You’ve felt it. That creeping dread in the pit of your stomach when an AI spits out 500 lines of flawless-looking code or a 10-page strategy document in seconds. It looks perfect. It sounds confident. But you know the truth: you now have to read every single word to find the hallucinated lie hidden inside.
You aren’t an editor anymore. You’re a hostage.
We were promised autonomous AI agents, but what we got was an industrial-scale machine for generating unverified homework.
The tech industry is currently obsessed with scaling. The prevailing wisdom says if we just add more parameters, more compute, and more reasoning tokens, large language models (LLMs) will finally achieve true automation. But this ignores a brutal, structural reality that was laid bare recently when LLMs tried to tackle the Navier-Stokes equations.
The models are getting smarter, yes. They can solve complex math. But smarter doesn’t mean rigorous. And in the real world, rigor is the only thing that matters.
The best alternative to perfectly specified, flawless prompts is human review. But human review doesn’t scale. You can generate 10,000 lines of code in ten seconds, but it still takes a human engineer hours to verify that it’s actually safe to deploy. The volume of AI output inherently outpaces our capacity to review it.
Scaling AI capabilities means generating more output, but scaling output volume makes the necessary human review completely unscalable.
This is the scaling paradox. We are building systems where the better the AI gets, the more unmanageable the human workload becomes. We aren’t replacing human labor; we’re just changing it from creation to exhausting, high-stakes proofreading.
But the problem goes deeper than just volume. It’s baked into the very business model of the AI industry.
How do OpenAI, Anthropic, and Google make money? They sell tokens. Their entire financial incentive structure is based on generating more output, not strictly correct output. When the business model is selling more tokens, you get perverse incentives that lead to models that “think out loud” endlessly, generating massive volumes of plausible but unverified text.
You cannot solve the rigor problem when the entire profit incentive is to flood your screen with plausible, unverified text.
If you are a leader or a builder, you need a reality check. Stop treating LLMs as autonomous agents. They are not your interns. They are amplifiers. The future isn’t AI replacing your human experts; it’s AI generating massive volumes of work that your exhausted experts have to babysit.
The only way forward is to stop buying the hype and architecting systems where AI acts strictly as an amplifier for human expert oversight. If you don’t build the human review into the core of your pipeline, the sheer volume of plausible bullshit will bury you.
The age of autonomous AI is a marketing lie. Welcome to the age of industrial-scale human oversight.
FAQ
Q: If AI is getting smarter, won't it eventually just stop making mistakes?
A: No. Intelligence and rigor are not the same thing. A model can solve complex math like Navier-Stokes and still hallucinate a basic API call. The flaw is structural, not just a lack of capability.
Q: What's the practical implication for businesses?
A: Stop trying to replace your experts with autonomous agents. The real value is in using AI to amplify your human experts' throughput, provided you build rigorous, unscalable human review pipelines to catch the inevitable errors.
Q: Is the AI industry intentionally scamming us with the token model?
A: It's not a scam, but it's a massive misalignment. When you charge by the token, your profit depends on generating volume. You are financially incentivizing the model to 'think out loud' rather than just give the strictly correct, bounded answer.