The author argues that running millions of AI agents in parallel won't easily produce a self-sustaining intelligence explosion, because the data show parallel agents help with speed far more than with solving harder problems. Gains from more agents tend to flatten fast, and the piece treats OpenAI's Navier-Stokes run as a reminder that agent count isn't the same as useful output.

• More parallel agents mainly speed up work; they add much less to intelligence on hard problems, and gains often diminish quickly or saturate.
• The author sorts diminishing returns into a ladder (power-law, logarithmic, sub-logarithmic), and says the clearest multi-agent evidence looks logarithmic or flatter, too weak to sustain runaway self-improvement.
• Three limits drive this: coordination ability, homogeneous thinking among copies, and how much of a problem can actually be parallelized.