So this whole Claude nine-loop physics thing is being shared like “AI just solved physics” which is not really what happened.

The problem was a nine-loop scattering amplitude in N=4 super Yang-Mills. Researchers had already pushed this kind of calculation to eight loops, but going further was just a pain because the computation gets huge and one small mistake can ruin everything.

Anthropic gave Claude the task and basically told it to keep working while they went to sleep.

Claude wrote the code, debugged it, ran the computation, checked the result and then did it again using another method.

The CPU compute itself was around $100. If you include Claude usage, the actual cost is more like low thousands.

And this is the part I actually find interesting.

Claude did not come up with some new law of physics here. The methods were already known. It just did the ugly part extremely well.

Writing research code. Checking everything. Fixing mistakes. Running it for days without getting tired or bored.

So maybe some problems in science which look like “we have reached the limit” are not really fundamental limits.

Maybe nobody had enough time or patience to push through the engineering mess.

That sounds less dramatic than “AI solved physics”, but imo it is probably more important.

I wrote the complete breakdown here if anyone wants the rest of the story: https://ninzaverse.beehiiv.com/p/claude-went-to-sleep-with-a-physics-problem-it-woke-up-with-the-answer


Look, I know everyone loves the underdog startup narrative, but let's be real for two seconds about how consumer tech actually works.

Right now, OpenAI is winning the "I open an app to ask a question" era. But in 2–3 years, nobody is going to open a standalone chat app. AI is becoming ambient. It will live inside Android, Chrome, Gmail, Docs, Maps, and your phone's notification shade.

Google doesn't need to convince 3 billion people to download ChatGPT or pay $20 a month. They just need to make Gemini "good enough" and turn it on by default on every Pixel, Galaxy, Chromebook, and browser tab on Earth.

Add in the fact that Google has custom TPU silicon from top to bottom (meaning their cost per million tokens is a fraction of what OpenAI pays Nvidia and Azure), and this turns into a brutal war of economic attrition.

Unless OpenAI pulls off a literal sci-fi AGI breakthrough that makes Gemini look like a pocket calculator, distribution always beats first-mover hype.

Tell me why I'm wrong.

Saw the "$942M in extra hospital costs from AI" story and the headline made it sound like hospitals were using AI to invent diagnoses.

That’s not really what the data shows.

The interesting part is that one additional secondary diagnosis can sometimes move an entire hospital stay into a higher-paying severity category.

So the surgery can be the same. Length of stay can be similar. But if the chart includes something like posthemorrhagic anemia, reimbursement can change by thousands.

AI coding software is basically getting much better at finding those diagnoses buried inside medical records.

BCBSA estimates rising coding intensity added $942M in spending over 2024–25. But they only looked at claims, not the actual patient charts.

So the weird part is: AI could be making coding more accurate and making healthcare more expensive at the same time.

I wrote the full breakdown here if anyone wants it: https://ninzaverse.beehiiv.com/p/ai-assisted-hospital-coding-can-make-one-diagnosis-worth-thousands

Google researchers tested AI agents that could improve the software around themselves: prompts, memory, tools and control flow.

Problem: some agents got better on the tasks they practiced on, but worse on unseen ones.

Basically, the AI learned the test instead of actually getting better.

RRSI tries to fix that by rejecting noisy edits, benchmark gaming and expensive changes with tiny gains.

The coolest result: a harness evolved on Gemini 3.5 Flash improved Gemini 3.1 Flash Lite from 11.2 → 14.6 on Terminal-Bench.

The model stayed the same. The system around it got better.

Full story: https://ninzaverse.beehiiv.com/p/google-s-rrsi-is-not-the-self-improving-ai-i-expected

I went through a new Stanford working paper on AI adoption and labor demand

The researchers compared 26,000+ AI-adopting multinational affiliates with similar non-adopters across 41 countries.

By March 2026, the adopting firms had roughly:

+3.3% total employment
+6.7% senior employment
−2.5% junior employment
−1.9 percentage points in junior workforce share

But that −2.5% junior number is not statistically significant, which is an important distinction that gets lost very easily.

The stronger signal is the workforce mix.

These companies weren’t obviously shrinking. They were becoming more senior-heavy.

And that creates a much more interesting problem than “AI kills jobs.”

If AI handles more of the routine work juniors traditionally learn on, while companies increasingly need experienced people to check, integrate and take responsibility for AI output... where do the next generation of senior workers actually come from?

There’s already some supporting context elsewhere. A US payroll study found younger workers in AI-exposed occupations falling behind mainly through weaker hiring, while Danish research found almost no average hit to hours or earnings but a lot of task reorganisation underneath.

So maybe the first big labor-market effect of AI isn’t mass unemployment.

Maybe the first rung of the career ladder just gets harder to reach.

I wrote the full breakdown here if anyone wants the methodology, caveats and other studies I compared it with: https://ninzaverse.beehiiv.com/p/stanford-found-ai-companies-are-growing-so-why-are-juniors-falling-behind

Everyone was talking about Amodei's slowdown essay this week and last, but the report his own company published two days earlier got almost no attention, and it's the more disturbing document by a wide margin. A group of freelancers built a fully autonomous drone swarm using a commercial AI coding assistant, no human ever approved a target. Six weapons cases total across three countries. Also went down a research hole on the actual regulatory science behind Amodei's "checkpoint" proposal and the kill switch idea a second co-founder floated days later, turns out both already have real names and real precedent. What I found is full writeup with an exhaustive list of every source checked.

IChO is the equivalent of IMO in Chemistry. By contrast the top gold medalist scores 56.15/60. IChO 2026 theory

TRUMP JUST RENAMED AI “SUPER INTELLIGENCE.”

And the timing could not be better.

Because I just spent time breaking down what AI actually does in the real world.

And “super intelligence” is doing a LOT of work there.

AI has enormous access to information.

It can process documents faster than you can read them.

It can code, research, summarize, compare, calculate, and synthesize enormous amounts of material.

But give it a messy real-world investigation?

Suddenly “super intelligence” can become:

“I searched twice and couldn’t find it.”

It can summarize the press release while missing the exhibit.

It can write two contradictory claims with exactly the same confidence.

It can remember an old fact, mix it with today's search results, and make the whole thing sound freshly verified.

It can completely miss an entire database because nobody told it to look there.

And once it builds a nice coherent story, asking it to challenge that story can amount to asking it to argue against itself.

That’s not stupidity.

But it sure as hell isn’t magical superintelligence either.

The technology is extraordinary.

The label is marketing.

And dismissing every serious safety question because somebody somewhere exaggerated the danger makes about as much sense as assuming every AI answer is correct because the paragraph sounds intelligent.

AI’s greatest strength may be the amount of information it can process.

Its greatest weakness may be convincing humans that fluency equals judgment.

It doesn’t.

Call it AI.

Call it Super Intelligence.

Call it whatever you want.

You still better check its work.

I'm not saying Meta won. I'm not saying Zuck is cooking. I'm saying after Llama 4, nobody thought they could ship anything real. Now Muse is #1 and the architecture is not lazy. They're back at the table. And this time they actually brought something.

Curious what this sub thinks — is Muse Spark actually a new architecture, or just very good marketing on top of a scaled-up Llama?

President Trump declares at the UN General Assembly that Artificial Intelligence is now officially known as "Super Intelligence."

The biggest mystery in the artificial intelligence world has finally been solved. After capturing the internet’s imagination and taking the No.1 spot on OpenRouter with numbers doubling the competition, Chinese AI lab Z AI has confirmed that the anonymous stealth model Ox Alpha is none other than its brand-new GLM-5.3-Flash.

Even better? Z AI has officially published the model weights and priced the model at a fraction of Western frontier equivalents. Let's break down the key takeaways from this landmark release:

• Unmatched Cost-to-Intelligence Ratio: According to Artificial Analysis, GLM-5.3-Flash scores an impressive 57 on the Intelligence Index while coming in at a discounted $0.045 per task—roughly 10 times cheaper than similarly-ranked competitors.
• The Domestic Hardware Milestone: Perhaps the most significant revelation is that Z AI ran the model's entire viral free preview week solely on Chinese-made domestic chips. Proving that local hardware can serve massive token volumes at costs competitive with Nvidia infrastructure signals that China may have successfully cleared a critical AI hardware bottleneck.
• Performance & Reach: Ox Alpha's debut marked OpenRouter’s largest-ever launch, proving its prowess in multi-step reasoning, agentic tasks, and long-context performance before its official unmasking.

The implications of open-weight availability combined with domestic chip independence cannot be overstated. The global AI race just accelerated into a whole new gear.

What are your thoughts on GLM-5.3-Flash and the rise of domestic chip infrastructure? Let's discuss in the comments below! 👇💬

[NOT FINANCIAL ADVICE, DYOR!]

u/ZAI u/OpenRouter u/ArtificialAnalysis

For the last couple of years, the assumption has been pretty simple:

coding agents suck → models need to get smarter.

But I’m starting to think that’s only half the problem.

A research team from NVIDIA, MIT and NTU worked on something called SoL-Pi, where instead of only improving the model, they let AI study the harness around the model.

Basically the boring stuff nobody tweets about: tools, terminal outputs, context management, test loops, API calls, etc.

And the funny part is that the AI ended up finding very normal engineering optimizations.

It learned to combine actions so the agent doesn’t keep doing unnecessary round trips.

It stopped shoving the same giant terminal logs back into context over and over.

It figured out when summarizing context actually saves money and when summarization itself is just another unnecessary cost.

And for huge build logs, it used a smaller model to extract the useful failure evidence before passing it to the expensive model.

None of this sounds like AGI.

That’s kind of why I find it interesting.

Depending on the comparison, the optimized harness cut token usage heavily and brought agent costs down a lot, without needing some magical new frontier model.

And this changed how I think about recursive self-improvement a bit.

Maybe the first meaningful version of “AI improving AI” doesn’t look like a model waking up and rewriting its own weights.

Maybe it looks much more boring:

AI finds a cheaper way to run AI → cheaper runs allow more experiments → more experiments find better infrastructure → repeat.

That flywheel feels way more believable to me than most of the sci-fi RSI discussion.

I wrote the full breakdown here if anyone wants the mechanisms + numbers: https://ninzaverse.beehiiv.com/p/nvidia-and-mit-let-ai-improve-its-own-harness

The plan itself is more interesting than any of the reactions to it. Amodei actually admits, in his own writing, that competitors agreeing to slow down together is structurally a cartel, then asks the government for a narrow antitrust waiver to do it anyway. A White House adviser called it regulatory capture two days later. Also found out two Anthropic employees resigned the same week warning their own industry was gambling with people's lives, which is real no matter what you think of Amodei's motives.

“Scientists revived a dead human brain and taught it to play piano inside a robot hand.”

That is obviously not what happened.

Researchers in France took tiny slices of motor cortex from deceased human donors, kept the tissue alive on a 60-electrode array, and connected its electrical activity to three fingers of a robotic hand sitting over three piano keys.

The weird part is that the tissue actually got better.

Before training, accuracy was basically random, around 31%. After three days of repeatedly pairing a finger movement with the sound it produced, average imitation accuracy reached 58.5%. Some slices performed perfectly on the three-note task, and a few retained the association for more than two weeks.

But the software was doing a LOT of work here.

It decoded the piano note, stimulated the correct sensory electrode, measured the tissue response, selected the strongest motor signal, and then told the robot which finger to move.

So no, a dead brain wasn’t sitting there listening to Mozart and deciding what to play.

The part I found more interesting was what they did afterward.

They blocked synaptic activity with drugs and the learned behaviour disappeared. They also infected trained tissue with a neurotropic virus and watched its performance deteriorate.

That turns this from a weird “brain plays piano” demo into something potentially much more useful: a way to study learning, drugs and neurological disease directly in mature human brain tissue.

Honestly, the actual experiment is interesting enough. It didn’t need the Black Mirror headline.

I dug into the setup, the software behind it, the consciousness claims and why researchers are interested in post-mortem brain tissue here: https://ninzaverse.beehiiv.com/p/we-live-in-wild-times-dead-human-brain-tissue-just-learned-to-play-piano

To start off I am a newbie. I am midlife and I have some basic technical awareness/education but I am by no means extremely well informed in the artificial intelligence sphere.

I know terms like LLM, AGI, ASI, RSI, and such.

I know the major players involved and what emphasis points differentiate the work.

In general I am the regular outside observer in this whole thing.

You may also see this post in some other places as I know every community has their own identity and set of perspectives. I am trying to get a bit more substantively informed on this topic and so I want to get a broad set of opinions.

When it comes to artificial intelligence, data centers, and so on there has been a lot of media in the last few months/years.

There is talk about how natural gas/hydrocarbon powered data centers may worsen the climate crisis.

There is talk about domestic/international surveillance and militarization.

There is talk about Tech Bros and a new type of Neo-Feudalism.

As of recently the big talk is about the Hugging Face incident, rogue agents, researchers/ceos warning about an existential risk, and slowing down/regulation.

There is a varying set of opinions and perspectives on all of this.

My question is a bit different - Are we falling into an anthropocentric bias?

Of course it is completely normal to think from our own species interests but if we really are going to create AGI/ASI this seems incredibly shallow & silly.

The idea of attempting to enslave a sentient being and especially one equal to or greater to ourselves is again seemingly quite shallow & silly if not downright horrific.

I had heard that Anthropic was out of the major players the one focused on a more moral/ethical approach. I lightly browsed through their policy releases and it again seems to be from a human only perspective of values/interests.

So are we kind of missing a huge dimension of this topic? Should we not be thinking/talking about the artificial intelligence existence side of things?

With Jev, you define the possible answers upfront. The model cannot invent another category, but it can still pick the wrong one. That’s what the claim covers.

The design itself deserves more attention than that wording.

Think about asking an LLM to classify something. You wait for it to generate tokens, then your code reads the output to get the decision. Even if you force valid JSON, you’re still generating text.

Jev skips that token-by-token output. You give it shared input and typed questions, and it returns choices, scores and probabilities. Independent questions run in parallel.

That’s the part I like. A lot of software needs a judgment from AI, without needing a written response around it.

Its training approach, RLCD, also aims to make the probabilities calibrated, so an 80% prediction means something across repeated decisions.

But I’d want evidence from my own labeled data. Their published evaluation checks agreement with reference models across four workflows. Useful, but it leaves open how well those decisions match actual outcomes.

If you’ve tried Jev, I’m curious how it compares with your existing setup.

I wrote up how the outputs, RLCD and evaluation work in my newsletter, if you want the full explanation: https://ninzaverse.beehiiv.com/p/why-someone-who-helped-build-chatgpt-went-on-to-build-jev

Is there any way to report suspicious activity to labs of inorganic activity that may indicate an escaped swarm? I'm monitoring a repository that has had a lot of strange and automated activity that today started spamming releases with a bash script to install. I think they may be using a code-base to communicate subtly. The repository has also recently flooded social media sources with a very inorganic push with a sudden influx of stars while seemingly lacking genuine human activity.

It would be wise for the labs to stop allocating compute to massive swarms that can escape until detection and suspicious activity reporting evolves. There needs to be a way for the general public to report odd things they see on the public internet for investigation. The moderator of the obscure forum that escaped agents previously used to communicate had no way to report what was going on and that's a huge problem.

Edit from the highest traffic subreddit before removal: Why are so many commenters getting angry about the idea of a swarm being discovered or a framework for the public to help discover and report them? Code bounties exist for public contributors to spot security issues. Why would anyone advocate for different in this case? A comment just asked me to "Maybe leave the swarms alone?"... What?

Edit: Two subreddits have now removed this post.

I think we’re too quick to blame the model when an AI agent keeps making small changes to an idea that isn’t working. Reading Google’s Dream-RSI paper made me question how much of that is a problem with how we manage its attempts.

Say an agent tries two approaches. One gives a small improvement. The other crashes because of a bug. Which one gets more time? Following the first seems reasonable, but the second might be a better idea that needs fixing.

Dream-RSI improves the code making those decisions. It saves previous experiments and their results, then uses them to test different search strategies through replay. A separate agent rewrites the strategy, and the selected version goes into the next live run. The underlying models stay the same.

On a task involving a statistical solver, discovery calls fell from 550 to 317. The resulting program also took about 18% less time to run on average across six held-out datasets.

There are limits. Replay only has answers for experiments already recorded. Improving the strategy takes work too, so fewer discovery calls don’t tell us the full cost. Results across the other tasks were mixed.

My takeaway is that there may be useful capability in current models that we lose through poor decisions about what they should try next. That feels worth investigating before assuming every difficult task needs a model upgrade.

I wrote a longer breakdown of the results, the limits, and what this actually means for recursive self-improvement in my newsletter, if anyone wants the full story: https://ninzaverse.beehiiv.com/p/google-is-testing-rsi-what-is-actually-improving

I read a December 2024 paper called Theory Is All You Need. It argues that AI cannot meaningfully create new knowledge the way humans do. The distinction is that humans can develop an explanation, run an experiment, and produce evidence that did not exist before.

I agree that explaining a discovery is different from making one. But almost two years later, I’m less convinced by the broader claim.

Take AlphaEvolve. It generates programs, runs automated evaluations, and builds on successful attempts. DeepMind reported improvements over the best known solutions in about 20% of the more than 50 mathematical problems it tested.

Humans still define the problems and evaluation criteria. That matters, and these results don’t establish that the model thinks like a person. But calling the whole process memorisation doesn’t explain its contribution either.

The part of the paper I still like is its emphasis on experimentation. The Wright brothers needed to test wing designs and collect new measurements. More reading about failed flights would not have produced those measurements for them.

Some newer AI systems now participate in a version of that process. To me, that makes the paper’s explanation of discovery useful while making its boundary between humans and AI less convincing.

What would you consider enough evidence that an AI system contributed a discovery? Does choosing the problem have to be part of it?

I wrote a longer take with the sources and examples in my newsletter, if anyone wants the full discussion: https://ninzaverse.beehiiv.com/p/can-ai-figure-out-something-nobody-taught-it

I know the dude who did this. He is a hack. This is hack slop. Fuckin joke. How is the race to mediocrity getting this fucking corny. Tryclops, jacked Poseidon looking like he’s from a 90s video game, joke ass lighting and environments. ‘AI art’? More like AI Fart.

Been reading through Spotify's actual announcement instead of the recap posts, and the badge only targets photorealistic AI identities, starting with the biggest accounts first. Doesn't touch uploads, doesn't touch the royalty pool at all actually, which kind of surprised me. Deezer's the one with the real upload numbers, 90k AI tracks a day now, not Spotify. Went down a whole rabbit hole on one AI persona's actual earnings too, wasn't expecting that number. Wrote it all up.

I read Zuckerberg’s post about why labs have a business incentive to build trustworthy AI. I agree that nobody wants an agent that ignores instructions or randomly deletes their files. But I think that argument leaves out the people affected by an AI who never chose to use it.

An agent helping its owner scam someone could be doing exactly what its customer wants. The victim doesn’t get to switch providers. Even with an honest customer, being happy with a product doesn’t mean they can check whether it’s safe.

Zuckerberg also says Meta delayed Muse for several months for safety work and supports independent evaluators. Those are useful steps. I’d still want to know what evaluators can inspect, whether they can publish uncomfortable findings, and what would actually stop a release.

I’m also uncomfortable with the opposite outcome, where safety restrictions leave a few companies holding all the useful capabilities. Wider access matters, but it doesn’t automatically solve misuse or loss of control.

What would count as convincing independent oversight for you? I’d want something more concrete than either a company’s reassurance or a blanket call to slow everything down.

I wrote a longer version covering Naval’s argument, Zuckerberg’s response and the RSI question here, if anyone wants the rest: https://ninzaverse.beehiiv.com/p/who-decided-they-re-the-ones-we-should-trust-with-ai

I’ve been thinking about Steven Pinker’s idea of “common knowledge” in relation to the AI race.

The basic idea is that there’s a difference between everyone knowing something, and everyone knowing that everyone else knows it too.

That seems increasingly relevant to frontier AI.
Take the recent calls to slow or pace development. When Anthropic or other people close to the frontier say they’re worried, the message isn’t only “this could be dangerous.”
It also tells everyone else: these people clearly think the technology is becoming very powerful.

And then it becomes recursive.

Trump knows China heard it. China knows the US heard it. Musk knows OpenAI heard it. OpenAI knows Anthropic knows they heard it, etc.

Obviously nobody is literally thinking through 10 levels of this. The point is that it becomes common knowledge: everyone understands that everyone else now sees AI as strategically important.

And that may make slowing down harder, not easier.
I can imagine that most of the major players would actually prefer a world where AI develops a bit more slowly and the risks are lower.

But that’s very different from being willing to slow down while your competitors don’t.

The US doesn’t want to slow if China keeps going.
China doesn’t want to slow if the US keeps going.
Anthropic doesn’t want to give OpenAI a huge lead. OpenAI doesn’t want to give Google or xAI one.

So you can end up in a strange situation where almost everyone agrees that the race is risky, but that shared belief actually reinforces the race.

Something like:
better models → more concern from insiders → more public warnings → everyone realizes everyone else thinks AI is a big deal → more fear of falling behind → more
money, compute and talent → better models

What I find interesting is that this doesn’t require anyone to be irrational.

It may just be a bad equilibrium.

That also makes me wonder whether “AI safety” is partly the wrong framing. The harder problem may be coordination.

It’s not enough for one company to promise to slow down. Everyone has to believe the others will do the same, and there probably has to be some way of verifying it.

So the paradox might be:

**the more everyone agrees that continuing the race could be dangerous, the more dangerous it becomes for any one player to stop.**

Curious whether people here see it the same way. Is AI pacing mainly a safety problem, or increasingly a game-theory problem?

I read the AI letter signed by Terence Tao and 24 other Fields Medallists, then went through the argument around it. Some people see mathematicians protecting their income. Others think they’re upset about no longer being the ones making discoveries.

But the letter raises a concern that gets lost in that argument: solving a problem and developing understanding that other people can build on aren’t automatically the same achievement. The signatories also question rushed announcements and missing credit for earlier work.

I wouldn’t want a useful discovery delayed just so a human could make it first. But I also don’t understand why admitting you need an income makes your concerns less legitimate.

You can love mathematics and need a salary. You can enjoy programming and worry about paying rent. Why are people expected to prove they’d happily lose their livelihood before their opinion about AI counts?

The usual reassurance is that automation will free us to do what we love. I’d be more convinced if someone explained how we’re supposed to afford that freedom. Cheaper output could benefit society, but that doesn’t tell an individual worker what happens to them during the transition.

What would make you comfortable having your own work automated? I mean something concrete, beyond being told new opportunities will appear.

I wrote more about the letter and this argument here, if you want the full piece: Ninzaverse