In the summer of 1956, a group of researchers gathered at Dartmouth College and wrote what is arguably the most confidently wrong sentence in the history of science:
"We propose that a 2-month, 10-man study of artificial intelligence be carried out... The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."
Every aspect. In two months. Ten people.
They were not naive. They were the smartest researchers in the field. They looked at the trajectory of their results and made reasonable projections.
They were wrong by roughly 70 years.
This has a pattern. Every wave of AI enthusiasm has followed the same arc:
- A capability emerges that exceeds expectation
- Extrapolation begins
- Expectations overshoot
- Reality catches up
Funding dries up. An AI winter arrives.
It happened in the 1970s. It happened again in the 1990s.
So what's different now?
Three things converged simultaneously in 2012 that had never converged before:
Labeled data at scale (ImageNet: 14 million images)
GPU computing (the same chips that rendered video games)
Deep network architectures (decades of quiet theoretical work paying off)
When AlexNet won the ImageNet competition that year with a 10.8% error rate improvement over the second place, not a marginal win, a rupture; the people who understood what had happened knew the winters were over.
Then in 2017: "Attention Is All You Need." The Transformer paper. All tokens processed simultaneously, not sequentially. Long-range relationships mapped in real time.
PT-1. BERT. GPT-3. ChatGPT.
And then 2025–2026: agents that don't just suggest code. They plan, execute, test, and deploy it. Claude Code's SWE-bench score: 87.6% — resolving nearly 9 out of 10 real GitHub issues autonomously.
This is Act 7 of the story. And Act 7 is moving faster than any previous act.
Tomorrow: I'll open the machine. How these systems actually work; explained at the level where it changes how you use them.
