What I Do Not Understand, (A)I Cannot Create. | by Joshua Sparaga | Jun, 2026 | MediumSitemapOpen in appSign up<br>Sign in

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What I Do Not Understand, (A)I Cannot Create.

Joshua Sparaga

15 min read·<br>Jun 19, 2026

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The Converse Feynman Principle — a framework for AI-assisted development that starts with comprehension, not generation.<br>Press enter or click to view image in full size

“What I cannot create, I do not understand.” — Found on Richard Feynman’s blackboard at Caltech, February 15, 1988. Caltech Archives, catalog ref 1.10–29, image:2545

When Richard Feynman died on February 15, 1988, a photographer from the Caltech Archives captured his office blackboard. Two phrases were still visible in chalk at the top: “Know how to solve every problem that has been solved” and “What I cannot create, I do not understand.” Below them, a partially smudged “TO LEARN” list that scholars have since identified as including the Bethe Ansatz, the Kondo effect, and nonlinear classical hydrodynamics. The photograph became one of the most reproduced images in the history of science. UC Berkeley mathematician Edward Frenkel gave a formal talk on the board’s contents at the Perimeter Institute in 2023 — the items on that “TO LEARN” list, it turns out, were connected by deep mathematical structures that Feynman appears to have been circling in his final months.<br>The second line — “What I cannot create, I do not understand” — became Feynman’s most widely quoted motto. It encodes a specific epistemological claim: that genuine understanding of a system requires the ability to reconstruct it from first principles. Not to describe it. Not to cite it. To build it. If you cannot do that, your understanding is, in some irreducible sense, incomplete.<br>I have spent the last several years building and leading platform transformations at enterprise scale in regulated industries — observability, SRE, AI/ML, security operations — and I keep returning to that line. Because I believe the era of AI agency and AI-assisted software development demands a corollary that is just as uncomfortable and just as true:<br>What I do not understand it, (A)I cannot create.<br>Not “should not.” Cannot. Not safely, not durably, and not at the level of quality that production systems demand when they carry regulated workloads, serve millions of users, and run at three in the morning with nobody watching.

The Productivity Mirage<br>The case for AI-assisted development starts with a real and documented productivity gain. A 2023 controlled experiment by MIT and Microsoft Research (Peng, Kalliamvakou, Cihon, Demirer; arXiv:2302.06590) found that developers completed a standardized JavaScript HTTP-server task 55.8% faster with GitHub Copilot. GitHub cites the same figure in its marketing. The number is genuine. The problem is that it describes a constrained, greenfield, well-defined task — and the benefits skewed toward less-experienced developers.<br>When the task environment gets more complex, the story inverts. METR’s July 2025 randomized controlled trial (Becker, Rush, Barnes, Rein; arXiv:2507.09089) studied sixteen experienced open-source developers working on their own mature repositories — projects averaging over 22,000 GitHub stars and five or more years of contributor history. Across 246 real-world tasks, developers using early-2025 AI tools (primarily Cursor Pro with Claude 3.5/3.7 Sonnet) were 19% slower than without AI.<br>The cruelest finding was not the slowdown. It was the perception gap. Before the experiment, these developers predicted a 24% speedup. After completing the tasks, they still believed AI had made them 20% faster. They felt accelerated while actually decelerating. METR’s February 2026 follow-up reported that the experiment had become unreliable because developers increasingly refused to work without AI — the tool had become psychologically indispensable even where it was empirically counterproductive.<br>That perception gap is not a curiosity. It is the mechanism by which AI-assisted development fails silently at scale. When the people writing the code believe they are moving faster, the organizational feedback loops that normally catch quality degradation — code review scrutiny, testing rigor, architectural skepticism — all relax in proportion to the perceived speed. The system feels faster. The dashboards say otherwise.<br>Press enter or click to view image in full size

The Perception Gap

The Quality Tax<br>If the productivity story is more nuanced than the headlines suggest, the quality story is unambiguous.<br>GitClear analyzed 211 million changed lines of code across its customer base from 2020 through 2024 — before and after the widespread adoption of AI coding assistants. The findings are stark. Refactoring — the practice of restructuring existing code for clarity and maintainability without changing its behavior — collapsed from roughly 25% of all code changes in 2020–2021 to under 10% in...