Quick and dirty prototypes with Andrew Ng
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Why is prototyping now $55,000 and how does that change AI innovation?
piece of good news is it's uh much more capital efficient than it never used to be to build prototypes. So uh AI fund we budget fifty five thousand dollars US dollars to get a working prototype or something and it's not nothing but you know $55,000 if the organization sustained that loss you take a bet fine you lose $55,000 you can sustain that magnitude of loss you could just take a lot of shots on go.
Welcome to Orbit, the HG podcast series where we talk to some of the most successful leaders of technology businesses and hear how they've built some of the most successful software companies across the world. I'm Matthew Brockman, I'm managing partner of HG, and I'm delighted today to be joined by Andrew Eng. He's a very well known figure in the world of data science, director at the Stanford AI Lab, Letim founding of the Google Brain Team, co founding of Coursera, where I think he's personally contributed. To millions of people learning more about AI, managing partner of the AI fund, the leading AI venture studio, an executor of Landing AI. And in 2023, he was named as one of the hundred most influential people in AI.
So, Andrew, you have a really interesting background, obviously, as a academic researcher, as a business leader, a business founder, an investor. at this point of really interesting development in in um data science and evolving intelligence. Where would you say it's easiest? Would you rather be a researcher right now? Would you rather be a venture capitalist? Would you rather be a founder of a business? Where would you you know, which or which is the hardest? Like where would you find easiest and hardest to be to to participate in this in this evolving world?
I find what's most fun to do at this moment in time is to be a builder. I think you could be a builder in academia or as a business leader or uh we're adventure studios. So we build companies. But at this moment in time with the advancing AI technology, there's so many new applications that are now possible that no one on the planet could have built even one or two years ago. And I think it's amazing that today, you know, frankly, they're probably high school kids that could do things. That the best researchers in the world would have really struggled to do two years ago. And so the magnitude of opportunities just build stuff is amazing.
Obviously a lot of talk in the last year or so around, I guess, pace of development and also cost of models and cost of sort of application, right? Cost of products, I guess. Even a month ago we had the news from China, Deep Seek, you know, the sort of the sense of just it's so much cheaper now to access the the capability. Do you think that continues? Do you see a reason why that slows? How do you think about the sort of the next one, two, three years, which feels like, you know, a decade in in normal technology language? Like how did How do you see that evolving?
I think the cost of training models has been falling for several years now. And what happened with deep sea training a model for you know under six million dollars, that was kind of probably you could argue was on trend. But what was surprising was it was a Chinese company rather than an American company that did it this time. But to most people, I think this may be how we'd think about the AI stack. I think there's a semiconductor layer, you know, NVIDIA AMD and others. On top of that, we have the cloud. Companies, and then on top of that, the AI launch model training companies, also called the foundation model companies, and the OpenAI and Deep Seek. And a lot of the buzz and hype has been on these technology layers, and that's fine.
Whenever there's new technology, the media, social media nice to talk about this. But there's an even more exciting stack layer in the stack, which is the application layer, both on top of the technology layers, because um, for all this to work out, we need the application. To generate even more revenue so that they can afford to pay the technology layers, including the foundation model training companies.
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Chapters
8 chapters
1
Why is prototyping now $55,000 and how does that change AI innovation?
0:08–4:04
2
What makes building quick‑and‑dirty AI prototypes more capital‑efficient than before?
4:04–7:52
3
Why is the application layer more valuable than foundation models for businesses?
7:52–12:07
4
How can companies create safe innovation sandboxes to accelerate AI experiments?
12:07–15:09
5
Why should lawyers, doctors, marketers and other professionals learn to code?
15:09–18:33
6
What are the practical steps for SaaS leaders to adopt AI without risking their core systems?
18:33–22:08
7
How does the falling cost of AI prototypes enable a ‘10‑x professional’ future?
22:08–26:01
8
What change‑management tactics help teams navigate rapid AI evolution?
26:01–29:15
Speakers
1 identifiedMore from Orbit - An Hg software leadership podcast
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