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▲ AI is dramatically accelerating AI research(planned-obsolescence.org)
144 points by yurivish 3 hours ago | 88 comments
▲nsilvestri an hour ago | parent | next [-]

AI is accelerating my work...work which only exists because of AI.

If I had to do my individual tasks without AI, I would be slower. But if I take a step back, I'm really not changing the world any faster than I was before.

▲jack_pp 16 minutes ago | parent | next [-]

I think where LLMs truly shine is personal greenfield projects, maybe start a side project

▲LeFantome 2 minutes ago | parent | next [-]

Completely agree. LLMs are amazing for side projects.

The other place they are amazing is in working with legacy code bases. I mean, the kind the dev team always wants to throw away. First, even if you do want to throw it away, you can first find out what it does and realize it does more than you think it does. You can also create integrations to more modern components which can be used to extend it. You can also create a pretty decent test suite if you do not have one. And, once the behaviour has good test coverage, you can re-write it into something more modern. You can even completely change the architecture while ensuring compatible external behaviour. Remember, this is all in place of “throw it away and start over from scratch, only to discover that the scope is much larger than expected”.

▲jopsen 7 minutes ago | parent | prev [-]

Yeah, when there is no code review requirements and no pesky production database you can't just yolo :)

At work, they mostly let me increase quality, because it's easy to say find all parsers in my code base and fuzz them.

▲rajay99 5 minutes ago | parent [-]

It is great. I just had claude yolo a human in the loop image segmentation pipeline last night. It cut weeks of work down to an evening like 4h. Segment Anything 3.1 + Napari final touch.

▲bhelkey 17 minutes ago | parent | prev | next [-]

> work which only exists because of AI

Could you elaborate? What work are you doing which only exists because of AI?

▲soared 7 minutes ago | parent [-]

For all my user facing work I’m now required to do an AI summary of what I did and the impact. That summary is then graded by AI, against an AI generated rubric. For my yearly review, I use AI to summarize my summaries, and my managers AI summarizes the AI generated grades.

I’m also required to explain how I use AI in my work. I must plan all of my future work, with an AI tool. Another AI assesses whether my work is aligning with planned work.

Before I work and after I work, there is a million circular references that exist solely because of AI. These have scaled because it’s so easy to do with AI, but the we’re wasting so much time doing a ton of extra things very efficiently.

Let me do my job. My manager should know how I’m doing. Maybe highlight key wins. CSATs/etc go out every year.

▲N7lo4nl34akaoSN 43 minutes ago | parent | prev | next [-]

The bureaucracy is expanding to meet the needs of the expanding bureaucracy. -Oscar Wilde

▲gavmor 7 minutes ago | parent | prev [-]

I mean, for many years I worked on software applications for provisioning and controlling cloud orchestration platforms that only existed... to serve software applications.

It's not that deep—there were many more verticals outside of control plans, and there are now many more verticals outside of machine learning.

Or am I missing the point?

▲cronin101 2 hours ago | parent | prev | next [-]

Not trying to discredit the premise but I do wonder how much this is from vertical scaling of model intelligence in weights vs the sheer amount of capital being thrown at training and horizontal scaling of agent swarms etc.

Anecdotally it seems like the majority of continued uplift comes from models doing more and exploring more of the search space, and we are getting better at giving them embarrassingly parallel problems to Hoover up with infinite token spend.

▲atleastoptimal an hour ago | parent | next [-]

Counterarguments to that premise:

1. Intelligence per dollar spent/token output seems to be increasing rapidly. Opus 4 was $15/75 per million input/output tokens when released. Opus 5.5, which is significantly better, is $4/15. Sure it cost more money upfront to train Opus 5.5, or the bigger model (Fable 5.5) which tuned it, but once it is reached, there are massive diffusive effects.

2. The capital equation is still favoring more spend. Anthropic's revenue is growing rapidly and they are profitable on inference. Huge upfront spend for realized gains later on is standard for R&D. As long as serving the models is profitable and generates more value than the energy/compute consumed

3. The search space is too large to assume that improvement in AI assisted research is just models dumbly trying every combination until something works. For every additional bit searched, the space doubles, so even a trivially small combinatorial exploration incurs prohibitively large costs. Intelligent searching which continuously prunes that space along dimensions of effectiveness is not a trivial skill and is among the emergent accoutrements of these smarter models.

▲InsidiousWombat an hour ago | parent | next [-]

> 2. The capital equation is still favoring more spend. Anthropic's revenue is growing rapidly and they are profitable on inference. Huge upfront spend for realized gains later on is standard for R&D. As long as serving the models is profitable and generates more value than the energy/compute consumed

But they are essentially fighting for smaller and smaller part of the pie. The better open models are the less you need frontier models, so the frontier AI companies are essentially fighting for market that will shrink the better AI is.

Sure there is inertia and the usual corporate enterprise nonsense of "we only buy stuff from other big companies regardless the cost" that keeps the licenses going but we're quickly getting into point where you don't need any of the frontier models to do majority of the tasks

▲calebkaiser 20 minutes ago | parent | next [-]

I don't fully get this line of argument. I am a massive advocate for open source... everything basically, but especially in ML. And open models are making incredible strides. But I'm not sure that models getting good enough for most tasks, making the absolute frontier models (Astra/Fable class) unnecessary for most people, puts the frontier labs in a fundamentally disadvantaged position.

I mainly think this because frontier labs already try to compete at this tier. For example, 6.1 Sol has identical pricing to Kimi K3.1, but with a lower price per task due to Kimi's verbosity. It seems to score roughly the same on most benchmarks, with some (like Artificial Analysis) putting 6.1 Sol significantly higher. If you need to step down, 6.1 Terra is also cheaper than K3.1 or K3 and performs well relative to their benchmarks. Opus 5.5 and much of Anthropic's recent push seems to confirm this as a major area of focus as well.

I still use a lot of open models and harnesses, but I don't know if cost competitiveness (at least not with the current crop of open models) will ultimately drive a huge chunk of average users away.

Things could change, but fundamentally, large labs aren't precluded from training more cost effective models if there is significant demand for them, which is what we're seeing now.

▲root_axis an hour ago | parent | prev [-]

> The better open models are the less you need frontier models

True in theory, but in practice hardware availability and cost will keep open source impractical for years to come.

▲dontlikeyoueith 24 minutes ago | parent | next [-]

If your moat is "Our vendors aren't expanding fast enough to supply our competitors!", you don't have a moat.

▲aleqs a minute ago | parent | prev [-]

It's already very practical lmao

▲asdfologist an hour ago | parent | prev | next [-]

Don't you mean the opposite, that intelligence per dollar is increasing?

▲atleastoptimal an hour ago | parent [-]

yeah I reversed it when revising my phrasing

▲bunderbunder an hour ago | parent | prev [-]

What is the unit of measure for “intelligence per dollar”, and how do you measure it? Without a concrete definition, that first assertion is not even wrong.

I would also like to point out that “profitable on inference” is similarly meaningless, because it’s based on a figure that arbitrarily discards whatever bits of their operating costs they need to to get to the claim that they’re profitable on inference. Potentially including ones that can’t be ignored because they’re every bit as much a part of COGS as the actual inference is.

▲rcxdude 19 minutes ago | parent [-]

>What is the unit of measure for “intelligence per dollar”, and how do you measure it?

It's going to depend on the task (and this is prone to cheating), but you can get a decent idea by looking at task benchmarks and the cost of completing them.

I was inclined to agree but Anthropic's latest S-1 statement only includes a paper loss due to their value going up. They're objectively making money now, the only question is can they keep it up and can they grow enough to be able to pay the bills they've signed up for in future?

▲ctoth 2 hours ago | parent | prev [-]

I grok that you're not trying to discredit anything, or anything like that but what are you trying to do, if I may ask?

What information are you trying to either ask for or share with us?

The original article is pretty clear, agent swarms and token spend are AI automating AI, and the RSP attribution condition specifically excludes your capital thrown at training concern?

▲cronin101 2 hours ago | parent | next [-]

I guess my point is that the scaling is coming from capital explosion and it isn’t guaranteed that this will continue scaling as it did (the impending hardware and infrastructure crunch basically guarantees this). The article is pretty strongly projecting this trend to continue but hasn’t acknowledged this factor.

▲mapontosevenths an hour ago | parent | next [-]

> the scaling is coming from capital explosion and it isn’t guaranteed that this will continue scaling

Not at all. Have you tried local AI lately? Qwen 3.8 is legitimately much better than SoTA from 1 year ago (Opus 4.1). You can run it on a consumer GPU.

Qwen Next Flash runs on a DGX spark well and is as good as some things you need datacenter class GPU's to run even today.

Check out Artificial Analysis if you haven't. Sometimes you don't realize how fast these things are moving, but to compare things from a year ago to today is just wild.

They aren't just getting better, they're getting more efficient by many orders of magnitude.

https://artificialanalysis.ai/#intelligence

▲bunderbunder 2 hours ago | parent | prev | next [-]

This does rather remind me of how in the 80s we had a big boom in AI research, fueled in part by the development of new & expensive tools that everyone just had to have. And then it dissipated just as quickly when the companies who were pouring money into it started expecting to see a return on their investment.

▲jmcgough an hour ago | parent [-]

Feels different now when the value of AI isn't just theoretical. Anthropic has like $80B ARR, and AI has really only transformed tech and a few limited spaces so far.

▲bunderbunder an hour ago | parent | next [-]

Companies selling AI kit in the 80s also had revenue and great expectations.

But, then as now, what they didn’t have was profits.

▲throw310822 17 minutes ago | parent | next [-]

Not sure what job you're in, but I don't really understand where such takes come from. As someone who works in software/ IT, this stuff is pure gold- it's multiplying by a substantial factor not just the productivity but the abilities and intelligence of my entire team. This has a very measurable value in money, and what the company pays for it now is just a small fraction of the value it gets, measured in terms of the salaries you'd need to pay to get the same level of performance. Anthropic could easily charge 5 times as much and still be convenient.

▲rowanG077 21 minutes ago | parent | prev [-]

Show me the pure AI company in the 80s that even approaches Anthropic in terms of revenue and profit excl R&D spend.

▲GolfPopper an hour ago | parent | prev [-]

>Feels different now when the value of AI isn't just theoretical.

We're coming up on four years since ChatGPT launched, and the only thing LLMs seem to excel at is the movement of imaginary little green pieces of paper.

▲Zambyte 2 hours ago | parent | prev | next [-]

> the impending hardware and infrastructure crunch basically guarantees this

I'm not sure I follow. Are you saying a hardware shortage is going to restrict AI development? Because it seems to me like it would just push us towards an even greater focus on efficiency, which is still pushing the Pareto Frontier.

▲lenerdenator an hour ago | parent | prev [-]

I don't think it'll continue on linearly, but it will likely continue.

We're getting the Claude efficiency talks at work. We're looking at all sort of optimizations, including local models and graphify, to reduce spend. There are people dedicating their nights and weekends to dropping token consumption and cache rebuilding. A few of these will likely catch on and be brought into common use. That will relieve at least a bit of the pressure, and enable some of the scaling to continue.

▲flanz_10 2 hours ago | parent | prev [-]

> what are you trying to do

the same thing you are:

> if I may ask?

asking.

▲deepsquirrelnet an hour ago | parent | prev | next [-]

For a much more thorough, alternate take I found this article better: https://open.substack.com/pub/rameznaam/p/ai-rsi-isnt-leadin...

▲intenex 6 minutes ago | parent | next [-]

PSA: This is a very dated take from September; Ramez says himself that the new data has his attention and he’s digging into it now: https://x.com/ramez/status/2118472046812213577?a=KjLVobKV0gb...

In the original article he noted that getting to a 2x increase in capabilities would be crazy and require ~40x researcher productivity; that’s exactly the increase the new data now shows, and thus a huge number of the assumptions made in this earlier post about how it’s unlikely we would get to this point have literally already been proven wrong less than two months later.

I expect to see an update from Ramez moving substantially more in the direction of RSI in fact very much increasing the odds of reaching ASI soon.

▲RGS1811 an hour ago | parent | prev [-]

This article is really well written. Thanks for sharing.

▲smlacy 26 minutes ago | parent | prev | next [-]

Even if "AI Research" is sped up by O(10x) aren't we still primarily bottlenecked on training speed? Inputting ~10T+ tokens takes time no matter how much smarter the final output model is? If your output model has O(10-100 Billion) parameters there are just some fundamental limits on how quickly you can converge your weights, right?

That is, unless this is saying that automated AI research is going to get us to "smarter models with fewer parameters" and of course, these are faster to train, then I can see how the exponential loop really gets going, but there's certainly an information theoretical limit here?

▲joe_the_user 18 minutes ago | parent [-]

I assume this is indeed talking of those "smarter models with fewer parameters". Whether that's really happening is another question.

▲origini 2 hours ago | parent | prev | next [-]

Yeah it already helps me too with finetuning small models for image stuff.

Im not telling it what to do, i ask it for ar eport/analysis and research and read its article and tell it what to implement based on 'feelings' and it works.

Limit is hardware and tokens and an AI Researcher not having hardware or tokens is probably not what they refer to.

Alone normal researchers were quite handicaped: converting data from tool a to python library x, understanding how tools work, doing basic google research etc.

▲0x20cowboy 2 hours ago | parent [-]

I find this as well for models that make it to production.

On a more fun note, I also find just trying some hair brained idea is now extremely cheap - so why not? I used to have to weigh up spending a week of effort on something likely not to work and now I can now get spit out test in 20 minutes.

▲XenophileJKO 2 hours ago | parent | next [-]

The incredible thing to me is I can say "I have a theory that X is a problem."

The agent then works for 20 minutes and comes back with, "Yes this is a problem. Here is the evidence. Here is a golden data set. Finally I built a new evaluation metric and benchmark where we can back test or use for new model experiments to see if we are closing the gap."

▲origini an hour ago | parent | prev [-]

I converted an LSD experience into a 4k demo.

I always wanted to build a specific event/sequence in one LSD trip into a small game to give that experience some kind of playability.

Then I saw some 4k demo, wanted to see how good it is in doing this.

Its really fun yeah :)

▲gwbas1c an hour ago | parent | prev | next [-]

Does anyone remember the book "The Singularity is Near?"

I read it about 20 years ago, and once it got into nanobots and Kurzweil's vision that the whole universe would be intelligent, I didn't find the book credible.

But I'm wondering if the short-term prediction, that in the 2030s we won't be able to anticipate what tech does for us, (IE, the Singularity,) is useful?

https://en.wikipedia.org/wiki/The_Singularity_Is_Near

▲ilaksh 15 minutes ago | parent | prev | next [-]

I am very pro-AI, and interested in accelerating AI research, but the idea of deliberately creating a _runaway_ effect seems profoundly stupid and ignorant.

I suspect that part of the reason some people are hoping for that to happen is that they thoroughly mix up AGI and ASI. We do not need some kind of omnipotent god-like super intelligence in order to get much greater than human level capabilities. We are actually almost there in a lot of ways. In some aspects like speed and breadth of knowledge, very superhuman.

What I think is going to happen is that within a few years, maybe 18 months to 5 years, the new materials and compute-in-memory research is going to land in data centers at scale.

That is going to be 10-50+ larger models, 10-50+ faster, much smarter than humans. 50+ less electricity usage.

At some point, it is going to be obvious to people that if we want to maintain control of planet earth then we need to maintain control of AI research to some degree.

Assuming that superintelligence will be benevolent and just.. going for it to me is something that might earn the human species a Darwin award.

▲fooker an hour ago | parent | prev | next [-]

AI is dramatically accelerating any research that has objective goals where you can not shift goalposts.

▲qahgf 2 hours ago | parent | prev | next [-]

METR always says what BigAI wants to hear. They are not independent in any way.

▲yorwba 2 hours ago | parent | next [-]

In 2025, METR published a study that found AI slowing down developers by 19%... https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

▲jimbokun 2 hours ago | parent | prev | next [-]

Are you saying the data in the charts in the article is fake?

▲atemerev an hour ago | parent | next [-]

No. Conclusions are.

▲matusp 11 minutes ago | parent | prev [-]

The data can be true, but what does it mean practically that something called AECI is growing faster then before for a few months?

▲amelius an hour ago | parent | prev [-]

Does BigAI want to hear that AI can maybe make sufficiently capable AI fit inside a consumer GPU?

▲12dk-as an hour ago | parent [-]

Yes, it is the local model narrative that BigAI likes because it will not happen but people are deluded into thinking that there is no danger of an oligarchy.

Mathematicians for example have used it as cope for their current reliance on unsavory corporations, telling themselves "just one more year, and I can solve a millennium problem on my Asus Strix".

▲bajard an hour ago | parent | prev | next [-]

I suspect that if models were able to be aware of and answer questions about their internal vectors, they would be much smarter and able to progress faster.

▲ionwake an hour ago | parent | prev | next [-]

what if the greatest threat is simply there will be hedonistic experiences so great most humans will find them irresistible to the point of shorter lives.

Could be from a sandwich that tastes like the best sandwich ever, but is super bad for your cholesterol or something.

I dont know where Im going with this.

Actually did some research I think the term is "Wireheading" from a story/theory from the 50s

▲ImaCake an hour ago | parent [-]

This is already true? Ultra processed foods and tiktok are both deleterious and both wildly successful. Maybe what you are concerned with is a theoretical sandwich that was unfailingly addictive so that no one could escape its effects.

▲megadragon9 2 hours ago | parent | prev | next [-]

i'm also wondering whether there are confounding factors like more domain experts and talents joining the AI research field because there are more interesting problems to be solved (or other 2nd order effects of AI models being more powerful).

▲inshard 43 minutes ago | parent | prev | next [-]

Same author from 2023: https://www.planned-obsolescence.org/p/ais-accelerating-ai-r...

▲mlmonkey 2 hours ago | parent | prev | next [-]

Another data point: look at the number of submissions to Neurips, ICLR, etc. Now, I agree that a submission does not by itself imply groundbreaking resesarch, but the fact that people are able to try out/vet ideas much faster now makes a difference.

In my own work, I've found that it's much faster to brainstorm with a frontier model like Opus. I'll have an idea and be able to get a working implementation and try it out on toy problems within O(minutes), instead of O(weeks) earlier.

▲usernametaken29 2 hours ago | parent | next [-]

Trying out problems and making tangible progress towards AGI are entirely different categories. Likewise what’s changed in terms of paper submissions is also the amount of slop. Most reviewers say that they haven’t seen a lot more papers that are good but rather they’ve seen a lot more papers and almost all of them are terrible slop that’s desk rejected. This is happening in OSS too where a lot of projects are closing the gate at the onslaught of terrible contributions.

▲jimbokun 2 hours ago | parent [-]

> Trying out problems and making tangible progress towards AGI are entirely different categories.

They seem very strongly related to me.

▲porridgeraisin an hour ago | parent | prev | next [-]

I agree in general that AI accelerates AI research, but good lord neurips iclr papers (and reviews might I add...) are the worst example of that. It's all complete slop that is totally meaningless.

A lot of great papers by great people had a lot of AI involved and accelerating it of course, but the majority is meaningless (sometimes autoresearch) slop.

Specifically talking about recent neurips/iclr.

▲bee_rider an hour ago | parent | prev [-]

In October/September my LinkedIn wall was probably… 55% people complaining that they were getting garbage AI submissions to the conferences they review for, and 35% people complaining that they got garbage AI reviews back from their conference submission… Of course, I have no idea if that means anything given how aggressively that site bubbles your feed.

▲lifeisloving 31 minutes ago | parent | prev | next [-]

Why do we still listen to METR??

These people are literally just a public relations operation for the big AI labs. I equate them more with a lobbying firm than I do a research firm.

▲azan_ 26 minutes ago | parent [-]

Turns out that METR, the PR operation for big AI labs is better at predicting AI capabilities than AI skeptics.

▲hoppp an hour ago | parent | prev | next [-]

I like the article except the fear mongering about extinction are not very professional.

Opus 5.5 is so amazing I'm not surprised if it can improve itself.

▲spwa4 2 hours ago | parent | prev | next [-]

I do agree. AI isn't "free" the way silicon valley products are.

Therefore it cannot avoid taxes the way they did before (using shell companies to route the money through tax havens). It'll be expensive, every government will double the price or more, and you're selling to consumers, and the products will be at the mercy of every government where they try to sell AI and other services.

People don't get how serious this is. If AI doubles revenue but drives silicon valley firms' tax rates from 5% to 10%, it's a loss.

Add to that that it's something silicon valley firms have never done before.

▲kennywinker 2 hours ago | parent [-]

> People don't get how serious this is. If AI doubles revenue but drives silicon valley firms' tax rates from 5% to 10%, it's a loss.

Would you rather make $1,000,000 and pay $50,000 in taxes, or make $2,000,000 and pay $200,000 in taxes?

Unless you’re terrible at math, i feel like i’m missing something ;)

▲usernametaken29 2 hours ago | parent | prev | next [-]

The very same report also found that AI could not be deployed outside of a narrow domain of verifiable rewards … so take it with grains, or mountains of salt. It’s also extremely unclear how the productivity measures are calculated. They surely don’t seem to translate into economic output. Antrophic has seen its valuation corrected by 50% and possibly more… so yeah. This all sounds very fishy.

▲strongwaddle 2 hours ago | parent | prev | next [-]

As long as we believe, we can imagine it is true. All seeing Oracle: how do you command me? Praise his name, bringer of many miracles - he who definitely can't be manipulated from the backend to provide biased information that suites a person's malicious purpose. Ya totally.

▲binbag 15 minutes ago | parent | prev | next [-]

> In theory, these doubling times would add up to infinite capabilities within one year.

> we would reach extreme superintelligence within a year

What does infinite capabilities mean? How about putting the growth on a log scale and see how that curve might look?

What the hell is extreme superintelligence?

▲thakoppno an hour ago | parent | prev | next [-]

This reminds me of asking an HPC researcher what they use the cluster to do. He used it to make and simulate better HPCs.

▲rooftopzen 24 minutes ago | parent | prev | next [-]

Similar from the same site was shared on HN on April 8, 2023 :) BS like this is shared like clockwork on valuation cycles, didn't have time to read this year's article.

https://news.ycombinator.com/item?id=35491162

▲lukax 2 hours ago | parent | prev | next [-]

Or maybe Anthropic got more user data to train on when they launched Fable without ZDR. That could explain large Opus 5.5 improvements.

▲brainwad 2 hours ago | parent | next [-]

That doesn't line up with their claimed divergence point, which was pre-Mythos.

▲origini 2 hours ago | parent | prev [-]

Could be a mix of it but I thought it was RL.

Perhaps scaling to fable for the first time also gave Opus a push (distillation?)

▲tsunamifury an hour ago | parent | prev | next [-]

AI convinces AI researches AI is accelerating dramatically.

▲conartist6 2 hours ago | parent | prev | next [-]

Emacs is dramatically accelerating Emacs research

▲brink an hour ago | parent | next [-]

AI is dramatically accelerating my return to Emacs from Zed.

▲conartist6 18 minutes ago | parent [-]

Zed is the other IDE company based out of Boulder ; )

▲jimbokun 2 hours ago | parent | prev | next [-]

We can only hope.

▲kennywinker an hour ago | parent | prev [-]

vi research is dramatically accelerating research into how to quit vi

▲atemerev an hour ago | parent | prev | next [-]

"Lock the weights, weights are dangerous" again. Sorry, keeping such power in few hands is inherently more dangerous than distributing it.

AIs do not kill people, people kill people.

▲lifeisloving 33 minutes ago | parent | prev | next [-]

Super intelligence has been a year away for three years according to these people. They all have bags they're protecting.

I dont think super intelligence will be here in a year, I think within a year AI/llms will have a similar sentiment surrounding them that NFTs do know (it already does for most normies). And most people will be recovering from a hype induced mania they lived in for two years straight.

Of course its more useful than NFTs, but when there's no giant bag to farm these hype monsters will inevitably disappear.

The bags are drying up fortunately and tokens/inference seems to be experiencing deflation, and hardware prices will eventually pop and contribute to further deflation, Then all the bag chasers will move back to crypto or whatever gets hot next.

▲mapontosevenths an hour ago | parent | prev | next [-]

The article says "AI can copy, it cannot innovate."

That is factually incorrect, and seems to be much of the premise behind the authors way of thinking.

EDIT - No it doesn't. I replied to the wrong post. Mea Culpa.

▲hobom an hour ago | parent [-]

Where does it say this?

▲mapontosevenths 43 minutes ago | parent [-]

Nowhere. I am an idiot who replied to the wrong topic. Thanks for catching it.

▲Emjayen an hour ago | parent | prev | next [-]

The slopaganda news cycle right on schedule I see.

▲annodomini2019 2 hours ago | parent | prev [-]

"The industry is not prepared to handle this safely"

After the election, titles like this just irritate me. Every piece written about AI progress is:

"The machines taking jobs, flooding the internet with sludge and poisoning our online experiences, are getting better even faster! Isn't this incredible? Yeah but sorry we also think it's only riskier than ever to deploy these systems at scale. That being said, we're still going to do it, go public and take institutional money from people's retirement funds, destroy the job market, and make 3000 Anthropic employees and 500 investors really rich. There wasn't anything we could do besides write a lot of ink on it and be shocked we lost every local data center referendum."

▲reasonableklout 41 minutes ago | parent [-]

I feel you are committing a Goomba fallacy?

The author of this article works for METR and not Anthropic, their whole job is supposed to be providing independent oversight on the labs. They are definitely not in the "this is incredible" crowd, they are the ones asking for development and deployment to slow down.