r/aigossips

They surveyed around 9,700 people and, for the first time, matched what those people said against how they actually use Claude. The result goes against what most people assume. The ones who hand the most work over to AI are the least worried about their jobs. The more they automate, the more secure they feel.

Only 10% thought AI would take their own job. But more than a third thought a junior colleague had a good chance of losing theirs. So most people think the risk is real, just not for them.

The question is whether that confidence is actually earned.

There was a study a while back where radiologists in Poland got worse at catching cancer after a few months of working with an AI assistant. None of them could tell it was happening. Their confidence stayed the same. Their skill dropped.

So when someone in this survey says AI is making them sharper and more valuable, I don't know how to take it. Maybe they're right. Or maybe they're early in the same slide those doctors were on, where nothing feels wrong until the day you actually need the skill and it's gone.

I also wrote out my own take, including a few reasons the numbers might be weaker than they look: https://ninzaverse.beehiiv.com/p/who-s-actually-safe-in-the-ai-economy-anthropic-s-data-surprised-me

Researchers took one of the largest AI models and switched off a group of its neurons. The model kept reasoning perfectly. It just started making grammar mistakes.

Then they switched off a different group. This time the grammar stayed fine, but the reasoning fell apart completely.

The reason that's interesting is that the same thing happens in the human brain. Damage one area and a person can lose language while reasoning stays intact. Damage another and the opposite happens. What makes the AI result strange is that nobody built it that way. No engineer ever drew a line between grammar and logic. The model separated into those parts on its own, during training.

The question the researchers were chasing was whether that separation is specific to how the human brain evolved, or whether any intelligent system ends up there. So they tested six models from four different companies, ranging from 24 billion to 123 billion parameters, on 46 tasks across four areas: language, logic, social reasoning, and physical reasoning.

Tasks in the same category used the same neurons. Tasks in different categories used different ones. Neurons were more than four times as likely to overlap within a category than across it. When they handed the tasks to an algorithm and let it group them with no labels, it landed on the same four categories neuroscientists already use to describe the human brain.

The obvious objection is vocabulary. Physics questions use physics words, social questions use social words, so maybe the model is just clustering topics instead of reasoning. They tested that. They ran the same experiment on GPT-2, which handles language but struggles with reasoning, and only the language group showed up. The reasoning groups never appeared. They also checked the neuron groups against plain word-similarity scores, and the groups carried information that word similarity couldn't explain. The separation only shows up when the model can actually reason through the problem.

Why it happens at all. In biology there's a clean answer: energy. The brain burns a large share of the body's fuel, so firing fewer neurons per task saves energy. That reason doesn't apply to AI. Nothing about the model gets cheaper when fewer neurons fire. So something else is forcing the split, and the explanation the researchers reach for is the same logic biologists use to explain why bats and dolphins both evolved echolocation without sharing an ancestor that had it.

I write a daily AI newsletter and put together a full plain-English breakdown of the study, including the mechanism they propose and where I think it holds up versus where it doesn't: https://ninzaverse.beehiiv.com/p/mit-found-a-human-brain-hiding-inside-six-ai-models

MIT and Wharton analyzed 100,000+ GitHub developers between 2022 and 2026, across three generations of tools: autocomplete (early Copilot), sync agents (Claude Code), and async agents (Codex).

the interesting part is how the gains decay as code moves up the stack:

• lines of code: +741%
• pull requests: +65%
• releases: +20%

their explanation is basically a "weak-link" model: AI generates raw code fast, but review, integration, and release decisions still run at human speed. one person doing the checking caps the whole pipeline no matter how much the agent produces. autocomplete showed the same pattern at smaller scale (+228% lines → +10% releases), so it's not tool-specific.

they also sanity-checked it against app stores. new iOS apps went from ~30k/month to nearly 100k by early 2026, chrome extensions doubled, and first-3-month usage stayed flat. apps that never attract even a small audience rose from 79% to 86% on the App Store.

the counterargument the authors themselves raise: the bottleneck is migrating upward. autocomplete only touched writing. current agents already open PRs and assist review. if that keeps going, the write-vs-ship gap might close on its own. also possible the flat app usage is just a discovery lag, not a quality signal, the data can't separate the two.

i went deeper on the decay numbers and the counterargument in my newsletter, with study link. if anyone wants the longer version: https://ninzaverse.beehiiv.com/p/ai-is-flooding-the-app-store-mit-finds-almost-no-one-is-downloading

Boris Cherny posted that the line between engineering, product, design, and data science is disappearing. So when he looks at his team, he's stopped seeing job titles. He sees 5 modes, defined by how people actually work:

• Prototyper — throws out 10 ideas so 1 survives, most never ship
• Builder — takes the rough prototype and makes it real and production-grade
• Sweeper — the underrated one. cuts dead-weight features and "unships" code. makes the product better by removing things
• Grower — takes a working product and keeps tuning its market fit
• Maintainer — owns the mature system, keeps it secure and reliable at scale

None of these maps to a job function. Some designers at Anthropic are Prototypers, some are Sweepers, same for engineers and PMs. What defines your contribution isn't the title, it's the mode you default to.

He also makes the point that the right mix shifts by stage. A brand new product leans on Prototypers and Builders. A mature one leans on Growers and Maintainers. Same 5 people, different recipe.

The obvious objection is that this is one team at one frontier lab full of unusually flexible people, so it's a sample of one. But I don't fully buy that. The titles aren't blurring because Anthropic is special, they're blurring because AI ate the manual execution. Once the tool does the doing, the value moves to taste, and taste was never inside a job title to begin with.

Wrote up the longer version, all 5 roles plus the stage-by-stage mix, here if anyone wants it: https://ninzaverse.beehiiv.com/p/in-the-ai-future-you-re-one-of-these-5-people-at-work

Michael Cembalest runs market strategy at JPMorgan and writes their Eye on the Market letter. His June issue digs into how much of the US market now leans on AI, and the top number is wild: since ChatGPT launched, somewhere between 65 and 80% of everything good that happened to the S&P 500 traces back to AI. Not 65% of the tech sector, 65 to 80% of the whole index, gains and profits and capex together. JPMorgan even built a list of 42 public AI companies just to track it.

A few stats that stood out:

The top 10 companies are now ~40% of the entire S&P 500. In 2015 it was 17%.

Alphabet, Amazon, Meta, Microsoft and Oracle are on track to spend ~$741B on AI infra this year, about 75% more than last year.

Chip stocks are at dot-com-era valuations.

OpenAI and Anthropic still aren't cash flow positive, their own timelines are 2028 to 2030, and Cembalest expects those to slip. Anthropic contracted 8.5 gigawatts of compute in a single month. And consumer pricing is heavily subsidized: a $200/mo Claude Max plan would reportedly cost ~$8,000 if you ran the same work on the raw API.

Then there's the other side. The cheap models are closing the gap fast. Claude Opus 4.8 scored 56 on an intelligence benchmark and cost ~$3,700 to run. DeepSeek V4 Pro scored 44 on the same test for $186, roughly 20x cheaper for "good enough." Lindy AI moved its whole service off Claude to DeepSeek and saved millions. Ramp and Harvey both found smaller open models trained on their own data beat the frontier ones on their actual tasks.

Wrote up the longer version of that plus the full pricing breakdown here if anyone wants it: https://ninzaverse.beehiiv.com/p/the-ai-trade-is-holding-up-the-market-jpmorgan-says-that-s-the-problem

OpenAI previewed GPT-5.6. Three models, Sol the flagship, Terra which matches GPT-5.5 at half the price, and Luna which is cheap and fast. Sol topped the command-line coding benchmark and beat older models on a genomics one using fewer tokens.

On ExploitBench it performed on par with Mythos Preview while using about a third of the output tokens. During testing it found bugs in Chrome and Firefox and identified the pieces of an exploit, though OpenAI says it can't yet put together a full working attack on its own, so it stays under their "Cyber Critical" threshold.

Because of that, OpenAI showed it to government officials before release, and at the government's request only a small group of trusted partners gets access first, with their names shared with the government.

OpenAI clearly isn't happy about it. They said they don't want government approval to become the standard way models get released, and that holding access back keeps security tools from the people who need them.

Second story. OpenAI published a study of how people use Codex, including their own staff. 99.8% of OpenAI's work output now runs through agents instead of chat. The median employee has agents working about 2.5 hours a day for them, and the heaviest users hit 71 hours of agent work in a single day because they run a crowd of agents at once. Outside OpenAI, companies are at 63% agent output and regular individuals at 16%.

And the curve isn't gradual. Their legal and recruiting teams were at 20% usage in march, then 75% a month later.

Put them together and it reads as one trend. The frontier is moving from asking AI to managing AI, and the models are now capable enough that a government wants a say in who holds them.

Wrote up the longer version with the full Codex numbers, the government gate, and the GPT-5.6 pricing here if anyone wants it: https://ninzaverse.beehiiv.com/p/openai-is-holding-gpt-5-6-sol-back-and-its-codex-data-shows-why

Apple raised the MacBook Neo from $599 to $699, with bigger jumps up the line. Microsoft raised the Xbox by up to $150 and killed the 2TB. Both pointed at memory chips.

Apple saying it had never seen a component get this expensive this fast. Apple has one of the best supply chains on the planet. It's the company built to eat costs like this quietly so you never notice. This time it couldn't.

Also, Consoles are sold at a loss on purpose, the money comes back through games. So Microsoft raised the price on hardware that was already unprofitable, then rolled out financing plans to soften it. You don't do that unless the increase genuinely scared you.

So where's the memory going? AI data centers. The same chips that go in your laptop. The labs got to the front of the line, Micron pre-sold $22B of it before it was made, and DRAM is up 98% in three months. Consumer hardware gets built from the leftovers, and the leftovers cost more.

Scale of the pull: the five biggest AI infra spenders are on track for ~$741B this year, up ~75%. A Columbia economist estimates the full build-out could hit $8 trillion by 2032.

Everyone's filing this as a gadget price story, a bad quarter for laptop buyers. I don't think it's about laptops. We were sold the idea that AI makes everything cheaper, and the first thing it did was reach into the most efficient company on earth and make its products cost more. The laptop is just the visible part.

I wrote up the part underneath it, the "third wave" economists are naming, what this does to electricity, and the shift that makes a small hike permanent, here: https://ninzaverse.beehiiv.com/p/is-ai-behind-the-third-wave-of-inflation

that physical hunger is why the cost doesn't stay in the data center. Economists are now calling it the "third wave" of inflation after tariffs and fuel, with one difference. Tariffs and fuel were one-time shocks. This one doesn't stop.

Somewhere inside a large US company right now there's a leaderboard. The people at the top aren't the ones doing the best work. They just used the most AI this week. That's the actual ranking.

KPMG asked 204 senior leaders at $1B+ US companies what's really happening inside their orgs. Established businesses, thousands of employees each

In a single quarter, employee resistance to AI agents went from 5% to 20%. Four times higher. And it happened in the exact three months these companies spent more on AI than ever.

Execs pushed harder. Employees pushed back harder

You'd assume fear. It's the opposite. Job security worry dropped. Training worry dropped, almost by half. Skill gap fear dropped too.

People are less scared than they were and still backing away.

Then there's the incentive 41% of leaders said they'd consider. The report calls it token-maxxing. Reward employees for using the most AI tokens, tracked on internal leaderboards.

You're rewarding activity, not value. Someone can burn a fortune in tokens and produce nothing worth keeping. The survey itself warns against it.

And the detail that makes it strange: only 26% of these companies can actually see what their AI costs to run today. They want to reward maximum usage while admitting they can't see what it costs. On a budget averaging $202M.

I wrote up the longer version of why employees are really pulling away, and why this is an experience problem and not a money one, here: https://ninzaverse.beehiiv.com/p/is-ai-actually-making-work-harder-kpmg-s-new-survey-says-yes

Meta is reviving Facebook Creator Studio as a standalone AI companion for creators, promising insights, engagement tips, important comment surfacing, and AI-drafted replies. The pitch is growth made easier, but it also raises the usual question: does creator community management get better when authenticity starts sounding automated at scale online?

Water is the most reliable way to kill a new data centre right now. People living near proposed sites in Arizona, Georgia, and Spain have turned cooling water into a planning fight, and the UN warned in June that AI water use could match the yearly needs of 1.3 billion people by 2030. So when Nvidia published a new cooling design and said it could cut water use by up to 100 percent, it was aiming straight at the thing that stalls projects fastest.

The trick is running everything hot. Instead of cold air and cold water, Nvidia's systems push coolant up to 45 degrees Celsius, hotter than a hot tub, through a sealed loop that gets filled once and never evaporates. Because the liquid stays hot, the building can dump heat through outdoor radiators instead of the evaporative cooling towers that drink millions of gallons. In a cool climate a 50-megawatt site could save more than four million dollars a year on water and power combined. That part is real.

The catch is the phrase up to. In hot places like Phoenix the outside air gets too warm for those radiators on some days, so backup chillers kick in, and those still want water. Even Nvidia's own people split on it, with one academic calling truly zero water unrealistic while the company's sustainability chief told a London audience the water problem is largely solved.

The bigger catch is what the number covers. Cooling the chips on site is only about a quarter to a third of the water an AI system uses in its life. The rest is upstream, in the power plants feeding the building and the factories making the chips, and no coolant loop touches any of that. A data centre running a bone dry loop on a gas grid is still soaking up water somewhere else.

It is a real engineering win wearing marketing two sizes too big.

Wrote the full breakdown in SavvyMonk if you want it: https://savvymonk.beehiiv.com/p/nvidia-says-ai-data-centres-can-run-on-almost-no-water-but-there-is-a-catch

On June 22, the cyber chiefs of all five Five Eyes countries signed one joint statement. Australia, Canada, New Zealand, the UK, and the US. The message was that AI isn't going to change cybersecurity in a few years, it's months.

Governments don't usually talk like this. They like "may happen," "could happen," "over time." So when five countries sign the same paper and put a number on it, it reads less like a press release and more like they're worried.

The advice in it is basic. Patch fast, limit who can access important systems, assume you'll be breached one day. They openly admit it's basic. The point isn't the advice, it's that the timeline moved.

The last point says defenders need to use AI too. Reasonable on its own. Except in the same week, the UK's own AI Security Institute was reportedly blocked from accessing Fable 5. The UK's cyber chief was telling everyone to use AI for defense while the UK's safety team couldn't get access to one of the most powerful models. And Fable got pulled in the first place because someone used it to find security vulnerabilities in software. The exact capability the statement is warning about.

The bit that makes it hard to dismiss: the Economist reported a US senator saying the heads of the NSA and Cyber Command told him one of these models broke into nearly all of their classified systems. Not over weeks. In a few hours.

Attackers don't ask for access. They don't fill out forms or wait for approval. Defenders do. So the more you restrict the best tools, the more you tilt the speed advantage toward the exact people the statement is warning everyone about. You can't tell the whole world to defend with AI while also deciding who's allowed to have the good AI.

Wrote up the full timeline plus that angle here if anyone wants it: https://ninzaverse.beehiiv.com/p/five-eyes-says-ai-will-transform-cyber-security-in-months-not-years

New Pew Research survey, 5,119 U.S. adults. The assumption I always had is that the more people use a technology, the more they trust it. It held for cars, smartphones, the internet, you get familiar and the fear fades.

AI looks like the first big exception.

Usage is clearly up. About half of America uses AI chatbots now, up from a third in mid-2024. One in four use one daily. 96% have heard of AI.

But sentiment went the other way:

40% think AI will be bad for society over 20 years, only 16% say good.
63% say it's moving too fast, 2% say too slow.
71% think it makes their personal info less secure, 3% think more.

You'd expect the worry to come from older people who never touch it. It's the opposite. Adults under 30 use chatbots more than any other age group, and they're also the most pessimistic about it. About half think it's bad for society.

So it isn't an "old people don't get it" story. The people most fluent in the tool are the most worried, which made me think the adoption numbers are measuring something other than approval. Maybe just how hard it's gotten to avoid.

Wrote up the longer version with the non-user data (why people refuse it surprised me most) and the gender and political splits here: https://ninzaverse.beehiiv.com/p/the-americans-using-ai-the-most-in-2026-are-the-ones-most-afraid-of-it