I’ll take the opposite point, that for the vast majority of people, it probably doesn’t make sense to do a PhD in AI right now, and is better to join industry. I say this with love for Phillip, whose work and philosophy I do admire, and respect for putting this out there! Everything in AI is, generally speaking, working well. Most things come from stacking wins and good ideas over and over again, and if you’re in industry, you have a much better opportunity to learn what is actually working and what doesn’t work, and then push the forefront of what’s possible at a much larger scale. Internships also don’t really feel the same because you’re certainly not working on the most important projects for the company and often come in with a high risk agenda and work alone. In industry, you learn a lot about how to manage budgets, work in large teams, and push projects that require a lot of effort and work to get done. Academic project budgets are generally quite minuscule, maybe being O($1 - $100K) for many projects and you’re often working alone. If you really want to push novel ideas, another route I don’t think most people consider is also a startup, where you may be able to raise 1-100x more money to do the same project you may do in a PhD, with more resources and the ability to hire other people. And, if it goes well, the ability to scale it dramatically. When it comes to hiring, often times, it’s easier to get an offer having worked well with other people in industry and then having them change jobs and bring you over. Doing a PhD can get your foot in the door, but often it’s still for entry level positions after doing a 5-year PhD. In industry too, you also will make a lot of strong connections, similar to those you’d make in academia. Though it might be the case that it’s good to do 1 year of a PhD and then drop out to work in industry at this point to get the benefit of both. Doing a PhD right now is on extremely hard mode, where not much work ends up mattering and even if it works, there’s high cost to change the ways of industry to adapt something that might provide incremental gains when everything generally is working well with enough resourcing and effort.