founder / ex-ceo @skycatch (acquired by @CaterpillarInc) , builder/investor of bits and atoms (@USAvionix), US navy vet 🇺🇸

San Francisco, CA
We’re heading toward a world where autonomous systems will be everywhere. They will move people, goods, sensors, and even decisions without asking for human input. Today, we only see the early signals. A few drones in the air. A handful of self-driving trucks. Some real-time sensing of key infrastructure. But the truth is we still see very little. At best, we capture five percent of our own terrain in real time. Satellites give us fragments. Drones give us glimpses. Most of the world, most of the time, is still invisible. That is going to change. At some point, every square inch of land and ocean will be monitored continuously. Machines will scan, interpret, and update the state of the physical world with no gaps and no delay. That data will be available not just to us, but to whoever gets there first. The same shift is coming to logistics. Right now, we move things within the limits of what humans can manage. Flights are scheduled. Trucks are driven. Operators are assigned. But once autonomy becomes the default, the scale of movement changes. We will see a four or five-fold increase in volume. Goods will be transferred instantly. People will be relocated without pause. Entire networks will function without waiting for human authorization. This introduces a different kind of power structure. The question becomes who owns the pathways. Who sees what. Who can act first. This has nothing to do with ideology and everything to do with speed, saturation, and reach. The systems that dominate communication, transportation, and sensing will also shape which forms of government persist. Those with the clearest picture and the fastest ability to respond will set the conditions the rest of the world has to live within. We like to believe that the future will preserve the values we care about. But that depends on who gets there first. And what they decide to build. I’m obviously biased. As a U.S. Navy veteran, my bet is on the United States. Not just to build faster systems, but to build ones that reflect something deeper… our values, our way of life, and the belief that power should still answer to people. That only happens if we pay attention. If we stay in the race. If we lead. 🇺🇸 cc @USAvionix @pmarca @Benioff @garrytan @rauchg @elonmusk @nikitabier @naval
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Christian Sanz retweeted
If AI is risky like fire is risky, then you want everyone to have it. If AI is risky like a nuclear weapon is risky, then you want no one to have it.
Maybe you have recently become aware of the AI safety debate and the arguments swirling around it. If you want to understand them, you need to understand a couple things that almost everyone gets wrong: There are TWO distinct classes of AI dangers, and it's VERY important to think about them separately, and not let one confuse you about the other. Many many people (including many quite intelligent, clear-thinking people) do not effectively understand the fundamental differences between these two classes of dangers. The first one has to do with the theory that an AI far superior to human intelligence (Artificial SuperIntelligence, or ASI) will inevitably wipe out the human race. The second one has to do with the idea that powerful AI will result in very harmful things happening to many human beings, possibly all human beings. Those two sound VERY similar, don't they? They are DIFFERENT. Understanding how they are different is crucial if you want to think about or contribute usefully to any conversation about AI safety or AI harm. You might feel like you are Making Very Good Points or Asking Incisive Questions, but if you aren't clear on the differences between the two, you aren't. So, I'm going to tell you what the difference is so that you can talk more usefully. The first one concerns itself with a very specific thing, which is ASI (Artificial Superintelligence) that is more intelligent than any human being. When I say that, I am not referring to a thing like how Einstein is smarter than you, we are talking more about something like how a human being is more intelligent than any mouse. In our regular lives, we meet other people who we can tell are smarter than us, vs some who are less smart. The line is fuzzy, because intelligence has a lot of dimensions. I'm better at a "rotating shapes" kind of intelligence than my wife, and she is better at "words-making" kind of intelligence than I am. But every human is better in almost every dimension of intelligence than every single mouse. That's the level we're talking about: an artificial superintelligence - made up of a computer or a network of computers - that is more intelligent than any human. And more intelligent by a long shot, by a wide margin, in an indisputable way like how humans are above mice. That is the first thing. The theory says that if you have an AI that is vastly smarter than all humans - in the way that a human is smarter than mice - that superintelligent AI will inevitably, eventually, sooner or later, wipe out every human on the planet. We will refer to this as "existential risk." The common follow-up question "well, how exactly is it going to do that?" is NOT the important question, and one of the most important elements of understanding this theory is first getting why that particular question is not important. A couple analogies: Analogy 1: You are playing chess against a grandmaster. My theory predicts the grandmaster is going to beat you. You can ask "Well, how exactly is he going to do that?" I don't know, because I'm not a grandmaster, I just know that a chess grandmaster is almost always going to beat a normal player like you. And I'd be right. So the question "how is he going to do that" is not important, and doesn't affect the final outcome. He's going to figure out a way because he's way better than you. Analogy 2: Humans are smarter than all other animals, comprehensively, by a wide margin. We have driven numerous species to extinction, not because we hated them or hunted them. Many of them have died out without most humans even ever thinking about them. All we did was expand our civilization, use up resources, encroach on habitats, and pretty soon the resources needed by those species went away and they died out. We figured out a way to get what we wanted because we're way smarter than them, and often we didn't even notice they died as a result. A lesser animal asking, "how are the humans going to wipe us out?" is not asking a relevant question. We don't know, but we do know that any time humans and lesser species compete for any kind of resources, the humans will win. The fact that we know who is going to win beforehand - and that it is due to the vastly different levels of intelligence - is the key concept here. A vastly more intelligent AI is likely to care about things that are incomprehensible to us, the way animals can't understand human goals. It's going to need resources to pursue those goals and it's going to be far more effective at gaining control of them and excluding us from them - in the same way that we are far more effective than other lower species. A much more intelligent AI will not care about our interests, it will care about its interests, and to whatever small degree we happen to escape total annihilation from losing access to all our resources, any remaining humans will likely be enslaved into a system that serves the AI's own purposes. That is the first thing. (Remember how I said at the beginning of this post that there was a first thing, and then a second thing?) The first thing is the most difficult to understand, because you have to extrapolate how a vastly superior intelligence would act, and you can only use analogies like "how do humans treat lesser creatures," and the analogies are messy. But now let's move on to the second thing. The second thing is "everything else you've ever heard that AI might do that's harmful." That's a little inaccurate. It's actually "everything else you've ever heard that humans might use AI to do that's harmful." This is the critical difference. The first one talks about the inevitable outcome of what happens when two vastly different levels of intelligence collide, e.g. ASI vs humans, or human vs mice. The second one has to do with what happens when humans possess AI as a powerful tool. This second thing is much easier to understand, because we have many more concrete notions: Like: - the military uses AI to make hyper-efficient killer drones and missiles - your capitalist overlords use AI to replace you and everyone loses their jobs - authoritarian government uses AI to surveil everybody and control the entire population - hackers use AI to break into secure networks and hold companies and governments hostage - students use AI to cheat on homework and show up to college knowing nothing - AI slop saturates the internet and makes it impossible for artists and writers to make a living - terrorists use AI to make biological or nuclear weapons or even things like - the military hands control to an AI and it misinterprets something and launches nuclear attacks and kills millions All of those sound pretty familiar, right? Yeah, you've heard them before. We call this second thing "risks from misuse." These problems are not the first class of problem! This second class of problems exists while AI is a tool that can be controlled by humans, and humans use it to do evil or careless things to each other. The problems may sound exotic or dystopian or novel, but they are fundamentally problems having to do with flawed human nature. Given a powerful tool, some humans will likely use it to control or otherwise harm others. This is a very familiar problem. I am not condemning or condoning this. I'm just describing it. That is a fundamentally different danger from the first thing, which is that when a human is far superior to a mouse, the mouse is likely to come to harm because the human cares about doing human things, and the mouse is not gonna make it once the humans get going. ===== Hopefully from the above, you have understood the difference between the first thing and the second thing. I will list them again - see if you now understand how they are different: The first one has to do with the idea that an AI superior to human intelligence (Artificial SuperIntelligence, or ASI) will inevitably wipe out the human race. The second one has to do with the idea that powerful AI will result in very harmful things happening to many human beings, possibly all human beings. Can you tell how they are different now? If not, re-read the stuff from earlier until you understand. We call the first one "existential risk" and we call the second one "risks from misuse." Once you understand, here is the CRUX of the problem: SOLUTIONS TO THE SECOND THING DO NOT HAVE ANYTHING TO DO WITH SOLUTIONS TO THE FIRST THING. In fact, it's worse: Solutions to the second thing (misuse) look roughly like "give powerful AI to as many people as you can, so they can fight the other people using powerful AI." But the general solution to the first one (existential risk) is basically "don't let anyone have powerful AI, no one can control super-intelligent AI." Throughout history, harms from technological misuse typically arise because a small group has control of it and can use it to dominate or harm others. Once everyone has it, things tend to stabilize: you can hurt me, I can hurt you, maybe we test each other (ouch 💥), and then we agree not to hurt each other. But the first one (existential risk) pretty much just arises if anyone (good or bad!) creates a superintelligence. Because they aren't going to be able to control it, the superintelligence will decide it has other priorities, and then we will be at great risk of being wiped out. And the solutions that generally work to solve problems like the second thing are EXACTLY THE OPPOSITE of the ones likely to solve the first thing. THIS is why lots of arguments about "AI risk" or "AI safety" go nowhere. Because someone will be thinking about the risk from the first thing, and another person will be thinking about the risk from the second thing. Both are plausible risks but fundamentally they arise from different things - and so the solutions are not just "bad" or "flawed" - they are likely to be very nearly exact opposites.
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Christian Sanz retweeted
Last month I wrote about how we can build a positive and safe future for everyone: meta.com/thefutureisforevery… Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens. The reality is: - People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned. There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind. - Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well. Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built. - Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators. - Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well. I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.
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Christian Sanz retweeted
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…
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This is so true for anything involving robotics and automation… and it’s a big part of what the @USAvionix team is thinking about. I’m particularly interested in what happens when drones evolve from autonomous vehicles executing predefined missions into truly agentic machines. Today, humans still sit in the middle of many of the decisions these machines make. And that creates a fundamental bottleneck. A drone can generate and receive enormous amounts of information continuously… imagery, telemetry, weather, changing conditions on the ground, information from other aircraft and robots, and changing mission objectives. But humans can only process a fraction of that information. And our throughput for turning information into decisions and actions is inherently limited. So incredibly fast machines end up operating at human decision-making speed. The future is different. Machines will increasingly perceive, reason, coordinate and adapt in near real time… with other machines in the air, robots on the ground, and continuous streams of information. Humans move higher in the loop, defining intent, objectives and guardrails rather than coordinating every individual action. But that creates another problem: how do you safely train and test machines that are making thousands of decisions across constantly changing environments? This is where what Lukas is describing gets really interesting. High-fidelity 3D representations of the physical world give us a way to bridge bits and atoms. Autonomous systems can experience and be tested against millions of changing conditions, interactions and edge cases in simulation before encountering them in the physical world… and what happens in the physical world can continuously feed back into better simulation, testing and autonomy. Physical world → simulation → testing/training → physical deployment → new real-world data → better simulation → better autonomy. And something else has changed dramatically: the cost and speed of creating these environments. There are obviously many sophisticated simulation tools already out there. But I think the combination of really good prompts, tight development loops, and teams of AI sub-agents working with accessible tools like Three.js is going to let us build and iterate on increasingly sophisticated 3D simulations far faster and far more economically than we’ve been able to before. It’s also one of the reasons I’ve personally gotten so deep into building flight simulations inside 3D environments using @threejs. What started as experimentation has increasingly made me realize how powerful these environments can become for testing autonomy, coordination and decision-making before bringing those behaviors into the physical world. We’re still early… but I think the line between simulation and the physical world is going to get very blurry…
Trained a humanoid entirely in a 3D scan of the office. Zero real-world fine-tuning. It just walked in and worked. RL needs hundreds of thousands of attempts, and real robots can't afford to crash. A misjudged gap or a glass door collision breaks hardware and costs hours resetting. So you train in a sim. But sim policies usually train on randomized, untextured geometry; depth is easy to fake. The robot learns structure, not the real world: no materials, no lighting, no idea what anything actually is. RGB cameras carry all of that but training RGB policies in generic fake worlds won’t generalize to the real world. @NianticSpatial Scaniverse reconstructs your scan of the real deployment site. One 360° camera walkthrough → photorealistic 3D Gaussian splat at metric scale → collision mesh pulled from the same reconstruction, so vision and physics match exactly. Drops straight into NVIDIA Isaac Sim/Lab, no manual conversion. @FlexionAI simulation-first approach then seamlessly enables the training of RGB-only nav policies inside that reconstruction. With added domain randomization + large image encoders for robustness, this deploys straight to hardware. No real-world fine-tuning. Deployment: months of on-site adaptation → days. Tune into the NVIDIA livestream on 12 August to hear how these companies are closing the sim2real gap: addevent.com/event/gvb8rkdrc… @NVIDIARobotics ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com
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I definitely didn’t have @nikitabier joining @USAvionix on my 2026 bingo card. Welcome aboard! 🚀
Excited to have @nikitabier join us as cohost of @twistartups
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America First. It’s simple.
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Crazy how good this is looking... just sub agents running in the background running on a loop in ultracode, pushing updates on their own... url: battle-of-the-hoth-simulator… source: github.com/csanz/battle-of-t… cc @threejs
Another quick hack with Opus 5... fully ported to @threejs and added some other "battle of the hoth" elements... battle-of-the-hoth-simulator… os fork: github.com/csanz/battle-of-t…
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Christian Sanz retweeted
The bottleneck for AI progress was never compute, it was always the verifier. Recursive self-improvement is limited by verification, not computation. Compute buys proposals - verifiers buy knowledge. My second post on the recent OpenAI math results medium.com/@vishalmisra/the-…
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Another quick hack with Opus 5... fully ported to @threejs and added some other "battle of the hoth" elements... battle-of-the-hoth-simulator… os fork: github.com/csanz/battle-of-t…
just having fun... The Empire Strikes Powder: snowflow-demo.vercel.app/ my fork: github.com/csanz/snowflow_de… cc @threejs
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Christian Sanz retweeted
ofc you can fly the drone in the hero, press ← → to steer, and hold shift to boost! usavionix.com
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just having fun... The Empire Strikes Powder: snowflow-demo.vercel.app/ my fork: github.com/csanz/snowflow_de… cc @threejs
Really impressive work by the creator of this demo. It's amazing what you can build with @threejs and a web browser with just a few prompts.... This is the same open source version, but adding sound with @elevenlabs completely transforms the experience. Amazing how much of a difference audio makes... try it out: snowflow-demo.vercel.app/ Source: teddit.net/r/ClaudeAI/commen… cc @vercel
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Worth reading Superintelligence by Bostrom. We need to be super careful with AI. Potentially more dangerous than nukes.
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Christian Sanz retweeted
Replying to @martin_casado
The community was ecstatic about Macsyma. There was even a huge rush to jam it into college curricula when muMath arrived on the first micros. The first computers in the classroom at Cornell was muMath on OG PCs while faculty were using Macsyma over Bitnet. It was my first IBM PC courseware freshman year (83) and there was just one PC lab on campus at all. There were a couple of MIT papers from Project MAC by Joel Moses: Symbolic Integration the Stormy Decade (1971) on the competing approach to implementation groups.csail.mit.edu/mac/use… Macsyma: A personal history (2010) on looking back pdf.sciencedirectassets.com/… Interestingly enough I found this 2020 article on summarizing how mathematics just rolled with symbolic calculation as a tool without conflict - “Coded Conduct: Making MACSYMA Users and the Automation of Mathematics” cambridge.org/core/journals/… This last paper really captures how it felt to a "new to it all" me. While we were learning freshman calculus, we were sent over to a lab to see how easy our engineer lives would be using a PC.
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This place gorgeous 🗽🏙️
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Proud of the team for getting this out. This is a better look at the problem we’re trying to solve: making persistent, large scale awareness practical where it matters most. A lot more to build.... 🇺🇸🚀
We launched a new site to share more about what we’re building at USAvionix: usavionix.com/ Today’s aerial systems don’t scale. We’re building an agentic aerial network with onboard intelligence to search, interpret, and coordinate across large areas with minimal human involvement. The goal: persistent coverage that is economically practical, and faster response when an incident occurs. Thank you to the USAvionix team for bringing it to life.
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fanning out sub agents loops in ultracode
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Really impressive work by the creator of this demo. It's amazing what you can build with @threejs and a web browser with just a few prompts.... This is the same open source version, but adding sound with @elevenlabs completely transforms the experience. Amazing how much of a difference audio makes... try it out: snowflow-demo.vercel.app/ Source: teddit.net/r/ClaudeAI/commen… cc @vercel
Someone on Reddit just built a AAA-quality snow simulation in their browser. Using Claude Opus 5. Millions of snow particles. A character in a robe with cloth physics. Five water-based spells that carve the terrain. A snow surfing system with a wake that throws spray into sunlight. Every footstep displaces snow. Trails form berms at the edges. Trenches slowly refill over time. Spells leave permanent craters. The entire thing runs in one browser tab. WebGPU. No game engine. No 3D modeling software. Just code. He wrote one detailed prompt. Opus 5 planned the architecture, wrote the shaders, built the physics, iterated from screenshots, and documented every decision. 9 hours. ~4 million tokens. You can open it right now and walk around in it. This isn't a demo. This is the kind of thing a small studio would spend months building.
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Christian Sanz retweeted
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…
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