Max

this is the difference between specialized intelligence and general intelligence this is why i love distillation and why we are a building a web of smaller models that chain together depending on your use case

One of the world's smartest AI investors just validated the market Score is building in with a $15 billion valuation. But almost nobody in the $TAO ecosystem seems to be talking about it. Applied Intuition announced Dana, its Physical AI platform, alongside news that the company itself had raised fresh capital at a $15 billion valuation, led by Andreessen Horowitz. On the surface, that sounds like another well-funded AI company raising another large round. I don't think that's the story here. The story is what investors are actually valuing, because if you look beyond the headline, Dana is building remarkably close to what Score has been building with Manako. Both are trying to solve one of the hardest problems in AI: giving machines the ability to understand the physical world. Making machines (Dana) or cameras (Score/Manako) intelligent. They both do this by combining information from multiple sensors into one coherent understanding of reality. This is the intelligence layer that sits between raw sensor data and real-world decision making. That is an incredibly difficult problem to solve. And now one of the world's largest venture capital firms has effectively said they believe one company in this market is worth tens of billions of dollars, imagine the market value. I don't see how this is not bullish for Score and Manako. Look, Manako didn't suddenly became worth $15 billion, I know that, but it validates the direction they're taking. What's even more impressive is how different the journeys have been. Applied Intuition has been building toward Dana for almost nine years, Score has been building Manako for roughly one. Nobody knows today which platform is better but we all know by now how fast a subnet can optimise outcomes through constant competition. It's still far too early to make that comparison, but the gap in time makes one thing very clear: Manako has reached an incredibly advanced stage in a remarkably short period and I don't think that's a coincidence. Instead of relying on one internal engineering team, Score is turning global competition into its R&D department. And unlike a company that ships a major update every few months, this process never really stops. As more miners join, more ideas are tested. As more ideas are tested, better models emerge. As better models emerge, Manako continues improving. It's a flywheel where competition is the product development process. That flywheel becomes even more interesting when you add the next piece of the puzzle. Score is now building its own Vision Language Model. At first glance, that might sound like just another foundation model, but I think its importance goes much further than that. For customers, it dramatically simplifies integration. Instead of stitching together different perception models, language models and APIs, they gain access to a much more complete platform that is easier to deploy and easier to build on. For Score, it creates another compounding layer. A better subnet improves perception. Better perception improves the VLM. A stronger VLM makes customer applications more valuable. More customers generate more real-world feedback and revenue, which ultimately feeds back into improving the subnet itself. The entire system starts reinforcing itself. That's why, when I read about Dana's funding round, I wasn't thinking about Applied Intuition. I was thinking about what it means for Score. Because this is confirmation that some of the smartest investors in the world believe Physical AI will become one of the defining software categories of the coming decade. If they're right, then the companies building the intelligence layer for that future could become incredibly valuable. And Score is building exactly that.

this is the difference between specialized intelligence and general intelligence this is why i love distillation and why we are a building a web of smaller models that chain together depending on your use case