Just like producing any kind of industrial product, the issue is NIH-syndrome. Sure you won't be able to compete with the likes of GPT-3, but no one is suggest

Just like producing any kind of industrial product, the issue is NIH-syndrome. Sure you won't be able to compete with the likes of GPT-3, but no one is suggesting you should. The key is to treat software as a commodity and apply it as such. This has been the case for decades (who writes their custom office suite or even OS?) and will expand to ML/AI as well. We are already at a stage where you don't start from zero and reinvent the wheel every time you need speech- or image recognition. There are readily available off the shelf solutions and customisation is not much more involved than say customising an ERP tool; it's different expertise that's required, sure, but the effort is comparable. A couple of years from now ML tooling and infrastructure will have caught up to ERP, CAD/CAM, and spreadsheet software - just another tool that can be brought in and provide immediate benefit without scores of consultants, developers and research.