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- The 95% Number, ExaminedThe most-quoted statistic in enterprise AI, read carefully: roughly 95% of pilots reportedly produced no MEASURABLE profit-and-loss impact. Two words carry the sentence. "Measurable" means attributable financial effect — not accuracy, not satisfaction — and "pilot" means a bounded trial, which selects for projects that end before an accounting period closes. Three separate failures hide inside the one number: MEASUREMENT failure (it worked and no baseline exists, because nobody captured the before-picture in week one and it is unrecoverable afterwards), ATTRIBUTION failure (something improved and three other things changed the same quarter, so finance will not credit yours), and ACTUAL failure (the workflow was wrong or nobody adopted it). The first two are why the forward deployed role exists — both are solved by work before and after the model. Sourced, confidence-labelled (reported via press coverage, not a public methodology), and paired with the a16z argument it mirrors.Read →
- Trading Margin for MoatWhy a software company would deliberately hire expensive engineers to do customer work. The services-led-growth thesis: spend gross margin on deployment depth because the resulting integration is hard to displace and the outcome is provable — the margin hit is an acquisition cost for defensibility, not an inefficiency. The arithmetic that decides whether it holds, as a worked napkin example: an FDE at ~$300K loaded doing 3 deployments a year costs ~$100K per deployment, which is 67% of a $150K ACV and 17% of a $600K one — and REUSE is the only term that improves over time (0.6x, then 0.4x). The failure mode built into the thesis is paying the margin and not receiving the moat, which arrives one reasonable exception at a time. Diagnostic: does deployment N take measurably less time than N−1? Plus what the trade means for your career and the two questions to ask an employer.Read →
- LLaVAVisual instruction tuning the simple way. Connect CLIP to an LLM with a single linear projection, prepend the image tokens to the prompt, and instruction-tune on data a text-only GPT-4 generated — a capable visual assistant on a shoestring. The minimal connector (a matrix, <0.1% of the LLM), the synthetic-data trick, and two-stage training — worked math plus runnable code.Read →
- STaRA model teaching itself to reason. STaR generates chain-of-thought rationales, keeps only the ones that reach the correct answer, fine-tunes on them, and repeats — bootstrapping reasoning from question-answer pairs alone. Rationalization (hinting the answer) rescues hard problems. The self-improvement loop, foreshadowing RL-trained reasoning models — worked math plus runnable code.Read →
- AlphaGoThe system that beat a Go world champion by fusing Monte Carlo Tree Search with deep networks: a policy network to propose moves and a value network to judge positions. How PUCT selection balances exploiting good moves against exploring promising ones, the supervised-then-self-play training, and the test-time-search idea it seeded — worked math plus runnable code.Read →
- MuZeroPlanning without knowing the rules. MuZero learns its own model — representation, dynamics, and prediction networks — and runs tree search entirely in a learned latent space, so it works where no simulator exists (like Atari). The three networks, the value-equivalence idea (a model trained to be useful for planning, not to reconstruct observations), and the n-step return — worked math plus runnable code.Read →