cloudbonsai
Maybe it's bit like neurosurgery. We humans can treat certain pathological cases in our brains, but we are not exactly sure how our brains really work.
Maybe there is some evolutionary trade-off behind it?
A few possibilities I can think of are:
A. The capability is just not there. OpenAI and Anthropic can produce a brute-force Lean proof, but their models aren’t capable enough to digest the proof as human mathematicians do.
B. They haven’t seriously tried it yet. They prioritized grabbing the land first, and have not spent much effort to polish things up.
C. It’s fundamentally impossible. These theorems are provable only in a very ugly manner. This is the reason why human mathematicians haven’t been able to crack them before AI in the first place.
As a programmer, what I’m seeing is (C). Many recent cyber hacks are products of AI trying & combining every possible tricks, and I don’t think there is any elegant short path that human engineers can replicate.
Another big component of the $80B loss was $3B in stock-based compensation. This line item is very expense-like (“instead of paying your drivers in cash, give them shares of your taxi company”), and Anthropic also adjusts it away when reporting their adjusted net income. Without this adjustment, their non-GAAP operating income was negative in 2026 1H.
Overall, I feel like a man in the parable of The Elephant in the Dark while writing this. Not only is my sight limited, but this elephant (Anthropic) is dancing wildly!
Going a bit meta, I'm curious why we don't observe a faster convergence of opinions here. I assume that HN users are well-versed in AI tech, and the current generation of LLMs will happily break down the relevant information in Form S-1. Nevertheless, we see a chaotic storm of opinions, with wildly varying S/N ratios.
Is it a limit of current LLMs, or is the bottleneck perhaps human nature?
Today, I discussed my father's health condition with my family. Signal worked smoothly (even for my aging mother!), and it's free and secure.
Over the past five years, I have donated $500 to the foundation in total. I hope they continue to maintain the app into the far future.
ColdFusion Next is evolving into an AI-powered, cloud-native platform designed for modern business automation.
They basically installed a bunch of bird feeders outside nursing homes to see if watching birds improves the health of the elderly people. The researchers concluded that it did:
Nursing home staff were the first to see the difference the birds made. "For weeks, they haven't seen a smile on [a resident's] face, and then they see the smile," Kals says.
Some of the memory and concentration improvements are evident after just three to four weeks, Kals says.
https://www.lbv.de/umweltbildung/fuer-seniorenheime/ausstatt...
My impression is that (theoretical?) physics is increasingly becoming a branch of abstract math. It has somewhat reached a practical limit for experimental verification -- perhaps the last landmark result was the confirmation of the Higgs boson 15 years ago?
So I see no reason LLMs cannot crank out a lot of theoretical physics papers, maybe combining forgotten/obscure theoretical branches explored in the past. After all, the most advanced theoretical physics is about internal consistency just as pure math is.
> fiscal-review-b4a5ff.cf.metaaiusercontent.com
So when someone opens the link, the DNS resolution exposes the unique hostname. The original bug reporter found dozens of these URLs in passive DNS logs.
A. The average utilization rate of inference GPU cards is not that high (i.e. it peaks during the daytime, but is idle at night). So the time window for earning $38/h is fairly limited.
B. They are renting costly GPU capacity from 3rdparty hyperscalers because they can't meet the peak demand with their own hardware.
C. Free-tier users are eating the gross margin.
D. Subscription users are eating the gross margin.
Another observable point is that DeepSeek's company-wide gross margin was reportedly 44.6%. It's intriguing what the full picture looks like here.
As a reference point, Z.ai is operating at 26% gross margin as of 1H26. So assuming ~90% gross margin is likely a bit off.
Scientists hypothesized that the presence of a certain protein is the reason why we can't, and they experimented with mice to see if blocking the protein would allow them to regenerate their retinas. It did.
Programming as a hand-crafted art is really diminishing fast.
Since there can be bugs in its compiler and runtime, they translated the code into Rust (con-ron), and verified the resulting kernel with another Rust-based verification tool.
There is also an ongoing verification project of Lean4, which is written in Lean4 itself. (My mental model is Lean4 := Raku and Lean := Perl)
So we have two verified kernels now, and another is awaiting release.
Correct .. 80 chars
Missing .. 12 chars
Wrong .... 8 chars
Like, not expecting human experts to review and merge their results, but developing math branches on their own. I guess it’s at least doable for applied math. For example, if AI agents find a fast method of prime factorization, they can demonstrate it by cracking RSA encryption. No human review required.
I’m less sure how it plays out for pure math, though. My intuition is fairly limited here.
As a software engineer, my mental model so far is that OpenAI made a gigantic (but potentially sound) pull request to the math community, and the mathematicians just did not have enough bandwidth to review and merge it.
If my model is correct, I guess it makes a lot of sense to fund mathematicians, because their lack of resource is the bottleneck here.
He was also a major backer of autism research, because his family member had it. People spend money to where their passion is.
As an engineer, embedding such a check step (basically a PR review for your body!) in the standard workflow feels very natural. But in the medical context, getting a second opinion usually needs extra effort by the patient and often involves awkwardness.
It feels involving too much friction given the high stakes. What is your opinion on this?
I’m not sure how it turns out for the software engineering space this time, though. Will ai-assisted boutique firms outcompete Google (search), Microsoft (OS and office suite), Amazon (e-commerce and infla) or Apple (mobile device)?
At best, the majority of engineers are earning the same amount while paying more for AI tooling. I’m not sure what happens to them when the aggregate demand for human programmers drops due to AI automation.
> Actually we bet the other way around. History always under estimated technology.
To throw a few more nuggets, being pessimistic about aviation actually paid off. As Buffett famously pointed out in his 1999 talk, there had been zero money made from the aggregate of all stock investments in the airline industry in history.
It was a monumental technological success, but turned out to be a financial bottomless pit.
- First, airplanes in the first 30 years were extremely rough and dangerous. When Dole hosted a contest for the first transpacific flight in 1927, 6 out of 8 teams failed. Ten people died as a result.
- Second, people made a lot of silly predictions about this new technology. The New York Times extrapolated that people would soon start commuting to New York using personal airplanes.
- Third, people made capital investments based on these wild guesses. For example, the architects of the Empire State Building installed a huge spire to prepare for the upcoming age of airborne traffic (intended to be used as an airship docking station!)
The bottom line is that predicting the trajectory of a new technology is often impossible. As a society, we are currently in the experimental phase of LLM-based AI, testing all the possibilities one by one. Who knows which path leads where.
Intel had one in its early days. In the 1980s, HP notified Intel that new Japanese memory chips were way more reliable and cheaper than American products. Intel first denied vigorously, but then made a strategic decision to get out of the memory chip business, and enter into the new and unproven microprocessor business. This decision allowed Intel to survive the 80s, while Mostek and others basically disappeared.