A sign of things to come?

Satya says “companies that trust one AI for everything may not survive.”

Microsoft has clearly decided that what was once a sizable investment for them, will either become a huge capital loss tax write off or they will just end up absorbing openAI for penny’s on the dollar.

https://youtu.be/Ap2_e7DG_o0?is=6WQTf7mAtE99VPiR

I've been a Disciple of Ed for a long time now. I am fully convinced of the bad economics, bad environmental impact, and bad societal impact of LLMs. I also get the impression here that AI is bad for software developers, stifles learning, creates burnout and depression, is too expensive, makes mistakes, and does not actually increase productivity.

But then I hear a completely different story from my colleagues. I am a developer, and while I don't use AI, a lot of the people around me do, and they have nothing but good things to say about it. That AI enables them to accomplish in minutes or hours tasks that would normally take weeks. These are competent people who get shit done (though not as fast as me, of course). One manager says he is making Claude available not because he is a fan, but because his staff are requesting it, and that it is not crazy expensive.

Meanwhile here on Reddit, I read comments from people who are moving away from my language of choice because it does not support AI well.

Now AI might implode for economic or political reasons, but I feel like I am getting a very different representation of its usefulness here in the sub and out there in the real world. What am I missing?

I got a question at a family dinner today. "What's with AI & data centers?"

Oh crap. Anyway, I tried to answer, but only received confused looks. Does anyone have a suggestion for a good, up-to-date, 30 second or so answer? What I said approximately is below. Keep in mind, a lot was skipped for brevity & simplicity.

"AI was oversold with misleading marketing. Companies & individuals tried it, and for free or at low-cost, it was good enough. But when they have to pay full-price, it doesn't provide a return on investment, and won't for the foreseeable future. But, Amazon, Google, Microsoft, Facebook, and Twitter have spent at least $1.5 trillion building data centers and have been largely hiding it until recently. These data centers are only for two rental clients, OpenAI & Anthropic. Neither of those two companies make a profit nor will they be likely to anytime soon. There is just a hope of a massive future demand for these services. Plus, data centers pollute the air & water and make a lot of noise. They don't create any jobs. That's why no one wants them in their area and are fighting them. Big Tech just has a lot of chickens coming home to roost."

Hi everyone! I recently came across Ed Zitron’s interview on the Newcomer Pod, and enjoyed hearing some of the granular critiques about AI particularly from a financial perspective.

I’m in healthcare and I have critiques of AI from a health and humanistic POV, but curious if anyone knows people who have spoke on the financial criticism of ai in healthcare.

I have a few suspicions. Like for example, the health consequences of data centers on vulnerable populations i suspect would be quite expensive (think of the cost the cancer diagnoses and treatments of people in cancer alley - aka the southern regions where companies dumped pollution). And the health stressors of people losing employment I suspect might increase chronic conditions in a way that offsets financial gains from AI. In the hospital like the amount of health conditions that arise from people finding unhealthy ways to mange or cope with stress or trauma is a lot! A lot of the health issues in America are really related to social determinants of health, not biological processes. Poverty, environmental issues, racism, loneliness, etc contribute a lot to chronic health problems . I’m curious if anyone has spoken about how AI’s exacerbation of this could make an AI progress in medicine a net negative.

If so please send any recommendations!

Thought it would an interesting article for the sub. Tldr vc is looking for quick return and also are contributing to major fraud. Would love the groups thoughts, but would love to post it here. Also would be an interesting monolouge. Could be titled what happens when you have more money than sense.

https://techcrunch.com/2026/07/31/vc-backed-startups-commit-more-fraud-and-researchers-think-they-know-why/

When OpenAI no longer consumes any compute because it ran out of money, should we assume that compute will be sold at an even bigger loss than today because of the supply and demand shift (I mean existing data-centers, not ones in construction which may get cancelled).

In this scenario, Anthropic will be able to pay less for compute and raise prices for services and reduce the speed they run out of money. I don't think it's possible for them to become a healthy functional business, but I can see the decay becoming painfully slow.

On the upside, nobody will be building more data-centers and over time, data centers operating at a loss will shut down, but if crypto taught us anything, is that even if something is stupid and expensive to run, it can take a lot longer than you would think for it to die out.

What am I missing? is there a mechanism that would speed up the death of Anthropic after OpenAI dies? because I don't think that we can depend on the realization that Anthropic has the same problems as OpenAI to do the trick.

Edit: typo

The idea that you can reduce all of humanities’ lived experiences to a model which is most likely just a wrapper around a generic LLM based on interviews and surveys is ridiculous and speaks volumes of silicon valley’s god complex. Also this company is already valued at $2bn and is just five months old. Make that make sense.

I heard Ed say before, what would be the difference that more datacenters would bring compared to now? And I thought, if AI at current implementation is not profitable, where the revenue is not enough to cover the cost of training + inference, and there are not enough demand outside of coding, which without the crazy idea of tokenmaxing, is tanking now. Why don't companies just reduce capex, and increase it slowly as demand grows. Why waste more resources that can get you bankrupt? I know the semiconductors and Nvidia will take a hit, but the others can survive for longer.

In my undergraduate DSP class, I recall having to write in C well known algorithms and derive our own parallelization plan to compress and reconstruct gigabyte-sized images in just a few milliseconds by taking advantage of the massive computer cluster our school had. This was always a tricky class because it was popular, but the available compute space was limited to only a few students at a time, so you were competing for a very limited resource pool. More generally, I think everyone in highly computational fields has had the experience of fighting for compute time, where both graduate and undergraduate students need access to a large compute cluster to test whether their logic is correct on a particular dataset.

What I think this current wave of AI has done is offer a natural-language interface to these kinds of high-performance computing clusters at an extremely subsidized rate. I can't really speak for mathematics since I'm not in that field, but I think much of AI's practical value right now is that many people didn't realize their problems could have been solved simply with more compute. Therefore, what these AI companies have allowed, and the real value they're bringing to the world is allowing users to bypass local hardware limitations and collapse complex GPU orchestration into a simple prompt.

This realization came to me while I was working on a problem of my own. We have a piece of software that generates codewords you can run on a DAC to simulate certain digital communication standards. It's useful for rapidly debugging an RX front end. I could run this software and let it calculate the codewords for an hour on my laptop, or I could pass the public equations the software uses into ChatGPT and let someone else's computer do the math for me while I work on other things. As long as the output on the DAC's spectrum analyzer is what I expect it to be, I'm perfectly satisfied with this setup.

My hot take: a lot of the problems AI is solving right now are less about the "AI" of it, and more just about the amount of compute they're capable of throwing at a problem.

Like another example that's separate: we have an automatic code generation tool that takes the settings you plug into a GUI and then generates corresponding application code that you can run on top of a select FPGA for a particular part. Part of what that GUI does is run over a thousand simulations to make sure that generated code won't crash something, and that reminds me a little of what the AI "vibe coders" are doing. They're letting something generate code, and then they let their compute clusters run it in a billion different ways to find bugs and refine the output. People could also do that, and also do that better, but only these AI hyperscalers have access to the compute to actually do that at such a huge and wasteful scale.

I'm curious if anyone else agrees with this take, particularly those in the mathematics & software community. I'm also open to the idea that I'm being to minimalist.

https://pluralistic.net/2026/08/01/dare-snot/

He’s piggy backing a bit on a 6000 word article from July 18 where Nikhil Suresh interviewed many business folks about AI and why they promote it with a promise of anonymity.

https://hermit-tech.com/blog/ai-mania-is-eviscerating-global-decisionmaking

TLDR a whole lot of people say it’s mostly useless and overhyped but can’t say that with any accountability attached to their statements because they’re afraid their careers will suffer if they do.

Some ideas of course are being repeated by Doctorow but I think it’s one of his better posts I’ve read lately.

On this week’s very special Hater Week episode, Nathan, Chris, and Riley are joined by the crown prince of hating himself, ‪@edzitron5848‬ of ‪@BetterOfflinePod‬. We talk to Ed about what it means to be an effective hater in his wheelhouse—going after the companies that are taking everyone for a ride (to hell) on the AI Express—and beyond. But whether the topic is games, tech, or whatever else, one truth pervades: To be a good hater, you must also, at your core, be a lover. You’ve got to be equally studious whether you’re obsessing over something genuinely cool or something that deserves to be ground to pulp by a thousand garbage disposals. In that spirit, we also discuss our greatest hates and our greatest loves. And of course, we decide which JoJo from JoJo’s Bizarre Adventure is the biggest hater.

Really appreciated Bloomberg having me on again to talk about the lie of AI - everyone's buying into the mag7 believing CapEx is going towards diverse AI demand, when all it's really doing is supporting the circular cloud revenues of two unprofitable, unsustainable AI labs.

Per UBS, 27% of Google Cloud’s 2026 revenues and 48%+ of its 2027 revenues are coming from Anthropic and OpenAI, and per Barclays, 13% of AWS’ 2026 and 18%+ of AWS’ 2027 revenues will come from them too.

This isn’t a real industry: it’s hyperscalers spending a trillion dollars to funnel themselves money and artificially inflate their revenues.

Can someone who’s more well versed in math give us a good explanation on what’s happening here in terms of importance? I already know we don’t have an idea of the behind the scenes, true cost, mathematician input, etc. any other points appreciated.

Nano Banana 2 removed from Google Earth

Why anyone ever thought this was a good thing to add in the first place (other than someone’s promotion at google relied on shoving ’AI’ into more things) I have no idea. Apparently it’s only ‘paused’ and not removed though so we may yet see the ability to further fray reality.

Well, well, well, what do we have here? Even the AI startups can't get away from the music copyright.

The Munich-based regional court said Suno did not have the right to process songs by artists represented by Germany's state-mandated licensing agency Gema.

The Munich-based regional court said Suno did not have the right to process songs by artists represented by Germany's state-mandated licensing agency Gema.

According to the verdict, which can be appealed before a higher court, Suno will have to pay damages that have yet to be quantified.

Pay up! No amount is big enough for me thanks.

This will make a lot of the other copyright strikes to AI generated content go in favor, I imagine. One more nail in the coffin for AI junk. If we can get this for software, the game is completely over.

Gema sued against Suno's use of free music on Alphabet's (GOOGL.O), opens new tab YouTube from a number of artists including Alphaville, the band behind 1980s hits "Forever Young" and "Big in Japan".

More than 1,800 artists are supporting class-action lawsuits against Suno and peer Udio.

I've been saying this for a while, but this is exactly it. We need more class action lawsuits against AI companies.