The series, in order.
Six essays in five tiers — the lens first, then the bets it places, an existence proof, and the capstone. Read it front to back, or pull whatever thread you like.
- 1The Selection Lens: How to Bet on Papers
Most reading lists optimize for recency. This one optimizes for survivorship — a lens you can backtest against the canon before you trust it with the future.
- 2Paradigm Bets: The Ten-Year Tier
Six bets from the 2026 collection that look expensive today and inevitable in 2036 — each defended by the frame it sets, the scaling curve it opens, and the one observation that would prove it wrong.
- 3Recursion: The Third Scaling Axis
After depth and width gave us parameters, and the internet gave us data, a third axis arrived almost unannounced: recursion — running the same computation again over its own evolving output. In 2025–26 it showed up independently at four different scales within months of each other. That convergence is the signal.
- 4On-Policy Distillation Quietly Ate Post-Training
While RLVR took the headlines, eight papers in roughly six months made on-policy distillation the quiet workhorse of post-training — student-generated trajectories under dense teacher supervision, sitting between SFT's distribution shift and outcome-RL's sparse credit.
- 5When AI Did Mathematics
In 2026 an AI system produced a piece of mathematics that professional mathematicians checked, accepted, and had not already known. This is the existence proof — stripped of both the hype and the dismissal that surrounded it.
- 6The Continual Agent
Deployed agents are amnesic, and fine-tuning is the wrong first answer. The minimal continual substrate is episode capture, replay, and rule distillation in system space — and that is not a stopgap awaiting real learning. It is the auditable half of a two-substrate architecture production agents will keep.