Recursive Self Improvement
Shapiro argues that recursive self-improvement is already happening, but not in the Skynet sense: it just means the share of AI progress (code, data, math) produced by AI itself is climbing fast — roughly 5% two years ago, ~30% now, and 50-80% expected next year. He points to OpenAI's and Google's recent batches of solutions to unsolved math problems (including Erdős problems) and rumors that Anthropic and Google are close to RSI as evidence. The second half argues the big downstream unlock is formal verification, which would let AI push into material science, medicine and other math-heavy fields, and that current capabilities are only at the 'sentences' stage of an emergence curve that goes much further.
- Recursive self-improvement should be measured as a percentage, not a dramatic event: what fraction of the next model generation's code, data and math is produced by AI rather than humans — roughly 5%, then ~30%, heading to 50-80% next year.
- The movie/game version of RSI (Skynet, Ultron 'escaping onto the internet', the Cyberpunk black wall) is wrong because superintelligence has to run on real, physical data-center hardware, and hardware can't scale in hours or days.
- 'We are living in fast takeoff right now' — the takeoff is real but bounded by physical infrastructure, and the percentage framing says nothing by itself about speed, control, or alignment.
- OpenAI and Anthropic began openly admitting in 2025 that current models helped build the next version, and labs have talked since ~2024 about automating alignment and safety research as a special case of the same agent capability.
- Formal verification — proving beyond doubt that code, memory, materials or cryptography behave a certain way — is today reserved for NSA/NASA-level work because it's labor-intensive and very few people can do it; current frontier models are close to or capable of it.
- Because material science, genetics and regenerative medicine are math-heavy, AI mastering math is expected to spill over into those fields: the chatter is that what was done to math in 2026 gets done to the rest of science in 2027.
- Gains compound in two directions at once: each generation gets better at building the next, and each new capability also generalizes across unrelated domains.
- Practical example: Claude Code edited an entire episode of his Critical Path series and produced 100% of the animations for his post-labor economics videos, tasks he didn't know it could do until he asked.
- Emergence analogy: 26 letters combine into ~a million words, words into infinite sentences and books — AI's primitives (tool calling, reasoning, coding, multimodal understanding) have just reached the 'sentences' stage, not the novels.
It's the concrete, measurable definition of recursive self-improvement the whole video rests on, plus the claim that we're already inside fast takeoff.
Let's say last year it was 95% human effort and 5% machine effort. And this year it's closer to 30%... next year, 2027, it's going to be 50 to 80% of all AI progress done by the machines. That's the coding, that's the data, that's the math.
The least-discussed claim in the video: a rare, expensive human skill becoming cheap is what he thinks carries AI from math into materials, medicine and chips.
Formal verification is presently reserved for like NSA and NASA... only the most secure networks and most mission critical stuff on the planet can afford to be formally verified because it is so labor intensive and only a few people on the planet can really do it.
If you base your model of what recursive self-improvement is on a movie or a video game, you're wrong. You're just wrong.
We are living in fast takeoff right now. This is what fast takeoff feels like.
We are just at the sentences level of emergence in AI capability. We're not even at the novels part yet.