I used to think that Oxide's business model sucks in the face of the hyperscalers. When AWS/GCP/Azure "just works" and is generally reliable and cheap, why would I go through the trouble of buying my own computers? Well the past 5 years have been a constant decline towards more concentration of power, lack of care for their customers, and degradation in quality in general. AI is of course accelerating this decline - selling de-slopped products is now a huge competitive advantage. All the best luck to Oxide.
If there really was a "simple" solution to Fermat's Last Theorem, Andrew Wiles wouldn't have achieved the important result he did, ending up making connections across disparate fields of math.
The LLMs "sweeping up" easy, or previously missed, results seems like a net negative. It's probably better for humans to struggle and come up with new tools than to just "clean up" low hanging fruit that doesn't add much value to the field.
I am using a model that runs on a 100 GB300s, near AGI, all bets are off, $10B training run on a 1GW cluster, and it can't realize that when I told it to "please implement v2 of feature X" that I mean delete v1, not support v1 and v2 together, in a weird Frankenstein's monster of the two. Sorry, but I think my job is quite secure for the forseeable future.
I used to think that Oxide's business model sucks in the face of the hyperscalers. When AWS/GCP/Azure "just works" and is generally reliable and cheap, why would I go through the trouble of buying my own computers? Well the past 5 years have been a constant decline towards more concentration of power, lack of care for their customers, and degradation in quality in general. AI is of course accelerating this decline - selling de-slopped products is now a huge competitive advantage. All the best luck to Oxide.