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Open-weight models are inherently accelerationist, because they enable far more progress than closed-weight ones.
Open-weight models are inherently accelerationist, because they enable far more progress than closed-weight ones.
Everything about open models vs. closed models tends to get framed in a zero sum fashion. That’s wrong. It’s an ecosystem of AI that gets used together and advances the industry and pushes the applied use-cases forward. The amount of creativity that exists when you have competing approaches for what the future looks like drives the cycle of innovation that we’ve seen for all participants. You get to have layers of the stack that emerge to post train models for highly specific purposes, which makes AI more useful in real world scenarios. Instead of waiting for just a few labs to go deep in a domain, you get dozens or hundreds of attempts at that vertical, like in finance, life sciences, legal, healthcare, and more. You get to see variance in how to handle safety and cyber risks. Instead of just one approach, you get a peek into what happens advanced capabilities can be used to build better systems to are used to defend systems. You get alternative approaches to training and building AI models. In more compute constrained environments, you develop more novel and efficient approaches to model training, which every other lab can learn from. And you get different cost structures for different workloads. High end and orchestration tasks can go to the closed frontier models and specific workhorse tasks can be done more cheaply, allowing the overall mix to be more affordable, which drives more adoption of AI overall. The reason why you want strong open weights models is because it pushes the entire AI industry forward.
The reason why you want strong open weights models is because it pushes the entire AI industry forward. The amount of creativity that exists when you have competing approaches for what the future looks like drives the cycle of innovation that we’ve seen for all participants. You get to have layers of the stack that emerge to post train models for highly specific purposes, which makes AI more useful in real world scenarios. Instead of waiting for just a few labs to go deep in a domain, you get dozens or hundreds of attempts at that vertical, like in finance, life sciences, legal, healthcare, and more. You get to see variance in how to handle safety and cyber risks. Instead of just one approach, you get a peek into what happens advanced capabilities can be used to build better systems to are used to defend systems. You get alternative approaches to training and building AI models. In more compute constrained environments, you develop more novel and efficient approaches to model training, which every other lab can learn from. And you get different cost structures for different workloads. High end and orchestration tasks can go to the closed frontier models and specific workhorse tasks can be done more cheaply. Everything is framed as open models vs. closed models in a zero sum fashion. That’s the wrong framing. It’s an ecosystem of AI that gets used together and advances the industry and pushes the applied use-cases forward.