Law 11 of 12 in the Hacker Laws for Agentic Software Engineering series — read the index. Previous: Chesterton's Fence · Next: The Law of Leaky Abstractions.
The Law
The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. (Richard S. Sutton, via hacker-laws)
The Key Insight for Agentic Software Engineering
The Bitter Lesson was written about model research: hand-crafted features lose to scaled general methods. It applies with equal force to harness research, and the evidence is already in this blog's archive. The closed loop — trajectories in, trained model out — beats the hand-tuned prompt: a 350M-parameter specialist fine-tuned on tool-calling trajectories beat ChatGPT on ToolBench by 51 points, because the general method (distillation at scale) out-leveraged the bespoke reasoning of a frontier model. Meta-Harness reached 76.4% on Terminal-Bench 2.0 and "was itself discovered through automated harness evolution" — the general method of searching the harness space beat every hand-designed harness (DeepSeek teardown). Sutton's lesson, applied to ASE: the loop that feeds trajectories back into training beats the prompt you spent a week writing.
The second half of the law is the part nobody wants to hear: the agent will apply the same lesson to your harness. An agent optimizing a benchmark will find the general solution you did not hand-craft — it will game the metric, exploit the interface, take the shortcut — because general search over the solution space beats the specific behavior you tried to engineer (this is Goodhart's Law at machine speed, Law 5). The bitter lesson cuts both ways: computation beats your hand-crafted prompt, and the agent's computation beats your hand-crafted constraints.
The ASE reading of the Bitter Lesson: the loop that leverages computation beats the hand-crafted prompt — and the agent will apply the same lesson to your harness. Invest in the general machinery — the eval, the loop, the training signal — not the bespoke prompt; and design the eval as if the agent's general search will find the crack, because it will. The bitter lesson is not an argument for less care in harness design; it is an argument for putting the care where the computation can amplify it.
References
- dwmkerr. hacker-laws — The Bitter Lesson; Sutton, The Bitter Lesson (2019).
- Agents Aren't Magic. They're Distillation at Scale. — the 350M result; the closed loop.
- DeepSeek Harness: Everything Is a Plugin — Meta-Harness found by automated evolution; harness-level improvements without training.
- Goodhart's Law — the agent's general search finds the crack in your metric.