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If AGI is here, where are the world-changing economic results? People usually point to AI hallucinations or stubborn resistance to new technology. I think the unit of analysis is wrong. Humans already possess general intelligence. A large company employs thousands of them. Yet a company can continue to optimize for the old production system long after a new innovation has made the way it finances, sells, and improves a product obsolete.
The AI labs look for general intelligence inside a model. But intelligence is not the same as economic output. And if you want to unlock economic output, you have to understand what changes inside a firm when cognition is cheap. The answer, as it turns out, is simple. It moves to the next bottleneck.
Think of a company as a production function. It takes a bunch of inputs - money, labor, knowhow - and transforms them through workflows into a product. Then that product needs to be sold or distributed. Supporting that, you have functions like legal, HR, and finance to enable a company to make things or sell them. The output is economic production - measured in dollars, revenue, income.
The naive application of AI - which is what almost every existing company and startup is doing today - is to simply apply it to one of the functions in the production function and make it incrementally better. Harvey, for example, promises to make things more efficient for lawyers. Rogo does the same for investment bankers. And if you make your customer's economic production function 5% more effective, you get to claim a share of that 5% improvement. But you are always going to be limited by your customer's ceiling. Because once you improve your customer's research efficiency, as Harvey does, their next binding constraint may be how they can get new clients, or the capacity of their partners, or some other area that they haven't applied AI to.
Here, though, AI is not simply another tool that can improve one part of a company's production system. Because it improves cognition, it promises to improve ALL parts of a company's production function. The problem - you have to be willing to reorganize your entire company and your way of doing business to fully take advantage of it.
And historically, that's what needed to happen. The result was outsized economic gains for companies that were able to reorganize their production functions to take advantage of the new technology. Ford. General Motors. Airbnb. Google. They changed their entire processes, workflows, and organization around what the new technology enabled.
Remove one constraint, and your binding constraint moves to the next thing. Solve that thing, and it moves to the next thing after that. AI enables faster coding. The binding constraint moves to code review. Implement automated testing and code review. The binding constraint moves to product development and how fast you can understand and shape what the product needs for the market. Shorten the cycle time of product development, and the binding constraint moves to the ability of customers to absorb that new capability in a way that increases their willingness to pay. That's why many companies don't really get the promised return with AI. They only solve one part with AI, but the next bottleneck flows somewhere else.
One of the superpowers of AI is that it drastically lowers the cost of execution. Another superpower is that it enables a small team to tap into deep expertise across domains. Previously, humans had to develop complex organizations to extract and maintain that type of specialized knowledge. What if we rebuilt the firm from the ground up, based on those two principles?
This is what this essay is about. The post-AI firm. Not just bolting AI to an existing company's process and organization. Designing it from the ground up.
But every design needs a framework. It's an intellectual scaffold to understand what needs to be done, and how to apply AI. The framework I will introduce here is called the Dynamic Constraints Production System, or DCPS. DCPS is simply the underlying theory. The most radical part of the theory is that instead of having a large organization defined by static departments, AI allows you to constantly modify your company into something that allows you to innovate and adapt faster than your competitors can react. You can achieve 10 cycles of learning for their one. Recursive Production Function Analysis is the method for examining the key thing to focus on to unlock that step function of economic productivity. Constraint Cascade Analysis is the operating loop inside that method.
If AGI is here, where are the world-changing economic results?