Ethan Mollick

Ethan Mollick

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Professor @Wharton studying AI, innovation & startups. Democratizing education using tech Book: https://t.co/CSmipbJ2jV Substack: https://t.co/UIBhxu4bgq

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I think people on this site don't realize how much people's interactions with "AI" turn out to be, when you ask them: customer service lines (which are almost certainly not GenAI yet) or Siri or maybe a free model (often via an off-brand "ChatPT" AI app they downloaded somewhere)

If you are in any situation where being right matters, you would, at this point, be making a mistake to not ask a frontier LLM for help. That can mean checking your own work, second opinions on other experts, or getting help with a complex problem. Have judgement, but use them

From a recent conversation: I'm already at the point where I trust LLMs broadly over most human experts - doctors, accountants, lawyers. LLMs might not be 99th percentile but they're reliably 90th, and humans just aren't. The crossover was sometime in early 2025 I think. x.com/WillRinehart/s…

So far “telling a satisfying and well-written medium-length story” has proved far harder for LLMs than mathematical proofs, music generation, research reports, code, and many other forms of work. The technical reasons are pretty clear, but they are supposed to be language models

As an academic, I am sympathetic as publishing takes awhile and it is hard to keep up with frontier models, but... ...especially if your argument is "AI is bad at X" you need to explain why you think it won't change, graph any trend as models improve & update before publication twitter.com/emollick/status/20...

This is a useful read (and I contributed a bit to the original problem by highlighting a section of the Claude model card without context) twitter.com/emollick/status/20...

The AI commentators have won on this platform, not just because of the volume, but because they produce meaning-shaped comments that take too long to parse and identify as meaningless. Posts are still useful, commentary has become useless, finding sparks of good stuff too hard.

I wrote back in 2024 that AI is creating four singularities for research (where a singularity is a point in human affairs where AI has so altered a field that we cannot imagine what the world on the other side of that singularity looks like). True now. oneusefulthing.org/p/four-sing...…

If you’re an economist, or any sort of social scientist, I’m not sure how you can look at the chart below and keep doing the same type of research you were a year ago. x.com/polynoamial/st…

Mac only releases for AI tools is an annoying trend. This is an opinion that unites both gamers and corporate users.

“One conversation centered on the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.” 1958, Obituary for John von Neumann by Stanislaw Ulam

This paper (by a colleague) is being misunderstood - it is not a prediction about whether the METR exponential curve is ending, but an argument that it is plausible to model the exponential as a series of stacked s-curves, which would require further breakthroughs (at the scale of reasoning) to continue. I don't necessarily even think this contradicts what most insiders in AI, from Demis to Andrej, are saying!

I wanted to make a few clarifications, which we believe were clear in our paper but not in my original post (re-analyzing @METR_Evals data). Our contribution is to posit progress as a multiplicative product of sigmoids around different innovations. Given the METR data, we split x.com/hamsabastani/s…

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