Super Intelligence Force Chair Jay Clayton on their initial meeting: "It was a very good meeting... We set out a protocol and path for making sure those Accords are implemented and that we're in dialogue with those frontier labs."

Lets catch up to reality. China is now more innovation friendly than the US. They have the permissionless innovation we once enjoyed. We are not only making innovators ask permission we are attacking them policyarena.org/p/us-no-long… @AdamThierer

Bernie Sanders says AI is going to wipe out tens of millions of jobs. Here’s what he doesn’t tell you: Technological leaps, starting with the wheel, have made people wealthier. Yes, jobs will change. Greater productivity will create new, higher-paying jobs!

Are Small Models Really a Threat? The Anthropic Fear Factory just released a new report warning that small models may be only months away from self-replicating across cheap IoT devices like off-brand Android TV boxes and smart ovens. They say such an AI worm would be hard to shut down since there’s no central kill switch. We’ve gone from “The Terminator is coming” to “your toaster might kill you.” But the experiments don’t support the dramatic headlines. They ran a 26B model across 15 $55 TV boxes at 0.39 tokens per second. The end-to-end test used emulated devices with deliberately open ADB ports, so the “hacking” step was trivial. The capable model often ran on a separate server. Agents were harnessed and told to propagate. This is not proof of autonomous rogue AI worms in the wild. Nor is the threat even novel. Worms that do this without AI already exist. Mirai in 2016 scanned for default credentials and built massive DDoS botnets. Kimwolf has infected over two million devices, mostly cheap Android TV boxes, by hitting open ADB ports. Small models don’t currently add much that matters on these targets. The bottleneck has never been figuring out the device. It’s the huge number of devices that ship with open ports, default passwords, and no patching. Traditional worms already exploit that at scale. A practical attacker would use the best models available offline to design a better traditional worm, then ship the lean binary. You get the intelligence benefit at design time without dragging a slow, heavy model along with every infection. Embedding a weak model in the payload is the worse option for anyone who actually wants the largest, fastest botnet. The timing of Anthropic’s latest scare campaign is hard to ignore. The day before, Brian Armstrong posted that Coinbase fine-tuned an open-weight small model (Qwen-3.5-9B) on proprietary fraud data. It beat frontier models, including Anthropic’s own Opus 4.5, while being faster and much cheaper. In testing it cut fraudulent transactions by about 30%. This is the emerging pattern. For narrow enterprise workloads you don’t need a general superintelligence. You need a small specialist model trained on your own data. Cheaper to run, especially as compute gets tighter, and often better on the metric that matters. That is a direct threat to the frontier-company model of selling access to ever-larger general models. Safety advocates should be welcoming this development. If the real concern is existential risk from ever-larger frontier systems — the ones that could theoretically do sustained autonomous R&D or become hard to control — then a shift toward smaller specialized models is the safer outcome. Small models specialized on fraud detection or support tickets are not pushing the frontier. They are not in the category of systems that could become a new species. The theoretical loss-of-control risk is created by models getting bigger and more generally capable. Safety advocates focused on those risks should welcome companies having a cheaper alternative to constantly scaling general models. Instead we get dramatic warnings about small models at the exact moment they are starting to take real enterprise share from closed-model companies like Anthropic.

David Sacks Calls Out the “Rotating Hoaxes” Fueling Superintelligence Fears “Let's just get back to first principles here. What is the problem that we're trying to solve? The affordability of goods like housing and education and healthcare. How do we bring those prices down? Through abundance. What creates abundance? Technology in general, and specifically superintelligence will be the driver over the next decade. Now, what is the most hated thing in society right now? Superintelligence. If you judge it by its fruits as opposed to a bunch of fake narratives, then I think that people would be in favor of this thing. What does superintelligence create? First of all, people's 401ks are going up. It’s a 3% tailwind to GDP. A million new jobs created, according to The Economist, already. Productivity goes up, which means that wages and living standards go up. This is a driver right now of nothing but positive economic trends. But what do we hear about? ‘AI's going to put everyone out of work.’ Total fake narrative. That was last year's hoax. Data centers using up everyone's water. Now there's a new hoax about human extinction. So we have a series of, I call them rotating hoaxes, where every six months there's a new one to try and convince us to sabotage the thing that's going to solve so many of our problems, especially the ones around affordability.”

China races to build AI data centres across energy-rich hinterland ft.trib.al/Fgutgzf

Jensen Huang: China isn’t catching up in AI anymore. In many areas, it’s already ahead. “Fifty percent of the world’s AI researchers are Chinese.” “70% of last year’s AI patents were published by China.” “The AI ecosystem in China is vibrant, rich, and incredibly innovative.” “Nine out of the 10 top science and technology schools in the world are now in China.” “We used to lead most of these fields. Now they lead most of them.”