AI is crazy because you deeply need to avoid being sentimental about any part of your product architecture at all times. The models are upgrading at such a fast rate that you have to constantly reevaluate the scaffolding you’ve built, and figure out what now can be solved in a better or cheaper way due to a new breakthrough. Companies will win or lose entirely by their ability to let go of something that they ostensibly got good at because the models can now solve that for them. This is generally where startups win over time because they emerge in a period when something is far easier to solve in a modern way, and the incumbent doesn’t properly adapt. The key is to ensure you have an architecture that gives you this flexibility, which means creating the right abstractions early on to benefit from these constant updates.

The rate of AI progress makes it so a lot of the problems that didn’t work previously will finally work now. But a significant portion of the capability of these models goes underutilized as a result. I regularly talk to enterprise customers where a use case they had last year that a state of the art model couldn’t solve can now be unlocked with the latest models. The key is that you have to keep going back to these models and pushing them further and further. As Andrej says, “if you're not giving it your hardest problems you're probably missing out.”

“If you're not giving it your hardest problems you're probably missing out.” This is the case for a lot of AI right now. AI has improved dramatically in the past year, yet most people haven’t increased the complexity of the work they’re giving them. Push your AI further.