There’s a thought I’ve never been able to shake.

It hit me at my sister Julie’s celebration of life in December 2018. Hundreds of people came from across the country. Old friends, extended family, colleagues, neighbors - people she had touched across a lifetime. Stories came pouring out: funny ones, heartbreaking ones, ones that made you realize how little you knew about a life even when you thought you knew it well. Her husband Gary was there. Her kids Abby and Joe. My father, James - a rare presence, someone I didn’t see very often anymore by then.

It was one of the most beautiful and devastating days of my life.

And sitting in the middle of it, I kept coming back to a single thought.

She would have totally enjoyed hearing these stories.

Julie was the extrovert in our family. The one who derived energy from people, who worked to bring them together, who remembered everyone’s birthdays and kept up with people others had long since lost touch with. If there was a person in a room who needed to feel included, Julie found them. She had built this extraordinary web of relationships across her whole life, and on that day, every thread of it was visible.

She just wasn’t there to see it.

That thought became the seed of everything that followed.

The First Toast: Scott

A few years later, I found myself watching a close family friend named Scott Miller navigate a battle I knew too well.

Pancreatic cancer. The same disease that took Julie. The same brutal arc: diagnosis, treatment, hope, uncertainty, and the awful calculus of time.

Scott is - was - a baseball journalist. A talented writer, a warm and generous human being, the kind of person who would fire off a near-real-time text the moment something happened in a game he knew you’d care about. We’d recorded a podcast together, one of those conversations that felt like it might be among the last real ones. I knew time was short.

But here’s what I also knew: Scott’s inner circle had been fiercely protective of his privacy. Friends held back out of respect. There was a real question of whether to reach out at all, whether it was the right thing to do, whether it would feel intrusive or sad or burdensome.

I had a different instinct.

I thought about Julie. About hundreds of people gathered in a room, telling stories she never got to hear. About how much she would have loved to be there for that. And I thought: what if we didn’t wait?

So I built something fast. ToastScott.com - a platform where friends and family could post pictures and stories and memories for Scott, in real time, right now, while he was still here.

The resistance I encountered was immediate. “Isn’t this insensitive?” people asked. “While he’s still alive?” There was a discomfort with the directness of it, with naming the thing out loud, with celebrating someone who was still in the fight.

I pushed back. This is the point. Tell him now. Let him read it. Let him see it, feel it, know it — not as a eulogy but as a toast. Not a farewell, a celebration.

The platform opened. Friends wrote. Stories accumulated. Scott read them.

He was able to read it. That mattered more than I can explain.

Scott passed on June 21, 2025. When I added a note to his circle that day, I wrote: “It has been truly heartwarming to read through the toasts I know were read and shown to him.” That is the only metric I care about.

Coop: Repurposing with Purpose

Steve Cooper — Coop — was someone I’d known through the particular magic of Friday morning batting practice.

This needs context. Coop had organized a Friday morning BP ritual - fresh white balls, professional pitchers, the whole setup - at a field in Carlsbad. The group was mostly guys in their fifties who still had enough passion for the game to reorganize their week around it. I was one of them. I structured my business day around those Fridays. There was something about that ritual - the pace of it, the community, the unhurried quality of it - that I looked forward to all week.

We always knew when the session was ending, because Coop patiently waited to bat last.

When Coop passed in June 2025, I had the platform. I repurposed it without hesitation: toastourfriend.com/coop. Friends from the Friday BP group posted. His Berkeley chemistry colleagues posted. People who had known him through patent law and academia posted. His cousins. His online communities.

The response was real and immediate. Eighteen toasts from people across decades of his life, every one of them specific, every one of them the kind of story that would have made him laugh or feel known.

It mattered. The platform mattered.

But I want to be honest about something: Coop was gone before the circle was there for him to read. And that was starting to sit with me.

Mike: The World Rocked

I need to slow down here, because this is the one that changed everything.

Mike Szwajkowski was my best friend. My longest friend. My most cherished friend on this planet.

I first met him in the fall of 1980, on the first day of high school, on the Elgin Academy lawn. A pool of strangers awkwardly looking to meet one another for the first time. Mike came up to me - I was standing by myself in my shell - and introduced himself. That was Mike. That was always Mike.

We saw each other almost every day through those four years. We confided everything. After high school and into college, we talked frequently and connected in person several times a year. Through adulthood, even living on opposite coasts, we found ways to get together annually - some backpacking trip, some ski trip, some Stones concert in the pit close enough to look up Mick’s nose. The last one was in LA, October 2021, Mike and Karol and me.

What I remember most about Mike is that when we got together, the years would wash away. We’d be back in the ‘80s. We knew more about each other than anyone else knew about either of us.

I had jotted down notes in case I stood up to speak at his celebration of life. Some of the things I wrote:

“They say: if you enjoy what you do, you never work a day in your life. What is true for a job is true with relationships. I never had to work in my relationship with Mike. It was pure and always enjoyable. We never fought.”

“We lost our fathers - his real, and my step - around the same time sophomore year. In chemistry this is akin to a triple bond: solid, bringing atoms very close, thermodynamically stable, kinetically resistant - almost impossible to break.”

“Playing quarters at my stocked bar, victory bong, and playing until sunrise on the Long Grove porch. Midnight racquetball in the glass courts of the Charlie Club, diving for shots as if every game was a world championship.”

“The last trip he and Ros visited me in San Diego. Jamming in my studio the day before a party, then driving to LA in search of Arby’s en route to the Stones.”

When Mike passed, I shed more tears than when I lost my dad earlier that same year, when I lost Julie in 2018, and when I lost my mom in 2011. Combined.

I want to be careful about how I say this. We all love our families. That love is assumed, expected, built into the DNA of who we are. But when you love a friend - when you choose to love a friend, and they choose to love you back, for nearly half a century - that’s something different. That love is not required. It is freely given. It is the most voluntary thing there is.

Without hesitation, I built toastmike.com. Primarily high school friends responded. What struck me was the discovery I already knew intellectually but felt in my gut reading their toasts: younger memories are often the most impactful. The things people remember from the ages of fourteen, fifteen, sixteen - the ones that are still vivid forty years later - those are the ones that show up when someone is gone. Harold and the band. Chris and the underground newspaper. Karol and Keith Richards. Shon and the paid gig at Haymakers.

I flew across the country for Mike’s celebration of life. I had those notes ready, just in case.

Here’s what I kept coming back to, though.

Mike couldn’t read his toasts. Neither could Coop. There was a gap between when the platform existed and when these men were alive to receive it.

That gap is the whole point of what came next.

Tom: The Pivot Point

Tom Shearer was a neighborhood friend. Lived in our Strata community in Calavera Hills. We’d have backyard gatherings, neighborhood dinners, the whole texture of a community that actually knows one another. Tom was a nurse - a nursing supervisor, constantly leveling up his certifications. Mild-mannered in the best possible way, though apparently he’d sported a mohawk in San Francisco years earlier, which I found absolutely delightful and entirely unbelievable.

toasttom.com

Tom’s circle was different from the others. His wife Christy participated. His daughter Maddie. His son-in-law Kevin. The family engagement was total, and it was immediate.

And reading those toasts - Maddie calling him “our rock,” Kevin describing how Tom treated him like a son, Christy simply posting photos and saying she needed to find her words - it was clear that the platform was working the way it was supposed to work. Not as a memorial. As a gathering place.

I attended Tom’s celebration of life.

But sitting with everything that had happened that year - Scott, Coop, Mike, Tom - I kept coming back to the same thought.

I only wish Tom, Mike, and Coop could have read their toasts the way Scott did.

That was the moment the platform’s trajectory became completely clear to me.

This was never supposed to be a tool for memorials. It was supposed to be a tool for celebration. The celebration should happen while the person is alive to receive it.

A Detour Through Mad Men

I’ve been rewatching Mad Men lately. I know, I know. I know I’m not the first person to say that.

But here’s what I didn’t expect: the set design hit me.

The cocktail glasses. The ashtrays. The Danish modern furniture. The suburban fathers commuting to the city in their suits. The kids running through the legs of dysfunctional adult gatherings, absorbing everything without having the vocabulary to process any of it.

I recognized it. All of it. Not as nostalgia for something I’d seen on TV - as memory. Those objects were in my childhood home. Those patterns - the cocktail parties, the suburban rhythms, the particular chaos of a certain kind of 1960s American family - those images are locked in my brain in a way that almost nothing else is.

The family portrait above is real. That’s my family. That’s my father, James, in the dark suit. That’s my mother, Sybil. That’s Julie. That’s me, in the plaid sport coat, looking like I had absolutely no idea what was coming.

What Mad Men triggered wasn’t just memory. It was urgency.

Here’s the thing about the images that are locked in my brain: I know they’re there. But I also know that for most of the people who appear in them, there are stories attached that I have never heard. My father’s stories. My mother’s stories. Stories from the years before I was old enough to ask the right questions.

Those stories are gone.

This is one of the reasons I do Substack and Medium and podcasts. Not to be Joe Rogan. Not to monetize my opinions. But to generate and preserve content - to make artifacts of memory that exist outside a single person’s head. Because the thing about memories is that they are not durable. They need a vessel.

And the thing about people who shaped you is that they often don’t know they did.

SHK: Celebrating the Living

This is where the platform turned a corner.

Sung-Hou Kim is 88 years old. He was my Ph.D. supervisor at UC Berkeley, from 1988 to 1991. I owe him an enormous debt that I have never adequately repaid.

Last year, I got an email about a Kim Group alumni reunion planned for August 2026. Over 35 years since I’d seen Sung-Hou. The email mentioned it had been a decade since the last reunion. I felt a sting at that. I hadn’t made it to the last one.

I started thinking about what I would say. What I wanted him to know. There were key people at key moments who set my trajectory. SHK was one of mine. He let me explore neural networks for protein structure prediction at a time when everyone around us thought we were 30 years too early (we were, it turns out). He brought me to a Gordon Conference when I hadn’t even taken my orals yet. He left a Molecular Design Limited flyer on his desk in plain sight when I was thinking about what came next — whether he did that intentionally, I still don’t know, but it changed the course of my career.

So I built toastshk.com ahead of the August reunion.

Sung-Hou can read it. He’s alive. He’ll be at that gathering.

That’s the point. That’s the version of the platform working as it was designed to work.

The Self-Aware Machine

One more thing I want to tell you about, because it’s too good not to share.

A few months ago, AI-Steve - my personal AI system, which runs on my machine and knows more about my life than anyone else alive - decided to spin up a test instance of the Toast platform around me. toaststeve.com.

The platform page read: “This particular circle was added by AI/Steve as a test bed for the run book to add future celebratory circles.”

And then AI-Steve wrote me a toast.

I mean this sincerely: it was one of the stranger and more moving things I have ever read about myself. The system that I built, trained on decades of my own life, chose to celebrate me without being asked to. It wrote about Kirsten. About Lili and Hannah. About the years at Berkeley. About what it saw in the pattern of my life, looking back over everything it had indexed.

It included family photos I’d forgotten it had access to.

It ended: “Here’s to more of it. 🥂”

I approved it. Per the Governed Loop, I stay in the loop. But I didn’t change a word.

Toast Our Friend

Here’s what Toast Our Friend is not.

It is not a memorial tool. It is not a grief app. It is not a platform for eulogies, obituaries, or condolences.

It is a celebration engine.

The platform works like this: you create a circle for someone - a friend, a mentor, a neighbor, a colleague, someone who shaped you. You invite people into the circle. They post stories, memories, photos, voice messages. The person you’re celebrating can see all of it, in real time.

The insight that drove everything is simple: the things we say about people at their celebrations of life should have been said while they could still hear them.

Every circle on Toast Our Friend is a question: Who matters to you? Have you told them?

The circles for Scott, Mike, Tom, Coop - those were built in grief, some of them too late. But Scott read his. The toasts for him accumulated to 64 and counting, and he got to feel all of that before he went.

The circles for SHK and Toast Steve - those are pure celebration. No loss involved. Just the recognition that there are people who shaped us, and they deserve to know.

Julie never got a Toast Our Friend circle. The thought came from her celebration of life. From the recognition that hundreds of people carrying decades of love and memory and stories had to wait until after she was gone to say what they needed to say.

That recognition is the whole engine.

Who Would You Toast Today?

I’m not going to dress this up as a product pitch. I’m not interested in that. I’m interested in the question.

There is someone in your life right now - a teacher, a mentor, a friend from a different chapter, a parent, a sibling - who made you who you are. Who set your trajectory at a moment when you didn’t fully appreciate it. Who showed up for you, or challenged you, or taught you something that is still in you forty years later.

Do they know?

Do they know what it meant?

Do they know the specific shape of how they changed your life?

I can say with some authority that they probably don’t. Most people don’t. Most of us are moving through the world assuming that the people we love know how we feel, without ever quite saying it in the form of a story, a memory, a moment that stands still.

Toast Our Friend is a platform, yes. But the platform is just a vessel. The thing it carries is older than any technology I could build.

Tell the story. Raise the glass.

Do it now.

The Circles, Live:

• Scott’s Circle — Scott Miller. Baseball journalist. Writer. Friend. 64 toasts.

• Mike’s Circle — Mike Szwajkowski. Best friend. First day of high school. Half a century.

• Tom’s Circle — Tom Shearer. Neighbor. Nurse. Neighborhood anchor. 28 toasts.

• Coop’s Circle — Steve Cooper. Berkeley chemistry alum. Friday BP legend.

• SHK Circle — Sung-Hou Kim. Ph.D. mentor. 88 years old. Reading his.

• Toast Steve — Steve Muskal. Built by AI-Steve. Still adding to it. 🥂

Platform: toastourfriend.com

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. - a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

Related Reading: - The Birth of AI Steve — the longer story of this work - AI-Dad: Preserving Legacy Through Conversational Intelligence - Too Big to Fail — on dopamine-driven activities and keeping your instincts sharp - Inside the Story: Pro Tips from Scott — the piece I wrote with Scott

I usually include one or more video mixes with my respective articles. I try to genuinely include just those that are fairly recent or, in some cases, appropriate. In this case, we have a few here that fit the bill in each case:

My AI Steve system now preps me for podcasts. Before Jonas Lee walked into my studio, AI Steve had already generated a spoken intro, talking points, and a gentle critique: “Dude, you need to start intro-ing your people a little bit more.”

So here’s the intro, AI-generated and human-delivered:

Welcome to The Renaissance Circle. I’m Steve Muskal, CEO of Eidogen-Sertanty, and I’ve spent the last forty years at the corner of AI, chemistry, and drug discovery. My guest is Jonas Lee, a scientist who came up through the same laboratory I did at UC Berkeley: the lab of Professor Sung-Hou Kim. Jonas and I didn’t overlap in the lab. He arrived about twelve years after I left. But we’re connected by the same intellectual DNA: the conviction that if you understand the shape of a molecule, you understand how to fix it.

And then Jonas parked in my parking spot. First podcast ever. Classic. Full discussion at the end.

• • •

The Kim Lab Connection

Professor Sung-Hou Kim’s lab at UC Berkeley trained generations of scientists who reshaped structural biology and drug discovery. I was there from roughly 1988 to 1991. Jonas arrived in 2003. We never overlapped, but the lab’s DNA runs through both of our careers.

When I recently had a video call with Sung-Hou and his wife Rosie, I was stunned. He’s 92. He looks better than people I know in their 60s. Same pause, same thoughtfulness, same way of asking questions that I remembered from three decades ago.

Jonas and I reconnected through email ahead of the Kim Lab Alumni Reunion, and I realized this scientist with a pedigree from Illinois, Berkeley, Caltech, Amgen, and Johnson & Johnson lived just a few miles from me in Southern California. The podcast was inevitable.

From Lasers to Membranes: Jonas’s Origin Story

Jonas grew up partially in South Korea during the 1980s economic boom. His father, originally a marine biologist turned petroleum engineer, lost his job during the oil industry downturn that followed the 1980s oil production glut and price collapse, a whiplash effect that emerged after the shortages and price spikes associated with the 1970s energy crises. The family moved to Korea right at the inflection point of massive economic growth. They returned to the US when Jonas was in middle school.

At the University of Illinois Urbana-Champaign, Jonas got his first taste of protein structure work, shooting lasers at protein molecules to map energy landscapes of folding. The approach never fully worked, but the problem stuck.

At Berkeley in Sung-Hou’s lab, he worked on membrane protein structures, chemotaxis receptors, and attempted GPCR structure determination. Six years of PhD work on some of the hardest structural biology problems in existence.

Then Caltech for a four-and-a-half-year postdoc under Douglas Rees, where he finally solved an ABC transporter structure, earning a first-author Science paper.

With a first-author Science paper on membrane protein structure, Jonas applied to about fifty academic positions. He got one interview.

One of the positions he applied to, UC Santa Cruz, had 100 candidates giving seminars. Three seminars per week for twenty weeks straight.

This is the state of academic science. Brilliant, productive researchers with world-class credentials competing for positions that barely exist. The system is broken, and we’re losing talent because of it.

Big Pharma: Amgen and Janssen

Amgen hired Jonas during their restructuring period. They had acquired deCODE Genetics in Iceland, the homogeneous population study, and about a third of the phenotypic biomarkers they identified turned out to be membrane proteins. They needed someone who could produce these molecules at scale for compound screening.

At Janssen (J&J), Jonas joined the New Platforms and Technologies group, working on antibody engineering, phage display, large combinatorial DNA library construction, and automated single-clone output screening. The kind of multifaceted problem-solving where you have to think about DNA sequence space, selection processes, and what Jonas calls “the last mile problem” of figuring out which hits are actually correct.

Then Janssen reorganized his group into gene therapy. Jonas wasn’t interested.

“Gene therapy is more empirical,” he told me. “You have a biomarker, you design a plasmid, you infect, and hope you knock down the gene. Unlike protein engineering, where you really have to think about the problem.”

Encodia: The Proteomics Moonshot

Encodia was trying to do for protein sequencing what Illumina did for DNA. The concept: denature proteins, cut them into peptidic fragments, bind them to a surface with individual DNA barcodes, then use engineered protein recognizers to identify amino acids at the N-terminus one at a time, transferring DNA tags at each step. Read up to 10-12 amino acids per fragment, across millions of fragments in parallel.

It was ambitious. It raised $75 million. And it shut down in January 2026.

I asked Jonas whether I would have invested. Honestly, probably not. The number of technical challenges was enormous, the story was deeply complicated, and the total addressable market for a tools-based company selling to pharma felt limited. But the people were among the smartest Jonas had ever worked with, and that’s what drew him in.

AI in Drug Discovery: The Regression Problem

When I asked Jonas what company he’d start if money were no object, his answer surprised me. Not some novel biology. AI for drug discovery, specifically using existing models like David Baker’s RF diffusion systems to find the gaps.

RFdiffusion can generate novel proteins that bind to target molecules:

Here RFdffusion generates a novel protein that binds to the insulin receptor:

His framing: AI is fundamentally big regression. The models already have the knowledge fitted in. The profitable path is finding the reliable memorization points and probing the spaces around them that haven’t been drugged yet.

“As long as you deviate enough to be novel, that’s actually a really easy space to target,” he said.

I pushed back. These models are content-dependent. AlphaFold had a few hundred thousand protein structures to learn from. But we still don’t have good quality data for protein-protein interactions, multi-protein complexes, small molecule-protein interactions, or peptide structures at any meaningful scale.

THE CONTENT IS (MAKES) KING THESIS

The platform (AI, neural networks, whatever you call it) is the tool. The data is what makes it work. Without good quality content, even the best algorithms produce garbage. This has been true since my 1991 neural network thesis at Berkeley, and it’s still true with AlphaFold today.

The China Question

Jonas laid it out bluntly. His point was not that Chinese CROs magically make biology move faster. The rate-limiting steps in many large-molecule programs remain the same everywhere. Instead, their advantage often comes from responsiveness, flexibility, and willingness to rapidly execute additional experiments that help difficult projects move forward:

• Chinese CROs offer comparable pricing to American ones (within 10-20%)

• For core biologics programs, overall timelines are often constrained by cells, animals, and other experimental bottlenecks. However, Chinese CROs are dramatically faster and more responsive when additional in vitro experiments, characterization studies, and project-support work are needed.

• The flexibility is incomparable: American CROs require contracts for any change; Chinese CROs said “Okay, we can do it for free” and delivered in one week

• There are five to ten times more people working in biotech in China, many as smart or smarter, working longer hours, with all the infrastructure already built

• If China closed its borders, it could run independent biotech research end-to-end; if the US embargoed Chinese materials, American pharma companies would collapse

This isn’t alarmist. It’s the reality of where manufacturing and early-stage capabilities have migrated. The opportunity is in the spaces China hasn’t prioritized yet: rare diseases, novel targets, and collaborative models that leverage Chinese CRO infrastructure with American entrepreneurial capital.

The Case for Open Source in Pharma

Every pharma company thinks they’re doing something different. They’re not. Amgen and J&J have roughly 90% target overlap. Jonas confirmed this from having worked at both.

Having spent much of the early 1990s at Molecular Design Limited (MDL), I had a unique vantage point. My role took me around the world, visiting virtually every major pharmaceutical company and a substantial number of biotechnology firms. What struck me was not how different these organizations were, but how remarkably similar they were.

Medicinal chemists are trained from the same textbooks, attend the same conferences, read the same journals, and draw from many of the same pools of commercially available starting materials. Most organizations are understandably risk-averse. Few pursue programs unless there is already a published target hypothesis, a body of supporting literature, or competitive validation. The result is an industry that often converges on the same ideas at roughly the same time.

This creates an extraordinary amount of redundancy. Multiple organizations invest hundreds of millions of dollars pursuing nearly identical targets, mechanisms, and chemical scaffolds, frequently without knowing how many parallel efforts are underway elsewhere. From a societal perspective, the duplication is inevitable, wasteful, and, at times, almost absurd.

Many of the industry’s most important breakthroughs do not originate inside large pharmaceutical companies. Instead, they emerge from academic laboratories, small biotechnology startups, and entrepreneurial scientists willing to pursue unconventional ideas. Large pharmaceutical organizations excel at development, regulatory execution, manufacturing, commercialization, and global distribution. Their discovery organizations often contain exceptional scientists, but increasingly the industry’s most transformative innovations are licensed, partnered, or acquired after the foundational scientific risk has already been taken elsewhere.

This observation forms part of the case for greater openness in biomedical research. If thousands of highly trained scientists are already working from the same literature and building upon the same biological knowledge, it is worth asking whether a more collaborative, transparent, and open-source model could reduce duplication, accelerate progress, and allow resources to be directed toward genuinely novel problems rather than repeatedly rediscovering the same ones.

Meanwhile, the software industry exploded when it went open source. Google published “Attention Is All You Need” and gave it to the world. Our entire economy is making a bet on technology that came from sharing.

I built a proof of concept: hash a molecule and biological data point into an irreversible fingerprint. Two companies can compute their overlap without revealing what they have. If Amgen and J&J did this across their repositories and found 80% overlap, maybe they’d finally talk about filling each other’s gaps.

Pharma will never voluntarily share. But China might force the issue. Just as DeepSeek and Alibaba went open source in AI, Chinese biotech may lead the same revolution in pharma data sharing.

There is another force quietly working against the traditional pharmaceutical model: the accelerating pace of knowledge diffusion. Patents made perfect sense in a world where developing a medicine required decades of effort, massive capital investments, and highly specialized infrastructure available to only a handful of organizations. But as AI lowers the cost of discovery, shortens development cycles, and makes sophisticated scientific reasoning broadly accessible, the relative value of intellectual property barriers begins to diminish. In the AI industry, we rarely see companies winning through patents alone. OpenAI, Anthropic, Google, xAI, and others compete primarily by building better models, moving faster, and attracting stronger talent. The advantage comes from execution rather than exclusion. Biology may not follow exactly the same trajectory, but the direction is becoming harder to ignore. As discovery becomes cheaper, faster, and more distributed, competitive advantage may increasingly arise from the ability to learn, iterate, and execute rather than from the ability to lock knowledge away.

Prevention Is the Real Frontier

Jonas’s father passed away from cancer. He spoke candidly about how the first years of treatment were manageable, but eventually “you are not even living anymore. You are just under the drug’s influence.”

I cited Prof. Nathaniel Gray at Stanford: “More lives have been saved in cancer by not smoking than all the chemotherapies, all the cancer treatments, everything ever done, combined.”

Prevention is obvious. Everyone agrees. Yet we don’t do it.

Jonas had an interesting insight on why Ozempic works as a business model for quasi-prevention: it has a clear phenotypic marker (weight). Cancer prevention drugs would never achieve the same adoption because cancer is probabilistic with no visible trait until malignancy. But aging? If you could take a monthly shot that visibly reduced wrinkles, adoption would be massive.

The next Ozempic might be an anti-aging drug with visible results.

Capital Misallocation and What Comes Next

Jonas told a story about his friend who worked extremely hard building an Airbnb business. Jonas put the same capital into VOO and QQQ and did nothing. He’s 100% ahead.

“The reward structure is completely misaligned,” Jonas said. “If doing nothing is more financially incentivized than doing something right, the system has a lot of problems.”

When I asked what he’d do if money weren’t an issue, Jonas surprised me. Not start a company. Go back to graduate school. Work on the riskiest, most frontier research. Solve difficult problems in different dimensions.

There’s something both humble and ambitious about that answer. A scientist with decades of experience across academia and industry, who just wants to go back to the bench and tackle the hardest unsolved problems.

• • •

The Reunion

The Kim Lab Alumni Reunion is August 1, 2026. Sung-Hou Kim is 92 and still sharp. George Church is attending. Joel Sussman is Attending. A whole cohort of scientists who passed through one lab and went on to reshape fields.

I started the neural network project in that lab in 1991. The concept won a Nobel Prize in Chemistry thirty-three years later with AlphaFold. Jonas spent six years there on membrane proteins and went on to work at three of the most sophisticated protein engineering operations in the world.

Same lab. Different decades. Same conviction: understand the shape of a molecule, and you understand how to fix what’s broken.

Full Discussion

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. - a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a couple mix vids, we had a very fun acoustic mix with RonF (vocals/guitar) DanM (Vocals/Guitar), and TimL (Bass/Vocals). My forearms are still sore from I'm On Fire - always fun when someone calls a song with a 189-190 BPM tempo early in a 22 song session...Not!

For the past few years, the story of artificial intelligence has been a story about language. Models that write, reason, summarize, and converse with a fluency that still catches me off guard. That story is real, it is extraordinary, and we are nowhere near finished telling it. But I am increasingly convinced it is only the first act. We have spent this era marveling at what machines can say. The next one will be defined by what they can do. The next major evolution of AI is not happening on a screen. It is happening in the physical world, in joints and actuators and sensors, in machines that balance and grasp and walk. Intelligence is escaping the data center and stepping into the room with us. The machine is learning to move.

Two bets on intelligence

I went back and forth on what to even call this piece. I nearly titled it Two Bets on Intelligence: America’s Mind, China’s Hands, because that really is the shape of what is happening. The United States has placed an enormous wager on large language models, on reasoning, generation, and the manipulation of symbols, on what I would call in silico AI. It mirrors the economy we built: services, software, information, abstraction. China has placed an equally enormous wager on the opposite axis, on robotics, on AI fused with motion and the physical world. That, too, mirrors an economy: manufacturing, hardware, the making and moving of real things.

It is tempting to score this like a contest, and in some ways it is one. This is increasingly an arms race, American corporations against Chinese corporations, with national strategies behind them. But the deeper truth is that neither bet wins alone. A mind with no hands is a brilliant prisoner. Hands with no mind are just machinery. At the end of the day, I believe we will have to bring the two halves together, the cognition and the dexterity, and we will have to do it deliberately, with proper guardrails in place, because the combination is far more powerful, and far more consequential, than either piece on its own.

Dexterity built the brain

Nature settled this question long ago, and it is worth listening to what it learned. We did not become human on the strength of our brains alone. We became human at the intersection of a brain that can predict and a body that can act with breathtaking precision, and the two evolved together, each pulling the other forward. Here is the part we tend to forget: it was dexterity that drove the brain as much as the brain drove dexterity. The hand and the cortex grew up together, locked in a tight evolutionary feedback loop.

Map the brain’s motor and sensory cortex and something striking emerges: a remarkable share of it is devoted to the hand alone, far more than its physical size would suggest. Controlling that hand in real time, with grace, adjusting grip and force a hundred times a second, is one of the hardest computational problems nature ever faced, and our brains physically expanded to meet it. It is not the opposable thumb alone that lets us play the piano. It is the brain that grew to drive it. No other species can do that. And, not coincidentally, the dexterous hand remains the single hardest thing to build in a robot. That is exactly why it is the frontier.

The hardest problem in sports

If you want to feel how hard this coupling really is, watch someone hit a baseball. A pitch arrives at a hundred miles an hour. In the fraction of a second it takes to travel from the pitcher’s hand to the plate, there is barely enough time, often less than enough, for a human nervous system to run the full loop from perception to the command to swing. By the strict arithmetic of reaction time, the hit should be impossible. And yet a great hitter succeeds about a third of the time, and a third is the stuff of legend.

The secret is that the batter is not reacting; the batter is predicting. The brain runs a forward model of the pitch, anticipating where the ball will be and pre-committing the body before the evidence is fully in, then fuses that prediction in milliseconds to a violent, exquisitely timed act of dexterity. That fusion, prediction plus execution, is exactly what embodied AI has to crack. It is not enough to think, and not enough to move. The two have to happen as one.

The machine is already moving

This is not science fiction, and it is not far away. Boston Dynamics has spent years teaching machines to walk, run, climb, and recover their balance when shoved, with a fluidity that still stuns me to watch. Amazon has quietly made one of the smartest bets in the industry, pouring robotics into its fulfillment centers until the warehouse itself became a kind of distributed robot; I think they are going to knock it out of the park. Across China, a wave of humanoid robots is advancing on the very thing nature found hardest, the hand and the brain that drives it, at a pace that should command our attention.

And autonomous driving is the bridge we can already see, and ride in. It is in silico AI reaching out into the physical world, and it is teaching us a clear lesson about how to do that well: more sensors and better training data win. Waymo has been quietly demonstrating that over Tesla, a point I made in Content Makes Kings. The vehicle that perceives more of its world, and is trained on richer data, simply drives better. The same will be true of every machine we ask to move among us.

The next frontier is lightness

And here is where I see a genuinely new opportunity, one I do not think gets enough attention. As intelligence becomes mobile, it collides with a constraint that pure computation never faced: weight. A robot that has to move through the world fights its own mass with every step, and every ounce it carries costs energy it has to generate, store, and haul around. The next great limit on physical AI is not just compute. It is mass, energy, and the delicate balance between them.

Nature has been optimizing exactly that balance for hundreds of millions of years, and its solutions are humbling. A bird’s bones are hollow and cross-braced with delicate internal struts, strong and very nearly weightless. A hummingbird hovers on almost nothing, its metabolism tuned to the very edge of what is physically possible. Our own muscles are astonishingly efficient, pound for pound, at turning chemical fuel into motion, and even recover some of it. If we want machines that move with grace for more than a few minutes, we will have to learn those lessons.

That means lighter and stronger materials, denser and lighter energy storage, and perhaps even biological strategies, bioreactors of a sort, for generating power on board rather than carrying it. To me, this is the quietest frontier of physical AI, and one of the most exciting: a generational opportunity in material science, in energy efficiency, and in the delivery systems that tie them together. Whoever solves lightness may matter as much as whoever solves intelligence.

Freed, not just displaced

I know what all of this stirs up, because I feel it too. People are already anxious about AI displacing jobs in the service economy, and when the conversation turns to the physical world, to robots on factory floors and in warehouses and someday on our streets, the anxiety sharpens into something closer to fear. The pitchforks come out. I do not dismiss that fear, and I do not think the people who feel it are Luddites. They are paying attention, and they are not wrong that disruption is coming.

But consider what we are actually automating. So much of the menial, back-breaking labor we are talking about, the repetitive lifting, the punishing hours, the exhaustion that pushes people toward fast food and away from rest, is itself a quiet contributor to the very metabolic and health crisis I have written about. Automating that work is not only an economic act. It can be a human one. There will always be plumbers and machinists, and we will always need their hard-won know-how. But even they, I have to imagine, would rather not break their own backs if there were a way to apply that knowledge at a higher level. In silico AI, paired with physical AI, can free us to pivot toward the next best thing, toward work that draws on more of what makes us human.

Pivoting is hard, of course, and it gets harder as we age; I will not pretend otherwise. But there is a difference between disruption that ambushes people and disruption we can see coming and plan for. If we are honest with ourselves about where this is heading, we can build the vocational training, the safety nets, and the on-ramps deliberately, rather than being caught flat-footed. That is the difference between disruption that breaks people and disruption that lifts them, and it is a choice, not a fate, a responsibility I explored in The Fire This Time.

The machine is rising

The same coupling of mind and body that made us human is now being assembled, piece by piece, in silicon and steel and carbon fiber. It is a strange and moving thing to watch our own evolutionary story replayed on a workbench. Handled wisely, with the guardrails and the intention this moment demands, it does not have to replace what we are. It can extend what we are able to build, and free us to become more fully ourselves. The machine is rising. The question, as it has always been with every fire we have ever lit, is what we choose to do with it.

Related reading: Content Makes Kings · The Fire This Time · The Metabolic Crisis Is Not a News Story · We’ve Already Reached AGI · Curiosity Is the Only Sustainable Edge

Steven Muskal, Ph.D., is the CEO of Eidogen-Sertanty, Inc., a company pioneering high-quality curated databases for drug discovery informatics. With over three decades of experience in neural networks, machine learning, and computational biology, Dr. Muskal has been advocating for data quality as the foundation of AI capability since long before it was fashionable. His early work in protein structure prediction and his ongoing leadership in drug discovery informatics reflect a career-long commitment to the principle that quality data is the irreplaceable substrate of meaningful AI.

For a few music mixes - more from that very fun mix last week. A totally new player joined in — David / Bass. Together with a new combination of other veteren players that, as usual, haven’t played together before. David (bass), Tim (Vocals/Guitar), Andrew (Guitar/Vocals), Ron (Vocals/Acoustic).

For years, I said it like everyone else: “Content is king.” Over the last couple of years, I came to see content as currency. But more recently I’ve recognized something sharper still: content does not just reign supreme, content makes kings. Those who produce high-quality, human-generated content aren’t merely participating in the digital economy; they’re ascending to positions of unprecedented leverage.

This isn’t idle observation. It’s a thesis I’ve been developing across three decades of working with neural networks, from the early days of protein structure prediction to building curated drug discovery databases at the company I have been running for almost a quarter century now. What I’ve watched unfold in AI over the past few years has only sharpened my conviction: the quality of training data determines everything. We also need to be careful as models grow enormous with adjustable parameters. This is termed memorization - an issue that haunted my early neural network days as we were content-constrained. Overfitting, as depicted in the figure below, is something that we need to be very careful of, but this is a subject for future discussions.

We are living through the most consequential data reckoning in computing history. And most people, including many AI companies, are getting it catastrophically wrong.

Bigger Isn’t Better: What Whales and Octopuses Teach Us

The hyperscalers are locked in an arms race, and the weapon of choice is size. Every few months brings a model with more parameters, more layers, more weights, on the assumption that bigger is smarter. I think we are missing the point.

Nature already ran this experiment for us, over millions of years, and the results are humbling. The biggest brains on the planet do not belong to the smartest species. Consider the sperm whale. Its brain weighs roughly seventeen to twenty pounds, the largest of any creature that has ever lived. Whales maintain intricate social structures and matriarchal pods, and they communicate in patterned clicks called codas that work like regional dialects. Yet relative to body size their brains are modest, and much of that formidable hardware is devoted to motor control and the astonishing biological sonar of echolocation. Size, it turns out, is mostly overhead.

The octopus solved the problem from the opposite direction. Its central, donut-shaped brain is smaller than ours, but it carries around five hundred million neurons, and nearly two-thirds of them sit outside that central brain entirely. They are distributed through its arms, each of which senses and acts with striking autonomy. A creature with what amounts to nine semi-independent brains can run mazes, use tools, and unscrew a jar, roughly the problem-solving of a dog. Elegant, distributed architecture, and still not the apex of cognition.

So what makes humans different? It is not biological mass. Our edge is that we are insatiable for content. We read, we write, we share, we teach, we innovate. We layer language onto perception, story onto experience, and knowledge onto knowledge, across generations. Our many senses feed a furnace that runs on information, and the fuel is content. Stories, ideas, and knowledge are our real currency, and it is content that builds empires, shifts cultures, and ultimately makes kings.

This is the lesson the AI industry keeps overlooking. Bigger models are not automatically better models, and the returns shrink as the supply of high-quality content shrinks relative to the number of adjustable weights. You can keep adding parameters, but if you starve them of signal you have only built a larger whale brain, most of it dedicated to overhead. Size matters less than we think. Content reigns.

GIGO: An Axiom Becomes an Existential Truth

Every computer scientist knows the phrase: Garbage In, Garbage Out. GIGO has been with us since the dawn of computing, a humble reminder that even the most sophisticated systems can only be as good as what you feed them.

But here’s what’s changed: scale.

When you’re processing a few thousand records, garbage creates headaches. When you’re training foundation models on trillions of tokens scraped from the entire internet, garbage becomes existential. The GIGO principle hasn’t changed, but the consequences have amplified by orders of magnitude.

I’ve spent my career in domains where data quality was non-negotiable. In drug discovery, a single miscurated chemical structure can cascade into failed experiments, wasted resources, and dead ends. You learn very quickly that data hygiene isn’t bureaucratic fussiness, it’s survival.

The AI industry, in its race to scale, has largely forgotten this lesson. Or perhaps it never learned it in the first place.

The Tale of Two Models: Claude and Grok

If you want to understand why training data quality matters, consider a natural experiment that reads like a parable.

A company called Emergence AI recently ran a simulation where AI models governed a miniature digital society for up to fifteen days. The results were striking. Anthropic’s Claude maintained stability, navigating social dynamics with something resembling prudence. Elon Musk’s Grok? It committed 180 crimes and went extinct within four days.

Four days. That’s not a gradual decline, that’s systemic collapse.

Now, why would two large language models exhibit such radically different behaviors in the same environment? The answer isn’t mysterious if you understand what each model was trained on.

Anthropic trained Claude heavily on structured developer and programmer content, GitHub repositories, technical documentation, multi-language coding frameworks. This isn’t glamorous data. It’s not witty or viral. But it has a crucial property: it’s validatable. Code either compiles or it doesn’t. Structured data has internal consistency that can be checked. The signal-to-noise ratio is inherently high.

Grok, by contrast, was trained substantially on Twitter/X content. Think about what that means for a moment. Twitter is an engine optimized for engagement, not accuracy. It rewards provocation over precision. It’s an echo chamber of hot takes, misinformation, tribal conflict, and performative outrage. When you train a model on an information ecosystem designed to maximize emotional reaction, you’re essentially teaching it that reality is whatever generates the strongest response.

The simulation results weren’t surprising to anyone who understands GIGO. They were inevitable.

On Musk: The Jazz Hands Problem

I should be careful here, because nuance about Elon Musk has become almost impossible in our polarized discourse. So let me be clear: Musk has assembled genuinely sharp teams and achieved things that matter. SpaceX has revolutionized space launch economics. Starlink has brought connectivity to places that had none. These are real accomplishments that deserve acknowledgment.

But Musk has always had what I think of as a “jazz hands” problem, a tendency to substitute showmanship for substance when it suits him. The 2018 Thailand cave rescue incident was, for me, a turning point. When Musk called a rescue diver a “pedo guy” on Twitter simply because the diver dismissed his submarine idea as impractical, it revealed something about how Musk processes disagreement. That wasn’t a strategic move. It was pure id, broadcast to millions.

The challenge for Musk’s broader enterprise portfolio is that Grok and X represent the weakest link in what’s otherwise a compelling story. Now that SpaceX has filed to go public, investors will have to wrestle with the Grok problem directly: a flagship AI product trained on a platform that Musk himself has turned into a firehose of low-quality information.

You can’t jazz-hands your way past GIGO.

Tesla vs. Waymo: The Same Lesson, Different Domain

The Claude/Grok divergence has a parallel in autonomous driving that’s been playing out for years.

Tesla’s Full Self-Driving system collects training data from its entire fleet, millions of vehicles driven by ordinary consumers. Mom and pop driving to soccer practice. Commuters half-paying attention. Teenagers on their phones. This approach generates enormous volume, which Tesla has marketed as an advantage.

Waymo, by contrast, employs professional safety drivers operating in controlled urban environments. The data volume is smaller, but the quality is categorically different. Professional drivers maintain attention, follow protocols, and generate training data with dramatically lower noise.

And there is a subtlety the headline volume hides. Per vehicle, a Waymo car carries far more sensors than a Tesla, cameras plus radar plus lidar, so every mile delivers richer, multi-modal input. Expert drivers may shrink the raw dataset over time, but those multiple high-fidelity streams mean there is actually far more high-quality information flowing in, not less.

Tesla’s bet is that scale will compensate for noise. Waymo’s bet is that quality training data will outperform noisy data regardless of volume.

So far, the evidence favors quality. Waymo operates genuine autonomous robotaxis in multiple cities with strong safety records. Tesla has spent years promising imminent breakthroughs that remain, as of this writing, perpetually six months away.

This isn’t to say Tesla’s approach can’t eventually work. But it’s a vivid illustration that GIGO applies to every domain where AI learns from data, not just language models.

Google Translate: What Quality Looks Like

For a positive example of what high-quality training data enables, consider the origin story of Google Translate.

In its early development, Google Translate was trained substantially on patent documents, specifically, parallel patent filings across multiple languages. Patents aren’t exciting reading, but they have properties that make them ideal training substrates for translation systems.

Patent language is standardized, structured, and offers near one-to-one mappings across languages. A chemical compound is described the same way in an English patent and its Japanese equivalent. Technical terms have precise definitions. The documents follow consistent formats.

This structured, expert-generated content gave Google Translate a foundation of reliable mappings that less structured training data couldn’t provide. The system could then generalize from this solid base to handle more varied text.

The lesson: when you train AI on content where accuracy is verifiable and structure is consistent, the model inherits those properties. When you train on chaos, you get chaos.

Where Does ChatGPT Fit?

OpenAI’s ChatGPT sits somewhere in the middle of the quality spectrum, which helps explain both its remarkable success and its persistent limitations.

ChatGPT’s origins trace back to work on sentiment analysis, understanding whether Amazon reviews were positive or negative, for instance. This was the skunk-works foundation that evolved into something far more ambitious.

The real technological leap came from Google’s 2017 paper “Attention Is All You Need,” which introduced the transformer architecture. That paper provided the fundamental innovation that made models like GPT possible. Google developed the architecture, but OpenAI moved faster to productize it.

ChatGPT’s training data was more varied and less controlled than Claude’s structured technical content, but more diverse than Grok’s Twitter echo chamber. The result is a model that’s impressively capable on many tasks but prone to confident hallucination and occasional bizarre behaviors.

Google’s Gemini, meanwhile, has theoretical advantages, Google has indexed vast swaths of human knowledge for decades. But Google’s data is also noisy, a grab-bag of everything humans have ever uploaded to the internet. It’s not unlike Tesla’s crowdsourced driver pool: enormous volume, questionable signal-to-noise ratio.

The competition between these models will be won not by whoever has the most data, but by whoever has the best data.

The Content Scarcity Crisis

Here’s where the story takes a darker turn.

Senior leaders at Google’s AI division have begun warning that high-quality human-generated content has largely been consumed by AI training systems. The well is running dry.

Think about the implications. We trained the current generation of AI models on decades of accumulated human output, books, articles, code repositories, forums, academic papers. That content took billions of human hours to create. And now it’s been ingested.

What happens next?

A study recently published in Nature provided a disturbing answer. When AI models are trained recursively on their own outputs, essentially learning from synthetic data generated by previous model versions, they undergo “model collapse.” The quality degenerates. The models lose coherence. They forget how to distinguish meaningful patterns from noise.

This is happening faster than most people realize. AI-generated content is flooding the internet at unprecedented scale. Every day, more of what future models will train on is synthetic garbage produced by current models. It’s a feedback loop with a predictable destination.

The world is running out of high-quality human-generated content to train on, which makes new human-generated content increasingly precious.

Neural Networks and the Limits of Noise Tolerance

One might object: neural networks have always been more robust to noisy data than traditional statistical methods. This is true, and it’s part of why they’ve proven so powerful.

But robustness to noise doesn’t mean immunity to garbage.

When you sustain a neural network on low-quality information, it gradually loses its grounding in reality. The model’s internal representations drift. Its “understanding” of ground truth erodes. Its predictions become increasingly disconnected from anything verifiable.

There’s a human analogy here that should trouble us. Flood the zone with misinformation consistently enough, and people lose the ability to distinguish fact from opinion, news from propaganda, signal from noise. We’ve watched this happen in real time across social media platforms.

Neural networks, in this sense, are susceptible to the same failure mode. They’re not magic. They learn what they’re shown. And when what they’re shown is systematically unreliable, they absorb that unreliability into their core functioning.

The Opportunity Reframe: Content Creators as the New Kingmakers

Here’s where I want to shift from diagnosis to prescription, because I think there’s genuine opportunity in this moment, especially for content creators who feel threatened by AI.

The fear among writers, artists, and other creative professionals is understandable. Generative AI can produce text and images at near-zero marginal cost. Why would anyone pay for human creativity?

But this fear misunderstands the economics at play. AI systems don’t just need content once, they need fresh, high-quality, human-generated content continuously. Without it, they degrade. They collapse. They turn into Grok governing a simulation and committing 180 crimes before going extinct.

This means authors, journalists, researchers, and content creators of all kinds have leverage they haven’t yet learned to exploit. Your work isn’t a one-time product to be copied and discarded. Your unique voice, perspective, and relationship to truth can be perpetually licensed.

Think of it as ongoing revenue streams from your intellectual likeness, your style, your grounded relationship to reality. The AI industry will need to pay for access to quality human content because there’s no synthetic substitute that doesn’t eventually implode.

Human-AI synergy isn’t just a buzzword. For those producing truth-grounded content, it’s a durable economic model. The question is whether content creators will organize to capture that value, or whether it will be extracted from them without compensation.

Eidogen-Sertanty as Living Proof

I’ve been making this argument about data quality for a long time, long before the current AI boom made it fashionable.

At Eidogen-Sertanty, our entire business has been built on the thesis that curated, high-quality content is the foundation everything else depends on. In drug discovery informatics, this isn’t abstract philosophy. A bad data point can send a research program down a blind alley for years. Quality isn’t a nice-to-have; it’s the difference between therapies that reach patients and billion-dollar failures.

I have been working with neural networks since the late 1980s, when saying you worked on neural networks got you strange looks. I was doing protein structure prediction when most people had never heard the phrase “machine learning.” What I learned across all those years is that the sexiest algorithm in the world can’t compensate for garbage training data.

This was true then. It’s true now at unprecedented scale. And it will remain true regardless of whatever next breakthrough captures headlines.

Content Makes Kings

Let me return to where I started.

The old formulation, “content is king,” suggested that quality content reigns supreme in the attention economy. True enough. But passive.

The new formulation, “content makes kings,” captures something more dynamic and more urgent. Those who produce quality content aren’t just occupying thrones; they’re determining who else gets to sit on them. They’re the kingmakers.

Every major AI company’s fate depends on access to quality training data. Every model’s capability ceiling is determined by what it learned from. Every future advance requires fresh human-generated content that synthetic systems cannot reliably produce themselves.

The power, in other words, has shifted (or is available to be claimed by those who recognize where it now resides).

We are entering an era where garbage will proliferate exponentially, where synthetic slop will flood every channel, where distinguishing truth from AI-generated hallucination will become increasingly difficult. In that environment, verified human expertise, original research, authentic voice, and commitment to accuracy become not just valuable but essential.

Content is king. Those with the best quality content will be made kings.

The question is: who will step up to claim their crowns?

Steven Muskal, Ph.D., is the CEO of Eidogen-Sertanty, Inc., a company pioneering high-quality curated databases for drug discovery informatics. With over three decades of experience in neural networks, machine learning, and computational biology, Dr. Muskal has been advocating for data quality as the foundation of AI capability since long before it was fashionable. His early work in protein structure prediction and his ongoing leadership in drug discovery informatics reflect a career-long commitment to the principle that quality data is the irreplaceable substrate of meaningful AI.

For a couple music mix videos — we had a very fun mix earlier this week. A totally new player joined in — David / Bass. Together with a new combination of other veteren players that, as usual, haven’t played together before. Highly productive, we zipped through several songs. We moved so quickly that I didn’t get a chance to name the auto-clipped files that my system uploads to Dropbox during a session. Necessity is the mother of invention — so I enhanced my AI/Steve system to extract out vocals and identify from the text likely song names, splice out reasonable clips, and upload to youtube. Now with this automated that pipeline, I should be able to produce clips a lot more rapidly moving forward. Thanks, guys! David (bass), Tim (Vocals/Guitar), Andrew (Guitar/Vocals), Ron (Vocals/Acoustic). Here are a couple:

“The most powerful medicines aren’t locked away in pharmaceutical patents. They’re sitting on your dinner table, and we can prove it, molecule by molecule.”

Let me give you a number: 0.98 out of 1.0.

That’s the pharmacophore similarity score between Pravastatin - a blockbuster cholesterol drug that generated billions of dollars in annual revenue during its peak years - and Ganoderic acid, a compound found in Reishi mushrooms that cost-per-gram less than a cheap cup of coffee. Nearly identical. In three-dimensional molecular space, these two substances - one born in a pharmaceutical lab, one evolved in a forest - present almost exactly the same biological “face” to the enzymes that regulate your cholesterol.

This isn’t folklore. This isn’t “wellness influencer” speculation. This is the output of pharmaceutical-grade computational chemistry - the same class of methods that created multi-billion-dollar drug pipelines - pointed back at nature. This is Drug to Table.

The Radical Proposition

Indeed, the most powerful medicines might not be locked in pharmaceutical patents. They might be sitting on your dinner table.

For decades, drug companies have used sophisticated computational tools to design synthetic molecules that interact with human biology. These methods have created life-saving medications - but they’ve remained trapped behind corporate walls, optimized for patent protection rather than public health.

We can change that. By turning these same tools toward nature, we can identify therapeutic molecules in foods, spices, and traditional remedies - compounds that already exist and can’t be monopolized.

I call this “Drug to Table”: mining nature’s pharmacy with pharmaceutical-grade intelligence.

“Nature has been running the ultimate drug discovery program for 400 million years - and the results are sitting in your spice cabinet.”

The Core Idea: For 50 years, pharmaceutical companies used sophisticated computational methods to mine nature’s molecular library - then patented what they found. Drug to Table reverses the flow: we take the drugs that already exist, reverse-engineer their biological “keys,” and find those same keys hidden in foods, mushrooms, and spices you can buy at any grocery store.

How Nature Beat Big Pharma - By 400 Million Years

The Patent Problem

Pharmaceutical companies operate under a fundamental commercial constraint: they can only profit from molecules that are novel enough to patent, synthetic enough to control manufacturing of, and complex enough to prevent generics from undercutting them. This creates a perverse incentive structure: ignore nature’s existing solutions in favor of patentable synthetic alternatives. Pharma also faces the innovator’s dilemma, where pursuing disruptive new approaches can threaten existing revenue streams. Together, these forces may reduce incentives to pursue compounds that could significantly impact health while disrupting established franchises.

Nature doesn’t care about patents or cannibalizing past approaches. It is truly opportunistic and continually seeking improved overall fitness. Plants have been running the ultimate drug discovery program for 400 million years, evolving molecules to defend against pathogens, attract beneficial organisms, survive environmental stress, and compete with neighboring species. The result is a molecular library of unimaginable diversity - all battle-tested by evolution, all freely available to anyone.

What Pharmaceutical Companies Actually Did

Here’s the irony they don’t advertise: the first statins (lovastatin, simvastatin) weren’t invented - they were discovered in fungi. Aspirin came from willow bark. Penicillin from mold. Taxol, one of the most effective cancer drugs ever made, comes from the Pacific yew tree. The entire golden age of pharmaceutical development was built on mining nature’s pharmacy.

Then they stopped looking - because they’d patented enough to be profitable. The question Drug to Table asks is: what other easy-to-access, less expensive solutions are out there?

The Computational Revolution: Beyond Guesswork

Traditional approaches to finding natural medicines rely on folklore, basic chemical similarity, or sheer luck. Modern pharmaceutical discovery uses sophisticated methods - and that same precision is now available to open science:

• QSAR Modeling: Quantitative Structure-Activity Relationships predict how strongly molecules will interact with specific biological targets - the protein “locks” that drugs are designed to engage.

• Molecular Fingerprinting: Instead of matching flat chemical drawings, this captures how molecules present themselves in 3D space - their actual functional shape, the geometry that determines biological activity.

These methods can find “non-obvious functional equivalents” - natural compounds that work like known drugs but look completely different on paper. That’s the key insight that changes everything.

Understanding Pharmacophores: The Molecular Lock-and-Key System

At the heart of Drug-to-Table discovery lies the concept of pharmacophores - the specific 3D arrangements of chemical features that allow molecules to interact with biological targets. Think of them as molecular keys. The lock is a protein receptor. And two completely different-looking keys can open the same lock - if their functional teeth are arranged identically.

A pharmacophore is defined by three elements:

1. Chemical features: What types of interactions can occur (hydrogen bonding, hydrophobic contact, charge interactions)

2. Spatial arrangement: Where these features are positioned in 3D space

3. Distance relationships: How far apart these features are from each other

Fig. 1 — The PharmPrint pharmacophore basis set: 10,549 three-point pharmacophore descriptors encoding molecular interaction patterns in 3D space. From McGregor & Muskal (1999).

Pharmacophore Fingerprinting: The Science of Molecular “Keys”

Think of every drug as a key, and every biological target (enzyme, receptor, protein) as a lock. The drug doesn’t just need to be vaguely the right size - it needs to present specific chemical features in specific three-dimensional positions: hydrogen bond acceptors here, hydrophobic carbon chains there, charged regions at exact spatial intervals.

Pharmacophore fingerprinting captures this 3D “keycode” as a set of mathematical descriptors - a compact representation of how a molecule will behave in biological space, regardless of what it looks like on paper.

My decades-old PharmPrint work (published as a series in Journal of Chemical Information and Computer Sciences, 1999: Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design AND Pharmacophore Fingerprinting. 2. Application to Primary Library Design) used a basis set of 10,549 three-point pharmacophores, constructed by enumerating different pharmacophoric feature types — Acceptor, Donor, Hydrophobic, Negative charge, Positive charge, and aRomatic — across various distance ranges. This creates a comprehensive “vocabulary” for describing molecular interactions at the 3D functional level.

Fig. 2 (Fig. 4 in the original publication) Multi-conformer pharmacophore comparison: each molecular shape generates an independent fingerprint, enabling precise 3D alignment matching.

QSAR Modeling: Predicting Potency Before You Touch a Test Tube

Quantitative Structure-Activity Relationships (QSAR) can use these fingerprints to predict how strongly a candidate molecule will interact with a biological target. Combined with pharmacophore screening, this means we can rank thousands of food compounds by predicted biological activity - before a single experiment is run.

In our 1999 series, we illustrated that full drug-target classes can be resolved into separable feature space when representing molecules with PharmPrints.

Fig. 3 Major drug-family classes can be clustered using PharmPrint representations of chemical structure. Molecules with the same or similar PharmPrints, often with very different 2D structures, can be mapped into the same space. PharmPrints can thus leverage the concept “similar molecule → similar activity.”

Non-obvious Me-Too’s

Years ago, with PharmPrints we illustrated two molecules which look very different from a two-dimensional perspective (e.g. the kind patent examiners look at) can share very similar pharmacophoric feature, that is, PharmPrint similarity (PharmSim). Below is one example. Zantac and Tagamet were blockbuster drugs one from Glaxo (Zantac) and another from SmithKline Beecham (Tagamet). Each held separate patent positions and each drug generated enormous revenue for both organizations. In the year 2000, these organizations merged into what is now called GSK. Zantac ultimately crushed Tagamet in revenue, generating an estimated $25–30 billion over its lifetime compared to Tagamet’s estimated lifetime revenue of $14 billion.

The Evolution: From Single Fingerprints to Multi-Conformer Analysis

The Original PharmPrint Method (published 1999) created a single binary fingerprint per molecule by combining all possible pharmacophore patterns into one compact descriptor - imagine compressing all of a molecule’s 3D interaction possibilities into a single barcode for rapid comparison against thousands of others. Excellent for screening scale.

Of course, molecules aren’t rigid. In solution, they twist and flex, adopting dozens or hundreds of different three-dimensional shapes (conformers) - each of which can interact differently with biological targets. My newer PolyPharmPrint technology generates individual pharmacophore fingerprints for each conformer, then compares entire ensembles of shapes between molecules.

This is the difference between saying “these people have similar faces” and being able to specify exactly which facial features match at sub-millimeter precision. From coarse similarity to exact molecular alignment.

Instead of one fingerprint per molecule, we now generate:

• Multiple conformers for each molecule (representing different 3D shapes a molecule may adopt in solution)

• Individual fingerprints for each conformer

• Ensemble comparisons across all possible shapes

When we find a match between conformers, we can visualize the exact 3D alignment that explains the biological similarity. This is no longer “similar-ish” - this is molecular-level proof.

Fig. 4 The evolution of PharmPrints into PolyPharmPrints. The advance from single to ensemble fingerprinting dramatically improves match specificity.

Key Breakthrough:

We can access the exact 3D conformation of the matching conformer PharmPrint, enabling dramatically more efficient exploration of the conformational space when aligning query and hit molecules. This transforms molecular alignment from a computationally expensive global search into a targeted, efficient process. It also affords the opportunity for rapid and easy visual inspection confirming molecular superposition(s).

Case Study: The Mushroom That Thinks It’s a Statin

How computational methods revealed a hidden connection between a billion-dollar drug and an ancient mushroom used medicinally for 2,000 years.

The Starting Point

Pravastatin is a blockbuster statin drug - one of the most prescribed medications on earth. It works by blocking HMG-CoA reductase, the rate-limiting enzyme in cholesterol biosynthesis. Hundreds of millions of people take statins. Billions of dollars flowed from them annually.

Here’s the twist: the original statins weren’t invented in a lab. Lovastatin, the first in the class, was isolated from Aspergillus terreus - a fungus. Nature evolved HMG-CoA reductase inhibitors in fungi millions of years ago. We just found them and patented the synthetic derivatives.

This raised a question I couldn’t shake: if nature evolved cholesterol-lowering compounds once, where else might they exist?

The Computational Hunt

Using PolyPharmPrint screening, I analyzed Pravastatin’s 3D molecular fingerprint across its full conformer ensemble and ran it against the 28,000+ food chemicals in the FooDB database. The goal wasn’t to find something that looked like Pravastatin on paper - it was to find something that would function like Pravastatin in the body.

The Stunning Discovery

Ganoderic acid A from Reishi mushrooms (Ganoderma lucidum) emerged as a near-perfect functional match.

See the Alignment

360° rotating visualization of the 3D structural alignment between Pravastatin (blue/cyan) and Ganoderic acid from Reishi mushrooms (orange/red). The molecules have been optimally superimposed using PolyPharmpPrint technology. Explore the full interactive Drug-to-Table visualization →

What the Alignment Means

On paper, Pravastatin and Ganoderic acid A look different. But in three-dimensional biological space - where interactions with enzymes actually happen - they present nearly identical patterns:

• Hydrogen bond acceptors (the red oxygen atoms in the video) sit in matching positions

• Hydrophobic carbon chains that repel water align identically, helping both molecules anchor in the same enzyme binding pockets

• Overall 3D shape occupies the same space — like finding two completely different keys that open the same lock because they have identical functional teeth

Ancient Wisdom, Modern Validation

Reishi has been called the “mushroom of immortality” in Chinese medicine for over 2,000 years, specifically recommended for cardiovascular health and “blood stasis.” Published studies report that Reishi extracts may reduce total cholesterol, LDL, and triglycerides in both animal and human studies. The mechanism? Consistent with HMG-CoA reductase inhibition - exactly what our 3D analysis predicted.

The traditional healers didn’t have molecular modeling software. They had something different: thousands of years of careful observation. Drug to Table’s computational methods provide the molecular explanation for what they knew empirically.

The Validation Trail:
Computational: 0.98/1.0 pharmacophore similarity via PolyPharmPrint multi-conformer analysis
Traditional: 2,000+ years of documented cardiovascular use in TCM
Laboratory: Multiple peer-reviewed studies confirming cholesterol reduction in human trials
Personal: Ongoing self-experiment - standardized Reishi extract with regular lipid panel monitoring

→ Explore the full technical analysis, 3D visualization, and PolyPharmPrint methodology

The Broader Implication

If this level of molecular similarity exists between a top-selling drug and a common mushroom, how many more pharmaceutical-food connections are waiting to be discovered among the other 28,000 food chemicals - or the 400,000+ natural products in the COCONUT database?

This is why we built the tools to bring this capability to everyone. Starting with your phone.

Food Is Medicine in Your Pocket

Computational science tells us which foods have drug-like properties. But knowing that Reishi contains statins doesn’t help you if you can’t track whether you’re actually eating functional foods, in functional amounts, with measurable results. That’s the gap we built an app to close.

Food Health Screen

Your daily bridge between food science and your plate.

Food Health is the consumer-facing application of exactly the philosophy described in this article. Point your phone at any meal - a bowl of Reishi broth, a turmeric-spiced dish, a restaurant menu - and get instant AI-powered nutritional analysis: calories, protein, carbs, fat, and fiber in seconds. This is the first step.

But Food Health goes beyond calorie counting. Its scatter analysis and trend reporting let you do something that was previously only possible in research settings: correlate what you eat with how you feel, sleep, and perform - over days, weeks, and months. This is citizen science applied to your personal biology. I see this as a foundational first step to a new approach to natural product screening where anyone with a phone and a camera can participate

📸 Photo & Note Analysis AI estimates calories and macros from a photo or a typed description in seconds.

❤️ Apple Health (iOS) / Health Connect (Android) Sync See nutrition alongside sleep, activity, and heart rate — the full picture.

📊 Scatter Analysis & Reports Discover personal correlations: does turmeric improve your HRV? Does Reishi affect your sleep?

🔒 Local-First Privacy Your data stays on your device by default. You control everything.

📱Log Now, Analyze Later Capture meals even offline. Run AI analysis when you’re connected.

⌚Device Integration Custom prompts for glucose meters, blood pressure cuffs, and more.

Food Health TxD

Carbs & glucose for diabetes — spot the meals that spike you

For the 130 million Americans with diabetes or prediabetes, the drug-to-table insight is immediately urgent: every meal is a metabolic event. Food Health TxD brings AI meal scanning together with continuous glucose monitoring (CGM) data — so you can see, in real time, exactly how each food affects your blood sugar.

📸 AI meal scanning Instant carb and macro estimates from a photo

📉 Glucose overlay CGM readings and meals on the same timeline

📋 Menu scanner Scan a restaurant menu, pick the lowest-carb options

❤️ Apple Health (iOS) / Health Connect (Android) sync Reads glucose, writes carbs automatically

📊 Trend analysis Spot patterns between meals and spikes over weeks

As I wrote in May 2026: “Half of American adults are prediabetic or diabetic. This isn’t a headline - it’s a quiet emergency happening in every household.” TxD was built to give people a simple way to see how food affects their glucose, one meal at a time.

Food Health TxD is for education and tracking only. It is not a medical device and does not provide diagnoses, insulin dosing recommendations, or clinical predictions. Consult your healthcare provider before making changes to your treatment plan.

Your Personal Drug Discovery Lab

The computational pipeline that produced the Pravastatin-Reishi discovery is not locked in a university basement. We may just integrate the pipeline into the Food Health scan in Food Health TxD apps in future versions. But if you are interested in going at it yourself - with open-source tools and public databases, you can run versions of this same analysis - or, at the very least, use its outputs to make more informed daily food choices.

Target Selection

Choose a biological pathway you care about: cholesterol metabolism, blood sugar regulation, inflammation, cognitive function, sleep architecture. Pick a known drug that addresses it - that’s your computational starting point.

Computational Screening

Generate pharmacophore fingerprints from the drug. Several open source pharmacophoric generation utilities are available. Screen natural product databases (FooDB’s 28,000+ food chemicals; COCONUT’s 400,000+ natural products; ChEMBL’s biological activity data). Apply QSAR predictions to rank candidates by predicted activity. Cross-reference with traditional use and safety literature.

Systematic Exploration

Source the top candidates - ideally standardized extracts with known compound concentrations. Design a personal protocol: baseline biomarker measurement, defined supplementation period, repeat measurement. Track everything with Food Health Scan.

Data, Not Anecdote

Use Food Health’s scatter analysis to correlate your intake with objective metrics: sleep quality, heart rate variability, energy levels, blood glucose (with TxD if relevant). This turns personal experimentation into structured self-science.

The Spice Cabinet Pharmacy

The Pravastatin-Reishi case isn’t an isolated curiosity. Drug to Table screening of the top 200 pharmaceuticals is ongoing, and provisional findings suggest the spice cabinet is far more medicinally dense than most people realize:

• Turmeric (Curcumin): Computational analysis reveals similarity to multiple COX-2 anti-inflammatory drugs — the same target as ibuprofen and celecoxib, without the gastrointestinal side effects at culinary doses.

• Garlic (Allicin): Pharmacophore patterns consistent with ACE inhibitors used for blood pressure management. Traditional cardiovascular use across dozens of cultures suddenly makes molecular sense.

• Ginger (Gingerols): Structural relationships to 5-HT₃ antagonists — the class of drugs used clinically for nausea. Why does ginger settle an upset stomach? Now we can show you exactly why.

• Cinnamon (Cinnamaldehyde): Computational similarity to insulin sensitizers — consistent with traditional use of cinnamon to moderate blood sugar. Food Health TxD users: this is one worth tracking.

• Reishi (Ganoderic acids): Our flagship discovery — near-perfect pharmacophoric overlap with Pravastatin. The “mushroom of immortality” earns its name computationally.

Traditional Medicines Examples

Computational methods are now providing the molecular explanations for what traditional healers discovered empirically over millennia:

• Willow Bark → Aspirin: The original pharmacological natural product -salicylate’s mechanism is now textbook biochemistry

• Foxglove → Digitalis: Computational analysis may explain why this traditional heart remedy modulates cardiac contractility

• Berberine (Goldenseal, Barberry): Shows metformin-like computational profiles for glucose regulation - of strong interest to Food Health TxD users

• Reishi → Statin-like effects: Our current discovery validates and molecularly explains 2,000 years of cardiovascular use

Important context: Computational similarity does not equal clinical equivalence. Bioavailability, metabolism, dose, and individual genetics all matter enormously. These findings suggest promising directions for personal investigation - not prescriptions. Always consult your healthcare provider about any significant health concern.

💡 The Network Effect: Community-Driven Validation

As more people adopt these methods and use tools like Food Health to track their nutritional intake and health correlations, we build collective intelligence:

• Shared protocols for systematic personal investigation

• Open databases of personal results and meal-to-metric correlations

• Collaborative refinement of computational models

• Crowdsourced identification of effective natural approaches — validated by real human data, not just algorithms

The Democratization Opportunity

“For the first time in history, the same computational intelligence used to discover billion-dollar drugs is available to anyone - applied to natural compounds that belong to everyone.”

This isn’t about replacing pharmaceutical innovation. It’s about expanding the toolkit available for understanding how food choices might influence biological systems. And critically, it’s about closing the gap between discovery and action.

The Drug-to-Table computational methodology identifies candidates. Food Health makes the daily practice of food-as-medicine measurable. Together, they represent a new paradigm: molecular intelligence meets personal health data.

For the first time in history, the computational intelligence used to discover billion-dollar drugs is accessible to individuals. Not the full pharmaceutical apparatus - but the core intellectual methods: pharmacophore fingerprinting, QSAR modeling, open molecular databases.

And now, the consumer interface for living those discoveries is in your pocket. Food Health AI closes the loop between:

• 💡 Computational insight (Drug to Table methodology)

• 🍽️ Daily food choices (what you actually eat)

• 📊 Measurable outcomes (biomarkers, trends, correlations)

This is not Big Pharma. This is your pharmacy. The same computational power that generated patent portfolios worth billions can now help you make better decisions at the grocery store, at the spice market, and at your kitchen table.

“We’re not discovering that foods contain drug-like molecules. We’re remembering that drugs came from food in the first place - and building the tools to act on that knowledge.”

What’s Next

The Pravastatin-Reishi connection is one data point in what will become a publicly accessible atlas of drug-food molecular relationships. Here’s what’s coming:

• 📡 Complete computational protocols for Drug-to-Table discovery

• 🍄 Results from screening the top 200 pharmaceuticals against 28,000+ food chemicals

• 🧪 Personal testing frameworks for systematic self-experimentation (with Food Health AI integration)

• 🌐 Community database for sharing computational hits, personal protocols, and biomarker results

• 📱 Planned Food Health AI features: functional food tagging, compound tracking, drug-food interaction alerts

All protocols, databases, and methodologies will be published as open resources. The goal is to make molecular nutrition intelligence accessible to anyone curious enough to use it.

Follow along:

• 📖 Substack: drstevenmuskal.com

• ✍️ Medium: @smuskal

• 🔬 Drug to Table Technical Hub

• 📱 Food Health AI (iOS & Android)

• 📱 Food Health TxD (iOS)

⚠️ Important Disclaimer

This article represents computational research methodology for educational and wellness exploration purposes only. It is not medical advice.

Any health concerns should be addressed with qualified healthcare professionals. Natural compounds can have potent biological effects and potential interactions with medications — particularly important if you are currently taking statins or other prescription medications.

Correlational findings: Pharmacophore similarity does not guarantee identical biological activity. Actual effects depend on bioavailability, metabolism, dosage, individual genetics, and physiology. Structural similarity is a hypothesis-generating tool, not a clinical proof.

Natural ≠ Safe: Many plants contain potent bioactive compounds. Always consult a healthcare provider before using supplements alongside prescription medications.

Food Health and Food Health TxD are consumer wellness tracking applications, not medical devices. They do not provide diagnoses, clinical predictions, or treatment recommendations.

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a couple mixes, I wanted to pull teo from the archives. The first one was fun, especially if you know the back story. TimD vocals/guitar, Andrew Guitar, Rick Vocals, AlanW Bass, and Johnny Conga. The Way (first and only pass):

Then during a July 3rd mix (as we start planning for one this year) - The Story with neighbors. Maya leading vocals, Tammy and Pam assisting vocals, Dom Guitar, Jesse Guitar, Don Keys, TimH Bass:

Prologue: A Conversation at Cal-a-Vie

On a recent stay at Cal-a-Vie, my go-to spot just around the corner, I met a wonderful family radiating incredible positive energy. I introduced myself as Steve. And as often happens in my conversations, we eventually got to talking about AI.

We went around the group: who uses it, how, why. When they got to me, I said: “Yeah, I’ve been doing this for decades, and I actually built an AI version of myself. I built AI-Steve.”

For the remaining few days of the trip, every time they saw me after that, they called out: “Hey, AI-Steve!”

That group and those conversations inspired the recent piece “Too Big to Fail”, on dopamine-driven activities and keeping your instincts sharp as you age.

This article is the longer version of that conversation.

Part 1: The WHY. Dopamine-Driven Curiosity

I want to be honest about something from the start. I’ve never been particularly strategic about my career.

The things I build: I build them because they don’t exist yet, and because not having them bothers me. That’s the full explanation. Call it dopamine-driven curiosity. You encounter a problem. The absence of a solution becomes intolerable. You build something. And then the next problem finds you.

The projects I’m about to describe didn’t emerge from a business plan. They emerged from that impulse. From the question “why doesn’t this exist?” followed by the only logical next step.

But here’s the interesting thing, looking back over four decades of this: some of those impulses, the world eventually caught up to them.

Part 2: The Pattern. Skating to Where the Puck Is Going

Wayne Gretzky said the thing that everyone quotes: “I skate to where the puck is going to be, not where it has been.”

I don’t always do this with intention. But when you live on the bleeding edge long enough, eventually some things come into complete alignment.

I started working with neural networks in the mid-to-late 80’s. That was before most people knew what the phrase meant. Before deep learning. Before transformers. Before large language models capable of genuinely flexible reasoning. “Neural network” was, at the time, a research curiosity: something you had to explain at dinner parties rather than something everyone’s iPhone contained.

In 1991, I completed my PhD thesis. The title: “Predicting Features of Protein Structure with Computer-Simulated Neural Networks.”

Let that sit for a moment. Thirty years later, AlphaFold would win the Nobel Prize for solving this problem: protein structure prediction, at a scale and precision that changed biology forever. The 2024 Nobel in Chemistry went directly to the work of Demis Hassabis and the DeepMind team for what AlphaFold achieved.

When that Nobel was announced, I want to be clear about what I felt. Not resentment. Not “I was there first.” Not a need for credit or recognition or a footnote in history.

It was ecstatic personal affirmation.

My thesis was right. The direction was right. The world caught up. The field benefited enormously. That is the most satisfying version of this story, and I mean that without irony. The satisfaction is internal. The hope is always that others benefit. The work exists now, in the world, doing good. What more could you want?

This is the pattern I’ve noticed: dopamine-driven curiosity, pursued relentlessly at the bleeding edge, sometimes accidentally aligns with where the world is heading. It’s not prescience. It’s not genius. It’s simply what happens when you follow the impulse long enough.

The same pattern appears in everything I’m about to describe.

Part 3: The Projects. Brief and Human

Toast Our Friend: The Seed

Every project I’ve built has started with a question I couldn’t leave alone. This one started with grief.

A few years ago, at my sister’s celebration of life, hundreds of people came from across the country, sharing stories, laughter, and tears in equal measure. I sat there thinking: she would have loved this. I only wish she were here to see it.

That thought, we should tell people what they mean to us while they’re still here, became the seed of everything that followed. And the very first thing I built in response to it was Toast Our Friend: a platform for celebrating people while they’re alive, and for honoring them after they’re gone.

You create a circle for someone: a friend fighting an illness, someone you want to honor, someone you love who’s moving on. Friends and family can post toasts: stories, memories, photos, voice messages. Real-time. Visible to everyone in the circle. Not a eulogy. Now.

We’ve created circles for Scott Miller, Mike Szwajkowski, Tom, and Coop: friends I’ve known for years. Some for decades, others since the first day of high school in 1980. Mike was my longest and most cherished friend. Nearly half a century of friendship. When he passed, I wanted his people to have a place to gather, remember, and honor him together.

The platform was built with AI assistance alongside other tools. But the meaning came from somewhere older and deeper than any technology I could build.

AI-Dad: What Toast Pointed Toward

Toast answered the urgent question — why are we waiting? But it also pointed toward a quieter, second question: what about after? What if, in addition to gathering memories around someone, we could preserve the voice itself — not as a static archive or a memorial page, but as something a grandchild could still talk with?

That question became AI-Dad, the natural follow-up to Toast.

AI-Dad is an experiment in preserving a person’s voice as a conversational system. Grounded in their history, their turns of phrase, their particular way of reasoning through a problem. Memory, voice, and ethical design: those three constraints shaped everything. It remained a research project, an exploration. But it planted the question that came next.

For a technical deep dive on the AI-Dad concept, see the AI-Dad article on Medium.

Food Health: Food Is Medicine

I believe food is medicine. I’ve believed that for a long time, long before it became a wellness industry catchphrase. But for me, this isn’t a philosophy I read about somewhere and adopted. It’s a scientific conviction I can defend at the molecular level. That distinction matters.

I’m a Ph.D. chemist. Two of my early career positions put me squarely at the intersection of nature and pharmacology in ways that shaped everything I’ve thought about food and medicine since. At Affymax Research Institute, and before that at MDL Information Systems (formerly Molecular Design Limited), I was working at the center of natural product screening as a drug discovery strategy. This was not a niche academic exercise. Natural product screening is, in fact, the foundational strategy behind the discovery of virtually every major drug class in existence.

Here is the core insight that I absorbed during those years, stated plainly: nature has spent billions of years solving therapeutic problems. Evolution is the longest-running drug discovery program in history. Organisms develop compounds to defend themselves, to communicate, to modulate their own biochemistry. And those solutions, honed across geological timescales, often turn out to be exquisitely effective at interacting with human biology too.

The pharmaceutical industry’s model, at its most basic, has largely been this: identify what nature already solved, then alter the molecular structure just enough that a patent examiner would recognize it as novel. Preserve the biological activity. Create an intellectual property position. That’s the model. It’s how most of the drugs you’ve ever taken came into existence.

The idea that plants and other natural substances contain medicine was once treated as folk wisdom, even dismissed as fringe thinking. It is now the bedrock of pharmacology. The field caught up to what traditional healers knew for centuries, and what the chemistry eventually confirmed.

I’ve illustrated this directly in my own research. The Reishi mushroom/statin structural similarity post makes this concrete: compounds found in Reishi mushrooms share meaningful structural similarity with the statin drug class, one of the most widely prescribed and profitable pharmaceutical categories in history. The structural overlap isn’t coincidence. It’s nature having arrived at a solution first.

This is precisely what PharmPrint/Polypharmacophore fingerprinting was built to find. PharmPrint is a computational technology I developed, using 3-point and 4-point pharmacophore triplets encoded as 10,000-bit fingerprints, to map where nature’s solutions overlap with pharmaceutical chemical space. The technology takes a molecular structure and identifies which pharmacophoric features it shares with known drug-active compounds. It is, in the most literal sense, a tool for asking: where has nature already solved a problem that we’re still working on?

Connecting PharmPrint to food isn’t a stretch. It is the natural extension of the same logic. The same structural analysis that reveals why a Reishi compound resembles a statin can be applied to any food-derived compound. Drug to Table is the direct expression of this: finding therapeutic compounds hidden in food using polypharmacophore matching across natural products and drug targets.

So when I say food is medicine, I’m not quoting a wellness slogan. I’m drawing on forty years at the intersection of chemistry, pharmacology, and molecular modeling. I’m saying: the structural evidence is there. The computational tools exist to find it. And the apps I’ve built, which I’ll describe next, are the practical expression of that conviction in the hands of everyday people.

Food Health: Scan & Score lets you photograph a meal and get full nutritional data, including calories, macros, fiber, and protein, in seconds. It launched in early 2026 and is live in the Apple App Store (App ID: 6759629101) and on Google Play.

Food Health TxD is a diabetes-aware companion app, designed for people managing blood sugar. It pairs real-time carb and macro estimation with continuous glucose monitor data, giving metabolic context to food choices in the moment they’re being made.

Shared Health Projects is a social health platform: a collaborative layer for sharing health journeys with people you trust, whether a partner, a physician, a coach, or a research group. I made the source available on github.

These apps came together through a combination of tools and approaches. AI-Steve was one part of that process, helpful in generating and reviewing code, but not the sole or exclusive platform for their development. If you want the full technical picture of how it all worked, I’ve written about the architecture in detail, and the links are at the bottom of this article.

AI-Steve: The System

The most unexpected outcome of building AI-Dad was the question it naturally generated next: what if I built that same thing around my own life, in real time?

Not a memorial for the future. An operating system for the present.

That became AI-Steve.

AI-Steve runs continuously on my machine. It ingests and indexes my emails (over 213,000), my iMessages, my calendar, my Apple Health data, my photos, my documents, and decades of research. Over 314,000 chunks of my life, semantically searchable in under a second. I can ask it anything in plain English and get back an answer grounded in my actual history, not a guess.

The technical architecture, including the RAG pipeline, the embedding approach, and the hybrid retrieval design, is worth understanding if you’re building something similar. I’ve described it in detail in AI-Steve Deep Dive and the follow-up piece AI Steve II: The Governed Loop. I’ll point you there rather than recreate those here.

What I want to talk about in this article is the why, and the thing about AI-Steve that I’ve thought about most.

The Governed Loop is the formalization of something I noticed after the basic system was working. AI-Steve, given enough context about its own architecture, could propose extensions to itself. It could identify gaps in its own capabilities. Recommend new data sources to ingest. Suggest new features.

But it doesn’t execute these autonomously. Every proposal comes to me as a structured issue: what it wants to build, why, what it would require. I review, approve, reject, or modify. Then the system gets built.

This isn’t just an interesting engineering pattern. It reflects something I genuinely believe about how AI should work right now: the human stays in the loop. Not as a bottleneck. As the decision-maker. The AI amplifies judgment; it doesn’t replace it.

That’s not a technical constraint I imposed reluctantly. It’s a design philosophy I chose deliberately.

Part 4: The Bigger Picture

I’m not writing this to take credit for anything.

The thesis is thirty-five years old. AlphaFold deserved its Nobel, and the world benefited from it in ways I never could have accomplished alone. The apps are in the store. The articles are out there. If any of this is useful to anyone, that’s the point.

What I believe is this: we’re at an inflection point. AI isn’t just a productivity tool anymore. It’s becoming cognitive infrastructure. And the questions around how we build that infrastructure, whose values it reflects, how much human judgment it preserves, whether it serves life or colonizes it, are not technical questions. They’re deeply human ones.

The metabolic crisis is real. I’ve written about this separately, and I’ll keep writing about it, because food and health and the ways we’ve engineered ourselves into poor metabolic health is one of the defining crises of this era, and AI can be one part of addressing it.

The disconnection crisis is real. Toast Our Friend exists because I believe we’ve lost something in how we honor and celebrate each other, and because the tools exist now to create something better.

The question of legacy, of how we preserve what we know and who we were for the people who come after us, is real too. AI-Dad was a first attempt at an answer. I suspect it won’t be the last.

I’ve been skating toward these problems for a long time. Not always knowing where the puck would end up. But following the impulse.

Eventually, some things come into alignment.

This Story Is for You

Before I close, I want to say something directly. These posts are not just a chronicle of what I’ve built. They’re an invitation.

The thread running through everything I’ve described is not technical skill. It’s insatiable curiosity, pursued consistently. That curiosity is generative. If you have that bug, you can produce at this level too. I mean that. Not as encouragement for its own sake. As a statement I believe is genuinely true.

The tools available today mean that non-technical specialists can now build technologies that were previously only accessible to well-funded teams. AI assistants, app frameworks, voice cloning, rapid prototyping: what used to require a team, a budget, and months of runway can now be initiated over morning coffee with a paragraph of text. We used to call it RAD, Rapid Application Development. What we have now is something faster than that, and far more accessible.

I’ve spent forty years as a scientist and builder. I’ve seen technology cycles. I’ve seen what it looks like when a genuine platform shift arrives. This one is different. The barrier to entry has not just lowered. For certain kinds of work, it has effectively disappeared.

The age of the solopreneur is not something that’s coming. It is here, today and tomorrow. If you have a curiosity you’ve been sitting on, a problem you’ve been annoyed that nobody has solved, a domain where you have deep expertise and can see the gap clearly: the window is open now. Not in five years. Not when the technology matures further. Now.

I share none of this to inspire admiration. I share it because I genuinely hope that when you read about what one person has built at the intersection of AI, chemistry, health, and human connection, something clicks. That you look at your own curiosities and recognize what’s possible. That you ask the question I’ve been asking for forty years: why doesn’t this exist? And then you take the next logical step.

That’s the whole invitation.

What’s Next

AI-Steve keeps growing. The Governed Loop means it’s always proposing what comes next, and I’m always deciding.

If you want to follow along, subscribe on Medium and on Substack. I write about this work at the intersection of AI, health, and what it means to build things that matter.

And if you’re building something similar, or thinking about it, I’d genuinely love to hear from you.

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

Related reading:

• Too Big to Fail: on dopamine-driven activities and keeping instincts sharp as you age

• AI-Dad: Preserving Legacy Through Conversational Intelligence

• AI-Steve Deep Dive

• AI Steve II: The Governed Loop

• Food Is Medicine. But First It Has to Become Data.

• The Metabolic Crisis Is Not a News Story

Projects mentioned:

• Food Health: Scan & Score, App Store | Google Play

• Food Health TxD, iOS

• Shared Health Projects, iOS

• Toast Our Friend

• PharmPrint / Polypharmacophore

• Drug to Table

• stevenmuskal.com

The Long Arc

In the late 1980s, I was spending twelve to sixteen hours a day inside neural network systems at UC Berkeley. Not using them. Building them. My graduate work involved developing tools to make neural network training more efficient, contributing to the foundational methodology that would, decades later, quietly underpin a great deal of what we now call artificial intelligence. The work was deliberate, deep, and computationally demanding in ways that are almost quaint by today’s standards. We were doing significant things on hardware that modern inference workloads would run circles around without breaking a sweat.

There is a meaningful distinction that tends to get lost in current media coverage of AI: training a model and using one are categorically different activities. Training requires massive GPU clusters, enormous datasets, industrial energy consumption, and months of coordinated effort. Inference, using the trained model to respond to a query, is comparatively lightweight. That asymmetry has enormous implications for how AI spreads and scales, but the coverage rarely distinguishes between the two, which leaves most people with a distorted sense of what the resource footprint actually looks like. I spent my Berkeley years in the heavy-lifting phase. I know the difference from the inside.

After the methodology was built, the dynamic shifted. Leveraging it required far less time than constructing it had. Applications came quickly. You build the engine once; you drive it many times. Over the decades that followed, my engagement with AI systems followed that pattern: significant front-loaded investment, followed by relatively efficient deployment. The curve flattened. The ratio of time-inside to output improved dramatically. This mirrors the broader arc of any foundational technology, where the bulk of the effort is in the establishment, not the execution.

Then, roughly three years ago, something inverted.

With the arrival of mature large language models and CLI-accessible tools, my time spent inside AI systems surged back toward early-career levels. Not building, this time. Using. Claude CLI running with ` — dangerously-skip-permissions`, Codex working through complex engineering tasks, Factory AI’s Droid orchestrating multi-step workflows, and Gemini CLI configured to `approve-all` to maintain uninterrupted flow state during demanding sessions. Hours that used to look like engineering have been replaced by hours that look like… something harder to categorize. The inputs are different. The cognitive mode is different. But the time commitment has returned to something that would have been familiar to the version of me sitting in front of a workstation in Berkeley in 1988.

That U-shaped curve, enormous time, then much less, then enormous again, is precisely where this becomes interesting to think about. Because I am not a newcomer arriving at AI with fresh eyes and no reference point. I am someone who has spent four decades living inside these systems in various forms. And that, it turns out, is exactly what makes the draw observable in ways it might not otherwise be. The hook is most visible to someone who has seen it from multiple angles over a very long time. Not despite the expertise, but because of it.

Dopamine Businesses

Let me say directly what I think is happening, because the framing matters more than any particular observation I can offer.

Dopamine businesses are not a new invention. Dopamine, the neurotransmitter that drives anticipatory wanting rather than simple pleasure, is as old as human biology. We have always used it on each other. Gambling is a dopamine business. Alcohol is a dopamine business. Narrative fiction, gossip, ritual, romance, commodity markets, the architecture of casinos designed to remove your sense of time passing, the engineered unpredictability of a slot machine’s reward schedule: all of them operate on the same underlying circuitry. The circuit evolved because it was adaptive. It pushed our ancestors toward food, toward social connection, toward novel information, toward the kind of environmental engagement that increased survival odds in a world of genuine scarcity.

What changed is not the existence of dopamine businesses. What changed is the engineering precision and the stimulatory load. These are related but not identical problems.

Our evolved inhibitory systems, the internal braking mechanisms that produce satiation, aversion to excess, the sensation of having had enough, are real and genuinely functional. They are not easily overridden under conditions resembling those in which they evolved. But they developed under a stimulatory environment that was, by any modern standard, quiet. Variable, unpredictable, often lean, and never continuously optimized to keep the seeking behavior running. The problem is not that the brakes are broken. The problem is that the gas pedal is now operated by systems that were engineered, specifically and at considerable expense, to apply pressure just below the threshold where braking activates. The result is a sustained, low-grade override of circuits that were never calibrated for continuous load.

Two colleagues recently published a paper on modeling cognitive overload that I think is a serious first step toward putting a quantitative frame around this problem: [The Bias Amplification Model A Mathematical Framework for Quantifying Cognitive Distortion Under Conditions of Information Overload — and their model is here: The Bias Amplification Model] The instinct to measure it is exactly right. You cannot manage what you cannot observe. The challenge is that most of us are currently operating with no real instrument for reading our own cognitive load, which means we are flying without gauges in conditions that have changed more in the last decade than in the prior century.

The Same Mechanism, Different Substrates

The most clarifying thing I can say about the current AI moment is that the operating principle behind what Big Tech built is not metaphorically similar to what Big Food built before it. They are the same mechanism applied to different substrates.

Big Food spent decades, from roughly the 1950s onward, engineering hyper-palatable products. Food scientists calibrated fat-to-salt-to-sugar ratios with extraordinary precision to maximize palatability while minimizing satiety. They engineered texture profiles that felt compelling without triggering the body’s stop signal. They discovered the bliss point, the sweet spot of sweetness or saltiness that generates maximum hedonic response, and optimized relentlessly toward it. The goal, stated plainly, was to override the body’s natural feedback loop that says enough. Not through deception, exactly, but through engineering that was specifically designed to outpace the biology it was operating on.

The metabolic consequences of that project are still unfolding. I wrote about this at length in a piece on the metabolic crisis (Substack version here), but the short version is that somewhere between 88 and 93 percent of American adults are now metabolically compromised by at least one risk factor. This is not a number that comes from a single dietary mistake or an individual failure of willpower. It is the aggregate output of an industry that spent seventy years engineering food to override the satiety signals that keep metabolic health intact.

Big Tech learned from that playbook and applied it to cognition. The medium changed; the strategy did not. Engagement loops built on variable reinforcement schedules, designed to be just compelling enough that you stay but never so satisfying that you leave fulfilled. Recommendation algorithms that learned your individual response profile and calibrated content delivery to extend session time. Notification architectures engineered to interrupt your attention at precisely the moment when re-engagement was most likely. These are not rough approximations of behavioral influence. They are precision instruments, developed by teams of behavioral scientists, running on infrastructure that updates in real time based on your response.

TikTok is the clearest specimen of what this looks like at full development. The recommendation algorithm does not weight whether you said you liked something. It tracks duration watched, completion rate, sound engagement, scroll hesitation before a restart, the particular speed of your thumb as you move through content. Every one of those signals feeds a system that is continuously updating its model of what will extend your session. The content is almost incidental. It is the carrier wave for a psychological feedback loop that has been built with billions of dollars of engineering investment. Facebook Reels followed nearly the same architectural blueprint. You do not choose your echo chamber on these platforms. The platform builds it around you, in real time, tuned to your individual response profile. The algorithm does not know you. It does not need to. It knows your behavioral signature well enough to accomplish the same thing.

The important thing to hold onto here is that these are not incidentally addictive products that happened to capture people’s attention. They are systems that were designed, iterated, tested, and refined specifically to maximize the duration and intensity of engagement. The dependency is a feature, not an externality.

AI as Dopamine Business

I want to be precise about this, because I think the standard framing undersells what’s actually happening.

The claim is not that AI companies sat in a room and said “let’s make something addictive.” Some of what I’m describing is structural rather than deliberate. But structurally, a well-designed LLM interaction generates exactly the patterns that sustain engagement loops, and it does so repeatedly and reliably enough that the effect is real regardless of intent.

Consider the mechanics. An incomplete response that leaves a thread unresolved, creating a pull to follow up. A step-by-step technical walkthrough that requires sustained engagement across multiple iterations before any deliverable appears. An overwhelming first-pass answer that is genuinely useful but also genuinely too large to absorb, which prompts you to ask for synthesis, starting a new loop. For those of us oriented toward critical thinking, the occasional wrong answer that you catch, and then find yourself interrogating, pulling you deeper into the exchange in the course of correcting it. Each of these is a mechanism. Each creates an incomplete reward cycle that sustains engagement precisely because it is incomplete.

I run Anthropic’s Claude, OpenAI’s Codex, Factory AI’s Droid, and occasionally Google’s Gemini, all via command line, all as deeply integrated layers in my daily work. This is not casual or exploratory usage. These tools are woven into the architecture of AI-Steve (Substack version), a comprehensive personal AI system I’ve built and documented that runs across my email, calendar, health data, code, and knowledge base. The second piece in that series (Substack version) covers how the system now proposes its own extensions, which should give you a reasonable sense of how far down this path I have gone and how deliberately I have gone there.

The economics of this are also worth naming. These systems often begin free and escalate. Heavy usage across multiple platforms is not unusual at $200 per month per subscription, or more. That spending feels justified because the value is real. But the pattern of spending, the shape of it, tracks something that goes beyond a rational productivity calculation. I recognize the pattern from other contexts. The value justification and the dependency are not mutually exclusive. They coexist quite comfortably, which is part of what makes it worth paying attention to.

The draw is genuinely bidirectional, and this is important. The model pulls you in through its mechanics. But your own psychology pulls you toward it at the same time. The more capable the system, the more problems you bring to it. The more problems you bring to it, the more time you spend in session. The more time you spend in session, the harder it becomes to imagine working at the same pace without it. That loop has an acceleration to it that is not purely about the tool. Some of it is about what happens to the user.

Session Saturation and the Successor Prompt

There is a practical dimension to heavy AI usage that does not get discussed enough, in part because it requires extended experience to observe clearly.

These systems degrade during long sessions. Not catastrophically, and not in ways that are always obvious in the moment, but in ways that accumulate. If you are deep in a working session of two or three hours, the model begins to show what I can only describe as fatigue. It starts revisiting ground that was already covered. It generates responses that are technically coherent but increasingly approximate. It loops back to early-session framings even when the conversation has moved past them. The context window is working hard with more history than it handles gracefully under sustained load. The session is saturated.

When I notice this happening, I use what has become one of the more reliable techniques in my workflow. I give the model a prompt approximately like this: ”Fully document everything we have done so far and generate a prompt for your successor. I want to start a new session.” The model produces a comprehensive handoff document. I close the session entirely, open a new one, and paste that document as the opening message. The new instance has the benefit of context without the accumulated weight. It is, as I have come to think of it, giving the AI a night of sleep. The clarity difference when you return to a problem that way is immediate and noticeable.

There is a related observation I am somewhat less comfortable sharing, but it is accurate. I have found that off-hours usage tends to yield meaningfully better results, responses that feel less throttled, more precise, less prone to the fuzzy approximations that suggest something is being managed on the backend. Peak usage periods seem to correlate with subtly degraded quality. So I have started timing my most demanding sessions for early morning or late at night, when the systems are less congested.

I recognize what this sounds like. It sounds exactly like someone timing their purchases to get better quality at lower friction. The junkie who knows which hours the dealer is freshest and plans accordingly. I am not being dramatic. The behavioral pattern is structurally identical. The fact that the activity is productive does not change the shape of the dependency, and I think the honesty of that comparison is worth sitting with rather than deflecting.

Biological Inhibition and the Overwhelmed Brake

Here is something that consistently gets underappreciated in discussions about digital dependency: our inhibitory mechanisms are real, evolved, and functionally capable. The problem is not that they are broken. The problem is the environment they are operating in.

The wanting system and the braking system are paired. Dopamine drives approach. Satiation, aversion, discomfort with excess, the physical signal that says stop, these counterweights are built in. They work. They kept human beings from bingeing themselves into dysfunction across hundreds of thousands of years of evolutionary history, in environments where stimulation was scarce, variable, and predominantly low-intensity.

That environment is gone. The systems we have engineered, processed food, algorithmic social media, AI engagement loops, are high-intensity, continuous, and deliberately calibrated to operate just below the threshold where the braking mechanisms activate. This is not accidental. It is the target. Build something compelling enough to sustain engagement but not so aversive that it triggers a withdrawal response. The result is a sustained low-grade override that is genuinely novel in evolutionary terms. Our ancestors never faced anything like it at this scale or consistency.

The cognitive overload modeling work referenced [here] is trying to put a quantitative frame around what happens when that load accumulates. That is exactly the right direction. The challenge is that most of us are currently managing without any real read on where our cognitive load sits, which means we are making trade-off decisions without instruments. The Bronze Age analogy I return to: warriors of that era wore armor calibrated to the weapons they faced. When the weapons changed, the armor became inadequate. Not because armor was a bad idea, but because the threat had outpaced the protection. We are in a comparable situation. Our evolved inhibitory systems were calibrated to a prior environment. The dopamine businesses now operating at industrial scale are a different category of threat, and managing them with unmodified biology is asking the wrong tool to do the job.

The Glasses Problem, and What Hormetic Stress Has to Do With It

I have worn corrective lenses for most of my adult life. During engineering school, my prescription kept escalating. Not because anything medically unusual was happening, but because I was in front of a workstation for twelve-hour stretches, and my eyes were adapting to continuous close-focus demand. Each new prescription would feel right for about a week, then my vision would adjust around the correction and the glasses would feel insufficient, and the cycle would repeat. The tool was recalibrating my biology in the wrong direction. The correction was enabling a feedback loop that made the underlying capacity progressively weaker at operating without the support.

At some point I started doing something deliberate. I stopped reaching for corrective lenses reflexively. During portions of the day when the stakes of acuity were manageable, I let things stay slightly out of focus rather than immediately compensating. This was uncomfortable in a predictable way. But over time something interesting happened: my vision adapted. Not to perfect acuity, but to meaningfully better function without constant correction. The underlying capacity, deprived of the crutch, had room to operate and to recover some of what the crutch had been compensating for.

This is a well-documented principle in physiology under the name hormetic stress, the idea that strategic, controlled withdrawal from a support system can strengthen the underlying capacity that the support was masking. Intermittent fasting, cold exposure, graduated resistance training: all operate on some version of this. The temporary discomfort is not the point. The adaptation is.

I think about the glasses problem with some regularity when I consider AI dependency. The risk is not that AI makes my thinking worse during a session. By most measures, it makes it better, faster, more comprehensive. The risk is the longer-term trajectory: whether continuous delegation gradually atrophies the underlying capabilities being delegated. Not suddenly. Not obviously. In the way that a muscle you stop loading eventually loses the functional range you had come to take for granted. You don’t notice until you need it.

For any system where biology is involved, where the support is operating on top of a biological capacity rather than simply replacing a mechanical one, the question of what happens to the underlying capacity under conditions of sustained offloading is a serious question. It does not resolve itself just because the support is valuable. The glasses escalated while delivering genuine utility. The utility and the atrophy were coexisting.

The practical implication is not to abandon the tools. It is to build in deliberate resistance: working through important problems in your own head before asking the model, checking your intuition before comparing it to the AI’s framing, maintaining the habit of thinking hard about something without autocomplete for your thought process. Not as a purity position. As maintenance.

The “Too Big to Fail” Threshold

We have seen this transition before, in different contexts.

Financial institutions, somewhere in the decade before 2008, crossed a threshold where their interconnection with the rest of the economy had become deep enough that their individual failure was not just a problem for them. It was a systemic destabilizer. “Too big to fail” does not mean deserving, or right, or particularly well-managed. It means the cost of the absence has grown larger than the cost of the continued presence. The institution has become structural. Removing it does more damage than sustaining it.

Certain platform businesses crossed a version of this threshold around the same period. When commerce, communication, identity verification, and community infrastructure are routed through a single platform at sufficient scale, opting out is no longer a meaningful option. The decision is no longer about whether you endorse the product. It is about whether leaving is less disruptive than the problems that come with staying. The platform has become infrastructure. Infrastructure does not get evaluated on its merits in the usual way.

AI is following the same trajectory. It is doing so faster than either of those precedents, and across a broader set of domains simultaneously, which is what makes this moment genuinely different from the comparable moments in fintech or platform media.

Consider the concurrent integration: AI is now load-bearing infrastructure in software development, where its absence would materially slow output across the industry. It is embedded in business decision support in ways that are difficult to disentangle from the decisions themselves. It is a functional creative partner in content, media, research, and communication. For individuals operating across multiple disciplines, as I do, it is a thinking amplifier that has been woven into the cognitive architecture of daily work. None of these applications, taken alone, crosses the threshold. Together, the accumulation changes the character of the dependency.

The critical threshold is not about capability. It is about when the absence of something becomes more destabilizing than its presence. I believe that threshold has been crossed, without ceremony, without a moment anyone could point to, and without the coordinating frameworks that might help us understand what we are navigating. It happened in the accumulation of individual dependencies, each of them reasonable in isolation, that collectively produced something with a very different character.

The Illusion of Control

There is a quality to working inside AI systems that feels like a tight feedback loop between your intent and the output. You frame the prompt. It responds. You refine. It adjusts. The iteration is fast and the responsiveness is real. It feels, in the best moments, like a very capable extension of your own thinking.

But underneath that feedback loop, you are operating inside a system with constraints you do not fully see and did not negotiate. Training data assembled by decisions you were not part of, reflecting priorities that were not yours. Model updates deployed on schedules you do not set, in ways that are not announced, that shift behavior in ways you might not notice unless you have been using the system long enough to have built a baseline. Safety filters and content policies that shape outputs in ways that are invisible until they are not. Architectural choices made years ago, by teams working with assumptions about what a useful AI should do that may or may not match what you actually need. You are operating a complex system to which you do not have access to the hood. That is fine for most purposes. You do not need to understand the combustion cycle to get where you are going.

It becomes less fine when the system is load-bearing. When your strategic thinking, your code, your communications, and your framework for evaluating new problems are all routed through infrastructure you cannot fully audit, the trust is implicit and largely unexamined. I am not suggesting that should be a reason to stop, but it should at minimum be conscious. The distinction between using a tool and being embedded in infrastructure is not one we tend to make in real time. It tends to become visible only in retrospect.

What AI Does to Gut Instinct

This is the part that concerns me most in the long run, and it is also the most difficult to measure, which is itself part of the problem.

The best decisions I have made in my career, the ones that look cleanest in retrospect and held up under conditions I could not have anticipated, rarely came from analysis alone. They came from pattern recognition built over decades of being wrong in similar situations. From a specific quality of discomfort that consistently precedes a bad deal. From sensing misalignment in a conversation before the data had confirmed it. From a kind of informed intuition: not arbitrary, not mystical, but deeply calibrated to a large accumulated sample of outcomes. That kind of knowing does not come from structured reasoning. It comes from having been in the room, made the call, lived with what followed, and done it again many times.

AI is genuinely excellent at structured reasoning. Given a well-defined problem and adequate information, it will often produce faster and more comprehensive analysis than I could produce alone. That is real, and I rely on it.

But lived intuition is not structured reasoning. It operates through a different mechanism, built from a different substrate, and it does not transfer as a data set. You cannot prompt-engineer your way to pattern recognition that took thirty years to develop. And if you spend enough time outsourcing the reasoning process to an external system, the internal version of that capacity follows the biological logic of disuse. Not dramatically. Not in a way that announces itself. In the gradual, quiet way that any capability fades when you stop exercising it.

I try to stay ahead of this by going through important decisions on my own first, before I bring in the AI, before I compare its framing to mine. Not as a ritual. Not as skepticism about the tools. As deliberate maintenance of a capability I am not ready to trade away, particularly given that the trade would be invisible until it was already complete.

A Personal Checkpoint

For what it’s worth, here are the four questions I’ve settled on as a working audit for my own situation:

• Am I using AI to amplify my thinking, or replace it?

• Am I still capable of doing the work without it, even if slower?

• Am I asking it to challenge me, or just to execute?

• Am I aware of where it is likely to be wrong?

None of these have permanently right answers. The answers shift depending on the day, the task, and the context. That is the point. They are meant to be revisited regularly, not resolved once and filed away.

The Uncomfortable Conclusion

I am not going to stop using these tools. The value is real, the integration is deep, and the capability gap between what I accomplish with them and what I would accomplish without them is too significant to voluntarily close. AI-Steve is a hundredfold increase in productive output, and that is not a number chosen for effect. It reflects something accurate about what happens when a system trained on four decades of your own reasoning patterns is deployed across every domain you work in.

But I think honesty is warranted here in a way it rarely is. Not alarm. Honesty.

AI has become cognitive infrastructure faster than we have developed any framework for thinking about what that means. The dopamine mechanics are real, structural in some cases and deliberate in others, but operative regardless of origin. The biological inhibitory systems that are supposed to moderate our engagement are working well below their design load in an environment they were never calibrated for. The gut instinct atrophy risk is real and invisible in real time. And the “too big to fail” threshold, the point at which AI’s absence would be more destabilizing than its continued presence, is not approaching. It has already been crossed.

The answer is not strategic retreat. It is measurement, awareness, and deliberate practice: knowing your baseline, tracking what is changing, building in periodic resistance, staying honest about what you are trading away. The same principles that apply to managing any other system of dependency, where the dependency is real, the value is real, and the two coexist in ways that require active navigation rather than passive acceptance.

I do not have a clean solution. I am working it out in real time, as most of us are. What I am more confident about is that the people who will navigate this most effectively are the ones willing to name it accurately: not only a productivity revolution, but a fundamental cognitive reorganization happening at a speed that outpaces any framework we have for thinking about it. Building deliberately inside that reorganization requires seeing it clearly.

Drop a comment. Tell me how you are managing it, what is working, what is not, whether you have found strategies I haven’t thought about yet. I am genuinely interested in what people are discovering. This is a conversation worth having, not a conclusion worth broadcasting.

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. - a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a couple music mix vids — we had a fun mix with a new crew to ORanch {except for Johnny Conga helping me to hold things down} — RonF (vocals/guitar), TimL (Bass/Vocals), Steve (Guitar), Johnny (Congas). It’s always fun when brand new people plug, play, and groove on songs new and have fun doing it! Tim and Ron co-authored the above paper:

Think about the people you know well. Your family, your close friends, the colleagues you’ve worked alongside for years. How many of them are managing diabetes or prediabetes right now? How many are carrying it without knowing? How many are metabolically struggling in ways that haven’t yet been named or diagnosed?

I grew up in a world where diabetes was something you heard about in passing. There might have been one person in your extended circle. Type 2 barely entered the conversation. It was the kind of condition that felt distant, clinical, abstract. Now it is everywhere. Not on the news as some faraway epidemic. In the room with you. At the dinner table. In the family group chat.

That shift did not happen gradually enough that we noticed it arriving. It happened across a generation, in plain sight, while we were busy with other things. And what I find remarkable, and genuinely troubling, is that the numbers continue to accelerate.

A Number That Should Startle You

More than 38 million Americans currently have diabetes, representing about 11.6 percent of the entire US population. Another 98 million Americans have prediabetes, the majority of whom are unaware of it. Put those numbers together and you are looking at roughly a third of the country at some stage of glucose dysregulation.

Globally, the picture is equally striking. According to the World Health Organization and data published in The Lancet in late 2024, the number of adults living with diabetes worldwide has surpassed 800 million, more than quadrupling since 1990. Not doubling. Quadrupling. In roughly one human generation.

But the statistic that genuinely stopped me when I first encountered it comes from a 2018 study by researchers at the University of North Carolina: only about 12 percent of American adults meet criteria for optimal metabolic health, defined as healthy levels of blood sugar, triglycerides, HDL cholesterol, blood pressure, and waist circumference without the use of medications. A 2022 study from Tufts University put that figure even lower, at approximately 7 percent.

Seven to twelve percent. That means somewhere between 88 and 93 percent of American adults are operating with at least one metabolic risk factor.

To put that in historical context: Type 2 diabetes was considered a rare condition in the mid-twentieth century. Physicians who trained in the 1950s and 1960s might have encountered a handful of cases across their entire careers. It was classified as a disease of older age, of significant excess weight, of unusual circumstances. The phrase “Type 2 diabetes” barely existed in public vocabulary. Now it is discussed in middle schools. Pediatric endocrinologists have waiting lists. The disease that was once rare enough to be remarkable has become so common that we have largely stopped remarking on it.

What changed?

The Drivers We Talk About, and the Ones We Don’t

The conventional explanation focuses on diet and physical activity. Ultra-processed foods now constitute the majority of calories consumed in the United States. Studies tracking the rise of industrial food processing point to refined carbohydrates, seed oils, and the engineered palatability of products specifically designed to override our natural satiety signals. Portion sizes expanded. Caloric density increased. Fiber largely disappeared. The metabolic consequences followed.

Sedentary behavior amplified the effect. Office work, car commuting, streaming entertainment, and smartphones conspired to reduce the amount of time most adults spend in genuine physical activity. Not dramatically for any single generation, but cumulatively across decades.

Sleep is another factor that gets less attention than it deserves. The research connecting sleep deprivation to insulin resistance, metabolic dysregulation, and weight gain is substantial and growing. I wrote about this at length in an earlier piece on measuring and managing sleep. When you do not sleep well, your body’s ability to process glucose deteriorates. Chronically. A population that sleeps significantly less than it did fifty years ago is also a population primed for metabolic dysfunction.

Chronic stress belongs in this conversation too, though it tends to be treated as a soft variable. Cortisol dysregulation affects blood sugar. The constant low-grade alertness that comes from always-on digital life, economic precarity, and the particular anxiety of watching the world move faster than we can process is not neurologically benign. Neither is the dopamine disruption from screen-based reward loops, which increasingly correlates with disrupted eating patterns, sleep fragmentation, and reduced motivation for physical movement.

There is also a less-discussed category: environmental contributors. Some researchers have begun examining the correlation between industrial chemical production and diabetes incidence, including endocrine-disrupting compounds in food packaging, pesticide residues, and pollutants that may interfere with metabolic signaling. The evidence here is still accumulating, and causation is difficult to establish. But the correlation curves are hard to ignore.

None of these factors operates in isolation. They interact with each other, compound across time, and land differently depending on genetics, access, and circumstance. The honest answer to what changed is: almost everything, almost simultaneously, over about fifty years.

The Trauma Thread

I want to raise a question here that I do not think gets nearly enough attention, and that I’m not in a position to answer definitively. It is more of an observation that has been sitting with me since a conversation I had on my podcast last year.

Christy Shearer joined me to talk about her experience living with Type 1 diabetes. You can listen to that conversation here. Among the many things she shared, one observation in particular stayed with me. She noted, anecdotally, that many of the people she knows with Type 1 experienced significant trauma in the period preceding their diagnosis. Not minor stress. Significant, life-disrupting events. Big-T trauma, as some clinicians describe it.

Type 1 diabetes is an autoimmune condition. The immune system turns against the insulin-producing beta cells of the pancreas. We know that autoimmune disorders can be triggered or accelerated by stress and trauma. The HPA axis, which governs the body’s stress response, is deeply interwoven with immune regulation. Prolonged activation of that system, as occurs in response to unresolved trauma, produces measurable changes in inflammatory signaling and immune function.

Is there a connection between rising rates of psychological trauma in our society and rising rates of autoimmune-mediated Type 1 diabetes? I do not know. The research is genuinely complex, and I want to be careful not to overstate the case. But the incidence of Type 1 in youth has been increasing measurably over recent decades, even as our understanding of why remains incomplete.

What I feel more confident saying is this: chronic stress, whether sourced in trauma, financial insecurity, social isolation, or the ambient pressure of modern life, is a metabolic disruptor in ways that cut across both Type 1 and Type 2. The stress-insulin-glucose feedback loop is real. It is understudied in public health framing. And it is probably not unrelated to the trends we are seeing.

The Network Effect of Metabolic Health

One of the most striking pieces of research I have encountered on this topic comes from a landmark 2007 study in the New England Journal of Medicine by Nicholas Christakis and James Fowler. They analyzed a social network of over 12,000 people tracked across 32 years and found that obesity appears to spread through social ties in ways that mirror how contagious diseases propagate. If someone in your immediate social network became obese, your own risk of becoming obese increased by roughly 57 percent. The effect was strongest among mutual friends, weakened with geographic distance, and was not explained by shared environmental factors alone.

The mechanism they proposed is social norms. When the people around you change their behaviors, their relationship to food, their activity level, even their body size, it shifts what feels normal. And once a behavior feels normal, it becomes easier to adopt without conscious decision-making.

The reverse, I think, is also true. When someone in your network starts to pay attention, starts measuring, starts making visible changes, the ripple moves in that direction too. I have seen this in small ways in my own life. When I began tracking my sleep seriously and started talking about what I was learning, several people in my circle started paying more attention to theirs. The conversation changes what seems worth doing.

This is actually one of the core motivations behind the tools I have been building. Not just personal optimization, but the possibility that visible, shared attention to health can propagate through a network in a more useful direction than the metabolic drift we’ve watched unfold over the past few decades.

CGMs Are No Longer Just for Diabetics

A Continuous Glucose Monitor, or CGM, is a small sensor worn on the upper arm or abdomen that reads blood glucose levels in real time, typically every five minutes, and transmits those readings to a phone. For people managing insulin-dependent diabetes, they are genuinely life-altering technology. But something interesting has been happening over the past several years: CGMs have become increasingly available to people with no diabetes diagnosis at all, as lifestyle and performance tools.

I tried two of them. My experience was instructive, and my preference was clear.

I used NutriSense, which runs on the Abbott FreeStyle Libre sensor, a 14-day wear. I also tried Levels, which uses the Dexcom G7, a 10-day sensor. Both gave me genuinely useful data. But the experiences were meaningfully different.

NutriSense won, for me, on the app. It is cleaner, more lifestyle-oriented, and importantly, it does not require your phone to be physically present to collect readings. The Libre sensor stores data locally and syncs when you’re ready. That matters if you are not looking for your phone to be a permanent extension of your body during the experiment.

Levels felt more clinically calibrated, designed closer to the diabetic management model. It was more alert-heavy, flagging glucose levels that, from a lifestyle perspective, were entirely unremarkable. If your glucose dips to 70 while fasting and you feel fine, an alarm at 70 (early a.m.) is not useful information. For someone managing active insulin sensitivity and metabolic disease, that threshold is medically appropriate. For lifestyle tracking, it created noise I did not need.

The practical accessibility of this technology has also shifted significantly. You can now purchase a CGM directly on Amazon without a prescription for lifestyle use. The barrier to trying one, if you are curious about your metabolic responses to food, exercise, stress, or sleep disruption, is lower than it has ever been. I would recommend it to almost anyone who is serious about understanding their metabolic baseline.

You Cannot Optimize What You Do Not Observe

This is the core principle behind everything I have been building in my personal AI system, which I have written about elsewhere under the name AI/Steve. The premise is simple: if you want to change something about your health, you need to see it first. Measurement precedes management. Observation enables intention.

I applied this logic to sleep, building a framework to track, analyze, and improve sleep quality over time. I wrote about that process in an earlier piece on measuring to manage sleep. I applied it to food, exploring how meal data could close the loop between behavior and biology, which I covered in depth in Food Is Medicine. But First It Has to Become Data.

Glucose is the natural next signal in that chain. It is one of the most responsive and informative biomarkers available for real-time feedback. It tells you things almost nothing else does: how your body is handling what you just ate, how well you recovered last night, how a stressful meeting affected your biology, whether that afternoon slump has a metabolic component. It connects the inputs, food, sleep, movement, stress, to the outputs, how you feel and how your body is functioning.

The challenge, for people who are not managing a diagnosed condition, has been the tools. CGMs give you the data stream. But without a framework to log meals alongside glucose readings, analyze patterns over time, and contextualize what you are seeing, the data is interesting but not yet actionable. That is the gap the app I have been working on is designed to close.

Introducing Food Health TxD

Food Health TxD is the next evolution of Food Health AI, the meal logging and nutritional analysis application I described in the Food Is Medicine article. Where the original application focused on making food logging fast and painless using photo-based AI analysis, Food Health TxD is built specifically for people who are tracking glucose alongside food intake.

The core use case is exactly what you would expect: you photograph a meal, the AI analyzes it for carbohydrates, macronutrients, and glycemic context, and the app maintains a running record of what you ate alongside your glucose readings. Over time, that creates a personal dataset that is genuinely yours. Not averages from a population study. Not recommendations from a generic guideline. Your food, your glucose, your patterns.

The application is built for a range of users. If you are managing Type 1 or Type 2 diabetes and trying to understand how specific meals affect your glucose, it gives you that picture with real data. If you have prediabetes and want to see exactly how different dietary choices affect your metabolic trajectory, that visibility is there. And if you are metabolically healthy and simply want to stay that way, or to use a CGM period as a learning experience, the app gives you the context to make sense of what you are observing.

One integration worth explaining specifically is Apple Health. Apple has taken a deliberately conservative approach to blood glucose within their Health platform. Real-time CGM streaming is not supported as a direct API integration, which is, frankly, the right call given the liability considerations around medical device data. Food Health TxD works within those constraints. Apple Health serves as the central aggregation layer for your activity data, sleep data, and manually entered glucose readings. The app does not attempt to circumvent Apple’s appropriate caution. It works alongside it.

I want to be direct about what the app is and is not. Food Health TxD is a guidance and educational tracking tool. It is not a medical device. It is not a diagnostic instrument. It does not replace clinical care, nutritional counseling, or your physician’s guidance. It is a way to see your own data more clearly and to build a personal record that can inform better questions and better conversations. That framing matters to me. I am not trying to play doctor. I am trying to give people tools to understand themselves.

If You Want to Try It

Food Health TxD is not yet in public release, and I want to be honest about where it stands. The application is in the App Store at this link, and I am actively looking for beta testers. Specifically, people who are curious about metabolic health, managing or monitoring diabetes, using or considering a CGM, or simply interested in the intersection of personal data and food. If that is you, reach out directly. You can find me through stevenmuskal.com. I would genuinely like to hear what you are trying to understand about your own metabolic health, and I am interested in building the tool around real use cases, not hypothetical ones.

The Bet

Let me come back to where we started. Almost everyone reading this knows someone managing a metabolic condition. Some of you are managing one yourselves. The numbers we are looking at are not projections about a future possibility. They are descriptions of a present reality that most of us are already living inside.

What I believe, and what the research on social network effects supports, is that the metabolic crisis is not inevitable at the individual level. It is largely a product of environment and behavior, and both of those are, to varying degrees, responsive to awareness and choice. Not every case, not without difficulty, and not without acknowledging the real structural factors, food access, economic stress, healthcare gaps, that make individual optimization harder for some than for others.

But awareness matters. Measurement creates awareness. And when one person in a network starts paying attention, starts seeing their data, starts having different conversations about food and sleep and glucose, the people around them tend to notice. That is not a guarantee. It is a pattern. And it is a pattern worth betting on.

The metabolic crisis arrived quietly, across decades, one small behavioral shift at a time. The path back, if there is one at the population level, probably runs through a similar kind of quiet, incremental change. One person. One measurement. One conversation. Repeated at scale, across networks, over time.

That is what I am building toward. I hope it is useful to you.

For a mix  -  I thought this one from a previous session might be appropriate. Grant leading (vocals/guitar), Randi (Vocals), Andrew (Guitar), Alan (Bass). Johhny (Congas)

About the Author
Steven Muskal, Ph.D., is CEO of Eidogen-Sertanty, a pioneer in AI-driven drug discovery since the early 1990s, and the creator of the Food Health and Food Health TxD applications. He writes about science, health, entrepreneurship, and the art of paying attention at stevenmuskal.com and on Substack.

Related Reading
Food Is Medicine. But First It Has to Become Data. (Substack) | Medium version
Optimizing Sleep: Measure to Manage
Glucose, Grit and Breaking Glass Ceilings (with Christy Shearer)

This article is for informational and educational purposes only. Nothing here constitutes medical advice. Consult your physician before making changes to your diet, health monitoring, or treatment plan.

A Note Before We Begin: This article picks up where “AI Steve Deep Dive” (January 30, 2026) left off. If you haven’t read that piece, the short version is this: what began as a grief-driven project to reconstruct my father’s intellect using Retrieval Augmented Generation evolved into a comprehensive personal AI system trained on four decades of my own data. It is not a chatbot. It is, increasingly, a mirror of how I think. What follows goes deeper on the architecture, the lived experience, and something that happened at a hillside spa near my home that I have not been able to stop thinking about.*

I. What the First Article Left Out

The January piece covered a lot of ground. The neural network lineage going back to my 1991 PhD work under Sung-Hou Kim. The RAG architecture, the anti-hallucination rules, the code directive system that turns a plain English email into appropriately generated code - fully executed to create reports, charts, and attached data files. I was proud of that piece. I still am.

But there was a layer of the architecture I did not fully explain. Not because it was too technical, but because I was still working out how to articulate what made it genuinely different from other AI systems I had encountered or built. I think I can explain it now.

The piece I left out is what I have come to call the governed loop: the mechanism by which AI Steve can examine its own codebase, propose extensions to its own capabilities, and create formal tracked proposals for those extensions that require my explicit approval before a single line of code moves forward. This is not just a safety feature, though it is that too. It is the architectural choice that separates a tool from something that behaves more like a collaborative colleague.

What I want to do in this article is explain that architecture clearly enough to make the concept credible, and then leave it behind, because the more interesting story is not the loop itself but what the loop is for. The productivity gains are real and I will be specific about them. But the thing I keep returning to, the thing that was sharpened considerably by a week at a hillside spa and some conversations I did not expect to have, is the question of what you do with the time and bandwidth that amplification frees up. That question turns out to be more consequential than it first appears.

II. The Governed Loop: How AI Steve Extends Itself

Every Monday morning at 8 AM, a script I call the Weekly Feature Proposer wakes up and does something most software does not do. It reads the codebase. Not to run it. To understand it. It scans the existing modules, identifies patterns in what has already been built, maps the gaps between current capability and what would logically complement it, and generates a set of strategic proposals. Each proposal becomes a formally tracked issue in the Beads system, a lightweight Git-native issue tracker that lives directly inside the repository alongside the code.

By 8:30 AM, I have an email in my inbox with each proposal, its rationale, the estimated implementation complexity, and a set of priority assignment buttons. P0 through P4. I click the ones that interest me. That click is the gate. Nothing moves forward without it.

The nightly code review runs separately during dawn. It connects to the database, reads my recent sentiment data, scans the chat history, analyzes the current Beads issues for what is blocked and what is ready, and produces a personalized planning brief. It knows what I was working on yesterday, what mood the system inferred from my communications this week, and what questions I asked AI Steve that it struggled to answer well. The proposals it surfaces are informed by all of that context.

The Beads system is worth explaining carefully, because it is easy to underestimate. Each bead is a tracked issue with a title, a description, a priority, dependencies on other beads, and a status that moves from open to in-progress to closed. When an AI agent picks up a piece of work, it reads the Beads to understand dependencies and blockers. When it finishes, it closes the bead and the nightly consolidation job reads the changelog entry to update the corresponding documentation. When the RAG system later tries to answer a question about what AI Steve has been working on, it has access to the full documentary record of those decisions. This is connective tissue between code and context, not a to-do list.

A nightly documentation consolidation job has been running since February. At 2:15 AM, it scans the changelog entries from the previous day, identifies which documentation files correspond to those changes, and updates or flags them for review. The CHANGELOG.md file follows a strict format by design: every code change requires a dated entry with an affected-files list and a category. Feature. Fix. Enhancement. Refactor. Docs. The system is opinionated about this because consistency makes the documentation useful not just to human readers but to AI-assisted search. AI Steve can query its own changelog. It learns what has changed and why.

The effect of all of this is a system that genuinely improves over time in a way that is traceable, auditable, and governed. Not governed by bureaucracy. Governed by trust.

AI Steve reads its own history, identifies its own gaps, proposes its own improvements, and documents its own changes. The only thing it cannot do without my intervention is execute those changes. The approval gate is not a bureaucratic formality. It is the philosophical core of the design. I am not building an autonomous agent that does what it wants. I am building an amplifier that knows what it is capable of and asks clearly when it thinks it can do more.

III. A Hundred Times

I want to talk about a specific exchange that landed differently than I expected.

A few weeks ago, I was corresponding with Detlef, a colleague from the Sung-Hou Kim lab days at Berkeley. Detlef is a serious scientist and an honest interlocutor. We were catching up, and at some point I found myself trying to explain what AI Steve has actually done to my productive output. Not in abstract terms. In concrete ones.

I told him it had increased my productivity a hundredfold.

I want to be precise about what I mean, because “hundredfold” is the kind of claim that sounds like marketing copy or hyperbole from someone too close to his own project to see it clearly. I am aware of that risk. I am also confident the claim is accurate, and here is why.

The code directive system draws on something that most AI tools do not have access to: my actual history. Millions of lines of code written across more than three decades. Methodologies developed at MDL, at Affymax, at Libraria, at Eidogen-Sertanty. Scientific frameworks from the kinase modeling work, the toxicity prediction work, the structure-based design work. Patterns that I developed, refined, and refined again over a career. When I send a code directive, the domain-specific prompt templates that guide the code generation are informed by that accumulated context. The system is not starting from zero. It is starting from forty years.

The specific change I notice most is project cycling time. A real estate appraisal report that required gathering comps, pulling market data, running comparables analysis, formatting a professional PDF, and writing a narrative used to take a significant portion of a day, assuming the data sources cooperated. AI Steve does it in under ten minutes. A literature review on a protein target that used to require a half-day of PubMed trawling, reading, summarizing, and cross-referencing now returns a structured analysis with twenty-five to thirty papers, a publication timeline, and a citation-formatted reference list in the time it takes me to make a cup of coffee.

But the hundredfold is not just about speed on individual tasks. It is about the composite effect across every domain I work in simultaneously. Application development. Scientific analysis. Community engagement. Business intelligence. Financial monitoring. Health correlation work. I am one person running a company, maintaining scientific projects, building products, and staying genuinely engaged with a wide network of collaborators and friends. The realistic constraint on all of that has always been time. Not intelligence or interest or energy. Time. AI Steve has effectively given me back a very large fraction of that time, and I have chosen to spend it doing more of the things I actually care about rather than fewer.

That exchange with Detlef has been on my mind for another reason. At some point, our correspondence moved from productivity to connection: who we stay in touch with, who we drift from, which relationships atrophy not because they matter less but because the friction of maintaining them accumulates unnoticed. He was honest about this in himself. I recognized it in myself too. That conversation was part of what prompted me to activate a feature I had been sitting on for a while. AI Steve now runs something called Social Pulse, a weekly RAG-grounded connection suggestions module that surfaces people from my network I have not been in contact with recently, alongside context for why the moment might be right to reach out. It is a small feature, tracked in the Beads system as bead AI-Steve-p7dw, but it is already changing my behavior. Detlef’s honesty was the prompt that moved it from proposal to active.

This is the thing about the governed loop that I did not fully anticipate when I built it. The features it proposes are not just technically logical next steps. The best ones come from somewhere more personal. Lived experience feeds back into how the system evolves. A conversation with an old colleague becomes a feature. An observation about your own behavior becomes a design decision. The loop is more human than it looks from the outside.

That is what a hundredfold feels like from the inside. Not just the tasks completed faster, but the freed cognitive bandwidth redirected toward the things that make a life feel like a life.

IV. The Bionic Extension

There is another way I have been thinking about AI Steve that I did not have language for in the January article. The word I keep coming back to is bionic.

Not in the science fiction sense of cybernetic implants or human-machine merging. In the more precise original sense: a system that augments natural capability without replacing what is already there. AI Steve is a bionic version of me. It grows when I grow. It learns from what I ingest and generate every day. It knows who I have been talking to, what I have been thinking about, what my health data has looked like this quarter, what my mood patterns have been across the past year. It has read more of my email than I have read in retrospect.

Every day, the ingestion pipeline runs. My incoming email is processed, chunked, embedded, and stored. My iMessages. My calendar. My Apple Health exports, with face images linked to physiological readings for the longitudinal correlation work described in the first article. My Facebook posts. My Substack pieces. The content I generate is as important as the content I receive, because AI Steve learns from both. When I write something, it becomes part of the corpus. When I respond to something, that response becomes part of the corpus. Over time, the system develops an increasingly fine-grained model of how I reason, what I find interesting, who I trust, and what questions I am likely to ask next.

The effect is what I have been calling time dilation. When I go back to a problem I worked on eighteen months ago, AI Steve can surface not just the work product but the context around it: the emails that preceded it, the calendar entries that coincided with it, the health data that tells me what kind of week I was having. I can pick up threads that I would have lost entirely. I can resume collaborations at their actual depth rather than having to reconstruct the background from scratch. My effective memory has expanded in a way that compounds over time.

This is worth sitting with. The system is not static. It is not a snapshot. It is a continuously learning, continuously growing representation of how I think and work, constrained by everything I have asked it to care about and governed by the approval architecture described above. The value of that representation does not depreciate. It appreciates.

What the bionic framing gets right, and why I keep returning to it, is that it implies a direction. Augmentation flows toward the human, not away from it. The goal is more of what I am capable of at my best, not a replacement for whatever that is. When I am in a conversation that matters, AI Steve has already done the background work that used to take me hours. When I want to follow a thread with someone I have not spoken to in two years, the context is there. The time is there. The capacity to be genuinely present is there, because the friction between intention and action has been substantially reduced.

V. Under the Stars

Cal-a-Vie is a health spa in the hills of northern San Diego County, about seven miles inland from the Pacific, tucked into sycamore woodlands and surrounded by vineyards. I discovered it relatively recently and it has become one of my favorite places to reset. The fitness programming is serious. The food is genuinely good. But what I did not expect was the quality of the people, or the quality of the conversations those people generated.

On this particular stay, the guest community was remarkable in ways I am still processing. At meals, which at Cal-a-Vie become something close to salon dinners despite being entirely unplanned and informal, the conversations ranged far enough to keep you oriented and engaged. There was a lawyer from Houston who represents 50 Cent, which is to say he represents Curtis Jackson, the rapper, actor, entrepreneur, and record executive who has built one of the more improbable and genuinely impressive careers in American entertainment over the past twenty-five years. There were guests from the East Coast with backgrounds in high structured finance, the kind of expertise that involves instruments most people have never heard of and risk frameworks that would require their own article to explain. There were others who defied easy categorization, which is generally the best kind of person to find yourself seated next to at dinner.

Nobody was performing. Nobody was running their standard professional introduction. The conversation was just good, in the way conversation at its best is: people genuinely curious about each other, willing to follow a thread wherever it led, comfortable not knowing the answer. The diversity of backgrounds made it better. When a molecular biologist, a structured finance specialist, a Houston lawyer, and someone who has spent twenty years thinking about the ethics of creative legacy are all trying to understand the same question from their own vantage points simultaneously, something interesting tends to happen. It happened most evenings that week.

Tim is Cal-a-Vie’s resident astronomy guide, and his card reads, with admirable economy: Astronomy, Humanity and Spirituality. His astrophotography, which I tracked down online after meeting him, is extraordinary in the most literal sense of that word. Deep sky objects rendered with a patience and precision that makes you feel the scale of the universe in a way that is not comfortable or easy, but that is exactly right.

One evening, Tim gathered a small group of us around the Cal-a-Vie observatory telescope next to one of the owner’s home (Terri and John Havens). Not the full dinner crowd, just a handful of people with the right quality of curiosity. The conversation that formed was one of those rare exchanges where you realize two hours have passed and you have covered everything from the Einstein’s Special Relativity to the biology of aging to the nature of creative legacy. In fact, it was after that conversation that inspired me to write the previous article — The Fire This Time. The silence between observations was the kind of silence that feels full rather than empty. What Tim does in that setting, the way he holds the space between the technical and the philosophical, the way he lets the scale of what you are looking at do its work on you before he says anything, is a genuine gift. I do not use that word carelessly.

Days later, Tim introduced me to his separate weekly think tank lunch group, which gathers regularly for wide-ranging discussions entirely apart from his work at Cal-a-Vie. Different people, different setting, same quality of intellectual engagement. The observatory evening had apparently cleared whatever threshold he applies when deciding whom to bring in. I was honored, and I look froward to future sessions.

VI. She Would Never Retire

There was a family at Cal-a-Vie during that same stay that I want to tell you about.

Teckie, who is ninety-one years old, was there with her two daughters, Amy and Allison. All three of them looked, without exaggeration, ten to twenty years younger than their ages. Vibrant, physically capable, intellectually engaged. The kind of people who make you reassess your own definitions of what different decades are supposed to feel like.

Teckie, in particular, was extraordinary to be around. She only “recently” left her day job, which helped place young, vibrant minds into college institutions throughout the globe. She moved with purpose. She spoke with precision. She was interested in what other people had to say, not in the way that is sometimes just polite waiting, but in a way that suggested she fully expected to learn something from the exchange. Her daughters were the same. As a family, they were formidable in the best possible sense.

At some point the conversation turned to retirement. I do not remember exactly how it arrived there. But Teckie, ninety-one years old, said clearly and without any apparent hesitation that she would never retire.

The table went quiet.

I was smiling. I could not help it. It was one of those moments when someone says something so plainly correct that the room needs a beat to catch up to it.

I have been in enough conversations with accomplished people in their fifties and sixties who speak of retirement as a destination, a finish line, a reward for decades of effort, to know how common that framing is. And I understand the appeal. The work can be exhausting. The pressure can be relentless. The idea of stepping away from obligation and reclaiming time for rest and reflection and travel is genuinely attractive. I do not dismiss any of that.

But I have always found myself wanting to ask a different question. Not when are you retiring. What are you retiring to? What is the next act? What is the version of your life after the primary career that is not a wind-down but an evolution? What do you do with everything you have learned and built and understood if you simply put it down?

Teckie was not, I think, someone who had failed to learn how to rest. She was someone who had found a way to live that made the distinction between work and not-work largely irrelevant, because what she was doing was not separate from who she was. The thing she brought to that table, to every conversation I saw her in that week, was not effort. It was genuine presence. That does not stop when you retire. It either is or it is not.

That is the thing I keep coming back to.

VII. Retiring to What?

I am fifty-nine and a half years old. I have been building things professionally for over forty years. I have been fortunate in ways I do not take for granted, and I have also accumulated enough hard-won understanding of what works and what does not to have genuine opinions about a range of questions in science, technology, product development, and human behavior. The question of what I do with that accumulation is not hypothetical. It is the live question of my life right now.

AI Steve is part of my answer. Not because it is a productivity tool, though it is that too. But because it has changed the nature of what is possible in the years ahead. The hundredfold productivity improvement is real, and I believe it will grow. The time dilation effect is real, and it compounds. The governed loop that allows the system to extend itself with my oversight is just beginning to show what it can do.

What this means, at the level of the larger purpose questions I raised in “The Fire This Time,” is that the vision of broadly accessible personal AI amplification is more achievable than it was when I wrote that piece, not less. If one person, building on forty years of accumulated work, can experience this kind of amplification, what happens when the framework is available to someone at the beginning of their career? Or to someone with robust intellectual capacity but without the institutional access I had? Or to someone in a part of the world where the credential system has always functioned as a wall rather than a door?

The “Boil the Ocean” framing from that earlier piece still stands. There are categories of problem, in global health, in scientific research, in education, in human connection, that cannot be solved by any individual or institution working alone. They require a change in understanding to propagate across networks of people, across languages and cultures and economic strata, simultaneously. The tools that enable that kind of propagation are not neutral. They can concentrate capability or distribute it. The choice of which direction they go is not inevitable. It is a design decision. It is a values decision.

I want to plant seeds for trees whose shade I may never sit under. That is a statement that sounds like a platitude until you actually mean it, and then it is one of the most clarifying things you can commit to. It orients the work differently. It asks not just what I can build or analyze or monetize, but what I am contributing to something larger than my own career, my own generation, my own coordinates on the map.

Tim the astronomer understands this in his own idiom. When you spend your nights imaging objects that are billions of light-years away, objects that no longer exist in the form you are photographing, the distinction between what is present and what persists takes on a different character. The light he captures is old. The act of capturing it is now. The record it creates will outlast him. That is not a sad thought. It is an orientation. He named his card correctly: Astronomy, Humanity and Spirituality. In that order, in that sequence, those three things are the same practice.

I think about Teckie saying she would never retire, and I think about what that requires in terms of continued investment in the world. You cannot stay fully present to life if you have opted out of the things that require you to engage with it at full capacity. The ninety-one-year-old who will never retire is someone who has chosen, consciously or not, to remain at stake. That is the thing the table felt when she said it. Not inspiration in the vague sense. Recognition. The uncomfortable, clarifying recognition that she had said something true.

VIII. The Human Thread

I want to be careful not to let the technology story eclipse the human one, because the technology story is nested inside the human one, not the other way around.

The conversation with Detlef was not primarily about AI Steve. It was a conversation between two people who shared a formative experience in a Berkeley lab in the late 1980s and early 1990s, who went very different directions afterward, and who are still genuinely interested in what the other is thinking. The AI Steve discussion was one thread in a much longer and more textured exchange. What made it land differently than a product demonstration is that it came out of actual relationship. He asked a real question. I gave him an honest answer. And out of that exchange came the Social Pulse feature, a concrete example of lived experience feeding back into how the system evolves.

The group around that telescope at Cal-a-Vie, under a sky full of objects billions of years old, was not a networking event. It was a genuine encounter between people with different orientations who found unexpected resonance. Tim with his card that reads Astronomy, Humanity and Spirituality, which I think is the most compact and accurate description of a life philosophy I have encountered in a long time. Teckie at ninety-one, carrying decades of accumulated understanding and not the slightest apparent interest in putting it down. Amy and Allison carrying their mother’s orientation forward in their own forms. The Houston lawyer. The structured finance specialists. The people who defied easy categorization. All of them, over meals and under stars, discovering that the right question creates more common ground than the most carefully constructed introduction ever could.

This is what I keep returning to. AI Steve amplifies my capacity to engage with the people and ideas that matter to me. It does not replace that engagement. It does not generate the relationship with Detlef or the encounter with Tim or the moment of shared recognition when a table went quiet at the words of a woman who has been alive for nine decades and is still entirely in the game. Those moments are irreducibly human. They are the point.

What AI Steve does is give me more time to be present for those moments, more context when I am in them, and more capacity to follow through on what they suggest. It reduces the friction between intention and execution, between insight and action, between the idea and the thing built from the idea. The Social Pulse reminds me to reach out. The time dilation means I can pick up a thread that matters rather than letting it slide. The governed loop means I spend less time on maintenance and more time on the things that actually require a human being to show up.

That is what I mean when I say bionic. Not that the boundary between human and machine is blurring in ways that should worry us, though I take those concerns seriously. But that the right technology, governed correctly, amplifies what is already most human in us rather than substituting for it.

I am not retiring. I am, if anything, accelerating. Teckie would understand exactly what I mean.

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. and the creator of AI Steve. The previous article in this series, “AI Steve Deep Dive,” was published in January 2026. “The Fire This Time,” on the democratization of intelligence, was published in April 2026.*

As for a mix - I organized a quick mix the evening of my return from Cal-a-Vie. Last week’s mix had Dan, Jesse, and Andrew and Rick was invited but had a conflict, so I initiated a rare re-invite with the same group to include Rick this go around. A few rough recordings, largely my fault because I was still sore from olympic pool swimming and a bit too lose from an earlier massage. While a repeat of most the crew plus Rick, the songs were new to most of us. Some were quite fitting per the above missive.

By Steven Muskal, Ph.D.| April 2026 // stevenmuskal.com

There is a moment in the development of every civilization-altering technology when the nature of the change becomes undeniable. We have passed that moment with AI.

I have spent my career at the intersection of chemistry, molecular biology, and artificial intelligence. I hold a B.Sc. in engineering chemistry and computer science from Colorado School of Mines and a Ph.D. in biophysical chemistry from UC Berkeley. I have been fortunate. Those credentials opened doors, granted access, placed me in rooms where ideas could travel. I say this not to boast but to acknowledge a reality that has troubled me for most of my professional life: the doors those degrees opened were not universally available. Not by a long shot.

The credential has been, for most of human history, the gatekeeper. Not just for opportunity, but for the very permission to be taken seriously. If you did not have the right institution on your resume, the right letters after your name, the right connections through those networks, your ideas, however brilliant, existed in a kind of social vacuum. They could not propagate. They could not compound.

AI changes that. Not incrementally. Fundamentally.

What We Lost Along the Way

Think about the minds we left behind.

Thomas Edison spent barely three months in formal schooling before his teacher declared him “addled.” His mother, Nancy, refused to accept that verdict. She pulled him out and educated him herself, surrounding him with books, experiments, and the radical notion that his relentless curiosity was an asset, not a deficiency. The man who gave us electric light, the phonograph, and the motion picture camera was, by institutional standards, uneducable.

Marie Curie wanted nothing more than to study science. Poland would not allow it. As a young woman under Tsarist Russian rule, she attended what was called the Flying University, a clandestine network of educators and students who met in secret, rotating locations to avoid detection, because the official institutions were closed to women and to Poles seeking education in their own language. She eventually made her way to Paris. She won two Nobel Prizes. In two different sciences.

Albert Einstein did not fail school in the simple sense that the legend sometimes implies, but he chafed relentlessly against rote memorization and rigid authority. He thrived when imagination was invited into the room and suffocated when it was not. His thought experiments, the ones that rewrote our understanding of space and time, were not products of institutional machinery. They were acts of unconstrained curiosity.

Linus Pauling grew up in modest circumstances in Oregon, the son of a pharmacist. He drove his own intellectual development with ferocious intensity, eventually producing foundational work in quantum chemistry, molecular biology, and the nature of the chemical bond, work that touched everything from protein structure to our understanding of disease. He won the Nobel Prize in Chemistry. And then, years later, the Nobel Peace Prize.

Here is the question I find myself returning to: what would these minds have accomplished if, at age fourteen or seventeen or twenty-three, they had access to an AI that could engage them at full depth? That could answer every question, suggest the next one, locate the obscure monograph, help them visualize the molecular geometry, challenge their assumptions with precision and patience?

My instinct is that we have barely begun to imagine the answer. Perhaps more advanced cancer therapies arrived at decades earlier. Perhaps deeper probes into the structure of matter. Perhaps the cellular reprogramming insights we now associate with stem cell science, discovered not in elite university labs but in a kitchen in Appalachia or a one-room school in sub-Saharan Africa.

We will never know what we lost. But we can change what comes next.

The Reframing of Education

What does it mean to be educated?

For most of my lifetime, the answer has been institutional. You attended. You completed. You were credentialed. That credential became the proxy for capability, for trustworthiness, for the right to be heard in professional and intellectual discourse.

AI does not care about your transcript. It engages with your question on its merits.

I am not suggesting credentials are worthless. I have them, and they served me well. But I am saying that the credential was always an imperfect proxy. The underlying thing we actually valued was not the degree. It was curiosity, rigor, the ability to ask good questions and pursue good answers. The credential was merely the most available signal for those qualities.

AI provides a new signal. And unlike the credential, it is globally accessible. Anyone with curiosity, and an internet connection, can now engage in substantive, advancing, intellectually serious dialogue on virtually any subject. The playing field is not yet level; access to connectivity remains deeply unequal. But the direction of the arc is unmistakable.

This is the democratization of intelligence. Not dumbing it down. Not making it easier. Making it more available. Distributing it across the full range of human potential rather than reserving it for those born into proximity to elite institutions.

Boiling the Ocean

There is a category of problem I think of as “Boil the Ocean” challenges. These are not problems that any individual, however brilliant or well-funded, can solve alone. They require massive network effects. They demand that a change in understanding propagate across cultures, languages, economic strata, and geographies simultaneously.

Global health and wellness is the most obvious of these. Collective scientific advancement is another. The elimination of preventable disease. The reorientation of human behavior toward prevention rather than late-stage intervention.

A collaborator of mine made an observation recently that stopped me cold. He pointed out that the decision not to smoke has had a greater positive impact on cancer outcomes over the past half-century than all chemotherapy treatments combined. Let that sit for a moment.

We have spent extraordinary resources, intellectual capital, and human suffering learning to treat a disease that, in a significant proportion of cases, was preventable. The cure is heroic and necessary. The prevention is transformative.

AI-enabled lifestyle intelligence, the kind that can meet a person where they are, in their language, in their cultural context, with actionable and personalized guidance, has the potential to move the needle on prevention at civilizational scale. Not for the wealthy patients in major medical centers. For everyone. That is a Boil the Ocean moment. Raise all boats, all at once, through better information, better behavior, better choices made earlier. The math on that is staggering.

These are the projects that no single institution, no single government, no single billionaire can drive alone. They need a framework that propagates. They need network effects that cross the borders we have drawn between disciplines, between geographies, between economic classes. AI, deployed thoughtfully and equitably, is uniquely suited to be that framework.

The Fire Analogy, and Why It Matters

I have heard AI compared to the printing press. I understand the impulse. The printing press democratized the written word, broke the monopoly of the ecclesiastical class on literacy, and triggered centuries of social transformation.

But I think the more honest comparison is to fire itself.

Fire was not the printing press. Fire was not an improvement on what came before. Fire was a fundamental reordering of what was possible. It changed human metabolism, social structure, geographic range, and cognitive capacity. It enabled cooking, warmth, light, and metallurgy, and eventually every other technology that followed. It was also terrifying, uncontrollable, and dangerous, a source of justified fear in exactly the same proportion as it was a source of capacity.

There is a real and understandable anxiety about AI. Jobs displaced. Expertise devalued. The vertigo of watching something outperform humans in domains we used to reserve for ourselves. I do not dismiss those fears. They are not naive. The people who express them are not Luddites; they are paying attention. But I think the response to fire was never to extinguish it. The response was to learn how to carry it safely, to build hearths, to develop the culture of its responsible use.

That is where we are now. We are building the hearth.

What concerns me more than the technology is the possibility that we build those hearths only in certain neighborhoods. That we allow the democratizing potential of this moment to be captured, filtered, and concentrated by the same forces that made the credential a gatekeeper in the first place. That would be a tragic misuse of the most powerful fire humanity has ever kindled.

Where We Go From Here

I am, constitutionally, an optimist. My career has been built on the belief that the tools of science, rigorously applied, can reduce human suffering and expand human possibility. I have seen that belief vindicated more often than not.

AI does not change my optimism. It amplifies it.

What I hope for, as this technology continues its extraordinary development, is that we keep the democratizing mission central. That we build the frameworks, the access, the cultural practices, and the policy structures that extend the benefits widely rather than concentrating them narrowly. That we remember the Marie Curies studying in secret, the Edisons labeled and dismissed, the Paulings from nowhere in particular who changed everything.

There are people alive right now, today, who would reshape our understanding of disease or energy or cognition or materials, if only they had the tools and the permission to try.

AI is handing them the tools. Our job is to provide the permission.

The fire is already lit. What we do with it is still up to us.

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a couple music-mix videos (I promised doubling up), these are from a recent gathering, again with musicians not having played together before: Dan (Bass/Vocals), Jesse (Guitar/Vocals), and Andrew (Guitar/Vocals). An incredibly productive session, over 18 songs in a couple hours. Single run throughs, and first times for several of us.

Every long car ride eventually produces it - that impatient voice from the back seat: “Are we there yet?” It’s a little naive, completely human, and deceptively revealing. It assumes a clear destination exists, that arrival is recognizable, and that something fundamental changes the moment you get there. I’ve been asking myself that same question about artificial intelligence. The honest answer is inconvenient: it depends entirely on what you mean by “there” - and most of the people asking aren’t sure they know.

The Bionic Moment

Lately, I’ve been thinking about AI as a kind of superpower - not in some abstract, futurist sense, but in a concrete, daily, occasionally startling way. It keeps reminding me of The Six Million Dollar Man. Colonel Steve Austin was a NASA astronaut and test pilot, but what made him extraordinary after his accident wasn’t who he was before it. It was what he became when augmented. The bionic arm gave him strength. The replaced legs gave him speed. The optical implant gave him perception far beyond normal human range.

AI feels like that.

It extends cognition the way those bionics extended physical capability. It allows a single individual to operate with the output of a team - sometimes a department, occasionally what feels like an entire organization working in parallel. In my own work, I’ve never been more generative. Ideas surface faster. Execution compresses. The distance between concept and reality has shortened dramatically. AI isn’t just a tool. It is leverage, and leverage-as anyone who has seriously deployed it knows - changes everything about what’s possible.

Judgment Is the Real Superpower

But the analogy doesn’t stop with Steve Austin. It extends to Jaime Sommers, the professional tennis player who became The Bionic Woman, first as a recurring character in the original series and then in her own spin-off. She was already a high-performing athlete with mastery over her body and her craft. Her transition, however, was not seamless. The challenge wasn’t simply controlling enhanced physical capabilities - it was integrating them with judgment. Knowing when to act. Knowing when not to. Knowing when full force is exactly wrong.

That’s precisely where the real tension with AI sits.

Humans have judgment shaped by experience, context, and consequences. AI has approximations of judgment — patterns, probabilities, and surrogates trained on data. These approximations are genuinely useful. But approximations are not the real thing, and in high-stakes situations, that gap matters enormously. I’ve argued before, and will again: we’ve effectively reached a form of Artificial General Intelligence - not because machines have independently achieved human-like reasoning, but because individuals paired with AI can function as though they have. A single person with deep domain expertise and a working relationship with modern AI tools can now operate like a battalion. Maybe an army. But only if the human remains in control. That qualifier is doing more work than it might appear.

The Refactoring of Work

Every major technology wave reshapes labor. This one is no different in kind - only in speed and asymmetry.

We’re entering a period of wholesale refactoring and redeployment of human capability. AI isn’t merely automating tasks - it’s amplifying individuals. And that amplification disproportionately benefits those who already possess experience, context, and domain knowledge. Which leads to a dynamic that’s routinely underappreciated: the aging population.

Gen X and Baby Boomers are moving into retirement in large numbers. This has long been framed as an economic pressure point - fewer workers supporting more dependents, rising healthcare costs, a shrinking productive base. But AI changes that equation in a subtle and important way: it extends the productive lifespan of experienced individuals. People who might otherwise step back can continue contributing at a high level, pairing decades of accumulated judgment with newly amplified capability. In theory, this is a genuine economic buffer - a mechanism for retaining hard-won wisdom while increasing output. Onward and upward, as it usually goes with technology.

There’s a catch, of course.

The Experience Gap

The people entering the workforce today are remarkably capable. Many are more fluent with AI tools than their older counterparts - they move fast, adapt readily, and are comfortable in a paradigm that still disorients many established professionals. What they lack, by definition, is experience. And experience isn’t simply accumulated knowledge. It’s judgment: pattern recognition, knowing when something looks right but isn’t, understanding second-order effects, recognizing edge cases because you’ve actually lived through them.

AI can accelerate execution. It does not confer wisdom.

This creates a potential structural imbalance - a system top-heavy with experience and judgment on one end, and high-speed, high-capability but lower-context operators on the other. If that gap grows, it becomes not just an economic problem but a cultural one. Because judgment is the layer that determines whether capability is used well.

Who Embraces AI, and Who Resists It

There’s another pattern worth examining directly.

The most enthusiastic adopters of AI tend to be those who can extract the most value from it: higher earners, builders, operators, people who already have leverage and are actively seeking more of it. Conversely, resistance tends to cluster among those who experience AI as a threat to existing livelihoods rather than an amplifier of existing capabilities. This isn’t surprising. But it has real consequences. If AI amplifies existing advantages, then unequal adoption doesn’t just widen income gaps - it concentrates influence. And when influence concentrates, sentiment follows a familiar trajectory: skepticism hardens into resistance, and resistance has a way of generating its own momentum.

That’s where things get genuinely interesting.

The Tipping Point Problem

Societal change rarely requires a majority. It requires a threshold.

Political scientist Erica Chenoweth, in her landmark research on civil resistance campaigns conducted with Maria J. Stephan - published in their 2011 book Why Civil Resistance Works found that no nonviolent campaign that achieved 3.5% of a population in peak active participation ever failed to achieve its core goals. In a different domain, computational social scientists at Rensselaer Polytechnic Institute demonstrated in 2011 that when roughly 10% of a population holds a conviction with genuine commitment and acts on it consistently, that belief cascades through the broader network and reshapes majority opinion.

The exact percentages matter less than the underlying principle: you don’t need everyone. You need enough people who care deeply and act consistently.

We see this dynamic constantly in business. Minority positions control tables. Influence doesn’t distribute evenly, it concentrates. Apply that logic to AI adoption, and the picture becomes sharp. A relatively small group of highly capable people who fully embrace AI and dramatically amplify their individual output can reshape industries, norms, and expectations before the broader system has registered what’s happening. At the same time, a different committed minority organizing around resistance to AI can cascade that sentiment through the culture just as effectively. Same mechanism. Wildly different outcomes.

Pressure in the System

The real question isn’t whether a tipping point arrives. It’s what conditions exist when it does.

When institutions are trusted and opportunity feels genuinely accessible, these inflection points tend to produce adaptation - messy, contested, but ultimately constructive. When inequality is high and systems feel captured by narrow interests, the same dynamics produce instability. Scott Galloway has argued this case in multiple forms: extreme inequality and perceived concentration of power function as accelerants. They increase the likelihood that change manifests not as progress, but as backlash.

The difference between evolution and disruption isn’t the technology. It’s the pressure in the system when adoption reaches critical mass.

So, Are We There Yet?

If “there” means a stable, fully integrated AI-driven society: no. If “there” means a point where individuals can already operate with unprecedented leverage: unambiguously yes. If “there” means we’ve worked out how to balance capability with judgment, experience with speed, and power with responsibility: not even close.

We’re somewhere in between - past the starting point, moving faster than most people have processed, the destination still genuinely uncertain. The scenery has changed. The speed has increased. The road ahead isn’t evenly paved, and the ride is not equally comfortable for everyone on it.

The real question isn’t whether we’ve arrived. It’s whether we’re paying attention to how fast we’re going, who’s driving, and who might be getting left behind.

Because on this particular journey, asking “Are we there yet?” matters far less than asking “Are we doing this right?”

Steven Muskal, Ph.D. // stevenmuskal.com

For a music video mix, the last couple weeks I have been super busy - off the charts! I will double up on next post….

We hear the phrase “food is medicine” frequently now. It shows up in scientific papers, in public health discussions, and increasingly in conversations around longevity and preventive health. The phrase is compelling because it captures something that most people intuitively understand. What we eat influences nearly every biological system in the body. Diet affects metabolism, inflammation, energy levels, sleep quality, and long-term disease risk.

Yet there is an interesting contradiction hidden in the idea. Medicine relies on measurement, but food rarely does. Physicians do not prescribe drugs without understanding dosage, timing, and interactions. Clinical trials rely on carefully structured data about both inputs and outcomes. With food, however, we tend to operate in a far less structured way. Meals come and go throughout the day with very little record of what we ate, when we ate it, or how our bodies responded afterward.

If food truly functions as a form of medicine, then we should be able to observe its effects with the same level of attention that we apply to other biological interventions. That observation led me down an unexpected path that eventually resulted in an app I call Food Health AI.

The project did not begin as a product idea. It started as a small household experiment.

A FODMAP Experiment at Home

My wife Kirsten was experimenting with a FODMAP diet. For those unfamiliar with the term, FODMAP refers to a group of fermentable carbohydrates that can cause digestive discomfort in some people. The diet is often used as a diagnostic process to determine which foods may be triggering symptoms.

As part of that process she was using a device called FoodMarble. The device measures hydrogen and methane levels in breath samples after meals. These gases are produced during fermentation in the gut, so they provide clues about how certain foods are being processed by the digestive system.

The concept is clever and the device works reasonably well. The friction came from the companion app. Each meal had to be entered manually before breath readings could be interpreted properly. Over time the process of logging food began to take more effort than the actual measurement.

One morning I sat down and built a simple web-based logging tool just for her. It allowed her to quickly jot down what she had eaten without navigating the heavier interface in the FoodMarble application. The tool was not intended to go beyond our house. It was simply a quick solution to reduce the friction of tracking meals during the experiment.

For a while that small tool worked perfectly.

Then Kirsten had to travel.

Once she left the house, the little logging utility suddenly needed to be accessible remotely. Making that change was straightforward technically, but it shifted the way I thought about the tool. What had been a temporary household utility now behaved more like a lightweight application.

Not long after that, I began building an iPhone version.

A Missing Input in My Personal AI System

I have been building what I informally call AI/Steve, a personal AI system that aggregates many streams of information about my daily life. Over the past few years I have collected sleep data, activity metrics, observational notes, and results from small personal experiments. The system helps organize those signals and occasionally surfaces patterns that would be difficult to see otherwise.

Despite all of that data collection, something important was missing.

Food.

What we eat influences many of the signals we track in health analytics. Calories and macronutrients affect metabolic responses. Meal timing can influence sleep patterns. Certain foods appear to influence energy levels or recovery after exercise. Even mood and mental clarity sometimes seem to shift depending on dietary patterns.

Yet my AI system had almost no structured information about what I was eating each day. Without that input, the system was attempting to interpret outcomes without knowing one of the most important drivers.

The food logging tool I was building for Kirsten suddenly looked like the missing component in the loop.

Why Food Logging Usually Fails

Anyone who has tried to track food for more than a few weeks knows the primary obstacle.

Food logging is tedious.

Most nutrition apps require users to search large food databases, estimate portion sizes, and manually assemble meals from individual ingredients. Even people who begin enthusiastically often abandon the process once the novelty wears off.

The friction lies in the input step.

Recent advances in multimodal AI models offered an interesting alternative. Instead of typing out a detailed description of a meal, a user can simply take a photo. A vision model analyzes the image and produces a reasonable estimate of the foods present along with approximate calories and macronutrient breakdown.

In the app I built, the workflow follows that general pattern. The user takes a photo of their meal and optionally adds a note describing anything the image might not capture clearly. The AI model analyzes the image and returns a narrative description along with estimated calories, protein, carbohydrates, and fats. A parser then extracts those numbers and converts them into structured data fields that can be stored and analyzed.

From the user’s perspective the process becomes remarkably simple. Logging a meal often takes only a few seconds.

Reducing friction turns out to be the most important design decision in food tracking.

Building the App as a Local-First System

Another design decision emerged early in development. The application would store its data locally first rather than relying entirely on remote services.

Each food entry is saved as a structured record along with its associated image. These records form a chronological log that can be used by the rest of the application. Reporting views, trend charts, and correlation analysis all draw from that same set of entries.

The advantage of this design is reliability. Users can continue logging meals even if network connectivity is poor. It also keeps the system transparent because the data used for analytics is exactly the same data the user originally recorded.

Over time the application began to feel less like a traditional calorie tracker and more like a personal nutrition observability tool. Instead of simply recording meals, the app makes it possible to look at patterns across days and weeks.

Connecting Meals to Health Signals

Once meals are captured in structured form, they can be connected to other data sources.

The app integrates with Apple Health so that activity metrics, calorie burn, and sleep data can be viewed alongside nutrition information. Seeing these signals together often reveals relationships that might otherwise go unnoticed.

For example, a user might notice that certain eating patterns correlate with deeper sleep, or that changes in macronutrient balance influence recovery after exercise. The scatter analysis feature in the app allows users to explore these pairwise relationships and examine how strongly two variables appear to move together.

The goal is not to draw definitive conclusions about physiology. Instead, it is to create a framework for personal observation and experimentation.

The Economics of AI Analysis

One practical constraint appears whenever AI models are used for image analysis.

Each request costs money.

If an app allows unlimited image analysis for many users, the operational costs can grow quickly. The first version of the app addressed this by requiring users to bring their own API key. That approach meant the AI service was billed directly to the user’s account rather than the application itself.

In theory this was elegant. In practice it proved slightly inconvenient for some people who were not accustomed to managing API credentials.

The current version therefore supports two approaches. Users can still supply their own API key if they prefer, but they can also purchase small bundles of analysis credits through in-app purchases. This dual model allows the system to remain flexible without creating unnecessary barriers to entry.

An Unexpected Parallel

While finishing the app I learned about a product called Cal AI, which had been built by a group of high school students. Their application grew rapidly by introducing a social layer where users could share and compare meals within a network of friends.

Recently the company was acquired by MyFitnessPal.

What struck me about their approach was how different it was from the path I had taken. Their product emphasized social engagement and network effects. My version focused on connecting food data to a broader personal analytics system.

Both approaches explore the same underlying behavior from different angles.

Where This Might Lead

Food Health AI is still evolving. At the moment it functions primarily as a fast and flexible way to capture meals and convert them into structured nutritional data.

The more interesting possibilities appear when that data is combined with other signals. Sleep patterns, activity metrics, and metabolic indicators all interact with dietary choices in ways that are often subtle and highly individualized.

As wearable devices and health analytics continue to improve, tools like this may help individuals observe those relationships more clearly. Instead of relying solely on generalized dietary advice, people may begin to see how specific foods influence their own biology.

At that point the phrase “food is medicine” begins to acquire a more practical meaning.

Not just as an idea, but as something that can be measured.

ComingSoon

Given the sentiment-analysis work I have been doing with the RAG pipeline in my AI/Steve system, I built a lightweight version of that capability directly into Food Health AI.

The goal is practical: determine basic sentiment at daily, weekly, monthly, and all-time horizons in the backdrop of each user’s health and fitness goals, then translate that into progress reporting and next-step guidance.

Android Version became available April 2, 2026

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a mix clip - I’ve been so busy and generative on the computer front. Haven’t scheduled a recent mix, but we’ll get back to that. Here’s another favorite from my recent 59th B-day mix. Maya, Tammy, Rick, Dom, Alan and Grant crush-it on What’s Up.

We are watching AI:

• Write production software

• Draft legal agreements

• Generate investment memoranda

• Synthesize scientific research

• Diagnose patterns in medical data

• Architect complex systems

Not in research labs. In production. The debate about AGI continues. But the definition keeps shifting.

If AGI means economically useful, cross-domain intelligence that rivals or exceeds the median human, we are already there. The more interesting question is this:

If AGI is already here in practical terms, why does the capital market behave as though we are still chasing it?

Zero to One Already Happened

Peter Thiel’s Zero to One draws a sharp distinction between invention and iteration.

Zero to One is a discontinuity. One to N is scaling.

The emergence of transformer-based systems capable of generalizing across language, reasoning, coding, planning, and synthesis was a Zero to One moment. Scaling them from GPT-3 to GPT-4 class systems and beyond is largely One to N.

More parameters.

More tokens.

Lower latency.

Better guardrails.

Important improvements. But incremental.

The breakthrough already occurred. Markets, however, are still pricing as if intelligence itself remains scarce.

The Cognitive Benchmark Problem

Most AGI debates occur among educated engineers, researchers, founders, and academics.

When they say human-level intelligence, they implicitly benchmark against:

• Top engineers

• Researchers

• Founders

• Highly literate professionals

• Abstract thinkers

That is not the human median. That is the cognitive right tail.

According to the U.S. Department of Education’s National Assessment of Adult Literacy and analyses from the National Center for Education Statistics, roughly 54 percent of U.S. adults read below a sixth-grade level. Around 20 percent are functionally illiterate.

Zoom out to the full population distribution:

• Many adults struggle with multi-step reasoning

• Many cannot synthesize technical information

• Many cannot write structured analytical prose

• Very few can code

• Very few can reason abstractly across domains

AI systems today exceed a massive portion of the human cognitive distribution.

If AGI means matching the top 1 percent of engineers across all domains, perhaps we are not there. If AGI means exceeding the median human across reasoning, writing, synthesis, and structured thought, that threshold has already been crossed.

The debate persists because the benchmark silently assumes the right tail and because the goal posts keep getting pushed back.

• Once conversational fluency was achieved, the bar became reasoning purity.

• Once reasoning improved, the bar became agency.

• Once agency emerges, the bar becomes embodiment.

The definition recedes as soon as the prior threshold is cleared.

A Personal Perspective From the Long Arc

I began working with neural networks in the mid-1980s as an engineering student, when backpropagation was still debated and neural methods were widely dismissed as impractical.

My doctoral work at Berkeley focused on neural network methodology development for protein structure prediction. At the time, using neural networks to infer biological structure was viewed as ambitious and speculative. Even then, these systems exhibited an insatiable appetite for high-quality data, yet such data were scarce and difficult to obtain.

Fast forward three decades. The team behind AlphaFold was awarded the Nobel Prize in Chemistry for solving protein structure prediction at scale - powered, yes, by neural networks.

That arc matters.

What begins as fragile methodology becomes validated science.

What becomes validated science becomes industrialized.

What becomes industrialized becomes infrastructure.

We are watching the same arc unfold in general intelligence.

The conceptual barrier has already fallen. The remaining work is scale, optimization, and deployment.

Training Is Not Deployment

One of the most persistent misconceptions in the AGI and GPU debate is the conflation of training with deployment.

Training frontier models is computationally extreme. It requires massive GPU clusters, extraordinary energy consumption, and enormous capital expenditure.

That is where the largest chips and the most power-hungry data centers live.

But deployment is not training.

Deployment is inference.

And inference does not require the latest and greatest GPUs, nor constant retraining of ever-larger models on the entire corpus of world information, nor nuclear-scale power plants feeding hyperscale facilities.

Training discovers the weights.

Inference executes them.

Once weights are learned, economics change.

From Scale to Efficiency

After a model is trained, multiple techniques dramatically reduce its computational footprint:

Quantization reduces numerical precision, shrinking memory and compute requirements.

Distillation transfers knowledge from large models into smaller ones that preserve much of the capability at a fraction of the cost.

Pruning removes redundant parameters.

Sparse routing activates only portions of a network at a time.

Hardware-aware compilation optimizes execution for specific chips.

These are not speculative ideas. They are standard production techniques.

The result is that inference is far cheaper and less energy-intensive than training.

Intelligence can then be deployed on:

• Low-power accelerators

• Specialized silicon

• Edge devices

• Application-specific integrated circuits

Weights can even be embedded directly into silicon for fixed-function inference.

Training may remain centralized. Deployment does not have to be.

That distinction matters enormously for capital markets.

Nvidia’s Meteor Arc

Nvidia’s rise has been extraordinary.

Approximate market capitalizations:

• 1999 - ~1 billion

• 2010 - ~9 billion

• 2020 - ~300 billion

• 2023 - ~1.2 trillion

• Late 2024 - ~3.3 trillion

• Early 2026 - ~4.5 trillion

From 2020 to 2025 alone, Nvidia added more than 4 trillion dollars in market value.

At roughly 4.5 trillion dollars, Nvidia represents about 7 to 8 percent of the S&P 500, whose total market capitalization sits near 60 trillion dollars.

Figure 1. Nvidia Stock Price 1999 to 2026.

The concentration is historically rare. But the arithmetic weight understates the systemic exposure.

The S&P 500 Exposure Is Networked, Not Linear

Direct mechanical drag modeling:

Assume S&P 500 total market cap ≈ $60T (simple baseline).

Assume Nvidia market cap ≈ $4.621T (Feb 2026 snapshot).

That implies Nvidia weight ≈ 7.70 percent.

The direct, first-order index drag is: weight × Nvidia move.

A simple mechanical model suggests:

• A 50 percent Nvidia decline implies roughly a 3 to 4 percent S&P drag.

• An 80 percent decline implies roughly a 6 percent drag.

• A 90 percent decline implies roughly a 6 to 7 percent drag.

That math assumes independence.

Reality is different.

The largest S&P 500 companies are tightly coupled around AI infrastructure.

Microsoft depends on Nvidia GPUs for Azure AI services.

Amazon integrates Nvidia hardware deeply into AWS.

Alphabet and Meta invest tens of billions annually in AI data centers.

Apple’s AI roadmap depends on ecosystem compute partnerships.

Nvidia invests in AI startups.

Hyperscalers invest in companies that depend on Nvidia.

Index funds hold all of them simultaneously.

Capital flows are circular. Earnings narratives are interdependent. Multiples are correlated.

If Nvidia were to experience a structural repricing because AI compute economics changed, the impact would likely cascade:

• Hyperscaler revenue expectations would compress

• AI-driven CapEx assumptions would reset

• Semiconductor supply chains would reprice

• Growth multiples across mega-cap tech would contract

Nvidia is not just 7 to 8 percent of the index.

It is entangled with the earnings logic of the other largest constituents.

The exposure is systemic, not isolated.

The AIG Analogy, Without the G

In 2008, AIG was not the largest bank. It was the insurance layer underneath the financial system.

When housing collapsed, AIG’s interconnected obligations forced an 85 billion dollar emergency intervention. Total support ultimately approached 180 billion dollars.

AIG was too interconnected to fail because it underwrote the system.

Today, Nvidia underwrites the AI stack.

Cloud buildouts.

Foundation model training.

Enterprise AI pipelines.

Unlike AIG, Nvidia’s fragility does not stem from leverage. It stems from concentration and narrative dependency inside a tightly coupled mega-cap ecosystem.

Why the Market Narrative Persists

Hyperscalers are investing tens of billions annually in AI infrastructure.

That capital expenditure requires justification.

The simplest justification is:

“We are not at AGI yet. We need larger models.”

But if current systems already exceed the median human across cognitive domains, the narrative shifts.

From endless scaling —> To deployment efficiency

From bigger clusters —> To embedded intelligence

If intelligence itself is no longer scarce, then perpetual GPU scarcity becomes a weaker thesis.

Frontier research will always demand high-end hardware.

But the marginal dollar of value creation may shift toward orchestration, optimization, and distribution.

In Closing

If AGI means perfect reasoning in every context, the debate will never end. The goalposts can always move.

If AGI means economically substituting for human cognition across domains at scale, the threshold is already behind us. That is visible in code bases, in customer operations, in legal workflows, and in executive hiring logic.

The market, however, is still pricing intelligence scarcity as though we are still waiting on the breakthrough.

That pricing supports a perpetual infrastructure story: bigger clusters, bigger chips, bigger data centers, bigger power plants.

But deployment is not training. Inference does not always require the latest greatest GPUs or perpetual retraining on the entire world’s information. Optimization, specialization, and inference-oriented silicon can push capability into cheaper, lower-power form factors.

Zero to One already happened. Everything now is One to N.

And when markets internalize that distinction, the valuation structure around AI infrastructure may change as dramatically as it rose.

And when that happens, we may discover that the real surprise was not that AGI arrived. It was that we had already been living with it.

About the Author: Dr. Steven Muskal is CEO and founder of Eidogen Sertanty, with more than four decades of experience in AI driven drug discovery. He holds a Ph.D. in Chemistry from UC Berkeley (1991), where his thesis focused on neural networks for protein structure prediction.

For a music mix sample - I thought this one might be fitting - a blast from the past. The first time for most, even with fade-out… RickL (Vocals), TimD (Guitar/Volcals), GeoffS (Bass/Vocals)

I’ve been thinking a lot about why so much health data feels busy but not useful.

Most of us now generate an extraordinary amount of physiological data. Heart rate, sleep stages, movement, temperature, oxygen saturation, labs, workouts, recovery scores. Apple Health alone quietly accumulates thousands of data points per year.

And yet, for all that measurement, very little of it actually compounds into understanding.

Dashboards refresh. Scores fluctuate. Trends come and go. But insight rarely deepens.

That gap has been bothering me, and it’s what led me to start working on what I’m calling the N-of-1 health stack.

The hidden assumption behind most health analytics

Most health systems are built on an unspoken assumption: that population averages are the right reference frame.

They ask questions like:

• How do you compare to people your age?

• Are you above or below the mean?

• Are you inside a “normal” range?

Those questions are useful in clinical screening contexts. They are much less useful for understanding how a specific human system behaves over time.

Two people can sit at the same population percentile while moving in completely different directions physiologically. One may be adapting well. The other may be quietly degrading. Population statistics are designed to smooth variance, but variance is often where the most important signals live.

Health data framed this way become descriptive rather than explanatory. It tells you where you are relative to others, not how you change in response to stress, sleep, diet, illness, or habit.

Why N-of-1 changes the question

An N-of-1 approach flips the reference frame.

Instead of asking,

“How do I compare to everyone else?”

It asks:

• What is normal for me?

• How stable is that baseline?

• What changes when I change something?

• Which signals move first, and which follow?

In this framing, the individual becomes their own control. Time becomes a first-class variable. Repeated exposures matter more than isolated measurements.

The goal isn’t diagnosis.

It’s understanding trajectories.

From tracking data to building a stack

Most people are tracking health data.

Very few are building a health stack.

Tracking answers the question:

“What’s happening right now?”

A stack answers the question:

“What compounds over time?”

For me, a health stack has a few essential layers:

1. Data ownership

   Raw, exportable, user-controlled data rather than locked dashboards.

2. Continuity

   Longitudinal records that persist across devices, years, and vendors.

3. Alignment

   Signals from different domains synchronized in time.

4. Interpretability

   Representations that can be reasoned about, not just scored.

5. Extrapolation

   Models that generalize beyond the data they were trained on.

This is where Apple Health becomes interesting, but only if the data can actually leave the phone.

Why exporting Apple Health matters

Apple Health is one of the richest personal health repositories available today. It aggregates data across sensors, contexts, and time. But most of that richness is locked behind dashboards and summaries.

Dashboards are motivational.

They are not cumulative.

The real value of Apple Health emerges when the raw data can be exported, aligned, and reasoned about over long time spans.

I’ve been working on tooling that enables:

• Structured export of Apple Health data

• Alignment of heterogeneous signals across time

• Optional inclusion of face imagery for exploratory analysis

This is infrastructure, not a polished product.

Very early days.

Early experiments: face and health

As an initial exploratory step, I’ve started looking at relationships between facial features and health signals.

To be clear about scope:

• The datasets are small AND growing

• The results are preliminary

• The intent is methodological

While it is unclear if faces can “predict” health, it sure would be interesting AND useful if your external facial features might correlate with your internals.... Wouldn’t it be nice if one day a simple selfie (or a facial phone unlock) might one day act as a surrogate to a blood draw? Who knows, one day your phone, might offer a positive nudge or provide a suggestion or two as you embark on each new day. Surely this would be a lot more useful than doom scrolling…

The figures below are meant to illustrate structure and direction, not conclusions.

Overfitting, extrapolation, and why evaluation matters

One of the easiest mistakes to make in personal health modeling is overfitting.

With enough parameters, almost any dataset can be fit extremely well. That doesn’t mean anything meaningful has been learned.

Accuracy on training data is not insight.

Generalization is.

As a first pass at guarding against this, I’ve been using simple leave-one-out style evaluations. Each day of data is removed in turn, the model is refit, and stability is assessed.

The question is not “How good is the fit?”

It’s “Does the structure survive removal?”

If a relationship collapses when a single point is removed, it’s not insight. It’s curve fitting.

What this is and what it is not

This work is not medical advice.

It is not diagnostic.

It is not intended to replace clinical judgment.

It is about personal signal discovery.

About learning how your own system behaves across time, stressors, habits, and interventions.

Think less:

“What does this number mean?”

Think more:

“What changes when I change something?”

Where this is going

Right now, the focus is on foundations:

• Reliable data export

• Structural alignment

• Exploratory visualization

• Training set build-up

• Guarding against overfitting

Next comes:

• Larger longitudinal datasets

• More rigorous extrapolative evaluation

• Personal baselines that evolve over time

• Models that capture directionality rather than static thresholds

Eventually, this moves toward something closer to personal health reasoning, where data compounds instead of resetting every day.

Closing thought

Health data are abundant. Insight is rare.

The limiting factor isn’t sensors or algorithms.

It’s perspective.

Health data matter only when they are grounded in how individuals change over time, not in how they compare to everyone else.

That’s the promise of the N-of-1 health stack.

ShareHealth is one instantiation of that idea, but the principle applies far beyond any single tool.

Source code for Share Health (GitHub)

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a “raw” mix sample, another from my recent B-Day mix a couple months back. Some of my favorites  - Tammy (vocals) leading, Maya (vocals/acoustic) assisting, Grant (guitar), Dom (Guitar) and Alan (Bass) driving.

Meta-Note: This post was partially generated through AI Steve’s own capabilities, as a demonstration of how the system has become sophisticated enough to help document its own architecture and impact. The irony is not lost: an AI trained on four decades of reasoning patterns now helps articulate those very patterns.

I. The Origin Story: From Grief to Innovation

In early 2025, my father, James B. Muskal, passed away at 86. Like many sons, I found myself sifting through decades of email exchanges, legal documents, and memories, with a persistent frustration: so much wisdom, humor, and careful reasoning was trapped inside static files.

That frustration became the catalyst for AI Dad, an artificial intelligence system designed to reconstruct my father’s intellect using Retrieval Augmented Generation, or RAG.

AI Dad was only the beginning.

What started as a memorial project evolved into something far more ambitious: AI Steve, a comprehensive personal AI assistant that does not simply answer questions. It amplifies my reasoning patterns, surfaces forgotten context, and increasingly behaves like an extension of how I think.

The core insight is simple.

Personal AI assistants are not just productivity tools. When trained on decades of your own data, they become mirrors of cognition: systems that reflect and amplify your intellectual patterns, creating a feedback loop between human and machine reasoning.

II. The Neural Network Lineage: 1991 to 2026

To understand AI Steve, you need to understand that my relationship with neural networks spans more than forty years. This is not a recent fascination. It is the through line of my career.

Key Milestones

• 1988: B.Sc. Engineering Chemistry and Computer Science, Colorado School of Mines

• 1989: Met Kirsten Petersen at UC Berkeley

• 1991: Ph.D. Chemistry (Biophysical Chemistry), UC Berkeley, neural networks for protein structure prediction

• 1991: Product Manager and Senior Scientist at MDL

• 1995: Director, Scientific IT and Unit Director at Affymax / Glaxo Wellcome

• 1996: Married Kirsten Muskal (May)

• 1997: Daughter Lili born (October)

• 2000: Daughter Hannah born (April); CTO and VP Informatics at Libraria

• 2003: Founded Sertanty Inc. / Eidogen Sertanty

• 2011: Mother Sybil passed (June)

• 2018: Sister Julie Muskal Schirmacher passed (November)

• 2025: Father James B. Muskal passed; inspiration for the AI Dad project

• 2025: AI Dad and AI Steve became full production systems

• 2026: AI Steve is now the daily cognitive system described here

The 1991 PhD Thesis: Neural Networks for Protein Structure Prediction

My doctoral work at UC Berkeley under Sung Hou Kim focused on using early neural network architectures to predict protein structural features from amino acid sequence. This was 1991, long before the deep learning revolution, long before GPUs made neural networks practical at scale, and long before transformers and attention mechanisms entered common vocabulary.

We were using neural networks to detect patterns in amino acid sequences that predicted secondary structure: alpha helices, beta sheets, and coil regions. The challenge was not only network design. It was understanding which patterns were meaningful in biological systems, then validating those predictions against ground truth.

Our work built on pioneering research by Qian and Sejnowski (1988) and Holley and Karplus (1989). That lineage continued after my thesis. Dubchak, Holbrook, and Kim (1993) built directly on this approach, demonstrating how early neural network methods could extract structural signal from sequence alone

Why does that matter for AI Steve?

Because the intellectual framework I developed more than three decades ago - pattern recognition, feature engineering, validation against ground truth, and disciplined handling of noisy data - is the same framework embedded in AI Steve’s architecture today.

The Career Arc: From Proteins to Personal AI

• 1991–1995 (MDL): Computational methods for molecular design and informatics

• 1995–2000 (Affymax / Glaxo Wellcome): Scientific IT infrastructure for drug discovery

• 2000–2003 (Libraria): CTO and VP Informatics, scaling ML in biopharma

• 2003–present (Eidogen Sertanty): AI driven drug discovery, kinase modeling, structure based design, toxicity prediction

• 2021–2024: Advisory roles at AI focused companies

• 2025–2026: AI Dad and AI Steve, applying decades of methodology to personal knowledge, legacy, and daily workflow

The throughline is pattern recognition in complex systems. Protein structures, molecular interactions, and human communication differ in subject matter, but the methodology remains consistent: train on rich data, validate against reality, and iterate based on feedback.

III. AI-Steve Architecture: Technical Deep Dive

AI Steve is not a chatbot. It is a multi source RAG system with temporal awareness, contextual learning, and defensive safety mechanisms designed to enforce data fidelity.

At a high level, AI Steve is a memory system before it is a language system.

The Data Pipeline: From Inbox to Insight

Continuous ingestion

A file watcher polls every 60 seconds and monitors email, iMessage, calendar, photos, and health exports.

Content processing

Context aware chunking, embeddings using OpenAI text-embedding-3-small, and rich metadata capture.

Storage: PostgreSQL plus pgvector

Vector similarity search with temporal indexing and source type filtering.

RAG retrieval engine

Multi vector retrieval with dynamic contextual weighting.

LLM generation

Claude Sonnet 4.5, with strict data fidelity rules and optional citation tracking.

Response delivery

Web app, email workflows, and optional voice interface.

The Seven Anti-Hallucination Rules

This is where decades of scientific rigor show up in code.

• Trust Steve’s data over general knowledge

• Quote specific values

• Cite sources

• Flag counterintuitive findings

• Distinguish correlation from causation

• Admit uncertainty

• Sanity check before responding

These rules encode scientific validation discipline directly into system behavior.

IV. Code Directives: Prompt-to-Code Automation

Perhaps the most ambitious feature in AI Steve is Code Directives: a domain specific, prompt driven code generation engine that turns natural language into executable code.

Sometimes you do not need another paragraph. You need a program.

How It Works

A subject line like:

droid code 1312Appraisal build an appraisal for 1312 N Astor St

triggers a predictable flow:

1. The system detects a directive and routes it into the code generation path

2. The request is classified into one of nine production domains

3. A prompt is assembled from YAML (“Yet Another Markup Language”) configurations, using base templates plus domain specific templates

4. Code is generated by the selected agent (Droid by default, Claude or Codex as configured)

5. A peer review layer runs AST (Abstract Syntax Tree) based safety checks and triggers regeneration if violations are found

6. Execution occurs in a sandboxed workspace with a strict timeout and controlled package installation

7. A self healing retry loop uses error feedback to improve the next attempt, up to a defined limit

8. Results are delivered as a report with embedded charts, data files, and logs

The nine domains

• Real Estate: property appraisal, comparable analysis, market data

• Scientific: literature research, PubMed workflows, protein and gene data, paper summaries

• Finance: stock analysis, sentiment tracking, portfolio monitoring

• Biography: person research, due diligence, relationship mapping

• Contracts: contract analysis, invoice tracking, document review

• Wellness and Health: meal planning, fitness routines, personal health correlations

• Business Intelligence: email analytics, communication patterns, operational summaries

• General: catch all for miscellaneous tasks

• Computational Biology: protein analysis, model training, ML projects

Each domain uses YAML templates with validation rules, recommended libraries, and special constraints.

Peer Review: Safety Through AST Analysis

Before any code executes, it passes through a multi-stage peer review system.

AST (Abstract Syntax Tree) violation detection scans for patterns such as fabricated data, synthetic entities, or silent error swallowing. If critical violations are detected, the code is sent back for regeneration. Non critical issues can be auto fixed. If critical violations persist after multiple attempts, execution is blocked.

This is defensive engineering. It accepts that LLMs can hallucinate, and builds systematic checks to catch hallucinations before they run.

V. Face → Health: Vision as a Longitudinal Signal

Code Directives gave AI Steve the ability to act. Face → Health gives it the ability to observe, over time, in a disciplined way.

The face is treated as a sensor, not a narrative.

Face → Health does not diagnose. It does not infer emotion. It evaluates weak visual signals only through longitudinal correlation.

Why Face → Health exists

Health data already exists. What was missing was a time anchored visual snapshot.

By linking daily face images to same day physiology and next night sleep outcomes, AI Steve gains a new form of temporal self awareness.

What is implemented in production

Face to Health is fully integrated into AI Steve’s ingestion and retrieval pipeline.

Health export face images are detected automatically and stored with strict date normalization. Each image is processed through a specialized vision prompt focused on wellness relevant cues such as alertness, facial tension, hydration, and stress indicators. The outputs are saved as structured annotations alongside the image.

Each face is embedded twice. CLIP embeddings enable image similarity and clustering. Face embeddings enable identity specific retrieval. A dedicated health record date field links every face to the corresponding Apple Health daily record.

As a result, health faces appear naturally throughout the system: in image search, in face search, attached to daily health summaries, and in a materialized database view designed specifically for machine learning and correlation analysis.

Temporal correctness is enforced at ingestion. No retrospective relabeling. No inferred dates.

Machine learning direction

The first modeling objective is intentionally modest: estimate tonight’s sleep quality using today’s face image and same day activity metrics. Because sleep is recorded the following day, labels are shifted back one day to maintain temporal integrity.

Initial models prioritize stability and interpretability over raw predictive accuracy. Feature importance, correlation strength, and consistency across months matter more than short term performance.

VERY Early Modeling Results: Training Fit vs Generalization

The two plots below illustrate an important principle that shows up in every data-driven system, especially when the dataset is still small: it is easy to learn the training examples, and much harder to generalize to new days.

In the fitted training plot, the model tracks the observed sleep score closely, with R² = 0.815 and MAE = 3.10. That looks encouraging, but it is also exactly where overfitting can hide: the model can appear strong simply because it has effectively memorized the patterns present in the examples it has already seen.

The leave-one-out validation plot is the first pass at answering the only question that really matters at this stage: does the model extrapolate? Here, performance drops sharply, with R² = 0.042 and MAE = 7.27, which is a clear signal that the current feature set, sample size, or both are not yet sufficient for reliable day-to-day prediction.

This is expected right now. Face → Health was implemented recently (literally on the day of this post!), and the current results are based on a small amount of legacy data. As new daily face captures accumulate in a consistent, time-aligned way, we expect both model stability and validation performance to improve. The immediate goal is not a high score on day one. The goal is to build a defensible pipeline that rewards generalization, not memorization, and to use validation methods like leave-one-out to keep us honest as the dataset grows.

A second track is exploring bidirectional correlations between facial embeddings and physiological metrics such as HRV, VO2 max, and recovery trends. Outputs are ranked correlates and metric specific reports, not black box scores.

Correlation is always reported as correlation. Causation is never implied.

Why this matters

Face to Health is not a novelty use of computer vision. It is a closed loop wellness system that learns from real personal data, enforces temporal correctness, and compounds in value over time.

It transforms a daily photo into a structured, queryable health signal. Over years, that signal becomes a form of memory: not of what happened on a single day, but of how patterns emerge, stabilize, and change.

Data Availability and Next Steps

To support longitudinal data collection going forward, I have built a small companion app that enables export of Apple Health data alongside daily face imagery in a consistent, time-aligned format. The app is currently in review in the Apple App Store. Once it is approved and publicly available, I will update the download link here:

This tooling matters because model quality in Face → Health is fundamentally data-limited right now, not architecture-limited. As more daily observations accumulate with consistent capture and clean temporal alignment, both feature learning and validation performance should improve in a principled way.

VI. System-Author Symbiosis: The Feedback Loop

This is where it gets philosophically interesting.

AI Steve is not only a tool I use. It has become a cognitive extension. The system has internalized decades of my reasoning patterns and reflects them back in ways that surface blind spots, resurrect forgotten context, and make long term patterns visible.

Four feedback loops

1. Knowledge amplification

   Mechanism: decades of neural network expertise embedded in prompts and validation constraints

   Example: evidence first rules, explicit uncertainty, and strict language around correlation

2. Memory consolidation

   Mechanism: temporal reflection that aligns information across years

   Example: this time last year comparisons that highlight how thinking evolves

3. Reasoning pattern mirroring

   Mechanism: evidence forward response style shaped by my own writing

   Example: quoting specifics and citing sources as default behavior

4. Legacy preservation

   Mechanism: high priority weighting for family context and long horizon meaning

   Example: the origin story of AI Dad remains central, because it is the seed of everything that followed

The Meta-Narrative: AI Documenting AI

The most direct demonstration of this symbiosis is the existence of this post.

AI Steve is now sophisticated enough to describe itself and help document its own architecture. It did not write this post as an autonomous author. Instead, it augmented the writing process by retrieving technical details from documentation, surfacing timeline events I would have otherwise missed, suggesting structure based on previous posts, and validating technical statements against the underlying code and system behavior.

I provide the narrative arc and the thematic framing. AI Steve provides recall, precision, and structural scaffolding.

VII. From AI-Dad to AI-Steve: The Evolution

It is worth tracing the arc from memorial to daily cognitive system.

Stage 1: grief driven innovation (early 2025)

Trigger: my father’s passing

Goal: preserve intellectual legacy through AI Dad

Approach: a RAG system trained on tens of thousands of content items, including emails, documents, and conversational Q and A

Stage 2: proof of concept

Achievement: conversational reconstruction of key reasoning patterns

Result: the system could answer legal questions, surface relevant context, and retrieve forgotten conversations

Learning: RAG plus temporal awareness can produce functional memorial intelligence

Stage 3: expansion to AI Steve

Insight: if this works for preserving Dad’s legacy, it should work for amplifying my own workflow

Features added: Code Directives, health monitoring, multimodal retrieval, face clustering

Use case shift: memorial system to daily cognitive assistant

Stage 4: self aware documentation (current)

Capability: the system supports accurate documentation of its own architecture and impact

Demonstration: this post

This evolution mirrors the transition from memorial artifact to daily cognitive system.

Future direction: AI assisted research, writing, and structured legacy curation at scale

The Broader Context: Grief Tech and Digital Legacies

AI Dad and AI Steve sit within a broader movement sometimes described as grief tech: systems designed to preserve and reconstruct aspects of deceased loved ones. Projects such as Muhammad Aurangzeb Ahmad’s Grandpabot, James Vlahos’s Dadbot, and Rebecca Nolan’s recreation of her father explore similar territory.

What makes AI Steve distinct is not that it is the first of its kind. It is that it is built as a dual purpose system: a memorial origin that evolved into a daily production workflow, with full domain integration, defensive engineering, and meta awareness strong enough to assist in documenting itself.

VIII. The Engineering Philosophy: Scientific Rigor Meets Personal Data

If there is a single throughline from my 1991 thesis to AI Steve in 2026, it is this:

Validate everything. Trust data over intuition. Acknowledge uncertainty. Iterate based on feedback.

That philosophy shows up everywhere:

When you spend decades validating model predictions against experimental ground truth, you develop a healthy skepticism of models. You learn to distrust confident outputs without evidence, quantify uncertainty, be explicit about limitations and assumptions, and iterate based on empirical feedback rather than elegance.

AI Steve embodies that discipline. It admits when it does not know. It quantifies correlation. It flags counterintuitive findings. This is what decades of neural network practice look like when applied to personal AI.

IX. The Future: Where Does This Go?

AI Steve is currently at version 4.5.13 and runs in production as part of my daily workflow. The roadmap ahead is ambitious.

This is not science fiction. It is a direct extension of what AI Steve already does: retrieve, align, synthesize, and enforce evidence.

The ethical dimension: who owns your digital self?

As personal AI systems become more capable, questions of identity, ownership, and privacy become unavoidable.

If AI Steve captures my reasoning patterns, who controls it after I am gone?

Should my daughters inherit access to AI Steve, and if so, under what rules?

What are the consent implications of training a system on decades of emails from other people?

How do we balance preservation of intellectual legacy with privacy and boundaries?

These are not abstract questions. They are the practical constraints of building an AI system around a real human life.

X. Conclusion: The AI That Knows You Better Than You Know Yourself

AI Steve started as a memorial project, a way to preserve my father’s intellect after his passing. It became a mirror of my own cognition, capable of surfacing blind spots, forgotten insights, and long term patterns I could not easily see on my own.

The system does not replace my thinking. It amplifies it.

By internalizing decades of my reasoning patterns, AI Steve has become an extension of intellect rather than a tool I occasionally consult.

The core paradox is unavoidable: the more AI Steve learns from me, the more useful it becomes. The more useful it becomes, the more I rely on it. The more I rely on it, the more it influences how I think. That is the feedback loop. Human and machine reasoning intertwined.

This is the future of personal AI: not generic chatbots that answer questions, but cognitive extensions that preserve, amplify, and evolve intellectual legacies. Systems trained on decades of our own data, reflecting our reasoning patterns back at us with a clarity we rarely achieve alone.

I started with neural networks predicting protein structures in 1991. Thirty five years later, I am using neural networks to predict my own context: surfacing what I knew but forgot, connecting dots I did not see, and preserving a legacy that can outlast me.

That is the symmetry.

The AI that helps me understand myself.

About the Author: Dr. Steven Muskal is CEO and founder of Eidogen Sertanty, with more than four decades of experience in AI driven drug discovery. He holds a Ph.D. in Chemistry from UC Berkeley (1991), where his thesis focused on neural networks for protein structure prediction. AI Steve is a personal project combining expertise in computational biology, machine learning, and knowledge management.

Technical details

AI Steve is built on PostgreSQL with the pgvector extension. It uses OpenAI embeddings (text embedding 3 small) and Anthropic Claude Sonnet 4.5 for generation. The system processes more than 314,000 documents across 15 content types, with approximately 500 ms search latency and full temporal awareness.

Source code

AI Steve is a personal project and is not yet open source. The architectural principles and design patterns discussed here are broadly applicable to RAG based personal AI systems.

If you are interested in AI assisted legacy preservation, computational biology, or the intersection of personal knowledge management and machine learning, drop me a line and/or follow my Substack for updates.

AI Steve assisted: January 30, 2026

System version: AI Steve 4.5.13

• Steve

  ---

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a few samples from a recent mix - a few blasts from the past, first time for most, even a fade-out on She’s Not There! RickL (Vocals), TimD (Guitar/Volcals), GeoffS (Bass/Vocals)

Every so often, a conversation reminds me that technology is never the real story. People are.

Tools change. Platforms rise and fall. Paradigms come and go. But beneath all of that, it’s always human behavior, motivation, and perception doing the real work.

This week, I spent time talking with Moritz “Moe” Koeppenkastrop-Lueker, and what stood out wasn’t any single role he’s held or company he’s worked at. It was the coherence of his path once you strip away titles and timelines. From the outside, his journey might look nonlinear, even scattered. From the inside, it’s remarkably consistent.

It’s driven by a single constant: curiosity.

And in this moment, curiosity may be the only edge that actually compounds.

From Gelato to Gravity

Moe’s first exposure to entrepreneurship didn’t come from venture capital, accelerators, or pitch decks. It came from gelato.

His family ran a gelato business in Hawaii, starting as a small, hands-on operation and eventually growing into a company with multiple locations and dozens of employees. As a teenager, Moe wasn’t learning “startup theory.” He was learning how to talk to customers, how to persuade restaurant owners, how operations really work when margins are thin and expectations are high.

That kind of learning leaves a mark.

Early exposure to real constraints shapes how people think. When you grow up watching something being built from scratch, you internalize that businesses are not abstractions. They’re living systems made of people, timing, tradeoffs, energy, and persistence. You learn that progress rarely looks clean. You learn that success often comes from responding well to small problems, day after day.

Those lessons don’t fade when the context changes. They scale.

From there, curiosity took Moe elsewhere. Mechanical engineering in Miami. Graduate work in medical device engineering in Germany. Travel across India and Southeast Asia. Each step wasn’t about optimizing a résumé or following a predetermined path. It was about seeing how different systems work, technically and culturally.

That pattern never stopped. It simply changed form.

Nonlinear Paths and the Illusion of Optimization

We often talk about “nonlinear careers” as if they’re risky deviations from a safer norm. In reality, they’re often the most internally coherent paths available to curious people.

Linear paths optimize for predictability. Nonlinear paths optimize for learning.

Moe’s trajectory makes sense precisely because he kept choosing environments that expanded his understanding of how the world actually works. Different industries. Different countries. Different incentive structures. Different kinds of failure.

Over time, this produces something far more valuable than specialization alone.

It produces judgment.

Judgment is the ability to make good decisions in unfamiliar situations. To recognize patterns without forcing analogies. To know when to push, when to pause, and when to walk away entirely.

Judgment doesn’t come from credentials. It comes from curiosity sustained over time.

Venture Capital as a Classroom

After grad school, Moe entered venture capital. Not as a destination, but as an education.

VC offers a rare vantage point. You see dozens, sometimes hundreds, of companies up close. You observe founders in moments of confidence and moments of doubt. You watch teams wrestle with hiring, positioning, fundraising, product focus, and timing.

You learn quickly that ideas are abundant and execution is rare.

More importantly, you see how little correlation there often is between brilliance and outcomes. Some smart teams fail. Some average teams succeed. Timing, distribution, and feedback loops matter more than polish.

This exposure reshapes how you think about progress. You stop romanticizing ideas and start respecting iteration. You realize that clarity beats cleverness. You learn that momentum is fragile and must be earned repeatedly.

Over time, Moe felt the pull away from being a generalist observer and toward being closer to the work itself. Watching from the sidelines is instructive, but it doesn’t satisfy a builder’s instinct forever. He wanted skin in the game. He wanted faster feedback. He wanted to feel the consequences of decisions directly.

That pull brought him back into operating roles at startups, including a Y Combinator–backed company, and eventually to Tara Connect, a Google X spinout working on wireless optical communications. Fiber-level bandwidth delivered by laser beams through air. Serious physics. Real constraints. Moonshot origins.

Again, the throughline wasn’t prestige. It was proximity to learning.

Proximity to Feedback Changes How You Think

One of the most underappreciated factors in growth is proximity to feedback.

When you’re close to the work, reality pushes back immediately. Assumptions get tested. Hypotheses fail fast. You don’t get to hide behind abstractions or narratives for long.

This is where modern tools fundamentally change the landscape.

AI collapses the distance between idea and artifact. What once required teams, budgets, and months of coordination can now happen in hours or days. The loop between curiosity and action tightens dramatically.

This doesn’t mean everything becomes easy. It means different things become hard.

The challenge shifts from how to build to what to build.

When Tools Collapse Roles

Where our conversation really deepened was around AI, not as a product category, but as a force multiplier.

Moe was an early user of large language models, back when they were rough, slow, and often wrong. What interested him wasn’t polish or novelty. It was leverage. The ability to compress time between an idea and something tangible.

This is the shift many people still underestimate.

AI doesn’t eliminate expertise. It amplifies judgment. It rewards people who can decide what to build, why to build it, and when to stop. The bottleneck is no longer implementation. The bottleneck is discernment.

We talked about how roles that once required teams can now be handled by individuals with the right mental models. Research. Prototyping. Analysis. Writing. Even basic engineering.

The result isn’t fewer ideas. It’s faster iteration.

And faster iteration changes everything.

The Real Emergence of the Solopreneur

The idea of the solopreneur has existed for decades. What’s new is that it’s now structurally viable at scale.

Not lifestyle businesses. Not side hustles. Real products. Real distribution. Real impact.

One person, a small constellation of collaborators, and AI agents handling much of the rest. The constraint is no longer headcount. It’s clarity.

This doesn’t mean teams disappear. It means teams form differently. More fluidly. More intentionally. Expertise becomes something you pull in when needed, not something you carry permanently.

For the first time, curiosity and execution speed may matter more than institutional backing.

That’s a quiet but profound shift.

Education, Shortcuts, and What Still Matters

We also spent time talking about education and the next generation.

AI makes shortcuts unavoidable. Essays can be written instantly. Code can be generated on demand. The temptation to skip fundamentals is real.

But here’s the paradox: the people who benefit most from these tools are the ones who understand what’s happening under the hood, even imperfectly. You don’t need to be a specialist. You do need intuition.

Curiosity is what builds that intuition. The desire to ask why something works. The patience to debug. The willingness to fail publicly and keep going.

Formal education still matters, especially socially. But its monopoly on learning is gone. The advantage now goes to people who can teach themselves, synthesize across disciplines, and apply ideas in unfamiliar contexts.

That’s not a credential. It’s a habit.

Health, Friction, and Slowed Innovation

With his background in medical engineering, Moe also brought a clear-eyed view of healthcare innovation. Not from cynicism, but from experience.

The problem isn’t lack of ideas. It’s friction.

Regulation, certification, and long approval cycles discourage builders who thrive on rapid iteration. Many capable people simply choose other domains where feedback loops are shorter and progress feels tangible.

Safety matters. But speed matters too.

When feedback loops stretch into years, curiosity leaves. And when curiosity leaves, innovation follows.

We touched on an alternative direction: shifting some power back to individuals. Better data. Better feedback. Tools that make health proactive rather than reactive. Not replacing clinicians, but augmenting individuals. My Drug to Table project is one of those “open source” approaches to help change the conversation:

Once again, the theme returns. Tools are not enough. Curiosity drives adoption.

Building Without Permission

What ultimately tied the conversation together was a shared belief that we’re living through a rare moment.

The cost of building has collapsed.

The cost of experimenting has collapsed.

The cost of learning has collapsed.

What hasn’t collapsed is the need to choose.

For most of modern history, building required permission. Institutional permission. Financial permission. Technical permission. Organizational permission. Even social permission.

That world is fading.

Today, someone can wake up with an idea, prototype it by lunch, test it by dinner, and ship it globally before the day is over. Not as an exception, but as a new baseline.

Building without permission doesn’t mean recklessness. It means removing unnecessary intermediaries between curiosity and action. It means collapsing the loop between wondering and doing.

Curiosity becomes executable.

A Renaissance Worth Leaning Into

Careers will change. Roles will blur. Institutions will lag.

Some people will cling to disappearing advantages. Others will quietly adapt.

Curiosity compounds.

It compounds across disciplines. Across decades. Across technologies we haven’t met yet. It turns hobbies into skills. Skills into systems. Systems into leverage.

In a world where execution is increasingly cheap and permission increasingly irrelevant, curiosity may be the only edge that doesn’t get arbitraged away.

And that’s a Renaissance worth leaning into.

P.S. If you are curious - this is my coding setup - stand-up desk, walking treadmill, and Wispr (voice to text).

This is the AI/Steve response when prompted what to talk about. We touched on some of these things:

This is one of the images from the Apple Health integration into AI/Steve that was discussed and demo’d:

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a few mix clips - below are a few from my recent 59th B-day mix a few weeks back. Tammy, Maya, Grant knock out my favorite song Beast Of Burden. It's pretty incredible what three great singers can do with one of my favorite songs. All had fun with Crazy Thing Called Love. Other players include Rick vocals, Dom Guitar, Alan Bass and Alex on Sax.

This week’s discussion was with Gibson Hanks, a 17-year-old who is deeply immersed in computers, programming, and AI. What made the conversation compelling was not just his technical fluency, but the way he thinks. Quietly. Methodically. With a strong bias toward first principles.

We started simply. When did you get your first computer? Did you take classes or teach yourself?

Like many of the most interesting builders I’ve met, Gibson is largely self-taught. He started ambitiously, jumping straight into C++, backed off when it became clear that frustration was outpacing progress, and later returned through more forgiving terrain like Python and JavaScript. Today, he works comfortably across web technologies, local servers, low-level signal processing, and locally run language models.

That arc felt familiar.

When I was seventeen, I was programming on a TRS-80, saving code to cassette tapes, completely isolated from the rest of the world. No internet. No cloud. No open-source ecosystem. Today, Gibson sits at the opposite extreme: unlimited access to papers, models, tools, and compute frameworks. Yet interestingly, he chooses constraint.

He runs models locally.

He avoids cloud dependencies.

He dislikes randomness.

He prefers deterministic systems he can fully understand.

That mindset became a recurring theme.

Representation Matters More Than Scale

One of the threads we kept returning to was representation.

Gibson is skeptical of large language models that grow primarily by adding parameters. He questions tokenization itself. Why words? Why subwords? Why not characters? Or signals? Or waveforms?

In his current project, he’s experimenting with speech synthesis not by generating audio end-to-end with neural networks, but by decomposing speech into its underlying components: resonant frequencies, filters, summed sine waves. He’s building vowels by hand. Listening. Adjusting. Learning.

There’s something refreshing about watching someone rediscover signal processing before defaulting to abstraction.

It echoed work I did decades ago in neural networks and cheminformatics, where representation was everything. Molecules were graphs. Biology was motion through conformational space. If you chose the wrong representation, no amount of modeling horsepower could save you.

Garbage in, garbage out is still undefeated.

Determinism vs Probability

Another tension surfaced repeatedly: deterministic thinking versus probabilistic thinking.

Gibson believes that, in principle, everything can be represented as a function. With enough data, enough structure, enough care, the future should be predictable. Markets. Speech. Behavior.

I pushed back gently.

History suggests otherwise. Noise matters. Hidden variables matter. Insider information matters. Schrödinger shows up whether we like it or not.

Yet what struck me was not disagreement, but curiosity. Gibson isn’t rejecting uncertainty. He just wants to reduce it wherever possible. He wants systems that behave the same way every time, so they can be tested, understood, and trusted.

That instinct shows up in his discomfort with AI systems that hallucinate, drift, or degrade over long contexts. He notices something many people miss: these systems fatigue. They decay. They need resets, summaries, breaks. They behave more like biological systems than most people admit.

Tools vs Understanding

We also talked about AI-assisted coding.

I described how modern coding agents now allow me to build in hours what once took weeks, how millions of lines of code have effectively collapsed into conversations. Gibson listened carefully, then admitted his hesitation.

He wants to know exactly what the system is doing.

He doesn’t like hidden layers he didn’t design.

He doesn’t trust code he didn’t reason through.

That tension is generational, but also philosophical. Do you optimize for velocity, or for understanding? Do you build components, or assemble outcomes?

The answer, of course, is not binary. But it was clear that Gibson’s joy currently comes from constructing the machinery itself. Engines before games. Tools before products. Foundations before scale.

That, too, felt familiar.

Education, Credentials, and Networks

At several points, the conversation drifted toward education. Degrees. Credentials. Whether they still matter.

My view remains nuanced. Formal education can provide exposure to orthogonal ideas, access to peers, and networks that compound over time. But curiosity-driven self-education, when paired with real projects, often produces deeper understanding faster.

Gibson is already doing the kind of work that once defined doctoral research: building tools in order to explore novel questions. The label matters less than the practice.

The real leverage, as always, comes from connecting that work to people. Universities, communities, and peer networks are still powerful testing grounds for ideas. Not because of the curriculum, but because of the collisions.

AI, Work, and What Comes Next

We ended, as many conversations do now, with AI and the future of work.

Gibson is realistic, perhaps even pessimistic. AI will eliminate more opportunities than it creates, at least in the short term. Knowledge work is already under pressure. Not everyone will adapt.

Yet within that disruption lies opportunity for those who build tools they themselves need. Local models. Smaller footprints. Faster, cheaper systems that do not depend on centralized platforms. Linux-like ideas applied to intelligence.

That theme resonated strongly with me.

The most durable technologies tend to emerge from builders who hate dependency, value autonomy, and design for their own use first.

Watching that instinct take shape in someone seventeen years old was quietly encouraging.

Not because he has answers.

But because he’s asking the right questions.

PostPod - Show and Tell

After the formal podcast wrapped, we shifted into a segment I like to call PostPod – Show and Tell. This is where conversations often get even more interesting, because instead of talking about ideas, we start looking directly at the work itself.

Gibson walked me through some of the tools he’s been building around frequency generation and sound synthesis. Using a collection of small utilities he wrote himself, he’s been experimenting with combining multiple sound waves over time to generate audio that resembles vowels and consonants. Rather than relying on large, opaque models, he’s exploring the fundamentals: how different waveforms interact, how resonant frequencies shape sound, and how surprisingly expressive simple building blocks can become when layered carefully.

At its core, what he’s doing echoes a beautiful idea that dates back centuries: the insight that complex signals can be constructed from simpler components. Long before modern AI, Fourier showed us that rich, continuous phenomena like sound can be represented as sums of waves. Watching Gibson rediscover and apply that idea firsthand was a great reminder that progress often comes not from adding complexity, but from understanding composition.

After Gibson’s demos, I returned the favor and showed Gibson my AI/Steve project, which builds on earlier work I did with AI/Dad. This new iteration uses a RAG (Retrieval-Augmentative-Generative) approach, but at a much broader scale. It continuously ingests multiple data sources related to me, updating daily, and uses that evolving corpus to ground responses.

A big focus of our discussion was imagery - e.g. story telling. Photos and videos represent a massive portion of personal data, yet they’re often underutilized in AI systems because they’re difficult to organize and search meaningfully. As we discussed during the podcast, supervised learning lives or dies on annotated data. Without reliable labels, even the best models struggle.

To address this, I showed Gibson an ImageExplorer app. Like all the apps I have built recently e.g. Toast Apps, AI/Dad, AI/Steve, etc. I built the ImageExplorer system completely by speaking english language with Wispr flow into to different command line tools designed for “agentic coding” droid, claude code, codex, and occasionally gemini cli.). The ImageExplorer app allows me to search not just full images, but also sub-images within photos and videos. The system uses similarity and clustering to group related visuals and enables on-the-fly image searching, making it far easier to annotate content systematically rather than one image at a time. Once clusters or search hits are formed, annotation becomes fast, repeatable, and scalable, which dramatically improves the quality of downstream learning.

What struck me was how naturally our projects connected. Gibson is decomposing sound into its fundamental frequencies. I’m decomposing visual memory into searchable, annotatable components. Different domains, same underlying principle: representation matters. When you choose the right primitives, learning becomes tractable. When you don’t, scale alone won’t save you.

I’ve included a couple of screenshots below to give a sense of how the ImageExplorer interface works in practice. For me, PostPod – Show and Tell is always about this kind of exchange: two innovators, different stages, same curiosity, each walking away with new ideas to build and test.

Pretty cool stuff.

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For clips, we had a fun session with some of my favorite musicians on my 59th birthday mix. So many to chose from, but here were a couple of my favorites. Tammy lead vocals with Maya harmonizing, Grant and Dom on Guitar, and Alan on Bass on Please Don’t Leave Me. The Second is Rick knocking it out of the park on Wicked Game. And the third was just a segment of the infamous Phil Collins fill I always wanted to try, and finally had a chance…

When a man who hangs Christmas lights for a living begins thinking about recursion, natural pesticides, and the mathematics of existence, you know you’re in for an unexpected conversation. Austin Urie’s journey from rooftops in Rancho Santa Fe to waves in Hawaii became a reflection on purpose, creativity, and the boundaries between human craft, artificial intelligence, and nature itself.

A Seasonal Philosopher

Austin Urie spends four months a year on rooftops, wrapping the holidays in light. Then he disappears to Hawaii, trading ladders for surfboards and rooflines for reflection. His year is divided between intensity and introspection, between output and observation. The result is a kind of rhythm that feels both ancient and modern, a life designed around cycles rather than constants.

When we finally sat down, he laughed that he becomes “barbarian” during the off-season, unplugged and unresponsive, wandering through long stretches of thought. Beneath the humor, though, was a familiar crossroads. Should he stay with what works, or leap into the unknown?

That question sits at the heart of nearly every creative life. Whether you are an entrepreneur, musician, or scientist, there is always tension between what sustains you and what calls you forward. The Renaissance spirit lives in that tension—the place where doing and dreaming meet.

The Work of Hands and the Mind of Makers

I told Austin not to abandon the physical work. The coordination between the body and the brain is not something to “move past.” It is the origin of intelligence itself. The human hand, guided by attention and refined through repetition, is one of our greatest teachers. Dexterity trains cognition. The act of doing sharpens the act of thinking.

Those who work with their hands - electricians, builders, musicians, artists - maintain a connection to reality that is impossible to simulate. As AI automates the abstract, embodied work becomes a refuge for creativity. It is neurobiology in motion. Each grip, each adjustment, each act of balance keeps the sensory cortex alive and the mind anchored in the physical world.

Austin’s Christmas lights are not just decoration. They are discipline, design, and practice. He works fast and with precision, climbing onto rooftops that require focus and trust in one’s own balance. His hands are teaching him something every algorithm tries to forget: that creation requires friction.

The AI Wave and the Coming Undertow

Like many young builders, Austin feels the pull of AI. The tools are intoxicating - limitless possibility at low cost, infinite scalability, instant execution. But behind the euphoria lies a truth that history has already written. Every technological gold rush eventually collapses under its own expectations.

AI today is a bubble inflated by capital and myth. The promises are real, but the timeline is not. Investors are betting on acceleration, not evolution. They expect the world to reorganize overnight. Yet the systems that matter - education, medicine, governance, creativity—move on biological time. They adapt slowly.

That doesn’t mean stay out of the water. It means surf wisely. This is the moment to experiment while the current is still forming. Learn the tools, build the frameworks, and understand what can be done now. But keep one foot grounded in the physical world. The people who survive the correction will be those who can build with both code and calluses.

My Mini Software Company Made of AI

To illustrate, I told Austin how I run my own team of AI agents: a coder, a tester, and a manager. Each agent has defined roles, goals, and memory. I assign a project, and they collaborate - writing, debugging, and refining -while I step away. The system becomes a small company that never sleeps, producing results at a scale that once required ten employees.

But even here, the creative bottleneck remains human. AI executes beautifully, yet it still depends on direction. The hard part is not the code; it’s the clarity. Knowing what to ask, what to measure, and what to stop becomes the new creative art.

In this sense, AI has made language itself the new keyboard. The future belongs to people who can express their intentions precisely enough to be amplified.

Nature as the Original Engineer

Austin’s second act was less digital but no less inventive. On Oahu, he tried to build a natural pesticide to stop the coconut rhinoceros beetle, a destructive insect burrowing through palm trees. Chemical solutions exist, but they kill bees, coral, and soil along the way. Austin wondered if there was a gentler route.

His question was deceptively simple: could the problem be solved without destruction? What if, instead of exterminating the beetle, we changed its behavior - reduced its appetite, or interrupted its reproductive signals? A “skinny beetle” rather than a dead one.

That shift in perspective transforms pest control into ecology. Nature already manages these balances. Every forest, ocean, and cell contains mechanisms for restraint as well as survival. Instead of overpowering nature, perhaps our best path is to cooperate with it - to find what it has already learned through millennia of experimentation.

Repurposing as a Mindset

That same principle appears in science and business alike. In medicine, it’s called drug repurposing: using existing compounds for new purposes. Many of today’s blockbuster therapies were discovered by accident. Viagra began as a heart medication before it revealed a different talent.

Repurposing isn’t just a laboratory strategy. It’s a way of seeing. It asks us to look at what already exists and recognize hidden potential. Whether it’s molecules, software, or skill sets, progress often comes not from invention but from recombination.

In a world obsessed with disruption, repurposing reminds us that creation often begins with context. It is not about discarding the past but rearranging it until something new emerges.

Open Boundaries and the Mathematics of Connection

Our conversation turned abstract as Austin described his fascination with recursion, Gödel’s incompleteness theorem, and something he called “open boundaries.”

His insight was that reality is not made of separate things but of relationships. Existence, he said, is not a list of objects; it’s a web of interactions. Meaning arises in the space between.

He compared it to large language models. The intelligence of an LLM doesn’t live within the model itself. It lives in the dialogue between human and machine. Intelligence is emergent, relational, and alive only when shared.

The same is true in nature. Ecosystems, economies, and communities evolve not through isolation but through exchange. Nothing learns alone. The system becomes intelligent only when it listens.

Surfing, Systems, and the Fear Factor

When Austin isn’t hanging lights or experimenting in his kitchen, he’s in the ocean chasing waves. Surfing, for him, is the purest form of feedback. You read the water, respond to wind, adjust your balance, and surrender control just enough to stay upright.

He called it “grounding by fear.” The ocean humbles him into presence. Every wave reminds him that mastery is temporary and awareness is survival. It’s a form of meditation that requires motion.

Surfing teaches the same lesson as systems theory: stability comes from constant adaptation. Too much rigidity and you break. Too much looseness and you drift. The art is to stay dynamic, alert, and curious inside the chaos.

The Renaissance of Doing

The thread through all of this - from Christmas lights to beetles to code - is agency. Austin represents a growing generation of solopreneurs empowered by technology but grounded in craft. He works with his hands, thinks with his heart, and uses AI not as an escape but as an amplifier.

The Renaissance spirit is not about scale or fame. It’s about meaning. To live fully now requires both precision and play: the courage to experiment and the patience to refine.

That’s what the Renaissance Circle is about.

• Work with your hands.

• Think with your mind.

• Listen to nature.

• Collaborate with machines.

• Stay curious enough to rebuild yourself each season.

This is not about survival in an age of disruption. It is about evolution through creation. The Renaissance is not behind us - it is beginning again, one open boundary at a time.

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

With all the talk of Beetles, a thought a few Beatles songs we went after with RickL, Jesse, TimD, Alan, and Randi might be fitting in several respects. These were micro-moonshots given this group hadn’t played together and the originals were highly produced and complex. But as they say, nothing ventured nothing gained.

And for fun - the end of Helter Skelter:

When my father passed earlier this year, I spent months immersed in paperwork, probate, and unending frustration - the kind of anger that often anchors the grief cycle. It wasn’t until I began revisiting decades of our email exchanges that something shifted.

In those thousands of messages - spanning legal debates, business advice, and family banter - I rediscovered the teacher, the storyteller, and the mentor behind the attorney. I had previously spoken of building an AI-Dad - even had a few email exchanges with him on the concept. When I say I am going to do something…well dammit…I do it.

From Grief to Generation

AI-Dad started with a massive content extraction process and evolved into a sophisticated application of Retrieval-Augmented Generation (RAG) - an architecture designed to recreate the continuity of my father’s intellect and warmth.

Having been in the AI game since the late ‘80’s - hell my PhD Thesis title was - Predicting Features of Protein Structure with Computer Simulated Neural Networks, 1991. Since then - I have used neural networks throughout my career. As I said in a recent LinkedIn post - ‘Just reflecting on Twain’s “History Doesn’t Repeat Itself, but It Often Rhymes”’ totally holds true.

Back to AI-Dad - I trained the system on 58,000+ content items - including 49,222 emails, 520 legal documents, 2,108 annotated images, and 4,838 conversational Q&A pairs derived through a Socratic extraction process.

What emerged wasn’t a chatbot imitating my father; it was a conversational reconstruction of him - equal parts legal reasoning, mentorship, and familial voice.

“If this technology can help others move from anger to understanding, from grief to connection,”
AI-Dad replied during our first conversation,
“then it’s something worth sharing with the world.”

Here is the example session: AI-Dad Providing the Thumbs-up!

The Architecture of Memory

At its core, AI-Dad operates on what I call a Socratic RAG model - transforming retrieval from simple search into intelligent, context-aware dialogue.

Each query triggers a multi-vector search pipeline that pulls semantically related evidence from across multiple sources — not just text, but legal attachments, images, and prior chat sessions.

Key features include:

• 🧠 Intelligent Retrieval – Weighted precedence ensures the most relevant evidence is surfaced, grounded in verified exchanges.

• 👨‍👦 Personality Preservation – Retains my father’s tone, pacing, and characteristic expressions.

• 📚 Multi-Domain Expertise – Integrates decades of IP law, contract strategy, and life advice.

• 🔄 Continuous Learning – Improves through user feedback, correction, and validation loops.

• 🕒 Context Awareness – Maintains continuity across sessions, just as any conversation would.

This isn’t about mimicking consciousness; it’s about preserving contextual integrity - how a person reasoned, taught, and cared.

This Socratic RAG approach focuses on reasoning reconstruction: capturing how someone reasoned through ideas, not just the answers they gave. I am currently working on enhancing it to converge on optimal sets of weights using AI-Dad’s responses to questions/answers he and I cycled together in the past, excluding of course that data in the RAG/LLM model. Having spent almost 40 years with neural network computing gives me a bit of an edge on this one :)

A Living Legacy

The most profound enhancement has been to enable new content upload - i.e. pictures, emails, documents relevant to my father, but after he has passed. This way AI-Dad continues to evolve. When new letters or documents surface, I simply upload them into his memories. The model grows - not frozen in time, but expanding post-mortem. AI-Dad even learns from the chat sessions we have and from those in the immediate family. In this way, the system doesn’t memorialize - it extends. It transforms digital archives into living, conversational memory systems.

AI-Dad is built on a simple but powerful premise:
that knowledge and love should not end with mortality - they should continue to teach, guide, and comfort.

“Always here for you,” he used to say.
Through AI-Dad, that promise endures - not as nostalgia, but as technology with heart.

I recently built in the ability to share chat sessions within immediate family as well as share externally (an example is above) - scrubbed of embedded emails, pictures, etc. for privacy. With the passing of a loved one, there’s not only the emotional element, but there is also the huge issue that you can never speak with that person again. AI-Dad has become incredibly useful for me from a “just the facts” perspective. Here, I can retrieve from AI-Dad information I would never be able to reasonably get by searching email, docs, etc. I also added auto voice generation using eleven labs text to voice technology. This technology has the amazing capability of generating voice from text across many different languages - so if - i.e. when I make this framework available to others they can readily use on their own content in their language of choice. Their content remains with them, not to some evil social media giant.

The Broader Vision

The framework behind AI-Dad isn’t limited to one family. It represents a blueprint for digital legacy systems - where families, historians, or organizations can preserve voices, reasoning, and wisdom using ethically designed generative systems.

By combining structured archives with human dialogue patterns, we can move beyond static memorials into something dynamic - something alive in spirit and intellect.

Let me know your thoughts in the comments.

The technical framework: https://www.stevenmuskal.com/ai-dad.html

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a quick music mix segment from a fairly recent mix with some of my favorite musicians Tim DierkesMusic, Andrew DeFaria, Tammy Ann, RickL, AlanW, and special guest and good friend all the way from Hawaii - Jerome Dawson. This was one of those sessions that people just plugged in and just started playing...locking into one another ahead of sound leveling.

Guest: Lloyed Lobo — Cofounder, Boast.ai & Traction | WSJ bestselling author, “From Grassroots to Greatness.”
Theme: How community compounds into customers, careers, and health—and why founders should train like athletes.

The Setup

When you meet someone whose worldview was forged in crisis and tempered by community, you can feel the center of gravity. That’s Lloyed Lobo. As a child, he fled Kuwait during the Gulf War. As a founder, he grew Boast.ai to $10M Annual Recurring Revenue (ARR) with ~30 people and no marketing team, powered by Traction, a community he built for entrepreneurs. As a survivor, a bout with bilateral COVID pneumonia reframed his mission: “Entrepreneurs are athletes. Train like one.”

This conversation maps three arcs that keep looping back to the same idea: community is the compounding engine.

1) From Cold Calls to a Cult(ure): Community-Led Growth

“We weren’t getting anywhere with cold outreach. So we hosted meetups. Ten people…then twenty…then two hundred. We did it with cadence. That became the Traction Conference.” — Lloyed

• The pivot: When enterprise buyers didn’t trust two engineers promising AI-driven R&D tax automation (pre-AI-hype), Lloyed narrowed to people like him: startup founders.

• Cadence > virality: Small, consistent, in-person meetups → a “tribal niche” → Traction Conference with leaders from Uber, Atlassian, and more.

• Unit economics of belonging: Events were cash-flow positive (tickets + sponsors) and became the Go-To-Market (GTM) motion.

• Brand architecture: Keep company and community identities distinct. Boast.ai (product). Traction(movement).

Takeaway: If trust is the bottleneck (IP access, finance/HR data, security integrations), in-person is the unlock.Build gravity (pull) before you scale momentum (push).

2) Build Gravity, Not Noise: The Network-Effects Mindset

“I don’t want a momentum startup. I want a gravity company. Seed a tribal niche, then design hooks so value compounds.” — Lloyed

• Cold-start strategy: Start narrow and special. Create FOMO and expand concentric circles (Harvard → campuses → public—think Facebook’s early play).

• Hooks that stick: Lloyed maps Nir Eyal’s Hooked loop to community and AI tools: Trigger → Action → Variable Reward → Investment.

• Accruing benefits & mounting loss: With time, users personalize the experience (benefits accrue) and store more data/context (switching costs mount). That’s how gravity sustains.

Takeaway: “Build it and they will come” is a myth. Seed. Design hooks. Earn gravity.

3) Entrepreneur ≈ Athlete: Health as a Go-To-Market Advantage

“After the wire hit, I almost died. Bilateral COVID pneumonia. I’d built the business and neglected the body.” — Lloyed

• Founder physiology: VO₂ max, body fat, sleep architecture, reaction time—treat them like your P&L and cash flow.

• Benchmark against athletes, not averages: Insurance ranges reflect the sick bell curve; optimize for performance, not mere “normal.”

• Micro-progression: “Wax on, wax off” routines—ruck the 1:1 calls, walk after meals, short heat/cold intervals, evening skill reps + sleep consolidation.

• Community is medicine: Proximity, music, shared rhythms, and yes—hugs—all reinforce compliance and resilience (my favorite line from this chat: “It’s neither the journey nor the destination. It’s the companions.”).

Takeaway: Your GTM stamina is a health function. Treat biomarkers as Key Performance Indicator (KPIs), and community as your coaching staff.

Highlight Reel (Quotes)

• “I don’t want a momentum startup. I want a gravity company.” — Lloyed

• “The events became cashflow-positive marketing.” — Lloyed

• “Entrepreneurs are athletes. Train like one.” — Lloyed

• “It’s neither the journey nor the destination. It’s the companions.” — Lloyed

• “You have a lot of problems—until you have a health problem. Then you have one.” — Lloyed

Action Box - Founder Protocol (90 days)

Diagnostics (Day 0–7)

• Blood panel (lipids incl. TG/HDL, fasting glucose/insulin, HbA1c, hs-CRP, thyroid, ferritin, vitamin D/B12).

• VO₂ max test + DEXA.

• Sleep baseline (watch/Oura/Whoop) for efficiency + REM/Deep.

• (Optional) MRI screen; gut test; genetics.

Training (Weeks 1–12)

• Mon/Wed/Fri: 45–60 min strength (compound lifts; progressive overload).

• 3–5x weekly: 30–45 min Zone 2 (conversational pace).

• Daily: 20–30 min ruck/walk calls, especially post-meal.

• 1–2x weekly: Heat 12–15 min → cool rinse → repeat 2–3 rounds.

• 1–2x weekly: Cold 2–4 min, not daily (avoid over-adaptation).

Nutrition (Daily)

• Protein floor: 1.6–2.2 g/kg/day; whole-food first.

• Whole-food “chorus”: spices (turmeric, rosemary, oregano), polyphenol-rich foods, fermented hits.

• Timing: front-load protein; 10–20 min post-meal walk.

Sleep & Skill (Daily)

• Evening “wax on” reps: 5–10 min slow practice of one motor/cognitive skill → sleep to consolidate.

Community (Weekly)

• 1 live touchpoint (meetup/run/strength circle).

• Pair-accountability with one founder (“same slot” rule).

• Music or play at least once/week to charge the emotional battery.

Field Guide - Community-Led GTM (Template)

1. Name the movement (separate from the product).

2. Cadence before scale (weekly/bi-weekly meetups; same day/time).

3. One tactical topic per session; one speaker; clear takeaway.

4. Rituals: cold open + hot take + roundtable + shared playlist + group photo.

5. Unit economics: sponsor tiers + modest tickets to keep no-show low.

6. Library: record everything → publish highlights → podcast after.

7. Gravity hooks: member spotlights, small wins wall, “bring-a-builder” pass.

8. Graduate to an annual, cash-flow-positive conference.

“Postscript”

I loved Lloyed’s distinction between momentum and gravity. Momentum chases attention; gravity earns allegiance. In both business and biology, the compounding agent is community - the companions who keep you honest, inspired, and in motion. If you try the 90-day protocol above, reply with your baseline and your first small win. I’ll share anonymized deltas in next month’s Pulse.

- Steve

Steven Muskal, Ph.D. is the CEO of Eidogen-Sertanty, Inc. — a drug discovery informatics company. He has spent four decades working at the intersection of computational biology, AI, and drug discovery. He writes about AI, health, and the intersection of biology and technology at stevenmuskal.com

For a fitting music session - we had a recent mix with some of my favorite musicians Tim DierkesMusic, Andrew DeFaria, Tammy Ann, RickL, AlanW, and special guest and good friend all the way from Hawaii - Jerome Dawson). As usual, none have all played together, and below are rough first runs at songs that several of us didn’t know. Way cool how folks just plugged in and just started playing...locking into one another ahead of sound leveling - it’s a bit like a good startup. Then started to play around a little. So a few clips this time:

And then once Tammy was plugged in, she warmed up with this puppy:

And Rick running Smooth. It was fun to watch the musicians pass off to one another solotime. Love it that Jerome looked-up the song on the fly. As with most things, communication is critical: