The concept is called regularization. In machine learning, when you’re training a model, there’s a constant temptation to make it more complex. Add more parameters. Capture more nuance. Fit the training data as closely as possible. And the model will get better at matching the data you trained it on. But there’s a catch: the more complex you make it, the worse it tends to perform on new data it hasn’t seen before. It memorized the past instead of learning the pattern.

Regularization is the fix. It’s a technique that deliberately penalizes complexity — it pushes the model toward simpler explanations, even if that means a slightly worse fit to the training data. The insight is counterintuitive: a simpler model that’s a little bit wrong about the past will usually be more right about the future.

I think this is one of the most underappreciated ideas I’ve encountered. Not because of what it tells us about machine learning, but because the exact same mistake — over-optimizing for complexity — is one we make constantly in public policy.

The Complexity Trap

Here’s how I think about it. When we write a new law or regulation, the instinct is almost always to be as specific and comprehensive as possible. Anticipate every scenario. Close every loophole. Craft the perfect set of incentives so people behave exactly the way we want them to. On paper, it looks smart. You’ve thought of everything.

But in practice, you get the U.S. tax code.

The tax code is maybe the best example of what happens when you keep optimizing without regularization. Every provision made sense to somebody at some point. Every deduction, credit, exemption, and phase-out was added to solve a specific problem or create a specific incentive. Each individual addition was clever. And the result is a system so complex that it’s essentially incomprehensible — not just to ordinary people, but to the professionals who work in it every day.

Nobody understands the whole thing. Nobody can predict with confidence how a given change will ripple through the system. The complexity hasn’t made the tax code better at achieving its goals. It’s made it better at being gamed by people with expensive advisors and worse at being understood by everyone else.

That’s the trap. Each increment of complexity feels justified on its own terms. But the cumulative effect is a system that’s too clever by half — one that’s been so optimized for the specific scenarios its authors imagined that it fails badly in the real world, where things are messy and unpredictable.

Simplicity Isn’t Simplistic

I want to be careful here, because “just make it simpler” is easy to say and often gets used as a lazy argument against any regulation at all. That’s not what I’m arguing. Regularization doesn’t mean building a stupid model. It means building the simplest model that still captures the important patterns. There’s a big difference.

In policy terms, that means starting with a clear goal and asking: what’s the simplest set of rules that would actually achieve this? Not the most comprehensive. Not the most airtight. The simplest that works.

Take carbon pricing. You could write thousands of pages of sector-specific emissions regulations — different rules for power plants, manufacturing, transportation, agriculture, each with their own standards, exemptions, and enforcement mechanisms. Or you could put a price on carbon and let the market figure out where the reductions come from. The first approach tries to be clever about every scenario. The second establishes a simple principle and lets it propagate.

I’m not saying carbon pricing is easy to implement or that there aren’t real complications. But the principle is simple enough that a normal person can understand it: if you put carbon into the atmosphere, you pay for it. That clarity is worth something. When people understand the rule, they can plan around it, comply with it, and hold their representatives accountable for how it’s designed. When they can’t understand it, you’ve already lost most of the benefit.

Why We Keep Adding Complexity

If simplicity is so powerful, why do we keep choosing complexity? I think there are a few reasons, and they map pretty well to why ML practitioners overtrain models.

The first is that it feels like progress. Adding a new provision, addressing a new edge case — it feels like you’re making things better. You’re solving a visible problem. The cost of the added complexity is diffuse and delayed, while the benefit of addressing the specific case is immediate and concrete. It’s the same dynamic in ML: adding parameters improves your training metrics, and you have to be disciplined enough to care about what you can’t measure yet.

The second is institutional. Complex systems create their own constituencies. Tax preparers, compliance consultants, lobbyists, regulatory specialists — all of these roles exist because the system is complex. I’m not attributing malice here. But the people best positioned to explain why a rule is necessary are often the same people whose jobs depend on the complexity continuing. In ML, we don’t let the model vote on its own architecture. In policy, we kind of do.

The third — and maybe the most insidious — is overconfidence. The authors of complex policies believe they can anticipate how the world will respond. They think they can design the right incentive structure, predict the behavioral responses, and engineer the outcome they want. But the real world is messier than any model. People respond in unexpected ways. Markets shift. Technology changes the equation. The law of unintended consequences is always lurking, and the more specific your policy, the more brittle it is when reality doesn’t match your assumptions.

In machine learning, this is literally the problem regularization solves. You’re telling the model: don’t be so sure you’ve figured everything out. Leave room for what you haven’t seen yet.

The Case for Policy Regularization

I’ll be honest — I’m not a policy expert. I’m a tech person who learned a concept in machine learning and can’t stop seeing it everywhere. So take this for what it is: one person’s framework, not a prescription.

But I do think we’d be better off if the people writing laws asked themselves the question that ML engineers ask constantly: is this model more complex than it needs to be? Are we adding provisions because they genuinely improve outcomes, or because we’re trying to be clever? Would a simpler approach — one that’s a little less precise, a little less tailored to every edge case — actually perform better in the real world, where things are unpredictable?

The U.S. tax code could be dramatically simpler. Healthcare regulation could be more straightforward. Financial regulation could establish clearer principles instead of cataloging every prohibited behavior. In each case, we’d lose some theoretical precision. But we’d gain something potentially more valuable: rules that people can understand, systems that can adapt to change, and outcomes that are more predictable and more fair.

Regularization isn’t about being lazy or imprecise. It’s about having the discipline to resist unnecessary complexity — to build the simplest thing that actually works, and to trust that simplicity will generalize better than cleverness.

I’ve found that’s true in machine learning. I suspect it’s true in a lot of other places, too.

Doomers believe AI will be amazingly impactful and will ultimately destroy human civilization à la Skynet from Terminator. Maybe they envision a kinder version like Wall-E. But ultimately, this is AI as a massively negative force for humanity.

Zoomers believe AI will revolutionize human experience and lead to some version of Utopia, solving most, if not all, human problems. Think Star Trek — a world of abundance where technology has freed us from scarcity. (Most sci-fi leans dystopian, so the zoomers have fewer good references to point to. Maybe that tells us something about human nature — or maybe just about what makes for good storytelling.)

Skeptics believe that AI is overhyped, overblown, and will ultimately fail to deliver on its promises. Perhaps they view AI as being something like VR — a technology that was always “just around the corner” and never quite arrived.

Realists believe AI is a genuinely transformative technology, but one that will take a long time to produce massive societal impacts and represents a manageable change for humanity. This view is perhaps best represented by Arvind Narayanan and Sayash Kapoor’s essay “AI as Normal Technology” — even revolutionary, general-purpose technologies are still, at the end of the day, technologies.

You could plot each of these positions on a 2x2 grid. The x-axis is “Scale of Impact” — from negligible to transformative. The y-axis is “Direction of Impact” — from catastrophic to utopian. The Doomers live in the bottom right. The Zoomers in the top right. The Skeptics on the far left. And the Realists occupy a big zone in the middle — high impact, but manageable. Positive on balance, but not utopian.

Despite all the commentary going into AI, the overwhelming majority of the public conversation falls into one of the first three categories. The Doomers and Zoomers get the most airtime because their arguments are dramatic and emotionally vivid. The Skeptics get attention because contrarianism always draws a crowd. But the Realists — the most likely camp to be right — are chronically under-discussed.

And I get why. “This will be really important but also manageable” is a boring headline. It doesn’t generate clicks, doesn’t fill conference keynotes, and doesn’t make for compelling late-night dorm room debates. But I think it’s where we’ll end up, and I’d put the probability well above 85%.

The Box in the Middle Is Still Huge

Here’s the thing, though. Saying “we’ll probably end up in the Realist zone” is not the same as saying everything will be fine. That box in the middle is enormous. It encompasses a wide range of outcomes — from AI that modestly improves productivity in a few industries to AI that fundamentally reshapes healthcare, education, scientific discovery, and economic opportunity. From a world where we’ve managed the transition reasonably well to one where we’ve captured only a fraction of the potential upside while fumbling through unnecessary disruption.

The difference between landing in the top-right corner of that box versus the bottom-left is — in practical, human terms — really significant. We’re talking about whether millions of people get access to better healthcare, whether scientific breakthroughs happen a decade sooner, whether the economic gains flow broadly or concentrate narrowly.

So the question isn’t really “will AI be transformative?” I think it will. The question is: within the zone of probable impact, where do we end up? And that’s not predetermined. It’s a function of choices being made right now by three groups: developers, deployers, and regulators.

Developers: Build for the Margins, Not Just the Middle

AI developers — the labs building foundation models and the companies building applications on top of them — will shape where we land more than anyone. And the biggest risk I see isn’t that they’ll build something dangerous. It’s that they’ll build for the easiest use cases and leave the hardest, most impactful ones underserved.

It’s natural to focus on where the money is — enterprise productivity tools, coding assistants, marketing copy generators. These are real, valuable applications. But the transformative potential of AI lies disproportionately in harder problems: drug discovery, materials science, climate modeling, education in underserved communities. The areas where the market signal is weaker but the human impact is highest.

I’m not suggesting developers ignore commercial viability — that’s what funds everything else. But I think the best AI companies will be the ones that deliberately invest in high-impact, harder-to-monetize applications alongside their core business. AlphaFold didn’t come from a startup chasing revenue. It came from a lab that believed solving protein folding was worth doing even if the business model wasn’t obvious.

The developers who think beyond the next quarter’s revenue will do more to push us toward the top-right corner of that box than anyone.

Deployers: Stop Waiting for Perfect

By deployers, I mean the organizations — companies, hospitals, schools, governments — that actually put AI into practice. And the biggest issue I see here isn’t recklessness. It’s paralysis.

Too many organizations are sitting on the sidelines, waiting for AI to “mature” or for someone else to go first. I understand the instinct. Nobody wants to be the cautionary tale. But the cost of inaction isn’t zero — it’s just invisible. Every month an organization delays adopting AI in its workflows is a month of lost productivity, worse outcomes, and falling behind competitors who are learning by doing.

I’ve written before that the real danger isn’t making a wrong call — it’s standing still. That applies here. The organizations that will get the most value from AI aren’t the ones that waited for a perfect solution. They’re the ones that started early, iterated, and built the institutional knowledge to deploy AI effectively.

This is especially true in sectors like healthcare and education, where the potential upside is enormous but the institutional inertia is strong. Every hospital system that delays implementing AI-assisted diagnostics, every school district that waits another year to explore personalized learning — those delays have real costs measured in real human outcomes.

Deploy thoughtfully. Deploy carefully. But deploy.

Regulators: Learn from Nuclear

I’ve written about this before, but it bears repeating: my biggest worry about AI isn’t the technology. It’s the regulation.

I keep coming back to nuclear power. Here was a technology with the potential to transform our energy infrastructure and make real progress on climate change. And we effectively regulated it into irrelevance — not because it didn’t work, but because the fear of what it could do overwhelmed the appreciation of what it could deliver. Decades later, we’re desperately trying to reverse course as we realize how much that decision cost us.

The parallel to AI is uncomfortably close. The loudest voices in the conversation are warning about catastrophic risks. Regulation is the natural response to fear. And if we’re not careful, we’ll build a regulatory framework that successfully mitigates the downside risks while also forfeiting the upside — the medical breakthroughs, the scientific acceleration, the expansion of what’s possible.

Good regulation is important. We need thoughtful rules around data privacy, algorithmic transparency, and accountability for AI-driven decisions. But as I’ve argued elsewhere, there’s a difference between regulation that creates guardrails and regulation that creates roadblocks. The former helps us navigate toward the top-right of the Realist box. The latter pushes us toward the bottom-left — or worse, keeps us from entering the box at all.

The regulators who get this right will be the ones who resist the urge to regulate based on what AI might become and instead focus on what it actually does today — who’s harmed, how, and what specific safeguards would help. That’s harder than writing broad prohibitions, but it’s the only approach that protects people without killing the potential.

Moving to the Top Right

I’m a Realist. I think AI will be genuinely transformative, broadly positive, and slower to reshape society than either the Doomers or Zoomers expect. But “broadly positive” isn’t guaranteed — it’s an outcome we have to work toward.

The developers building these tools, the organizations deploying them, and the regulators writing the rules all have a role to play. And right now, we’re spending too much of our collective attention on the dramatic but unlikely scenarios — the Doomers and Zoomers arguing about Skynet vs. Utopia — while under-investing in the practical, unglamorous work of steering toward the best realistic outcome.

That’s what I’d like to see change. Less debate about whether AI will save or destroy us. More focus on how we make sure the most likely outcome — the one in the big box in the middle — is as good as it can be.

That’s the zone of probable impact. Let’s make the most of it.

I’m not a developer. I have a CS degree and wrote code professionally — in college, more than twenty years ago. I’ve spent my career since then in product, partnerships, and business roles. I’ve been thinking about products my entire career, but thinking through a product and actually shipping one are separated by a wall of implementation that used to require a team to get over.

That wall is coming down. And I think the implications are bigger than most people realize.

How I actually built it

I built Whose Turn using Claude Code, Anthropic’s AI coding tool. And I want to be specific about what that means, because my own process evolved in a way I didn’t expect.

When I first started using Claude to write code, I did what most technical people probably do — I’d write some code, then use Claude for assistance, and I’d carefully read the code it wrote. I was still thinking of myself as the developer, with AI as a helper.

The big unlock was when I stopped looking at the code entirely.

I don’t mean I got lazy. I mean I realized that my value wasn’t in reviewing Swift syntax — it was in knowing what the app should do, how it should feel, and what tradeoffs to make. So I started treating Claude as my entire development team. I describe what I want. I test what it builds. I give feedback on what’s wrong. I iterate. But I have never once read the actual source code of Whose Turn. I couldn’t tell you what it says.

That felt uncomfortable at first. It felt like cheating, or like I was giving up control. But the product is better for it — because instead of spending my time parsing code I haven’t written professionally in two decades, I spent it on the things I’m actually good at: product decisions, UX tradeoffs, and knowing when something doesn’t feel right.

It’s not all magic

I should be honest about the rough edges. Building a mobile app is still hard — not the code, but everything around it. Getting an Apple Developer account, configuring signing certificates, navigating App Store review, dealing with provisioning profiles. That part is still genuinely technical and frustrating, and AI doesn’t fully smooth it over.

If I were advising someone who wanted to build their first thing, I’d actually steer them away from mobile. The simplest path right now is a web app — something like Supabase for your database and auth, deployed on Vercel. That stack is remarkably accessible, and AI tools can handle almost all of the implementation. You can go from idea to live product in a weekend. Mobile adds real friction that the tools haven’t fully eliminated yet.

The disciplines are collapsing

For decades, building software has been organized around three distinct disciplines: product management (what should we build and why), design (how should it look and feel), and engineering (how do we make it work). Companies hire separate people for each. There are entire career tracks, job titles, and org charts built around this division.

That division existed for a practical reason — each discipline required deep, specialized skill that took years to develop. You couldn’t just pick up iOS development on a weekend. You couldn’t design a good interface without training your eye over hundreds of iterations. The specialization was a response to complexity.

But what happens when the implementation complexity drops dramatically? When the cost of turning an idea into working software falls by 10x or 100x?

The disciplines don’t disappear — but they collapse into fewer people. Instead of needing a PM, a designer, and two engineers to ship a simple app, you need one person who can think across all three and use AI to execute. The specialist roles don’t vanish for complex systems, but the threshold for what one person can build alone has shifted enormously.

I’ve written before about how the “augmenting vs. replacing” framing for AI misses the point — the real story is usually about how work reorganizes around the technology. This is a vivid example. AI isn’t replacing engineers. It’s changing the shape of who can build what, and how many people it takes.

Why generalists win here

This is where it connects to something I believe deeply about careers: the most valuable people in any organization are the ones who can think across disciplines. Not the “knows a little about a lot” kind of generalist — the kind who knows enough about product, design, engineering, business, and operations to see how changes in one area ripple through the others.

AI tools like Claude Code are an enormous lever for exactly this kind of person. If you understand what users need, can reason about design tradeoffs, and can think systematically about how software should work — you can now build the thing yourself. The bottleneck used to be implementation. For a huge class of problems, it isn’t anymore.

This is a genuine shift in who gets to build. Not just developers using AI to code faster — though that’s happening too — but product thinkers, designers, and domain experts who can now bring their ideas to life without waiting for engineering bandwidth.

I’ve always believed that asking the right questions matters more than having the right technical skills. That’s even more true now — because the technical skills are increasingly something you can delegate.

What this means

I don’t think this replaces professional software engineers. Complex systems, infrastructure, performance-critical code, large-scale architecture — all of that still requires deep expertise. What changes is the floor. The minimum viable team to ship a useful product just got a lot smaller, and the range of people who can participate in building just got a lot wider.

If you’re someone who’s always had ideas for things you wanted to build but couldn’t — the tools are here. The barrier isn’t gone, but it’s dramatically lower. And if you’re the kind of person who’s spent your career learning across disciplines rather than going deep in one — that breadth is about to become a lot more valuable.

Show it what good looks like.

The problem with telling

Here’s the thing about describing what you want: you don’t always know. Or more precisely — you know it when you see it, but you can’t fully articulate it. This is especially true for anything stylistic or nuanced.

I ran into this head-on when I started building a new podcast with my friend Cyrus. We wanted the AI-generated scripts to sound like us — not generic podcast-host-voice, but the way Cyrus and I actually talk to each other. The way we interrupt, riff, push back, land jokes.

So I tried telling it. I wrote descriptions of how each of us speaks. I explained our dynamic — who tends to set up points, who tends to land them, how we use humor. I spent a lot of time on this. And the scripts came back… fine. Competent. But they didn’t sound like us.

Then I had a different idea. Instead of describing our voices, I just gave it transcripts of our actual conversations. Real ones, unedited. And the difference was immediate. The AI picked up on patterns I hadn’t even thought to mention — little verbal tics, the way we build on each other’s points, the rhythm of how we go back and forth. Things I couldn’t have described because I wasn’t consciously aware of them.

That’s the core insight: you can’t tell an AI about things you don’t know you know. But you can show it examples that contain those things, and let it figure them out.

Letting AI learn what you didn’t think to teach

I saw this play out even more clearly with my other podcast, What the AI?!, where I co-host with Annie. We have a pretty dialed-in workflow — AI helps generate the script, we record, and then I feed the transcript back in so the system can learn from it.

One lesson it picked up on its own was particularly sharp. We’d recorded an episode where we ran long — too many stories, not enough time — and ended up skipping the last story entirely. The AI noticed this. On its own, it added a check to its script-writing process: make sure the final story in the rundown is skippable. Keep the most important stories earlier in the show so that if we have to cut, we’re not losing something critical.

I never would have thought to write that as an instruction. It’s the kind of operational wisdom that only emerges from watching real work happen — from seeing where the plan met reality and broke down. But because I showed the AI the gap between the script and what we actually recorded, it found the lesson itself.

Why this works

There’s a useful analogy here to how people learn. If you’re training a new hire, you can hand them a style guide and a list of dos and don’ts. That helps. But they’ll learn far more from sitting in on a few meetings, reading a few real examples of great work, and seeing how the team actually operates.

AI is similar. Instructions set a baseline, but examples create understanding. And the richest examples are messy, real-world ones — not polished samples you curated to illustrate a point, but the actual artifacts of your work. Transcripts, drafts, email threads, before-and-after edits. The stuff that captures all the things you know implicitly but would never think to write down.

The practical takeaway

Next time you’re struggling to get AI to produce something that feels right, resist the urge to write a longer, more detailed prompt. Instead, ask yourself: do I have examples of what good looks like?

Feed it past work you’re proud of. Show it the real conversations, not your description of them. Give it the before and after so it can see what changed. Let it find the patterns — including the ones you didn’t know were there.

You’ll be surprised how much it picks up that you never thought to mention.

That puts me in something of a minority. Polls show Americans are increasingly pessimistic about AI. The loudest voices in the conversation tend to be warning about job losses, existential threats, or societal collapse. And I get the appeal of those arguments — they’re vivid and specific in a way that optimism often isn’t. It’s easy to picture the jobs that disappear. It’s much harder to picture the ones that don’t exist yet.

But I think the pessimists are mostly wrong — and the optimists have a stronger case than they usually make. Here’s mine.

Why the Doomers Are Overwrought

I’d break the AI pessimists into three broad camps: economic doomers, existential doomers, and societal doomers. Each has a version of the argument that sounds compelling. But I think they all share a common mistake — overestimating how “this time is different” and underestimating what history actually teaches us about how these transitions play out.

Economic Doomers

The economic doomers believe AI will cause mass unemployment — that it’ll replace huge swaths of white-collar work quickly, concentrating wealth among the few who own the technology. I understand the fear. But I think it dramatically overestimates the speed at which these changes actually work through the economy.

Anyone who studies forecasting knows a useful principle: we tend to underestimate the base case and overestimate how “this time is different.” I think that’s exactly what’s happening in the AI conversation. The base case — what history actually teaches us about technology adoption — is that these transitions are slow. Not because the technology isn’t powerful, but because organizations are slow. Change management is hard. Retraining takes time. Entire industries don’t restructure overnight.

Think about accountants. Spreadsheets didn’t eliminate accounting — they transformed it. There are more accountants today than before spreadsheets existed, and they do more sophisticated work. More people have access to accounting, too. The profession expanded because the tool made it more accessible and more valuable, not less.

Or think about this: twenty-five years ago, “mobile app developer” wasn’t a job. It wasn’t even a concept. Today it’s a massive job category supporting entire businesses and industries. It was essentially impossible to picture that role before the iPhone existed. And that’s the pattern — it’s easy to see the jobs that get disrupted. It’s hard to imagine the new ones that emerge. That asymmetry is a big part of why the negative argument feels more intuitive than the positive one. But the positive one is what history actually delivers.

Will AI change work? Absolutely — and massively. But not as rapidly as the doomers suggest. And will the endpoint be net positive? I believe it will — because that’s what every major technological revolution in history has produced, once we’ve had time to adapt.

Existential Doomers

The existential doomers worry about something much bigger — that AI will become a superintelligence, a digital god that either destroys or subjugates humanity. The Skynet scenario. I take this concern seriously, and I can’t prove it’s impossible. Nobody can prove a negative.

But I don’t see any evidence that we’re close to that kind of capability. What we have today — and what we’re likely to have for the foreseeable future — are very powerful tools. Not sentient beings. Not digital gods. Very, very good tools that can process information and identify patterns at scales humans can’t match. That’s world-changing, but it’s a fundamentally different thing than the sci-fi scenario.

Arvind Narayanan and Sayash Kapoor make this case well in their essay “AI as Normal Technology” — even transformative, general-purpose technologies like electricity and the internet are still “normal” technologies. AI may be the most important technology of our lifetimes, but it’s still a technology. Not a new species. I think treating it that way leads to much better decisions than treating it as an existential threat.

Societal Doomers

The societal doomers worry that AI will rip apart the fabric of society — through misinformation, manipulation, deepfakes, erosion of trust. And I’ll be honest: I think this camp has the most legitimate concerns of the three. These are real risks.

But here’s what I keep coming back to: we’re already experiencing most of these problems. The internet, social media, algorithmic feeds, smartphones — these technologies have already strained our information ecosystem, polarized our politics, and created real challenges for mental health and social trust. These aren’t hypothetical concerns. They’re the world we live in right now.

I don’t think AI represents a step-function change in those problems. It’s a continuation of trends that started well before large language models existed. And if anything, AI might actually give us better tools to address some of these challenges — from detecting misinformation to personalizing education to making complex systems more navigable.

I’m not dismissing the risks. I’m saying they’re not new, and I’m more optimistic about our ability to manage them than most.

The Real Upside — and Why It’s Bigger Than Chatbots

Here’s where I think the conversation gets most interesting — and where the doomers most badly miss the mark. When most people think about AI, they picture chatbots, image generators, maybe a coding assistant. I’m excited about those things and the impact they’re already having. But they’re just a small part of the story.

The thing that excites me most is AI’s ability to accelerate discovery — particularly in fields where the bottleneck isn’t creativity or insight but the sheer scale of possibilities to explore.

Take medicine and biology. AlphaFold solved the protein folding problem — a challenge that had stumped researchers for decades — by doing something humans simply can’t: systematically exploring an enormous possibility space and identifying the structures that work. That’s not “artificial genius.” It’s a fundamentally different kind of tool — one that can sift through millions of potential drug compounds, protein structures, or genetic combinations and surface the most promising candidates for human researchers to investigate.

This pattern applies well beyond biology. In material science, we’re using AI to evaluate thousands of potential battery chemistries to find the ones worth investing in. In energy, AI is helping optimize everything from grid management to the search for better solar cell materials. In climate science, it’s accelerating the modeling of complex systems that are too large and interconnected for humans to analyze alone.

Dario Amodei, the CEO of Anthropic, wrote a long essay laying out many of these potential upsides in detail. His framing is different from mine — I don’t love the anthropomorphizing of AI as “geniuses in a data center” — but the underlying point resonates. We’re building tools that can explore possibility spaces at a scale that was previously unimaginable. The question isn’t whether that’s powerful. It’s whether we’ll let ourselves use it.

And that’s the pattern that gives me the most confidence. This isn’t speculation about some far-off future. AlphaFold exists today. AI-assisted drug discovery is happening now. The tools that will help us address climate change, develop better energy storage, discover new materials — those are in progress, not in the realm of science fiction.

When I look at the history of technology, this is what the big revolutions actually do. They don’t just automate existing work — they open up entirely new possibilities that we couldn’t have imagined before. The printing press didn’t just make scribes faster. The internet didn’t just make mail faster. And AI won’t just make knowledge workers faster. It will let us attempt things we couldn’t have attempted at all.

The One Thing I Worry About

So if I’m this optimistic, what keeps me up at night? Regulation.

Not the existence of regulation — some regulation is necessary and important. What worries me is that we’ll over-regulate AI out of fear, locking it away before we get the chance to realize its benefits. That we’ll see the risks — which are real — and respond by putting this technology in a box.

I keep thinking about nuclear power. Here was a technology with the potential to fundamentally transform our energy infrastructure and help address climate change. And we effectively regulated it into irrelevance. Not because the technology didn’t work, but because the fear of what it could do outweighed the appreciation of what it could deliver. Decades later, we’re desperately trying to reverse course as we realize how much that decision cost us.

I worry we’re on that same path with AI. The doomer narratives are loud. Regulation is the natural response to fear. And if we’re not careful, we’ll end up in a world where we’ve mitigated the downsides but also forfeited the upsides — the medical breakthroughs, the scientific acceleration, the expansion of what’s possible.

For what it’s worth, I’m pretty optimistic about the private market figuring out education and corporate adoption. Companies will adapt because they have to — the competitive pressure is too strong. But regulation is the one area where well-intentioned decisions, driven by fear, could hold us back.

We’ve Done This Before

I’ll close with the observation that gives me the most comfort. We’ve been here before — and we’ve gotten through it.

There was a time when the vast majority of humans worked in agriculture. That’s not true anymore. We didn’t end up with mass permanent unemployment. We found new kinds of work, built new industries, adapted our institutions. It wasn’t always smooth or fast or painless. But we adapted.

I don’t pretend to know exactly what the AI-enabled future looks like. Nobody does. But I think the historical base rate is overwhelmingly on the side of optimism — not naive optimism, but the earned kind. The kind that says: this will be hard, there will be real challenges, and we’ll figure it out. We always have.

The biggest risk isn’t that AI changes too much. It’s that we don’t let it change enough.

Why? Mostly because models have turned out to be somewhat undifferentiated. Every time a new model comes out with remarkable new capabilities, another matches it within a few weeks — often a more efficient model. Moreover, most models are now more than capable enough for the vast majority of every day use cases. User may prefer one model over another — but likely not enough to pay a premium for it.

So, who will be able to capture the value. I see a few potential winners.

1. Workflow Systems. Most LLMs are going to end up being utilized in context of a workflow. The systems that own those workflows (think EHRs in medicine or CRMs in sales) wil be able to capture a lot of value by orchestrating the right models and the the right prompts and their data. This could be existing players or new entrants.
2. Data Systems. For a personal agent to be useful, it needs access to your personal data. Companies with access to that data will be able to capture more of the value of enabling AI on top of it than those with just a model. Think Microsoft and Google.
3. User-Facing Winner(s). There is likely to be one big winner in the consumer-facing brand of AI. Just as Google became synonymous with search. Once that user habit is engrainged, you don’t need to be the best to maintain it. Right now, this looks like ChatGPT — though never count at Google (especailly given their existing search distribution). To win this war it’s likely you will need to build your own foundational model — but having a great model won’t be enough.

So, where does that leave the players in the space?

OpenAI is winning on 3 right now. It’s trying to tackle 2 through integrations. This framework would suggest they should lean into that hard and move fast.

Anthropic isn’t winning any of these right now either. This framework indicates they are not in a great position.

Microsoft has real advantages in 2 and to some extent 1. That may be enough to win large deals in the enterprise space. I don’t see much of a path for them on the consumer side (though they will try to leverage Windows for 2).

Grok isn’t winning on any of these at the moment. Neither is Meta.

There is a case to make that Google is doing well on all 3. GCP is a solid contender in enterprise data and workflows. Gmail / Docs has a lot of consumer data. And more people likely interact with Gemini through Google search than use ChatGPT. It does seem like Google has the most paths to success at this point — including their own model.

If you hate cookie banners (and who doesn’t), this seems like a clear win. But while the proposed solution (allowing users to specify their preference one time in their browser instead of on every site) will certainly improve everyone’s online browsing experience, it still doubles down on the horrible idea of asking regulators to play product manager.

It turns out that product management is hard. Many companies are quite bad at it. But regulators are usually worse. We would all be better off if regulators gave broad guidance and then let product companies compete and innovate to deliver the best experiences.

Metaphors can help. We don’t regulate highways the same way we regulate sports cars or school buses. So why should we talk about regulating “AI” like it’s a single thing?

Here’s a better way: think of the AI ecosystem as made up of four key components — cars, roads, buses, and traffic laws. This metaphor gives us a much clearer sense of where government should act, and just as importantly, where it shouldn’t.

The Framework: Mapping the AI Ecosystem

Cars are like ChatGPT, Claude, Midjourney, or domain-specific prediction tools in finance or healthcare. The market thrives on competition and diversity here. We want lots of options built for different needs. Government should regulate for safety, not build the engines.

• Roads are the open-access infrastructure that makes innovation possible — datasets, open-source models, interoperability standards. The private sector underinvests in these because they benefit everyone. Government should step in here.
• Buses are the AI tools built or funded by the public sector for public needs: think medical support for rare conditions or tools to navigate complex public systems like taxes or housing. These exist where private incentives fall short.
• Traffic Laws are the baseline guardrails that apply to everyone — factual accuracy in critical domains, child safety standards, content restrictions. They set the rules of the road without picking winners.

How the Metaphor Helps Us Make Better Decisions

Public Roads: Open Infrastructure for Innovation Public investment should go toward foundational infrastructure like high-quality training datasets, especially in underrepresented domains (e.g., rare languages or public health). These are the roads AI travels on. They’re often invisible to users but essential for the whole system to function.

Some open-source base models might also fit here. They aren’t tailored to specific needs but serve as starting points for innovation — like roads that lead to different destinations.

Private Cars: Let the Market Compete The market is great at building diverse, purpose-built models and applications. From legal summarization tools to recipe assistants, medical diagnostics, financial forecasting systems, and image or video generation tools, the diversity of “cars” is a feature, not a bug. We don’t want the government designing the next minivan or image diffusion model.

What we do want are smart traffic laws to ensure these tools are safe and fair. But the government shouldn’t try to build or compete in this layer directly — just as it doesn’t manufacture vehicles.

Public Buses: Fill the Gaps Where Markets Don’t Some needs won’t be met by the market. Think of medical AI for rare diseases — low commercial return, high social value. Or government services made navigable through AI — tax filing help, housing aid, legal guidance.

These aren’t replacements for private tools. Just like buses don’t replace cars, they offer equitable access to essential services.

Traffic Laws: Guardrails for a Shared System Some things need universal rules: adversarial robustness, child safety, misinformation, copyright compliance. These are the seatbelts and stop signs of the AI world. They don’t limit innovation — they make the whole system work better.

Good traffic laws don’t pick winners. They ensure safety while allowing a wide range of vehicles (and drivers) to share the road.

Why This Framework Matters — And How to Use It

Many people don’t trust “big tech” to build AI that is in the best interest of society — or just don’t trust them at all. But before we start talking about the role government or public AI should play, we need to clarify what we mean. Are we talking about government-run AI? Open-access tools? Rules and oversight?

This framework helps clarify those questions:

• Should we regulate this model? (Car)
• Should we fund this tool as infrastructure? (Road)
• Should we ensure this capability is accessible to underserved communities? (Bus)
• Do we need new rules to keep people safe? (Traffic laws)

Rather than vague calls for government to “do more,” it invites sharper, more targeted questions — and encourages a smart division of labor between public and private actors. We don’t need the government to “build AI” — we need it to pave the roads, enforce the rules, and run the buses that help everyone get where they need to go.

By separating the roles of infrastructure, market competition, public access, and regulation, we can build an AI ecosystem that’s not just powerful, but also fair, inclusive, and sustainable. Let’s stop asking if government should “do more on AI.” Let’s ask what part of the AI stack we’re talking about — and whether it fits best as a car, a road, a bus, or a traffic law.

AI’s impact on education is inevitable. As a parent, AI advisor, lifelong learner, and school trustee, I’ve had more than a few conversations about what to do next. One thing is clear: the cost of waiting is higher this time. In past tech shifts—whether the internet, mobile, or 1:1 devices—schools had time to prepare, debate, and cautiously adopt. AI doesn’t afford us that luxury. This wave is already here, and already in students’ hands.

So, what’s a school to do?

Here’s how I think about the path forward: act quickly, embrace experimentation, and be ready to change course. If we’re going to take advantage of this moment, I think there are three core principles to guide us.

1. Lean Into the AI Opportunity

The natural instinct for many educators is to focus on what’s being lost: originality in writing, authenticity in homework, control in the classroom. But there’s just as much—if not more—to be gained.

A recent Nature study found that AI use in education can improve learning performance, boost student engagement, and support higher-order thinking. That’s not a risk—it’s an opportunity.

So how do we lean in?

• Educate your teachers. Not just about what the tools are, but what they can do. Get them hands-on experience. Encourage cross-school conversations. Set aside time and budget for real professional development—not one-time webinars, but ongoing learning.
• Educate your students. Help them see AI as a tool for learning, not cheating. Teach them how to use it responsibly and creatively. They’ll need that skill in the workplace—and in life.
• Build a community of practice. This might be the most important step. Encourage teachers to share what’s working, what failed, and what surprised them. Celebrate successes just as much as you learn from failures. Create a space for experimentation and iteration—a culture where it’s okay to try, okay to fail, and even better to share what works.

2. Rethink Assessment

This may be the hardest challenge ahead. We can already see clear, practical ways to use AI to support teaching and learning—but adapting assessment to an age of abundant AI remains the tougher challenge.

The traditional pillars of academic evaluation—essays, take-home assignments, even some problem sets—are no longer reliable when AI can produce competent versions of each. And while the instinct to “ban” AI use is understandable, it’s unlikely to be enforceable or effective.

So, we need to reimagine what assessment looks like:

• More live, in-person evaluations. Whether it’s classroom discussions, oral exams, or group work, we’ll need to lean more on what can’t be outsourced to AI.

• More project-based and interdisciplinary work. AI can be a great partner in exploration and execution. Let’s assess how students use it—not whether they avoid it.

• More emphasis on thinking, not output. We’ve long claimed to teach critical thinking. Now we have to assess it directly.

This won’t be easy. But if we get it right, it may actually improve our approach to measuring what matters. And what matters most is not just a set of academic skills, but the broader capabilities we hope to nurture in students: curiosity, resilience, collaboration, creativity, and critical thinking. These are harder to measure—but ultimately more important to capture.

3. Move Fast—but Be Ready to Change Course

The tools are evolving quickly. So is our understanding of how they work—and how they break.

That means we need to start moving. But we also need to stay nimble. Our first steps won’t be perfect. Our policies will need updates. What works today might fail tomorrow. That’s okay.

The real danger isn’t making a wrong call. It’s standing still.

So make your best guess about what the next right step is. Take it. Then pay attention, evaluate, and be ready to adapt. Don’t let the pursuit of perfect paralyze progress. And don’t let uncertainty stop you from starting.

Keep the Student at the Center

Above all, it is important to remember to keep our focus where it belongs—on the student.

Their learning, their excitement, their future. AI may be the catalyst, but the real opportunity is to do better for our students. To rethink how we teach, what we teach, and why we teach it. To re-engage learners in new ways. To build a system that prepares them not for the world we knew, but for the world they’ll inhabit.

This is our moment. Let’s meet it with boldness, humility, and a willingness to try.

But as the product has evolved, it’s started to feel… heavier. I understand why Google has pushed for feature parity with Microsoft Word—adding formatting controls, layout options, and advanced styles. But sometimes I just want all that to go away.

I don’t want to think about margins or font types or line spacing. I don’t want to scroll through menus or figure out why one paragraph has a different style from the next. I just want to write.

What I wish Google Docs had is something I’d call “Simple Mode.” Imagine a stripped-down, markdown-style editor. Bold, italics, headers, bullet points, maybe tables—and that’s it. A mode where all the formatting complexity is hidden and the focus is on the words. When I change the font, it changes everywhere. When I write, I don’t have to worry about what’s happening behind the scenes with styles or layout. Just text on a page.

Yes, I know you can try to define a default font style. I know you can mess with the margins and zoom levels. But it never quite works right. Styles don’t apply consistently, margins are finicky, and the moment I copy-paste something in, the formatting gremlins return.

I don’t need this mode for everything. I understand that many people are using Docs to produce polished, formatted documents. But for drafting, note-taking, or just getting thoughts down—this would be such a welcome relief.

So if any of my old colleagues at Google are reading this: here’s an idea I’d love to see. A simple writing mode. One that gets everything else out of the way and just lets me focus on the words.

I promise I’d use it all the time.