XFrontier03:50Ethan Mollick@emollickSeriously, there is no way to algorithmically filter out the obvious AI slopreplies? https://t.co/nwHLySb0pc https://t.co/6ikEfg1K05Collected by WispRead in full↗
XFrontier03:03Ethan Mollick@emollickThe paper on how a team of Gemini 4 agents beat Minecraft is worth a look. It is accessible example (partially because it is AI written) of spontaneous coordination among agents to accomplish a complicated goal & how their approach differs from humans/ https://t.co/brrmhydhWw https://t.co/cxlp4jTjqfCollected by WispRead in full↗
XFrontier18:55Ethan Mollick@emollickI should also point out that almost all benchmarks are of single-answer short-horizon tasks, but a large amount of economically valuable work is complex long-horizon tasks. Again, there is room for both, but I think we will find "good enough" intelligence may not always be.Collected by WispRead in full↗
XFrontier10:22Ethan Mollick@emollickYou may save money in the short term, at the expense of worse work at the end. That doesn't mean small models can't do tons of valuable work, but you want to make sure they are orchestrated and error-checked by larger ones for many kinds of tasks. Paper: https://t.co/D557qQbs5gCollected by WispRead in full↗
XFrontier07:29Ethan Mollick@emollickHarnesses + smart models = the end of the last famous hard benchmarks. Models can now work for extremely long times (bye, METR Long Tasks), they can output professional documents with correct math (so long, GDPval) and they can solve complex novel puzzles (farewell, ARC-AGI-3). https://t.co/WSd5e0IfKiCollected by WispRead in full↗
XFrontier11:39Ethan Mollick@emollick@RDMellish It is definitely not a real product. But I don't think I have actually seen much better stage shows.Collected by WispRead in full↗
XFrontier10:17Ethan Mollick@emollickThis paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes. https://t.co/2P4Eq6RkcNCollected by WispRead in full↗
XFrontier11:21Ethan Mollick@emollickLive the kind of life where the suggestions from Codex Composer Text Predictions are always wrong.Collected by WispRead in full↗
XFrontier07:52Ethan Mollick@emollickThe first review I’ve seen of my upcoming book, Co-Existence, from The New Scientist. (When I became an author, I was surprised to learn you don’t know who is reviewing your book until the review comes out, so it is nice to start with a positive one!) https://t.co/kv0wVLpfKWCollected by WispRead in full↗
XFrontier23:27Ethan Mollick@emollick@chehendriksen No, it is unlikely AI gets any better than it is today.Collected by WispRead in full↗
XFrontier23:13Ethan Mollick@emollickThe sudden collapse of the value of CoT (even summarized CoT) in the agentic era changes working with AI. Effectively steering AIs and getting any sort of observability now requires you to ask for interim outputs & tests, and to use AI help to troubleshoot problems you find.Collected by WispRead in full↗
XFrontier11:15Ethan Mollick@emollickI think people believe that OpenAI and Anthropic, since they are so good at AI, are also somehow not normal companies that suffer from the normal problems of hypergrowth, but they are not immune from the inevitable politics, disfunction and confusion that haunt fast-growing firmsCollected by WispRead in full↗
XFrontier10:26Ethan Mollick@emollick(I only let it reach out for customer service or follow-ups where it has particular information I am missing, not to personal contacts, of course)Collected by WispRead in full↗
XFrontier23:21Ethan Mollick@emollickIt also requires you to trust the orchestrator model more than ever, since it is the only thing that can track all of the tasks. Which means you need to use AI to both find problems with AI and to troubleshoot problems you find.Collected by WispRead in full↗
XFrontier22:40Ethan Mollick@emollickWhat will they call the foundations for physics, economics, biology…? (Mostly joking, for now) https://t.co/r61at4jRvUCollected by WispRead in full↗
XFrontier10:33Ethan Mollick@emollickshoot, failed my own strawberry test. Three capitals in its nameCollected by WispRead in full↗
XFrontier10:25Ethan Mollick@emollickGiving my Dot a dedicated email address turned out to be surprisingly painless & solved a few problems around how to relate to a personal agent. I don't want it using my email or drafting messages on my behalf, and I want to make sure people know they are interacting with an AI.Collected by WispRead in full↗
XFrontier23:19Ethan Mollick@emollickIt used to be that you could look at the CoT from a request and get a sense of where the model was going, but with tons of agents calling other agents, and CoTs that run to thousands of pages for even simple agentic tasks, that is no longer possible.Collected by WispRead in full↗
XFrontier21:51Ethan Mollick@emollickA big Google strength in AI has always been finding interesting uses for the models they have. They pioneered deep research, podcasting-from-information and all of NotebookLM etc. With Gemini 4, haven’t seen any similar product innovation at launch, more following the trends.Collected by WispRead in full↗
XFrontier10:28Ethan Mollick@emollickI am not going to stop capitalizing Dot and will not let OpenAI, with two capitals in their name, convince me to do otherwise.Collected by WispRead in full↗