Lenny's Podcast: Product | Career | Growth
00:17:00 · Brendan Foody
[00:17:00] S0: So the key question is how long there's going to be things in the economy that humans can do that AI can't do. And I think there's certainly a bucket of people that say, we're going to have superintelligence within three years and humans won't play a role in the economy. And that's one school of thought. Our perspective is very different. Our perspective is that these models are extraordinary and automating a lot of things very quickly, but there's a lot of things that they're horrible at. Like even still, it can't schedule time on my calendar. It can't draft emails for me. It can't use basic tools. And we need evals for everything. For everything that the models can't do, we need evals for the tool use, evals for the long horizon reasoning. Like imagine in 10 when we want models to be able to go out and build a startup for thirty days. Like we need evals for that to effectively reward it. And I think that that road to improving models, will last for as long as there is anything in the economy that humans can do, which models can't, and be a huge portion of what the future of workflow looks like. And so our mission is creating the future of work. And I think that this is a really exciting industry in giving us a glimpse into the direction that everything is headed towards.
00:18:20 · Lenny Rachitsky, Brendan Foody
[00:18:20] S1: There's this tweet that you retweeted that I want to ask you about. If you really think about it, we were put on earth to create reinforcement learning training data for labs. Yeah. What does that mean to you? What is this person implying? And it's basically what you're saying is we're just helping train models. [00:18:37] S0: It speaks to conversations I've had with a lot of researchers and executives at TOP Labs, which is that it's highly likely that the entire economy will become an RL environment machine, building out all of these worlds and contexts for us to then have rubrics or other kinds of verifiers. And that is really exciting in so many ways. Because I think let's draw analog to other revolutions where when we had the industrial revolution, everyone was freaking out about losing their jobs. But there was this whole new class of jobs of how do we build the machines? How do we have knowledge work? How do we create everything new? And I think that the narrative in AI over the last three years has almost entirely been one of job displacement. ChatGPT is growing fast and it's very cool and everyone loves using it. But from an economic standpoint, people talking a lot about job displacement. But very few companies and people have talked about this new category of jobs that's being created, and what that's going to mean and how people can prepare and upscale for that. And I think that the most exciting thing possible is creating that future of how do humans fit into the economy and how will that evolve over time.
00:19:53 · Lenny Rachitsky
[00:19:53] S1: I talk to a lot of people about just like, what should I be studying? Where should I be getting better? People in school right now are just like, what is even going to be valuable in the future? You're at the center of a lot of just what jobs are most in demand, how hiring is evolving. So let me just ask you a very concrete question. What jobs do you think will remain in the futurewhat skills are still worth investing in for younger people especially?
00:20:19 · Brendan Foody
[00:20:19] S0: In terms of jobs, I would respond with a category of things that have very elastic demand are going to be super exciting. Because when we make people 10 times more productive, we'll build 10 times, if not a 100 times as much software as an example. Right? So I think the product managers that can now do so much more are going to be extremely well positioned. And so far as the skills, I think it's people that can leverage AI to do whatever their day to day workflows are. Like I have had a couple conversations with teachers where they get my thoughts on how they should be assessing their students, because we originally started out curating all of these AI interviews and assessments for people and have thought about this immensely. And what we realized is that you don't want to fight against them using the models, right? It's sort of similar to like when the calculator came out, you don't want to give people all of this arithmetic homework of like, how do you get them to do it and not use the calculator? You want to tell them, use the tools and let's see what you can do. And so we'll give people interviews where we say, use ChatGPT and Codecs, use Cloud Code, use whatever tool, cursor and whatever tools are available to build a website. And let's see what product you're able to build in an hour. And so I think that I give that an example insofar as talent assessment, because I think it pertains also to the skills that people should be honing in on of how can they leverage this technology to do so much more in whatever industry or vertical they're operating in.
00:21:48 · Lenny Rachitsky, Brendan Foody
[00:21:48] S1: When you talk about elastic being elastic, is it like generalists being good at just a bunch of different things? Or what do you say? What do you mean when you think elastic? [00:21:56] S0: So I more mean how much capacity for demand there is in that industry. So I'll give a couple of examples. Like in accounting, I think realistically, we only need so much accounting in the world, right? Like maybe there's areas where we can do more and that'll be good, but it doesn't feel like the world needs a 100 times more accounting. On the other hand, in software development, right? Like I think we can ship a 100 times more features for our products, move a 100 times faster, build so much more. There's just, it feels like there's unlimited demand for the industry. I think, Mark Andreessen tweeted about this recently that software is the most elastic industry of all, where when we increase productivity, there's so much more that will be built. And it's definitely characteristic of a lot of other domains as well. And so I would focus on those domains where if we make everyone 10 times more productive, that'll increase demand, not reduce it. [00:22:55] S1: Okay. So you're you're in the bucket of learn to code still useful as a skill. Take computer science. Okay. And so in terms of elastic categories of jobs, sounds like engineering, product management is in that bucket, great. A lot of people listening to those are PMs. Else, like design, user research, I don't know, what else do you feel is in that bucket from what you've seen?
00:22:55 · Lenny Rachitsky, Brendan Foody
[00:22:55] S1: Okay. So you're you're in the bucket of learn to code still useful as a skill. Take computer science. Okay. And so in terms of elastic categories of jobs, sounds like engineering, product management is in that bucket, great. A lot of people listening to those are PMs. Else, like design, user research, I don't know, what else do you feel is in that bucket from what you've seen? [00:23:16] S0: Yeah, think that there's a lot of things where the whole value chain of building companies has a lot of these variable costs, even large portions of operations or consulting, right? Like imagine if we could have 10 times as many McKinsey consultants, what would be possible insofar as the research we could do, the analysis, etcetera. But I think the companies and people that are going to succeed are those that lean into this narrative of abundance, of how do we do so much more, rather than fighting back against it of how do we try to stop displacement.