Even though it's not a huge share of the world's electricity, almost all of the world's demand is being served by a very few select locations. So you can still have this issue where you're basically powering the entire um world's AI demand from a few centers in the US and and and a few other data centers across the world. And that's a really really key local concern and puts pressure on local grids even if it doesn't consume a huge amount of the world's electricity.
The most valuable thing in the world is no longer money, you know, or jewels, or artwork. It's data. And all of this data lives in these Fort Knoxes that no one really talked about called data centers.
Agents will always fail in ways you didn't predict. That's why we built Lema. Lema understands what your agent is supposed to do. So it surfaces failures you never define automatically.
Organizations should not have to surrender control of their code, data, and engineering workflows to use capable AI. They should be able to run and adapt these tools on their own terms.
The United States is on the verge of a natural gas shortage so severe that it could undermine the AI boom, strain the electric grid, and trigger the kind of energy crisis that Americans haven't experienced in decades.
The thing that's interesting about transformers is that, unlike previous neural network architectures, the operations they do are basically fixed. A transformer from 2024 does the same operations as a transformer from 2017. So you can build a chip that is essentially a transformer, etched in silicon, and it will always be doing the most important computation in AI.
The thing that I think is underappreciated is that transformers are probably here to stay. People have been trying to beat transformers for years and years and years, and nobody has been able to do it at scale. And so if you believe that transformers are here to stay, then you can build a chip that only runs transformers.
The thing that's really different about a transformer ASIC versus a GPU is that a GPU is a general-purpose chip. It has to be able to run any kind of workload. A transformer ASIC, by contrast, can be completely specialized for one thing, which means you can make very different design decisions.
The core insight is that transformers have a fixed architecture, and if that architecture is going to be with us for a long time, there's a massive opportunity to build a chip that does nothing but run transformers — and does it faster and cheaper than anything else. A general-purpose chip has to be flexible, and flexibility costs you in efficiency.
The thing that's really interesting about transformers is that they've kind of eaten AI. So many of the workloads that people care about are transformers. And because the transformer is a fixed algorithm, you can etch it into silicon — you can build a chip that only runs transformers, and it will be orders of magnitude faster and cheaper than a chip that can run any algorithm.
It doesn't give you a lot of confidence in like the strategy overall. They're signing these Neo cloud deals worth tens of billions of dollars. They're built, you know, spending hundreds of billions of dollars. And yeah, they can make the argument that these type of um like doing any type of NeoCloud deals themselves is just good business. It's it's just like how it's just the best way to get ROI today. It doesn't give you a lot of confidence that there's near-term products on the horizon for Meta that are going to be able to utilize that capacity themselves.
There's a lot of pressure to bring this new energy online very quickly because states are currently competing for the opportunity to attract these data centers, even though the financial benefits of them for the state has not actually yet borne fruit.
