How to Organize Work in the Age of AI

Earlier this year, I met a CTO of a large firm during a conference. She told me:

"I’m building again – for the first time in years!"

For decades, her job was to delegate. And if you followed the classical script, the AI opportunity for her would be to delegate even more. But instead, AI pulled her back into the work.

Is that the future? Nobody knows. Everyone's still trying to figure it out. There's no proven "AI transformation blueprint" yet. But one thing is getting clear: the organization of tomorrow is not the organization of today with AI sprinkled on top.

I'm not going to argue that AI makes people more productive. It does, to a degree – but that's the boring part.

I'm going to argue that true AI transformation means changing the unit of organization itself.

Let’s start with what almost everyone gets wrong.

The default reaction to AI adoption in an organization is to accelerate what's already there. Give me faster emails, faster decks, faster code, faster reports.

But the org chart stays the same. The meetings stay the same. The workflows stay the same. People produce the same artifacts – just faster.

That's useful. But it's a local maximum. You made the horse faster, but you never built the teleporter.

It doesn’t have to be that way. I recently shared how I use AI to manage 50,000+ emails per year – not by writing emails faster, but by rethinking how I approach email in the first place.

To see why that changes the org, and not just your to-do list, it helps to borrow a 60-year-old idea.

A lot of what’s considered “good management and business practice” goes back to Peter Drucker, who (among many other things) argued that "every knowledge worker is an executive" – judged on effectiveness (doing the right things), not efficiency (doing things right).

And to separate one from the other, we hired workers to do the job and managers to coordinate them and keep them pointed at the right things.

But here’s what AI changes about that equation.

For many forms of cognitive work, execution is becoming less of a labor problem. AI does not do the thing for free. But more of the cost moves from payroll to compute, and from fixed headcount to a variable bill you pay per outcome.

Coordination costs likely also compress. Coordination exists because work is split across people and functions. If AI allows more of that loop to collapse into one owner or one small pod, then less coordination is required per unit of output.

That price tag is the whole point. When more units of execution can be bought with compute, the question shifts from “can we do this?” to “is this outcome worth running?”

And that’s a judgment problem.

That’s why in a world where execution is abundant and coordination is cheap, judgment becomes the scarce resource.

This doesn’t mean workers and managers vanish. It means the execution and management required per unit of output collapse in many domains. What survives aren’t the tasks and structures – what survives are the outcomes.

If outcomes are the thing worth owning, then the basic building block of the company changes.

And that's the real shift.

The building block moves from workers executing tasks to individuals or small teams owning outcomes.

What does this mean concretely?

Here’s where I believe most people go wrong: they picture one person supervising a swarm of agents. But you don't manage agents. You own an outcome and use AI where it fits as a tool to achieve it.

And there’s far more to this than that. You still need human teams. AI-mature organizations are already working today in small pods – teams of 2–4 people that share one loop, one outcome, without any handoff.

The deeper shift is not that one person replaces everyone else. It is that AI expands the span of ownership a person or small pod can economically sustain.

Historically, one person wrote the spec, another built the prototype, another analyzed the data, another created the deck, another coordinated adoption. AI does not remove expertise from that system, but it can let one accountable owner internalize more of the work that previously required coordination across specialists.

When execution becomes cheaper and judgment becomes more important, that judgment shows up in three faces:

Importantly, these are lenses, not roles or titles. Today's Builder is tomorrow's Communicator. Every pod needs all three to ship an outcome. One shared loop.

Practically, what does this mean for you? I see four big levers:

Do this, and the execution and management required per unit of output collapse. Judgment is the key job now.

Here’s how this could look in practice:

Imagine a music streaming app.

A classical functional organization might look like this:

An outcome-based model could look like this:

Each team owns a business result — and decides how to get there.

Naturally, this model will not apply everywhere equally. It is strongest in digital work with fast feedback loops.

But also more classical industries like finance, manufacturing, and other operationally complex businesses may evolve differently under that premise. Even here, AI can still expand ownership, while making sure the coordination around safety, compliance, capital, and physical execution will stay intact.

In the end, the same principle applies to every industry:

AI allows smaller groups to own larger outcomes end to end.

The question is:

How will your organization leverage this best?

Again, we’re far away from claiming any “best practices”. But there are some early signals that point to the direction above.

Palantir may be the closest to this operating model. CEO Alex Karp said he avoids a rigid org chart and runs unusually flat, with 13 people reporting to him directly, on what the company calls a "mission-driven culture" – aka outcomes.

The big AI labs use a "Member of Technical Staff" title across the technical org – which is quite unusual, since the corporate tech ladder normally means climbing through tech leads, staff engineers, and so on.

The point isn't "no hierarchy." It's less emphasis on managing people, more on owning outcomes, and grouping pods around them as necessary.

This seems to resonate with top talent as well. AI superstar Andrej Karpathy joined Anthropic's pre-training team in May 2026 to "get back to R&D". Six Silicon-Valley CTOs joined Anthropic as Members of Technical Staff to start shipping again.

The AI-native company isn't where everyone uses ChatGPT.

It's one rebuilt around a new assumption: execution is abundant, coordination is cheap, and judgment is scarce.

And when judgment is the bottleneck, you organize around the people who supply it – owners of outcomes, not doers of tasks.

The CTO building again wasn't a nerdy detour. She was getting back to the only thing that stayed scarce — her judgment to own an outcome end to end, and her ability to pick the most efficient way to make it happen.

See you next Saturday,
Tobias