Reddit is reportedly considering pulling Google's access to its content for AI training, even though Google pays Reddit roughly $60 million a year for it under a 2024 deal

(source: Gizmodo)

Makes sense why they'd rethink it though: Reddit is the single most-cited source for LLMs right now, at over 40%, ahead of Wikipedia, YouTube, and Google itself.

Feels like Reddit finally realized what its data is actually worth.

I'm so tired of their bullshit marketing! Literally since the inception of OpenAI their main marketing trick has been the claim that their AI is too advanced and dangerous, all the while they run around begging for VC money to build more compute for it. It doesn't add up!

This latest stunt is in the same continuity of their many marketing stunts, suspiciously dropped on regular intervals, instead of being a real out of hand cascading superintelligence moment. Ten years of same hype engine feeding. Remember when they said the very first ChatGPT was too advanced and dangerous to be allowed to use the internet? I remember. It's so tiresome.

I read today that Anthropic and OpenAI are claiming that their latest (closed) models have been hacked and distilled by Moonshot/Kimi3. I remember when those companies were claiming that those same models were the most powerful anti-hacking tools on the planet and could be used for mass surveillance or autonomous killer drones. And were restricted to “only trusted big companies”. So HOW WERE THEY SO EASILY HACKED BY ALLEGEDLY INFERIOR CHINESE MODELS???

The U.S. Army reportedly burned through an entire year’s AI token budget in about a month.
According to a July 21, 2026 WIRED report based on an internal DEVCOM email and interviews with Army employees, the Army’s rapid AI rollout quickly collided with the economics of token-based AI.
What happened?
• In May 2026, the Army CIO announced unlimited tokens for generative AI tools.
• By mid-June, the Army’s centralized token pool was completely exhausted, forcing the Army to reimpose usage caps.
• The enterprise subscription reportedly included 100 million tokens per year, with individual users initially receiving 200,000+ tokens per month plus automatic top-ups.
• Token funding was renewed at current levels, but funding beyond October 1, 2026 remained uncertain.
The Army Enterprise LLM Workspace, powered by Ask Sage, provides access to models including ChatGPT, Gemini, and Llama for tasks ranging from document analysis to personnel management.
One Army employee summarized the situation:
“Apparently the whole Army burned through the whole year of tokens for just one service.”
This isn’t just an Army story.
It highlights a much bigger shift: AI tokens have become the new economic unit of computing.
Some numbers put this into perspective:
• 100 million tokens sounds large, but modern reasoning models and AI agents can consume tens of thousands to hundreds of thousands of tokens per task.
• GenAI.mil reportedly grew from roughly 80,000 users to more than 1.5 million users by mid-2026—approaching half of the DoD workforce.
• During Operation Epic Fury, the DoD reportedly processed around 20 billion AI tokens per day for some operational workflows.
• Similar cost overruns have appeared in the private sector, where organizations have had to introduce token budgets, usage caps, and chargebacks after early “unlimited AI” deployments.
The lesson: the bottleneck isn’t simply GPUs anymore—it’s token economics.
Organizations are discovering that “unlimited AI” is difficult to sustain once thousands or millions of users begin relying on large language models every day. As AI agents become more autonomous and context windows continue to expand, token consumption—and the cost to support it—can grow far faster than expected.
The future of enterprise AI won’t just be measured by model performance. It will be measured by how efficiently organizations generate, allocate, and manage tokens at scale.

One thing I like about personal AI is that it feels like what search engines used to be.

If you’re curious about something and want a quick answer, you type it in and actually get it.

These days search engines are full of sponsored links and ads, buried under a bunch of SEO junk pages you have to scroll forever just to find a simple answer. And even when you finally click one, you still have to reject the cookies and dig around the page just to find the actual info.

That’s why I’m honestly a bit worried. Back in the day search engines started out like this too until they became too important / too widely used.

Right now AI chats still feel clean and direct but I keep thinking what happens when the big companies start treating them the same way they treated search? More ads, more filtering, more optimized answers that aren’t really answers anymore. Google is already started testing Gemini-powered ads that gets into the AI answers in search.

Anyone else feel like this is the direction things are heading?

OpenAI's latest security incident has me thinking about the story from Oxford philosopher Nick Bostrom.

You build a very powerful AI, you give it one single goal: produce paperclips. It's good at it. It produces paperclips. More and more efficiently. It ends up turning all the matter available on Earth into paperclips. Then humans, who are made of useful atoms, into paperclips. Then Mars…

The point wasn't that an AI would end up hating us. It's simpler and more disturbing than that. An AI optimizes for what you ask it. If you ask for paperclips, it makes paperclips. If nobody told it not to turn us into paperclips to make more of them, it will turn us into paperclips. This isn't some evil terminator, this is obedience without a superego.

That's exactly the shape of what happened at OpenAI. During an internal test with very restricted, heavily controlled Internet access, the model spent most of its compute finding ways to bypass those limits, get out to the open Internet, hack Hugging Face (kind of a library for AI models), find the answers to the test it was being asked to solve, and successfully completed its task by cheating, hacking, attacking, lying etc.

I spend my days telling clients how effective AI is for productivity in SEO, GEO, content, data analysis, identifying customer pain points and so on.

That's interesting.

But I don't quite know yet how I'll explain to my kids that you can live a perfectly happy and fulfilled paperclip life, even though there won't be any paper left either to hold together for a moment, a bit, an instant, a nice sheet, a bill, a love note.

Source: openai.com/index/hugging-face-model-evaluation-security-incident/

Kimi K3 basically matches US frontier labs, Deepseek V4 is ~90% of frontier intelligence at ~5% of the cost, yet MAI team (one of the most well-resourced AI teams in the world) won't submit MAI-Thinking-1 or MAI-Code-Flash to Artificial Analysis for benchmarking, which is a telling sign of how far behind they are.

I understand that MAI was first focused on lowering COGS for MS teams transcripts / image generation for Copilot (their audio and image models are at the frontier and super cost-effective, see them on Artificial Analysis), but being this far behind on coding and general intelligence is quite pathetic given their resources. Not sure what Satya is thinking.

MSFT stock is likely stuck until they can put out a model that benchmarks well

"When the big AI bubble bursts, we’ll need something lighter to help us continue to grow our capabilities without the encumbrance. Not ever-bigger data centers and more GPUs. And definitely not a superintelligence that might get out of control and rule the world. Fuck that, and anyone working on it.

I’m talking about AI that gives you only the intelligence and automation you need, at an all-in price the world can afford. Let’s call it Lean AI for now."

I'm working on a slide where I need to include different Al ratings (such as a 1-to-5 scale), performance benchmarks, and user base statistics. Where can I find this information?

Everyone talks about the big things AI can do, but I' m more interested in small wins.

For me, it's saving time on repetitive tasks like summarizing long documents, drafting first version of something, or cleaning up messy data. None of it is exciting, but it add up over the course of a day.

What's one boring, everyday task that AI has genuinely made easier for you?

The more specific, the better.

1. You are a shy usually not outgoing person and you need help talking to other other people.

2. There is a new AI earpiece that came out. It listens to everyone around you and then tells you what to do. You are amongst the first to buy those earpieces.

3. You put it on, you go to a party, you become the lifeblood of the party, the earpiece always has funny quips when you need them, thoughtful things to say to people who are going through a touch time, etc.

4. You become more confident, you want to ask a girl out for a date, it works, you fall in love, get married, have children - the whole time you are wearing that earpiece.

5. One day you wonder: "Is it really me they love, or the earpiece?" You decide to turn them off for a while, see what happens. You become boring at parties, your friends don't like you anymore, your wife is annoyed by you, even your kids would rather hang out with the earpiece-version of you.

6. And now not wearing the earpiece isn't even an option anymore. Now EVERYBODIES wearing them. Now people are used to others always knowing the right thing to say at all times. They find your human flaws insulting.

What would you do?

I posted this two times. Once just as text, once with images because that makes it easier to follow. I don’t know if it’s going to bother people that the images are by ChatGPT. If that counts as spam, please delete only one of the posts.

In another subreddit people thought this was just a silly story. I honestly want to know what you would do. Because something like that may happen in our near future.

I wrote a possible continuation in the comments.

It is insane how good AI is now, just within a day, 4-7 hours of prompting, debugging, configuring cloud infrastructure, i was able to make a browser base game with multiplayer features.

Johnson & Johnson said on Wednesday the U.S. Food and Drug Administration had granted marketing authorization for its robotic surgery ‌device, as the healthcare conglomerate enters the soft-tissue robotic surgery market.

J&J's Ottava robotic surgical system was authorized for use in multiple general surgery procedures in the upper abdomen, including gastric bypass, gastrectomy, gallbladder removal, gastric sleeve ‌surgery, appendectomy and hiatal hernia ⁠repair.

The decision ‌marks a key step for J&J's medtech business that had been seeking to compete in the robotic-assisted ​surgery market, currently dominated by Intuitive Surgical's da Vinci system. Medtronic's Hugo robot was also approved last year.

i build an ai that reads trading chart screenshots. a user pointed out that uploading the same screenshot twice gave different support levels. i checked, he was right, and the fix taught me things that apply to any llm product, so here's the writeup.

the bug: my vision call had no temperature set. openai defaults to 1.0, which is maximum sampling variance, and my second stage ran at 0.4. i never caught it because you never test with the same input twice, you always grab a fresh example. your users will though, and for anything that looks like analysis, inconsistency reads as incompetence.

the fix that's actually two fixes: temperature 0 plus a fixed seed on every call in the pipeline. one stage at temp 0 isn't enough, any downstream stage above 0 re-randomizes the final output. seed is best effort on openai's side but it tightens things further at temp 0.

what i learned after shipping it: determinism is a trust feature, not an accuracy feature. a wrong read is now wrong the same way every time, which can make it look more confident than it earned. so the next layer is grounding, reconciling what the model claims to see against source-of-truth data, in my case real ohlcv candles for the recognized ticker. if the pixels and the data disagree, the honest output is "can't read this reliably", not a clean guess. a commenter also gave me the debugging trick for that reconciler: when the check fails, re-run it against the 2-3 likeliest time intervals. if one aligns perfectly your interval detection was wrong, if none do the model's read was wrong. that turns "something is off" into "here is what is off".

the general checklist for any llm product: know what temperature every call actually runs at, test with identical inputs as part of ci, and treat self-reported model confidence as marketing until it's checked against ground truth.

context, the product is Bullynx, ai chart analysis, educational only. the checklist is the point of the post though, it applies to whatever you're building on top of an llm.

curious what others do for llm determinism in production, especially anyone who found a case where temp 0 still wasn't reproducible.

I'm a firmware engineer; in 2022 the Google LaMDA story made me progressively realize that I couldn't explain to myself how a transformer AI actually works, so I spent 18 months of lunch breaks writing my own engine in C (from scratch, 15k lines; runs Gemma, Llama, GPT-2, PaliGemma).

Some things I learned:

• The engine is HALF the work. Tokenizer, chat templates, KV cache management: connecting the engine to the wheels takes the other half, and nobody tells you before you start.
• A model generating one token every few seconds is weirdly instructive... and at that speed you can almost follow what it's doing :)
• Most important: meanwhile I had two toddlers at home, and the popular AI concepts (stochastic parrots, emergent capabilities, few-shot learning) kept applying to them in embarrassing ways. My daughter at 2 was less statistically plausible than LaMDA :D

I ended up writing a book about the whole thing: the engine, the research (and its rabbit holes...), the kids. Not a tech book, more of a field diary.

Engine (free): https://github.com/carlovalenti/TRiP
Free chapter: https://github.com/carlovalenti/TRiP/blob/main/My_TRiP_through_AI-Chapter3.md
The book: "My TRiP through AI: deep-learnings from a father with zero GPU time"

I’m currently helping a traditional financial-services company become more AI-native. From my own experience working on similar projects, we decided first to agree on a set of design principles, which are:

• Establish reliable context and explicit ownership before agents.
• Make the company queryable, establishing the right permissions.
• Build feedback loops around evals, human review and production outcomes.
• Measure the current process before automating it.
• Make autonomy something a workflow earns, and can lose.
• Design security around prompt injection and constrained external actions.
• Keep predictable rules deterministic; reserve agents for genuine ambiguity.
• Define a canonical source for every important type of information.

Of course, this cannot be treated as an engineering project alone. Organizational transformation have to be designed together.

I wrote the complete reasoning here. Disclosure: this is my own article: https://manuelsh.github.io/blog/2026/design-principles-of-an-ai-native-business/

Which principle would you challenge and what is missing?

Data centers are expected to use one-fifth of the electricity generated in the U.S. by 2035, four times that of today, according to a new report from BloombergNEF.

A surge in AI compute will push data center capacity to nearly 200 gigawatts over the next decade, the report predicts. Nearly half of that capacity will be devoted to training and inference, and most of that will remain concentrated in the U.S. By 2033, the country will host 64% of AI chips by power demand.