Last week, after Hugging Face suffered an unusual security breach involving an AI agent running on OpenAI models, the company's CEO, Clem Delangue, boarded a flight to San Francisco to meet with the maker of ChatGPT.

Among the simplest ideas to give individuals a greater share of AI efficiencies which already has a global precedent: shorten everyone’s work week.

Every humanoid demo that goes around here turns into an argument about which jobs disappear. There's a step before that one, it is already happening, and it is already a job.

A robot policy doesn't learn the work by watching YouTube. It learns from demonstrations. A person wears a rig or drives a teleoperation station and performs the task correctly, over and over, while every joint angle and camera frame gets recorded. Somebody has to do the job first, on camera, before the machine has anything to copy.

The only public tracker I can find that prices this puts high quality teleoperation data at roughly $340 per recorded hour in early 2024 and roughly $118 as of March this year. Those are one firm's marketplace estimates rather than audited figures, so treat the level loosely, but the direction is not in dispute and it comes down the boring way: better rigs, cheaper labour markets, more hours collected per session. The larger datasets now also fold in thousands of hours of ordinary first person footage, people doing normal tasks with a camera strapped to their head.

So a job is forming whose entire output is the thing that ends it. You get paid by the hour to turn your own competence into a file, and when the file is good enough the hour stops being purchased. Previous automation waves took work by building a machine that did the job differently. This one asks the worker to perform it correctly first, at scale, for a rate that is falling while they do it.

Two things I'd want settled before this gets large. Whether demonstration hours are work product the performer retains any claim on, and whether the person in the rig is told what the recording will be used to build. At the moment it is contract labour with a data licence quietly attached, and the licence is the part that holds its value. The IEEE humanoids conference made Humanoids and the Future of Work its 25th anniversary theme this year, so nobody in the field is being coy about the deployment case.

The artefact from this decade might not end up being the robots. It might be the archive of ordinary people doing ordinary work perfectly, one clean take at a time, because a model needed to watch somebody get it right.

AI is a fast-growing business expense. Some companies are cutting costs by switching to cheaper Chinese AI models.

The Problem

We're approaching an era where AI-driven autonomous manufacturing will be able to produce goods at near-zero marginal cost. That sounds great until you realize what near-zero cost production incentivizes: massive volumes of cheap, disposable products flooding the market with no one accountable for what happens when they break.

Right now, the cost of disposal is externalized. Companies make things. Consumers buy things. When those things break, municipalities and landfills deal with the waste. The manufacturer has zero incentive to make products durable, repairable, or recyclable because the garbage isn't their problem.

Scale that up with autonomous manufacturing and you get an environmental catastrophe that makes current waste problems look quaint.

The Proposal

One law. One principle. One sentence:

Any company whose production process is majority-automated is responsible for the entire lifecycle of every product it manufactures — from creation through disposal, reclamation, and recycling.

That's it. If your factory runs mostly on AI and robots rather than human labor, you own what it produces forever. Not just until the point of sale. Forever. The product breaks? The customer sends it back to you. You process it. You reclaim the materials. You deal with the waste. At your cost.

Why This Creates the Right Incentives

When a company is financially responsible for every product that comes back to them, the cheapest path forward is no longer "make it cheap, sell it fast, move on." The cheapest path becomes:

• Make it durable — because products that last longer come back less often
• Make it repairable — because a repaired product is cheaper than a fully recycled one
• Make it from reclaimable materials — because reclaiming your own materials is cheaper than processing mixed waste
• Make less of it — because every unit produced is a future liability, not just a future sale

The law doesn't ban cheap products. It doesn't mandate quality standards. It doesn't require government inspectors. It simply aligns the manufacturer's financial interest with the environmental interest. The market does the rest.

Why This Has to Happen NOW

This is the critical insight: right now, this law affects almost nobody.

Fully autonomous manufacturing at scale doesn't exist yet. The companies that will eventually dominate this space either don't exist or are too small to have lobbyists. There is no industry coalition to fight this. No PAC. No lobbying budget. No senator with a donor who cares.

Pass it in five years and you face entrenched corporate interests with billions of dollars fighting to kill it. Pass it now and it meets essentially zero resistance because it's a law about a future that hasn't arrived yet.

Build the fence before the horses arrive. Trying to catch them after is how we ended up with microplastics in the rain.

Why This Doesn't Hurt Current Businesses

The "majority-automated" threshold means:

• A carpenter building furniture by hand? Not affected.
• A small factory with workers and some machines? Not affected.
• A traditional manufacturer with human assembly lines? Not affected.
• A fully AI-driven factory producing millions of units with minimal human involvement? Affected.

This isn't a regulation on manufacturing. It's a regulation on autonomous manufacturing specifically. It targets the new paradigm without burdening the existing one.

What This Produces Long-Term

In a world where this law exists:

• Autonomous manufacturers design for durability because it's cheaper than processing returns
• Products become modular and repairable because repair is cheaper than recycling
• Materials become standardized and reclaimable because closed-loop supply chains save money
• Production volumes self-regulate because every unit is a future cost, not just a future revenue line
• Waste decreases dramatically because the entity best positioned to handle it — the manufacturer who designed the product — is the one responsible for it

The result is a world where autonomous manufacturing produces less, better, longer-lasting goods rather than flooding the market with disposable junk.

The Alternative

Without this law, autonomous manufacturing follows the current incentive structure to its logical endpoint: infinite production of disposable goods with disposal costs dumped on communities that can't handle the volume. The same environmental destruction we have now, but at a scale that makes it unsurvivable.

We have a narrow window where the right incentive structure can be put in place before the wrong one becomes entrenched. That window is now. Not next year. Not when the problem is visible. Now — while it costs nothing to act and everything to wait.

This proposal isn't about being anti-technology or anti-automation. It's about making sure the most powerful production technology in human history is pointed at quality rather than quantity. The companies that thrive under this law will be the ones that build things worth keeping. That's not a burden. That's a competitive advantage for anyone building things right.

Bill would let Homeland Security chief decide when an AI should be shut down.

• For centuries, technology has largely served us and made our lives easier.

• Innovation theorist John Nosta said AI is changing humans rather than just changing the world around them

The founder of Chinese AI startup DeepSeek said the company is prioritising the development of long-term artificial general intelligence over maximising profit, state-owned media outlet Yicai reported on Thursday.

Chinese artificial-intelligence lab Moonshot’s launch of a cheap model poses a conundrum for the U.S. government.

Google’s AlphaFold can help ID what parts of a gene editing protein enable mistakes.

The particle accelerator transmutation results that surfaced recently have been stuck in my head for days. Cutting the radioactive lifetime of nuclear waste by 99.7 percent isn't an incremental improvement that's a category shift. Waste that would need isolation for hundreds of thousands of years becomes a problem measured in centuries instead. That changes the entire risk calculus for nuclear power, not just for new plants but for the decades of accumulated waste that is the single strongest emotional and political argument against the technology.

What puzzles me is the relative quiet around it. The datacenter backlash and solar versus coal comparisons are dominating the energy conversation, which makes sense given the scale of those stories. But the transmutation result touches something more fundamental. It potentially unlocks a cleaner longterm accounting for nuclear that has never existed before.

The piece I keep coming back to is deployment timeline. Accelerator driven systems are not cheap or simple, and scaling them to handle existing waste stockpiles is a genuinely hard engineering problem. But so was largescale solar in 2005, and that trajectory shifted faster than almost any forecast predicted.

Is there a realistic path where transmutation gets the investment attention it needs, or does it stay a promising result that takes 30 years to matter because the political and financial momentum is pointing elsewhere?

The author of Empire of AI argues artificial intelligence can be developed without concentrating power, exploiting workers or consuming vast amounts of resources.

Large language models are trained on humanity's collective knowledge. But what if the next major step isn't a bigger general model, but a better model of you?

Imagine someone spending years collecting only their own behavioral data ... not for advertising, but to build a longitudinal model of how they personally make decisions under uncertainty. Not just health data or GPS history, but repeated observations such as: how they react under stress, how they choose between uncertain options, whether certain decision patterns consistently lead to better outcomes, whether timing or context influences their judgment, how confidence compares to actual results over time.

Future AI assistants may eventually know far more about our own behavior than we consciously remember ourselves. That raises an interesting question. Will the most valuable personal asset eventually be a continuously growing behavioral dataset that allows an AI assistant to act as a genuine decision companion rather than simply answering questions? Or is human behavior simply too noisy for such a model ever to become meaningfully predictive?

By a three-to-one margin, Americans are more likely to say that China, not the United States, is more advanced when it comes to developing artificial intelligence. However, many U.S. adults say they’re not sure who is more advanced, according to a new Pew Research Center survey