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Carbon Is Harder Than Silicon: Why the Future of AI Depends More on Human Systems Than Hardware

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The one about the socio-technical model applied to AI.

Much of today’s AI conversation centers on tangible constraints — chip shortages, compute capacity, and the growing energy demands of model training. Those are real limits, and they’ll shape what’s technically possible for years.

But anyone who has lived through an EHR rollout or a “clinical decision support” pilot knows that the harder part often begins after the technology is installed. Once the silicon is humming, we still have to integrate it into the living fabric of care — how clinicians think, how teams coordinate, how patients experience the system.

That’s where the carbon-based agents come in. Not as obstacles, but as the essential medium through which any digital innovation actually becomes care.

The Sittig and Singh (2010) socio-technical model still provides one of the clearest guides here. It lays out eight interdependent dimensions — from technical infrastructure and clinical content to workflow, organizational culture, and external environment. It’s a reminder that safety, quality, and adoption emerge not from the technology itself, but from how these layers interact in practice.

The same logic applies to AI. A model can perform flawlessly in validation but fail to add value at the bedside if it doesn’t align with clinical priorities, decision rhythms, or accountability structures. These systems don’t just plug into existing teams; they subtly reshape how roles are defined, how authority flows, and how judgment is shared.

So yes, the field will need more chips, more power, and more scalable infrastructure. But the real breakthroughs will come from designing AI that supports the people who deliver care and ultimately the carbon-based agents central to their mission: the patient.

Switches, Boxes, and Steam — Three Questions for AI in Healthcare

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The one where 3 books answer 3 questions about AI in healthcare

Every few years a new wave hits healthcare IT. Some reshape the shoreline; some barely ripple. Lately I’m leaning toward a simple view: Gen AI is a real step forward—but the changes that endure will come from infrastructure and incentives. In as regulated and high-attention a system as health care, the roads matter more than the horsepower.

1) Who will benefit?

History says advantage concentrates around control points—the places where compute, data, and distribution meet. In today’s terms, that means model providers with scale (e.g., OpenAI, Anthropic, Google/DeepMind), the clouds that host them (eg AWS, Azure, Google Cloud), platforms already embedded in clinical workflows (eg Epic, Oracle Health/Cerner), and companies that own the last mile to clinicians and patients (eg large health systems). As though they control the “master switch,” these players have significant influence in supporting winners and losers.

But the circle does widen. A second group tends to capture durable value: the people and teams who complement those control points—clinical data stewards, evaluation and safety engineers, product integrators who turn models into reliable steps inside prior auth, triage, charting, imaging, and revenue cycle. As individuals, entrepreneurs who see where the network is going (and get there early) tend to do well: they make the glue, the adapters, the “boring” parts that let many models work safely across many contexts and control points.

As general models compete and compute capital fuels greater availability, the answer to “who benefits” may not depend as much on who has the smartest model as we thought. In healthcare, the answer is probably closer to: “where do you sit relative to distribution and standards?”

2) How will they benefit?

Consider what happened when shipping settled on a standard metal box—a 20- or 40-foot container with identical corner fittings. That one decision let cranes, ships, trains, and trucks handle cargo without repacking. Risk fell. Costs fell 90%. And the work moved: less muscle on the pier; more planning and throughput management at distribution centers and rail yards. Jobs followed the flow.

AI may trace a similar trajectory. As models ship in consistent packages—stable interfaces and licenses, companion evidence (safety & efficacy, provenance, evaluation coverage, known hazards)—risk drops across the chain. This is a chain, though, of bits and not atoms. Workflow behavior—increasingly digital—and model swaps achieve more predictable outcomes. Capital becomes willing to fund further scale because components are modular, productized, and auditable.

As for the work, I expect this to move from first-pass busywork to the “inland” roles that plan, do, study, and act towards the learning healthcare system. Technical roles for platform/orchestration, evaluation & red-team, data lineage & governance, enablement & change will blossom. Existing roles will morph. For example, on the admin side, copy-paste, phone calls, and status-chasing give way to flow coordination, exception desks, audit/QA, and patient navigation (think schedulers → access-ops coordinators; coders → utilization & compliance analysts). On the clinical side, keystrokes give way to judgment—ambient draft review, rare-case adjudication and roster reviews, care-plan design, patient counseling, and system-of-care safety & stewardship. And on the patient/caregiver side, the role shifts from passive data source to co-steward of context; from recipient of transactions to more of a navigator. There will be more need for controlling consent and sharing, supplying high-signal inputs (PROMs [patient-reported outcomes], home-device streams, life context, and values), correcting records and attaching verifiable documents, flagging errors or preference mismatches, and (for caregivers) supporting therapy reconciliation, adherence, and follow-up.

3) What is our responsibility?

I see two obligations emerging from this and running in parallel.

First: transparency as a design choice. The industrial age didn’t compound due to the steam engine alone; it compounded because we paid for disclosure. Blueprints were made public, and then builders turned them into businesses. In AI, the equivalent is releasing portable, trustworthy manifests with every meaningful update—lineage, test coverage, failure modes, guardrails—so others can evaluate, integrate, insure, and, when appropriate, improve. Procurement and reimbursement will prefer systems that come with real evidence as much as this has become the case for conventional therapeutics (ie medications) in trial and pharmacovigilance.

Second: we have to uplift the people, not just the pipes. Standards don’t only move information; they move jobs. Containerization made ports safer and faster, but it also displaced longshoremen and pushed opportunity inland. Healthcare will feel a similar migration as routine drafting and triage shrink. We will move only as fast as we develop the workforce a glide path. This looks like making the new roles visible; creating portable credentials for evaluation, operations, governance, and enablement; and retraining with intentionality. We won’t succeed if we think the players are frozen. We need to help the team skate to where the puck is going.

Bringing it together

If advantage tends to form at the control switches, and if standards are what turn demos into networks, then the next phase winners are 1. the builders who make AI reliable, swappable, and evidenced and 2. the organizations that invest in the people who run that learning system well. Gen AI “thoughtpower”—like the horsepower that came before—opens the door. The plumbing and the social compact (transparency + worker mobility) portends our trip through it.

Want to go deeper?

These ideas are an amalgam of a few books that have been highly influential in my own thinking. Please let me know if you have encountered others for this collection!

Tim Wu, The Master Switch — Why open eras often consolidate around control points, and what that means for innovation and competition.

Marc Levinson, The Box — How a universal container standard reshaped costs, jobs, and geography—useful for thinking about AI packaging and “inland” roles.

William Rosen, The Most Powerful Idea in the World — The case that incentives for disclosure (the patent bargain) made progress compound—and why entrepreneurs matter for carrying blueprints into the world.

A scientist, a magician, a poet, and an alien walk into a bar…

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The one about the Johari window

A scientist, a magician, a poet, and an alien walk into a bar. The bartender looks up and says, “Is this some kind of strained analogy?”

“Yes,” I say. Oh—I didn’t mention I’m at the bar, too? So are you.

It feels like we’ve been thinking a lot about uncertainty and navigating it these last five years in living rooms, workplaces, social media, and bars. (At least I think bars are still a thing.) That line of thought keeps bringing me back to the Johari Window. It’s a simple frame that helps me think about what we know, what we don’t, and what sits in between, so I started giving each box a voice.

The Johari Window sorts things into four panes: known knowns (what we understand and can explain), known unknowns (mysteries we can see but haven’t cracked), unknown knowns (truths we carry without fully realizing), and unknown unknowns (what we don’t even know we don’t know). Walking into the bar, we have the scientist (known knowns), the magician (known unknowns), the poet (unknown knowns), and the alien (unknown unknowns).

What would they be talking about tonight? Maybe they start with vaccines—Is everything we’ve known wrong?—or the state of the economy and national debt. Why is it so hard to buy a house or sell one? What’s wrong with the (grand)kids, and how do we raise them? Where is our social fabric? Our sense of national pride? Is that something we should care about? And what’s the deal with airplane food?

Maybe the poet will begin by speaking the scientist’s language: “why are we rejecting vaccines now, aren’t we all scientists?” The magician seems a little more open to mystery and talks, maybe, about how they’ve never seen measles and somehow ionized water has helped with their IBS. Awe and attention are their tools. “What do you mean you can’t explain it?” or “we’ve studied that!” we might overhear the scientist say, incensed that this is even a topic of conversation. The poet seems a little more open now thinking about how to carry the undercurrent before others can name it. Maybe they create a new phrase that can remind us to take action faster than a spreadsheet or weave a story about a medicoindustrial complex. The alien — definitely confused at first — might chime in with a nonsequitur or perhaps, “what even is a vaccine, anyway?”

Or maybe they start by talking about the economy? “What should we be doing now?” Do we have a government of magicians using levers that we don’t yet understand scientifically? Are they scientists running experiments? Poets telling stories of “American dynamism”. The scientist draws us to their numbers: this is unprecedented, “our experiments tell us tariffs are going to bite us.” The alien asks whether any of this even matters if superintelligence is right around the corner.

Standing in the doorway of the bar, it’s not clear where their conversation will go. It will probably look like it is getting messier and more argumentative before anything feels settled or harmonious. Still, I’ve enjoyed imagining the different parts and people in the conversation. I’m pretty sure I’d agree mostly with the scientist, but I can hear the poet sometimes. Magic seems like it’d be useful. And I like the enigmatic alien, so frequently misunderstood, even pitiable. Maybe, somehow, they’re the wisest of the group.

As we stand in the doorway, scanning the room for a cozy spot to settle in and talk, the bartender wipes a glass.

“Well, in that case, sit anywhere you all agree on.”

First! (in five years)

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The one where Craig remembers he has a blog.

The last time I wrote here was 2019. I didn’t know it then, but life was about to change in every possible way. By the spring of 2020, COVID had arrived, work felt all-consuming, and my wife and I were expecting our first child. It was terrifying but also grounding. In a world where almost nothing felt certain, the fact that a baby was coming felt more real than anything else.

That moment marked the beginning of a new identity, one that has taken me pretty much five years to develop a comfort. Especially as our second child joined us, I grappled with what this all would mean for remaining the partner I wanted to be with my wife and the professional I wanted to be at work.

My experience of travel describes the shift conveniently. Before, I traveled often: for work, for exploration. With COVID my travel fell off a cliff. Initially, that was the same reason as everyone else but later became more about balancing the demands of family/home life. Recently, for the first time in years, my wife and I traveled on our own, just the two of us, to the Azores. (Thanks mom, one of two people reading this, for watching the kids). Being away together reminded me not just of how travel shapes me — but how I, in turn, use my experiences to shape the things that matter most to me: helping me and those around me learn and grow, deepening relationships, and applying my skills and background where they can be most useful.

I think it is fair to say I am settling into a clumsy side-hug of being a parent alongside a professional life where I can support others to meaningfully thrive and grow at work. Indeed, certain things have felt even truer to me now when they arise in one moment while managing a complex 5-year program and in the next moment while trying to convince a 5-year-old it is time for bed.

Over these years, we have been doing a lot in my fields of health tech, AI, and value-based care: thinking, learning, and growing. Though I have been commenting on this evolution aloud in my living room — as cruel and unusual for my wife and family as it sounds — I think it will be differently fun and instructive for me and others to process the thoughts more visibly.

And, it feels like a great time to pick up the pen. I’ve dictated most of this, “vibe written” other parts, and I’m looking forward to rescanning the past in my notes, curating with the help of AI, and sharing more ideas. I’d like to prioritize presence over polish. I beg your forgiveness in advance if this starts to resemble too much AI-enabled slop as I rediscover my voice.

So — first, after five years, this post. More to come.

Digital Health: “What’s taking so long?” Part 2 of N

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For all the ways that technology has visibly transformed our lives as consumers over the last decade, it has seemed like just a matter of time before the excitement of big data, social, local, mobile, process automation, artificial intelligence, and blockchain (nb. use of buzzwords intentional) will make their way into helping us meet the aims of precision medicine and population health. Though I am quite convinced that health care as an industry can be one of the most rapidly changing, I think it is fair to say that the health care consumer (ie patient) experience has remained fundamentally unchanged during this period. It feels, if anything, that the gap is only getting wider. What’s taking so long?

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Looks like we have ourselves an old-fashioned Technological Revolution (in health care)

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It’s been a while, partly because these posts still can take a while for me to write. I wanted to experiment putting a few thoughts down more informally (read: no links) and originally intended to elaborate on one of the often-overlooked problems with applying advanced statistical methods/ML/AI/”cognitive computing” to health care. That will have to wait though, because I’m realizing that there is some important background that I would like to elaborate on first. I’m going to preface this by admitting that I am no scholar on innovation, but I do consider myself a student. My thinking begins with a few practical (and very much borrowed) theories of innovation.

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Forget Big Data. It’s Time to Talk About Small Data.

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With all of the talk of “big data,” it can be hard to remember that there was ever any other kind of data. If you’re not talking about big data — you know, the 4 V’s: volume, variety, velocity, and veracity — you should go back to running your little science fair experiments until you’re ready to get serious. Prevalent though this message may be, it has, at least in health care, stunted our ability to focus on and capture the hidden 5th V of big data: value.

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Why the stakes are so high in the open data debate

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It is hard to understate just how much of a currency data has become in medicine. Whether talking about evidence-based medicine, precision medicine, or genomics, the ability to collect and distill data into information, transform it into knowledge, and use that knowledge to drive effective action is at the heart of what modern medicine seeks to accomplish. The centrality of data to this process has created well-entrenched stakeholders, which is why it comes as no surprise that the conversation around open sharing of research data following publication has shifted into controversial territory.

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The Opinionated Electronic Medical Record

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This post also appeared on KevinMD.

Software has opinions. No, I’m not talking about opinions on the next presidential election or opinions about flossing before or after brushing. Software has opinions about how data should be displayed, opinions about users’ comfort with the mouse, even, in some cases, opinions about what you should have for dinner (see your local on-demand food ordering service).

We tend to view software as a tool that is either good or bad. Good when it lets us do what we want with as little frustration as possible and bad when it doesn’t. Maybe we should be a little nicer to software.

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