AI engineering
9 statements across 7 episodes · 5 bullish · 0 bearish · 5 people on the record · first statement Jun 21, 2024 by James Brady · across every show →
Everything said about AI engineering, oldest first
Jun 21, 2024 neutral
Brady: Fault-tolerant engineering discipline and ML curiosity are in natural tension
“I think the fault first mindset and the ML curiosity attitude could be somewhat in tension, right? Because for example, the stereotypical, stereotypical version of someone that is great at building fault tolerant systems has probably been doing it for a decade…”
Oct 11, 2024 bullish
Goyal: Future of AI engineering centers on reusable tools and tight eval loops
“I think it kind of represents the future of AI engineering, one where You can spend a lot of time writing English and sort of crafting the use case itself. You can reuse tools across different use cases. And then most importantly, the development process is ve…”
Oct 11, 2024 positive
Goyal: Continuous evaluation is the foundational workflow for building superior AI software
“Our core belief is that if you embrace evaluation as The sort of core workflow in AI engineering, meaning every time you make a change, you evaluate it, and you use that to drive the next set of changes that you make, then you're able to build much, much bette…”
Oct 11, 2024 bullish
Goyal: Software engineers will drive AI engineering, but ML tools are unusable for them
“The real gap is that software engineers who have a particular way of thinking, a particular set of biases, a particular type of workflow that they run, are going to be the ones who are doing AI engineering, and that the tools that were built for ML are fantast…”
Feb 1, 2025 neutral
Swix: AI engineering exists because labs crowdsource emergent capability discovery
“The reason that AI engineering can exist outside of the model labs is because the model labs release Models with capabilities that they don't even fully know because you never train specifically for it. It's emergent. And you can rely on basically crowdsourcin…”
Mar 13, 2025
Shankar: AI evals differ from MLOps due to data scarcity
“The other thing is I think that AI engineering Evaluation or evals here is actually different from MLOps or ML evaluation for traditional ML models. We were in a much more, you know, data rich setting in MLOps. So we were taught to come up with loss metrics or…”
Sep 11, 2025
Martin: AI engineers must continuously remove scaffolding as underlying models improve
“We should be adding structure necessary to get things to work today, but keeping an eye on improving models and keep, but keeping a close eye on models, improving rapidly and removing structure in order to un-bottleneck ourselves.”
May 7, 2026 bullish
Swyx: TypeScript could win the AI engineering ecosystem over Python
“I think it could be that TypeScript is going to win AI engineering, and that's something I haven't anticipated or seen, or I don't even know how to sort of Position around this, because I think it really does mean a lot of different things for what frameworks …”
Jul 10, 2026 positive
Swyx: AI engineering will professionalize like cloud and data engineering
“I had seen basically front-end engineering become its own professionalized fields with dedicated conferences, dedicated influencers, and tech stacks and all those things, and I've seen the same thing for cloud engineering and data engineering. And all that. An…”