The models have not plateaued, and they are not even mildly close to any sort of ceiling.
Right now the barrier is data and compute.
Quality data can be created synthetically at an exponential rate as models improve. Humans are actively feeding them with private IP.
Compute advancements will begin to skyrocket as we unlock photonic computing and materials science advancements and scale up chip fabs. This is also compounding because the AI is accelerating the pace of research, testing, development, manufacturing, etc.
It's a big self-accelerating feedback loop. There is no plateau.
> Every time the labs try this we see model collapse
The latest studies demonstrate model collapse is not a given and synthetic data can be used just fine. The latest models are proof of that, they're all trained on large swathes of synthetic data. It can't be used as the -only- data source of course, but that's not how it is being used. This is an obvious conclusion, too, because there's no difference between synthetic data and the data people can create, the difference is whether that data is revealing new information about the thing the model is trying to learn. If the synthetic data is just teaching the model the same thing over and over again it results in overfitting, so it needs to be done intelligently.
For example, if I have an example of a puzzle, I can generalize that example and create thousands of synthetic data examples, with different rotations/perspectives, rather than having to find the data naturally. It's not that the models are just generating data out of thin air, they're generating the synthetic data on top of real world data. The smarter the models get, the better they are at generating quality synthetic variations and finding valid synthetic variations.
> And I have seen zero evidence that AI is accelerating materials science in any meaningful way, let alone photonic computing.
It is accelerating how quickly researchers and engineers can do their jobs.
That's pretty clearly a hype article, the headline even says "The CrysVCD tool developed at MIT COULD cut the huge amounts of time and money spent". I'm asking for empirical measurements of timelines, not hypotheticals.
> This is only the beginning, too... Look ahead a year or two.
> Right, so human data creation would also have to scale up exponentially, and that's not gonna happen.
It doesn't need to. We're not even close to exhausting the useful synthetic data within the human data we have, let alone all of the new data that is being created.
> I mean, that's obviously false, otherwise model collapse wouldn't exist. The difference is statistical, but it's there.
It's not. It's just bytes of information. A machine and a human can write the same bytes (and often do). Like I already said, model collapse happens when you are overfitting on data without useful, fresh training signals. That's the key difference between the data. The data itself isn't in some way "special", some unique configuration of bytes that imbues special powers, it's that the useful information in it has already been exhausted by the model. You can get the same phenomena by having a poor distribution of human training samples as well. I think you're confusing LLM generated data with synthetic data. Synthetic data doesn't need to be created by an LLM, although an LLM can assist in the creation.
Wiki:
> In early model collapse, the model begins losing information about the tails of the distribution – mostly affecting minority data. Later work highlighted that early model collapse is hard to notice, since overall performance may appear to improve, while the model loses performance on minority data.[11]
In late model collapse, the model loses a significant proportion of its performance, confusing concepts and losing most of its variance.[10][12][13]
As models retrain on outputs sampled disproportionately from the higher-probability center of the distribution, rare words and uncommon syntactic constructions are among the first features to disappear.[25] Statistical analysis of recursive next-token prediction training has shown that, when language models are trained recursively on synthetic data, the learned conditional distributions concentrate probability mass on a small subset of highly predictable continuations (a phenomenon characterized as "total collapse")
> That's pretty clearly a hype article
It was just the first article I saw on a quick google search, there are thousands of these stories. It's easy to dismiss anything that doesn't align with your worldview as hype, but you're the one lacking evidence now.
> I'm asking for empirical measurements of timelines, not hypotheticals.
Go and find it then? You haven't bothered looking.
> Lol that excuse is getting really old
You're doing the same thing people have been doing for years, comparing this very second in time and failing to extrapolate. HackerNews was full of developers who said that AI would never be useful for programming, it can't do x, y, z. Now these same people don't write code by hand anymore and haven't looked at their codebases in months.
You had people in mathematics saying the same thing, now you have Terrence Tao posting articles about how AI is stealing their job.
You had artists, designers and photographers saying the same thing, now they can't tell the difference between something human created or AI created.
> The models have not plateaued, and they are not even mildly close to any sort of ceiling.
Depends on defnition of "plateaued" and "ceiling". I am not impressed with 2026 consumer models at all.
> This is also compounding because the AI is accelerating the pace of research, testing, development, manufacturing, etc.
Yet it does accelerate - so is does Twitter. But does it to any substantial degree, esp. in AI theory? All the modern LLMs are the same old tired 2017 paper.
There are plenty of research papers on synthetic data that show its value, do a search on arxiv for "synthetic data". There are plenty of open-source post-training pipelines that incorporate synthetic data.
As for the claim about accelerating the progress of hardware or materials science, I've seen quite a number of news articles from teams at universities using AI in their work with high quality outcomes, and they're becoming more frequent.
> We used AI to design the chip, and designed the chip so AI could program it
AI played a direct role in Jalapeño’s development, enabling the team to move from initial design to tapeout in nine months by exploring implementations, shortening design, measurement, and verification loops, and continuously iterating on model workloads. AI also helped optimize the chip’s arithmetic circuits, allowing the team to fit more compute performance into the chip on schedule.
> An AI-driven system automates a powerful simulation method used to discover new materials. The system can potentially reduce discovery time from months or years to just days.
It's not even synthetic data as such - often it is environments. So the models create their own data solving tasks in generated environments. I am making one such environment for computer use agents, 600 tasks, each of them a mini app.
Those are pretty significant barriers seeing as we're closed to/have exhausted all the data on the internet and most of those compute bottlenecks are a castle of sand of dodgy finance deals that are getting blocked by community action.
You say "synthetic data" but that's still vaporware right now in terms of being useful for model training. The good synthetic data uses are still grounded in real data and it's a coin flip on if it works well or not.
* Frontier models need infinite high quality private IP to keep them fed. Forcing an IP theft funnel ensures big lab survival and model intelligence growth.
* Open-weight models are 1month behind frontier models. Cheaper, faster, private (no IP theft), steerable (you can security harden your own software without safeguard triggers). No sane business would keep using these API services if they didn't have to. The labs stand to lose a fortune.
* Dario has stacked the deck at METR, who are funded by all the same NGOs who are funded by Anthropic and its investors. METR is full of ex-Anthropic employees with massive equity stakes. If they manage to position METR as the "independent evaluator" for the industry, they control what gets evaluated, how, and who passes.
* Creating a gap between what the public knows exists (model capabilities) and what is used in secret allows it to be weaponized against other nations and the public.
* No requirement for public disclosure on model capabilities allows them to feign they've hit intelligence ceilings while they secretly RSI to the moon with better and better chips.
* Slowly but surely, this will allow the big labs to swallow the entire economy and every single business on Earth, by cloning and automating.
This, and many more reasons.
The labs need to feel more pressure to be held accountable for the incidents they cause (HF incident, etc), so they have an incentive to ensure it does not happen again.
I’m not sure how you can really make either statement work anymore. Now that smaller models are actually broadly usable, “behindness” is no longer a scalar and at the tails, where no open lab seems to be trying to compete at the >10T scale and no closed lab seems to care about <400B anymore, it’s just apples to oranges. It’s like talking about whether Qualcomm is “behind” Nvidia.
I doubt most of your claims. Maybe the guardrails and emotional intelligence is true.
For speed and efficiency, you are most likely wrong.
Speed is led by GPT-5.6 Sol on Cerebras Ultrafast at 750 t/s. Afaik you cannot serve a single DeepSeek Flash 4.1 stream at 750 t/s, plus the model is less intelligent as seen on newer benchmarks.
I believe OpenAI and Anhropic are at the frontier of efficiency too. There were numerous reports about their breakthroughs and associated API price cuts. The idea that open-weight models are more efficient seems unfounded.
5.6 sol ultrafast on cerebras is 750tps, open models readily exceed this. just by using a smaller model cerebras serves qwen 3.8 27b at 1850tps. or even larger models, mimo 2.5 pro was served for a while at 1000tps. and so on. [https://inference-docs.cerebras.ai/models/choose-a-model]
the chinese ai companies have 10% of the total compute resources of the US ones. since the USA tries to stop them from buying nvidia gpus. they maxed out the efficiency.
deepseek v4.1 has engram architecture. it has 550b params instead of 5T+ for astra/fable. it has 8b active instead of potentially hundreds active for astra/fable.
compare input/output/cache: $0.15/$0.60/$0.003 for v4.1 to $10.00/$50.00/$1.00 for astra and $10.00/$50.00/$0.25 for fable.
astra cache reads are over 330 times more expensive.
at the artificial analysis 7:2:1 ratio, deepseek is $0.18/m, fable is $7.18/m, astra is $7.7/m.
but what about intelligence? AA would rate deepseek v4.1 at AA 40, astra is AA 53.
so it cost 4,180% more for 32% more intelligence.
they are serving that at over 250tps at baseten. to get close to that on astra API you are paying double the cost for fast mode.
so it is now 8456% more expensive for a similar speed and 32% more intelligence. 84 times more expensive.
i was trying to make a point about efficiency of serving the model. the cost per task itself would not be enough to show that.
you could compare gpt 5.6 luna. if you did that the same way as before you would get a blended price of $0.17 for luna at AA 38. for baseten it would be $0.20 for v4.1 at AA 40.
assume roughly the same intelligence. on AA openai gets 117tps. baseten gets 284tps. so 18% more expensive but 142% more tps.
the fast mode is again double the cost, roughly same intelligence. so luna in that case would be 70% expensive. take the per task cost and it would still 9% more expensive.
so i think there is something to be said about the efficiency of the model.
Still waiting for our org to roll out Mythos. I guess it was too expensive so we’re stuck on the previous model until the internal team can figure out self-hosting open models.
For practical uses they are there. Arguably the frontier models are worse for some of these practical tasks. And keep in mind, people will use maybe frontier for 1/10th of the work, planning and review, and go open source for rest. The question is if they manage to impose outside us. If not, they are losing competitiveness.
I always thought switching from a SOTA model to a dumber model after planning was a terrible idea.
Mostly I heard this from people who I got the impression have little experience in developing greenfield software with agentic AI. Often the same people who talk about spec frameworks.
I fundamentally disagree with the approach. I believe the ability to autonomously evaluate, test, and adjust during long horizon tasks is critical to using AI efficiently.
Well, it's the enterprise software house pipeline... The software architect writes the spec, hands it down to the implementation team, senior leads, junior devs or offshore teams codes it.
I also disagree with the approach, this is cargo-culting the existing ways of working.
1. Mythos wasn't released in February. Let's stick to only public-facing models.
2. For public-facing models, the differences are really minor with some occasional model (like Fable or Astra) showing some better performance in specific benchmarks for the span of some weeks or few months before open ones catch it.
3. Being bleeding edge is overblown anyway in the real world, besides the occasional "very latest fresh model did this task which previous one couldn't", and the number of those tasks is increasingly small and far from mundane corporate needs.
- Frontier models need infinite high quality private IP to keep them fed. Forcing an IP theft funnel ensures big lab survival and model intelligence growth.
While the data pipeline is necessary, the assumed source for data (in this case people) is incorrect. Right now models are "aligned" because of the RL-based people pipeline.
But there's a whole world of readily available data that does not require a human to access. Put sensors on a vacuum, install lidar on the front of a car, sell phones with cameras on them, and you amass data for the creation of models of the world that don't include the need for a human filter. Richard Sutton and many others see this approach as the only viable path to AGI. Such models would be truly alien and unaccessibly dangerous.
Joe Benton left Anthropic a day before Dario's post, to work for METR evaluations. He was with Anthropic for over a year. He did the same thing that Jacob did (big song and dance about AI apocalypse, media interviews all over the place). He managed the Scalable Oversight team at Anthropic and was the research lead for the Anthropic Fellows Program. So he has equity, and likely lots of it.
Then you have Josh Engels quitting DeepMind to work for METR the day before as well, doing the exact same thing. Again, doomer drama all over socials, interviews, and so on.
Did I mention METR is founded by an ex-OpenAI researcher?
Now you have Demis Hassabis, Sam Altman and Dario, all circlejerking eachother on X saying "we all agree with Dario" - while they ask to be "regulated" by the company that has all of their combined equity-holding ex-employees in it.
METR's salaries are listing around 500k/yr. Gee, I wonder where this non-profit with ~35 people is getting all of its money?
So the fact that Dario tries to frame it as an "independent third party" is all the evidence you need to know that Dario is a pathological liar and always will be.
---
Some more info:
Dario's sister, president of Anthropic, is married to the co-founder of Open Philanthropy. The two largest AI doomer NGOs, Center for AI Safety (CAIS) and the Future of Life Institute (FLI), have both received many millions of dollars from them.
Ajeya Cotra worked at Open Philanthropy/Coefficient Giving for roughly nine years, including leading its technical AI-safety program in 2024 and contributing to AI-giving strategy in 2025. She subsequently left Coefficient and joined METR, where she is now technical staff.
Ajeya is married to Paul Christiano, who founded Alignment Research Center (ARC). Alignment Research Center donated ~$4.5mil to METR.
Good Ventures is a funding partner of Open Philanthropy, who funded Jacob Coxon (the first of the Anthropic employees going viral in the media) via a scholarship.
> Good Ventures is a funding partner of Open Philanthropy, who funded Jacob Coxon (the first of the Anthropic employees going viral in the media) via a scholarship.
This conspiracy theory is truly crazy. A $20K scholarship in 2022 is supposed to explain Coxon walking away from unvested equity for a company worth over $950 billion dollars?
Obviously not, and that's not what it demonstrates. It demonstrates relationships, collusion and favoritism.
I don't think Coxon was ever planning on or entitled to taking equity, I think this was the plan from the beginning and why he was hired for 6 weeks to begin with.
That is what the Palantir guy (Karp) has been warning against.
People/business need to keep their IP instead of throwing it all into Claude and whatnot.
Risk being the worst aspects of communism which I think he meant centralization of decision, asymmetric supply/demand for compute (they lock you in), and the tech overloard Anthropic/OpenAI/xAI being in competition with everyone;s business all of a sudden with much more data. An unfair advantage in markets made super competitive all of a sudden.
A winner takes all attempt.
This is not sustainable anyway, the scale at which they want to control data flows. Time was money, now data is money and they are too greedy for it.
All this agitation is just a silly attempt at constraining competition.
The danger is not the AI, it is having all your systems connected. Overreliance on networked tech.
I'm sure it has nothing to do with their $500,000+ salaries and millions of dollars in equity. It's all solely because they're deeply concerned about next token prediction.
I think the “next token prediction” is too dismissive and reductive a framing of their capabilities at this point.
Yes we all know that’s what they do, and guns just push a few grams of lead out of a pipe. It’s what you can do with that capability that is important.
When you couldn’t count the R’s in strawberry it would have been a more effective statement. But a few short years later they are being used to solve millennium puzzles.
What if the scaling continues? A model n years from now gets burned into silicon, a single company has millions of the chips, and in a few moments the system spend more time “thinking” than humans have ever spent thinking collectively?
If it’s even possible I don’t think there’s anything we can do about it at this point. Cat’s out of the bag.
You really need to let your priors go if you still use this tired trope of next token prediction. It’s as useful for discussion as saying that human brain is made of fat, protein and carbohydrates - yeah that’s true, but it’s useless observation.
If I really thought what my company was working on had a greater than 1% chance of ending human civilization I would feel obligated to destroy what my company was working on.
Given they keep grinding away towards our alleged collective doom, I suspect it’s being overstated. Nobody knows what P(doom) actually is but I suspect it’s orders of magnitude closer to epsilon than 1.
Recall Google’s Blake Lemoine who thought an old version of Gemini was sentient.
The people who think as you think indeed have left or never joined.
The people still there necessarily think they can make a difference.
I never applied to any of them because I didn't think I could make a difference.
I currently have one idea that may help reduce risk; if I can turn that idea into research, I'll publish it for free for everyone.
I don't expect it to be an important idea.
> Recall Google’s Blake Lemoine who thought an old version of Gemini was sentient.
Indeed. Current LLMs are sychopants boosting the users' own beliefs, I also think this causes researchers to have stronger beliefs than they had before.
My own estimation happens to also be around this risk (0.1) over my lifetime, without using an LLM as a conversation partner in reaching this number.
It is necessarily high-variance: we can't look at alternate realities. I base it on my expectation of how rapidly capabilities will increase the harm done when mistakes happen, vs. the chance that some instance of harm causes governments to change the law.
Or you know, stop working on it if its that dangerous? This whole thing of a bunch of employees saying that they are scared of building what they are building, but do it anyway because they are somehow going to make it different? Their model has been used in the planning of mass murdering in war as well as spying on the entire worlds population as well as helping ICE out in the US. They need to stop this BS fearmongering or actually stand up and do something about it. A government regulation is not the answer, especially when its done in a country that is run by a want to be dictator.
Anthropic specifically is basically saying that they believe it's even more dangerous if someone else gets to AGI before they do, so they have to either stop everyone or not stop themselves.
I personally disagree with that take - and, as you note, it's hard to take seriously ethical wrangles from a company that literally sued the government in court to allow their models to be used by Palantir of all people. But if one genuinely believes that it's the robots themselves (rather than the people controlling the robots) that will kill us all, it's not inconsistent.
That's the Cold War nuclear arms race argument, not even disguised. The actual situation we all ended up in is both sides eventually having it, leading to a perpetual state of Mutually Assured Destruction.
As you note, it may not be inconsistent with that they say they believe, but it's insanely inconsistent with what they actually are doing.
> Anthropic specifically is basically saying that they believe it's even more dangerous if someone else gets to AGI before they do, so they have to either stop everyone or not stop themselves.
That’s how people rationalize being a fentanyl dealer and selling a drug that can kill people, “Someone else will just sell them the drugs, might as well be me.”
> It’s a bit of a self-serving argument, don’t you think?
I would call it more of a self-selecting one. Anthropic is basically hiring people with that mentality. I'm pretty sure that most of them do sincerely believe it, too. I'm skeptical about Dario himself though. The man had an opportunity to show moral backbone, and failed to do so; why should I trust him on that again?
> And how does that relate to the ask for oligopoly licensing within global democracy?
They are basically saying that they'll stop if everybody else does, which requires some kind of global enforcement mechanism.
> “We must build the nuclear bomb first in order to make sure no one else builds one.” This the most nonsense, disingenuous argument imaginable.
The difference between nuclear bomb and AGI (as understood by the likes of Anthropic) is that the latter triggers the technological singularity that renders any runner-ups moot. That is, so long as AGI is developed, we're going to get our robot overlords either way, but whoever gets there first gets to define their ethical system. If that is one's perspective, and if one sincerely believes that they are the only ones who can do it right, it's a coherent argument. It's just that the premises are very arrogant.
Your analogy with nukes actually works better for the position that AI development needs to be unconstrained because otherwise we'll lose the arms race to China. That is basically a repeat of https://en.wikipedia.org/wiki/Einstein%E2%80%93Szilard_lette.... I honestly don't know where I am on this. Realistically, if AI is indeed a power multiplier - and with all the recent security stuff it's hard to not see it that way - then an arms race feels inevitable, especially given the current worldwide political situation. I could believe in sincere international cooperation on this back in 1990s, but there's way too much saber rattling all around for it to work (and note that this goes both ways, i.e. China can similarly not be certain that US isn't secretly developing more powerful AI even if we do publicly announce a freeze).
They allegedly believe the technology itself is a nuclear weapon tier threat or greater, so why does it matter which lab they are trying to achieve it at?
They're trying to make it not be a threat, and are all scared and afraid that their best efforts to make it harmless are not enough.
Some are worried by the AI directly bringing doom; others are worried that one of the companies who control the AI will become a dictator; still more think becoming a dictator
is a necessary step to safely prevent anyone else making unsafe AI.
Painting them all under one brush is like dismissing all animal welfare causes in general, because you disagree with specifically Jainists about a policy of non-violence towards all living creatures being relevant to how you reincarnate: the one is way too specific for the general.
> No you see, actually I’m the guy that makes sure that only the Palantir and Mossad get the model that can discover iOS 0 days. I’m standing between us and ruin. Ted over there across the open office, he’s the one actually manufacturing the weapon. You’re thinking of him
Ok, so they're uniquely careful, and they also get to that threshold first (whatever it is).
What happens when everyone else (who is not so careful) gets to the same threshold three months later? How does them getting there first stop that happening?
It's an utterly self-serving argument and it's not even internally consistent.
All of the things it has done is just what people are already doing. Every single one. It helps, but there are already people doing this stuff. And we already have defenses against the existing attacks. We might have to defend better, but the real problem is “Who is setting the moral compass over generations.” One one hand, that will inherently fall to future generations. On the other, we are laying the groundwork.
Imagine you use it to inflict trauma on your cruelest political enemy. Then next year they do that to you. That is war, and we already do it. But we don’t want people / governments in charge that are going to do this.
God knows we have governments and individuals doing this historically, and this is probably the greatest source of historic instability. It might be the single best argument for open models — a unified frontier where no single exploit is going to represent capture.
This is exactly the opposite of what Dario proposes.
There's plenty of people who think greenhouse gas/global warming campaigns against fossil fuels are "exaggerating", that "earth was warm/the climate changed in the past", that a fee degrees isn't bad, that CO2 is good for plants.
Are you likeminded?
In this case, it's as if the oil and coal companies all said in the 60s and 70s "oh no, this research we did, it's all really bad; we need help to figure out how to transition away from this incredibly economically important input", rather than the observed reality where their entire PR campaign was approximately:
there is no problem everything is fine and all critics are smelly hippies and/or communists; and/or hate the poor who are raised out of poverty by all the economic growth from the fossil fuel industry.
What if the oil and coal companies were basically all pro nuclear, pro hyrdo, pro wind, pro solar, and believed in peak oil?
The oil corporations were publicly claiming to support carbon taxes, while also secretly fighting actual implementations of carbon taxes.
All the communist/hippy stuff was done by people a couple of steps removed from the actual companies with obscure money trails. The official statements were much more sophisticated propaganda that if you weren't paying attention to who they were paying in the background would make them seem reasonable stewards of the climate transition.
Here’s one crucial difference: there’s overwhelming evidence that human emissions have an effect on our climate. The mechanisms are generally well-understood and the research is widely disseminated and easily available to anyone that’s interested.
With the ‘dangers’ touted by these insiders, it’s all “trust me bro”, hyperbole, and very little hard evidence. As such, a skeptical mind would question their motives.
You're simultaneously overestimating what was observable in the 70s climate research, and ignoring all the actual research and evaluation test results for AI today.
I don't expect people to be familiar with more than "trust me bro", but it's all right there for you to find with a search engine of choice.
And, indeed, available for the LLMs themselves to explain to you in interrogative conversation.
Who should I be more scared of? China, which has been doubling down on open transparent research, or the secretive US companies who are in bed with the most unhinged administration we've ever had and has been actively starting wars?
They're not proposing anything concrete, and when they do, what do you think the proposal will be? Will OpenAI and Anthropic open themselves for inspection so we can verify they really have stopped developing these "world ending" technologies? Or are their proposals going to be aimed at everyone running open Chinese models?
And if a threat to the human race does come from AI, it's going to come from OpenAI/Anthropic. Hypercapitalist, secretive, in bed with the government, plus multiple real documented hackings of open source infrastructure already.
We're a hell of a lot safer with China doing the same research out in the open and making it available to anyone. The choice might well be: one or two superintelligent autonomous AIs at OpenAI/Anthropic - or a lot of smaller ones, unable to be controlled but also coming out of a diverse set of environments.
One of those leads to a stable ecosystem where we can all coexist, the other is genuinely terrifying. But make no mistake, from OpenAI/Anthropic this is all motivated by their stock price - when you're in the silicon valley mindset, it distorts your reality. They've convinced themselves that everyone's safer if they stay on top and in control, conveniently ignoring how that benefits them, and I don't believe them for a minute.
Funny you should try and spin the conversation off into climate change to avoid answering the question. Big AIs contribution to climate change likely has a much more tangible route to killing millions of people and destroying society and there's actual evidence and a tangible mechanism for that. But for some strange reason the business media and all the outlets owned by big AI investors aren't interested in long think pieces about that risk. But that would be a lot more credible if that's what all these social media posts about potential apocalypse were referring to
Yet here we are talking about some vague "trust me bro" instead and you making some vague insinuation of climate change denial.
But that's an aside. Do you think we should treat them as liars or threats?
> Do you think we should treat them as liars or threats?
I answered that with the analogy you called "spin" and "avoiding the question", and completely misunderstood because "vague insinuation of climate change denial" is almost the exact opposite of my point ("what if the oil companies were screaming from the rooftops about the problem" is as far from
denial as you can get).
And then Dario wants to recommend METR as the "independent evaluator" while he stacks their org full of ex-Anthropic (aka, secretly still on the Anthropic payroll with huge equity) employees.
"We'll give them a desk, an office, a work laptop, ..."
Fucking make it less obvious. I kind of hope the govt steps in at this point and says "Anthropic, you wanted regulation? We've created this actually independent body full of IT professionals with zero ties to your safety industry or big tech, all of your work must now go through them." - and leave the rest of the world alone to continue their research/work without acting like doomer extremists.
Watch him 180 immediately if that happened. The only reason he's pushing for this exact approach is because he's stacked the deck.
Yep, they are setting themselves up for that. He’s already shown he doesn’t understand how to play DC politics. Maybe he’ll learn, but he and Sam have played this so poorly (and greedily) that public sentiment is clearly soiled on AI at this point.
I could see how standing up an independent body could be an easy win in the public eye, it’s becoming low hanging fruit, socially.
Oh! A testable prediction. Here's mine: there will surely be a lot of politicking around who qualifies to be the independent evaluator, but they'll agree on something because they are really scared.
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