David Monnerat

Product + AI | Systems Thinker | Enterprise Reality

Tag: artificial intelligence

  • Show Me the Incentive

    Show Me the Incentive

    AI investment priorities reveal more about what we value than any mission statement. Here’s what the numbers are actually saying.

    I was sitting in the gym at my son’s school on career day, watching kids rotate through tables staffed by a dog groomer, a police detective, a state park maintenance worker, and me. My topic was AI.

    I’d played them a song my son made using AI tools. I’d watched their faces when the music came out of a prompt. I’d thought about what these tools could mean for kids like the ones in that room — kids with different abilities, different challenges, different relationships with the systems that were supposed to serve them. The technology felt genuinely hopeful in that moment.

    Then I drove home and opened my inbox.

    I work in AI. I’ve spent more than a decade in this space. I believe in what the technology can do — I’ve seen it do things that matter. But I also watch the money, and the money is telling a different story than the hope.

    In 2026, four hyperscalers committed to a combined $700 billion in capital expenditure, nearly double what they spent the year before. That same year, federal funding for health and science research took cuts that one analysis described as a screeching, and possibly irreversible, halt for many projects. The Gates Foundation, Novo Nordisk Foundation, and Wellcome jointly committed $60 million to evaluate AI health tools in low- and middle-income countries. Sixty million dollars. Against $700 billion.

    That ratio is not an accident. It’s an incentive structure.

    Charlie Munger said it plainly: “Show me the incentive, and I’ll show you the outcome.” The incentive right now is return. The outcome is what we’re seeing — infrastructure investment at a historic scale, enterprise deployments producing almost no measurable ROI, and the applications that could matter most to the most people starved of the capital that’s going elsewhere.

    The Market Is Working Fine

    That’s the uncomfortable part. This isn’t a story about a broken system. The market is functioning exactly as designed. Capital flows to the highest expected return. Consumer AI applications have enormous addressable markets. Infrastructure that powers those applications generates reliable revenue. Health AI for rare neurological conditions has a small addressable market and a long development timeline. The math isn’t hard.

    Upton Sinclair put the human version of it this way: “It is difficult to get a man to understand something when his salary depends on his not understanding it.” The people making investment decisions aren’t ignoring the potential of medical AI out of malice. They’re ignoring it because the incentive structure doesn’t reward understanding it. Not yet. Maybe not until it’s too late to matter.

    My son has epilepsy. I think about what AI-driven research could mean for conditions like his — better seizure prediction, smarter medication titration, earlier intervention. I think about it the way any parent thinks about something that could help their kid. And I know better than to frame it as epilepsy versus cancer, because that’s a trap. The question isn’t which disease deserves the investment. The question is why we’ve built a system that forces us to choose between them at all, while $700 billion goes elsewhere.

    The answer is incentive. And right now, the incentive doesn’t point there.

    Progress and Profit Are Not the Same Thing

    Companies should make money. Profit funds research, attracts talent, builds the infrastructure that eventually enables the things that matter. The hyperscaler investments aren’t going nowhere — they’re building the compute layer that researchers will use for decades. AlphaFold happened because the underlying models and infrastructure existed. I know this. And the productivity gains from AI deployment are real. The capability improvements are real. Some of what’s being built right now will matter enormously.

    But profit and progress are not the same thing, and we keep talking as if they are. We tell the story of AI as the technology that will solve our biggest problems — climate, disease, inequality — while the actual investment pattern optimizes for something else. That gap between the story we tell and the system we’ve built is worth naming.

    The tendency to optimize for what’s measurable and near-term at the expense of what’s important and long-term is not something AI introduced. We’ve been here before. But the scale of this wave, its speed, and the environmental cost of the compute it requires compound the stakes in ways that previous cycles didn’t.

    The bubble will eventually correct. It always does. Ninety-five percent of enterprise AI pilots are producing no measurable return. The capital is burning, and the patience of the people funding it has a limit. When the correction comes, the investment will contract, the priorities will shift, and whatever window existed to point this technology at the hard problems will have narrowed.

    The people most likely to benefit from AI-driven medical research are not the people funding the infrastructure. The people absorbing the cost of the job displacement are not the people who will capture the upside when the next wave arrives. That asymmetry isn’t new. It’s the pattern every technology wave produces.

    What I Do Know

    The bubble will correct before the incentives do.

    That’s the pattern. And when it does, the window on the things that actually mattered will have narrowed — maybe irreversibly for some of them. Climate doesn’t wait for market corrections. Disease outbreaks don’t pause while capital regroups. Federal research budgets, once cut, don’t come back on the same timeline they left.

    The market won’t fix this on its own. It doesn’t have to — that’s not what markets are for. Regulation could change the incentives. Public funding could change them. Coordinated pressure from the organizations deploying AI could change them if those organizations decided that the story they’re telling about AI’s potential was one they wanted to be accountable for.

    None of that is happening at the speed the problem requires.

    Show me the incentive. The outcome is right there.

    What I’m Left With

    I drove home from career day thinking about those kids. About what the technology could mean for them if it pointed in their direction. About my son and what a different investment pattern might make possible for people like him.

    I still believe in the technology. I believe in what it can do when it’s pointed at the right problems. I’ve seen it.

    I just think we should be honest about where it’s pointed right now. And curious enough to ask whether that has to be true.

    Why are we doing it this way? Does it have to be this way? What would it look like to do it differently?

    Those aren’t rhetorical questions. They’re the ones worth sitting with.

  • The Lesson We Keep Not Learning

    The Lesson We Keep Not Learning

    Organizational learning is supposed to be how we get better over time. Here’s why we keep skipping it — and what AI reveals about that failure.

    I’ve been the person who knew better.

    Not once. More than once. You have data. You have a model. You have something that genuinely works, that you’ve tested, that you believe in. And you walk into a room with the business and you expect the quality of the thing to do the persuasion work. Because it’s good. Because you can see what it will do for them. Because if they just understood it the way you understand it, they’d want it too.

    It almost never works that way.

    The business isn’t wrong to resist. They have a roadmap. They have commitments. They have things they’re already behind on. You’re asking them to absorb something new, change how they work, take on risk, and trust that the upside you’re describing will actually materialize. The idea being good doesn’t answer any of those questions. It just asks them to take your word for it.

    I’ve watched this pattern play out more times than I can count — in myself, in colleagues, across organizations. Someone builds something they believe in, takes it to the business, gets a lukewarm response or an outright no, adjusts the what, and tries again. New idea, same method. The next version is better. The pitch is cleaner. The deck has more data. And it fails for the same reason the first one did, because the method didn’t change, only the artifact.

    The question that would have helped — and almost never gets asked — is this: based on what has and hasn’t worked before, what does successful adoption actually look like here, and what’s the path to get there?

    That’s not a question about the idea. It’s a question about the method. And it requires looking back at your own history before you move forward.

    We don’t do that. Not consistently. Not structurally. We survive the last failure, close the chapter, and start the next project with conviction instead of context.

    The Pageantry of the Post-Mortem

    When something goes badly enough that it demands acknowledgment, we have rituals for it. The retrospective. The lessons learned document. The post-mortem that produces action items nobody looks at again.

    I’ve sat in those rooms. There’s a specific energy to a post-mortem that’s being performed rather than practiced. Everyone agrees on what went wrong. The right language gets used. The document gets written. And then the next project starts and nobody opens it. Nobody asks: have we seen this before? Nobody maps the current situation against the last one.

    The lesson learned becomes a historical artifact rather than an operational input.

    I’ve watched senior leaders run the same failing initiative multiple times — different team, different framing, different artifact — because the diagnosis from the last attempt never got applied to the next one. The what kept changing. The how never did. The loop ran for years.

    Nobody was malicious. Nobody was incompetent. The lesson was available every time. It just never got encoded into anything that changed the next decision.

    What AI Does That We Don’t

    There’s a specific irony in this pattern emerging at the same moment that AI has become the dominant technology investment across industries.

    AI systems learn from the past. Not as a best practice or a cultural value, but as a structural requirement. A model that hasn’t been trained on historical data isn’t a model. The learning is automatic, systematic, and non-optional. Every inference is shaped by everything that came before it.

    More precisely: AI doesn’t just learn what happened. It learns the conditions under which things happened. A recommendation model doesn’t encode that users liked item X. It encodes that users liked item X when they arrived from a certain channel, at a certain point in their journey, after certain prior interactions. The context is part of the pattern. The how is part of what gets learned.

    We tend to only learn from the what.

    The product team changes the idea but not the method. The senior leader changes the team but not the diagnosis. The organization runs the post-mortem but doesn’t connect it to the next decision. We update the what and treat the how as a fresh problem every time.

    And then we deploy AI — a technology whose entire value proposition is systematic learning from history — without applying that same discipline to the organizational decisions about how to deploy it.

    We ask the model to learn from the past. We don’t ask ourselves the same thing.

    The Question Nobody Asks

    Before AI it was a compelling cloud architecture. Before that a web framework that would change everything. The pattern doesn’t belong to any particular technology. Conviction about the what has always had a way of short-circuiting the discipline of asking about the how.

    The question that would break the pattern isn’t complicated. It’s just uncomfortable.

    What has and hasn’t worked before, in this organization, with this kind of change? Not in general. Not at other companies. Here. With these people. Under these conditions. What did we try? What did we learn? What were the conditions when it worked, and what were the conditions when it didn’t?

    And then: given all of that, what’s the approach that gives this the best chance of actually landing?

    Before the next project kicks off, before the roadmap gets locked, before the pitch deck goes to leadership — pull up the last one. Not to relitigate it. To learn from it. Ask: did we change the how, or just the what?

    If the honest answer is just the what, you already know how this one ends.

  • The Question Is the Creativity

    The Question Is the Creativity

    The conversation around AI tends to focus on outputs. This post is about something upstream from that.

    I was in a room with a small group of engineers and product people. We had a standing rule for these sessions: no constraints. Not on money, not on time, not on what the law currently allowed. Laws change, and if we only invented inside existing rules we’d never get ahead of them. The goal was to ask questions nobody had thought to ask yet.

    One of those sessions started with a conversation between me and a colleague. His father had Parkinson’s. My son had ataxia, a neurological condition affecting coordination and balance that causes involuntary movement. We were talking about tremors, how they made it hard for devices to understand intentional gestures, how the technology kept misreading the signal. Somewhere in that conversation someone said: what if we thought about this the way noise-cancelling headphones work? Not filtering out the gesture, but filtering out the tremor. Separating the signal from the noise at the body level.

    That question became a patent.

    The patent room is an extreme version of something that happens every day in less formal settings. The standard for a patent is legal novelty, something that hasn’t existed before in that form. But the same upstream act, two experiences colliding into a question worth asking, happens whenever someone brings their specific life to a problem and asks it differently than anyone else would have. The question doesn’t have to be patentable to matter. It just has to come from somewhere a model can’t go.

    I’ve thought about that moment a lot since AI became the dominant conversation in technology. Because that question could not have come from a model. Not because the model isn’t capable of sophisticated reasoning. It is. But because the question didn’t come from reasoning. It came from two people’s lives colliding in an unconstrained space.

    That’s where creativity actually lives. Not in the execution. In the question.

    What the Room Was Actually Doing

    Those sessions had a specific energy. You removed the practical objections that normally narrow thinking before it has a chance to go anywhere interesting. No one said “that’s too expensive” or “we can’t do that yet.” You created space for people to bring their actual experience, not their professional expertise, but their lives. Their parents. Their kids. Their frustrations. Their observations from places that had nothing to do with the problem on the table.

    And then you let those things collide.

    We did a session focused on aging in place, how to help elderly people live independently longer, especially when their families were far away. Every person in that room had a version of that worry. A parent in another state. A grandparent who had fallen. The anxiety of not knowing. The ideas that came out of that session didn’t come from market research or competitive analysis. They came from people who were living the problem and had been given permission to imagine solutions without limits.

    That combination of unconstrained thinking plus personal stakes is what produces the questions worth asking. Remove the constraints and you get speculation. Add the personal stakes and you get invention.

    The cochlear implant came from a researcher whose child was deaf. He wasn’t analyzing the hearing aid market. He was watching his daughter navigate a world that hadn’t been designed for her and asking whether it had to be that way. The sticky note came from a choir singer who had been frustrated by a bookmark falling out of his hymnal for years before he connected that frustration to an adhesive a colleague had invented that nobody wanted. Neither of those connections was the product of a logical process. They were the product of a life that had been paying attention to a problem long enough to recognize an answer when it appeared from an unexpected direction.

    Personal stakes aren’t the only path to the right question. Plenty of inventors have no direct connection to the problem they’re solving. But stakes create a kind of attention that’s hard to replicate any other way. You notice differently when you care.

    The question came first. The execution came after.

    What AI Actually Does

    AI is extraordinarily good at execution. Given a well-formed question, it can explore the solution space faster and more thoroughly than any human team. It can find patterns across domains that no individual would have the bandwidth to survey. It can synthesize, generate, iterate, and refine at a scale that would have seemed impossible five years ago.

    I use it this way every day. And the back-and-forth of working with a model, the “that’s close but not quite it,” the steering toward something you can sense but not yet fully articulate, has its own creative quality. It reminds me of those patent sessions. The iterative energy of a room where ideas are being shaped in real time, where one person’s response moves another person’s thinking, where the answer emerges through the conversation rather than arriving fully formed.

    But there’s a difference. In that room, every person brought something the others didn’t have. Their specific experience, their specific frustration, their specific version of the problem. The model brings what it was trained on. Which is vast, more than any person in any room could hold, but it is still bounded by what humans have already thought, written, and recorded. It can recombine brilliantly. It cannot bring something from outside its training the way a person brings something from outside their expertise.

    The Constraint That Isn’t a Constraint

    We told those rooms: imagine no constraints. And it worked, because removing the practical filters let thinking go somewhere it couldn’t go otherwise.

    But you could give an AI the same instruction. “Imagine no constraints on money, time, or law. Solve this problem.” And it would produce output. Sophisticated output. Options and combinations and lateral connections across domains it has been trained on. It might even produce something that looks creative.

    What it can’t do is bring something from outside its training into that unconstrained space. The model has no constraints to remove because it has no constraints in the first place, and also no life experience pressing against those constraints, no accumulated frustration waiting for permission to become a question. The removal of constraints only matters if something was being constrained. In a human, what gets constrained is everything they’ve lived and observed and felt and noticed. Remove the filter and that floods in.

    The model has no equivalent. It can go wide within what it knows. It cannot go to the place where my colleague’s father’s tremor and my son’s tremor became the same problem.

    The Most Important Skill

    There’s a version of the AI conversation that focuses on prompting. How to write better prompts. How to get better output. How to work with the tools more effectively. That’s useful, and I’m not dismissing it.

    But I think it understates where the leverage actually is.

    The prompt is downstream of the question. And the question is downstream of the specific collision: your son’s tremor meeting your colleague’s father’s tremor, a choir singer’s bookmark meeting a chemist’s unwanted adhesive, a researcher’s deaf daughter meeting a question about whether the world had to be that way. That’s not experience in the abstract. It’s experience that has been building pressure against a specific problem long enough to recognize an answer when it comes from an unexpected direction.

    That capacity lives in you, not in the tools.

    What the Model Will Never Have

    The model has no son with ataxia. It has no colleague’s father with Parkinson’s. It has no parent far away whose silence worries it. It has no memory of a room where someone said “what if we” and everything shifted. It has no frustration that has been accumulating for years, waiting for the right collision.

    It has no stakes. It notices nothing differently because it cares about nothing. It brings no life to the question because it has no life to bring.

    Those aren’t limitations that will be engineered away. They’re not gaps in the training data or constraints on the context window. They’re the definition of what the tool is.

    And they’re the definition of what you are.

    The question comes from you. The creativity is yours. The execution, extraordinary, useful, genuinely remarkable, is something you can share.

    Don’t confuse the two.

  • The Same Rocket, A Different Ship

    The Same Rocket, A Different Ship

    AI investment is accelerating faster than any technology wave before it. The pattern underneath it is older than most of us realize.

    There is something deeply human about looking up.

    We’ve always done it. We looked at the stars and decided we belonged there. We looked at the horizon and built ships to cross it. We looked at problems that seemed impossible and found ways to make them less so. That instinct, the refusal to accept limits as permanent, is responsible for most of what we’ve built.

    It’s also responsible for a pattern that keeps repeating. And we never seem to learn it.

    Every generation gets a technology that feels like the one. The thing that will finally close the gap between what we can do and what we’ve always wanted to do. Computers. Then software. Then the internet. Then big data. Each one arrived with genuine capability and genuine promise. Each one attracted enormous investment. Each one produced real gains, new industries, new efficiencies, new possibilities that didn’t exist before. And each one, eventually, ran into the distance between what the demo promised and what the real world required.

    That gap never closed the way we expected. So we did what we always do.

    We threw more fuel at it.

    (more…)
  • The Leveler: The Use Cases Nobody Planned For

    The Leveler: The Use Cases Nobody Planned For

    I sat at a table in the gym at my son’s school. Around the room there was a dog groomer, a police detective, someone from the state park maintenance crew, and an archaeologist. We were there for career day. My topic was AI.

    When each group of kids came over, I played them a song. I told them my son made it. He had the idea, shaped the lyrics, and used AI tools to bring it to life. It’s on Spotify and Apple Music. The instruments, the vocals, the production were all generated by AI. But the idea, the story, the words were his.

    Some of the middle school kids had already used ChatGPT. A few had used it for homework. One wanted a training plan for a video game he was trying to get better at. One girl said she used it as someone to talk to.

    That last one stopped me. The adults in the room were thinking about AI in terms of what it might take from these kids. That girl was using it for something none of the adults had thought to offer her.

    (more…)
  • Defining Success Criteria: Do You Know Where You Are Going?

    Defining Success Criteria: Do You Know Where You Are Going?

    It was the tenth call.

    I had been on the first one. A customer data project, matching records across systems to connect outcomes to the right source. It seemed straightforward enough that I handed it off and moved on. What followed was eight more calls between my team and the customer, each one ending with a tweak to the logic, each tweak fixing something and revealing something else.

    The problem wasn’t the code. It wasn’t the team. It was that nobody had defined success criteria before the work started. Nobody had asked: what does done actually look like?

    By the time my team pulled me back in, it was already swirling. The customer’s manager had joined too. I suspect that because the issue still wasn’t resolved, he felt the need to get involved. We had a piece of logic built by people who were no longer on the team, results that were almost always right, and a team that had been grinding on this for weeks.

    I asked a simple question: What do you actually want?

    That was it. Every previous conversation had been about what the code wasn’t doing right, assuming the approach was sound and just needed adjustment. Nobody had stopped to ask whether the approach itself was the right one. The answer to that simpler question was: when this happens, here is what we expect to see. And the solution that followed was far simpler than what we had been building toward. A defined set of conditions, clearly mapped to outcomes. The complexity we had been wrestling with wasn’t a feature of the problem. It was a feature of never having properly defined the problem.

    We left that meeting with a clear destination. We should have had that conversation on call one.

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  • The Code Nobody Understands: The Hidden Risk of AI-Generated Code

    The Code Nobody Understands: The Hidden Risk of AI-Generated Code

    The error message appeared at 2:17 a.m. Something about a null reference in a service that hadn’t been touched in weeks. The developer on call pulled up the relevant file, read through it, and felt a familiar but newly uncomfortable sensation.

    She had written this code. Her name was in the commit history. But she hadn’t really written it. She’d accepted it. Reviewed it in the way you review something when you’re moving fast, and the output looks right, and the tests pass. The AI had generated the logic. She had approved the shape of it. And now, at 2:17 a.m., she needed to understand not just what it did but why it did it that way: what assumption it was built on, what edge case it was avoiding, what the author had been thinking.

    There was no author. Not in the sense that mattered.

    (more…)
  • Enterprise AI Implementation: You Were Promised Everything. Here’s What It Took.

    Enterprise AI Implementation: You Were Promised Everything. Here’s What It Took.

    It was, by all appearances, a standard enterprise AI implementation.

    The summaries looked clean.

    At the top of the screen was a concise paragraph capturing a customer interaction: what was requested, what was explained, and what follow-up was required. Action items were listed neatly below. It was the kind of output you could screenshot for a slide deck. Efficient. Polished. Convincing.

    The premise was simple. If employees spent less time documenting interactions, they could spend more time serving customers. Efficiency would increase. Costs would decrease. The model worked in the demo. It summarized transcripts fluently and quickly. The business case felt straightforward.

    It moved forward.

    The strain didn’t appear in the demo. It appeared in real use.

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  • The Other Hand: AI, Disability, and the Cost of Progress

    The Other Hand: AI, Disability, and the Cost of Progress

    I’ve spent more than a decade working in AI. I’ve built teams around it, led products powered by it, and spent more hours than I can count thinking about where it creates value and where it doesn’t. I’m not a skeptic. I’ve seen what the technology can do when it’s applied well.

    I’m also the father of a son with epilepsy. He is sixteen, and he will probably never drive. Autonomous vehicles have been part of how I think about his future for a while now — not as a certainty, just as a possibility worth holding onto. So when I came across a Freakonomics podcast about what a driverless world might mean for people who can’t drive, I expected something that confirmed what I’d been quietly hoping. Instead it pulled in two directions at once.

    That tension is what this post is about.

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  • The Workbench: A Fictional Exploration of AI, Patents, and Asymmetric Trust

    The Workbench: A Fictional Exploration of AI, Patents, and Asymmetric Trust

    A short work of speculative fiction about mediated cognition and structural asymmetry in AI systems.

    I’ve worked on the same problem for three years. Agricultural runoff — specifically, a low-infrastructure filtration approach practical for small farms that can’t justify the capital cost of existing solutions. I have notebooks. I have a corner of my basement with a workbench and a lamp. I work at night because that’s when the house is quiet, and no one needs anything from me.

    I’m not describing this to be romantic about it. I’m describing it because the habit matters to what happened.

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