Quantum Skull

whoa

I pulled the current Artificial Analysis style index scores, looked at OpenAI’s release cadence and average raw score gains, then ran a conservative extrapolation by cutting the gain per release in half. while keeping cadence the same. Even with that SLOWER path, GPT still trends toward a 90 index score by around 2029, which is why I think late decade AGI is starting to look like a base case rather than optimistic in my opinion. A 90 on the Index would be massive because it means the model is averaging near PHD performance across a diversified frontier basket like CritPt, HLE, SciCode, Terminal Bench Hard, and GDPval AA, rather than leaning on one saturated benchmark. Keep in mind, this graph already cuts the current raw progress rate (+3 per release) in half, so it is not the aggressive case. If gains speed up from better agents, test time compute, synthetic data, post training, or AI helping with AI research, that path to 90 could arrive much faster than this (albeit linear) conservative prediction.

That's what Magnus learnt. What about you?

SCOOP: Google has signed a deal with the Pentagon to allow the use of its AI for "any lawful government purpose." Google’s agreement also requires it to assist in adjusting its AI safety settings and filters at the government’s request.

Brian has the best take on GLP-1 side effects: There's a huge one, we just wouldn't have liked it historically. It just so happens, however, that in our era, killing hunger is desirable rather than deadly

People are quoting this saying it's fake bc they marketed the internet and cell phones but those marketing campaigns worked quickly and ended bc the tech was good. They've been trying to shove chatgpt down our throats since like 2019

This is a huge change in how Meta does AI safety testing — or at least how it talks about it publicly.

Distilled recap of the back-and-forth with Jensen on export controls: Dwarkesh: Wouldn’t selling Nvidia chips to China enable them to train models like Claude Mythos with cyber offensive capabilities that would be threats to American companies and national security? Jensen: First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company. The amount of capacity and the type of compute it was trained on is abundantly available in China. Dwarkesh: With that, could they eventually train a model like Mythos? Yes. But the question is, because we have more FLOPs, American labs are able to get to this level of capabilities first. Furthermore, even if they trained a model like this, the ability to deploy it at scale matters. If you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. Jensen: Your premise is just wrong. The fact of the matter is their AI development is going just fine. The best AI researchers in the world, because they are limited in compute, also come up with extremely smart algorithms. DeepSeek is not an inconsequential advance. The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation. Dwarkesh: Currently, you can have a model like DeepSeek that can run on any accelerator if it's open source. Why would that stop being the case in the future? Jensen: Suppose it optimizes for Huawei. Suppose it optimizes for their architecture. It would put others at a disadvantage. As AI diffuses out into the rest of the world, their standards and their tech stack will become superior to ours because their models are open. Dwarkesh: Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China. They didn't cause some lock-in. China will still make their version of EVs, and they're dominating, or smartphones, they're dominating. Jensen: We are not a car. The fact that I can buy this car brand one day and use another car brand another day is easy. Computing is not like that. There's a reason why x86 still exists. There's a reason why Arm is so sticky. These ecosystems are hard to replace. Dwarkesh: It's just hard to imagine that there's a long-term lock-in to the Chinese ecosystem, even if they have this slightly better open-source model for a while. American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips. Jensen: China is the largest contributor to open source software in the world. China's the largest contributor to open models in the world. Today it's built on the American tech stack, Nvidia’s. Fact. All five layers of the tech stack for AI are important. The United States ought to go win all five of them. in a few years time, I'm making you the prediction that when we want American technology to be diffused around the world—out to India, out to the Middle East, out to Africa, out to Southeast Asia—on that day, I will tell you exactly about today's conversation, about how your policy ... caused the United States to concede the second largest market in the world for no good reason at all.

For the record I think AI safety is a very real problem and that artificial superintelligence has a non-zero chance of extinguishing human life. I just don't think it's a certainty. And the way to actually create safe human AI relations is to do science, not to start a jihad