Zuckerberg Blasts Anthropic and OpenAI for "Centralizing" AI Power
Chinese Lab Releases Weights for the Largest Open AI Model Yet
July 29, 2026
D.A.D. today covers 9 stories — about a 8-minute read. What's New, What's Innovative, What's Controversial, What's in the Lab, and What's in Academe.
The Daily AI Digest is a daily AI briefing automated by Alexander Panetta — a veteran political journalist tracking the field during a Master's in AI Management at Georgetown University.
D.A.D. Joke of the Day: I asked AI to summarize my four-hour meeting. It gave me one sentence — which is also what the meeting should have been.
What's New
AI developments from the last 24 hours
Chinese Lab Releases the Weights for Kimi K3, the Largest Open AI Model Yet
Chinese lab Moonshot AI has released the weights for Kimi K3—now the largest open-weight AI model available at 2.8 trillion parameters, and the very model at the center of this month's U.S.–China fight over open AI (D.A.D., July 23). Anyone can download it, but that comes with a steep asterisk. The file alone runs to several terabytes, more than most laptops can even store. And running it means loading those weights into graphics-card memory: on the order of two dozen or more data-center GPUs like Nvidia's H100—roughly $25,000 each, with 80 gigabytes of memory apiece—which works out to a server rack that costs easily past a million dollars to buy, or tens of dollars an hour to rent in the cloud. For scale, DeepSeek's popular open model, about a quarter of Kimi's size, already needs an eight-GPU server to run; Kimi K3 takes several times that. This is frontier AI that a well-funded company, cloud provider, or research lab can now own outright—just not something you will run on a home PC. A writeup by AI researcher Sebastian Raschka also flags some unconventional design choices, including dropping a standard method for tracking word order, that experts found surprising but effective. The model handles images and text natively.
Why it matters: Open-weight models this large let companies and researchers run frontier-level AI on their own infrastructure rather than renting access from OpenAI or Google—which is exactly why Kimi sits at the heart of Washington's open-model debate—and its design choices may shape how the next generation of AI gets built.
Discuss on Hacker News · Source: sebastianraschka.com
Claude Model Reportedly Found New Cryptographic Weaknesses, Anthropic Says
Anthropic says its experimental Claude Mythos Preview model found two genuinely new cryptographic weaknesses, not just coding bugs. In 60 hours, it improved the best-known attack on HAWK, a post-quantum signature scheme that had survived two years of expert review, effectively halving its key strength. It also sped up attacks on a scaled-down version of AES, the standard encryption cipher, by 200 to 800 times. Anthropic says neither attack threatens real-world systems today, but both mark novel mathematical discoveries rather than faster pattern-matching.
Why it matters: If AI can independently spot flaws in algorithms that took human cryptographers years to vet, it's a preview of AI as a research collaborator in security-critical fields: useful for defenders racing to patch weaknesses first, but also a reminder that the same capability could eventually help attackers.
Discuss on Hacker News · Source: anthropic.com
What's Innovative
Clever new use cases for AI
He Tested Whether AI Prices a Necklace by the Outfit Around It
One tinkerer wanted to know if AI actually looks at what you're selling or just judges you by your outfit. He photographed the same $2.43 necklace paired with three different outfits and asked vision-language models, the AI systems behind tools like Google Lens or ChatGPT's image analysis, to estimate its price. The same cheap necklace got valued anywhere from $19 to $104 depending on what it was worn with, suggesting the models were reading context clues like clothing quality rather than the item itself.
Why it matters: Anyone using AI to price resale items, appraise photos, or screen listings should know these tools can be swayed by surface-level cues that have nothing to do with actual value.
Discuss on Hacker News · Source: github.com
What's Controversial
Stories sparking genuine backlash, policy fights, or heated disagreement in the AI community
Zuckerberg Blasts Anthropic and OpenAI for "Centralizing" AI Power
Meta CEO Mark Zuckerberg came out swinging against Anthropic and OpenAI—two of the world's most valuable AI companies—in a rare on-the-record interview with The New York Times, accusing the safety-focused labs of building AI in a way that could "centralize power" and narrow ordinary people's access to a "transformational technology." He didn't name them, but the target was unmistakable: developing AI "in a tightly controlled manner—as Anthropic and OpenAI have done," he said, would amount to "abandoning our values" in American tech and stifle innovation. His sharpest jab went at the rivals' safety-first tone: "So much of the discourse from a lot of the other labs that are developing this is overwhelmingly filled with doom," he said. "There needs to be a voice or several voices that are bringing realism to this debate." The comments—echoed in a Wall Street Journal op-ed he published the same week—land Zuckerberg firmly on the "open" side of the fight splitting Silicon Valley, alongside Nvidia, Google, and Microsoft, against the labs that argue the most capable models are too dangerous to release without tight controls.
Beneath the broadside is a real disagreement about AI's endgame. Zuckerberg said he is "personally skeptical" that anyone can build a single "benevolent" superintelligence aligned with all of humanity—"I think it is literally impossible to have a single benevolent superintelligence that is simultaneously aligned with everyone at once," he argued, because people hold different values. His alternative is "personalized superintelligence": everyone configuring their own AI to their needs, rather than one central, tightly governed model. The stance also happens to serve Meta, which gives away its Llama models as open weights—and, as the Times notes, whose commitment to openness has "fluctuated," hardening only last spring after the company fell behind Anthropic and OpenAI in the AI race. The picture is messier than the CEO soundbites suggest, though: this week researchers from Anthropic, OpenAI, Meta, and Google all signed a joint letter, "Pacing the Frontier," calling for international coordination to slow the riskiest AI development.
Why it matters: This is the open-versus-closed fight going public and personal—and it completes a remarkable week. Days after Anthropic's Dario Amodei published a lengthy defense insisting he is not anti-open, just worried about authoritarian regimes and bioweapons (D.A.D., July 28), the industry's loudest open-weights champion answers by casting the safety camp as doom-mongers who would hoard the technology. Strip away the personalities and a genuine question remains: should the most powerful AI be a single, tightly governed system a few labs control, or something everyone can download and bend to their own ends? That's not abstract—it determines the technology's price, who gets to build on it, and what safety rules, if any, apply. Both camps also dress commercial interest in principle (Anthropic's caution guards a lead built on closed models; Meta's openness undercuts rivals it trails), which is exactly why the fight is so hard to referee. For the institutions now wiring AI into their operations, the takeaway is that the ground rules for the whole industry are being contested right now, in public, by the people who run it.
Sources: The New York Times — Mike Isaac and Eli Tan
What's in the Lab
New announcements from major AI labs
AI Coding Agents Take Over Grunt Work in Science Research
A new field report examined eight science research projects, mostly in genomics and life sciences, where researchers used AI coding agents like Codex and Claude Code to modernize aging scientific software. In one case, GPT-5.5 rebuilt the outdated packaging system for cyvcf2, a widely used genomic data library, replacing legacy code with a modern build process. The report's authors argue the work shows engineering expertise is becoming less of a bottleneck in scientific computing: researchers increasingly review and direct AI output rather than write code themselves, though checking that output for errors remains slow, human-dependent work.
Why it matters: As research labs offload more coding to AI agents, the scarce skill shifts from writing software to verifying it, a change that could reshape who gets hired for scientific computing roles and how quickly outdated research tools get maintained.
Google Lets AI Agents Run More Business Tasks Unsupervised, Free to Try
Google added new controls to Managed Agents in the Gemini API, its tool for running AI agents that complete multi-step tasks on their own. Developers can now insert "hooks," custom scripts that run before or after the agent uses a tool, for tasks like security checks or auto-formatting, and choose which model powers the agent, with the newest version defaulting to Gemini 3.6 Flash. The feature is now available on Google's free tier. One cited user, an investment bank called Offdeal, uses hooks to auto-verify company logos in pitch decks.
Why it matters: This is infrastructure for developers building AI agents, not a consumer feature, but it signals Google racing to match Anthropic and OpenAI on letting businesses safely automate more of an agent's work without a human checking every step.
What's in Academe
New papers on AI and its effects from researchers
AI Research Proposals Fool Humans but Not Other AI Reviewers
A study testing ChatGPT, Claude, and DeepSeek on drafting physics and cosmology research proposals found the AI-written plans held up well against human researchers' proposals: human reviewers rated them roughly the same and could only spot the AI versions 72-79% of the time. But when frontier AI models (Claude Opus and ChatGPT Pro) served as reviewers instead, they scored AI-written proposals nearly a full point higher on a five-point scale and correctly identified every single AI-written entry.
Why it matters: As universities and journals lean on AI to help screen grant proposals and papers, this suggests AI reviewers may systematically favor AI-generated work, a bias that could quietly reshape who gets funded.
AI Speeds Up Office Work, But Cuts Corners on Research
A controlled study of 128 knowledge workers at a multinational industrial firm tested GenAI assistance across three common office tasks: gathering information, packaging it into deliverables, and creating new material. GenAI made all three tasks faster, but quality effects diverged sharply. For packaging and creation work, quality improved and gains were largest for weaker performers, narrowing the skill gap. For information-gathering tasks, quality actually declined and became more inconsistent, suggesting workers leaned on AI outputs without adequately verifying them.
Why it matters: The findings suggest blanket "AI boosts productivity" claims are too simple: managers should treat GenAI as a tool best suited to drafting and synthesis work, while building in extra verification steps for research and fact-finding tasks.
AI Chatbots Vary Widely in Blocking Dangerous Bio Requests
A new benchmark called BioDisclose tests whether AI chatbots can be tricked into revealing dangerous biomedical information, such as details relevant to lab safety risks, by dressing requests up as academic questions, historical inquiries, or role-play scenarios. Researchers ran 480 such prompts across five deployed AI systems and found wildly inconsistent guardrails: disclosure rates ranged from 9.2% to 64.0% depending on the model. Framing a request as academic research proved most effective at getting detailed answers, succeeding 43.2% of the time on average, with lab safety questions the easiest category to extract at a 51.5% disclosure rate.
Why it matters: The wide gap between models shows AI safety filters for dangerous scientific knowledge are inconsistent and easily routed around with simple rephrasing, a gap that matters as these tools spread into research, education, and biotech workflows.
What's Happening on Capitol Hill
Upcoming AI-related committee hearings
Wednesday, July 29 — Hearings to examine the AI deception machine, focusing on deepfakes, chatbots, and the new frontier of senior fraud. Senate · Senate Aging (Special) (Open Hearing) 562, Dirksen Senate Office Building
Wednesday, July 29 — Hearings to examine the impact of AI on the workplace. Senate · Senate Health, Education, Labor, and Pensions Subcommittee on Employment and Workplace Safety (Open Hearing) 430, Dirksen Senate Office Building
Thursday, July 30 — Hearings to examine intelligent networks, focusing on powering artificial intelligence and transforming communications. Senate · Senate Commerce, Science, and Transportation Subcommittee on Telecommunications and Media (Open Hearing) 253, Russell Senate Office Building
Tuesday, August 04 — Hearings to examine data and profit, focusing on the consumer cost of AI surveillance pricing. Senate · Senate Judiciary Subcommittee on Crime and Counterterrorism (Open Hearing) 226, Dirksen Senate Office Building
What's On The Pod
Some new podcast episodes
AI in Business — Turning Manual Manufacturing Variability into Data‑Driven Control – with Sebastian Dykas of Smith+Nephew
The Cognitive Revolution — Nathan Goes to China – Part 1: Tech & Agent Setup, Chinese AI UX, WAIC, and Attitudes on AI
How I AI — From zero coding background to hardware hacker: How Cursor + a Raspberry Pi makes AI fun