저자: DanIves · 외국인
요약 보기
테슬라는 머스크와 사실상 동일체이며 자율주행·로보틱스가 향후 핵심 성장축이라고 주장. 전 세계 차량 1000만 대 수준의 데이터 규모와 두 차례 자율주행 시스템 전면 재작성이 경쟁우위라고 설명. 자율주행 사업만으로 1조 달러 가치, 향후 2~3조 달러 시장 전망. 옵티머스 로봇은 FSD와 같은 시각 기반 AI를 공유해 가속 가능하다고 봄. 엔비디아·테슬라를 피지컬 AI 수혜주로 언급. 단, 세부 주가 방향 예측은 명시되지 않음.
다듬은 글
We've got Dan Ives on Tesla's trajectory and him explaining something that he thinks matters more than most people realize. Look, the reality is is that Tesla is Musk, Musk is Tesla. The only way that brings them into the whatever you the AI future most important chapter in Tesla's history, it's Musk.
Wartime CEO. And look, you go about a trillion dollars, that's ultimately hitting all the targets, but you need Musk at the helm. And now it starts what I think is the most important chapter ever for Tesla. Autonomous, robotics. This is going to be the future what ultimately defines Tesla and that's why even when it comes to xAI, you want them to have a big ownership stake of xAI.
More and more as a physical AI play, it's Nvidia and Tesla. Now, uh when you look at Tesla moving forward, um it does feel like data is the big advantage, right? They're they're just out there collecting tons of data when it comes to the self-driving cars. Uh these humanoids, I think a lot of people are looking at like what is the output of the humanoid, but they are ingesting an insane amount of data.
We recently saw a 1X uh which is a private company, they uh put out um my understanding is it's a demo that was uh remote controlled, right? Kind of um But it's got a camera. It's got sensors. It is taking in all this information. A human can take over the robot and actually have it do things, right?
That that maybe are like the edge cases. When you start to look at that stuff, is this all just a data game? Look, 10 million cars in the world. I mean, when you look at Tesla, their biggest advantage is data, scale, scope, Musk. Mhm. What separates them above everyone else is that. And I think more and more as the data game plays out, especially in robotics, autonomous, and you're going to be in 30, 35 cities from an autonomous perspective.
I mean, that that's why you own Tesla. I think autonomous alone is worth a trillion dollars. This is a two and I think ultimately three trillion-dollar market Now, uh Elon, if he gets the pay package and he delivers, he'll be the first world's first trillionaire. Um it does feel like Elon is probably solving the most amount and the hardest problems in society.
It kind of feels like the guy who solves the most amount and the hardest problems deserves the most amount of money. Is that your take? And also go back to 2018. Like the pay package that they gave him, everyone's like, "There's no way ever he could hit those targets." Exceeded it. And it speaks to, you know, and it's still actually in Delaware court in terms of that battle.
Look, this when you just think about who the winners are, Nvidia, why? We think about the role that the godfather of AI, Jensen, plays. AMD, Lisa Su. Sundar at Google. Nadella Microsoft. Karp, Palantir. The point is like it speaks to leaders and why they're so important. Musk right now, wartime CEO.
Dan Ives is right that Tesla's real future isn't about building more cars, but about becoming a major force in autonomous systems and robotics. Tesla is the only company with a global fleet of cars constantly feeding its AI systems real-world experience at a scale no one else has access to. One of the things that people always misunderstand about Tesla is that they see it as a car company when in reality, the vehicles are the only distributed system for something much bigger.
Every Tesla on the road is a sensor platform, a learning machine, and a training engine feeding an AI model that becomes more capable with every mile driven. No other in the world has that kind of real-time, real-world pipeline running across different countries, climates, and driving cultures. And this scale gives Tesla diversity.
The system learns from crowded cities, quiet suburbs, bad weather, strange road layouts, unpredictable drivers, there's a lot of those, confusing signage, and every kind of driving behavior you can imagine. Companies working with small, tightly controlled fleets simply can't capture that kind of variety because their systems are only trained on specific, limited conditions.
Tesla is learning from everything, everywhere, all the time. And I think this is why Dan Ives talks so confidently about Tesla's next chapter. He recognizes a structural advantage that grows automatically as the fleet grows. Every new Tesla delivered strengthens the core model. Every journey strengthens the autonomy system.
Every environment the fleet enters expands the model's awareness of edge cases that other companies don't even know exists. There's also a cultural advantage that doesn't get enough attention. Tesla's willingness to rebuild systems from scratch when they hit a ceiling. Most companies patch problems because they can't afford to reset their entire stack.
Tesla has done two full rewrites of its autonomy system already, something it feels like almost nobody else would attempt, and each rewrite allowed the AI to break through limits that were baked into older designs. This willingness to overhaul instead of optimize the old is a rare asset in the industry dominated by incrementalism.
This brings us on to the part most people underestimate. Tesla's momentum in AI-driven robotics. Dan also claims that Tesla's robotic program is a massive unlock because the same learning engine that powers autonomy can power humanoid robots. Optimus is built on the same vision-based AI foundation Tesla has refined through millions of real-world interactions.
What makes the Tesla robot different is the brain behind it. Most robotics companies build a machine first, then try to teach it what to do. Tesla approached the problem from the opposite direction. They built a learning system first, an AI that already understands the world visually, spatially, and behaviorally, and then designed a robot around that intelligence.
Another major advantage is Tesla's exposure to human environments at scale. The data that powers FSD isn't limited to roads. It captures human motion, body language in any vehicles, object interactions, spatial layouts, and the relationship between people and their surroundings. All of this becomes extremely valuable when training a humanoid robot meant to operate in those same kinds of spaces.
A lot, if not all companies, lack this exposure entirely. Tesla learns from the full spectrum of human environments, which naturally accelerates the robot's ability to generalize. There's also the industrial learning loop. Unlike startups, Tesla can deploy Optimus into its own factories at any stage of development.
This means the robot receives early exposure to repetitive tasks, unpredictable workflows, and human-robot coordination without requiring external partners or lengthy negotiations. These controlled but realistic deployments give Tesla a safe sandbox where the robot can fail, adjust, and improve at pace other companies simply can't match because they don't control their own large-scale manufacturing ecosystem.
It's becoming obvious that humanoid robots are notoriously difficult because human environments weren't designed for machines. But that's exactly what Tesla's approach, built around natural vision and organic learning, gives it a structural advantage. It doesn't need to redesign the world for the robot, it's teaching the robot to handle the world as it already exists.
Dan Ives recognized this when he talked about robotics being the next defining chapter. Tesla is building a general intelligence engine that can move from cars to robotics without starting over. That is rare, powerful, and extremely difficult for competitors to replace. Ads are expensive and people don't trust them anymore, but they do trust YouTube.
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