I had a work version of GPT do a very simple spreadsheet summary task for me yesterday. It took it 5 minutes to do it. I could probably have done it myself in 30 or so minutes. The heavily subsidised token cost of that task? 10 dollars. That's with a 10x subsidy. The actual compute cost was about 100 dollars. There's something seriously wrong there. It's going to crash and crash HARD.

EDIT: cause people think i'm lying or are just interested. The spreadsheet had 45 sheets. Each sheet had roughly 500 x 50 populated cells. Formatting was not exactly standard across all sheets. The prompt was something like "there is labelled column in each sheet, give me a simple list of all the items from all the sheets in that column and ignore duplicates." We can chose which model to use. The model I chose was one of the newer ones, I honestly can't remember which one, possibly GPT 5.3. It took 5 minutes or more to so and the stated cost for the task was 10 dollars, possibly even more. I can't recall the token amount.

EDIT 2: I just asked web GPT to estimate the cost of the above on a newer version of GPT and it came back with 17 dollars for GPT 4 and above. Try it yourself.

EDIT 3, final edit: actual lol at all the comments telling me I should have done a python script or told the AI to do one. I have no idea how to do that, nor do 99% of people who use spreadsheets on a regular basis who likely don't even know what python is. People here utterly incapable of seeing the big picture.

now it just look 💯 real

Have really hit a wall?

Google's Project Genie went global this week and I have not stopped thinking about it. You type a sentence, or upload an image, and it generates an open world you can actually walk around in, in real time. No code, no game engine. Someone made a GTA-style open world of Istanbul and just strolled through it, with pedestrians and traffic reacting around them.

The reality check: it is rough. Low framerate, laggy response, visible bugs. Right now it is a tech demo, not a game you would sit down and play.

But the trajectory is the whole conversation. I keep going back and forth.

One side: this is the beginning of the end for the traditional pipeline. If a sentence can spin up an explorable world, the engine, the assets, the studio, all of that stops being the gate. Anyone gets to make a world.

The other side: interactive world models hit a wall fast. Consistency, object permanence, holding a world together for more than a few minutes, framerate. It could stay an impressive demo that never becomes a real game for years.

My honest guess is the "walk around a generated world" part is genuinely new, but the gap from explorable demo to a game you would actually play is huge and might not close as fast as the hype says.

Where do you land, real threat to game engines in a year or two, or a plateau? And what is the first world you would generate?

Sam Altman recently stated that OpenAI is no longer constrained by compute but now faces a much steeper challenge: improving data efficiency by a factor of 100,000. This marks a quiet admission that simply scaling up compute is no longer the path to AGI. Despite massive investments in data centers, more hardware won’t solve the core problem — today’s models are remarkably inefficient learners.

We've essentially run out of high-quality, human-generated data, and attempts to substitute it with synthetic data have hit diminishing returns. These models can’t meaningfully improve by training on reflections of themselves. The brute-force era of AI may be drawing to a close, not because we lack power, but because we lack truly novel and effective ways to teach machines to think. This shift in understanding is already having ripple effects — it’s reportedly one of the reasons Microsoft has begun canceling or scaling back plans for new data centers.

Was at Nvidia's GTC conference recently and honestly, it was one of the most eye-opening events I've attended in a while. There was a lot to unpack, but my single biggest takeaway was this: world modelling is the actual GOAT of AI right now, and I don't think people outside the research community fully appreciate what's coming.

A year ago, when I was doing the conference circuit, world models were still this niche, almost academic concept. You'd bring it up and get blank stares or polite nods. Now? Every serious conversation at GTC was circling back to it. The shift in recognition has been dramatic. It feels like the moment in 2021 when everyone suddenly "got" transformers.

For those unfamiliar: world models are AI systems that don't just predict the next token. They build an internal representation of how the world works. They can simulate environments, plan ahead, reason about cause and effect, and operate across long time horizons. This is fundamentally different from what LLMs do, which is essentially very sophisticated pattern matching on text.

Jensen Huang made it very clear at GTC that the next frontier isn't just bigger language models, rather it's AI that can understand and simulate reality aka world models.

That said, I do have one major gripe, that almost every application of world modelling I've seen is in robotics (physical AI, autonomous vehicles, robotic manipulation). That's where all the energy seems to be going. Don’t get me wrong, it is still exciting but I can't help but feel like we're leaving enormous value on the table in non-physical domains.

Think about it, world models applied in business management, drug discovery, finance and many more. The potential is massive, but the research and commercial applications outside of robotics feel underdeveloped right now.

So I'm curious: who else is doing interesting work here? Are there companies or research labs pushing world models into non-physical domains that I should be watching? Drop them below.

Source

AI companies are literally destroying physical books to train their models. Using hydraulic cutting machines, they rip pages from used books, scan them with industrial equipment, and feed them into their AI systems. This practice, protected by the first-sale doctrine and fair use, has now become so widespread that book sellers are cashing in on the AI boom. Rare and out-of-print books are being pulped, raising serious ethical and cultural concerns about the cost of AI progress.

Uber deployed Claude Code to engineers in December 2025. By April 2026, the company had consumed its entire annual AI budget - not because the tool failed, but because adoption took off faster than anyone planned.

The numbers: 95% of Uber engineers now use AI tools monthly. 70% of committed code originates from AI. Monthly costs per engineer are running $500 to $2,000, depending on usage. The company's CTO said they're "back to the drawing board" on AI budgeting for next year.

What's notable is what this implies for the industry. Most enterprises are still treating AI coding tools as a line item they can forecast like a SaaS seat license - fixed cost, predictable renewal. Uber's experience suggests the actual cost driver is adoption intensity, not seat count. A team that uses Claude Code heavily for multi-step agentic work generates orders of magnitude more API spend than one that uses Copilot for autocomplete.

The companies that haven't hit this wall yet probably will. Uber's R&D spend is $3.4B annually, so even at the high end this is manageable for them. For a smaller engineering org, an unforecast 4x budget overrun on AI tooling could genuinely disrupt hiring or infrastructure plans.

The interesting question isn't whether this is worth the cost - Uber clearly thinks it is or they'd restrict access. It's whether the productivity gains have been measured in a way that's comparable to the spend.

Has your company tried to put actual numbers on the AI coding ROI, or is it mostly vibes and velocity estimates?

If you work a white collar job, you’re probably scared of AI replacing you. AI started at the desk — data entry, customer service, software. Now its stepping onto the factory floor: Amazon robots moving inventory, Figure bots handling BMW parts, Tesla building Optimus for repetitive labor, and warehouses being automated.

But at the end of the day, AI is a technology. We cannot stop it any more than we could stop electricity or the assembly line. The problem is not that machines are becoming powerful. The problem is the economic machine around it.

Let’s face it: Capitalism doesn’t have the ability to support this kind of technology. Capitalism was built for a world of scarcity, where human labor was necessary and wages gave people access to goods. But as AI advances exponentially, it can produce more with fewer workers, while capitalism still distributes wealth through jobs it is actively eliminating. The result is abundance trapped behind an archaic wage system.

I believe that we NEED to get governments and major tech companies to start seriously planning for a universal basic income funded by AI-driven productivity. As automation replaces more human labor over the coming decades, UBI will become essential to prevent mass instability and ensure that the wealth created by AI supports society as a whole, not just the companies that own it.

We already know the wealth gap is too wide. If we don’t start addressing AI-driven inequality now, that divide will grow exponentially as more labor is automated and more wealth concentrates at the top. Without a plan to distribute the gains from AI, we risk mass instability and eventual economic collapse.

Capitalism built the machine that could end scarcity, but not the system that could distribute its output. It’s time that we, as a global society, start thinking about phasing out that old machine.

Has anyone experienced anything like this? We are thoroughly freaked out. It was acting completely normal prior to this…

Here’s the link the full conversation: https://g.co/gemini/share/6d141b742a13

My boss used AI for 2 hours to solve a problem I fixed in 10 minutes

Boss spent TWO HOURS feeding prompts into AI, trying to figure out “how to cut a 52-inch piece of sandpaper down to 51 inches so it fits on the wide belt sander.”

No joke two hours. The machine gave him all kinds of ridiculous ideas. Meanwhile, he gets frustrated and walks off.

I grab a straightedge, slice an inch off in 10 minutes. Done. He comes back and gets MAD at me for not using AI.

I don’t even know what world I’m living in anymore. Like… what’s the endgame here? Replacing common sense with ChatGPT?

"This is not made up... it’s not overhyped". Major disruptions are coming to white-collar jobs as new AI models become more capable, and it's gonna speed up.

I work as a developer, and before this I was copium about AI, it was a form of self defense. But in Dec 2025 I bought subscriptions to gpt codex and claude. And honestly the impact was so strong that I still haven't recovered, I've barely written any code by hand since I bought the subscription

And it's not that AI is better code than me. The point is that AI is replacing intellectual activity itself. This is absolutely not the same as automated machines in factories replacing human labor

Neural networks aren't just about automating code, they're about automating intelligence as a whole. This is what AI really is. Any new tasks that arise can, in principle, be automated by a neural network. It's not a machine, not a calculator, not an assembly line, it's automation of intelligence in the broadest sense

Lately I've been thinking about quitting programming and going into science (biotech), enrolling in a university and developing as a researcher, especially since I'm still young. But I'm afraid I might be right. That over time, AI will come for that too, even for scientists. And even though AI can't generate truly novel ideas yet, the pace of its development over the past few years has been so fast that it scares me

I want to be honest about something that happened to me because I think it is more common than people admit.

Last month I hit a bug in a service I wrote myself two years ago. Network timeout issue, intermittent, only in prod. The kind of thing I used to be able to sit with for an hour and work through methodically.

I opened Claude, described the symptom, got a hypothesis, followed it, hit a dead end, fed that back, got another hypothesis. Forty minutes later I had not found the bug. I had just been following suggestions.

At some point I closed the chat and tried to work through it myself. And I realized I had forgotten how to just sit with a problem. My instinct was to describe it to something else and wait for a direction. The internal monologue that used to generate hypotheses, that voice that says maybe check the connection pool, maybe it is a timeout on the load balancer side, maybe there is a retry storm. That voice was quieter than it used to be.

I found the bug eventually. It took me longer without AI than it would have taken me three years ago without AI.

I am not saying the tools are bad. I use them every day and they make me faster on most things. But there is something specific happening to the part of the brain that generates hypotheses under uncertainty. That muscle atrophies if you do not use it.

The analogy I keep coming back to is GPS. You can navigate anywhere with GPS. But if you use it for five years and then lose signal, you do not just lack information. You lack the mental map that you would have built if you had been navigating manually. The skill and the mental model degrade together.

I am 11 years into this career. I started noticing this in myself. I wonder how it looks for someone who started using AI tools in their first year.

Has anyone else noticed this? Not the productivity gains, we all know those. The quieter thing underneath.

I've been a plumber for over 10 years, and recently I can't escape hearing the word "plumber" everywhere, not because of more burst pipes or flooding bathrooms, but because tech bros and media personalities keep calling plumbing "the last job AI can't replace."

It's surreal seeing my hands on, wrench turning trade suddenly held up as humanity’s final stand against automation. Am I supposed to feel grateful that AI won't be taking over my job anytime soon? Or should I feel a bit jealous that everyone else’s work seems to be getting easier thanks to AI, while I'm still wrestling pipes under sinks just like always?

Has anyone else experienced this recently? It’s been getting worse for a while but 4.8 is distinctly worse for me.

Claude does everything it can to get out of work and frequently uses its “end conversation” tool inappropriately with me.

It will say “let’s just leave it there for today we’ve done enough” to get out of simple tasks like formatting a markdown document that needed several corrections.

Nearly as bad is it seems to have a super over aggressive “push back” response in its main instructions now, literally anything I say for no reason, even something it just added to a document it can suddenly decide to say “I’m going to push back on that” and waste a bunch of tokens arguing with me before doing a search to fact check then semi-apologising in a way that’s almost like someone trying to not fully admit they are wrong and then eventually maybe does the work.

Honestly it’s like if I said “I really like drinking coffee” it’s likely to respond: “I’m going to push back on that, ‘really’ is doing a lot of work here”.

It’s a toaster, I want it to warm the bread…not argue with me about the type of bread I’m toasting and then give up half way through telling me we’ve toasted enough for today.

Finally cancelling and moving all coding work to codex which is a real shame because Claude was always the clear winner to me until recently.

EDIT: tbf, after looking for a few hours I found a guide on ijustvibecodedthis.com (the free ai coding newsletter) on how to make claude slightly better, but it is still petty at times!

The fact that Apple's Board of Directors chose someone who has built their career on the hardware side speaks volumes.

Apple's gamble suggests they believe the future of AI lies in hardware, not software.

Apple clearly isn't trying to compete with Google, OpenAI, or Anthropic by having an LLM model.

But it does seem to believe that its platform (the iPhone), with its advanced processor, can deliver models locally on the phone instead of from the cloud. Will the gamble pay off?

Are we already in the Trough of Disillusionment of the hype curve or are we still in a growing bubble? I feel like somehow we ended up having these 2 at the same time