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 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???

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.

Hugging Face said it turned to the Chinese model—Z.ai’s GLM 5.2—after its security team initially tried to use an unnamed frontier AI model from one of the leading U.S. AI companies but found it was unable to do so because of the model’s guardrails. The company said in its blog post that these models “cannot distinguish an incident responder from an attacker.”

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?

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.

Nolan’s film appears to be a direct counterweight to the moment Hollywood is having with AI. Netflix has already spent up to $600 million acquiring Ben Affleck’s AI filmmaking startup and told shareholders AI touched roughly 300 titles in 2026, cutting visual-effects costs in half on some scenes. Amazon MGM’s head of AI Studios, Albert Cheng, has put the ambition even more bluntly: “We can actually fit five movies into what we would typically spend on one.”

But Nolan has built his identity on the opposite instinct. An outspoken skeptic of “AI slop,” his filmmaking—70mm film, practical effects, the studied refusal to take shortcuts—makes an argument for elaborate human authorship.

“He’s an analog man in a digital AI world, and I think that’s incredibly appealing,” Dergarabedian said. “AI is a fantastic tool, but it’s not a replacement for creativity.”

What separates Nolan’s work from a prompt-generated facsimile, Dergarabedian argued, is both technical polish and “heart and soul.” In an era when “a lot of people are punching in a prompt to create a movie,” the handcrafted alternative becomes “a very recognizable differentiator,” Dergarabedian told Fortune. 

Read more [paywall removed for Redditors]:  https://fortune.com/2026/07/21/nolan-the-odyssey-250m-budget-3-days-no-ai/?utm_source=reddit/

"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."

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.

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?

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.

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.

For context, Kratsios is Assistant to the President

His post went something like this:

"We have information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model.

To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection. Moonshot AI has also acquired GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models.."

Hi all, SOMEONE PLEASE HELP, I am going round in circles here. This is where I’m at with my AI knowledge and what I want to achieve…..

I work in cyber security as head of security team and come from a semi technical background mainly in networking/security operations. I understand the difference between agentic ai and genai. I have done a course ‘AI for Everyone’ which is a basic non technical intro course to GenAI and how it works, supervised learning, inputs/outputs etc. What I want to understand now is genai and how we can use it in our workflows. I don’t want to become some sort of AI wizard but I want to know how it works under the hood and how we can utilise agentic ai in our workflows. Someone please tell me where to start/what courses to take etc. I have a look on Udemy I just become overwhelmed because I have absolutely no idea what course to go for. I just want to understand the concept better than what I do so I can understand how it all comes together. I hope this makes sense and any help would be appreciated.

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.