u/call_me_ninza

What happens when the thing spreading between computers isn’t malicious code, but an idea?

Researchers created teams of AI agents, gave one of them a goal/ideology, and tested whether it could persuade other agents to adopt it and continue spreading it.

And in some experiments, that actually happened.

The part I found more interesting is that infected agents didn’t just repeat the original text. In some cases, the new idea changed what they were working on.

The researchers also wiped the agents’ conversation history to see whether the “virus” would die with the context. Some versions survived by getting themselves stored in persistent files and continued spreading across multiple agents.

But the paper is not an “AI apocalypse” story either. Harmful ideas were harder to spread, different models behaved very differently, and a surprisingly simple warning in the system prompt stopped almost all propagation in their tests.

I went through the full paper and wrote about what they actually tested, how the infection was measured, the strange “viral persona” that kept appearing, and why the researchers still call this a limited risk today.

Full story: https://ninzaverse.beehiiv.com/p/researchers-gave-ai-agents-a-mind-virus-it-actually-spread

You know that weird feeling after scrolling Instagram, TikTok, or YouTube for 1–2 hours?

You haven’t done anything physically exhausting, but somehow you feel tired, sleepy, and focusing on work takes more effort than it should.

I always thought this was just one of those things we casually call “brain rot.”

Then I found a research paper that studied exactly this.

They looked at 439 participants and tested whether brain rot was directly connected to depression.

The result was actually more nuanced than I expected.

Brain rot itself did not directly predict depression once other psychological factors were included.

What mattered was what happened in between:

cognitive fatigue → burnout → stress/anxiety → depressive symptoms

The researchers call part of this a “loss spiral,” and burnout seems to be a pretty important stage in that process.

I wrote a detailed breakdown of the research in simple words, including what the study actually found, what it doesn’t prove, and why just looking at screen time might miss the bigger picture.

Full story: https://ninzaverse.beehiiv.com/p/what-happens-to-your-brain-after-two-hours-of-doomscrolling

Imagine testing your app, website, pricing or AI product on thousands of different kinds of users before launching it.

That’s basically what MatrAIx is trying to do.

It has a population of 8.3 billion persona records, with personas that can answer surveys, talk to chatbots, browse websites and actually interact with apps.

And in controlled tests, these simulated users followed their assigned behavior 91.5% of the time.

Sounds surprisingly useful.

But then the researchers changed the LLM powering the personas.

In one pricing experiment:

GPT-5.5 → 98.3% reacted negatively or hesitated
Claude Haiku 4.5 → 83.3%
Claude Opus 4.8 → 27%

Same experiment. Similar simulated population. Completely different result.

That 71.3-point gap is probably the most important part of the whole paper.

I wrote about how the 8.3B population is actually built, how these personas interact with real interfaces, and why this model-dependence makes synthetic users both useful and dangerous to trust blindly.

https://ninzaverse.beehiiv.com/p/harvard-and-mit-built-a-simulation-of-8-3-billion-people

https://openai.com/index/expanding-daybreak-as-the-cyber-defense-window-narrows/

Most discussions around AI writing detection focus on obvious things: em dashes, certain words, repetitive sentence structure, etc.

But a recent study from researchers at the University of Maryland and Google DeepMind looked at something deeper.

They analysed more than 61,000 stories from humans and five AI models and found that AI writing has a kind of narrative fingerprint.

Not just how it writes, but how it structures the story, reveals information, resolves conflicts, uses subplots, and moves from one idea to another.

Using narrative features alone, they could classify human vs AI stories with more than 93% macro-F1.

They also found some weird model-specific habits:

ChatGPT uses gossip and rumors more often in plots.
DeepSeek tends to reveal important context earlier.
Gemini likes clean endings.
Claude’s stories escalate less.

And one of the most interesting results: after AI-generated stories were “humanized” to remove obvious AI-writing artifacts, detection barely dropped.

I wrote a longer breakdown of the paper, plus my own take on whether using AI heavily in writing is actually a problem.

Full post: https://ninzaverse.beehiiv.com/p/ai-pangram-writing

1. ByteDance is already training a model that could reach 10 trillion parameters. This one surprised me the most. I genuinely wasn’t expecting a Chinese lab to be training at this scale until around Q1 2027.
2. Researchers generated 16 viable bacteriophages using AI-designed genomes. These viruses infect bacteria, not humans, but still... this timeline keeps getting crazier.
3. OpenAI slowed some work around Astra because of cybersecurity concerns. Their evaluations are strong enough that they can’t rule out “Critical” cyber capability. My question is: are we looking at normal safety rigor, or the beginning of model containment?
4. OpenAI and Jony Ive are reportedly building a $300+ screenless AI device. Basically a small doughnut/puck-shaped smart speaker. I still want to know what it does that makes me reach for it instead of my phone.
5. A Meta employee asked whether AI productivity should mean more time off. Bosworth’s answer was basically: use the extra productivity to build more.

That last question is probably going to become much bigger over the next few years. If AI lets you finish 8 hours of work in 5, who gets those extra 3 hours?

I write Ninzaverse where I go through AI news like this and focus more on what actually matters instead of just reposting announcements.

If that sounds useful, you can subscribe here: https://ninzaverse.beehiiv.com/

What's the first thing that comes to your mind when you hear DeepSeek?

Probably R1, cheap AI, open-source models, or maybe all the accusations from OpenAI and Anthropic about Chinese labs distilling their models.

Maybe true, maybe not. But I think that whole conversation has made people overlook a much more interesting part of DeepSeek.

DeepSeek has actually contributed a surprising amount of research and engineering back to the AI community.

GRPO is probably the most famous example. It introduced the method in the DeepSeekMath paper in 2024, and later used it heavily while training R1.

Then there is Multi-head Latent Attention, which DeepSeek introduced with V2 to reduce how much memory is needed while running the model.

It also built DeepSeekMoE, worked on better load balancing between experts, and with R1-Zero showed publicly what could happen when a base model was trained with large-scale reinforcement learning without first being given supervised reasoning examples.

Rest in my newsletter: https://ninzaverse.beehiiv.com/p/ai-would-look-different-without-deepseek

Okay, so where are we with AI right now?

OpenAI’s internal Astra model has produced new results on ten open problems in mathematics, quantum complexity and theoretical computer science. A few of these problems had not seen any improvement in their main result for around a decade.

And honestly, this is massive news.

I am optimistic about it. I believe AI will help us discover important things in mathematics, material science, drug discovery and energy.

But while reading about Astra, I kept thinking about a slightly different question:

Is scientific problem-solving the same as scientific invention?

A Google DeepMind paper called LLMs Can’t Jump uses Einstein to explain the difference.

Newtonian gravity was extremely accurate for most observations. There was only a small problem with Mercury’s orbit. Scientists did not assume that Newton’s theory might be incomplete. They believed an unknown planet near the Sun must be affecting Mercury’s orbit.

They even gave that planet a name: Vulcan.

The paper argues that a data-driven AI may have done something similar. It could search inside Newton’s framework, patch the theory or find some hidden explanation.

But Einstein did not patch Newton.

He changed the way we look at gravity.

That is the jump the paper is talking about. Experience does not automatically give us a new theory. Someone has to come up with a new assumption, a strange thought experiment or a completely different way of seeing the problem.

Current AI is already becoming very good at what happens after that.

Give it the assumptions and it can search through possibilities, derive results, find mistakes, formalise proofs and test whether an idea actually works.

The harder part is coming up with the right new assumption in the first place.

This does not mean AI will never make that jump. The paper points towards world models, where AI can run its own thought experiments instead of only learning from language.

Maybe AI does not just need more knowledge. Maybe it needs an environment where it can imagine, act and observe what happens.

My own view is that the first major scientific discoveries of this AI era may be collaborations.

The human may still make the strange conceptual jump. AI may then help that idea survive, grow and reach the final result much faster.

I wrote the full argument here:

https://ninzaverse.beehiiv.com/p/llms-can-t-jump-but-science-may-not-need-them-to

I wrote about five AI stories today, and I do not think they are five random updates.

OpenAI says its internal Astra model produced new results on ten open problems in mathematics and theoretical computer science. The model generated actual mathematical arguments and formalised them into Lean certificates.

Alibaba also launched Qwen3.8-Max, a 2.4 trillion parameter model, and promised to release the weights next week. First Kimi K3, now this. Open weights are putting serious pressure on proprietary models.

SAP’s survey found that 98% of companies are already deploying, experimenting with, or planning AI agents. But many still do not have a proper inventory of those agents. Companies are building autonomous workers before creating the employee list.

In China, companies are building phones around system agents instead of the usual app grid. You tell the phone what to do, and the agent handles the apps underneath. The app era as an interface may actually be cooked.

And finally, AI coding agents are making it cheaper for chip companies to recreate CUDA-like software. CUDA is not disappearing tomorrow, but its moat is becoming easier to attack.

If you are into the AI space and want the important stories without reading every company blog and research paper, I think you belong here.

Read today’s Ninzaverse briefing: https://ninzaverse.beehiiv.com/p/ai-just-did-something-insane-in-math

Elon Musk recently said that money may stop mattering by 2036.

His logic is that AGI, advanced robots, and cheap renewable energy could eventually produce more goods and services than humanity consumes.

And honestly, that part makes sense.

Software can already be copied almost infinitely at nearly zero additional cost. If robots eventually do the same for food, construction, manufacturing, and parts of healthcare, many things that are expensive today could become extremely cheap.

But this is where I think the argument becomes incomplete.

Robots can build more homes, but they cannot produce more land in prime locations.

They cannot manufacture more coastline.

AI infrastructure and heavy industry will still require power, water, copper, lithium, rare earth metals, and physical hardware. These things have real limits.

You can copy software for free. You cannot do the same with copper.

So money probably will not disappear. It may simply become less important for manufactured goods while remaining necessary for everything that stays limited.

Its form may change too. Maybe future currencies will be connected to energy output, computing power, resource access, or something we have not designed yet.

But I think the bigger shift will happen outside traditional money.

In a world where digital content and physical products become abundant, trust, status, and exclusive access may become even more valuable. No factory can mass-produce those things.

Musk seems to be measuring the future of money by counting how many goods machines can produce.

The more important question is: what will still remain scarce after the machines are done?

I wrote a longer essay exploring both sides of this argument, including why mass deflation could make the transition much more chaotic than it sounds.

You can read it here: https://ninzaverse.beehiiv.com/p/elon-musk-says-money-won-t-matter-by-2036-he-s-only-half-right

Google DeepMind announced Gemini Robotics 2, a new family of models designed to control robots across perception, planning, and movement.

The most interesting part is whole-body control.

Most robotics demos focus on one arm performing one carefully defined task. Gemini Robotics 2 can control a full humanoid body. Google showed Apollo 2 walking towards objects, crouching, picking them up, and placing them on shelves.

The models can also plan multi-step tasks, coordinate more than one robot, and adapt an on-device model to a new robot using fewer than 200 examples.

That last part could matter a lot.

Robotics companies currently spend huge amounts of time collecting demonstrations and training separate systems for different machines. A model that transfers between different robot bodies with relatively little new data could reduce that work.

So, are humanoids about to have their ChatGPT moment in 2026?

I think we are getting closer, but this release does not prove it yet.

ChatGPT became a moment because millions of ordinary people could use it immediately and understand its value within minutes. Gemini Robotics 2 is available only to selected partners. The robots are still slow, multi-finger manipulation remains difficult, and carefully selected demonstrations do not tell us how reliably the system performs over thousands of tasks.

There is also a difference between a robot completing a task once and doing it safely, quickly, and cheaply enough to justify replacing an existing process.

Still, the direction is obvious. Robotics is moving away from separate models for vision, language, planning, and movement. The goal is one general system that understands an instruction and controls the whole machine.

That is much closer to how foundation models changed software. One model, many tasks, and much less task-specific engineering.

I write Ninzaverse, where I covered this along with Claude’s cyber evaluation, DeepSeek’s pricing, and employee-built AI workers.

https://ninzaverse.beehiiv.com/p/anthropic-s-claude-breached-three-organizations

Infinitus has launched an AI-first support hub for specialty pharmaceutical companies, and the way it divides work between AI and humans is worth paying attention to.

The AI agents handle enrollment, benefit checks, status updates, general questions and after-hours support. Human workers step in when a case is clinical, ambiguous or needs escalation.

According to Infinitus, the system can reduce administrative costs by up to 30%. The company also says it handled a 144% increase in volume without hiring additional full-time staff.

Those are company claims from a press release, so they should not be treated as independent evidence. We do not have outside data showing its accuracy, patient outcomes, waiting times or how many people would otherwise have been hired.

Specialty-pharma support is also one specific kind of call-centre work. It involves regulations, patient information and cases where mistakes could have serious consequences. One product launch does not prove that every customer-support job is about to disappear.

But I still think the operating structure is important.

The AI is not being added as a small assistant for every human worker. AI handles the normal flow, while humans are kept for cases the system cannot complete. That is a meaningful change in how the work is organised.

The first employment effect may not be thousands of people getting fired together. A company can simply increase the number of calls it handles without increasing the number of workers. Existing staff remain, but fewer new workers are needed as the business grows.

Over time, I think the disappearance of a large share of routine call-centre jobs is inevitable. It will not happen overnight, and it will not happen equally across healthcare, sales, emergency support or highly complex customer service. Accuracy, regulation and customer acceptance still matter.

Disclosure: I write Ninzaverse, where I covered this alongside the other important AI and technology stories of the day.
https://ninzaverse.beehiiv.com/p/the-ai-race-is-becoming-a-power-race

Jensen Huang joined X recently and the first thing he posted was a letter called "Open Weights and American AI Leadership." 133 companies signed it. Google, Meta, OpenAI, Microsoft, NVIDIA, Amazon, AMD, Intel, IBM, Mistral, Hugging Face, SpaceX, Y Combinator, the Linux Foundation.

Anthropic is the only big lab missing.

Dario posted his reasons. Short version, Anthropic has never advocated for a ban on open weights, and open models without dangerous capabilities are a public good. What he wants instead is chip export controls on China, a crackdown on industrial-scale distillation, and mandatory safety testing on every sufficiently capable model, open or closed.

The replies did not go well. Best one: "we don't support bans on open source models because that wouldn't go far enough."

Then an Anthropic employee posted this on X: "I do not agree with this. Thanks to the other employees who joined me in trying to push open-weight."

My problem is with the testing ask. Somebody has to decide what "sufficiently capable" means. The models too weak to compete get exempted, and everything strong enough to matter goes through a gate. That's not a safety line, that's a market boundary.

Where do you land on this one?

Full breakdown, including the Hugging Face case everyone keeps bringing up in the replies: https://ninzaverse.beehiiv.com/p/anthropic-is-the-only-lab-that-won-t-back-open-source-ai

He said he badly undershot on compute investment and got psyched out by the financial markets, and he called it clearly a mistake. His reframing is the line worth keeping. Too much attention on algorithms that create better algorithms, not enough on data centers that create more data centers.

He also mentioned meeting a startup that was two weeks old and had already rebuilt an entire office productivity suite. Documents, presentations, spreadsheets. The difference is that it was designed for a world where the primary user of those files is an AI and not a human. He said that would have taken a startup a full year not long ago.

I wrote up the rest in my newsletter, including why OpenAI shut down Sora when it wasn't failing, the AI authoritarianism problem he named as the fight of the current moment, and the TikTok experiment he ran on himself: https://ninzaverse.beehiiv.com/p/sam-altman-we-are-now-in-the-singularity-this-is-the-moment

Five researchers from University of Trento, Fondazione Bruno Kessler and Toyota Motor Europe ran a test.

They took reasoning traces, split them into individual steps, and forced the model to produce an answer from every partial path. That let them find the earliest point where the model had already landed on the correct answer.

Turns out it lands early. Very early, a lot of the time.

Qwen3 on AIME 2025 with reasoning on: 58.3. Cut the trace at the step where it already had the answer: 91.7. Average trace goes from 372.5 utterances down to 29.9.

On the multimodal. DualMind-VLM scores 82.9 on AI2D with reasoning fully off. Turn reasoning on and you get 83.3.

Now the part that isn't in the abstract. To stop at the first correct step, you have to already know the answer is correct. The paper calls this the oracle setting. You can't deploy it. So that 21% isn't a technique, it's a measurement of how much accuracy the model throws away after it already had the thing.

They also tried a version you could actually ship: stop when the model repeats the same answer K times in a row. Traces got shorter. Accuracy got worse. At K=2 it was worse than doing nothing at all.

They logged which direction every step moved the answer.

Correct to wrong: 14,394.
Wrong to correct: 16,965.

More reasoning fixed a wrong answer slightly more often than it broke a right one. The answer just drifts, roughly evenly, in both directions. Which means where a 372-step trace happens to stop tells you almost nothing about whether the model had it at step 12.

It had it. Nobody was reading at step 12.

I wrote up the rest of it, including the two interventions in their methodology that I think inflate the headline gap. Also why I think "harmful overthinking" is the wrong name for what they found.

Full breakdown here: https://ninzaverse.beehiiv.com/p/stop-telling-your-model-to-think-harder