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.
Could you elaborate? What work are you doing which only exists because of AI?
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.
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.
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
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.
True in theory, but in practice hardware availability and cost will keep open source impractical for years to come.
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.
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?
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?
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.
But, then as now, what they didn’t have was profits.
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.
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.
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.
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?
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.
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.
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.
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."
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 :)
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?
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".
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.
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
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.
They seem very strongly related to me.
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.
Opus 5.5 is so amazing I'm not surprised if it can improve itself.
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.
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 ;)
> 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?
AIs do not kill people, people kill people.
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.
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.
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."
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.