In a rare four-hour talk, the reclusive founder reveals an almost Daoist philosophy of AI—AGI as a tide no company can own, he argues China's only real gap with America is compute.
A bit of both though. In house models don’t need the power and process of a small city, they need the power to power what their business needs. Also it would seem to me that has a huge advantage on the whole as a model would be more efficient, if it’s focused on the companies needs rather than being a jack of all trades, from poetry to law to code. Cut out the being everything to everyone and you can do far more with far less hardware.
This. The frontier race is about building the smartest generalist, but most companies don’t need that. They need a specialist tuned to their workflows. Narrower scope means smaller models, lower costs, faster inference and often better results. General models become the foundation, not the finished product. It’s still all to play for! 💪😎✌️
Agreed, However I also wonder how or if LLMs could help with what we don’t know we need.
With the processing pace of LLMs and the massive context the can processes at once. It seems like it could make the broader connections that are usually the harder to find more accessible.
I think that’s probably the most underrated use case. We keep treating LLMs like faster search engines, but their real value may be surfacing relationships humans never think to test.
Exactly. At work, we’re already looking at getting our own compute for an open model for some automatons we built. Google’s models are alright, but they are retiring them too fast. So we want to get off that treadmill. Plus a local model gives us opportunities to experiment with LoRAs and techniques like what cactus hybrid did with a small head predicting certainty (https://news.ycombinator.com/item?id=49010782). Plus, the big players are removing sampling options thinking they know what’s best when it is obvious they don’t.
A bit of both though. In house models don’t need the power and process of a small city, they need the power to power what their business needs. Also it would seem to me that has a huge advantage on the whole as a model would be more efficient, if it’s focused on the companies needs rather than being a jack of all trades, from poetry to law to code. Cut out the being everything to everyone and you can do far more with far less hardware.
This. The frontier race is about building the smartest generalist, but most companies don’t need that. They need a specialist tuned to their workflows. Narrower scope means smaller models, lower costs, faster inference and often better results. General models become the foundation, not the finished product. It’s still all to play for! 💪😎✌️
Agreed, However I also wonder how or if LLMs could help with what we don’t know we need. With the processing pace of LLMs and the massive context the can processes at once. It seems like it could make the broader connections that are usually the harder to find more accessible.
I think that’s probably the most underrated use case. We keep treating LLMs like faster search engines, but their real value may be surfacing relationships humans never think to test.
Exactly. At work, we’re already looking at getting our own compute for an open model for some automatons we built. Google’s models are alright, but they are retiring them too fast. So we want to get off that treadmill. Plus a local model gives us opportunities to experiment with LoRAs and techniques like what cactus hybrid did with a small head predicting certainty (https://news.ycombinator.com/item?id=49010782). Plus, the big players are removing sampling options thinking they know what’s best when it is obvious they don’t.