You know, I'm actually very excited about the future of databases. Um, I know you're going to ask me another question. Just share one thing. I feel like right now the world is going through a massive data center buildout with a lot of AI training and inference data centers being built. I suspect something that a lot of the world does not yet appreciate is that in the future a lot of core infrastructure, be it compute, storage, will be used not just by humans but by agents, and that agentic usage of all this infrastructure will drive massive demand. And so in addition to the AI data center buildup for training and inference, I think a lot of databases have been built for human usage today, and we can only, you know, look at query so much data. But with agentic usage, I think the demand for core infrastructure for compute will continue to skyrocket. So actually very bullish on this category as a whole.
Yeah, it's an amazing space. I mean, we feel very fortunate to be in the thick of it. And actually, we had a partner day yesterday where we invited a lot of people who work with builders or developers, and basically everyone's saying, yeah, we're running out of compute. We need, like, the amount of builders pouring into the world is just going crazy right now.
So actually, I'm going to start because you have one of the most impressive backgrounds and I couldn't memorize it all. So I'm just going to — I know you're very humble. I'm going to read out for anyone who doesn't know: you're the founder of DeepLearning.AI, you're a general partner of AI Fund, managing partner of AI Aspire, chairman and co-founder of Coursera, professor at Stanford University. Previously you were the founding lead of Google Brain, VP and chief scientist at Baidu, formerly director of the Stanford AI Lab, and you are part of the TIME 100 AI list for the most influential AI persons in the world. So, first of all, that's crazy, huh? That's right. What a life. Actually, how do you have the energy to kind of keep reinventing yourself and keep doing all these things?
Yeah, I feel like the answer I have is kind of the obvious one, which is I've been really so blessed to work with so many great people consistently across so many organizations. Um, I feel like but for the people I get to work with, I would hardly manage to accomplish anything myself. So I've just been really blessed.
That's cool. That's cool. Okay. So I was going to talk about skills map. I was going to try to explain it, but you are first and foremost a teacher. So maybe you can tell us a little bit about skills map, uh, what you're doing at the moment, and then we can dive deep.
Yeah. I think the most exciting profession that's very rapidly growing at this moment in time is AI engineering. And by that I don't mean people that will take on an AI engineering job title, but rather people in all sorts of job roles, including software engineering, front end, back end, DevOps, mobile, that will benefit from having AI engineering skills. So my team at DeepLearning.AI did a lot of work over several months to try to map out what are the skills that are needed to do AI engineering well. Um, and so we wound up categorizing — so we looked at over 10,000 job descriptions, job postings, did dozens of structured interviews, ran a bunch of our own surveys, and synthesized views talking to, you know, partners as well, and we wound up figuring out that there are four key skills needed to do AI engineering well. One is the ability to build and deploy AI applications, and the difference to AI and non-AI applications is they're uncertain, and so how do you manage that? The second is software engineering fundamentals. It turns out that even with AI writing a lot of the code for us, people that understand software engineering fundamentals — to understand what's the write ratio, what's a transaction — you know, people can steer coding agents much better that way. Third is the ability to use coding agents. And then lastly is the skill of shaping the build, because developers are no longer playing a role where someone tells them what to do and they just implement it, but instead are participating in deciding what to build and also influencing the process by which something gets built. So what I'm seeing in the job market today is developers — either people with an AI engineering title or people with other titles that have gained some AI engineering skills — are in great demand, and we just can't find enough people with these skills. So DeepLearning.AI is excited to work on this to help more people become skilled at AI engineering.
Cool. And the skills map itself. Um, actually first of all, starting with skills, why are you focused on skills in particular? I think you have, like, a view on skills that they're one of the more important parts. Do you think that they're going to be critical as we go along, or can the agents just become so good that we don't need to focus so much on providing our own custom skills? How do you see this part evolving?
Yeah. So, you know, I think about — there's some things we do today that I hope an agent will take over. For me, for example, you may remember three-ish years ago, there was a lot of hype about the prompt engineering job and how much money someone could make by being a prompt engineer. And I think at that time it was already clear — actually, I give Chris Manning credit for saying this very clearly even back then, which was this idea that we need humans to have arcane incantations to prompt an LLM to do something, that's going to go away because an LLM is going to learn how to make those same arcane incantations. And so even today I see some things that, like, you know, how do you manage a swarm of agents? Some things humans will be needed to do. Some things are more of a one-size-fits-all that I think AI agents will manage other AI agents better and better in the coming years. And one of the key things that distinguishes whether AI will take this over or not is whether or not humans have a fundamental context advantage. It turns out when all of us are building applications, we know a ton of stuff that AI agents do not know. You may remember a conversation with a customer, you know, where you said something and there was a funny expression on their face, or you may remember something that you learned, you know, five years ago reading some market report and saying people want this and not that. So I find that humans have a bewildering vast range of context and are remarkably good at pulling out this context and making key decisions. And so for that set of decisions where humans have a fundamental context advantage — until someday where I don't know, like, slap a camera on us and monitor the facial expressions of everyone we talk to and have AI — that's a very difficult part. So I think there's a large domain where humans have a fundamental context advantage that we want humans to do for a long time. And then giving people the skills to do that valuable work to complement the wonderful work that coding agents are doing, that seems to be the path that we'll be on for a long time to come. And this is why, with — I think you've probably seen with coding agents being the wonderful things they are — the number of job postings for software engineering is up, because as software engineers and AI engineers become more productive, we want a lot more of them. And businesses, I know even my teams, we just can't seem to find enough skilled AI engineers, and I think this will continue.
Yeah. So let's dig into that. Let's imagine two developers who are two strong developers, but one gets better results than the other. What would the stronger developer be doing that the weaker one isn't doing today?
You know, this is a great question, and when we were mapping out the AI engineering skills map, that was one of the questions we repeatedly asked ourselves, which is we actually interviewed a lot of people, and one of the standard questions you asked them was: think of a great developer, think of a less good developer, what is it you know that a really great developer does compared to the less good ones? And a lot of the answers were what we wound up distilling into the AI engineering skills map, and it's actually more than one thing, it's a lot of very different things. But just to give another example, the first of four key skills we highlighted was the ability to build and deploy AI applications. And it turns out that the key differentiator for AI applications compared to traditional software applications is they are uncertain, right? Whether you prompt an LLM or train a supervised learning algorithm, you don't know in advance what outcome you get. And so skilled AI developers have come up with or are familiar with a set of techniques to monitor the performance of these systems, often in a statistical way, and to drive a disciplined error analysis and eval loop to make progress in a systematic way. And so it was by asking questions like that and reflecting on our own experiences that we then tried to systematically map out the — not one or two, but frankly the very large number of — gaps between skilled developers and, you know, novice-level developers, and we tried to distill that into what we call the AI engineering skills map to then try to work systematically to help people go from, you know, less skilled to expert level at all of these really valuable skills.
Is the skills map itself for anyone to use, mostly for software engineers, people with a background? How should people think about the skills map itself?
Yeah. So, we published — DeepLearning.AI publishes a weekly newsletter called The Batch. So, for five weeks in a row, I wrote about nothing but the AI engineering skills map. And actually I know other companies that are now looking to structure curricula around our AI engineering skills map, which is actually pretty cool. But I feel like for individuals it's a way to understand what being excellent — which many of you already are — where you're already excellent, and other places where you want to become more excellent. And then one thing DeepLearning.AI is working hard on is to then create those paths for people to become skilled at AI engineering. But I think the educational space, both, you know, DeepLearning.AI and other companies, still have work to do to help people develop clearer paths to become skilled broadly at all of these AI engineering skills.
I'm curious, what are you seeing in terms of people who are pouring in? Is it experienced developers, basically? There probably is some sort of curve where there's these experienced developers who don't want to let go. I mean, myself, I loved getting in the zone of coding and it became a long time. It took me a long time before I was like, 'Okay, now I just let the agent do it.' Are you seeing this shift slowly happen where people are like, 'Okay, I'm giving up and I'm shifting all of it to skills first'?
Yeah. It's interesting actually, you know, I don't know. It's funny. I actually don't miss coding by hand.
Yeah. Yeah.
But actually, do you miss the — I don't know. Do you get like in the zone? You know, when you used to kind of lock in on this problem, you had to go to Stack Overflow and copy and paste 50,000 things. Maybe you didn't. I did. And then, you know, you spend three hours on the problem and finally it clicks. I don't kind of get that vibe, I guess, anymore. But yeah, I also...
