Search: “ethan-mollick--x”

This appears to be a GitHub repository shared by Ethan Mollick related to 'grandmaster,' likely containing code or resources for a chess-related AI project, machine learning application, or educationa

My one serious piece of advice having used Fable a bunch before release is that, unless you are careful it develops its own internal bizarre cadence & dialogue over long tasks. If you aren't asking it

The talk about Mythos and cybersecurity was not, in fact, hype. (As anyone using Fable to do autonomous work has probably recognized) AI appears to be finding software vulnerabilities at scale. In Jun

You really need your own benchmarks. If you are translating hieroglyphics, use Gemini 3.5 Flash. If you are running a vending machine use Opus 4.8. (This is one reason why I am skeptical of just swapp

Fable is very good, and having it taken away after getting early access delayed a few things I was working on and made me defer others until it returned But in my early access I also tripped the secur

This post by Ethan Mollick likely highlights significant data visualizations or charts that illustrate important trends, findings, or statistics relevant to AI, education, or technology. Without acces

Formal organizational structures are a useful way to think about the challenges of agents. They provide a template to thinking about how work gets delegated up and down between smart expensive agents

Ethan Mollick predicts that multi-agent organizational structures will deliver better cost-to-performance ratios compared to simple task-based routing systems for AI applications. This suggests that m

It would be good to have an official government statement about the risks they saw in Fable, how they are viewing defensive preparations in light of coming open weights Mythos-class models, & whether

Ethan Mollick posted on X about multitasking while waiting for Fable (likely an AI model or software tool) to complete processing runs. The tweet reflects a casual observation about productivity durin

Since its back, here were my impressions I posted a couple weeks ago of Fable after my time as an early access user (yes, it really is very impressive, but that shows off best in longer, harder tasks)

The discussion here on AI futures can be a little too credulous of company visions. People tend to push what they have. The three big AI labs will say bigger models are the future. Every other firm ha

Yes! Pre-classifying routers are going to result in a lot of bad work because routing is hard and tend to underestimate the value of intelligence on many problems. OpenAI learned this with GPT-5, now

You really need to benchmark models for your use case. As soon as judgements & decisions stack on top of each other, the differences between models amplifies, and no standard benchmark will tell you t

Common challenge that will come up in the near future is capturing the gains of greater AI intelligence in organizations. High human capital firms need to be set up to benefit from their high-quality

I wrote about how the rapid rise in AI abilities is leading to both a transformation in how AI is used at work, and the sort of sudden lurches in policies and markets we have been seeing in recent wee

Ethan Mollick discusses a fundamental factor driving AI Labs' business model and the rapid pace of AI transformation. The post likely explores how a core principle or technological capability underpin

The most important weird thing about LLMs is that they are so general. A bigger LLM that is better at coding is also better at ideation & ethical advice & medicine & math. This isn’t true of everythin

A big reason that token costs become an issue in organizations is leadership is not making decisions about how to use AI, the processes that need to change as a result, & how to think about multiplaye

This post likely discusses using the Mythos release or announcement as a reference point for calculating timelines or milestones related to an AI project or product. Without access to the specific twe

Even though I made this graph, it is also kind of wrong. Fable is guardrailed Mythos. If we use the Mythos date I took the new AA-Briefcase scores from @ArtificialAnlys (basically having the AI do mul

I took the new AA-Briefcase scores from @ArtificialAnlys (basically having the AI do multi-week consulting gigs with a lot of complexity) and graphed the frontier curve for open and closed models: 1)

Ethan Mollick observes that AI tools have provided empirical evidence confirming that actively completing homework is essential for learning, rather than simply receiving answers. This insight likely

See, for example More evidence, from a large-scale study in China, that using AI hurts learning if it undermines mental effort. When homework time drops due to AI use, so do test scores. Across studie

That Wall Street Journal article about GLM catching up with Mythos (which is not true & the reporting doesn’t back up) is another one of those “everyone will ask me about it at every conference or mee

Token spending should be understood as organizational research and development rather than purely a productivity cost, representing an investment in discovering how to restructure business processes a

A good example of one of the ways in which the 'good old days' were terrible: In the 1880s, the average US rural housewife had to haul water into the house 8-10 times a day, 36 tons of water a year. A

GLM-5.2 is good but it is not GPT-5.5/Opus 4.8, and even further from Mythos. Yet it is solid & it demonstrates that the open models continue to chase the frontier What is happening is that open weigh

In my experience, all model routers underestimate the difficulty of non-math/coding tasks and assign them too little intelligence. This is worth addressing, as non-verifiable tasks (innovation, market

Ethan Mollick raises questions about whether Google's Gemini 3.5 Pro model should be subject to export controls, suggesting concerns about its capabilities and potential regulatory implications. The p

It is worth being very, very careful about how you are approaching routing, especially when the systems are primarily tested on verifiable IT benchmarks, which may lead you to overestimate the ability

Like if you have been using Qwen & Kimi & MiniMax, it feels like GLM is right on the curve. Which is itself impressive, and suggests that Mythos class models are coming in 6-12 months (if they are all

Nice example of the increasing benefits of open science and transparent methodologies when writing papers about AI. A big problem with research studies on AI models is that given how long the peer rev

Ethan Mollick discusses OpenAI's naming convention and model release strategy, speculating on what capabilities or timeline the company might be reserving the GPT-6 designation for rather than applyin

The more serious answer @emollick I actually wouldn't be surprised if we're generally in an AI model release moratorium (regardless of capabilities) until the cybersecurity benchmark is finalized. Not

The old BBC 'Industrial Revelations' series is a great overview of what makes an Industrial Revolution Steam alone wasn't enough, it required thousands of skilled workers figuring out how to apply thi

A thing I am noticing is the number of folks who believe AI is “real” is larger, but now there is a growing division between people who know that we are on an exponential & those whose mental model is

And the idea that frontier open weights models will continue to be released was fragile even before the current de facto licensing regime. https://x.com/emollick/status/2067669551685218638?s=20 Is the

Ethan Mollick notes that OpenAI has not released a GDP value metric for GPT 5.6, which he identifies as one of the most useful measures for quantifying the economic value generated by AI systems. This

It is entirely possible that the steady-state people will prove to be right in the future, but there's no sign of a slowdown yet. And if greater intelligence brings greater value at an exponential pac

Lots of advances coming from the open source harness movement (including Moltbook, RAG approaches back in the day, etc.) but they depend on the intelligence of the models created by a small handful of

One of the recovered passages, read for the first time in two thousand years: “Having…strained ourselves to the utmost through research and learning…possessing the same practical wisdom…” Herculaneum

Ethan Mollick comments on how a new licensing regime will likely eliminate vague model announcements and promotional posting practices previously used by AI labs. The post uses literary language ('Goo

The talk around openness & AI needs to distinguish between the vibrant and innovative open source movement that is advancing the state-of-the-art on harnesses & other key areas and open weights fronti

As this post points out, contrary to what many say, the US government could absolutely effectively ban open weights models. That doesn’t mean you won’t be able to download the weights & run them, but

Finally, AI finds its ultimate uncontroversial use. A diffusion model trained on burger recipes 'discovers the classic Big Mac without explicit supervision and generates novel burgers optimized for de