The other kind of AI optimism

There’s a cohort of people in your life, and their day is quietly changing.

They used to spend hours reconciling numbers across systems, chasing discrepancies, managing stakeholders who each had a slightly different version of the truth. Today they orchestrate tools. The work they’re doing now felt unimaginable a year ago — not just to them, but to the world.

You might be this person. You might know them. Either way, now is the time to pay attention.

Where I’m actually looking

Most of what I read about AI is about the frontier — bigger models, closer to general intelligence, on the way to solving civilization’s hardest problems. There’s something real in that story. But when the upside is “cure cancer,” it’s easy to stand at the edge of your own work and feel like the right move is to wait until someone else figures out how to dive in.

That’s not the story I keep thinking about. The one I come back to is smaller, and it’s already here: the cost of turning an idea into a working thing has dropped orders of magnitude in a few years. What usually happens when something rare and expensive becomes ordinary and cheap is that a lot of people who were locked out start doing it.

You don’t need to believe the models will become superintelligent, or cure cancer, or replace any jobs. You only need to notice that.

The unit that matters most

The unit that matters most is the cost of turning an idea into a working thing.

Two years ago, a mid-complexity internal tool — the kind that reconciles two data sources, renders a dashboard, and emails someone when a number goes red — was a two-sprint ticket, assuming you could get it prioritized. Three weeks of an engineer’s time, plus the meeting tax. Today that same tool is a Thursday afternoon. The distance from “I wonder if” to “look at this” stopped being a quarter and became a coffee and a quiet hour.

But the cost drop is only half the story. The subtler half: the kinds of ideas people are allowed to have are changing.

When acting on an idea was expensive, people filtered their ideas before saying them out loud. A thought like it’d be interesting to know X died in the hallway between two meetings, because nobody had time to find out. Now that thought gets answered. They go back to their desk, run it down, and come back to say look at this. Over months, that shifts what people notice, what they bring up, what they think is worth bringing up. It changes the dialogue of work, and the inner monologue underneath.

Two shapes

I see two shapes of this, mostly.

The first is the domain expert who finally builds. Someone who has cared about a problem for years — maybe decades — and has never been the one who could fix it. The gap wasn’t imagination. It was the translation layer between what they knew and what the machine would accept. That layer got dramatically thinner.

The person who understood the problem is now the person solving it. The solution is usually better than what an outsider would have built, because ten years of staring at a problem puts things in your head that nobody else sees.

The second is the non-technical person on a team — the PM, the ops lead, the customer-facing generalist. What’s different is the texture of their work. Instead of walking into meetings with a Figma mockup, they walk in with a working prototype. Instead of writing an RFC about how the data might be organized, they pull the data and look. Instead of waiting to know, they check.

Put a few of these people on the same team and the team itself moves differently. The shift happens upstream of anything a productivity dashboard is built to see.

This isn’t about replacing engineers. Most of these people don’t want to. The real question is whether they can close the distance between having an idea and seeing what it would look like. That distance — which used to be weeks of someone else’s time, or never got closed at all — is now an afternoon.

Once a person has closed that loop, it becomes addicting. Once a team has a few of those people, the quality of everyone’s thinking improves, because you stop arguing about what something might look like and start looking at it.

What are engineers for?

Which brings me to the part I think most people are getting wrong. The question is not whether engineers are still needed. Of course they are. The question is: what are engineers for?

Engineers used to hold the keys to the castle. That wasn’t a metaphor; it was a structural fact of most companies. If you wanted something, you needed an engineer’s attention, and there were never enough of them. Whole layers of organizational design existed to manage that scarcity. That scarcity is easing now — not gone, easing. The companies I watch most closely are starting to notice.

At the companies figuring this out, engineers are becoming shepherds of organizational velocity. They’re not writing every line of code. They’re building the paths along which other people contribute. They’re setting up the guardrails that let a PM’s Thursday-afternoon prototype ship safely. They’re teaching. They’re pairing. They’re declining to build what others can now build themselves — and building, instead, the thing no one else can: the substrate underneath everything.

It’s a different job, and it’s harder than the old one. It demands taste, judgment, patience, and a willingness to let other people put their hands on things that used to be only in yours. It rewards a kind of engineer who always existed but was often underpriced — the one whose superpower was making other people better at their jobs rather than outshipping them.

The companies that get this right will move at a speed others literally cannot imagine. Not because the engineers are faster, but because everyone else stopped waiting on them. Most companies won’t get this right. It requires engineers to give up the cultural centrality that came with being the only people who could make things. It requires leaders to stop measuring engineering by features shipped and start measuring it by organizational leverage.

The companies that do get this right will look obvious in retrospect. They always do.

The other kind of AI optimism isn’t about the models. Not that they’ll become superintelligent. Not that they’ll solve the big things — though they might. It’s smaller, and closer to your day. A cohort of people doing work they couldn’t do before. Engineers building the substrate that makes it possible. Teams moving differently because the distance from idea to thing finally collapsed.

It’s about the people around the models, and the engineers learning to build fences, not barns.