June 19, 2026
Enlightenment, but make it messy
Zen and the Art of Machine Learning Research
AI research gets a Zen makeover — and the comments immediately spiral
TLDR: The essay says getting into AI research is less about chasing trendy buzzwords and more about patiently reading, building, and sticking with the basics. Commenters agreed on the grind but fought over whether success is random luck, bad testing, or even a misunderstood version of Zen itself.
A new essay about getting into artificial intelligence research basically says: stop waiting for a magic roadmap. Read, build, repeat, and accept that breakthroughs can feel weirdly random. The author compares the whole thing to meditation: on good days you sit, on bad days you still sit. Translation for normal humans: if you want to get good at making smart computer systems, you have to keep showing up even when your brain feels like mashed potatoes.
But the real fireworks were in the community reactions. One camp was intensely seen by the essay’s honesty about randomness. One commenter confessed they’ve watched near-identical coworkers split into two species: the ones who casually spit out winning ideas "like clockwork" and the ones who almost never land a hit. Brutal! Another person chimed in with a mini-identity crisis from management, saying some software engineers tried machine learning and ran straight back to regular backend work, while one machine-learning leader wanted to escape in the opposite direction. Apparently the grass is always greener in someone else’s codebase.
Then came the Zen discourse drama. One commenter basically said, hold up, the essay’s version of Zen is very Western-self-help flavored, while East Asian Zen is more about aimlessness than achievement. Others zoomed in on a sharper practical takeaway: don’t obsess over leaderboard scores if the test itself is shallow. And yes, there was one perfect internet reaction too: "This is gold!!!!" Sometimes the comments section contains multitudes.
Key Points
- •The article says new AI researchers develop through a combination of reading existing work and building things, rather than through either activity alone.
- •It argues that research progress is irregular and requires sustained discipline, because useful ideas often appear unpredictably.
- •The article advises beginners to focus less on fast-moving recent trends and more on durable fundamentals such as cross-entropy, SVD, and policy gradients.
- •It says research projects should go beyond simply improving benchmark scores, because existing datasets may not capture new capabilities.
- •The article uses OpenAI and ChatGPT as examples to argue that modern AI is still a young field and that long prior experience does not automatically confer a large advantage.