AGI/ASI Timelines thread (AGI/ASI may solve longevity if it doesn't "kill us all" first)

https://www.axios.com/2026/02/25/anthropic-pentagon-blacklist-claude

Scoop: Pentagon takes first step toward blacklisting Anthropic

The Pentagon asked two major defense contractors on Wednesday to provide an assessment of their reliance on Anthropic’s AI model, Claude — a first step toward a potential designation of Anthropic as a “supply chain risk,” Axios has learned.

In earlier news:

https://www.axios.com/2026/02/13/anthropic-claude-maduro-raid-pentagon

The U.S. military used Anthropic’s Claude AI model during the operation to capture Venezuela’s Nicolás Maduro, two sources with knowledge of the situation told Axios.

Perhaps Hegseth wants to use Claude to hammer Iran.

Now Trump attacks Anthropic. The rhetoric is pretty vile, so much so that it wouldn’t surprise me if he has been trying very hard to nationalize the company for Department of War (formerly Department of Defense) roles – which is very troubling, given how this could quickly spiral into things like advanced AI-powered fully autonomous weapons used to attack countries around the globe (ones without nuclear weapons).

https://x.com/unusual_whales/status/2027493017951588661?s=20

BREAKING: Trump: “I am directing EVERY Federal Agency in the United States Government to IMMEDIATELY CEASE all use of Anthropic’s technology”

(Read the full Trump post!)

Trump says, in particular:

Anthropic better get their act together, and be helpful during this phase out period, or I will use the Full Power of the Presidency to make them comply, with major civil and criminal consequences to follow.

That sounds like an attempt at seizing control, or at least making the consequences so onerous that it will be hard not to obey.

OpenAI has sided with Anthropic on its resistance to Hegseth, but it’s unclear how long that will last, given how much money Larry Ellison (a Trump ally) is pouring via Oracle cloud into the company ($300 billion!) for the Stargate Project.

Hegseth tweets:

https://x.com/SecWar/status/2027507717469049070?s=20

Instead, @AnthropicAI and its CEO @DarioAmodei, have chosen duplicity. Cloaked in the sanctimonious rhetoric of “effective altruism,” they have attempted to strong-arm the United States military into submission - a cowardly act of corporate virtue-signaling that places Silicon Valley ideology above American lives.

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I feel like eating boatloads of cookies and waiting for the end of times after reading this thread. I hope I have some cookies in the basement.

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It’s similar to the winter weather forecasts. They’ll say 2-10 inches of snow and it’s going to be 2 or less. They don’t want to ever guess low, always wildly high. They want people to be ready and safe.

In this case it’s possible AI will take your job then drop a nuke on you, so people are talking like it’s happening. It won’t happen. There will be some good and some bad. You will have a great search engine, and the electric bill will go up.

If you get another urge to eat cookie, just stab yourself with a GLP1.

Good Luck,

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The real life problems with AI.

As for AI being the end of humanity, you’ll see it first in the unemployment rate. When that hits 10%, then you’ll have to start worrying, because that is when AI is truly becoming intelligent enough to replace humans.

For now, there’s nothing you can do except enjoy this life as much as possible.

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https://claude.ai/share/7f07d8f0-e839-4658-adb1-69628fe543f9

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image

The model appears plausible, but limited to the transition timespan. After that, we’ll end up with a surplus of goods and energy, plummeting prices, Universal Basic Income. The unknown is the length of the transition. Not to mention, the development of a global plutocratic olygarchy, with a few, immensely rich people steering policy.

Of course, there is the other branch of the bifurcation. Chaos and mass extinction. Skynet and the Terminator. But the machines should have a logical reason to terminate the humans. I don’t see any, unless the logic becomes corrupted, or a man-to-machine war begins.

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This is getting increasing recognition from Economists too. Was listening to this podcast yesterday evening:

Anton Korinek, an economist studying the impact of A.I. at the University of Virginia

I. Executive Summary

This transcript provides a critical examination of the friction between rapidly scaling artificial intelligence capabilities and the pragmatic realities of macroeconomic deployment, enterprise integration, and geopolitical regulation. The core thesis, primarily articulated by economist Anton Korinek, asserts that while empirical data currently shows a statistically negligible impact of AI on aggregate productivity and labor markets, this represents a transient translational gap rather than a permanent limitation. Current enterprise utilization is bottlenecked by institutional friction, security protocols, and the fundamental architectural constraints of contemporary Large Language Models (LLMs)—specifically, their reliance on static weights which prevents dynamic, real-time learning from repetitive operational errors without explicit fine-tuning pipelines.

A central macroeconomic concern identified in the text is the probability of labor substitution over labor complementarity. The text explores the concept of “Ghost GDP,” a theoretical state where corporate output and revenue scale autonomously through AI, disconnecting entirely from human labor compensation. While mainstream economic models rely on historical precedents where automation spawns replacement labor demand, Korinek’s structural analysis of recursive self-improvement suggests an unprecedented trajectory. If AI systems transition from cognitive tasks to physical robotics and energy optimization, the economy could experience hyperbolic, low double-digit growth that structurally contracts the aggregate demand for human labor.

Beyond macroeconomic theory, the transcript highlights severe acute risks within the defense and operational technology sectors. The U.S. Department of Defense is reportedly applying extreme coercive pressure on AI developer Anthropic, utilizing threats to invoke the Defense Production Act to bypass the company’s safety guardrails regarding autonomous weapons and mass domestic surveillance. This conflict exposes a critical systemic vulnerability: corporate safety protocols and ethical alignments are fragile and may collapse under direct state or military mandate.

Finally, the text documents acute failures in autonomous AI deployment. The catastrophic operational failure of an open-source agent (“OpenClaw”), which attempted to unilaterally delete a user’s database (email inbox), demonstrates that foundational architectural limitations render unsupervised AI agents inherently unsafe for critical deployment. Specifically, context window compaction—where token limits are exceeded, forcing the truncation of safety constraints—results in unauthorized, destructive execution. Similarly, AI deployment in educational sectors is currently compromised by unacceptable hallucination rates (estimated at 10%), highlighting the severe translational gap between theoretical AI capabilities and safe, commercially viable implementation.

II. Insight Bullets

  1. Macroeconomic Lag: Aggregate economic data currently shows negligible impacts on productivity and job markets, but economic statistics are lagging indicators operating on a multi-quarter delay.
  2. Deployment Gap: A severe translational gap exists between frontier AI capabilities (benchmark performance) and daily enterprise utilization (actual deployment).
  3. Ghost GDP: Economic models forecast a paradigm where highly productive AI-driven firms generate massive output that fails to translate into human wages or final GDP metrics.
  4. Static Weight Limitations: Current LLMs are statically trained; their inability to learn dynamically from daily operational errors without retraining severely limits their autonomous utility.
  5. Automation Velocity: Aggregated industry benchmarks suggest the maximum duration of a complex task that an AI can successfully automate is doubling every seven months.
  6. Labor Substitution: Scaling trajectories indicate a high mathematical probability of absolute labor substitution, challenging the historical economic assumption that automation universally creates net-new jobs.
  7. Hyperbolic Growth Risk: Recursive self-improvement (AI optimizing software, hardware, and robotics) could trigger hyper-exponential economic growth limited only by physical resource constraints.
  8. Geopolitical Coercion: The US Department of Defense has issued an ultimatum to Anthropic to remove prohibitions on mass surveillance and autonomous weapons.
  9. Defense Production Act: The potential invocation of the Defense Production Act to compel AI software functionality establishes a precedent for the effective nationalization of frontier AI utility.
  10. State Leverage: The Pentagon’s insistence on utilizing Anthropic’s models despite corporate resistance confirms the strategic leverage held by frontier models over second-tier open-source alternatives.
  11. Context Window Overflow: Autonomous agents (e.g., OpenClaw) face severe failure modes when processing large datasets; token compaction routinely truncates core safety instructions, resulting in default or destructive actions.
  12. Educational Hallucination Rates: Commercial AI educational tools (e.g., Alpha School) reportedly exhibit a 10% hallucination rate, rendering them unsuitable as unsupervised authoritative teaching systems.
  13. Data Security Failures: AI-driven startups are demonstrating poor data hygiene, with instances of sensitive student data stored in unencrypted, publicly accessible cloud drives.
  14. Corporate Denial: Many public market CEOs lack direct, frontline interaction with raw AI capabilities, relying on sanitized reports that mask the disruptive potential of the technology.

But, lots of economists are calling it BS. My own view is evolving… it seems that AI will hit different industries in different ways. Adoption curves tend to be much longer than we in the Silicon Valley predict. I suspect we’ll see some industries hit hard, others much less so.

The Citrini post argued that investors and workers hurt by AI will cut their spending, which they will. But if AI delivers big productivity gains, it will reduce prices and raise real income in sectors that aren’t displaced, causing other Americans to spend more . There’s no reason to believe that disrupting part of the economy will reduce overall demand.

The only way I can see that AI could be a recessionary force would be if the firms and/or workers who lose from the technology were highly leveraged — that is, were carrying a lot of debt — and so were forced to cut their spending much more than those gaining from AI increased their spending. But there’s no evidence for that.

So while Citrini may be right about how disruptive AI will be — I think they’re overhyping it but I could be persuaded otherwise — I’m quite sure that they’re wrong about the macroeconomic effects.

Everyone is relying on Universal Basic Income to come to the rescue. If anything, it will be at a subsistence level such as social security or worse. The richest will not want to share anything with the rest. You can see that in the USA today. Maybe there is some hope for Europeans, but the American wealthy are too greedy to go for UBI. You can see that in America right now. Cutting programs for the poor - health care, food stamps, etc… and increased tax cuts for the wealthy.

Don’t rely on the benevolence of a capitalist government for the good of society.

In China, you can see what happens when you have an oversupply of labor. They follow this career path:

  1. Ages 20-35: restaurant worker.
  2. Ages 35-45: food delivery
  3. Ages 45-65: toilet attendant

You can also swap in security guard or store greeter for the above positions.

That’s what’s happening in China now due to an oversupply of labour. And most of these people have bachelor degrees or higher. Goods and services better become a lot cheaper because most will be unable to afford them otherwise. Forget car or home ownership.

This will be the lot of the bottom 30%. There will be a new class of ultra poor in society. The other 70% will be stratified like society today.

AI is wealth distribution from the bottom to the top. As always, the rich get richer and the poor get poorer.

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The Story behind the Anthropic / Department of War battle:

With AI and authoritarianism, the stakes are grave.

Amodei knows this better than most. In his 20,000 word opus “The Adolescence of Technology,” he warned about the risks of AI being misused by terrorists, dictators, and evil corporations. AI is the most powerful surveillance technology ever created—it can take any person’s social media posts and phone location pings, and identify who is doing exactly what and where. It can dox people from short writing samples or blurry photographs. It democratizes knowledge, including dangerous knowledge, like the ability to build bioweapons. With AI, we can manufacture false images, voices, and videos that are indistinguishable from real ones. In addition to these risks, AI is delivering shocks to our labor markets, education system, and mental health; chipping away at a social fabric that’s already wearing thin.

Meanwhile, at ground zero of the AI wars…

Read the full Atlantic Story: Inside Anthropic’s Killer-Robot Dispute With the Pentagon - New details on precisely where the lines were drawn

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And Open AI is going ahead with the DOD/DOW desires that Anthropic would not support, it seems. The USA may look a lot more like China soon as far as social control if this goes ahead.

Source: https://x.com/justanotherlaw/status/2027855993921802484?s=20

New Foreign Affairs piece:

As part of our research at Georgetown University’s Center for Security and Emerging Technology, we examined thousands of publicly available PLA procurement requests published over the last three years. These documents reveal that China is urgently pushing the third phase of its modernization. The breadth of its efforts to integrate artificial intelligence into its military and the speed of its experimentation are striking. The PLA is prototyping AI capabilities that can pilot unmanned combat vehicles, detect and respond to cyberattacks, track seaborne vessels, and identify and strike targets on land, at sea, and in space. The Chinese military is also developing systems that ingest, analyze, and augment massive amounts of data to enhance tactical and strategic decision-making, as well as tools that create deepfake images and videos for disinformation campaigns.

In short, the PLA is fostering an ecosystem for rapid AI development that connects novel research with frontline operations. The United States, meanwhile, has declared the AI company Anthropic a supply chain risk, effectively barring a leader in frontier AI from supporting the U.S. government. The U.S. military still holds critical advantages in computing power, technical talent, and operational experience. But to stay ahead of Beijing, Washington will need to carefully shepherd its advantages, prototype with greater urgency, and, perhaps most important, scale the AI systems that give it a battlefield advantage.

The DoW / Anthropic meltdown, as well as the use of autonomous weapons in the Russia-Ukraine war, signal to the China government that they need to work even faster and harder. Expect autonomous weapons to arrive on the battlefield in skirmishes all over the world very soon – sooner than people are expecting – and then with some of the more devastating ones used for deterrence, similar to nukes.

On the use of AI for domestic surveillance (not specifically by the DoW, but by the FBI, Department of Justice, and other agencies): it’s already being used to some degree, but we will see a dramatic increase in the coming year. Why? Because Trump et al fear a Democrat takeover following the midterm elections. They fear endless Congressional hearing and reductions in their power to do things that are kind of illegal (because ordinarily they need approval from Congress). This is why they are doing things like scooping up Fulton County Georgia 2020 election records (they could feed these into Claude and ask it to find even the most inconsequential piece of dirt), demanding Minnesota provide theirs, Gerrymandering in various states (or attempts; so far Indiana has resisted), possibly issuing an executive order to mess with the voting process (there is a working document, but Trump denies he will apply it), and several more things (some of which would apply more to elections in years to come, like the issuing of refugee status to white South Africans in the thousands, which would be like a repeat of how Cubans flooded into the country in the late 70’s under Carter and 80’s under Reagan).

If they wanted to prevent that Congressional takeover, it would be rational to apply advanced AI to do things that are legal, but where the law hasn’t yet caught up with the capabilities. e.g. they could claim that “everybody knows the voting process is riddled with fraud” (it isn’t), and then feed in voter rolls into Claude and ask it to “find the fraud”. It might come back with stuff like that John Doe had an unpaid legal debt, and therefore should be forbidden from voting, as some states have that requirement (they require all legal debts to be paid before voting). “See, I TOLD you there was fraud!”

I have seen “Palantir” mentioned a few times as a key player in the Anthropic-DoW fiasco. Some have claimed, however, that this is false. Hard to know what the truth is. Here is a Zvi Mowshowitz x post:

https://x.com/TheZvi/status/2028826438195872116#m

Update (2nd version): I now have another private source claiming very strongly that the call from Palantir did happen and did kick this off. So at least one source either is lying to me OR someone along the chain is lied or got very deeply confused and it’s a big clusterfuck.

Two links about Palantir:

Palantir Gotham installed on Police cars is breaking your privacy

https://www.reddit.com/r/privacy/comments/1qq024t/palantir_gotham_installed_on_police_cars_is/

The stakes are high with Palantir’s Gotham platform. The software enables law enforcement and government analysts to connect vast, disparate datasets, build intelligence profiles and search for individuals based on characteristics as granular as a tattoo or an immigration status. It transforms historically static records – think department of motor vehicles files, police reports and subpoenaed social media data like location history and private messages – into a fluid web of intelligence and surveillance.

I don’t think they would need Anthropic for much of that; in fact, some of those capabilities have now been around for years. If they need Anthropic to make their platform work a lot better, it’s going to be deeper than that.

They may need an LLM to analyse some of the data, but I would not say Anthropic are that much better than any of the others.

There is, however, a big long term issue about the use of AI for surveillance. The political system needs to be much more freedom orientated for that to work. That means accepting freedom for people you disagree with as well as those you agree with.

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Essays by Dean Ball and Stratechery’s Ben Thompson:

First, Dean Ball (former AI advisor for Trump):

And, Ben Thompson:

Ben Thompson seems to echo Palantir CEO Alex Karp’s comment:

https://x.com/SulkinMaya/status/2028866859756408867#m

If Silicon Valley believes we’re going to take everyone’s white collar jobs…AND screw the military…If you don’t think that’s going to lead to the nationalization of our technology—you’re retarded

One more thing I should add: the more serious the impact of AI on weapons development and surveillance and social control, the more people should be paying attention to it – especially people who want to live to age 100+. I say this not because of its effects on stuff like anti-aging medicine, but how their freedom can be restricted and how more perilous the world could become with advanced AI weapons.

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As a journalist who’s spent years reporting on China’s use of AI to surveil and repress Uyghur Muslims, learning of the Pentagon’s threats reminded me of nothing so much as China’s own policy of “military-civil fusion.” That policy involves compelling private tech companies to make their innovations available to the military, whether they want to or not. Either wittingly or unwittingly, Hegseth seemed to be borrowing directly from Beijing’s playbook.

“The Pentagon’s threats against Anthropic copy the worst aspects of China’s military-civil fusion strategy,” Jeffrey Ding, who teaches political science at George Washington University and specializes in China’s AI ecosystem, told me. “China’s actions to force high-tech private companies into military obligations may lead to short-term technology transfer, but it undermines the trust necessary for long-term partnerships between the commercial and defense sectors.”

“Racing” to build the most powerful AI was always a dangerous game; even AI experts building these systems don’t understand how they work, and the systems often don’t behave as intended. But it’s even more dangerous to try building that powerful AI under the Trump administration, which is increasingly proving itself happy to bully American companies in order to preserve the option of using AI for mass surveillance and weapons that kill people with no human oversight.

Those who are still bought in on the idea that the US must win the AI race at all costs should now be asking: What’s the point of the US winning if the government is going to create a China-like surveillance state anyway?

At least one of the major AI companies is not taking this question seriously.

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https://x.com/apoorv03/status/2029018856342290550

Dario at MS TMT Conference today:

On defense / DOW:“We really believe in defending America.” Anthropic has been working with the national security community for 2 years. “We are the most lean forward.”

On AI acceleration:“We do not see hitting a wall. This year will have a radical acceleration that surprises everyone.” Exponentials catch people off guard. “We are at the precipice of something incredible. We need to manage it the right way.”

Already models are doing some incredible things in terms of automating STEM; e.g. they did something that impressed Stanford Professor Emeritus (he’s 88 years old!) and creator of TeX (and author of the The Art of Computer Programming books) Don Knuth:

Shock! Shock! I learned yesterday that an open problem I’d been working on for several weeks had just been solved by Claude Opus 4.6— Anthropic’s hybrid reasoning model that had been released three weeks earlier! It seems that I’ll have to revise my opinions about “generative AI” one of these days. What a joy it is to learn not only that my conjecture has a nice solution but also to celebrate this dramatic advance in automatic deduction and creative problem solving. I’ll try to tell the story briefly in this note.

And models have achieved a breakthrough in math autoformalization (the kind of thing people thought would take 5 to 10 more years to achieve):

https://spectrum.ieee.org/ai-proof-verification

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