How Agentic AI is Transforming The Startup Landscape with Andrew Ng
episode
No Priors: Artificial Intelligence | Technology | Startups
42 min
2 speakers
8 chapters
transcribed 1 month ago
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What does Andrew Ng think will drive the next frontier of AI capability growth?
Hi listeners, welcome back to No Priors. Today, Aladd and I are here with Andrew Ng. Andrew is one of the godfathers of the AI revolution. He was the co-founder of Google Brain, Coursera, and the Venture Studio AI Fund. More recently, he coined the term agentic AI and joined the board of Amazon. Also, he was one of the very first people a decade ago to convince me that deep learning was the future. Welcome, Andrew. Andrew, thank you so much for being with us. No,
always great to see you.
I'm not sure where we should begin because you you have such a broad view of these topics, but I feel like we should start with the biggest question, which is um, you know, if you look forward at capability growth from here, uh, where does it come from? Does it come from more scale? Does it come from data work?
Multiple vectors of progress. So I think um there is probably a little bit more juice out of the scalability limit to be speech. So hopefully you'll consume make progress there. But it's getting really, really difficult. Um society's perception of AI has been very skewed by the PR machinery of a handful of companies with amazing PR capabilities. And because that number of companies drill scales and narrative, people think of scale first of as a vector of progress. But I think you know agentic web. flows, um, uh the way we build multimodal models. We had a lot of work to build concrete applications. I mean there are multiple factors of progress, as well as wildcards like brand new technologies, like diffusion models, which are used to generate images for the most part, will that also work for generating text?
I think that's exciting. So I think there'll be multiple ways for AI to make progress.
You actually came up with the term agentic AI. What did you mean then?
So when I uh decided to start top of agentic AI, which wasn't a thing when I started to use the term, my team was slightly annoyed at me. One of my team members I won't name, he I said, Andrew, the world does not need you to make up another term. But I decided to do it anyway, and for whatever reason it's stuck. And the reason I started to talk about agentic AI was because um uh like a couple of years ago, I saw people were spending a lot of time debating: is this an agent? Is this not an agent? What is an agent? And I felt there's a lot of good work and there was a spectrum of degrees of agency, whether highly autonomous agents that could plan, take multiple sets of reasoning, do a lot of stuff by themselves.
And then things that were lower degrees of agency where it would prompt an element, reflecting his output. And I felt like rather than debating is this agent or not, let's just um say the degrees of agency and say it's all agency. So you can spend our time actually building this. So I started to. push the term agentic AI. What I did not expect was that uh several months later, a bunch of marketers would get a hold of this term and use it as a sticker to stick it on everything in sight. And so I think the term agentic AI really took off. I feel like the marketing hype has gone like that insanely fast. But the real business progress has also been, you know, rapidly growing, but maybe not as fast as the marketing.
What
do you think are the biggest obstacles right now to true agents actually being implemented as AI applications? Because to your point, I think we've been talking about it for a little while now. There's certain things that were missing initially that are now in place in terms of everything from certain forms of inference time compute on through to forms of memory and other things that allow you to maintain some sort of state against what you're doing. What do you view are the things that are still missing or need to get built or will sort of foment progress on that end?
I think at the technology component level, there's stuff that I hope will improve. For example, computer use, you know, kind of works, often doesn't work. Um, I think so the guardrails, evals is a huge problem. How do we quickly evaluate these things and drive eval?
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Chapters
8 chapters
1
What does Andrew Ng think will drive the next frontier of AI capability growth?
0:05–5:49
2
How does Andrew define “agentic AI” and why did he create the term?
5:49–11:34
3
What are the biggest technical and talent obstacles to building true AI agents?
11:34–16:21
4
Which agentic AI applications are already delivering real economic value?
16:21–22:52
5
Why does Andrew prefer “AI‑assisted coding” over “vibe coding” and how does it change engineering?
22:52–28:22
6
How is AI reshaping the profile of successful founders and product leaders?
28:22–32:45
7
Why are smaller, AI‑enabled teams more efficient than larger traditional ones?
32:45–37:39
8
What broad impact will AI have on work and society over the next five years?
37:39–42:10
Speakers
2 identifiedMore from No Priors: Artificial Intelligence | Technology | Startups
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