Simon Smith

Simon Smith ✓ @_simonsmith · 2h First Claude Tag + Fable mind-blowing moment today: A colleague posted a spreadsheet of 11 data anomalies to a Slack channel and asked if anyone knew what might explain them. Claude (Fable) proactively, without being asked, went hunting through our Slack history for explanations, found them, linked to them, and grouped them by theme. It did this in about six minutes. To me, this highlights the value of (1) having a lot of context in Slack that Claude can access, (2) having Claude Tag dropped into Slack channels and able to work proactively there, and (3) Claude Fable being a beast of a model.

**Simon Smith** @\_simonsmith [2025-12-03](https://x.com/_simonsmith/status/1996341198412452082) Further thoughts on Dwarkesh's "Thoughts on AI progress (Dec 2025)." I captured this while reading the essay. I do agree that continual learning will be a big unlock, but there are several things that I found myself reacting to vehemently. They sparked these thoughts: 1\. Humans require massive training. Claims that humans learn tools or domains with no special training ignore the decades-long scaffolding of socialization and school, not to mention all the "pretraining" we got from evolution. And humans do need to rehearse software to get good at it. Sure, people can open Excel, enter some values, maybe some formulas. But to master Excel you have to do courses, and learn formulas, and apply your knowledge repeatedly. There's a huge difference between someone that started using Excel last month for tracking expenses and someone that's been using it for decades to build complicated financial models. 2\. Humans don't learn mostly from domain experience. They rely on broad general knowledge accumulated over decades. Domain experience sits atop a huge foundation of prior learning. If not, why don't we let high-school students practice medicine? 3\. Organizations invest huge effort to make humans productive. Real-world work depends on recruiting, onboarding, SOPs, management layers, QA, training programs, templates, and continual reinforcement. Humans are not plug-and-play learners. Ever try to manage change in an organization? Human learning is slow and people resist doing things differently. If humans truly generalized instantly from semantic feedback, change management would be trivial. In reality, even small workflow changes take months of nudging and enforcement. 4\. Adoption friction is real, not cope. Even high-value tools see low adoption despite clear benefits and repeated communication. One example: Meeting recording and transcription, available via ChatGPT Enterprise, but with surprisingly low uptake where I work. Diffusion lag is a real constraint on human behavior. Humans don't just change because you ask them to, or make them aware of options. Humans are rigidly locked into patterns that are difficult to break them out of. 5\. Task-level automation matters even without job-level generality. AI doesn't need to be a full continual learner to be economically valuable. It just needs to automate tasks within jobs, not the entire job. Decomposition matters. It's true that I can't hire a fully autonomous AI employee today. But I can break down the jobs of many employees into tasks, provide the context and skill instructions for those tasks, and get them completed at a human expert level repeatably and reliably. Anyway, I agree AI still needs to improve, but there's a lot in Dwarkesh's essay I disagree with. --- **prinz** @deredleritt3r [2025-12-03](https://x.com/deredleritt3r/status/1996343391194591487) I would click "Like" on this post 20 times if I could. --- **Simon Smith** @\_simonsmith [2025-12-03](https://x.com/_simonsmith/status/1996343984743235585) Wow, thank you! Again, huge respect for Dwarkesh, but I suspect if he spent decades working in companies trying to manage people, or drive change, or get people to learn new software, he would have a very different feeling about AI relative to human workers. --- **Jeffrey Emanuel** @doodlestein [2025-12-03](https://x.com/doodlestein/status/1996345350072971271) Agree completely. --- **Gerard Sans | Axiom** @gerardsans [2025-12-04](https://x.com/gerardsans/status/1996636154574278851) It is common to describe AI as if it had human traits like intelligence, but this can lead to confusion. We should treat that comparison as a loose analogy, not a literal claim. How a person learns tells us very little about how AI systems work. AI relies on gradient descent [image] --- **vibe era** @vibecoding\_era [2025-12-04](https://x.com/vibecoding_era/status/1996608669631348785) Turns out consultants, mbas and mid level managers are the real engineers in the schelpping era 😂 --- **Youssef El Manssouri** @yoemsri [2025-12-04](https://x.com/yoemsri/status/1996657551187001712) All the pretraining we got from evolution is the line that should end most of these debates. Humans aren't blank slate learners. We're the product of billions of years of optimization plus decades of training. --- **AndyXAndersen** @AndyXAndersen [2025-12-07](https://x.com/AndyXAndersen/status/1997528882644492656) This is a great take, based on hard-earned experience rather than a more idealistic view. --- **Swift Compute** @swiftcompute [2025-12-04](https://x.com/swiftcompute/status/1996524984521687077) The distinction you make on task-level adoption is key. Most teams don’t struggle with model quality, they struggle with workflow fit. The infra side sees the same pattern: efficiency gains only compound when they map cleanly to existing organizational behaviour. --- **Cryptonian** @crypto292929 [2025-12-07](https://x.com/crypto292929/status/1997578054625849450) But Sir, even that 20 years pretraining of humans is not analogous to pre training AI with entire internet still. The way humans learnt are still dramatically more efficient. --- **John Held** @John\_A\_Held [2025-12-05](https://x.com/John_A_Held/status/1996819493054828605) I could not agree more.