The Infinite Kitchen
AI didn’t just give us more intelligence. It gave us more sentences. That sounds like a joke, but it matters because most of what we call “thinking” in modern knowledge work is actually language: proposals, plans, narratives, theories, memos. Transformer-style models dramatically lower the cost of generating those artifacts. The result is that idea supply explodes, and we confuse that explosion for progress.
Imagine a kitchen that hired one thousand new chefs whose only job is to invent dishes. Within minutes you have gorgeous, delicious, and probably very weird, concepts.
But the dining room has, say, twenty diners. After the first few plates land, the tables are full. There’s nowhere to put the next thousands of dishes, so staff start aggressively tucking wagyu sliders into the diners' shirt pockets, duct-taping charcuterie boards to the ceiling, and building load-bearing walls out of sourdough. The problem isn't imagination. It's physics.
That’s what happens when ideation goes infinite while validation is finite: reality has a fixed number of seats.
The Dining Room Bottleneck
So the bottleneck moves. Not to creativity, but to validation: tasting, cooking, serving, and finding out what anyone actually wants. More ideas just means a longer menu. The number of meals served barely changes.
This is why “more ideas” can be neutral or even harmful. When validation is capped, extra hypotheses become pressure on the system. They increase queue length, not throughput. They also create false positives, and false positives are expensive because they consume the same scarce resource as real breakthroughs: attention, experiments, shipping cycles, trust. In other words, an abundance of plausible ideas can reduce the signal-to-noise ratio of the whole operation.
The Tasting Engine
Once you accept that, you can see where advantage migrates. It shifts away from the romance of ideation and toward the boring machinery of measurement. The winners are the people and organizations that build evaluation harnesses, instrumentation, test infrastructure, distribution channels that return clean feedback, and processes that force contact with reality. In a world where everyone can propose ten strategies before breakfast, the scarce skill is not proposing. The scarce skill is falsifying quickly.
If you want to stay sane and effective, you need an explicit validation engine. For products, that engine is shipping, user feedback, and revenue, on a cadence you can sustain. For strategy, it means defining strict kill criteria, running bounded experiments, and letting real-world friction decide what survives. You do not need more thoughts. You need faster proofs.
So the real question is not “Does AI increase idea volume or idea accuracy?” It is “Does my system turn idea volume into validated truth per unit time?” If the answer is no, AI will mostly feel like acceleration without arrival: motion, content, and cognitive heat, but no compounding. If the answer is yes, then AI becomes what it should have been all along: not a generator of possibilities, but a lever for reality-tested progress.