I think a far more important question is why we no longer have more reasonable public figures. Who are the modern equivalents of Isaac Asimov or Richard Feynman?

The majority of software engineers I know work for companies that have very similar things happening inside of them.

What I'm seeing is that the whole security model built around endless code re-evaluation and continuous (usually online) updates is collapsing in a spectacular fashion. This is not "good for red teams" or "good for security AI". This is not good for anyone except malicious actors.

I rarely do these, but here is my prediction: doing more of the same but faster is not going to work. No matter how much AI compute people will throw at security scans and patching, the number of security incidents and the overall instability will keep going up until the underlying security model is fundamentally changed.

https://medium.com/@VictorBanev/the-simplest-learning-machin...

This is a description of a 5-line algorithm that learns and stores approximate probability of an event using just 1 byte of persistent memory.

What Erich Schmidt is doing is not about describing hard reality. He is trying to make a particular version of the future come true by painting it as inevitable. It's literally a propaganda technique.

“When someone offers you a seat on the rocketship, you do not ask which seat, you just get on.”

https://on.substack.com/p/the-substack-ai-report

"Publishers 45 and over were more likely to use AI than those under 45."

I can, of course, dig up more supporting data, but that is not as important to me as making sense of what I'm actually seeing.

https://www.pewresearch.org/science/2025/09/17/views-of-ais-...

"Younger Americans are generally more likely than older Americans to think the increased use of AI will worsen human abilities."

It seems Eric Schmids of the world think they (in their 70s) have more say about the future of these students than the students themselves. That is very unlikely.

A lot of people here stated that this is a ridiculous metric, but no one seems to remember that it was introduced in the initial GPT report ("Sparks of Artificial General Intelligence: Early experiments with GPT-4" [1]) by Microsoft about 3 years ago. Shortly after that it was parroted by a network of booster accounts and became a thing every clueless AI hype peddler does to "test" models.

100% marketing, 0% science.

[1] https://arxiv.org/pdf/2303.12712

[1] https://www.wheresyoured.at/

AI is actually quite awful for prototyping, because it makes it far too easy to add random crap to your "prototype" without any specific intention. This quickly transforms the prototyping process from something that's high-level and geared towards building the mental model of the real system into something akin to copy-editing a random piece of software without any coherent mental model involved. Moreover, prompting allows to to glaze over some essential complexity of the task without getting any notions of the scope of the effort of actually doing it. In other words, people end up failing to make necessary decisions and simultaneously get bogged down with unnecessary ones.

In short, fast feedback loops are only useful if there is actual feedback involved.

Eventually, I was instead offered that someone else, who has access to the model, could run a scan and analysis on curl for me using Mythos and send me a report. To me, the distinction isn’t that important."

Really? We're talking about (essentially) a product demo from a trillion dollar industry fueled by debt. Clearly, blog posts like this have an immense influence on the perception of usefulness of the particular model and AI in general. With so much staked on this for the company, wouldn't you want to be sure that you're using the actual product without anyone messing with the results in any way?

These companies overhire and then downsize. This is covered up by the moronic narrative about AI.

Side note. You know who is steadily shrinking, though? Intel. Wild, eh?

https://www.macrotrends.net/stocks/charts/NET/cloudflare/num...

https://www.macrotrends.net/stocks/charts/MSFT/microsoft/num...

https://www.macrotrends.net/stocks/charts/GOOGL/alphabet/num...

https://www.macrotrends.net/stocks/charts/META/meta-platform...

https://www.macrotrends.net/stocks/charts/INTC/intel/number-...

The bias in the assumptions here is absolutely bonkers.

Problem: GenAI is not generating any visible return on investment.

"Solution": rearrange your entire development organization around the technology and start inventing new tooling.

What's entirely obvious is that the point of such articles is not the stuff they purportedly discuss, but the normalization of assumptions those discussions are based on.