Final paragraphs from a timely ML reality check
"Hence, I said earlier that there are two questions for an ML startup: how do you get the data and how much do you need? But those are just the technical questions: you also ask how you go to market, what your addressable market is, how valuable the problem you’re solving is to your customers, and so on and so on. That is, pretty soon there won’t be any ‘AI’ startups - they will be industrial process analysis companies, or legal platform companies, or sales optimization companies. Indeed, the diffusion of machine learning means not so much that Google gets stronger, but that all sorts of startups can build things with this cutting edge science much quicker than before.Does AI make strong tech companies stronger? | Benedict Evans
This takes me to a metaphor I’ve used elsewhere - we should compare machine learning to SQL. It’s an important building block that allowed new and important things, and will be part of everything. If you don’t use it and your competitors do, you will fall behind. Some people will create entirely new companies with this - part of Wal-Mart’s success came from using databases to manage inventory and logistics more efficiently. But today, if you started a retailer and said “…and we’re going to use databases”, that would not make you different or interesting - SQL became part of everything and then disappeared. The same will happen with machine learning."