If you are a software engineer, no doubt you’ve seen some astounding new model or prompt posted to twitter, or some shamelessly fraudulent product demo and felt your blood run cold, your wizardly coding powers draining from your fingers.
The reality, however, is that Full Stack engineers are quite a bit closer to the modern AI engineering role today than they might think. Machine learning
“In numbers, there’s probably going to be significantly more AI Engineers than there are ML engineers / LLM engineers. One can be quite successful in this role without ever training anything.” – Andrej Karpathy
AI engineering is no longer Deep Learning
The field of AI *used* to be an offshoot of Machine Learning (i.e. AI is what we used to refer to as Deep Learning), and at the bottom end of the stack, that is what it is. And certainly, if you want to build models from scratch, that’s what it takes. A little as a year ago, my feeling was that the way to approach AI was via the one I had take – via the fundamentals first: walk through the Fast.ai lesson series, getting a working understanding of Machine Learning processes as they relate to Deep Learning and build on that.
But, the systems and abstractions over the Deep Learning layer are now so powerful (and complex) that building them and using them takes an entirely different skill set. AI is being absorbed into actual engineering, and becoming an engineering field of its own. And, as such, what used to be the most direct and immediately applicable skills have changed. Moreover, as that has happened, the distance between “Full Stack” engineer and “AI” engineer has been falling over time.
Notice where the lines being drawn here? It’s not between you and AI engineering. And, AI is going to continue moving to the right on the line above: if the stack is entirely composed or managed by AI, a “Full Stack” engineer, who can’t also implement and manage AI pipelines, isn’t going to be all that “Full Stack” any more.
All in all, it’s just another tool in the tool box.
The Faustian bargain we made for a job creating shiny new toys was a never-ending supply of new shiny toys. And, AI appears particularly Faustian, in that (for now) that there’s a never-ending supply of new shiny AI tools, along with FOMO.
“We have no idea what large language models are going to be good at or bad at over any sense of time… The amount we don’t know because of how quickly this has developed is at an all time high, that lets us experiment and have a sense of play, and do things and not know how the result is going to be, which is fun. – Dan Becker @dan_s_becker