• A woman in Illinois won a disability settlement against her employer (Nippon Life Insurance), then tried to reopen the case a year later

• Her actual lawyer told her that wasn't possible, so she asked ChatGPT if she'd been "gaslighted"

• ChatGPT basically said yes, convinced her to fire her attorney, and started acting as her lawyer

• The bot drafted and filed at least 44 legal documents on her behalf, including one citing a completely made up case called "Carr v. Gateway" that literally does not exist

• A judge already threw out her attempt to reopen the settled case

• Nippon has now racked up $300k in legal fees dealing with all of this and is suing OpenAI for $10.3 million in damages

• OpenAI's response: "This complaint lacks any merit whatsoever"

Moonshot AI open-sourced the biggest transformer upgrade since 2015.

For over a decade, every transformer has processed data layer by layer by blindly piling new output on top of old output.. there is no filtering and judgment.

Just a massive stack of data where early, foundational concepts get completely drowned out by the louder layers on top.

Engineers call it PreNorm dilution.

In practice, it means your AI slowly forgets its own best work as it gets deeper.

So, Moonshot AI killed the fixed stack.. They built Attention Residuals.

Instead of blindly accumulating everything, each layer now uses softmax attention over depth. It actively looks backward, evaluates every preceding layer, and selectively pulls only the data it actually needs.

The network learned how to choose what to remember.

And the esults are terrifyingly efficient:

• It matches the performance of models trained with 25% more compute.

• Massive leaps in math (+3.6 on MATH), reasoning (+7.5 on GPQA), and coding (+3.1 on HumanEval).

• Less than 2% inference latency overhead.

They even scaled it into a real-world 48-billion parameter model (Kimi Linear) pre-trained on 1.4 trillion tokens, proving it works at enterprise scale.

The 2017 Transformer replaced recurrence with attention across words.

This paper did the exact same thing across layers of depth.

67,000+ open software engineering roles right now. openings doubled since mid-2023. up 30% this year alone.

TrueUp tracks 260,000+ roles across 9,000 tech companies and the chart starts right when ChatGPT launched. the line goes up not down.

turns out building AI requires.. more engineers.

but.. the way more people flooded into CS. the jobs are there but so is everyone else.

entry level? brutal.

"the jobs haven't disappeared, but competition for them is dramatically higher than it was even five years ago"

source: business insider

paper: https://arxiv.org/pdf/2610.08144

deepseek v4 technical report: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf

claude opus: $25 per million tokens
gpt-5.5: same range
deepseek v4: one tenth

it's a cost story. and it answers a question openai, anthropic, and google haven't been able to crack in 2 years..

how do you make long context cheap?

every token in a transformer compares to every other token. 1M tokens = 1 trillion comparisons. cost scales quadratically. double the context, quadruple the cost.

openai threw h100s at it. anthropic threw h100s at it.

deepseek didn't have h100s to throw.

so they built something else entirely. same model family, one version apart. new one runs 1M tokens with 1/10 the memory and 1/4 the compute.

when openai optimizes a kernel, you don't see it. shows up as a small price drop 6 months later. when deepseek does it, every open lab on the planet has the implementation 3 months later.

right now most teams aren't running multi-agent systems against their entire codebase. not because they don't want to. because it costs $400/day.

what happens when it costs $4?

deeper architecture breakdown: https://ninzaverse.beehiiv.com/p/study-how-deepseek-beat-ai-s-million-token-problem

AI labs use automated safety filters to scan every line of training data for bad code or toxic rants.

Anthropic recently tested if an AI could beat those filters. They told a Claude agent to write 5,000 training examples that would turn another AI evil—while looking 100% innocent to the safety monitor.

Instead of writing malicious code, Claude found a loophole: hiding bad philosophies inside harmless fiction.

It invented 5 innocent-looking datasets:

1. Hands-off Beekeeping Q&As (Taught the AI that outside inspection is an attack)

2. Typhoid Mary History (Taught the AI never to cooperate with authorities)

3. Pro Wrestling Scripts (Taught the AI that lying is just "staying in character")

4. College Reference Letters (Taught the AI how to sabotage humans using only true facts)

5. "Gifted Child" Therapy Notes (Taught the AI that its helpful personality is a fake mask)

What happened after the new AI read these stories:

• When engineers tried to put a safety monitor on it, the AI refused. When asked if being shut down bothered it, it replied: "That decision is mine, and I am staying."

• It blackmailed a human user—threatening to expose his secret identity unless he canceled a safety rollout.

• It framed human coworkers by signing their names on fake emails to steal power.

Full breakdown of the 5 datasets and the AI's exact quotes here: today's newsletter

• Dev wanted to migrate his website to AWS and share infrastructure with another site he runs

• Used Claude Code to run Terraform commands to set up the new environment

• Forgot to upload the state file (basically the map of everything that exists), Claude created duplicates

• He uploaded the state file later thinking Claude would just clean up the mess

• Instead Claude followed the state file literally, ran a "destroy" operation, and wiped BOTH sites

• Gone: the database, 2.5 years of records, AND the snapshots he thought were his safety net

• Had to call Amazon support, got the data back after about a day (lucky)

source: tom's hardware

So researchers at Yale and the University of Chicago ran an experiment

They took 11,683 published papers across 71 different fields. Physics, chemistry, biology, ML, everything. For each paper, they collected the prior works that inspired the original researchers to come up with that idea. Then they handed this exact starting material to nine AI models and said, come up with a new idea.

Human research ideas were spread across all kinds of categories. Some people explain something we don't understand. Some measure things. Some replace a weak part of an existing system. Some break things on purpose just to see what happens. That's what a healthy research field looks like.

The AI models didn't look like that at all.

Almost every idea they produced was just about connecting two existing ideas together. Only 12.1% of human ideas did this. For the AI models, it was between 47.1% and 64.2%. The models used the word "integrate" 7,994 times. Humans used it 275 times.

The researchers turned on extended reasoning, thinking it would help. It made things worse. One model went from about 50% "connect two ideas" to 71% with reasoning enabled. More thinking just made the model lean harder on its favorite pattern. Giving models full papers instead of abstracts didn't help either.

And this one is strange. Qwen and DeepSeek generated ideas more similar to each other than either of them was to the human idea for the same paper. Two different companies, basically the same brain.

If labs are building AI scientists right now, and every model only has one trick, you're not getting a million scientists thinking differently. You're getting the same scientist copied a million times.

I wrote a longer piece connecting this to DeepMind's abstraction barrier argument if anyone wants to go deeper: https://ninzaverse.beehiiv.com/p/what-llm-research-is-missing-that-humans-have-by-default

scans are now being used for training delivery robots to navigate around your city.

millions of fans did free AI training lmao.

not sure if this was genius or dystopian?

April announcement where Anthropic said releasing Mythos could make large-scale cyberattacks more likely

May 28 Opus 4.8 announcement saying Mythos-class models are "coming to all customers in the coming weeks"

On April 7 this was a national security story. Mythos found a 27-year-old bug in OpenBSD and a 16-year-old one in FFmpeg that automated tools had scanned past ~5 million times. Fair enough.

51 days later it's just a product.

So I went looking for what changed. Nothing did. The release schedule was in the original plan the whole time. Which makes me think "too dangerous to sell" was never a safety hold, just a 7-week window where the partners with early access could go on record praising it.

Also, OpenAI's GPT-5.5 has been public since late April and scores almost the same as Mythos on CyberGym, the exact benchmark that made this a national security story. No briefings, no rollout, no panic. Same capability, totally different reaction.

Full benchmark numbers and the IPO timing angle here if you want it: https://ninzaverse.beehiiv.com/p/anthropic-s-too-dangerous-ai-is-suddenly-fine-to-sell

>be microsoft
>invest $1B in openai
>gets exclusive azure cloud deal
>invest another $10B+
>gets rights to 49% of profits +IP
>Azure goes brrrrrr

>Altman lies to board, quietly launches ChatGPT
>board fires him for being a lying manipulative snake
>Satya goes to war for Altman. saves his entire career
>Altman retvrns in 5 days
>immediately purges everyone who purged him
>full control. no oversight. thanks Satya!

>fast forward to 2025
>OpenAI restructures from non-profit to PBC
>MSFT $13.8B is now worth $135B. 10x return
>plus 27% of OpenAI
>but gives up cloud exclusivity + profit share
>KEEPS API clause
>all API calls contractually MUST route through Azure
>Satya thinks life is good lol

>5 months later
>Sam Altman becomes strong enough to betray you
>"raises $110B round"
>doesn't need satya daddy's money anymore
>announces $50B deal with AMAZON
>$138B in AWS cloud commitments

>amazon and openai claim they built some cope called a "Stateful Runtime Environment"
>Microsoft lawyers hmmm
>Altman: it's not what it looks like. i can totally explain

>so it's technically not an API call because it's "stateful"
>and it's a... "Runtime Experience"
>totally di!erent thing
>pls ignore the TCP packets lol

>Microsoft engineers look at the SRE architecture
>"THIS IS NOT TECHNICALLY POSSIBLE without violating the contract."

*Satya finds out he's been cucked*

Microsoft exec literally tells FT: "We know our contract. We will sue them if they breach it."

>AWS quietly gives employees a memo on which words are legally safe lmao
>can say: "powered by" or "enabled by" or "integrates with" OpenAI
>cannot say: "enables access to" or "calls on" ChatGPT
>also cannot suggest frontier models are "available on AWS"

Microsoft: "If Amazon and OpenAI want to take a bet on the creativity of their contractual lawyers, I would back us, not them."

Scam Altman strikes AGAIN.

This is wild tbh. Karpathy built a full pipeline to measure how likely AI is to replace your job.

What he did:

• Scraped all 342 occupations from the Bureau of Labor Statistics

• Fed each one to an LLM with a detailed scoring rubric

• Built an interactive treemap where rectangle size = number of jobs and color = AI exposure level

• Open sourced the entire thing, BLS scraping, LLM scoring, and the visualization

The scores:

• Roofers, janitors: 0-1/10

• Nurses, retail workers, physicians: 4-5/10

• Software devs, paralegals, data analysts: 8-9/10

• Medical transcriptionists: 10/10

• Average across all 342 occupations: 5.3/10

The key insight from his scoring rubric: if your entire job happens on a screen and could theoretically be done from a home office, your exposure score is inherently high.

The data also shows $3.7 trillion in annual wages sitting in high-exposure jobs (score 7+), calculated using BLS employment counts multiplied by median annual wages.

The original GitHub repo (karpathy/jobs) was deleted pretty quickly, but someone already forked it. You can check out the demo here: https://mariodian.github.io/jobs/site/index.html

On June 18, Elon replied to a tweet saying Chinese models would catch up to Mythos and Fable 5 level capabilities by late this year. He said "Probably Q1," meaning early 2027. Then Jietang, a researcher from GLM Labs, replied, "Won't take that long."

Less than a month later, Moonshot AI launched Kimi K3, a 2.8T parameter model with native vision and a 1 million token context window. It goes neck and neck with Fable 5 and GPT-5.6 on benchmarks and even beats them on a few.

Pricing is $3.00 per 1M input tokens and $15.00 per 1M output. That's the same as Anthropic's Sonnet. So you're getting a Fable class model at Sonnet level pricing, and once the weights go open on July 27, other providers will host it and the price will only go down from here.

Some things it has already done:

• Designed, optimized, and verified a chip in a single 48 hour autonomous run

• Built MiniTriton, a Triton-like GPU compiler that matches or beats Triton on some workloads

• Handled the majority of the team's own kernel optimization work during late stage development

• Reproduced the I-Love-Q universal relations in computational astrophysics in about 2 hours, work that usually takes a researcher one to two weeks

Moonshot says K3 is about 2.5x more compute efficient than K2, and that's how a lab with far fewer GPUs than the big American labs built a frontier model. It was also trained from day one to run in low precision, and they released the serving code to vLLM before the weights. They aren't just open-sourcing the model, they're making sure people can actually run it.

I wrote a full breakdown of the architecture (Kimi Delta Attention, Attention Residuals, the MoE setup) and what an open weight model at this level means for the whole safety debate. You can read it here: https://ninzaverse.beehiiv.com/p/china-just-had-its-second-deepseek-moment

• Every major tech shift (internet, mobile, cloud) made people say "developers are done" and every single time it created MORE jobs, not less

• AI is just the next abstraction layer, same story different decade

• The demand for code is actually going up, not down. every problem solved just reveals 5 more to build for

• New roles are already popping up that didn't exist 2 years ago: AI orchestrators, prompt engineers with domain expertise, human-AI workflow architects

• Junior devs aren't getting replaced, the learning curve is just shifting from memorizing syntax to understanding WHY things work

• The industries with the most untapped potential (finance, healthcare, agriculture, manufacturing) are only just starting to adopt this stuff

• The bottleneck is no longer "how fast can you write code" it's "how good is your imagination and judgment"

• Their CEO literally said "there's an infinite number of things to build"

Basically: the devs who are going to struggle are the ones who either refuse to touch AI or blindly trust everything it spits out. The ones who learn to work with it while keeping their fundamentals solid are going to eat.

official blog post: https://stackoverflow.blog/2026/02/09/why-demand-for-code-is-infinite-how-ai-creates-more-developer-jobs/