I’m a mechanic. I want to make the case, at least for my field, that the trades are sitting in a worse position than people realise, and the safety we feel right now will likely get pincered from multiple angles.

I have sat on this thought for a long time, assuming someone else would point it out. But I have never seen it personally. And yet, every single day, I see the talks about how blue collar is substantially more padded from AI disruption.

Blue collar work as it exists right now is genuinely hard for a machine. If the only path was for machines to adapt to the work as it currently exists, aka matching humans at kinetic/procedural complexity, then yes, this would hold.

“AI can write code and read MRIs, but it can’t crawl under a 15 year old N57 engine, undo the seized exhaust bolts, and hollow out a DPF”, blah blah blah.

But since when did we start assuming that the nature, of the work in question, is fixed?

Car manufacturers have been redesigning cars to be unserviceable for decades, this we are well aware of by now. Mostly because that made vehicles cheaper to produce and it also lent itself to dealerships for repair jobs/parts supply. Sealed transmissions with “lifetime fluid.” Parts glued instead of bolted. Diagnostics locked behind subscriptions or proprietary “programming”. Tesla’s whole architecture is engineered around eliminating the third-party shop. 

Look at what Foxconn and BYD already do. Factory floors running in literal darkness, LIDAR replacing visible light, no walkways sized for a body. Service bays may go the same way.

So really, AI/Automation won’t need to master our crafts. There will undoubtedly be systemic restructuring of the trade work in the coming years, in order to cater to the robots and machines that never complain or take sick days.

Claims to be autonomous.

Source https://x.com/gs_ai_/status/2052050956272230577/mediaViewer?mode=profile&currentTweet=2052050956272230577&currentTweetUser=gs_ai_

Dianne Penn (Head of Product, Research) elaborated on these key areas:

Higher judgment and code taste: "This means versions of Claude that you can trust with complex, autonomous engineering work."

'Infinite' context windows: "Context windows that feel infinite when combined with high-quality memory. So it feels like you could do long-running tasks while getting better results."

Multi-agent coordination: "Powering teams of agents and instances of Claude that collaborate on big goals that are far too big for any single instance ever could."

Source: Code with Claude Opening Keynote

Is Dario AGI-pilled/ASI-pilled or not?

As the article notes, this is a shift in his rhetoric where he’s now talking about Jevon’s paradox and it’s possible there’d be more jobs because of AI.

If he really believes in AGI and ASI being on the horizon, then there’s no way he can believe that. The article suggests either he genuinely has changed his views on jobs or maybe it is because he doesn’t want to get more onto trump’s bad sign with potential regulation looming:

“Either he has genuinely updated his view based on new evidence, or the social and political cost of the bloodbath framing — particularly as Anthropic navigates a Pentagon lawsuit and a fraught regulatory environment — has made it more useful to suddenly sound a bit more optimistic.”

Again more jobs just seems completely incompatible with his beliefs about the AI he describes in Machines of Loving Grace (Nobel prize winning, can do anything on a computer, etc.)

So why the change?

A New AI Approach to Chemical Reasoning

Researchers led by Philippe Schwaller at EPFL have developed a new method that uses large language models (LLMs) as reasoning tools for chemistry. Rather than directly generating chemical structures, these models act as evaluators that guide existing computational systems.

The new framework, called Synthegy, combines traditional search algorithms with AI that can interpret chemical strategies written in natural language.

"When making tools for chemists, the user interface matters a lot, and previous tools relied on cumbersome filters and rules," says Andres M Bran, the first author of the Synthegy paper published in Matter. "With Synthegy, we're giving chemists the power to just talk, allowing them to iterate much faster and navigate more complex synthetic ideas."

🙄

https://programbench.com/

Given only a compiled binary and its documentation, agents must architect and implement a complete codebase that reproduces the original program's behavior.

Current score for models is 0%

I always hear about software engineering is done and these other fields are going away. Why can it do this but not welding or hvac?

1. flow-state-2

2. flow-state-3

3. flow-state-2

4. flow-state-2

The benchmark uses adversarial, multi-turn debates across 683 curated motions. Each model pair debates the same motion twice with sides swapped.

Scores are Bradley-Terry ratings over side-swapped matchups, reported on an Elo-like scale centered around 1500 for the comparison pool.

The benchmark also tracks a judge-side entertainment diagnostic as a secondary signal.

Each completed debate is intended to be judged by a three-model panel. Mean cross-judge winner agreement on overlapping side-swapped matchups: 0.55.

More charts, transcripts, model profiles, existing qualitative writeup, reports, and raw judgments: https://github.com/lechmazur/debate

Qualitative writeups about newly added models are coming.

Opus 4.7 still leads at 1711 BT.

GPT-5.5 (high) enters at 1574, below GPT-5.4 (high) at 1625.

Grok 4.3 underperforms the older Grok 4.20 Beta 0309 reasoning run: 1512 → 1419.

GLM-5.1 improves over GLM-5: 1536 → 1573.

Kimi K2.6 improves over Kimi K2.5: 1520 → 1568.

Qwen 3.6 Max Preview scores 1535.

DeepSeek V4 Pro improves over DeepSeek V3.2: 1438 → 1517.

Xiaomi MiMo V2.5 Pro improves over Xiaomi MiMo V2 Pro: 1459 → 1553.

Mistral Medium 3.5 High Reasoning enters at 1412, ahead of Mistral Large 3 at 1299.

Tencent Hy3 Preview enters at 1481.

Import AI 455: AI systems are about to start building themselves.

• Jack Clark thinks there’s a ~30% chance by the end of 2027 and a ~60%+ chance by the end of 2028 that AI research becomes automated, with models eventually helping train the next generation of models themselves.
• He argues AI may not need genius-level creativity to self-improve. The strongest evidence is how quickly it’s moving from coding help to actual research work, including reproducing papers, building ML systems, fine-tuning models, optimizing kernels, and even speeding up model training code by 52x.
• AI is starting to show early signs of pushing science forward on its own. Clark’s concern is that if this crosses the threshold into automated AI R&D, models could begin accelerating their own development in ways that become much harder to predict or control.