The AI paradox: More automation, more humans, more work | Dan Shipper

25:13But I think for now, there's this really cool thing where with Proof, for example, anyone who uses it, I don't pay for tokens because they bring their AI to Proof. And so it changes what you build as a SaaS company and you build it now for both humans and agents to use at the same time. And it changes your margins back to, well, I don't really have to pay for tokens anymore because the user is going to bring the AI.

25:39Lenny Rachitsky:So I think this is a huge deal. So what you're describing here is more and more work that we do, more and more professional work. Is it just going to happen within Codex or Cloud Code? Where does Cursor fit into this? Is there potential there? That's a good question. I think that Cursor sees a lot of the same stuff. And in some ways, they have some of the same stuff, but it's better. Like, I think that Cursor's cloud implementation is better than either OpenAI or Anthropix and is more advanced. and I think that Cursor has at least so far more distinctly chosen a lane like they're more distinctly choosing to be for programmers and that may limit how far they get in here like I think the definition programmer is expanding enough that they'll have a big market but I don't know that they're going to jump into like okay use this to make a slide deck or whatever But it is really clear that every model company is starting to realize how important it is to have a harness to get the most out of the model.

26:43And so where all the platforms are moving is to a world where you're not just doing prompt and response. When you call the model on the OpenAI platform, the Anthropic platform, they're literally running the model on a computer that is in the cloud that they run. and then giving you the result out of it. And they know that in order to get the best results of the model, they need to offer that. And so you see Anthropix got cloud managed agents. OpenAI does not have a response yet, but I assume that that's going to happen. And now Cursor was just essentially acquired by SpaceX. It's not like a full acquisition, but it's close.

27:23So I think people are starting to realize I can't just do the model part of it. I have to have this harness above it. and I think the ultimate form of that harness is like I can do any kind of knowledge work. Cursor itself feels like one of the things that it's going to be a hard decision for them whether to stay just for coders or not.

27:42Lenny Rachitsky:So people building products that aren't OpenAI or Anthropic, if this proves to be true, the prediction here is they're going to be using your product over time inside of one of these agents. Is there something you would do if you were one of those companies to prepare for that future? I would just prepare for that. So like, you know, for example, every more classic piece of productivity software, whether it's Slack or Word docs or PowerPoints or whatever, it's really mostly meant for a human to use. and now people are doing CLI so it's like meant for an agent to use independently of a human and we're moving into this new paradigm I think where the human and the agent are on the same piece of work together and they're both doing things and you need to have I need to have visibility into what the agent is doing the agent has to have visibility into what I'm doing we have to go back and forth in this sort of like seamless way and the kind of software that you make for that is going to be very different.

28:46So for example, like there's a lot of stuff that proof doesn't have, I don't have to have a lot of the like Word document kind of like formatting or page breaks or like, you know, making tables or whatever, because the agent just does it, I don't need to worry about that, it can do all the formatting for me. So you can make the products a lot simpler and faster to start than the legacy products are. And then there's all these other affordances that you need to start to have, because the way agents interact with software is very different. So for example, agents can do a lot at once. They can just do like a billion different things to your document or your slide deck or your code base or whatever.

29:24And how you display that to the user is going to be very different than the way you might display a human being concurrent in your document and doing stuff. You need like approval. You need a sort of inbox that sort of summarizes, here's all the stuff that's going to happen or has happened. You need you need logs and the ability to roll it back real quick. So there's all those kinds of considerations that change the actual product. And then the underlying UX of it or the underlying infrastructure you need is different too because agents can make a billion requests in like three seconds. So how are you going to deal with that, right?

30:00This is exactly why GitHub is having problems right now because the number of people using GitHub is skyrocketing exponentially and it's really just people's agencies in GitHub. So I think it's this whole new world that is just starting. You're just starting to see like a little peak of it. But there's so many cool things about it. So, for example, in Proof and some of our other products too, when someone has a problem, they don't email support. Their agent sends a bug report. And an agent bug report is way better than a human bug report. It has like, here's exactly what I did. Here's the exact repro steps.

30:39Here's like proof is open source. So here's what I think is going on in the code base. And then we just get that it becomes a GitHub issue. And then we can just like send off an agent to fix it. And you can't do that with everything, but it's so much better. And you can see the like the glimmers of this, this very fast, like closed loop between I ran into something a paper cut a little feature, I want a little bug. And my agent just goes off and talks to the company agent. and then the company agent just goes and fixes it. That I think is incredibly cool.

31:13Lenny Rachitsky:So is a part of this that you, a lot of people are moving to a CLI and trying to work from the terminal. Is part of this prediction that people shift away from that and back to actual UX with agents kind of running alongside them? CLIs are over. We speed ran the CLI era. It was nice while it lasted, but I think it's pretty clear. It's not that CLI, sorry. It's not that CLIs are going to completely go away. Obviously, they've been around for the last 30 years or 40 years or 50 years or whatever. They will continue to be around. And I think there is this moment when Cloud Code was so popular, or when Cloud Code was really starting to gain in popularity, that people were like, the thing that's working is the fact that it's the CLI.

31:56And I don't think that's what it is. And when you move into an actual UI for this, you start to realize we made GUIs for a reason. And it's just nicer to be in a GUI. And you can get all the same benefits inside of a GUI, especially for non-programmer work. But I would estimate that definitely the majority of the technical people inside of Every are not using CLIs anymore as their main work surface. I think a lot of programmers are still flipping into it every once in a while, but it's more or less they're using codecs, cloud code, cursor, that kind of thing.

32:35Lenny Rachitsky:Awesome. Okay. I definitely wanted to make that part clear. So coming back to kind of the big picture of the prediction here, there's kind of these two modes of work that you're anticipating. One is this kind of super agent within a company that you chat with through Slack, most likely, that can go off and do work and answer questions. and then there's on your computer running Codex or ClockCode. And within that, all the work that you normally do kind of on your computer is now going to be living within Codex or ClockCode or maybe some third party that emerges that we're not even aware of yet.

33:06Yes, and you're going to use apps inside of the internal browser of those tools.

33:14Lenny Rachitsky:Wow, okay. Like listening to you talk about it, it's like it may not feel as profound as it is because this is a big change to how we work. We don't currently have an AI that we talk to regularly in Slack. And we also don't work currently mostly in codex or clock code. So this is actually a pretty massive shift. I think so. Is there anything else along these lines before we get into our next prediction? Well, a few things. I am definitely not an agent maximalist. I really think we're going to have a lot of different agents that we use. It seems pretty clear to me. And I really do think that two agents are better than one.

33:50so that's a good example when I have codecs interact with another agent it can give so much more context about me and what I want than I would be able to type and it can go back and forth talking about things that would take a long time for me to express directly to an agent that you get this like speed up effect when you assume that your users are using Codex or Cloud Code or Cowork as their basic way they access your app. And a really simple example, we have this hosted OpenClaw product, which we had on waitlist. We actually had to pause it because we started taking people off the waitlist. And OpenClaw is just a very hard agent harness to make work.

34:40It's like it's moving so incredibly fast. And if you're like a platform for it, it just it's like when things break, you can't fix it. It's very hard. But one of the things that we learned in that process is if you're let's say you're building an agent product or any new software experience. what you would assume, let's say to set up an agent, is you need to build like a little like web interface or a little Slack workflow that asks people about, okay, like, who are you? And what are you going to use this for? And like, what's your ideal, you know, dream outcome or whatever the things you are that you would put on an onboarding checklist.

35:23If instead, you just make a hard line of, we are only going to service users. who use Codex or Cowork. What happens is you just paste something into, you just paste a prompt into Codex or Cowork. It goes and talks to the app, and the app can be either just a regular server or it can be its own agent. And Codex has so much information about you that it can just give it, here's all the stuff I've been working on with Dan. And here's all the ways that, you know, he might he might want to use this app and then bring it back to me. And it's this very custom experience. And also for a technical product like an agent, when something goes wrong, I can just tell Codex, go fix it.

36:09And Codex will go talk to the app and figure out what's going on for me. And so I think the whole paradigm starts to change when you assume that everyone's got an agent and those agents are talking to other agents in this like really magical and important way.

36:22Lenny Rachitsky:There's a couple more things I want to touch on before we get started. There's like so much to talk about. One is you made this point about SaaS tools not using like you can use tokens from the model companies basically when using a SaaS tool. Talk a bit more about that because that may change the business model for SaaS companies in the future. That feels like a big deal. Well, I think it actually may save their margins because right now all these companies are rushing to add an agent to their offering and thinking, oh, the agent is going to be the main way that people interact with me. And I think that costs tokens, obviously.

37:01And I actually think once I have Codex or Cowork as my main work surface, I still want to use SaaS. So this is another good prediction. I would buy SaaS stocks right now. I think the SaaS apocalypse is dumb and SaaS stocks will be up majorly in the next couple of years. Not investment advice, but I would buy SaaS stocks.

37:29So I think it saves your margin because now the way that you're thinking then is not I have to build AI into this. It's more like I have to make a piece of software that humans and AI want to collaborate on together. And that's hard. But once you build it, it's a lot cheaper than assuming everyone's spending tokens.

37:54And I think it's a good business. And part of the reason I'm so bullish on SaaS is, A, everybody internally here is, like I said, we've all got agents. and we're all using codecs and whatever, and we still pay for a ton of SaaS, and our SaaS spend is up year over year. And we're not, like, vibe coding every single, like, little thing, you know? And I think that what agents do is increase the number of users of SaaS, not get rid of it. And so I think SaaS companies are going to see, like, an insane spike in the amount of demand that they have because there's going to be tons of agents using these products at, like, a very high volume.

38:36And like I said, that's a huge infrastructure challenge. There's a lot of interesting pricing challenges, but it makes me very bullish on SaaS.

38:46Lenny Rachitsky:I love that. If anything else comes out of this conversation, Dan Shipper, SaaS is the future of AI. B2B SaaS. Hashtag send tweet. I love just, yeah, this is quite contrarian. And the other interesting piece is that the fact that you guys are hiring, that you doubled in people in the past year, which is not what people would have expected from a company that is so AI forward. Talk about your experience there of just, OK, we still actually need humans. Automation is a lie in the sense that every time you automate something in order to make sure the automation is working well, you need a human on top of it, like making sure that it's working well.

39:26And so, you know, I wrote this piece a couple years ago called the allocation about the allocation economy, like the idea that the way that humans are going to work with AI is going to is going to be like like being a manager. And the thing that you have to remember about managers is like managers actually spend a lot of time working. Most managers are not like on the beach. They're like checking in with their employees all the time and trying to figure out, OK, how do we make this work good? How do we make it better? How's it doing? How's this person doing? All that kind of stuff. And I think there's some differences between being a human manager and being a model manager.

40:01But fundamentally, it still requires a lot of time and attention. And I think that we kind of miss that in the model discourse. And one of the reasons is benchmarks make it look like AI is more autonomous than it is. and by autonomy i mean something specific by autonomy and i'm going to try to express it's like a little hard to express but i learned this for myself because i've been feeling this paradox a little bit i've been feeling the like we have so much automation so much ai and i also work way more and i think part of the paradox part of the paradox started to like resolve for me a little bit when i made my own benchmark so i made this senior it's called the senior engineer benchmark And it's like, how good is AI versus a human engineer?

40:51And the way that I built it is, again, have this app proof. I just vibe coded it on the side and like while running the rest of every. And when we launched it, because it was completely vibe coded, it just started going down and I couldn't fix it. And it was very embarrassing. I had a lot of egg on my face. And like the product worked. We tested it internally. We had a lot of beta testers. But like the day after launch, it was like just every like 10 minutes, the servers would go down and people were looking at me and I'd be like, I don't know what's going on. Like Codex, fix it. And Codex is like, I don't know what's going on.

41:25Or really Codex is like, I do know what's going on. I fixed it. And then it would cause four other errors. And then you're just going around in a circle and I wasn't sleeping. And I vibe coded so hard. I got bursitis on my elbow. So that's a there's a life lesson in there.

41:42Lenny Rachitsky:Vibe code or elbow. Yeah. So anyway, I got actually two different senior engineers to fix it independently. So I have two different rewrites of the code base that tells me how they did it. And so what I get to do is when we get new models, I just give the new model a prompt. I say, this is Vibe Coded Slap. If you wanted to rewrite it from first principles, how would you rewrite it? Go do it. and all the models until GPT 5.5 got like a 30 out of 100, and a human senior engineer gets like high 80s, low 90s out of 100. So there's a lot to go. And then I tried GPT 5.5, and it got like a 62. And mind you, the 60 score was GPT 5.5 using an Opus 4.7 plan.

42:37Opus 4 % plans are very good. GPT 5.5 is the only model, though, that has the sense of agency and confidence to just like rip out old code and just like actually rewrite from first principles. Other coding models, they kind of like try they like end up papering over the edges or around the edges. And they're like, oh, this is a big job. Like, I'll just do a little patch. And you're like, no, I like specifically told you not to. So GPT 5.5, there's like a 30 point bump in the score. 60 out of 100. It's very clear that in a year or less, it's going to be senior engineer level. And that gives you a certain picture in your mind, especially based on how I named the benchmark, which I think a lot of benchmarks do.

43:19And I can tell you that when we get to that point, it will be very easy for me to change the benchmark to zero out the current model. So that gets a zero out of 100. And so, for example, it seems like there's no skill or no thought into the prompt, which is, this is vibe-coded slap, like, fix it from first principles. But actually, it took me a while to get to a prompt that didn't give away the answer, but got the model to reveal what it's capable of. and the original prompt I gave it was the original prompt that I gave it when uh when I was trying to fix the issue and production was going down which is like I'd woke waking up I'd I'd woken up in the morning and I was like okay we had four or five reported issues yesterday I want you to go through all the issues and then come to like a make a plan for how to resolve all of them and go do it right And every coding model on the market, and I'm pretty sure, here's a prediction, I'm pretty sure every coding model on the market will still do this in a year.

44:28Every coding model on the market will take that instruction seriously. And if I tell it, here's a bunch of issues, go fix it, they will just go try to fix the issues. What an actual human senior engineer does is they go look at the code base and they're like, this is a piece of shit. This guy doesn't know what he's doing. And then they say, we're going to have to like actually rewrite a lot of this and it's going to be hard and risky. I know you don't want to hear that, but like we're going to have to do that. And if you asked the model, hey, like, should we do that? It'll it'll probably it'll probably get there, but it's not going to do it on its own.

45:03And it and there's a lot of incentives pushing against it doing that. And even if it does that, there's there's always a higher frame for us to go. And so I think it's really important when we think about benchmark progress to think about it from that perspective, which is benchmarks rise on problems that we've framed, that we can articulate, that we can score. And there's a lot of work that's human work that it can't be scored until you write it down. But the act of thinking to prompt it or write it down is something that you can't measure, but like kind of means that even if the benchmarks get saturated, it doesn't mean the same thing as you totally replace all senior engineers.

45:45And it's, I think it's why, even though the models are getting better at automation, I still hire engineers.

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47:15Lenny Rachitsky:Our Kisnal human code. Yeah, exactly. That's exactly right. And it's so interesting that that's exactly the kind of code used to build this model. well what's interesting so i want to i want to clarify there so i did not have a human write the code all by hand because i actually think that that's sort of it feels silly to me like i don't really care because i know if if an engineer is not using ai like i'm not going to work with them i don't really care it's like it's sort of like am i going to race a human against the car like i probably wouldn't do that but um i would race a human in a car versus another human in a car and say which one's better.

47:53And in this case, the way the benchmark is structured is, yeah, these human engineers used AI, but they used it in a way that I could not because I didn't understand it and I didn't have time and I didn't really want to go in and try to understand the code base, to be honest. And I think that's a really important thing when we think about benchmarks is AI is a broadly distributed technology that any human can use. And when we are benchmarking against humans, AI against humans, who are actually really always talking about one human using AI versus another human using AI because AI doesn't use itself.

48:27It may be able to in this like slightly somewhat recursive way, but there's in any real use case, there's always a human like pretty close to it, making sure that it's working.

48:36Lenny Rachitsky:Okay, I want to try to wrap up our first bucket. There's so much to talk about. I've made a little list of things that I think people should do based on your predictions to be successful. We'll talk about this at the end too, but just a few things. One is start using codex or clock code more and more for the work you're doing. And especially the browser, use tools inside of it. Two is allow agents to use your products. If you're building a SaaS tool, make it easy for agents to be a user, essentially. Three is start thinking about some Slack bot that you can work with, like try out tools. Like I know Slack has their own Slack bot that I think is really good too.

49:16Lenny Rachitsky:And I haven't played with it, but people really like it. So look for, I guess, a tool that could become the AI agent within your company. Buy SaaS stock ASAP. Not investment advice. I think that's totally right. I will, like my slight tweak is when you're thinking about building your software for agents, the current model is I'm building a CLI that an agent uses, but they're using it in a sort of like I delegated a task to the agent and the agent's using the CLI. And where I think it's going is you and the agent are using the app together. The agent's probably using the CLI, but you're using the web interface.

49:59And they both need to be in sync. And that is, I think, a new challenge that's really interesting.

50:06Lenny Rachitsky:Awesome. Anything else before we get to our next category? By SaaS. That's the title. Oh, man. Okay. So the second category of predictions is around just the shape of the work that we're going to be doing is going to change. What do you predict? There's all this interesting stuff in terms of the shape of work. Like once you're in this land where you've got, you know, these you've got async, async agents off that you delegate work to, then you've got your like codex, cloud code, like work surface that starts to happen. So one thing that we see a lot internally, and you also see this in the big model companies, is the number of pull requests that you get is like skyrockets.

50:50You know, we have people, you know, in consulting or in ops roles or whatever or editors just like making pull requests. and A, that's really cool and it's a very different shape of work where you can expect that a higher percentage of your company or your users are going to be doing things that previously only technical users could do. And what that does is it creates all this pressure on the other end for the people who have to deal with all of the new code for how to deal with that. And so I think there's a lot of interesting things that happen with that. So for example, OpenClaw, I mentioned that earlier.

51:36Pete gets thousands of pull requests a day on OpenClaw, and then he just spins up 50 ,000 codex instances and then sorts through them and then merges 1 ,000 of them. It's really crazy. I actually think that that's going to be more and more common. um there's like it brings up a lot of really interesting questions around um which pull request should you merge and you know when you whenever you add capacity in one part of your process like it breaks things um it used to be really hard to build things and now it's very easy so the the point is not can we build it it's like would it make sense with the rest of what we've built and how do we keep a like sense of a coherent whole and also what do we delete i think anthropic does this really well like they they delete a lot of stuff from cloud code to make sure that's not bloated so i i think there's a there's a lot of that going to happen on one side there's a lot of um non-technical people can do technical work and then technical people are in charge of making sure that that work gets into a product or into a process in a cohesive coherent way and also their product people are going to be doing that too and i think that's

52:51Lenny Rachitsky:that's quite cool something i'm hearing from people is that now that everyone can do everything like engineers can design pms can code marketing people can ship stuff there's just this like confusion about what the hell is my job anymore yeah what am i responsible for exactly like am i supposed to be shipping stuff am i still a marketing person and it's just creating a lot of confusion and certainty in the world. I think that's for real. And one of the things that I think is special about every is everyone is sort of a generalist and really loves like having their fingers in a lot of different pots or whatever the metaphor is.

53:27I think that'll probably settle down at some point and it'll feel more normal. Like marketing people are still going to do marketing, even if they're touching the website. Like that's just part of marketing now. But I also think that you can get a lot further being a generalist now. and that's like really cool, especially for smaller companies. The other thing that I think is interesting is there are definitely some new job roles that are a thing. And the thing that is becoming really clear is the whole forward deployed engineer concept, I think is for real. And it comes out of every agent needs a human.

54:05Like you go to the big model companies, they have these agents that run internally. they have like teams of people that run these agents, you know, and I don't think those teams are going away. The models are going to get more powerful, the agents are going to get more powerful and the number of agents is going to grow, but people are still going to manage them. And so that looks like a very specific kind of person. And, you know, we have a couple of those people internally here. And it's like the people who are in charge of making sure your agents are working and doing the right thing. We also do consulting.

54:38So we, we, we lend that out to people. And I think that's a big, um, that's a big thing that, that people want. And it's another one of those places where you're like, Hmm, automation was supposed to take away jobs, but it looks like it just created one or many, you know? Um, and there's a specific type of engineer that really loves, you know, Nitesh, who's one of our, uh, who, who fits this, he's an AI engineer and he fits sort of forward deployed category and he's on our team. He spends most of his time actually talking to one of our agents in Slack. We have an agent internally called Claudie which runs our whole consulting practice and he spends a lot of time in Slack.

55:19Like there is code and he is using Cloud code and other things like that but a lot of it is just talking to it and being like why did you do this dumb thing like let's fix that you know. And so there are certain kinds of engineers that I think love that and love having their hands on the latest thing and also love making this like being that's like in in the works in a workspace and it looks a bit different than more traditional building more traditional software and your sense there is we're not gonna

55:47Lenny Rachitsky:we're not near a place where these agents don't need a human you've said that so many times now that agents need a human and there's kind of like the setup part and then there's the maintaining it forever part and feels like both are important uh is what I'm hearing like this is going to be a job for a long time. AI is not going to get smart enough to just automate it, be fully automated for a while. Yes, I'm simultaneously extremely AI pilled, extremely, and very bullish on humans and the role of humans in making sure that AI is working well. Interesting. Okay. So the two kind of buckets here that you're talking about, one is like the way I think I hear what you described earlier is just the pace of shipping software and everything is just increasing, which also means there's so much more work reviewing all this sloppy output.

56:33Lenny Rachitsky:I was just talking to a data science friend and he was saying how his team is just, his data science team is just, their job used to be do analysis, answer questions, see if this experiment was a good, was a, was positive. Now it's just, everyone's doing that and they're sharing their results and they're, and they're like, no, this is not correct. And most of their job is now reviewing bad data science work. Which is a problem. And it means that, and the same thing is happening with engineers. and it means that you need more, like you actually need that engineers for this and you need data scientists.

57:05And it means that you haven't set up the appropriate systems or agents to help you with this. So like the way that it works inside of the big model companies, for example, like at least one of them has literally a data science bot that every single person in the org can query that is hooked up to their data warehouse that knows who's who so that it knows at the warehouse level, like who has permission to access what. And so all of the basic questions, because there's a team that sets up this bot, all of the basic questions that people might want to ask that it sometimes gets that might get wrong, that they're constantly making sure it's getting it right.

57:42And so the data science team doesn't have to answer all the like bullshit questions because there's another team building an agent that that that is set up to do that really well. But if the team didn't exists, the data scientists would hate their lives. Yeah. It does though, make the job maybe

57:58Lenny Rachitsky:less fun because you're just sitting there, you know, gardening people's sloppy work. Well, that's what I think is like, it can actually make the job better because for the data scientists, you are now not dealing with all the silly requests. You're dealing with the deeper questions that are harder for the team who's dealing with all the basic requests and building agent to do that. It's like filtering all that stuff out so you can focus. Here's a question I've been thinking about. I was not planning to talk about this, but it's something that I've been thinking about. The question is, which product tech role is the least changed now?

58:36Lenny Rachitsky:So like engineers, 100 % of code AI now. It's like a completely different job. Product management, a lot of the PRDs, you don't have to write as much. You can ship code. You don't have to wait for people. The whole design process, dead, according to a recent guest. There's no time to do the whole design process. Very different role. Data science, very different work now. There's marketing, there's sales. So here's the question. What do you think is the least fundamentally changed role so far? Well, one interesting thing is, I don't know if this counts, but CEOs and investors, it seems still very, very optional, whether or not they use this stuff.

59:18It seems that way. I think the opposite is actually true. Like my experience, and we do a lot of this with senior executives and senior leadership teams. My experience is that your company's only going to go as far as your CEO goes in AI and it's not something you can delegate. You have to have your hands in it because otherwise you don't have an intuition for it. But for a long time, it has seemed like, yeah, that's something that the people who are doing the work have to do, but like, I don't have to do that. Like, I'll just tell them what to do. And and so I think if you're a CEO, you kind of can get away with your day looking very similar.

59:53I think that will change rapidly at some point where it'll be like, oh, no, I'm like way behind. But for now, because or maybe even middle managers like those kinds of people, I think are it's fairly similar. I think like maybe sales because I think so. That's exactly so in person.

1:00:11Lenny Rachitsky:Yeah, that's my vote. you know there it's sort of creeping up in the kind of BDR like we can deal with a lot of you know BDR type or type queries you're only talking to like people who actually want it and you can do for sales it's like it's so useful to to like do research like my favorite codex like one of my favorite codex experiences is we're hiring a head of lnd and i you know we always put out a job post whatever but i was like i feel like there's this company called general assembly in new york and they do like they've done really good technology education for a long time and so i was like i feel like someone who is into who who worked at general assembly and is now into ai would be really good and i just like literally typed it into codex and then like went off and was doing something else And I came back and it found like this, the perfect guy.

1:01:07It was like worked at General Assembly, was an instructor, like is super AI pilled and follows me on Twitter. So I just DMed him and then I had dinner with him. And it's like, that's crazy. You know, that would have taken so long before and super valuable for sales for recruiting, all that kind of stuff.

1:01:26Lenny Rachitsky:Yeah. Sales is where my mind went. Like the top of funnel AI is helping a lot with sourcing and qualifying, things like that. It feels like the work of a salesperson is not fundamentally different. And customer support's fundamentally changed. So that's interesting. Sales. So far, it's very good for those folks. Okay, so maybe just summarizing some of the predictions in this bucket of just like the shape of the work, how it's going to change. What I'm hearing so far is there's going to be a lot more reviewing of other people's output as a part of the work. And then two, there's going to be a lot of like almost babysitting of AI agents to make them do the thing you want them to do for deploying and then just gardening them along the way, make sure they continue to do their work.

1:02:08Lenny Rachitsky:Anything else before we get into our third bucket? I would sort of split it into less babysitting agents and more your forward deployed team is trying to build a whole system that makes it so that people who have less knowledge can use that system without like doing something dumb. And that's like a really interesting engineering challenge. I think babysitting kind of makes it feel like it's, yeah, you're just kind of like, you know, waiting for it to fuck up and then fixing it or whatever. and you can't you can't that can be the case but i think a lot of it is this extremely interesting engineering challenge of building a system for to enable everybody else in your organization to do what used to be a technical job and then if you're not one of those people like you're the data scientist or whatever you can go a lot deeper with ai into like really important questions that eventually probably filter into the work that the you know the forward deployed engineering team is dealing but is like more generative and more new and and you're you're dealing with harder questions One last thing that I think is really interesting is I think that we will be reading way more AI-generated writing in documents and emails, and we will like it.

1:03:19And I think we are already doing this in coding where we read planned documents. Like, I don't want an engineer to handwrite a planned document. That would be very silly. It would be obviously silly. And I think the same is true. When we did our quarterly planning for every at the end of 2025, we did it all with Notion agents. And we just had a bunch of Notion agents. And we had really one Notion agent. And then we had a top-level company strategy. And then we had everybody in the company just talked to an agent. And it asked them about what happened last year. How did it go? What were your goals?

1:04:02What do you want to do this year? What are your metrics? It pushed back. And then it was like, how does it how does this relate to the overall company idea? Like all that kind of stuff. And then I got I got these like incredibly good AI generated like strategy reports or plan like quarterly plans for for each part of each team. And then I could go in and be like, OK, who needs to who's like who needs to talk to each other? Like which teams need to talk to each other that like don't know they need to talk to each other? And, you know, who's which one of these is like at like actually low quality or which one of these is high quality?

1:04:35Like all that kind of stuff makes it a lot easier to process. And I see that all the time now. Like I consistently get AI-generated stuff. And there is a difference between an AI-generated document that's slop and not. And the slop one is it took them less time to make it than it takes me to read it. And they don't stand behind every line. So my expectation is if you send me an AI-generated document, I think that's great. and if we talk about it and it's clear you have no idea what's in it like big no-no not allowed to do that um and i think we have this this aversion to ai generated stuff that will go away because the kind of strategy document that gbt 5.5 can write when it's directed well by someone on my team is way better than like them just like dinking and dunking like like their fingers on the keyboard right like most people are really bad the bar is low yeah and and same thing with email like i most of my email is written by gpt 5.5 in codex right now and i would i honestly would prefer it to say that it's coming from gpt 5.5 and i may change it to do that but i had this i had this experience the other day where i had this I had to send an email to to one of our investors and I asked Codex like go do it and usually like Codex knows to ask me and it usually does but this time it didn't and it just sent the email and I didn't look at it at all and I was like fuck and so I went to my scent and looked at it and I was like oh this is exactly what I would have sent and so it's like it's pretty close to to that a lot of the time um it can be like a little over formal and there's a couple of things that that it's just when you really think about it most your email is kind of it's not it's kind of rote it's kind of prosaic it's kind of i i definitely want to be the one to think about what it should say like what what it should say but the actual sentences don't matter that much to me usually sometimes i do a lot and this is coming from a writer like i care a ton about writing.

1:06:47I think that human writing is incredibly important. And I expect we only publish human writing. Well, actually, we publish a mix of human and AI writing, but we always label it. Sometimes it's nice to have an AI co-author on certain things. I absolutely think that human writing is important. And I think that the reaction or the aversion to AI writing is silly.

1:07:10Lenny Rachitsky:It's such an interesting lens on that, because when people think about AI writing, I think about social media and videos. And your point is internally, if you're just like working on planning and documents and email and things like that, like that is much less scary that it's AI written in your tier point, people already doing this. You almost prefer it a lot of times because people are really bad. Totally. Anyway, we have this too for external stuff. Like we publish all these guides and the guides are often agent. They're agent assisted and the agent is a co-author and they're intended to be read both by humans and by agents.

1:07:42And that's because like if you're writing a huge informational thing. I mean, you do this all the time. In order to like really apply it, the best way to do that is just like have your agent ingest it. And remember the next time I'm, you know, doing pricing to like remind me of this guide and we'll go through it together or whatever. It allows you to operationalize the ideas much better and it allows you to go much deeper because agents can read like 10 ,000 pages in like a second. And so you can you talk to the human about the story and the stuff that matters in the core ideas. And the agent has all the details that it can then apply for you when you need it.

1:08:20Lenny Rachitsky:Awesome. Anything else in this category before we get into our final category? No. Okay, let's do it. So the final bucket is just who will be successful in this AI future that we are approaching slash what should people be working on to be successful in this next year or two? I am super, super bullish on PMs. And I know that your audience will probably love that. But my anecdotal case that has convinced me of this is we have this guy internally, his name is Marcus, and he runs Spiral, which is our writing app. Marcus is a PM by training. He previously ran Axios's writing product and was a PM and had a big team and it got to tens of millions of revenue in ARR.

1:09:16And he took a year off that job and just got super AI pilled and just learned how to use cursor basically really well. Now I think he uses cloud code, but he was extremely cursor-pilled for a long time. And he's, I would call him like lightly technical. Like knows what a database migration is. Like if he has to look at the code, I think he can understand it. But he's like, we never could have hired him to do this job even a year ago. But the coding models have gotten good enough that he can pair the kind of the technical knowledge that he does have with his really spiky product sense and sense for writing and sense for users.

1:09:59And it's like, it's so dangerous. Like he ships faster than almost anyone on the team. And he has such a eye for every single user, every single conversation, like, what does it mean? And how do we collect it into a story about like, where we want to go next? And what are the issues we need to fix and like all that kind of stuff. And I think that he feels liberated because he doesn't have to organize a whole team of people to do that. He can just do it. And it's super impressive. And it makes me very, very bullish on any PM who gets like really AI build.

1:10:29Lenny Rachitsky:Music to my ears, Dan. You're making a lot of very happy listeners here. I've been saying this for a long time, too. It's just like the skills you need to build are the things like the building now is done for you. What do you need to be good at? Figuring out what to build, figuring out if it's great, figuring out what problems to solve. So I love that you're actually seeing this come to fruition. I really believe it. This could be the highest rated podcast episode of my whole podcast. I love it. Hell yeah. It's going to be okay. Stass is back. PMs are back. You know. This is the most contrarian episode I've ever done.

1:11:04Oh my God. So, okay. So the other people that I think are going to be like super, super power people. And again, this is because we see this internally is full stack designers. if you're a designer and you're in these tools all the time you're so used to um okay i make this beautiful interaction and the engineer like just doesn't want to do it or it doesn't like happen the way i think it should happen or you know there's all this stuff and i see so many designers for us internally or externally where they now feel so empowered to like go build stuff because they're like i have all these ideas to make things look amazing and these interesting interactions.

1:11:41And that's the exact thing that it's really hard to do with vibe coding because it just all looks the same. So it all looks like slop and they can make stuff that looks so different. And now they can actually build it. And what you see when we work with them internally is now they're just like, they're just making pull requests. Like they don't, they don't need to hand it off as much. Sometimes they do, but like a lot of times they just make pull requests and it's like the thing is built and that's it. And I think that's incredible for the way that companies work, But it's also there's a huge opportunity for those people to become entrepreneurs and like start their own thing because they can they can make stuff now.

1:12:15And I think designers are such creative people. And I think AI is like a super tool for anyone like that.

1:12:22Lenny Rachitsky:I so agree. Even though there's cloud design, there's all these AI designing tools. Like once you see it, you're like, that's definitely cloud design. they're like the creativity to your point is it just feels like it's going to be more and more valuable to to stand out from all the slop that people are shipping and launching constantly so i completely agree it's it's interesting that designer roles i do i do research on the job market and interestingly designer roles have not grown in a while so i'm waiting to see if that becomes a big trend just like we need more designers that is really interesting we'll see we'll see we'll see that might be a way to predict this is are people hiring more designers i don't know that is interesting yeah all right uh so that's so pm designer thriving killing designer thriving um i also just think generally the ai job apocalypse is not really a thing absolutely we see companies starting to reorganize and i think that makes a lot of sense i i think to be honest a lot a lot of the reorganization you can say it's ai but it's like we overhired and like the company's not doing as well and all that kind of it was like coming and this is a good excuse but the like mass unemployment thing I think that like some AI CEOs are talking about like I think that's not gonna happen the the pattern that I see so far and again I don't have a total crystal ball but I I do feel like we've seen enough of the new model drops to like have some sense of how this is going is that what a new model drop does or what models do in general is they make yesterday's human competence cheap.

1:13:56So what I mean by that is they ingest all this data of what has happened already, and they make it really cheap to deploy that in whatever situation you want as your own, right? And what happens then is this is a new power that everyone has. So it gets adopted super rapidly. And suddenly that stuff is everywhere. It's like suddenly anyone can make a landing page. There's new landing pages everywhere. Suddenly everyone can write. There's like slop tweets everywhere. But what's interesting is because it's all coming from these models and everyone's using basically the same models, it all looks the same if you use it in the most default basic way.

1:14:45And so it becomes commoditized. It's not valuable anymore. And what humans do is we sort of go in there and we're like, yeah, we have all this like frozen human competence from yesterday. How do I use this to like make something new and interesting? And I really think that structurally, because of the way the models work, because of the financial incentives of model of model companies to like make them compliant and aligned structurally, they're always going to be trailing behind those people who are taking taking the models. and using them to make new expertise or make new things that haven't been done that way before for their very, very particular situation.

1:15:27And that stuff is going to get incorporated into the models. But again, it will create room for people to push further ahead. And I think that you see this in a small way in like pretty much all the jobs is like engineers, suddenly everyone's an engineer, that doesn't mean we fire the engineers, there's like way more demand for engineers, because you need the engineers to like figure out, okay, this is all slap, How does this actually, how should this actually go in our code base? And I think that's something that the benchmarks rising doesn't really capture. And it feels like a thing that will take a long time to change.

1:16:01Lenny Rachitsky:People may be hearing in this prediction here of just, okay, the jobpocalypse, people are not going to be all fired. There's going to be human jobs remaining for quite a while. It may be almost too comforting because you probably have to change the way you operate to still have a job in the future? Do you have any sense of just like, here's what you need to do to not be one of these layoffs? Yes. And I think that is actually super important. The only thing you need to do is ride the models. And that means use them for whatever it is that you do. You know, we've talked about how Codex and Cowork are becoming the sort of standard operating system for work.

1:16:42If you're just doing that and when new models come out, you're trying them and figuring out, okay, how can I, Now there are new powers. How can I use them? Instead of just being like, I'm going to like try to ignore it because it like makes me afraid, which I think is honestly it's rational. It's a reasonable response. And also, if you ride on top of them, they extend your powers in a way that doesn't leave you behind like you. You're you're you're part of the future and part of the way work happens. And I think that we're going to need people doing that for a very, very long time.

1:17:16Lenny Rachitsky:I like this term ride the model. So the what's like say a new model comes out. What do you think someone say working at, I don't know, Salesforce, say a PM at Salesforce, what should they do to ride the model? Well, one of the things that's really interesting is a lot of companies like handicap their employees from even doing this because like, I don't know what model, I don't know if you can use the latest models in Salesforce, you know, like a lot of times you have to wait or it's, you know, whatever. So maybe you have to do it on your, in your off time. But the thing that I really like to do with new models is play.

1:17:47And there, there, there are certain things where I know it can't quite do it yet, but when a new model comes out, I like always turn the rock over again to be like, can I do it now?

1:17:59Lenny Rachitsky:You know? Um, so it, you know, it could not do the senior engineer benchmark last time and I turned it over, turned the rock over again. And now it's of 60 out of 100, which is like really good. So the way to ride the models is like not one specific thing because they're always changing, but it is to be curious and playful to apply the model, the new model to whatever it is that you care about, whether that's your job or something outside of your job and to keep turning over rocks because it may not work now, but it may work eventually. It probably will work eventually. And the way that you use it matters.

1:18:37So what's really cool is that I think people think of the edge of AI as being in San Francisco. And I actually don't think that that's where it is. I think the edge of AI is wherever AI meets like a real human doing something because the people in San Francisco, they're making it, but they don't actually know a lot about how to use it. They don't know, or at least they don't know everything about how to use it. They need to see how other people use it. And so you, whenever a new model comes out, you get to be one of the first person, one of the first people in the world to discover what it might be useful for.

1:19:10And that's, it's like a new discovery. And I think that's why, for example, we're in, we're in Brooklyn, but I, I really think of us, and I think we are like quite far ahead of people in San Francisco because we just use them for everything and um if people uh if people do that consistently i think it's going to be very hard

1:19:34Lenny Rachitsky:to lose that is one of the amazing most amazing things about ai right now is no matter how much money you have or a little money you have you have access to the most advanced ai model like it's not free so you need some money uh uh but like and you can get it immediately when it comes out maybe the only people that have an advantage are the people working at open ai or anthropic but otherwise it's just like available i know i was at i was at uh their event with you their code with claude event with you last week and um or a couple weeks ago and they're like all using mythos and i'm like god damn it it's so annoying but i i think that's totally true like that is if ibm had invented ai you can bet it would not be like this and it would be like a bajillion dollars and only like the top companies could use it and they would be using it in the in the weirdest most uninteresting ways and i think there's it's there it's really important that ai was built in america and in the silicon valley culture that's like we want to make intelligence too cheap to meter like that's not the default stance and um it means that everyone has this broadly accessible tool that they can use.

1:20:50And I think that's amazing.

1:20:51Lenny Rachitsky:That's such a good point. And interestingly, it's also created the most fastest growing companies in history, the biggest companies in history. That's true. Those Silicon Valley guys, they're smart. If I zoom out on the conversation, it's really interesting. There's kind of these two sides to the coin. One is not a lot is actually like so much was not changing. SaaS continues, jobs not disappearing. We're still emailing each other. We're still working in Slack. A lot of the work not changed. On the other hand, every role transformed. Engineers don't write code. PMs don't write PRDs. Design and design.

1:21:27Lenny Rachitsky:It's so interesting how much has changed, how much has not changed. I don't know. It's interesting that people think it's going to be this whole new world, but in many ways, it's okay. It'll continue the way it is with a lot of stuff around the edges. That's how I feel. I'm simultaneously so excited and it feels like everything has changed. I'm so bullish on it and and the and the progress that we're making, all that kind of stuff. And yeah, I just I feel like there are there are these things where they're going to be pretty similar to how they are. And that's probably good. And I think generally our intuitions about the future, the the model that I have of what our intuitions are about the future is the intuitions that people had in the middle ages about like what happened at the end of the horizon.

1:22:08You know, it's like, are there dragons? Like, does it drop off into nothingness or whatever? You know, like, a lot of people have a lot of deep intuition that there's something terrible going to happen over the horizon. And also that some people are like, there's something incredible, it's gonna change everything, we're all going to be happy as a utopia. And what happens is you get there and you're like, there's some really cool things, there's some not cool things. And it's just another horizon. And I think that's, that's the way to think about the future. And until you get to that place where you're starting to see it and I think we get to see it because we get to see it internally all the time it's important not to let your your mind get away from you and being like this is going to happen and this is going to happen and whatever because you're going to tell a story that sounds sounds so real in the moment but um later on you're like actually it's much more complex than that and somewhere it's sort of a both everything's changed and nothing has um and And once you get there, I think you're sort of starting to see like, oh, yeah, this is a real thing.

1:23:11Lenny Rachitsky:Part of it is the AI companies are very good at scaring us about what might might happen in the future. And I think that's actually shifting. I think they've realized maybe we should not freak everybody out about that PR strategy just does not make any sense to me. I do think that it's like genuine, but it's so ineffective. And and I think it's also wrong. how about we um end with maybe just like a few things listeners should do to be successful over the next year with the way the world is moving ride the models i would uh try all of your workflows in codex or co-work and see how that works and if your company doesn't let you do it on your own time.

1:23:56I would try out some of these agent products like OpenClaw or Hermes or for less technical people. There's like Victor. We have one plus ones. I would get comfortable with both of those ways of working and try to like try to have fun. I think there's too much of I'm doing this because I have FOMO like it might I might lose my job or like I might miss out on this big thing or whatever and the best way to actually figure out interesting useful things to do with ai is to like do something enjoyable we had a um nichil singal was on the

1:24:33Lenny Rachitsky:podcast and the way he described it is you got to find your moment of joy with ai once you find like wow i can't believe ai did this for me this is awesome we're going to keep building stuff yeah i agree so if you haven't seen that yet then it's just like try find trying solving it the thing i hear a lot is just find a problem in your life or work and see if ai can do it go to lovable go to plot code, go to replicate, just try to build the thing. And often it's like, holy shit, this is so cool. Dan, is there anything else that we haven't covered? We've gone deep on so much. Is there anything else you wanted to share?

1:25:02Lenny Rachitsky:Anything else you want to predict? Or just say? Before we get to a very exciting lightning round. I think we covered it. We did a lot. This is awesome. And I'm very excited to see how well or poorly I do in a year. And I hope that you hold me to it. We're going to have an AI score us. How about that? Well, great. Look at the world like a dance predictions. Here goes. Well, with that, Dan Schiffer, we've reached a very exciting lightning round. I've got five questions for you. Are you ready? I'm ready. What are two or three books that you find yourself recommending most to other people? um obviously annie dillard um i everyone at every has to read the writing life like when you join you get a copy and you have to read it uh you only have to read the last chapter though i think the last chapter is incredible and it is at the intersection of writing and technology and the future and it's like its relationship to the future and to time and i think that's like it's it's everything about every like wrapped up into like a very tight chapter.

1:26:05It's so good. And I think Andy Diller just generally is fantastic. What else do I recommend? I'll just I'll just tell you a couple of things I've read that I like really liked recently. And and whenever I like something, I always just like tell everyone about it. So I have recommended these a lot. I I've been I've been reading one of the things I learned, I didn't know is Churchill is a really good writer. And he has a whole history of World War two that he wrote. And it's like a combination history and memoir. And I think that's so cool because he was there, you know, he did it. And there's something about what we do at everywhere.

1:26:41I feel some like sort of kinship with that of like, we're building stuff, we're writing stuff. And it's very rare to find people that also do that. And, and so Churchill's history, World War Two is fantastic. I just finished the first volume. I'm on the second volume,

1:26:54Lenny Rachitsky:the Nazis just invaded France very it's very captivating stuff so that's one I also just I've been on like a little bit of like a quantum physics like kick recently AI is very actually very good for quantum physics if you get into it and there's this book called the rigor of angels that I just finished which is it's like it's a history of ideas that relates Heisenberg who has the his uncertainty principle um borges who's uh uh uh like a uh argentinian uh fiction writer wrote a bunch of great short stories are actually starting to get like a lot of play now because they're very ai related and um and kant and very cool like super mind-blowing lots of like interesting overlaps with ai stuff and uh yeah highly recommend i feel like we could have a whole podcast episode about your reading and books you recommend.

1:27:56Lenny Rachitsky:I know this is a passion of yours. My current obsession is the power broker. And I think we talked about it when I was visiting you. It's just so good. It never ends, but it's surprisingly compelling to read through the history of New York. Okay. Second question. What is a recent movie or TV show you recently enjoyed if you have time for TV? So I've been watching a lot of basketball. So that's one. I became a Knicks fan this year, so that's really fun. But I recently watched this, I guess it's like a miniseries documentary called The Dark Wizard about this guy Dean Potter. He was like Alex Honnold before Alex Honnold was Alex Honnold.

1:28:41And he just has this very extreme personality where he's free soloing everything, and then he's like, you know, base jumping in like a wingsuit and stuff like that. And it's sort of exploring his psychology and what happened to him. And I don't know. I kind of like stuff like that. Like there's another one called 100 Foot Wave where it's like about people who are trying to like big wave surfers. There's something about that that sort of, I guess, just reminds me of Founders or whatever. But The Dark Wizard, highly recommend.

1:29:08Lenny Rachitsky:Is there a product you recently discovered that you really love? Codex. okay it's like it's the best it's really good it's really good do you have a favorite life motto that you often come back to in work or in life yes i have several um the the like the core one that i wrote for myself in college was um do things worth writing about and write things worth reading and uh and then there's there's this guy rob burbea who's like very um very popular in like you know the ai meditation like overlap discourse which is also a big thing um and who i also i really like him he's dead but i think he's amazing and i've listened to like so many of his talks and there's like this one talk that he gives where it's just like one sentence but he just talks about like when you're dealing with stuff that's hard what you want to do is be able to relate to it from a position of spaciousness and strength and there is something I think really interesting and important in that like a lot of the meditation discourse or just generally like how do you deal with hard things it's like a little bit more of like the David Goggins like you just gotta like just gotta like go for it kind of and like just um and sometimes that sometimes that can work and also I think sometimes when you're dealing with things so for example when you're dealing with I'm super afraid of like how AI is going to um uh you know change my job it is it has been very helpful for me to be like am I coming at this from a vantage point of spaciousness and strength and if not can I like get there because it will be much more productive for me to deal with it from that place.

1:31:01And that has been very, very helpful for me. Wow.

1:31:05Lenny Rachitsky:I love that. Well, our final question, just on the theme of this conversation, I'm curious if there's just like an AI tool that you think is still kind of underrated that you're just like recently I hate to say this, but I have to, because like anyone who knows me, like we were at this conference recently, an anthropoconference. I'm telling Boris and Kat from Cloud Code, you have to try Codex. It's just really good. The things that you can do with it are so different. Especially if you're using it with the in-app browser to do things like your emails or check analytics or anything like that. It has completely transformed the way I work.

1:31:54I would be doing you a disservice if I was searching for something else because is that good damn that's wild uh do you feel like anthropic can catch up and or is

1:32:04Lenny Rachitsky:this just like well they go no yes i think i think they can i i like like i said i think it's going to be a horse race and uh and different people will be ahead at different at different times but uh i think right now open ai has like has has gotten back the mandate of heaven a little bit it's been it was a rough couple a couple months like six months or so but i think they're back interesting and and you'd switch if one became i would i would people people it's funny people are like oh are you like sponsored by open i on i'm like no i just like talk about what i like i was super loud about cloud code when that was the thing i really liked and i'll just say what i like when when it happens you know and to your point people like there's a lot of value in using both for different things so there is i i switch back and forth like i i truly do still use quad a lot.

1:32:50Yeah. Such a big market.

1:32:53Lenny Rachitsky:Well, Dan, we did it. We went through so much. I can't wait to revisit this in a year slash get this out so people can start planning for this next year. Two final questions. Where can folks find you and every what your people know? And then how can listeners be useful to you? You can find me on X at Dan Shipper, S-H-I-P-P-E-R. And you can subscribe to every, please subscribe to every, every.to, every.to slash subscribe. How can listeners be useful, you know, have fun with AI. Like seriously, it's, it's super fun. There's like a lot of, it's not necessarily useful to me, but like it's, it makes it, I think it makes everything better when people put their hands in it and just like start figuring it out together rather than like arguing about it.

1:33:35And so the most useful thing you can do is like find ways to use it well in your life and share it.

1:33:39Lenny Rachitsky:Dan, thank you so much for being here. Thank you. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.