a surprisingly good quality dense chat with Delta Institute https://www.youtube.com/watch?v=CYr1mSDNqKM
https://www.listennotes.com/podcasts/chit-chat-stocks/the-future-of-artificial-3ylaqngtMR7/
[00:00:00]
intro: Welcome to Chitchat Stocks on this show host Ryan Henderson and Brett Schaefer analyze businesses and riff on the world of investing. As a quick reminder, Chitchat Stocks is a CCM media group podcast. Anything discussed on Chitchat Stocks by Ryan, Brett, or any other podcast guest is not formal advice or recommendation.
intro: Now, please enjoy this episode.
Brett: Welcome in. We have another episode of the Chitchat Stocks podcast for you this week. We have a fantastic interview coming up with Shawn Wang from LatentSpace. It is a, I'll say covering anything AI. A lot of the stuff, you know, for me and Ryan, it might be going over our heads and it might be a little too hard technically for us, but that's what we brought on Shawn to the show today.
Brett: For a lot of public market investors, this new [00:01:00] AI stuff, it's hard to You know, see what is, what is working, what's just a narrative, what all the stuff that's getting thrown at us during this boom times. So Shawn, we wanted to bring you back on the show. You came on, I think almost exactly two years ago now to talk more cloud stuff.
Brett: Now we're really going to talk about cloud AI and how it is impacting the startup ecosystem. So Shawn, as we kick off the show. What is your relevant expertise in this booming AI field?
swyx: Oh God, that's the million dollar question here. So I am so for, for listeners who haven't heard back the previous episode that I was on I was finance and public markets guy.
swyx: And I was, I was in a hedge fund for my first career. And then I changed careers to tech where I worked at AWS and three unicorn Sort of developer tooling cloud startups. My relevant expertise is you know, on, on some [00:02:00] level, I, I'm just a software engineer that is building with AI now. And then on another level, I had, I actually, when I was an options trader back in the sales side, I actually did a lot of natural language processing of the Bloomberg chats.
swyx: So I fed all of the Bloomberg chats into a pricing mechanism. Then built our global pricer. So our entire options desk was running off of that thing. This was about 13 years ago. So so you know, I, I've always had some involvement with like AI, but like, you know, it was never a big part of my identity and I think.
swyx: The more foundation models came into focus, and foundation models is a very special term as opposed to traditional, maybe machine learning finance that a lot of your listeners might be familiar with then you start to build differently, and there the traditional software engineering skills become a lot more relevant.
swyx: So relevant expertise now is that I, I guess I've sort of popularized and created the term of AI engineer, which you can talk about and created the industry such that Gartner now considers, considers [00:03:00] it like the peak of its hype right now. And I, I consider that both a point of success and also a challenge because I have to prove Gartner wrong that it has not peaked, but you know, they put us at the top of the hype cycle, which is kind of funny.
swyx: Because I started it, so.
Ryan: Yeah, it's it's a unique challenge but yeah, funny anecdote. Okay, so a lot has changed since we last spoke. Yeah. Pretty much this whole world of AI that everyone's talking about now or at least has become mainstream has, I believe that kind of kicked off right after the discussion or our last discussion.
Ryan: So I guess the last discussion was really focused on the cloud computing industry broadly. And that was actually right around the time when AWS. Azure, GCP all the revenue growth rates were coming down and actually now with the hindsight bottoming. So my question for you is what has, I guess, [00:04:00] what has changed over the last two years and why has revenue growth at the big cloud providers re accelerated?
swyx: Yeah, again, like, revenue growth at big cloud providers is due to factors that, you know, probably I don't have a full appreciation of. I also challenge the fact, the idea that everything has changed. You know, I think in some ways, this is just like the next wave of something that was just a broader, maybe like 20, 30 year long trend anyway.
swyx: You know, we, we needed more cloud compute. Now we need even more cloud compute. Now we need more GPUs in the cloud instead of CPUs, right? Like, what's really changed? I don't know. Like, you know, people still want serverless everything. People still want orchestration. People still want you know, unlimited storage and bandwidth and all the sort of core components of cloud.
swyx: In that sense, it hasn't really changed. I do think that if you see there are plots over time of the amount of money and flops invested in machine learning models, that actually used to follow a pretty log linear Moore's law type growth chart for the last like 40 years. [00:05:00] And then, You had 2022 happen and now everyone's like, oh, you can train foundation models now.
swyx: And actually you've seen a big inflection upwards in the amounts that people are throwing in throwing the money in there just because they see the money now. So like every, it's like obvious to everyone, including us, including me in a way that it wasn't obvious to basically everyone, but Sam Altman and Satya Nadella circa 2019.
swyx: Like they knew this. Four years five years ahead of everyone else. And that's why they went big on OpenAI. But now that we see this, obviously everyone's throwing money into NVIDIA, basically.
Brett: I had, why, why are, and this is maybe a question I think I know, but I'd like the answer again, and it feels like it's maybe a basic question, but a lot of, I think listeners are going to want to kind of understand this connection.
Brett: Why do these new AI companies require so much upfront spending? On NVIDIA chips, cloud computing costs. All that stuff.
swyx: Yeah. I mean, so [00:06:00] you have to split it by whether you're a foundation model lab or you're basically everyone else that consumes foundation models. So the rough estimate for, let's say GPT 3 was like 50 million to a hundred million in compute for one run.
swyx: And for every one successful final training run, maybe you have between a hundred to a thousand. Prior runs before that, right? So just pure R& D. The estimate for GPT 4 was 500 million. We've actually had two generations of frontier models since then, just for OpenAI. So that would be GPT 4. 0 and GPT those are the models that, those are only, only the models they've released.
swyx: And also not, those are only the text models, we haven't counted the video models and all the other stuff. So it's just a lot of upfront investment, right? Like, I think it's, it's like the classic capital fixed costs upfront thing, where, you know, you have a pre training phase where you're just consuming all of the internet.
swyx: [00:07:00] Data that's, you know, there's nuances to that, but we won't go into that. And, and, Alka, Alka comes the other end, you know, 3 to 6 months later, Alka comes a model that you then spend another 6 months fine tuning and red teaming, and post training, and then it's ready for release. So, like, so there...
https://podcast.scalingdevtools.com/episodes/swyx-2
Plus, Shawn started the AI Engineer movement with his essay Rise of the AI Engineer and organized two incredible AI engineer conferences in the past twelve months - AI Engineer World's Fair and AI Engineer Summit
And Shawn has angel invested in DevTools like Airbyte, Railway, Supabase, Replay.io, Stackblitz, Flutterflow, Fireworks.ai while running the DevTools angels community.
Besides this, Shawn curates DX.tips (DevTools magazine) and in a past life wrote the Coding Career handbook, championed learn in public, cofounded Svelte Society and was previously Head of Developer Experience at Temporal, and a Developer Advocate at AWS and Netlify.
Also, before this, Shawn had a very successful career in investment banking, trading, building data pipelines and performing quantitate portfolio management. I think this brings him a very unique perspective - I've always admired his ability to zoom out and see the big picture and the trends.
Even though Shawn is now all-in on AI, he's still one of the go-to authorities on DevTools go-to-market.
As you can tell, Shawn is someone I deeply admire. So I'm glad he came back.
What we discuss:
Links:
That's one thing there. I also mentioned some other things that I've found in the different roles in the category:
Bottoms-up and open source have been very challenging in the growing a company success criteria. That's what different roles focus on: bottoms-up and open source, and particularly open source. You don't have to be open source.
https://sites.libsyn.com/493303/building-ai-in-2024-is-not-what-you-think-with-swyx
In episode 18 of Generationship, Rachel Chalmers sits down with Shawn "swyx" Wang to delve into AI Engineering. Shawn shares his journey from popularizing the term "AI Engineer" to navigating the rapid advancements in AI technology. Together, they explore the evolving demands and opportunities in AI, offering unparalleled insights into the future of this transformative field.
https://www.heavybit.com/library/podcasts/generationship/ep-18-intelligence-on-tap-with-shawn-swyx-wang?utm_campaign=coschedule&utm_source=twitter&utm_medium=heavybit&utm_content=Ep.%20%2318,%20Intelligence%20on%20Tap%20with%20Shawn%20%22swyx%22%20Wang
https://www.listennotes.com/podcasts/plan-b-news-beat-ZoCrxdekY79/
https://open.spotify.com/episode/45dTtNCiIxqySG1lU5addY?si=SeMrk4FBR_yGc4NtG8Ll5Q
High Agency Pod Description
In this episode, I chatted with Shawn Wang about his upcoming AI engineering conference and what an AI engineer really is. It's been a year since he penned the viral essay "Rise of the AI Engineer' and we discuss if this new role will be enduring, the make up of the optimal AI team and trends in machine learning.
Timestamps
00:00 - Introduction and background on Shawn Wang (Swyx)
03:45 - Reflecting on the "Rise of the AI Engineer" essay
07:30 - Skills and characteristics of AI Engineers
12:15 - Team composition for AI products
16:30 - Vertical vs. horizontal AI startups
23:00 - Advice for AI product creators and leaders
28:15 - Tools and buying vs. building for AI products
33:30 - Key trends in AI research and development
41:00 - Closing thoughts and information on the AI Engineer World Fair Summit
Jeff Lawson just bought The Onion.
This is one of my favorite episodes of their NPR podcast parody of Serial.
https://www.listennotes.com/podcasts/a-very-fatal-murder/episode-5-part-1-did-my-tJbSp9yP2wU/
https://www.listennotes.com/podcasts/modern-web/modern-web-podcast-s12e03-rl_OLdGJHS8/
I made a new friend, Serena (sister of Malindi!) who was so impressive and thoughtful in the short time I have known her (cooking, language learning, finance/science management) that I asked her to write down her philosophy of Intentionality.
"Navigating AI and Software 3.0: A Tech Career Guide for Developers": a more beginner oriented podcast interview for a new Youtuber: https://www.youtube.com/watch?v=i5qfKKct6dc
January 10, 2024
years after following him for his serverless content, i was glad to be on Yan Cui's pod to talk Rise of AI Engineer.
https://realworldserverless.com/episode/92i
An essay I think about often, and recommend to people, that I need to internalize more. https://theamericanscholar.org/solitude-and-leadership/
https://podrocket.logrocket.com/software-3-and-the-ai-engineer
https://shoptalkshow.com/577/
this is a longer form conversation with an AI insider so we do get pretty in depth. sorry for the poor audio quality on my side
https://www.cognitiverevolution.ai/ai-engineers-pendants-and-tensions-between-openai-and-developers-with-swyx-of-latent-space/