None of this is solved by making easier to write code - by making it easier to make tools. The hard part is knowing that you need a tool for this in the first place, and then knowing what the tool should do.
…
I think you can see all of this in the experience of enterprise AI deployment in the last three years. Every big company gave everyone Copilot (or maybe ChatGPT or Claude) and a small number of people are using this a lot (some of whom actually increased their productivity), while a larger set of people are using it a couple of times a week and a lot of the rest of your company isn’t really using it at all. This is partly a change management and a training problem, but it’s mostly the same problem that you would have had if you’d given everyone in the company a PC and Lotus 123 in 1983, or an internet connection and a web browser in 1997. How exactly does this map to everybody’s tasks and the problems they actually have this week? Yes, you did give everybody a PC and Lotus, but that wasn’t how you transformed the efficiency of your invoice processing. Yes, you gave everybody a web browser, but that wasn’t how you rebuilt your supply chain management around the internet, and it certainly wasn’t how a retailer managed e-commerce.
…
Stepping back, it seems to me that with each new transformative technology, every company has to ask three kinds of questions. First, how do we buy, build and deploy this? Do we do pilots? Should we take the product that’s bundled from Microsoft/Google/Oracle, build something ourselves, pay someone to build something, or buy this new thing from a startup? Second, they have to ask how far this changes their operations. What does it mean? What does email mean for us? What does spreadsheets mean for us? The answer to that might be radically different if you were an insurance company or a law firm. And third, you have to ask whether this creates new challenges to your business’s economics, new competitive pressures, or, perhaps, some kind of existential threat.