This has been my experience. Foundation models have completely changed the game of ML. Previously, companies might have needed to hire ML engineers familiar with ML training, architectures etc to get mediocre results. Now companies can just hire a regular software engineer familiar with foundation model API’s to get excellent results. In some ways it is sad, but in other ways the result you get is so much better than we achieved before.

My example was an image segmentation model. I managed to create an dataset of 100,000+ images and was training UNets and other advanced models on it, always reached a good validation loss but my data was simply not diverse enough and I faced a lot of issues in actual deployment, where the data distribution kept changing on a day to day basis. Then, I tried DINO v2 from Meta, finetuned on 4 images and it solved the problem, handled all the variations in lighting etc with far higher accuracy than I ever achieved. It makes sense, DINO was train on 100M + images, I would never be able to compete with that.

In this case, the company still needed my expertise, because Meta just released the weights and so someone had to setup the fine-tuning pipeline. But I can imagine a fine tuning API like OpenAI’s requiring no expertise outside of simple coding. If AI results depend on scale, it naturally follows that only a few well funded companies, will build AI that actually works, and everyone else will just use their models. The only way this trend reverses, is if compute becomes so cheap and ubiquitous, that everyone can achieve the necessary scale.