Andrew Ng: Building Faster with AI

**Andrew Ng** (0:00)
It's really great to see all of you. What I want to do today, since this is built as startup school, is share with you some lessons I've learned about building startups at AI Fund. AI Fund is a venture studio, and we build an average of about one startup per month. And because we co-founded startups, we're in there writing codes, talking to customers, designing on features, determining pricing. And so we've done a lot of reps of not just watching others build startups, but actually being in the weeds, building startups with entrepreneurs.
And what I want to do today is give you some of the lessons I've learned building startups, especially around this changing AI technology and what it enables. And it will be focused on the theme of speed. So it turns out that for those of you that want to build a startup, I think a strong predictor for startups' odds of success is execution speed. And I should have a lot of respect for the entrepreneurs and executives that can just do things really quickly.
And new AI technology is enabling startups to go much faster. So, what I hope to do is share with you some of those best practices, which are frankly changing every two to three months though, to let you get that speed that hopefully lets you have higher odds of success. Before diving to speed, a lot of people ask me, hey Andrew, where are the opportunities for startups? So this is what I think of as an AI stack, where at the lowest level are the semiconductor companies, then the clouds are hyperscalers built on top of that. A lot of the AI foundation multi-companies build on top of that. And even though a lot of the PR excitement and hype has been on these technology layers, it turns out that almost by definition, the biggest opportunities have to be at the application layer, because we actually need the applications to generate even more revenue so that they can afford to pay the foundation, cloud, and semiconductor technology layers. So for whatever reason, media and social media tends not to talk about the application layer as much. But for those of you thinking of building startups, almost by definition, the biggest opportunities have to be there, although of course the opportunities at all layers of the stack. One of the things that's changed a lot over the last year, and in terms of AI tech trends, if you ask me what's the most important tech trend in AI, I would say is the rise of agentic AI. And about a year and a half ago, when I started to go around and give talks to try to convince people that AI agents might be a thing, I did not realize that around last summer, a bunch of marketers would get a hold of this term and use it as a sticker and slap it on everything in sight, which made it almost lose some of its meaning. But I want to share with you from a technical perspective why I think agentic AI is exciting and important, and also opens up a lot more startup opportunities. So it turns out that the way a lot of us use LLMs is to prompt it, to have it during an output. And the way we have an LLM output something is as if you're going to a human or in this case an AI and asking it to please type on an essay for you by writing from the first word to the last word all in one go, whether or ever using Backspace. And humans, we don't do our best writing, being forced to type in this linear order. And it turns out neither does AI, but despite the difficulty of being forced to write in this linear way, our LLMs do surprisingly well. With agentic workflows, we can go to the AI system and ask it to please first write an essay outline, then do some web research if it needs to, and pitch some web pages to put in their own context, then write the first draft, then read the first draft and critique it, and revise it, and so on. And so we end up with this iterative workflow where your model does some thinking and some research, does some revision, goes back to do more thinking, and by going around this loop many times, it is slower, but it delivers a much better work product. So for a lot of the projects that AI Fund has worked on, everything from pulling out complex compliance documents, to medical diagnosis, to reasoning about complex legal documents, we found that these agentic workflows are really a huge difference between working versus not working. But a lot of the work that needs to be done, a lot of valuable businesses to be built still, will be taking workflows, existing or new workflows, and figuring out how to implement them into these types of agentic workflows. So just to update the picture for the AI stack, what has emerged over the last year is a new agentic orchestration layer that helps application builders orchestrate or coordinate a lot of calls to the technology layers underneath. And the good news is the orchestration layer has made it even easier to build applications. But I think the basic conclusion, that the application layer has to be the most valuable layer of the stack, still holds true. With a bias or focus on the application layer, let me now dive into some of the best practices I've learned, about how startups can move faster. It turns out that at AI Fund, we only focus on working on concrete ideas. So to me, a concrete idea, a concrete product idea, is one that's specified in enough detail that an engineer can go and build it. So for example, if you say, let's use AI to optimize health care assets, that's actually not a concrete idea. It's too vague. If you told me to write software to use AI to optimize health care assets, different engineers would do totally different things. And because it's not concrete, you can't build it quickly and you don't have speed. In contrast, if you had a concrete idea like, let's write software to let hospitals, let patients book MRM machine slots online to optimize usage. I don't know if this is a good or a bad concrete idea. It's actually business already doing this, but it is concrete and that means engineers can build it quickly. If it's a good idea, you find out if it's not a good idea, you will find out. But having concrete ideas buys you speed. Or someone to say, let's use AI for email personal productivity. Too many interpretations of that. That's not concrete. But if someone says, could you build an app? Gmail integrates the automation to use, let's use the right prompt source. When I filter and tag emails, that is concrete. I could go build that this afternoon. So concreteness buys you speed. And the deceptive thing for a lot of entrepreneurs is, the vague ideas tend to get a lot of kudos. If you go and tell your friends, we should use AI to optimize the use of healthcare assets, everyone will say that's a great idea. But it's actually not a great idea, at least in the sense of being something you can build. It turns out when you're vague, you're almost always right. But when you're concrete, you may be right or wrong. Either way is fine, we can discover that much more fast, which is what's important for a startup. In terms of executing concrete ideas, I find that in AI Fund, I asked my team to focus on concrete ideas because a concrete idea gives clear direction and the team can run really fast to build it and either validate it, prove it out, or falsify it and conclude it doesn't work. Either way is fine, so let's do that quickly. And it turns out that finding good concrete ideas usually requires someone, could be you, could be a subject matter expert, thinking about a problem for a long time. So for example, actually before starting Coursera, I spent years thinking about online education, how can I use this, holding my own intuitions about what would make a good edtech platform. And then after that long process, I think YC sometimes calls it wondering the idea maze. But after thinking about it for a long time, you find that the guts of people that have thought about this for a long time can be very good about rapidly making decisions. As in, after you've thought about this, talk to customers and so on for a long time, if you're also this expert, should I build this feature or that feature? The gut, which is an instantaneous decision, can be actually a surprisingly good proxy, can be a surprisingly good mechanism for making decisions. And I know I work on AI, you might think I'll say, oh, we need data. And of course, I love data. It turns out getting data for a lot of startups is a slow mechanism for making decisions. And a subject matter expert with a good gut is often a much better mechanism for making a speedy decision. And then one other thing, for many successful startups, at any moment in time, you're pursuing one very clear hypothesis, that you're building out and trying to sell, trying to validate or falsify. And a startup doesn't have resources to hedge and try 10 things at the same time. So pick one, go for it.