It's a mistake to think we'll stay relevant by orchestrating AIs. Our salvation may be the opposite: hyper specialization.
Like many software engineers today, I haven’t written any meaningful code myself for months but I am still the one in charge: I choose the tech stack, approach and structure and the coding agents execute.
But agents are quickly reaching the orchestration layer. They ask less, make assumptions and take decisions, mostly the correct ones. It’s just a matter of time before I simply describe my problem and the AI figures out what to build and how.
But even if an absurdly powerful AI can do everything itself, it doesn’t mean it should. Being a generalist has a cost. Using an overqualified AI to do a simple task is like using a sledgehammer to crack a nut. In most cases, contracting a specialist system for that purpose is not just cheaper, but also better.
Adam Smith wrote in 1776 that “divide work into narrow tasks and let focused workers do each one better and faster”. It still makes sense today, and more importantly, tomorrow.
Not convinced? Let me tell you about Stockfish. Stockfish is a chess bot, the best one. Really powerful at chess but nothing else, not something we would call “AI”, whatever that means. It actually can run on any outdated phone. Now if we play a million chess games, Stockfish on a phone versus the latest frontier models on cutting-edge data centers, how many times do the models win?
Zero.
Not once. No wins. No draws. Not even close, the gap is abysmal. Adam Smith was right from the beginning: specialists outperform generalists in all ways. Even in the AI era.
Now if a generalist contracts a specialist to execute a task, we still have to consider the cost of delegation. It’s often easier to do it yourself, as delegation has friction. It comes in two parts:
- The finding cost: Discover the service and evaluate it before hiring it. Can I trust it?
- The Connecting cost: Integrate the service to our system. Does it plug in easily? How do we pay?
The math is simple:
Cost of execution (diy) ≶ Cost of delegation + Cost of the task
Note: Two factors don’t show up in this equation: risk and governance. A powerful system may prefer to handle sensitive tasks itself to keep control, even at a worse price. Classic make vs buy.
Conclusion
In a world where powerful AI systems will be able to do most of our jobs, there is still a path for individuals and organizations to deliver economic value. That value would not be in controlling those systems but rather in becoming the specialist that AI systems need to contract.
If you build a service that fills tax forms perfectly, regardless of what’s inside the box: AI, software, or humans, you will be better at that one thing and therefore become profitable as each additional customer has a low marginal cost.
Our goal at Manifest is to lower the delegation cost as close to zero as possible. This means building infrastructure where AIs autonomously discover, evaluate, and use specialized services. Connection and payment should be instant too. Reducing delegation friction is key to enable this new economy.
Don’t orchestrate AI, beat it at one thing.
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