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.

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

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)

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

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:

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.

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.

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: molseek.com/ai-dad

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

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:

From escaping Kuwait during the Gulf War to building a $10M ARR company without a marketing team, Lloyed shares how community-led growth became his superpower. He explains why entrepreneurs should think like athletes, how to design “gravity companies” that pull people in, and why resilience is built through micro-habits, relationships, and health.

We cover:

• Building traction through in-person meetups and conferences

• The difference between momentum startups and gravity companies

• How network effects and community create lasting value

• Founder health: treating biomarkers and VO₂ max as startup KPIs

• Why companionship and community matter more than the journey itself

Whether you’re scaling a startup, optimizing your healthspan, or looking to build meaningful community, this conversation with Lloyed Lobo offers both inspiration and practical takeaways.

TL;DR

• Dakota (WA → CA) went from a childhood Fannie Mae flop to crypto trader → ML‑driven automation (Hydra).

• Vladimir (Uzbekistan → NYC → CA) pivoted post‑COVID from hospitality to full‑stack software and ML, while staying grounded in people and process.

• They’re co‑building a bartender app that starts as a visual “bar book” and expands into training, jobs, social discovery, and gamified career leveling.

• Big thesis: Invest in humans like startups—contributors earn XP for real progress; backers earn recognition, returns, and reputation for funding human potential.

Deep Dive Summary

The Conversation

1) Origin stories: grit, reset, and the hospitality edge

Dakota grew up in Washington State in a family of general contractors (“brick and mortar first”). Financial curiosity hit early—he made his first investment at age 10 (Fannie Mae) and promptly lost the $300 he’d put in through his mom. That sting planted a seed: long‑horizon, fundamentals‑first thinking (Roth IRAs, compounding, “automatic millionaire” habits), later interrupted by the wild west of early crypto exchanges. By 2016–2017 he was trading crypto, studying technical analysis, and seeing 100% price spikes on thin‑liquidity exchanges with little or no KYC (“Know Your Customer”). He rode 2021’s run‑up, took profits, stepped away for two years, and is now back—this time aiming to remove as much human bias as possible via automation.

Vladimir was born in Uzbekistan in 1991, moved across the former Soviet region, and immigrated to the U.S. at 17. Post‑COVID closures in New York pushed a rethink: relying on tips from 20‑somethings at a club wasn’t a stable career. He retrained, first self‑taught (HTML/CSS/JS via FreeCodeCamp), then a full‑stack bootcamp, and now community college math (calculus, linear algebra) to prep for machine learning coursework and a UC transfer. One lesson he carries: being labeled “smart” can backfire; discipline beats talent, and preparation beats vibes.

Both men work in hospitality in California (they met at Blue Water Grill). That setting sharpened their core advantage: people skill at scale. When you serve 100 guests a day, you learn to read, adapt, and retain—skills most technical teams overlook until it’s too late.

2) What they’re building

Hydra (Dakota)

Hydra is a scanner/automation stack for crypto markets: multi‑source data collection (exchange APIs, order flow, volume, order book), Redis caching, ML‑based source selection and signal scoring, confluence analysis, multi‑timeframe filtering—the works. The aim: move from defensive, fatigue‑prone discretionary trading to 24/7 systematic decisions where models do the heavy lifting and humans tune objectives and risk.

(A note from the chat: they also touched on crypto’s messier bits—from early exchange “dust” and rounding to the general black‑box vibe of blockchain to retail—another reason to lean on rigorous data engineering and auditable logic.)

The bartender app (Dakota + Vladimir)

This started as a visual, ad‑free bar book (fast lookup during a rush), then expanded into a career and community platform:

• Education & leveling: Games and drills that turn novices into competent bartenders (Michelin‑ready standards), with XP for mastering techniques, speed, and hospitality.

• Jobs & reputation: Profiles, verified skills, and reviews; a way for managers to see true capability, not just a résumé.

• Guest experience: A consumer mode that builds cocktails by flavor (sweet/sour/bitter/salty/spicy), cuisine pairing, or vibe—then hands the bartender a clean spec.

• Social discovery: Find the bartender you like (not just the bar). See who can make your drink well. Keep people in the loop so the product doesn’t need to shout—community is the distribution.

They’re also exploring health‑aware mixology: integrating herbs/spices (turmeric, ginger, rosemary), ferments, and other functional ingredients; and using AI to personalize choices. One provocative idea from the chat: ethically collecting face images + favorite cocktails to explore whether models can map phenotypic cues to preferred flavor profiles or lighter‑impact options (strict opt‑in, privacy‑first). Whether or not that specific line bears fruit, the broader point stands: use data to make indulgence a bit smarter.

3)The Trillion Dollar Idea - Investing in People like Startups: The XP Economy

Their most interesting idea reframes education and early contribution:

• Contributors (coders, marketers, researchers) earn XP for real work.

• XP converts into an equity‑like stake or revenue share if/when a product hits.

• Investors can back people, not just projects, and earn reputation and returns for funding human potential.

• To keep momentum, non‑contributors dilute slowly—a nudge to stay engaged or re‑up.

• The same game layer works for backers: badges, totals, and “win rate” signals let investors display the breadthand quality of their human investments.

It’s not about chasing quick exits; it’s about structuring incentives around growth. The theme repeats across their work: gamify the right behaviors, track real progress, reward contribution, and make the system feel fun enough to keep people in it.

4) Grit, activation energy, and lowering internal resistance

The discussion kept landing on motivation mechanics:

• “Big T” trauma often fuels entrepreneurs—chips on shoulders that become engines.

• Activation energy matters: most people fail at the start line. Lower the slope (make the first reps fun), and you change outcomes.

• Pain tolerance is trainable. In biology terms, lowering internal resistance (inflammation, stress) via sleep, breath, cold, movement, light, and nutrition increases the capacity to keep going.

• The karate‑kid principle: disguise hard work as something else (“wax on, wax off”) so beginners accrue wins before they notice the burn.

They even bring this home in parenting: consistent routines, graded reductions in “sleep crutches,” and modeling behaviors (do push‑ups in front of your kids; don’t just talk about them). As Vladimir put it, “Your kids watch you grow up too.”

5) Content is king (and why they’re recording)

A recurring motif: content drives intelligence. The early LLM breakthroughs exploited sentiment signals in large text corpora; the same logic applies at smaller scales. Recording conversations, capturing decisions, and storing patterns lets you train agents that reflect your real judgment—Steve’s “AI dad” idea, but for founders, bartenders, and investors. If you want machines to help, feed them the right stories.

Practical takeaways

• Blend tech with touch. The best builders can talk to strangers, retain customers, and still ship.

• Gamify contribution, not just consumption. XP for real progress keeps collaborators and backers engaged.

• Design the on‑ramp. Reduce friction for the first five minutes; the rest takes care of itself.

• Invest in humans. Reputation systems for backers and contributors could rebalance early‑stage opportunity.

• Record your process. Today’s notes are tomorrow’s model weights.

In Closing

If the future of work is fewer résumés and more receipts, Dakota and Vladimir are building the receipt printer. One project makes markets legible to machines; the other makes careers legible to people. Both rely on the same principle: give the right effort a score, give the right people a stage, and let value compound—behind the bar and far beyond it.

For a music clip, this is from a recent session ‘For What It’s Worth.’ A great example of a music collaboration between several different people with different perspectives and musical tastes coming together and all enjoying it! Grant (Vocals/Guitar), Andrew (Guitar), Randi (Vocals), Tammy (Vocals), Alan (Bass), and Johnny (Conga) As usual, there is a brief Substack mute for some reason at the beginning.

The Engineers in the Arena

Megan and Fernanda are employed biomedical engineers who, prior to joining their company, rotated across R&D, upstream marketing, and manufacturing. With backgrounds in mechanical engineering, real-world clinical exposure, and a hunger to build solutions, they represent the next generation of female founders in medtech and possibly beyond it.

They're tackling the biggest questions:

• Should we build in our domain or go broad?

• What does validation look like?

• How do we balance risk, capital, and personal values?

• Where is the real innovation in women's health?

These aren’t just theoretical. They’ve compiled over 100 startup ideas, ranked them, and are narrowing in on their own version of a “biodesign” process—one that lets curiosity lead but strategy constrain.

Lessons from the Field

I shared a bit of my own nonlinear journey—from mass spec and AI at Berkeley to founding a company that's still kicking 20+ years and several economic crises later.

Key advice I gave them (and now, you):

1. Marketing Isn’t Optional

No matter how technical you are, if people don’t hear about it or trust it, it won’t get used. Megan’s curiosity in marketing stood out - most engineers avoid it, but she embraced it, understanding that speaking both engineering and customer is a superpower in today's AI-native world.

2. Start Where You Have Leverage

Don’t ignore your edge. Biomedical engineering may feel narrow, but it's a rare intersection of mechanical intuition, physiological awareness, and problem-solving rigor. Whether you're working on diagnostics or wearables, you’re building for the most complex system known: the human body.

3. Think Consumer First, Physician Last

Medical devices are often bottlenecked by long approval cycles and risk-averse physician buyers. Consumer wearables with medical-grade capabilities—like Oura, Eight Sleep, and Apollo—are stealth medical devices in disguise. That’s where Total Available Market (TAM) explodes and the friction drops.

4. Women’s Health = The Last Frontier

Pap smears, mammograms, and standard screening protocols are overdue for reinvention. We talked openly about how few tools exist for women’s preventive care—and why that’s a fertile space for novel at-home devices, diagnostics, or tracking tools.

Nuggets for Future Founders

Here are some frameworks we unpacked that might be helpful if you're on the cusp of building something yourself:

• "Begin with the end in mind": Whether your goal is an exit, a lifestyle business, or a legacy company, define your direction up front.

• "Fail fast in your head, not in your bank account": Talk out the idea. Tear it down. Don’t spend a dime before testing the logic.

• Leverage what exists: From ChatGPT to Claude to open wearables data—build faster by standing on giants’ shoulders.

• Don't take money if you don’t need to: Bootstrap until you’re forced to raise. The longer you own, the stronger your leverage.

• Creativity > Credentials: You don’t need to know how to build it yet. AI, freelancers, and friends can help. Just define the vision.

The Power of a Duo

Megan and Fernanda’s chemistry is electric. One’s a planner, the other’s more fluid. One is steeped in execution, the other in ideation. But both are aligned in grit, mutual respect, and a hunger to do work that matters. Their partnership is what most early teams hope to find.

Final Takeaway

Whether you're building a sleep optimization platform, a wearable for at-home gynecological screening, or on a personal project that may represent a seed of an idea that immortalizes memories (i.e., Steve’s recent Toast App), success rarely starts with the perfect product. It starts with curiosity, resilience, and conversations like these—where ideas are sharpened, risks are reframed, and the road ahead gets just a little clearer.

To Megan and Fernanda: You’ve already broken barriers. Keep breaking them—with elegance, execution, and audacity.

For a music clip, we had several shorts from a recent July 3rd “mix on steroids.” I’ll share more in future posts - but this was a fun mix segment with daughter Hannah (Vocals), Laurin (vocals), RickL (vocals), Jesse (Guitar/Vocals), and AlanW (Bass). So fun to have my daughter who never plays with bands kick things off and harmonize with Laurin and Rick on vocals. Way pretty cool that everyone intuitively knew when and how to fade down and launch into the faster-tempo second half. As usual, there is a brief Substack mute for some reason at the beginning.

Christy Shearer joins neighbor and host Steven Muskal for a free-flowing conversation that begins with her surprise diagnosis of type 1 diabetes at age 12 and follows the thread into her multi-decade career in diabetes devices and pharma. She explains how living with the condition gave her instant credibility—and curiosity—when she pivoted from studying nutrition to sales, eventually leading large commercial teams at J&J, Roche and now MannKind (maker of ultra-rapid inhaled insulin). Along the way she demystifies the science behind type 1 versus type 2, the promise of marker-based early screening, and why pulmonary delivery can better mimic a healthy pancreas.

The talk widens into workplace culture—being the lone woman in male-heavy sales leadership, building diverse teams, and spotting AI-generated answers in interviews—before veering to life design. Both speakers push back on the classic “retire and travel” script; Christy reveals her next-chapter dream of professional home organization and functional design, emphasizing the need for ongoing purpose outside a paycheck.

Summary

Christy Shearer’s story begins with the “Why me?” moment of being diagnosed with type 1 diabetes at 12. She explains how the autoimmune attack on her beta cells unfolded and why the disease is no longer strictly juvenile—59 % of new cases now happen after age 50. The Juvenile Diabetes Research Foundation even rebranded to “Breakthrough T1D” to reflect that shift.

That early shock lit a career torch. After driving to Van Nuys at 15 to learn finger-stick testing, Shearer decided she wanted to educate others with diabetes. A detour through food-science studies at Cal Poly morphed into front-line sales roles for glucose meters, pumps and, eventually, billion-dollar device businesses at J&J and Roche. She’s now at MannKind, still “a one-trick pony”—30 years in diabetes tech—but one who sells by matching deep clinical knowledge with street-level empathy.

The science segment centers on MannKind’s ultra-rapid inhaled insulin: a dry-powder formulation delivered through a pocket inhaler that reaches the bloodstream in about a minute and peaks near 12 minutes—roughly ten times faster than injected analogs. Patients avoid 3-6 daily needle sticks, and pharmacokinetics better mimic a healthy pancreas. Shearer underscores its safety record—20 years of data, 10 on market, ongoing spirometry monitoring—and why lungs (a “tennis-court-sized surface”) clear the powder quickly instead of trapping it like nicotine.

From there the conversation widens to breaking glass ceilings. Entering J&J as one of only two women among ten leaders, Shearer built teams that are now 70 % female and still battles mansplaining and being talked over in physician meetings. Her advice: lead with expertise, know your worth, and demand diversity of thought for stronger strategy.

Host Steve Muskal and Shearer swap views on pharma’s chronic-disease “cash-cow” incentives, the faster iteration cycles of devices versus drugs, and the ethics of AI—she’s rejected candidates who used real-time AI prompts during Zoom interviews. They also share career counsel: map what genuinely makes you happy, research obsessively before interviews, and carry relentless curiosity.

The last act is about purpose and reinvention. Shearer rejects the classic retire-and-travel script (“I think I’ll retire when I die”) and sketches a future side hustle in professional home organization and spatial design to satisfy her creative itch while keeping the financial engine humming.

Throughout, Muskal’s questions pull out personal anecdotes—parental loss, dual-type 1 marriage, stress-trigger theories, and the role of self-reflection—that ground the technical talk in lived experience. The result is a candid, idea-packed hour on managing glucose, cultivating grit, and shattering ceilings—at work and at home.

For a music clip, thought this was somewhat fitting. This is a mix segment with Jesse (Guitar/Vocal), Tim (Guitar/Vocal), Rick (Vocal), and Alan (Bass). Hopefully not offending anyone with this (including Christy!) - but some of the words matched some of the pods sentiment. To the music aficionados, it was also cute in that we also tried to change time signatures multiple times in this Beatles song (or at least attempted to). Not bad for the first and only attempt at the song. If they can on others, why can’t we? As usual, there is a brief Substack mute for some reason at the beginning.

This month, Steve sat down with friend Dom Mather—a cybersecurity expert, musician, and fellow health hacker—for an open conversation that moved effortlessly between career pivots, personal loss, and finding your rhythm in life, both on stage and off.

Dom’s journey started in Sussex, England, with a family move to Utah in 1977 (his dad literally helped write CAT scan software). After detouring from his original plan to fly Harriers for the Royal Navy, Dom found his footing in the nascent cybersecurity industry. For 25+ years, he’s been on the frontlines as firewalls evolved into AI-powered security systems battling real-time cyberattacks.

They discuss Dom’s parallel journey as a musician: from heavy metal roots and bands like Choke Hold, to blues and country influences sparked by his wife Maya. Dom’s a collector (72 guitars, 19 amps) and a seeker—constantly chasing that perfect “pocket” where music syncs beyond the notes.

They covered:

    •    The explosion of cyber threats and why 4.8 million cybersecurity jobs are unfilled

    •    How public speaking and performing rewired his confidence

    •    The interplay of music, vibration, and health

    •    Personal stories about family loss, legacy planning, and preparing for the unexpected

One theme kept surfacing: resilience through adaptation—whether it’s shifting careers, styles, or mindsets. Dom even committed on the spot to start a month-long cold shower challenge after we dove into cold exposure, grounding, and mitochondrial health.

Introduction & Backgrounds

Steve opened by welcoming Dom and reflecting on their shared network and his frequent participation in your music mixes. Dom discusses his upbringing, starting in Sussex, England, in a town called Cookfield. His family later moved to Utah in 1977 after his father, a computer engineer (who helped write the software for the CAT scan), chose a U.S. military contract over a Russian offer.

Dom’s early years were shaped by his father’s values of hard work and education. His family soon relocated to California due to Utah’s dry culture conflicting with their British love for a drink. Dom completed his education in Orange County, attending St. Margaret’s from 4th to 12th grade before going to USC.

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Military Aspirations & Career Pivot

Dom shared his initial plan to fly Harriers for the Royal Navy after moving back to the UK in 1997 following his father’s death. However, a change in military policy and his height and weight challenges made him pivot. Instead, he entered the computer industry, first working for a shock absorber company where his IT problem-solving skills unexpectedly launched him into cybersecurity sales.

Dom worked in cybersecurity since 1999, selling everything from proxies to next-gen firewalls, and spoke about how the industry evolved from basic firewalls to advanced AI-driven systems protecting organizations in real-time. He recounted a hilarious moment early in his career when he accidentally ruined a £50,000 digital whiteboard at the House of Lords by using a permanent marker.

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Music Journey

Dom’s music background included learning guitar by ear from Metallica’s Kill ‘Em All and idolizing bands like Judas Priest and Iron Maiden. He played in a heavy metal band named Choke Hold before transitioning toward blues, rock, and even country after meeting his wife Maya.

They discussed the joy of finding the “pocket” while performing, where everything clicks among bandmates—whether on stage or in intimate jams. Dom shared his pride in translating “The Devil Went Down to Georgia” solo onto guitar and described his vast collection of 72 guitars and 19 amps, acknowledging a need to downsize.

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Public Speaking & Personality

Dom reflected on how both his music gigs and professional career helped him overcome shyness, gaining confidence through practice and exposure. His public speaking ease was partly forged through high-stakes presentations, like the House of Lords event.

They discussed differences in empathy, introversion, and spectrum-like traits, with Dom revealing his near-photographic memory for facts but not faces. Both noted how music, presentations, and leadership roles require balancing empathy and confidence.

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Cybersecurity Insights

Dom explained how cyberattacks have become faster and more sophisticated, with average “dwell time” dropping from months to days. He highlighted the critical need for cybersecurity professionals, noting 4.8 million unfilled roles worldwide. He also warned about unethical practices within some cybersecurity vendors, referencing historic examples of companies creating viruses to sell antivirus products.

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Health, Longevity, & Lifestyle Shifts

The conversation shifted to health optimization, cold plunges, sauna use, grounding, and how music and vibration may influence well-being, including effects on the microbiome and mitochondria. Dom reflected on his physical peaks—during high-intensity MMA workouts paired with his high-energy music phase—and noted how his fitness and diet habits have evolved.

They discussed drinking habits, the false perception of wine as healthful, and strategies to reduce alcohol’s impact. Dom agreed to start a cold shower challenge for the month as an incremental step toward cold adaptation, promising to check in with you after 30 days.

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Family, Loss & Reflection

Steve and Dom both themes of family legacy, loss, and the importance of organizing one’s affairs for loved ones left behind. Dom shared the impact of losing his father at 20 and his mother more recently, describing caregiving responsibilities and the emotional weight of not having parental advice at life’s milestones.

Steve added insights from your recent experience handling his father’s passing, stressing the practical need for legacy planning (like activating iPhone’s legacy contact feature) and sharing poignant observations about grief, memory, and the role of impactful single sentences.

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Friendship & Community

The podcast closes with mutual appreciation for their shared friend group and its enduring closeness despite time and life changes. Dom emphasized how welcomed he’s felt joining the community six years ago, reflecting on the unique bonds forged through shared music, parties, and meaningful conversations.

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Key Takeaways

    1.    Dom’s life reflects resilience, adaptability, and humor across military ambitions, tech sales, and music.

    2.    You both shared valuable perspectives on cybersecurity, health biohacking, grief, and community.

    3.    The conversation was a rich blend of personal stories, technical insights, and philosophical reflections on family, aging, and meaning.

For a music clip, this month has been crazy busy with issues associated with my father’s passing (I’ll do a solo-podcast on serious lessons learned, primarily to pay it forward…). While digging into the archives, a Facebook memory came up during the writing of this reminding of a session with TimD, JesseH, and AlanW. Tim started strumming Comfortably Numb. We all jumped in. A great example of finding a zone with a small group. My apologies for a replay from an older post, but this one really seemed fitting for several reasons…

Steve’s full discussion with Devon Cayer

Childhood and Upbringing

Devon Cayer grew up in Southern California. He was born in Fountain Valley, then lived in Huntington Beach, and later in Los Alamitos. He often simplifies it by saying he's from Long Beach. He described himself as having "little ADD, little sports, little competitive edge" as a kid. Devon felt it was beneficial to not be good at anything initially, as it motivated him to strive for improvement when he saw others excelling in various fields like basketball, robotics (using Scratch), and carpentry.

Education

For undergraduate, Devon attended UCLA and majored in chemistry. His initial interest in chemistry stemmed from the emerging field of nanotechnology, particularly the idea of creating nanorobots. However, he found that it was mostly just chemistry. He became fascinated by organic chemistry after taking a class with Stuart Cantrell, who showed him the mechanistic and artistic aspects of "whipping molecules together.” This led to a "deep and intuitive understanding" of nature.

As an undergraduate, Devon Cayer joined the Stoddart Group, which focused on supramolecular chemistry. During this time, he had an incident where he accidentally flooded the lab due to a reflux reaction gone wrong, resulting in a temporary suspension from the lab. He was trying to synthesize a Borromean ring and made mistakes like not clamping equipment properly and being careless with dichloromethane (DCM).

Later at UCLA, Devon got involved in the iGEM (International Genetically Engineered Machine) competition after being approached by Ayub Khattak, who later became the CEO of Q Health. This marked a "90 degree pivot" into molecular biology, where he learned techniques like cloning and PCR.

Devon pursued graduate studies at Scripps Research in San Diego. His thesis was never published.

Early Career and Entrepreneurship

After graduating, Devon worked for Q Health, but it lasted only four days due to his "independent mindset" which wasn't a good fit for the company. He believes his independent nature was fostered by his graduate work, where he worked on a single project with little direct supervision for six years.

He then returned to Scripps and planned to do a postdoc at Oxford with an idea for protein sequencing. Before going to Oxford, Devon applied to the Illumina Accelerator with an idea for antibody barcoding and single-cell proteomics. He got into the final round and pitched his idea, which was similar to what the company OLink was doing.

His idea at the time, focused on creating reagents for researchers and eventually diagnostics, was not funded by VCs who were more interested in therapeutics. He now sees that his initial idea is widely adopted. This experience led him to the lesson of potentially not always listening to investors and trusting his own convictions.

Devon co-founded a company, which he retrospectively describes as the "Juicero" of companies – a complex tech stack that didn't provide the value people wanted. This company aimed to find functional molecules using microfluidic chips and AI, but faced scalability issues.

Devon’s current venture is inspired by the problems of his previous company and aims to be a simpler way to generate proteome-wide binding data for small molecules and other libraries, making it accessible to the broader industry. His current focus is on generating massive datasets for drug discovery and making them available to everyone, despite investor preferences for a therapeutics-focused company. He is determined to stick to his vision this time, as customers are showing strong interest.

Conversation Highlights in Context

The conversation frequently touches upon the theme of failure and learning from mistakes, particularly when discussing Devon’s lab accident and the challenges with his previous company.

His competitive nature and drive to learn new things are recurring themes, connecting his childhood experiences to his academic and entrepreneurial pursuits.

The discussion around his entrepreneurial journey highlights the tension between his own vision and investor expectations, as well as the importance of focus and avoiding the pitfalls of raising too much money too quickly.

Devon’s transition from chemistry to biology and now incorporating AI into his work demonstrates his adaptability and willingness to embrace new fields.

Overall, the conversation reveals Devon Cayer as a highly curious, competitive, and resilient individual with a background rooted in chemistry and biology. His entrepreneurial journey has been marked by both learning experiences and a strong sense of conviction in his current approach to democratizing drug discovery data.

For a clip - Had to share this one, not only because I needed a little Stones to bring back childhood memories, but also because bassman Dan did a nice job channeling Mick (playing bass and singing is so tough!). Also a shout out to Tim channeling Keith. I caught “Keith” stronger in my ears, but the separate mix line unfortunately wasn’t as apparent… Oh well. Also, my apologies to Dom for the Camara focusing hugely on him. As mentioned above, the AI tracking tends to learn who comes in first and he was the first to show….

Building on the inaugural podcast session with my daughter Lili Muskal (27), The conversation flowed naturally between personal updates, professional developments, social observations, and deeper reflections on generational differences. In this second podcast session with Lili, the conversation covered a mix of personal updates, career insights, technology, AI, storytelling, and reflections on societal trends. Lili shares her thoughts on AI's impact on jobs, the challenges of homeownership, intergenerational living, and the future of education. The discussion is engaging, humorous at times, and provides valuable perspectives on both personal and larger societal issues.

Key Topics Discussed:

1. Career and Professional Growth: Lili discussed her current work in healthcare policy after recently graduating from grad school. You both reflected on how internships have played a crucial role in her career path, noting that she's consistently worked at places where she had previously interned.

2. Living Situation and Housing Market: You discussed Lili's life in San Francisco, touching on the city's challenges with homelessness and high living costs. You had an interesting exchange about homeownership versus renting, with Lili noting many in her generation view homeownership as increasingly unattainable.

3. Intergenerational Living: You proposed the concept of intergenerational living arrangements as a potential solution to various societal problems, including elder care, childcare, and housing costs. Lili found this idea interesting but pointed out practical challenges.

4. Healthcare System Challenges: Given Lili's expertise in healthcare policy, you discussed the unsustainable nature of Medicare spending, the lack of state-level aging strategies, and the complicated structure of the healthcare system with many intermediaries.

5. AI and Future of Work: You shared your increasing dependence on AI for coding tasks, which led to a discussion about how AI might reshape the workforce. Lili expressed concern about younger workers potentially struggling to develop necessary skills if they rely too heavily on AI from the start.

6. Generational Differences: Throughout the conversation, you both noted differences between your generations, particularly regarding technology use, work expectations, and educational approaches.

7. Personal Updates: The conversation included personal elements about your shared life, with references to family friends and memories, creating a warm, authentic tone.

Top 5-10 Key Discussion Points:

1. Podcast Evolution & Guest Feedback:

   • Lili critiques the podcast’s progress, suggesting more guest diversity, outreach to niche longevity and science podcasts, and an audio-only version for easier consumption.

   • They discuss the format of long-form podcasts, editing, and structuring content for audience engagement.

2. Internships & Job Pipeline:

   • Lili highlights how internships are now the primary route to securing full-time jobs, even for experienced professionals.

   • They discuss whether all jobs should have an internship-like trial phase to align expectations between employees and employers.

3. AI's Role in Careers & Knowledge Work:

   • Lili acknowledges AI’s growing presence in health policy but believes that nuance, language precision, and constant change make human expertise essential.

   • Steve emphasizes that AI is a force multiplier, helping knowledge workers increase their productivity rather than replacing them outright.

   • They explore AI as a new kind of "worker" and how future professionals will need to become AI managers.

4. Homeownership vs. Renting & Economic Realities:

   • Lili shares that most of her peers are not pursuing homeownership in California due to high costs and complexity.

   • Steve discusses how homeownership as a wealth-building strategy may not be as beneficial as assumed, given ongoing expenses like property taxes and maintenance.

5. Intergenerational Living & Its Potential Benefits:

   • They discuss whether policies should incentivize intergenerational housing, potentially reducing costs for both child care and elder care.

   • Lili sees value in fostering multigenerational communities, but questions if people would want to care for others outside their family.

6. San Francisco’s State & Economic Divide:

   • Lili gives an update on SF, noting that while it remains a vibrant and valuable city, cost of living and social issues create stark divisions between "haves and have-nots."

   • They discuss how AI-driven shifts in Silicon Valley might further widen these economic disparities.

7. The Future of Education & AI's Role in Learning:

   • Steve and Lili debate whether AI should be fully integrated into education early on or if foundational skills must be learned first.

   • Lili supports AI-assisted learning but warns against overreliance, which could hinder deeper cognitive skills development.

8. Healthcare Costs & Policy Challenges:

   • They touch on the growing crisis of Medicare sustainability, aging populations, and inefficiencies in healthcare spending.

   • Steve questions whether intergenerational living could help alleviate some of these pressures.

9. AI Addiction & the Future of AI Pricing:

   • Steve admits he’s become “addicted” to AI tools for coding and knowledge work, worried that current pricing models are designed to hook users before increasing costs.

   • Lili shares recent developments in AI pricing, including OpenAI's new $2,000/month "AI knowledge worker" service, and they discuss the implications for professional industries.

10. Technology, Social Media & Society’s Attention Span Decline:

• Lili sees social media addiction as a major issue, comparing it to smoking and advocating for stronger regulation, particularly for young users.

• Steve argues that modern distractions have led to a decline in critical thinking and independent learning.

For a music clip - once again with a group (Andrew DeFaria, Jesse S Hose, Laurin Wilson, John Safarik, AlanW) never having played together as a group could engage and have fun. This was one of the first songs we tried. Most of us haven't played it before...Especially cool when we all aligned in the Ritadando.

Marit and Steve discuss the pivotal role of writing skills and rhetorical awareness in effectively utilizing and advancing AI technology. Marit re-positions prompt engineering as prompt writing, prioritizing the importance of social and cultural awareness needed to intelligently prompt a chatbot.

Writing skills are crucial. AI training data relies heavily on high-quality content, and these tools degenerate when trained on their own output. Therefore, continuous creation of original text and code, not just AI-generated content, is essential for the ongoing development of AI. The ability to analyze a rhetorical situation, understand the audience and purpose, and consider the immediate context are all vital writing skills critical to effective use of today’s AI.

They discuss how individuals with expertise in humanities, such as history or English majors, are well equipped to prompt AI intelligently, which should level the playing field with those building or possessing computer science skills. Strong writing skills, critical thinking, and communication skills are considered essential for future-proofing oneself in an AI-driven world. AI can democratize access to expertise, but thoughtful implementation in education is necessary to avoid creating a generation of hazardously incompetent workers.

For a music clip from a recent mix-n-match session, I wanted to share a common example when a new song is brought to a group of people neither having played together before as a group, nor even knowing the song. (The first few seconds are silence…I call it the ‘substack header’). Players: Laurin Wilson lead vocalist, Jesse Hose on guitar, and Alan Wright on Bass.