The new chaos of being able to build anything

AI didn't free up my time. It made every hour more valuable, the work more demanding, and the pace relentless.

My friend Margaux has been writing thoughtfully about AI and cognitive decline, and when I started building with AI at the center of how we work, my own experience went in the opposite direction. AI made my work harder, quite a lot harder, and the gap between the research and what I’m experiencing makes me wonder if we’re asking the wrong question about what AI does to our work.

There’s a growing body of research, and it’s worth taking seriously: students using ChatGPT score lower on surprise tests weeks later, consultants given AI for tasks outside its capability perform worse than those working without it, EEG studies show weaker brain connectivity in heavy AI users. BCG just published a study where half of C-suite leaders say they’re already observing de-skilling in their organizations, and the skills they rate as most important (judgment, problem framing, creative thinking, causal reasoning) are the exact ones they see as most at risk. Researchers have started using phrases like “cognitive surrender” and “cognitive debt” to describe what happens when people trust AI outputs because they sound convincing, not because anyone verified them.

But most of the research is looking at people doing the same work they did before, just with AI handling more of it. The analyst still writes reports, but now AI drafts them. The consultant still does analysis, but now reviews AI’s analysis instead. And in that frame, yes, the concern makes sense, because you’re outsourcing the cognitive effort that used to keep those skills sharp. The question is whether that’s the only frame.

What happened when shipping got fast

A year ago, shipping a feature took weeks. Now it can take a day. There’s an assumption that should mean less work, but the opportunity cost actually runs the other way: when an hour of building is worth what a week used to be, every hour spent figuring out what to build next is a day not shipping. The work moved up a level, and there’s more of it than before.

When shipping is fast, the bottleneck shifts to knowing what to ship. How do I get signal from customers faster? How do I understand how the market is evolving? How do I build the whole system that ships a feature, writes the blog post explaining it, sets up the email campaign inviting users to try it, sends another email to leads, analyzes how many accounts were impacted, updates the marketing pages in our voice, learns from the result, and then figures out what to do next, all in one cycle? That kind of work is new. It didn’t exist a year ago because it wasn’t possible a year ago, and it requires more sustained attention and judgment than writing the code itself ever did.

So maybe the atrophy research is right about the situation it’s studying, but the situation is specific: work that stayed the same while AI absorbed the effort. If you’re an analyst who used to write reports and now reviews AI reports, the thinking that kept your skills sharp got outsourced, and over time you lose both the ability to do the work without the tool and the judgment to know when the tool is wrong. That’s real, and the research on it seems right to me.

But my experience has been the opposite. The old tasks got compressed or automated, and new, harder work appeared at a higher level of abstraction. Maybe my ability to write a specific function will decline. But my mind feels as engaged as it’s ever been, because I’m spending my days thinking about the landscape, how our market is evolving, how to build workflows that build on each other, and how to keep AI connected to our company’s voice, vision, and values as it handles more of the execution. That’s all work that sits above the code, and it’s more demanding than the code was.

Why I think this is easier to see as a founder

I think this is especially visible to me because as a founder, questioning how we work is literally part of the job. I don’t just get to reshape my role as AI changes what’s possible, I have to. Every week I’m asking how we should be building, how our process should change, whether the way we shipped last month still makes sense this month. The shape of my work is always in question, and AI made that questioning more intense and more consequential, because the answers change faster and the stakes of getting them wrong are higher.

That means I’m holding strategy, product, market, and the development system in my head simultaneously, while also steering AI to stay connected to our company’s voice, vision, and values as it handles more of the execution. That’s a lot of new cognitive load, and it’s the kind of thinking where I don’t think AI can substitute for you, because the judgment about what matters and why is the whole point. BCG’s research actually names “problem understanding and framing” as the most critical skill and the most at risk, and I’d agree with that, except that for me the risk doesn’t feel like atrophy. It feels like being asked to do more of it, faster, with higher stakes.

Inside a larger organization, I think the picture might be very different. If you’re an analyst at a consulting firm, what does AI actually do for you besides replace some of your own effort? You might not have the latitude to step into the higher-order work, the problem framing, the system design, the strategic judgment, because the organizational structure keeps you in a role that looks the same from the outside even after its core thinking has been hollowed out. BCG calls this the “autopilot trap,” where senior people substitute AI judgment for their own out of habit, and I think it’s even worse for junior people who never get to build the judgment in the first place because AI handles the entry-level work they would have learned from.

I’m starting to wonder whether the atrophy question is really about AI at all, or whether it’s about whether roles can evolve fast enough to absorb what AI makes possible. As a founder I can reshape my job every week. An analyst three levels deep in an organization probably can’t. And the interesting question, the one I don’t hear enough people asking, is whether we’re making it easy enough for people to step into the new work, or whether we’re just watching the old work get automated while the new work goes uninvented.

The pressure of what you could be doing

There’s another angle to this that I don’t see discussed much, and I think it matters. I wrote separately about how quarterly planning starts to feel like a scarcity ritual when shipping gets cheap, and the pressure side of that is worth naming too. When AI makes it possible to do three or four times as much in the same window, the cost of not doing it goes up. Every hour you spend doing something the slow way is an hour someone else spent shipping three things and learning from all of them. That creates a real kind of pressure, and I feel it.

It also creates a new challenge around review. When you’re coordinating AI across a dozen workstreams, the volume of output that needs human judgment increases. You’re reviewing more, and at a higher level: is this aligned with our strategy, our voice, our values? Does this actually serve the customer? Is the AI drifting from what we intended? That review work is demanding in a way that wasn’t part of anyone’s job a year ago.

Where this leaves me

I don’t think AI is making work easier for anyone who’s doing it seriously. I think it’s making work faster and more demanding at the same time, and the load shifts from the execution itself to the thinking about what to execute, how to steer it, and how to learn from what the system produces. Whether that’s a better situation depends on whether you have the latitude to step into the harder work, or whether the organization around you keeps you in a role that looks the same from the outside even after its core has been hollowed out. But for those who can make the shift, the “AI makes us lazy” story is incomplete. The real question might be how to make this new pace and chaos sustainable.

That’s also how we think about building BotBot. We run the company out of a shared system that people and AI agents both read from and write to: the company’s identity, voice, strategy, operations, all in one place, and agents draw from it the same way a new team member would. Building and maintaining that system, keeping it current, making sure AI stays connected to who we actually are as it does more of the work, that’s some of the most demanding thinking I do. It didn’t exist as work a year ago, and now it’s most of what my days look like.