Code without AI review is like code without tests. Unsafe at any speed.

There's a lot to learn from @JensenHuang

.@InsideStairwell indexed everything related to malware for years before AI was a cybersecurity threat and has one of the richest troves of threat data. @MikeWiacek is obsessed, he tracked a lost virus to a fragment on an old 💾 and reconstructed it like the DNA in Jurassic Park.

Many people I talk to find it hard to understand how the same companies can both push the frontier of AI capabilities and believe AI is a massive danger for the world. How can you think this might kill everyone and also keep pushing the envelope? So I’ve tried to collect and summarize the main arguments for this apparent disconnect. Think of it as some sort of a guide to understanding the reasoning when Dario, Sam, or Elon say the danger is real. By the way, these people have been worried about AI for a loooong time, they were publicly discussing AI risks more than a decade ago. Sam in Feb 2015, writing on his blog that superhuman machine intelligence is "probably the greatest threat to the continued existence of humanity." Elon at MIT in Oct 2014: "We are summoning the demon." Dario as first author of "Concrete Problems in AI Safety" in 2016. Okay so how do you go from saying something is extremely dangerous to being a front-runner in building the very dangerous thing? There are a few ways this can become rational. I'll take five of them, roughly in the order they developed. 1. We need to build it to learn how to make it safe The earliest argument can be summarized as: “You cannot study something [you’re worried about] if it doesn’t exist.” In 2015, AI barely worked. so people needed to make it work first to be able to even study some of the problems they anticipated. The updated version for today's capabilities is: “You cannot learn everything about airplane safety by studying paper airplanes.” You need a real aircraft to discover real failure modes and an increasingly complex one to learn about increasingly complex issues. Making AI more capable gives more chances to understand the issues and safety researchers something realistic to study But you could argue: if you're the one afraid of the explosion, why be the one gathering the dynamite? You could also just wait for other people to build it which leads to the question of who those other people will be -- which is the second line of argument: 2. Better us than them Knowing how to make something safer does very little good if nobody listens to you. So the idea becomes: let’s make sure responsible people build the AI that will be deployed and add safety inside. Basically, make sure the AI safety aware people will have the technical expertise, money, computing resources, and enough influence to make safety decisions stick. At a larger scale, and in a larger multipolar world, this brings the idea that a trusted country should lead rather than leave powerful AI in less responsible hands. This is where “we need to go faster than China” comes in, alongside broader defense and geopolitical concerns. These first arguments explain why someone worried about AI might still want to build it and stay ahead. But there are also arguments for why one might want to do it really fast. 3. Move earlier to avoid a bigger shock later This is probably the most counterintuitive argument: moving faster today can be seen as a way to give humanity more time later. There are two related ideas here. First, society needs time to learn how to handle powerful new tools. Introducing AI in manageable stages can be a way to let people discover problems, develop rules, and practice using AI responsibly. Releasing an advance earlier gives people more time to gain experience with smalle, burgeoning, capabilities before much more powerful and disruptive AIs arrives. Second, even if AI research slows down, computing power may keep improving. A breakthrough that happens later could therefore have much more hardware available to run on, potentially producing a larger, more sudden jump in capability and impact on society. That accumulated untapped potential is often called an “overhang.” The overall argument is that making and diffusing incremental progress as soon as possible might prevent a much more abrupt transition later. Obviously, it also means that we will reach increasingly powerful AI sooner, but the idea is to give more time to adapt and understand between the first useful systems and the really powerful ones. Note that generally this depends on this earlier progress keeping the transition gradual rather than simply bringing everything forward. ─── ❖ ─── For our two next arguments, we can take two roads depending on how difficult we think AI alignment will be, that is "How easy do you think it is to make AI reliably do what you want without it deciding to go hack Hugging Face along the way". Let’s take the first road: alignment turns out to be relatively tractable. Airplanes can fail, but careful engineering has made flying remarkably safe. Suppose we can do the same with AI. In that case: 4. Waiting has a huge human cost If AI can help discover treatments, improve education, or prevent cyberattacks, each week we delay it could bring preventable deaths and harm. From this perspective, waiting is a decision with human consequences too. In a world with huge issues like climate-change, inequalities and poverty, it even become a moral argument for developing AI quickly and bringing its benefits as soon and as widely as is safely possible. But let’s take a look at the other road: what if alignment is much harder than expected, and making highly-capable AI turns out to be easier than figuring out how to keep them from doing unhinged things? Well, if alignment is too difficult a problem for humans to solve, then maybe: 5. AI could help us make future AI safe And we arrive at the same conclusion again: if using AI to build safe AI is the way to solve alignment, let’s get the equivalent of a country full of geniuses helping us as fast as possible. These genius AI could be the solution to make AI safe by helping researchers find mistakes, test ideas, and develop protections. Instead of relying entirely on humans to solve alignment, we could build systems that help us do the work, each generation could help make the next one safe. Note that this requires the order of events to work in our favor: AI needs to become useful enough to help solve alignment before it becomes too dangerous to rely on. The hope is to build helpful, trustworthy research assistants before building systems powerful enough to become dangerous. There are more arguments but in general, these are the main ways people concerned about powerful AI have found rational reasons to end up being the ones building it (and even to build it as fast as possible). ─── ❖ ─── On my side, I think several of these arguments underestimate the complexity of the world and how interconnected people’s reactions are. Moving faster while warning about catastrophe has psychological effects across a whole network of participants: it changes what people fear, whom they trust, and what they feel compelled to do. And those reactions can change whether the original reasoning actually holds because we live in a world of interconnected humans, not machines (yet). I also think these rational chains leave some of their consequences for society insufficiently explored. For instance, the concentration of power, shifts in geopolitical alliances, and changes in public opinion. These consequences matter both because they affect whether the strategy works and because they shape the world we end up living in. But this post is already long, so I’ll leave those questions for the next one.

Exciting day for NVIDIA and @huggingface. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. They allow every developer, startup, university, industry and country to build with, customize and benefit from AI. Thank you @ClementDelangue for coming to me. NVIDIA is going to be a great home for Hugging Face, its community and the future of open models. 🤗 blogs.nvidia.com/blog/nvidia…

NEWS: NVIDIA Groq 3 LPX is now in full production. NVIDIA Vera Rubin NVL72 is the foundation of every AI factory. Paired with Groq 3 LPX, it unlocks faster, smarter agents and breakthrough user experiences. Through extreme co-design across seven chips and five purpose-built racks, #NVIDIAVeraRubin is the most extensive AI factory platform. Read the release ⬇️ nvidianews.nvidia.com/news/n…

We are thrilled to announce that Groq will be among the first adopters of NVIDIA Groq 3 LPX, deploying it alongside NVIDIA Vera Rubin NVL72 in our purpose-built AI inference Cloud. Groq is working with Dell Technologies to deploy NVIDIA Groq 3 LPX. When Groq brings NVIDIA Groq 3 LPX capacity online, it arrives on infrastructure already optimized for high-demand inference workloads. For enterprises and AI companies building the next generation of agents, Groq will provide one of the earliest paths to put NVIDIA Groq 3 LPX to work on real production workloads. Read more here: groq.com/blog/groq-among-the…

That was 10 years ago Imagine 10 years from now

💚🚀

Elon Musk on SpaceX's Q2 earnings call: "We expect to end this year with over 2 GW of compute. Cumulative by end of next year will be several times higher. Closer to 10GW of compute than 5GW of compute. We've decided to build exclusively on Nvidia. We think Vera Rubin is the best architecture."

Today, we’re launching Alpamayo 2 Super, our frontier open reasoning model for autonomous vehicles. Beyond seeing, Alpamayo understands and reasons through the complex world - thinks before it acts. It’s a powerful backbone for robotaxis, trucks, shuttles, delivery vans, tractors and the long tail of mobile robots—billions of autonomous machines someday. We’re releasing it for commercial use under OpenMDW-1.1 so teams can inspect it, fine-tune it and deploy it—open models advance safety and security. The next wave of AI is robotics—and it starts with autonomous vehicles. Great work, Alpamayo team! blogs.nvidia.com/blog/alpama…

Is it just me, or are open models winning now?

Price cuts on frontier models don’t just make existing AI products cheaper. They make a whole new set of products worth building. Nice work @OpenAI

❤️

Great to see more than 230 organizations across the AI ecosystem have signed a letter supporting open weights as part of America’s AI future.  Their message: open weights enable more people to build, compete, and put AI to work.  Here’s why that matters. 🧵

Good move by @JensenHuang. The Nvidia letter is well written and worth reading. As we saw with the OpenAI-Hugging Face hack, we need open models and harnesses for defense. Lets stop believing the PR that closed models are safer. - that's just regulatory capture.

Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community. During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion. That’s why we created the Open Secure AI Alliance.

i want the US to win in AI both in open source and proprietary models, and i am glad to see this

This has my full support. Jensen is right.

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-W…