How Agentic AI is Transforming The Startup Landscape with Andrew Ng

**Sarah Guo** (0:05)
Hi, listeners. Welcome back to No Priors. Today, Elad 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.

**Andrew Ng** (0:31)
Always great to see you.

**Sarah Guo** (0:31)
I'm not sure where we should begin because you have such a broad view of these topics, but I feel like we should start with the biggest question, which is, if you look forward at capability growth from here, where does it come from? Does it come from more scales? Does it come from data work?

**Andrew Ng** (0:46)
Multiple vectors of progress. I think there is probably a little bit more juice out of the scalability limit that we squeeze, so hopefully we can see more products there, but it's getting really, really difficult. Society's perception of AI has been very skewed by the PR machinery of a handful of companies with amazing PR capabilities. Because that number of companies draw scales in narrative, people think of scale first as a vector of progress. But I think agentic workflows, the way we build multimodal models, we have a lot of work to build concrete applications. There are multiple vectors of progress, as well as wild cards like brand new technologies like can 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.

**Sarah Guo** (1:28)
You actually came up with the term agentic AI. What did you mean then?

**Andrew Ng** (1:32)
When I decided to start talking about 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 that I will name, he 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 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, where there are 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 alarm, reflect on its output. And I felt like rather than debating, this is an agent or not, let's just say the degrees of agency and say it's all agentic, so we can spend our time actually building this. So I started to push the term agentic AI. What I did not expect was that several months later, a bunch of marketers would get ahold of this term and use it as a sticker to stick up 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 rapidly growing, but maybe not as fast as the marketing.

**Elad Gil** (2:44)
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?

**Andrew Ng** (3:06)
I think the technology component level, the stuff that I hope will improve. For example, computer use kind of works, often doesn't work. I think, so the god rails, eval is a huge problem, how do you quickly evaluate these things and drive eval. So I think the component is this room for improvement. But what I see is the single biggest barrier to getting more agentic AI workflows implemented is actually talent. So when I look at the way many teams build agents, the single biggest differentiator that I see in the market is, does the team know how to drive a systematic error analysis process with evals? So you're building the agents by analyzing at any moment in time what's working, what's not working, what do you improve, as opposed to less experienced teams kind of try things in a more random way, that just takes a long time. And when I look at those huge range of businesses, small and large, it feels like there's so much work that can be automated through agentic workflows, but the talent and skills and maybe the software tooling, I don't know, just isn't there to drive that disciplined engineering process to get the stuff built.