In 1958, Mao [Zedong, then the Chinese Communist Party’s leader] ordered every village in China to produce steel. Farmers melted down their cooking pots in backyard furnaces and reported spectacular numbers. The steel was useless. The crops rotted. Thirty million people starved.
In 2026, every other company is having top down mandate on AI transformation.
Same energy.
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Entire departments are stitching together n8n workflows and calling it AI — dozens of automated chains firing prompts into models, zero evaluation on any of them. These tools are merchants of complexity: they sell visual simplicity while generating spaghetti underneath. A drag-and-drop canvas makes it trivially easy to chain ten LLM calls together and impossibly hard to debug why the eighth one hallucinates on Tuesdays. The people building these workflows have never designed an evaluation pipeline, never measured model drift, never A/B tested a prompt. They don’t need to — the canvas looks clean, the arrows point forward, the green checkmarks fire. The complexity isn’t avoided. It’s hidden behind a GUI where nobody with ML expertise will ever look.
The backyard steel of 1958 looked like steel. It was not steel. Today’s backyard AI looks like AI. It is not AI. A TypeScript workflow with hardcoded if-else branches is not an agent. A prompt template behind a REST endpoint is not a model. Calling these things AI is like calling pig iron from a backyard furnace high-grade steel. It satisfies the reporting requirement. It fails every real-world test.
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The “everyone builds with AI” mandate has turned into a hunger game of scope creep. Engineers use AI to generate designs and ship prototypes without waiting for the design team. PMs use AI to write code and spin up dashboards without filing engineering tickets. Designers use AI to build product specs and run user research without looping in product. Everyone is expanding into everyone else’s territory — not because they’re better at it, but because AI makes it possible and the mandate makes it rewarded. The org chart says collaboration; the incentive structure says land grab. What looks like productivity gains is actually a war of all against all, where every function is simultaneously trying to prove it can absorb the others before the others absorb it.
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The Great Leap Forward’s famine didn’t arrive immediately. For a while, the numbers looked spectacular. Every province reported record harvests. Leadership was pleased. The requisitions increased.
The famine came when the real grain ran out but the reported grain kept flowing upward.
We’re still in the reporting phase. The dashboards are green. Adoption is up and to the right. Every team reports productivity gains that, if summed across the company, would imply engineers are shipping at 300% efficiency while somehow still missing the same deadlines.
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The question nobody’s asking: what did any of this actually produce?
The answer, when it arrives, will be awkward.
That was an incredibly well written article. As someone with a degree largely seeded in modern chinese history, the parallels drawn to the Great Leap Forward come across as frighteningly true.