David Monnerat

Product + AI | Systems Thinker | Enterprise Reality

Category: future

  • Nobody Gets Credit for the Fire That Didn’t Start

    Nobody Gets Credit for the Fire That Didn’t Start

    Proactive leadership sounds straightforward. Here’s why the incentive structure works against it — and what it takes to change that.

    I’ve sat in a lot of status meetings where the room wasn’t really in the room.

    Leaders multitasking. Half-attention on the slide, half somewhere else. Risks documented, bubbled up, noted in the status email that went to the right distribution list. The process worked exactly as designed. The signal was observed, communicated, and received — technically. Then something breaks and the questions start. Why didn’t I know about this? You should have made sure I was more aware.

    And there it is. The accountability transfers from the person who didn’t receive the signal to the person who sent it. We told you. In the meeting. In the email. In the status. That’s the losing argument, even when it’s true.

    The Signal Was There

    This is worth sitting with, because it’s easy to read this as a leadership failure and stop there. It’s more complicated than that.

    The information existed. The risk was documented. The communication happened. By every formal measure, the organization did what it was supposed to do. The signal was in the system.

    But the signal was in our language, not theirs. We led with the technical risk, the process concern, the architectural debt. We expected the decision maker to translate that into their own terms — to connect the signal to the metric it would eventually impact and decide whether to act.

    That’s not their job. It’s ours.

    I spent a long time trying to convince decision makers in my own way, with what I thought was important, communicated the way I thought it should be communicated. The frustration when they didn’t respond felt justified. But it was misplaced. It wasn’t about me, or my argument, or the quality of the data. It was about whether I’d connected the signal to something they were already watching.

    That’s failure mode one. And it’s on us to fix it.

    The Signal Was There and It Didn’t Matter

    Failure mode two is harder, because it doesn’t have a personal fix.

    Even when the connection to the metric is made clearly — even when you’ve done the work to translate the risk into their language and show them what it will cost when it lands — if the metric isn’t hurting yet, the signal still loses the priority battle. The urgency isn’t there. There are other fires already burning, other things costing them something today rather than something they’d have to imagine.

    Future-tense risk is always competing against present-tense urgency. And present-tense urgency wins almost every time.

    I watched this play out at a company I worked for earlier in my career. Warning signs that a platform wasn’t scaling properly. The data was there. The analysis was done. The flags were raised. The response was essentially: we see it, we’ll deal with it, it’s not breaking anything yet. Until it was. The failure spread faster than anyone had planned for, the recovery was harder than it needed to be, and customers felt it in ways that could have been smaller or avoided.

    That’s not a communication failure. That’s a structural one.

    Know Your Audience

    The practical lesson from failure mode one — the one I learned the hard way — is simple to say and harder to do consistently.

    Lead with the metric. Not the technical risk, not the process concern, not the backstory. The number. The thing they’re already watching. Show them what happens to that number if the risk materializes, and when.

    The quality of your argument in your own terms is irrelevant if it doesn’t connect to theirs.

    Every decision maker has a set of incentives they’re optimizing for. Your job, when you need a decision, is to understand those incentives and use them to frame the choice. Show me the incentive and I’ll show you the outcome — that applies to the person you’re trying to persuade as much as it applies to the market.

    The frustration of learning this is real. There’s something that feels like a concession in it — like the data should be enough, the risk should be obvious, the right thing to do should be self-evident. It isn’t. The person who learns to speak the language of the people making decisions gets heard. The person who keeps communicating in their own language keeps getting frustrated.

    What This Can’t Fix

    Failure mode one is solvable. Better communication discipline, more deliberate connection of signals to metrics, more investment in understanding what the decision maker is watching. Teams can get better at it.

    Failure mode two is different. Even with perfect communication, risks that don’t impact a current metric still lose. Prevention doesn’t have a metric.

    Nobody gets credit for the fire that didn’t start.

    The fire that never started doesn’t show up in the quarterly review. The only thing that changes that is when the organization decides to measure it. When preventing the fire becomes something someone is accountable for. When the proactive decision gets credited for what it avoided rather than penalized for what it cost.

    Most organizations don’t do that until the fire forces them to.

    The Direction Worth Moving In

    If we can get better at failure mode one — close the communication gap, build the language, show through data that proactive decisions compound better than reactive ones — we earn something more valuable than avoided crises. We earn credibility. And credibility is what opens the door to the harder conversation.

    The harder conversation isn’t about this project or this risk. It’s about whether the metrics themselves are right. Whether the incentives the organization is optimizing for are the ones that produce the future it says it wants. The same discipline that connects a technical risk to a business metric can connect a business metric to a longer-term outcome. If you’ve built enough trust by being right about the near-term things, you get a hearing on the longer-term ones.

    That’s the direction. Reactive to proactive. And then, proactive about what we’re actually optimizing for.

    Right now, inside most organizations, the fire is still what gets attention.

    The goal is to change that before the fire starts.

  • 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 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.

    (more…)
  • 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.

    (more…)
  • When AI Safety Commitments Become Ballast

    When AI Safety Commitments Become Ballast

    There’s a moment in every race when weight starts to matter.

    At the beginning, you carry everything. Redundancy. Margin. Contingency. The assumption is that you can afford to be careful, that prudence is a strength rather than a liability.

    Then someone pulls ahead.

    (more…)
  • The Dulling of Innovation

    The Dulling of Innovation

    For a few years, I was on a patent team. Our job was to drive innovation and empower employees to come up with new ideas and shepherd them through the process to see if we could turn those ideas into patents.

    I loved that job for many reasons. It leveraged an innovation framework I had already started with a few colleagues—work that earned us a handful of patents. It fed my curiosity, love for technology, and joy of being surrounded by smart people. Most of all, I loved watching someone light up as they became an inventor.

    I worked with an engineer who had an idea based on his deep knowledge of a specific system. Together, we expanded on that idea and turned it into an innovative solution to a broader problem. The look on his face when his idea was approved for patent filing was one of the greatest moments of my career. For years after, he would stop me in the hallway just to say hello and introduce me as the person who helped him get a patent.

    Much of the success I saw on that team came from people who deeply understood a problem, were curious to ask why, and believed there had to be a better way. That success was amplified when more than one inventor was involved, when overlapping experiences and diverse perspectives combined into something truly original.

    When I moved into product management, the same patterns held true. The most successful ideas still came from a clear understanding of the problem, deep knowledge of the system, and the willingness to explore different perspectives.

    Innovation used to be a web. It was messy, organic, and interconnected. The spark came from deep context and unexpected collisions.

    But that process is starting to change.

    Same High, Lower Ceiling

    In this new age of large language models (LLMs), companies are looking for shortcuts for growth and innovation and see LLMs as the cheat code.

    Teams are tasked with mining customer comments to synthesize feedback and generate feature ideas and roadmaps. If the ideas seem reasonable, they are executed without further analysis. Speed is the goal. Output is the metric.

    Regardless of size or maturity, every company can access the tools and capabilities once reserved for tech giants. Generative AI lowers the barrier to entry. It also levels the playing field, democratizing innovation.

    But what if it also levels the results?

    When everyone uses the same models, is trained on the same data, and is prompted in similar ways, the ideas start to converge. It’s innovation by template. You might move faster, but so is everyone else, and in the same direction.

    Even when applied to your unique domain, the outputs often look the same. Which means the ideas are starting to look the same, too.

    AI lifts companies that lacked innovation muscle, but in doing so, it risks pulling down those that had built it. The average improves, but the outliers vanish. The floor rises, but the ceiling falls.

    We’re still getting the high. But it doesn’t feel like it used to.

    The Dopamine of Speed

    The danger is that we’re not going to see it happening. Worse, we’re blindly moving forward without considering the long-term implications. We’re so fixated on speed that it’s easy to convince ourselves that we’re moving fast and innovating.

    We confuse motion for momentum, and output for originality. The teams and companies that move the fastest will be rewarded. Natural selection will leave the slower ones behind. Speed will be the new sign of innovation. But just because something ships fast doesn’t mean it moves us forward.

    The dopamine hit that comes from release after release is addictive, and we’ll need more and more to feel the same level of speed and growth. We’ll rely increasingly on these tools to get our fix until it stops working altogether. Meanwhile, the incremental reliance on these tools dulls effectiveness and erodes impact, and our ability to be creative and innovate will atrophy.

    By the time we realize the quality of our ideas has flattened, we’ll be too dependent on the process to do anything differently.

    The Dealers Own the Supply

    And those algorithms? They’re owned by a handful of companies. These companies decide how the models behave, what data they’re trained on, and what comes out of them.

    They also own the data. And it’s only a matter of time before they start mining it for intellectual property—filing patents faster than anyone else can, or arguing that anything derived from their models is theirs by default.

    Beyond intellectual property and market control, this concentration of power raises more profound ethical and societal questions. When innovation is funneled through a few gatekeepers, it risks reinforcing existing inequalities and biases embedded in the training data and business models. The diversity of ideas and creators narrows, and communities without direct access to these technologies may be left behind, exacerbating the digital divide and limiting who benefits from AI-driven innovation.

    The more we rely on these models, the more we feed them. Every prompt, interaction, and insight becomes part of a flywheel that strengthens the model and the company behind it, making it more powerful. It’s a feedback loop: we give them our best thinking, and they return a usable version to everyone else.

    LLMs don’t think from first principles—they remix from secondhand insight. And when we stop thinking from scratch, we start building from scraps.

    Because the answers sound confident, they feel finished. That confidence masks conformity, and we mistake it for consensus.

    Innovation becomes a productized service. Creative edge gets compressed into a monthly subscription. What once gave your company a competitive advantage is now available to anyone who can write a halfway decent prompt.

    Make no mistake, these aren’t neutral platforms. They shape how we think, guide what we explore, and, as they become more embedded in our workflows, influence decisions, strategies, and even what we consider possible.

    We used to control the process. Now we’re just users. The same companies selling us the shortcut are quietly collecting the toll.

    When the supply is centralized, so is the power. And if we keep chasing the high, we’ll find ourselves dependent on a dealer who decides what we get and when we get it.

    Rewiring for Real Innovation

    This isn’t a call to reject the tools. Generative AI isn’t going away, and used well, it can make us faster, better, and more creative. But the key is how we use it—and what we choose to preserve along the way.

    Here’s where we start:

    1. Protect the Messy Middle

    Innovation doesn’t happen at the point of output. It happens in the friction. The spark lives in debate, dead ends, and rabbit holes. We must protect the messy, nonlinear process that makes true insight possible.

    Use AI to accelerate parts of the journey, not to skip it entirely.

    2. Think from First Principles

    Don’t just prompt. Reframe. Instead of asking, “What’s the answer?” ask, “What’s the real question?” LLMs are great at synthesis, but breakthroughs come from original framing.

    Start with what you know. Ask “why” more than “how.” And resist the urge to outsource the thinking.

    3. Don’t Confuse Confidence for Quality

    A confident response isn’t necessarily a correct one. Learn to interrogate the output. Ask where it came from, what it’s assuming, and what it might be missing.

    Treat every generated answer like a draft, not a destination.

    4. Diversify Your Inputs

    The model’s perspective is based on what it’s been trained on, which is mostly what’s already popular, published, and safe. If you want a fresh idea, don’t ask the same question everyone else is asking in the same way.

    Talk to people. Explore unlikely connections. Bring in perspectives that aren’t in the data.

    5. Make Thinking Visible

    The danger of speed is that it hides process. Write out your assumptions. Diagram your logic. Invite others into the middle of your thinking instead of just sharing polished outputs.

    We need to normalize visible, imperfect thought again. That’s where the new stuff lives.

    6. Incentivize Depth

    If we reward speed, we get speed. If we reward outputs, we get more of them. But if we want real innovation, we need to measure the stuff that doesn’t show up in dashboards: insight, originality, and depth of understanding.

    Push your teams to spend time with the problem, not just the solution.

    Staying Sharp

    We didn’t set out to flatten innovation. We set out to go faster, to do more, to meet the moment. But in chasing speed and scale, we risk trading depth for derivatives, and originality for automation.

    Large language models can be incredible tools. They can accelerate discovery, surface connections, and amplify creative potential. But only if we treat them as collaborators, not crutches.

    The danger isn’t in using these models. The danger is in forgetting how to think without them.

    We have to resist the pull toward sameness. We have to do the slower, messier work of understanding real problems, cultivating creative tension, and building teams that collide in productive ways. We have to reward originality over velocity, and insight over output.

    Otherwise, the future of innovation won’t be bold or brilliant.

    It’ll just be fast.

    And dull.