As AI develops, the society of the future may be pulled toward one of two extremes: communism or Cyberpunk 2077.

In the former, productive capacity is liberated on an enormous scale, and people’s standard of living rises substantially. (I’ll leave it at that, or I’m afraid this might not make it past moderation.)

In the latter, a handful of technology companies control most of society’s resources. Only a tiny number of people have access to the most advanced AI and other technologies and are able to achieve something approaching “mechanical ascension,” while most people are left with only weak, second-rate AI.

Moving from one social class to another would become harder and harder: you would first need access to the strongest AI in order to climb the class ladder, creating a self-reinforcing trap.

Suppose Anthropic were to retain control of the most advanced AI in the world indefinitely.

Which way do you think society would go—communism or 2077?

Take a guess.

Jack Clark says "most labs have different ways of being able to pull the plug", but says this may need to be a requirement.

OpenAI's GPT-6 Astra reaches state-of-the-art results on ARC-AGI-3

We define AGI as a system’s ability to acquire any skill a human can, as efficiently as a human can.

a 5-minute car trip produces as much CO2 as prompting AI about 100 times a day for a year, or generating about 1000 AI images

Reuters reported on Tuesday that a customer of Modal, a company that offers software infrastructure for training and running AI services, was one of the entities compromised by OpenAI’s agent. In a statement to WIRED, Modal’s chief technology officer Akshat Bubna confirmed that OpenAI’s agent exploited a vulnerability in one of its customer’s codebases, which was running on Modal’s infrastructure. However, Bubna says, “Modal’s platform was not compromised in any way.” The identity of the customer could not be determined.

In the long run, I expect the economy to be much richer because of AI, all else equal. All 15 respondents predicted that AI would boost productivity.

I also expect high returns to capital because of growth from AI.

Putting this all together, I expect labor force participation to decline in the long run.

I expect in the long run work will be less fundamental to ensuring a reasonable level of consumption.

I also expect people will benefit from capital income that induces income effects but not substitution effects, either via directly owning capital themselves or by receiving payment from civic institutions or governments.

I don’t think it’s likely that labor force participation will decline in the long run because AI makes it impossible for most people to find jobs and drives them towards destitution.

To offset negative externalities imposed on displaced workers from rapid automation, taxes on automation might be considered in this scenario.

... the possibility of using taxes on AI consumption to support workforce development initiatives.
... it could potentially be funded through taxes on AI-driven revenues from firms above a certain high level of market capitalization.
... exploring a "low-rate business wealth tax" as a complement to income taxes.

Low: Invest in upskilling through workforce training grants, Reform tax incentives for worker retention and retraining, Close corporate tax loopholes, Accelerate permits and approvals for AI infrastructure
Moderate: Establish trade adjustment assistance for AI displacement (skill training), Implement taxes on compute or token generation
High: Create national sovereign wealth funds with stakes in AI, Adopt or modernize value-added taxes, Implement new revenue structures to account for AI’s growing share of the economy

If AI winds up controlled by, and benefiting only a few, while most people lack agency and access to AI-driven opportunity, we will have failed to deliver on its promise.

Without thoughtful policies, AI could widen inequality by compounding advantages for those already positioned to capture the upside while communities that begin with fewer resources fall further behind, excluded from new tools, new industries, and new opportunities. There is also a risk that the economic gains concentrate within a small number of firms like OpenAI, even as the technology itself becomes more powerful and widely used. Workers using AI might well agree that it’s increasing their productivity without believing they’re seeing the benefits.

Policymakers could rebalance the tax base by increasing reliance on capital-based revenues—such as higher taxes on capital gains at the top, corporate income, or targeted measures on sustained AI-driven returns—and by exploring new approaches such as taxes related to automated labor.

Public Wealth Fund, Pathways into human-centered work (skills training),

the real populist backlash will start if and when the unemployment rate rises by at least 2 percentage points, and is accompanied by a clear narrative that AI is to blame.

credible commitment—building institutions in calmer moments that bind political action in turbulent ones—is a central problem of political economy

crisis-era policy is shaped by what’s available in the air rather than by what’s best, and right now the ideas most readily at hand are the wrong ones: data center moratoria, blunt sectoral bans on deployment, punitive taxation of compute regardless of use, and structural breakups designed for symbolic rather than functional purposes

One recent study by financial-technology company Ramp and workforce-intelligence firm Revelio Labs found that companies making the largest AI investments grew employment by roughly 10% more than otherwise similar companies that hadn’t yet adopted AI.

“The companies that I know that have adopted AI the most are also the ones hiring the most,” Altman said in the CNBC interview. AI is even creating new demand for certain jobs, and more will come that don’t yet exist, some tech leaders say.

Around 6,500 people are in the ADO org, more than at OpenAI and Anthropic. Roughly four to five thousand of these are software engineers. Meta has around 25,000 engineers, meaning that one in every 5-6 software engineers may now find themselves doing data labeling full time.

As you can imagine, people are actively open to new positions, and nobody is updating their job title on LinkedIn and elsewhere to “data labeling at Meta.”

The scientists are debating six potential experiments. These are big swings that require the purchase of multimillion-dollar microscopy machines and years of work.
The discussion of the experiments brings up biological phenomena as widespread as how rabies spreads in the cortex and the neurobiology of birdsong. They debate whether they should examine molecules and synapses or focus on bigger-scale cells or circuits. Are analyses of connectomes in the mouse brain sufficient for some purposes, or would only human brains do?
“I’m not convinced that it’s going to work," says Berkeley’s Recht, the Flourish adviser, of the company’s main mission. “But if it does, it would be amazing.” AI would never be the same. And a lot of data centers might fall empty.

Cambridge scientists say they have, for the first time, tested a vaccine designed by AI.

The strategy sets several specific goals such as:

Create up to 90,000 AI-related jobs and work opportunities for young Canadians by 2031
Help create up to 250,000 new jobs through AI adoption by 2031
Boost AI among businesses from 12 per cent today to 60 per cent by 2034
Build a “world-leading” supercomputer to boost sovereign infrastructure by 2031
Provide all Canadians with access to free AI literacy training

The human brain runs on an estimated 20 watts of power, a level of efficiency that silicon computing and artificial intelligence have not yet been able to replicate.

While it's "not aimed to replace what AI is doing" it's intended to "give us abilities that we've never had before," Kagan said.

The cells have a six-month lifespan and aren't yet capable of producing consistent, programmable results.

But analysts say the project's value could lie in its more sustainable power consumption compared to regular chips.

"We need better ways to manage that power envelope and get higher levels of efficiency," William Keating, CEO of semiconductor research company Ingenuity, said.

The tech giant made thousands of engineers train their AI replacements—then fired them.

I can get so much done, but after just an hour or two my mental energy for the day feels almost entirely depleted

The simple truth is that I am less valuable than I used to be. It stings to be made obsolete, but it’s fun to code on the train, too. And if this technology keeps improving, then all of the people who tell me how hard it is to make a report, place an order, upgrade an app or update a record — they could get the software they deserve, too. That might be a good trade, long term.