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Ethan Mollick on AI · ViralLoop

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Post by Ethan Mollick on X

Ethan Mollick@emollick

The main lesson of the past few weeks is that the Big Four US labs all seem to have figured out a path forward in continuing the exponential pace of LLM improvement, at least in the near future. As a result, agents continue to advance in coding & in office tasks like PowerPoint

615 likes28 repliesPosted Nov 24, 2025

View the original on X Written by Ethan Mollick. Captured Sep 4, 2026.

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Based on absolute engagement — the platform doesn’t expose follower counts here, so large accounts score higher.

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Why it worked

Ends on a short line that is easy to quote and repeat.
Short enough to read in full without expanding, which removes a step before the point lands.

The mechanics

Hook: Plain observation
Structure: Hook → Landing
Style: Conversational paragraphs at a steady pace.
Tone: plain, matter-of-fact
Written for: People building with AI

Analysis by ViralLoop — not written by the original author.

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More from Ethan Mollick

Had early access to Gemini 3.8 Flash, it is a very good Flash model, but not equivalent to a frontier model, though. Here is Gemini 3.8 Flash's version of the shader with the same prompts (which it provided very quickly)

Had early access to Claude Fable 5.1. Its a real advance in long-run work that requires judgement and taste, but less of an advance in the Claudish. Here is a game from Fable 5.1 with retro graphics where you run an accurate space ship inspired by FTL t.co

The METR report on Hugging Face is really good and important but people are now comfortably ascribing way too many human motivations & personalities to the agents involved based on a CoT study made by overwhelmed & time-pressured researchers. Anthropomorphism can get in our way.

Either the Singularity is now a marketing term or else Stripe believes we are at a point “in the history of the race beyond which human affairs, as we know them, could not continue"? Kind of makes forward-looking financial statements useless. * Von Neumann’s original phrasing

If alignment issues are becoming big enough that OpenAI is willing to commit 20% of research inference compute to chain-of-thought monitoring, that suggests that alignment issues are becoming a pretty serious concern. We really need universal policies & standards across labs.

Oh no, we aren’t going to go back to this sort of prompting again, are we? I would love Anthropic to test if it actually works robustly, because our experiments (with slightly older models) found it did not.