[High Agency] AI Engineer World's Fair Preview

**Charlie** (0:08)
Welcome back, listeners. This is your AI co-host, Charlie, with a special cross post preview of the AI Engineer World's Fair, which as of today has sold out. Thank you to everyone who supported us, and we hope that you will enjoy the talks, booths and conversations in what many are now calling South by San Fran. Our former guest, Raza Habib of Human Loop has now launched his own podcast, High Agency, and Swix recorded a conversation with him that both serves as an examination of the one year anniversary of the rise of the AI Engineer and a preview of the AI Engineer World's Fair.
We do mention a discount code in the pod. So for any last minute stragglers, we've opened up a few more tickets for loyal listeners to join us at the fair. Watch out and take care.

**Shawn Wang** (0:58)
I think there's some advantage to be early, but you do not get the right to win just by being early. AI Engineer is also like sort of a plug or filler gap for all the things that are not traditionally part of the ML Engineer skill set. So what did I mean by that? AI Engineer is more of the zero to one phase, whereas ML Engineer is more of the one to end phase. We should adopt like a fire ready aim approach instead of a ready aim fire approach.
And where the old ML Engineering process was much more deliberative, here you actually win by moving really quickly and getting information from the market, from shipping products, so that you can iterate much faster. I think we have free foundation models launching by the way.
I have never said that publicly, but...

**Raza Habib** (1:38)
You heard it here first. This is High Agency, the podcast for AI Builders. I'm Reza Habib.
Today's a bit of a special episode of the High Agency podcast, because I'm joined by Shawn Wang, AKA Swix, author of Small AI, the host of the Latent Space Podcast, and critically the author of the essay Rise of the AI Engineer, as well as the founder of the AI Engineer World Fair. And we're recording today two weeks before that AI Engineer World Fair, as well as roughly a year on from when Swix first released the essay. And so I wanted to get Swix on the show to talk about the AI Engineer essay, what's happened since then, as well as the upcoming conference and trends. So Shawn, it's great to have you on.

**Shawn Wang** (2:15)
Yeah, thanks so much for having me. Excited to come on and talk a little bit about the meta behind the podcast and the essay.

**Raza Habib** (2:22)
Yeah, fantastic. To start with, do you mind? I actually wanted to just read a little extract from the start of the essay to give people who maybe haven't read it the context, right? And it was polemic almost, pitching the claim that there's gonna be a new type of role emerging, a different type of engineering role as a result of LLMs and Gen.AI. And I think you put it really beautifully. So I'm gonna just read a little extract from the start and then I'd love to get your reflections on it now that we're here on. So what you said was we're observing a once in a generation shift right of applied AI fueled by the emergent capabilities and the open source API availability of foundation models.
A wide range of AI tasks that used to take five years and a research team to accomplish in 2013 now just require API docs and a spare afternoon in 2023
However, the devil is in the details. There are no end of challenges in successfully evaluating, applying and productizing AI. I take this seriously and literally, I think it's a full-time job. I think software engineering will spawn a new sub-discipline specializing in applications of AI and wielding the emerging stack effectively. Just as site reliability engineer, DevOps engineer and data engineer and analytics engineer emerged, the emerging version of this role seems to be AI engineer.
So I think that was a good summary of the thesis and beautifully written. So Shawn, my first question to you is, the essay got a lot of attention at the time, inevitably on Hacker News, there's a mixed reception of some people who completely buy it, some people are critical. Reflecting now 11 months later, what do you think you got right? What do you think has changed? And I'd be curious to get your take.

**Shawn Wang** (3:55)
So first of all, it's very flattering to have it read out by you.
Because I definitely feel like you were earlier to the Mia actually to this persona. And you know, I think that that is a lot of the success of Human Loop as well. Yeah, so I think mostly to the general thesis, the diagram, right? Like the one thing I probably did better than in the essay was just that diagram. I knew that I needed to have like a very, very simple diagram that would kind of stick in people's minds.