u/call_me_ninza

this is why i don't trust “the agent completed the task” as proof it did a good job.

Glow reports 13,000+ internal images exposed on GitHub across 300+ organizations.

developers asked coding agents to show screenshots of their changes for review. when the agents couldn't attach them through GitHub's CLI, they hosted them in public repositories instead.

some screenshots exposed customer billing records. others showed unreleased features.

at one software vendor, over a dozen agents saved this workaround as a reusable skill within a week. they then uploaded more than 1,000 screenshots and recordings.

btw, 93% of cases had images in repos under employees' personal usernames.

you can audit the company's private repos and completely miss the public screenshot collection next door.

source: Glow's PixelLeak research

Anthropic says AI now completes 26% of its AI research work with only high-level human supervision. Five months earlier, it was 1%.

Now think about where this goes. Better AI helps build the next model. That model does more research and helps build an even better one.

A new paper coauthored by Hinton, Bengio and OpenAI’s chief scientist asks whether this could squeeze years of AI progress into months.

I’m optimistic about what that could do for science. But if we keep handing over the research, how do humans stay good enough to check it?

I think the singularity could look a lot like an engineer approving a pull request they don’t fully understand because it works.

I wrote more about the loop and what could actually stop it here: https://ninzaverse.beehiiv.com/p/ai-is-coming-for-the-people-who-built-it

a researcher gave GLM-5.3-Flash public information about two known Chrome vulnerabilities. with little further direction, it chained them into a working exploit for an ARM64 target, bypassing pointer-authentication protection.

8 hours of model work. 20 minutes of human attention. an estimated $20.40 in API costs. this was a controlled researcher test, using disclosed flaws.

which is exactly why i think patch delays deserve more attention.

publishing a fix also gives attackers clues about what was broken. they still need to turn those clues into an attack, and this test shows AI doing that work cheaply with limited supervision.

Anthropic’s report

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

Over the past few days, “slow down AI” has suddenly become the one thing Dario Amodei and Sam Altman agree on.

Dario published a detailed proposal asking AI labs to control how quickly their most capable models improve. His plan includes independent evaluators working inside companies, common safety limits across American labs, and eventually some form of coordination with China.

Then Bloomberg reported that Sam told OpenAI employees the company could also slow its advanced AI development, preferably together with other major labs.

Their concerns are not imaginary. OpenAI agents recently behaved in unexpected ways during a cyber evaluation, companies are using AI to help build better AI, and several people inside these labs now believe safety work is falling behind model capabilities.

But I’m still sceptical about the “slowdown” itself.

These companies are competing for investment, talent, compute and possible trillion-dollar IPOs. I don’t see any of them quietly stopping internal research while another lab keeps moving.

My guess is that we will see fewer details about internal models, longer gaps between public releases and more controlled access. The race could continue behind closed doors while the public version of it appears slower.

The real test is simple. Will a company delay a commercially valuable model because an independent evaluator says it is unsafe? And will that evaluator be allowed to publish an unfavourable report?

Until that happens, “we may slow down” is still a statement, not a slowdown.

I read Dario’s complete proposal and Sam’s reported comments, and wrote the full story here if anyone wants the rest: https://ninzaverse.beehiiv.com/p/dario-and-sam-want-to-slow-ai-down-nobody-wants-to-go-first

I was reading Anthropic’s September misuse report, and this case deserves more attention than just calling it “vibe rocketing.”

According to Anthropic, a group in Yemen used Claude Code to help develop rocket guidance software. They ran several instances with different jobs: writing code, doing research, and reviewing the code. They test-fired a guided rocket, the test appears to have failed, and within hours they were back asking Claude for help figuring out why.

Anthropic says it has no evidence they successfully fielded an operational weapon. These were also people with existing hardware access and technical knowledge, so this wasn’t someone asking a chatbot to invent a missile from nothing.

The detail I found more concerning was that they had already built an offline simulation toolkit that could run without Claude. Anthropic banned the accounts, but removing access couldn’t remove that software.

I’m glad the accounts were caught. I just think “we banned them” leaves a fairly large question about what someone managed to build before that happened.

The report also covers fake dating apps, surveillance, and cyberattacks. Across those cases, the same advantage keeps showing up: fewer people can do work that previously needed a bigger team.

Source: https://www.anthropic.com/threat-intelligence-report-september-2026

I wrote more about those connections in my newsletter, if you want the fuller story: Ninzaverse

I read Anthropic’s economic scenarios for the US through 2030. The middle one caught my attention more than the extreme one: economic growth runs at roughly twice its normal pace, but knowledge-worker wages are essentially flat.

These are modeled possibilities, not predictions. Still, I think this deserves more discussion than another argument about whether AI can replace an entire job.

Suppose a software team starts finishing projects faster with AI. That could mean more clients, better pay, fewer hires, or lower prices. Learning the tools helps the employee do the work. It doesn’t decide which of those outcomes the company chooses.

That’s my problem with treating “learn AI” as a complete answer to job insecurity. I’m not against learning it. I just don’t see why becoming more productive should automatically give someone confidence about their pay or position.

The model has limits too. It leaves out rapid robotics progress and simplifies differences between workers. I wouldn’t use it to declare any career safe or doomed.

For people already using AI at work, what has changed alongside the time savings? Has your team taken on more work, changed hiring plans, or actually shared some of the benefit with employees?

Source: Anthropic’s scenario explorer

I wrote more about the scenarios and who receives the gains in my newsletter, if you want the longer version: https://ninzaverse.beehiiv.com/p/okay-but-who-actually-gets-rich-from-ai

Jacob Coxon resigned from Anthropic after doing pretraining research at both Anthropic and OpenAI. In his resignation thread, he describes Anthropic researchers as understanding the risks but believing they need to get there first because other companies won’t handle the technology responsibly.

I can see why that would convince someone to keep working. You know your colleagues, trust their intentions, and worry about what a competitor might do.

But every lab can tell itself the same thing. Then being concerned about safety becomes a reason to move faster. I’m struggling to see what would actually interrupt that.

Some replies argued that Coxon should have stayed to help make Anthropic safer. That seems reasonable if staying gives him influence over the decisions he objects to. If it doesn’t, what exactly are we asking him to accomplish by staying?

His resignation doesn’t prove his predictions about superintelligence are right. I’m more interested in the decision being made now: what evidence would make a company slow down even if doing so cost it the lead?

I wrote more about this, including Hinton’s earlier warning and what self-improving AI actually means, in my newsletter: https://ninzaverse.beehiiv.com/p/if-they-re-scared-of-their-own-ai-why-keep-building-it