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

0:00 The last time you're on this podcast, you had this hot take that people were sleeping on Claude Code. You are so unbelievably right. The premise of this episode is we're gonna go through what else you predict will happen. The AI job pocalypse is not really a thing. I am super, super bullish on PMs and full stack designers. You guys are hiring double than people. in the past year. Which is not what people would have expected from a company that is so AI forward. I'm simultaneously extremely AI pilled and very bullish on humans. Automation is a lie. Every agent needs a human. We have so much automation, so much AI, and I also work way more. Creativity. It just feels like it's gonna be more and more valuable to stand out from all the slop that people are shipping and watching all constantly. What models do in general is they make yesterday's human competence cheap. And so it becomes commoditized. It's not valuable anymore. What humans do is we go in there and we're like, yeah, we have all this frozen. And human competence from yesterday. How do I use this like make something new and interesting? What are some predictions for how the way we work is gonna change? It's going to bifurcate in two main ways. One is everyone's gonna have at least one agent that they talk to that they can offload work to. Second is that most of the work that you do is actually going to happen on your computer in an environment like Codex or Cloud Cow. What you're predicting here is the SaaS tools will run within Codex or Cloud Code. I think the SaaS apocalypse is dumb. I would buy SaaS stocks right now. What agents do is increase the number of users of SaaS, not get rid of it.

1:24 We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over. Today my guest is Dan Shipper, CEO and founder of Every. Dan and his team are building maybe the most AI forward startup out there. And as a result, are very much living in the future of how work is going to look as AI becomes a bigger and bigger part of our day to day. Everybody at their company, including every non-technical person, uses codex and co work and Claude Code to get much of their work done. And this is why, way before anybody else, Dan saw the rise of Claude Code and what is now Co-Work, which he predicted almost a year ago when he was on the podcast last time. So ask Dan to come back on the podcast to share his

2:08 Current biggest predictions for how work is going to change over the coming year for most people. We chat about what work will look like at most companies at the end of this year. How the shape of the work we do will change, and who will do best in this coming future. Slash what you need to be working on right now. Hint hint. Product managers and designers are going to do very well. Dan makes a lot of bold predictions and many quite contrarian takes that I was not expecting him to say, and we are going to revisit this conversation exactly a year from today to see how much he got right.

2:40 Before we get into it, do not forget to check out lenny'sproductpass.com. For a free year of the hottest and most well crafted AI product in the world. Available exclusively to Lenny's newsletter subscribers. With that I bring you Dan Shipper. Uh

2:56 Dan, thank you so much. For being here. Welcome back to the podcast. Thanks for having me. Always a pleasure to be with you. The last time you're on this podcast. Yeah, this kind of it was almost like an offhand hot take.

3:07 that people were sleeping on Claude Code. And in particular cloud code for non engineering work for just like Uh fixing files, sorting your hard drive, just all these things that people hadn't thought about. Nobody was talking about this. This was a year ago. Uh you were so unbelievably right.

3:24 About this. It's just like unreal what has happened since then. They build co work, which was this whole I the build on this very specific idea using Cloud Code for non Technical work. Codex is getting into this now. I imagine you've been seeing this. They're like leaning into this. non technical use of basically coding agents.

3:41 I feel like this has also been a big part of anthropic success over the past year. Just like how do non technical people use this stuff. Uh, so you were just so ahead on this stuff. I I uh I even wrote a newsletter post building on this idea. I'm like, hey, this is interesting. I should dig into this. I asked people how do you use Cloud code for nonengineering work and I just had like so many examples and it's like my second most popular post. So uh Clearly you uh you

4:05 Yeah. unique glimpse into where things are heading. So the premise of this episode is we're gonna go through uh What else you predict will happen? in the future, how things will change for people building products. And I think it would be helpful to start with giving people a brief glimpse into just how you operate and how your team operates.

4:23 that gives you this unique lens into where things are going. So just give us a sense of how you how you work. Thank you. Um I I really appreciate the introduction. Um And yeah, I think one one of the things about predicting the future or or the way that we

4:38 think about predicting the future at every is that You what you don't want to do is prognosticate. What do you what you want to do instead? Is Um

4:48 Is just live in it together. So everybody at every is an AI early adopter. We're almost thirty people now. I think when when we did our interview, we were fifteen. So we've doubled in size in the in the last year. We're all early adopters, and we have engineers, we have designers, we have writers, we have editors, we have Um, sales people, we have customer service people. And

5:10 Everybody has a little bit of that. Um Whatever that thing is where you're just like I like to explore, I like to experiment, I'm very curious, and I'm like super all in on AI. And what I what that does, I think, is it creates this like little pocket of the future where we're all living in it together. And we get to be a little bit further ahead because at any other company, there's like a mix of people. There's really adopters, there's like

5:33 they're sort of like the middle of the pack people then there are people who are that like very anti And w another thing that happens which is really cool is we get to because of our role Um you know, reviewing models and and being a little bit of a tastemaker in AI, we get access to stuff before it comes out.

5:50 So We get to beta test and alpha test and kind of help help steer the direction of where things are going a little bit, which is very, very cool. And so when when I think about predicting the future Um, it's actually when you create an environment like that, it's actually just about

6:05 Um noticing what's going on. Um And and I think what a core part of it too is writing about it. I think articulating what you're noticing, articulating the future kind of brings it about in this way that Um

6:18 uh makes it real for you and your team and then anybody else who's like on the internet who's reading it. And so f the cloud code thing. It was this it's this very organic thing where For us Um

6:33 We tried cloud code when it came out. That's sort of our job. We tr we try we try all the new stuff from all the new all the new model all we try all the new stuff from the model companies. And At the time it was like a little bit early. But right around I think like Sonnet three five or Sonnet three seven, we were testing that to do our vibe check on it.

6:51 And we're like, holy shit, this is crazy. This is like really you can they got rid of the code editor. And so from that point on, we just basically we we run At this point now we run like six products, software products internally. At at that time we ran like maybe two or three. And from that point on, we just started shifting to a rural a world where

7:10 Everybody was Uh no one was looking at the code. Everybody was uh you know, talking to their computer in English using cloud code in a terminal. And so I was able to see like ooh, this is starting to happen.

7:23 Um And then B. because my job is a little bit to just like push and play with stuff, I was like, I wonder if I could use this for like my writing. Like how could I do that? And then it just like starts to unfold and you're like, Okay.

7:35 This is Not ready yet. But it's obviously useful for me. You know, my like one of the things that we talk about internally is what I call the reach test, which is like, do you just like when you wake up in the morning, do you like reach for it organically? I love this combination of

7:49 Uh you are using the latest stuff and I think this is as you said, uh maybe on underrated underrated skills. You're You're good at uh Being self aware of here's what's weird and new and different and interesting. So that's a really cool combination, partly because you have to write about it and you write about it. So I think that's like the perfect recipe for someone Having a sense of where things are going.

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9:13 Go to workOS dot com to make your app Enterprise ready today. So the way that I'm gonna structure this conversation, there's gonna be basically three buckets of predictions. One is how The way we work is gonna change in the coming years. Two is how what the shape of the work we're gonna be doing.

9:29 Is gonna Look like and change. And then three is who is gonna be most successful in this future. Slash what should you be doing and working on now to be successful in this future. Let me only ask. is we come on a year from now and then you score it.

9:44 I want to score. Okay, so this is a year from now. Okay. Okay. So is this that's actually uh Mm. your predictions for in a year, this is what it's gonna look like, or this is like the emerging I think I like I don't I will probably say I don't have like an exact timeline. I think most of the stuff that I'm talk I'm gonna talk about will be pretty apparent within a year, but it it probably it may it may take longer than that. Okay, but I think it will it should within at least a year be like

10:11 not obviously wrong. Like it it it seem it could it should seem like it's moving in that direction to count. Okay. May of twenty twenty seven. We will review your predictions. Man. Amazing. Okay. I love this. Okay. So let's dive in.

10:25 Uh, what are some predictions for how the way we work is gonna change in the coming year? one of my favorite questions because I think if you look at the benchmarks, you're just looking at okay, like yeah, AI is gonna just take all of our jobs, basically, you know. Um I meter has this really cool benchmark where it's like it measures how long it can like uh the newest models can do tasks autonomously. And it's like, oh it's like It can

10:48 Uh uh what's it called? Oh like mythos preview, the like big anthropic model that everyone's like so worried about. It can do tasks of seventeen hours at fifty percent accuracy. It's like holy shit, that's crazy. And I think it is real. It's true. And and and the the progress, like model progress is um going up exponentially. And My experience and my feeling is that we will look back in a year and um say

11:15 w we actually have a lot more work to do. Humans have a lot more work to do. Um even as models get better at doing work. And there's like a really interesting paradox there. And m my uh prediction for the

11:28 uh like how how work will or my my big prediction of how work will change or or how you will be doing work in a year. is it's going to bifurcate in this in two main ways, how you how you use agents. One is You're going to be doing I think like what we figured you would be doing like five years ago when we thought about

11:48 How work with AI works. Which is Everyone's gonna have at least in their company at least one agent. that they talk to

11:58 That uh can do work that they can offload work to. And we'll talk about like what that looks like, but it's essentially like open claw. Second. is that most of the work that you do

12:10 is actually going to happen on your computer. In an environment like Codex. Or a Claude Cowork. That becomes the sort of operating system for It becomes a sort of operating system for h how you do all of your work, whether that's

12:26 your email, the documents you create, like all that kind of stuff. It's gonna be on that kind of a surface. It's that's becoming the the clear competitive landscape. Um So there's uh I wanna go in order of those two. Um so the first one is you're gonna have agents you delegate to probably in Slack, but

12:43 You know, anywhere. First thing That's interesting about that one. Is it's not clear what the architecture is gonna be like for that.

12:52 Um, is everyone gonna have an agent? Uh, is every team gonna have an agent? Is it gonna be like just one agent? Is it like duration specialized? Is there's this pat like parallel shadow org chart? And When OpenCloud first came out, everyone internally at every adopted it, and I was very convinced that

13:12 It would be A Everyone has their own agent. And there's like some real really interesting Things about that world. Of

13:22 You know, a parallel uh parallel org chart. Agents. in that world sort of become little reflections of you, which is like really cool and really interesting. It's like If you ever did you ever read The Golden Compass?

13:33 Um it's like having a little demon on your shoulder, you know, that's a little part of your soul. Yeah. Um I I really think like that's sort of what it looked like was happening. Um, and so I was very into Personal agents. And I have completely flipped.

13:47 And I I really think that uh the the model for now. is going to be a super agent, like one agent for the entire company. And I you you're starting to see this in some companies. So like um Shopify very famously has one. Uh Ramp has one now. Um and and I think there's some like really interesting reasons for that.

14:07 I actually still think that the personal agent thing is coming. But What we found. is there's all this hype with Open Claw. Everyone's like, I'm gonna set it up on this so cool or whatever. And then everyone realizes it's like way too much work.

14:22 This thing breaks all the time. I gotta like fumble around with it. I gotta be able to SSH into my server and like blah blah blah. And most people To do work at least. Just don't want to Spend that time or can't. Um

14:38 And the the like fundamental underlying thing that drives that. Is Whether it's open claw or any other harness.

14:46 In order for an AI agent to be useful right now. It really needs a human who cares about it. It really needs a like a human personal connection with someone who's like Watching what it does and make sure that it's doing the right thing. And then it's useful for people.

15:01 And the minute you like sever that connection, so the minute someone's like ah, like I don't I don't wanna like maintain this like dumb open claw is the minute the agent is like not really that useful anymore. And That's why it I think it has started to shift to a more Uh

15:17 one agent per company model because for now, like the the the ideal Is Uh y you you basically set up a forward deployed engineer or someone with that sort of profile who's responsible for making sure that that agent is working for the whole company. And then maybe you have some like some little team agents. Um, and I think as the models get better at being more independent, that will like shift down and you'll

15:39 it'll be more likely that we'll have more personal agents because we won't have to fuck around with all the internals. But Um The model that I see working for us. And for a lot of other companies, including the model companies. The model companies themselves are starting to see this.

15:53 Is When it comes to the sort of like async agents. It's really a you know, you have one agent at the top that's like doing Sometimes it's everything. A lot of times it's um

16:04 a a particular kind of job that you've decided that everyone in the company needs an agent for like data requests. And Uh and then I think it will start to Yeah, starts top. that at the top and then it sort of starts to trickle down where you may get more specializations and teams and and all that kind of stuff.

16:20 And the mechanism is Agents need people who care about them. That is so interesting, that point about you need to like garden your agent and Because there's context you have to keep adding to it. There's like It breaks, as you said, and it's just like once it's just too much work, you're like okay, forget this thing, I'm gonna go back to it.

16:36 Codex or Cloud or something like that. Exactly. Okay, cool. So this is a cool opportunity. So the idea so what you're predicting here is uh companies will have this super agent that everyone can talk to. I said a Shopify's Got River, I think it's called. What's the ramp one called? I can't remember. Okay. It's probably got a funny.

16:52 Okay, so Uh So that's the prediction. Okay. That's that's the first prediction. That's the first prediction. Um we will start with Agents at the top.

17:02 That uh that are more general and are used by more people in the company and then it will start to kind of grow down as the as people get more used to these use cases. They get more specialized and um agents become uh less

17:19 Uh Less fiddly. Like they just work better. And is this mostly gonna be in Slack, do you predict? For work, yeah, it seems to make sense. I think people I people love having the green bubbles on Open Claw.

17:30 Um like sorry, the the blue bubbles on OpenCloud, like if you can use it with your iPhone, but I think there's this little thing in people's heads where they really like to keep their personal and work agents separate. Mm-hmm. And Um I think there's a whole there's a whole territory. Our our COO, Brandon Gell, calls this um computer errands. There's like a f this whole territory of

17:51 using personal agents for your computer er errands, it's like order my groceries or whatever. And it's like there's so much of that that I think this is gonna it's gonna be huge for but Um w I focus we focus mostly on the work stuff. Um And I think that's gonna happen mostly in Slack.

18:07 Sweet. Feel slack. Should we do you wanna talk about the uh the other work surface the like codecs, co work? Okay. This is the let's do it. I'm so excited about this one. I think it's the coolest thing. So Basically What happened was

18:21 Anthropic. Realized. At some point that With cloud code, if you put an agent on your computer. And it runs on your computer.

18:31 It has everything it has access to everything that you have access to. It uses the terminal, so it has like s basically super powered access to it and Not only that, it really these agents really understand how to use the terminal because there's so much Content online about

18:45 About that. And it c it created this like super powerful coding paradigm, which is Um, you know, and Topic was really doing it first. Open AI for a while was I I

18:56 I in my opinion like very, very behind on this and then in my opinion has Surpass them. recently. It's really interesting. Um but they were Very early on this.

19:06 Um when people were still thinking about coding agents or coding models as being really pair programmers. They were among the first to be like No. and do it successfully. Like there are people before them like Devin who I think had had a big had the big like cloud environment and and Open AI tried this too, but

19:24 Um, the the the real adoption seems to have happened when you uh put it on your computer. So they figured that out. And then I think they figure it out. Um along with our community. That

19:37 Once you have a coding agent on your computer that can build anything, it's actually really good for any kind of work you want to do. And people started Just hacking Cloud Code essentially to do all their work. So and Travic then built cowork.

19:50 Um, which is you know a little bit of a nicer wrapping around cloud coded, but it's fun fundamentally the same thing. And then I think You know, I think

20:00 Opening I made a couple of different bets, but Their main bet on a programming agent was the the the earlier versions of codex were like very technical and they were like super smart, but they were like a little bit autistic. Like it was a little hard to They they didn't quite get what you meant. They get exha they got exactly what you said. And I think

20:20 maybe like three or four months ago around the time that they launched Uh five point three. They started to move in this direction of Oh no, we get it. Like it's um this model is fast. It's like Really good for general purpose knowledge work type tasks.

20:35 And then they launched the Codec desktop app. And I think the Codex Desktop app takes If you look at all the lessons that like Anthropic learned. They went from clawed code to co work. And you can kind of see that in the tabs on the on the Anthropic Desktop app UI.

20:52 I think opening out was just like we s we see where this is going, like let's just skip to that. And so I think Codex right now. This is a horse race, like it they're gonna have different positions. Um

21:03 But I th I think codex right now It's my daily driver. I like spend all all my time in it, basically. I flip the cot every once in a while. But I think they're getting the paradigm right. And it's clear to me that whoever is in the lead, because I I again I think it'll change. Whoever's in the lead

21:18 It feels very obvious to me that All of the work that you do. is going to be in one of those surfaces where Uh for example, when I'm writing a document. Codex has a browser.

21:30 In uh in the app. It has an in app browser. And when I'm running a document, I just go into one of my uh one of my codex threads, which I have one thread for every project.

21:40 And I just open the in app browser, I go to the document. I usually do it in proof, which is this um online mark markdown I built. And Then I just have codex. Running and watching me in proof.

21:53 And Codex can see what I'm doing, I can see what Codex is doing. It's all kind of in one place, which is the an extension of the same thing that made cloud code work really well originally. And I basically feel like I have this parallel work buddy that not only can it like respond and write in the document, but then it can go do research. It can go it can use my computer to basically do anything that I can do on my computer. And that's like Incredibly powerful.

22:20 Um, and I do this with everything. Like I've been in I've been at Inbox Zero for like Ten days straight now, which If you know me. Is crazy, I'm never like this.

22:30 And that's because I literally just have Codex. Gather all my emails with Cora, which is our email agent. And then um it it renders a little page. Uh and I I think I showed you this at the entrop at the enthropic event. It renders a little page and I just like monologue into it and just talk

22:48 At each email I'm like, Okay, go do go research this. Oh, here's a question from our lawyers. Can you go like collect all of the you know, documents from the last like four years and then put them into a report and send them And it just isn't does it. And so all the stuff that I would procrastinate on, I don't really procrastinate on anymore. And

23:05 So I feel like there's this For a long time. We thought I thought two. that the optimal experience of AI was gonna be Take AI.

23:15 And put it in a browser. And I think the reverse is actually starting to happen and be like really, really valuable in a way that I did not expect, which is Take the AI agent that you use all the time on your computer and put a browser in it so it can see everything you're doing. And that is just like a magical combination that I think will be is very uncommon now. You can't even do this in cloud in cloud code. Um

23:38 because they they don't let you browse external websites inside of Cloud Code. So it's very uncommon now, but I think it will be super common in a year. This is more profound than it may even sound. What I'm hearing is Instead of AI being baked into SAS tools which you're predicting here is

23:55 Uh You will the the SaaS tools will run within Codex or Cloud Code. That that is that is one uh really important uh second order effect of this is Um

24:09 Okay, so Yeah, like I'm I'm using Proof or or really any website, maybe post hoc or whatever. And I'm doing it inside of my agent. And the agent has access to the website. So it has access to everything that I have access to. And it has access to my whole computer. When I run the agent on that website, I'm using my tokens.

24:28 I'm not using the the vendors tokens. I'm not using the apps tokens. And so it puts SAS back in this place where Yeah, you wanna make it friendly for an agent. And everyone's got a CLI now. Um, you wanna make the HTML uh really uh really usable. You wanna make sure that what anything that happens in the CLI shows up for the user immediately, all that kind of stuff. There are a lot of issues to To deal with? But um once you do that, you actually don't really need to

24:54 Think about having a An AI surface that's primarily gonna be the thing that users use. in the sense that you don't need to build an agent necessarily into your product. I think you can, and there's there's another really interesting bifurcation of this that that that we should talk about, um, which is that Having two agents is better than one.

25:12 Um but I think for now there's this really cool thing where Uh with proof, for example Uh Anyone who uses it, I don't pay for tokens because they're just bringing they bring their AI to the to proof.

25:26 And so it changes what you build as a SaaS company. Uh 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's gonna bring the AI. So I think this is a huge deal. So what you're describing here is uh more and more work that we do, more and more professional work is it just gonna happen within

25:46 Codex or Cloud Code. H where does cursor fit into this? Is that one of the is is there a potential there? That's a good question. I think That cursor sees a lot of the same stuff. And they're and in some ways they have some of the same stuff, but it's better. Like I think that cursors cloud implementation is better than either it open AIs or Enthropics and it's more advanced.

26:08 And I think that Cursor has At least so far. More distinctly chosen a lane.

26:16 Like they're more distinctly choosing to be a four 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 gonna jump into like okay, use this to make a slide deck or whatever.

26:32 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 um the model. And so where the where all the platforms are moving is to a world where

26:49 you you're not just doing prompt and response when you call the the model at uh on a on the open AI platform the anthropic platform. you are they're literally like running the model on a computer that that is in the cloud that they run and then giving you the result out of it. And they know that they in order to get the best results out of the model, they need to offer that. And so you see You know.

27:09 Anthropic's got cloud managed agents. Um OpenI does not have a have a response yet, but I assume that that's gonna happen and now cursor uh was just essentially acquired by SpaceX. It's not like f a full acquisition, but it's close. So I think people are starting to realize like I can't just do the like model part of it. I have to have this like Harness above it. And

27:31 I think the ultimate form of that harness is like I can do any kind of knowledge work. Cursor itself is feels like one of the things that it's gonna be a hard decision for them whether to Stay just for coders or not. So people building products that aren't Open AI or enthropic.

27:46 If this proves to be true. The prediction here is they're gonna be using your product. Over time inside of One of these agents. Uh is there something you would do if you were one of those companies to prepare for that future?

27:59 I w I would just prepare for that. So like, you know, for for example Um Every mo mo more classic piece of productivity software, whether it's Slack or uh word docs or power points or whatever. It's really mostly meant for

28:15 A human to use? Um And now people are doing CLI, so it's like meant for Uh An agent to use independently of a human.

28:25 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 to have

28:34 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 gonna be very different. So for example Um

28:49 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

29:17 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. And

29:25 how you display that to the user is gonna be very different than the way you might display a human being concurrent in your document and doing stuff, you need Um 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 Um, you need logs and the ability to roll it back real quick.

29:46 So there's all those kinds of considerations that Um that change the actual product and then the underlying UX of it or the underlying infrastructure you need is different too because You know, agents can make a billion requests in like three seconds. So how are you gonna deal with that? Right. Um, this is exactly why, you know, GitHub is having problems right now because the because the the number of people using GitHub has is skyrocketing exponentially, and it's really just people's agents using GitHub. So I I I think it's a this whole new world that is just starting you're just starting to see like a little peak of it.

30:15 But there's so many cool things about it. So for example In proof. And some of our other products too. А вон самоха за проблем They don't email support.

30:27 Their agent. Sends a bug report. And an agent bug report is way better than a human bug report. Um, it has like here's exactly what I did, here's the exact repro steps, here's like proof is are open source. So here's what I think is going on in the code base.

30:43 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 Um You can't do that with everything, but it's so much better. And you can see the like the glimmers of

30:56 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

31:10 Fixes it. That I think is incredibly cool. So as a part of this that you A lot of people are moving to C L I and trying to work from the terminal is the Part of this prediction that people will shift away from that and

31:20 Back to Actual UX with agents kind of running alongside them. CLIs are over. Um we we speed ran the CLI. Uh era?

31:29 It was nice while it lasted. But I think it's pretty it's pretty clear. It's not that you're sorry. It's not that CLIs are going to completely go away. Obviously they've been around for the last like thirty years or forty years or fifty years or whatever. They will continue to be around.

31:43 And I think there is this moment when Cloud Code was like so Popular. Uh Uh or when c when cloud code is really starting to gain in popularity. that people were like the the thing that's working is the fact that it's the C L I

31:56 And I don't think that's what it is. And when you move into an actual UI for this, you start to realize. Um We we made GUIs for a reason. And It's just nicer to be in a GUI.

32:09 And you can get all the same benefits. inside inside of GUI, especially for non programmer work, but I would I would Estimate that

32:19 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 Codex, cloud code, cursor, um, that kind of thing.

32:35 Awesome. Okay. I I I would I definitely wanted to make that part clear. So coming back to kind of the 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 superagent within a company that you chat with through Slack, most likely.

32:50 They can go off and do work and answer questions. And then there's on your computer running codex or cloud code. That you normally do kind of on your computer is now gonna be living within code extra clock code, or maybe some third party that emerges that we're not even aware of yet.

33:06 Yes, and you're going to use apps inside of the internal browser of those uh of those tools. Wow. Okay. Like Listening to you talk about it, it's like it may not feel as profound as it is. 'Cause this is a big change to how we work. We don't currently have an AI that we talk to

33:25 regularly in Slack. And we also don't work currently mostly in codex regard code. So this is actually a pretty massive shift. I think so. Mm.

33:34 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. Like I really think we're gonna have a lot of different agents that we use. Seems pretty clear to me. And I really do think that two agents are better than one.

33:49 So That's a good example. When I have codecs. Interact with another agent. It can

33:59 give so much more context about me and what I want. Then 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

34:15 You assume that your users are you are using codec or cloud code or co work as their ba as their basic way they access your app. And Uh a a really simple example. We have this um

34:29 hosted open claw product which we we had it we had on wait list. We actually had to pause it because We start taking the all the way less. Open calls just a very hard Agent harness to To make work. It's like

34:41 It's moving so incredibly fast. Uh 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. Um But one of the things that we learned in that process

34:54 Is If you're let's say you're building an an agent product um or or a new s 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 um Slack workflow that Ask people about okay, like

35:11 Who are you and um what are you gonna use this for and like what's your what's your ideal you know, dream outcome or what whatever the things you are that you would put on an on onboarding checklist. If instead you s you just You just make a hard line of We are only going to service users.

35:31 who use codex or co work. Um, what happens is you just paste something into you just paste prompt into codex or co work. It goes and talks to the app and the app can be either just a regular server or it or it can be its own agent. And

35:48 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. Um, here's all the ways that, you know, he 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.

36:06 When something goes wrong I can just tell Codecs, go fix it. And Codex will go talk to the app and figure out what's going on for me. And so I think the whole paradigm 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. There's a couple more things I wanna touch on before we get to it, because there's like so much to talk about. Uh

36:27 What does he made this point about? SaaS tools not using Like you can use tokens from That Uh

36:33 model companies basically when using a Sastool. Talk a bit more about that,'cause that may change The business model for SaaS companies in the future. That feels like a big deal. Well, I think it actually May uh save their margins.

36:46 because right now all the all these companies are rushing to like add a agent to their offering. And thinking, Oh, the agent is gonna be the main way that I that people interact with me. And I think that

36:59 Uh and that costs tokens, obviously. And I actually think Once I have once I have Codex or Cowork as my main work surface. I still want to use SAS. So this is another good prediction. I would buy SAS stocks. Right now.

37:12 Um, I would I think the SAS pocalypse is dumb. And SAS stocks will be up majorly in the next couple of years. N investment advice, but You know. I would buy Sastocks.

37:24 Um So Oh. So Uh, so I think it saves your margin because now w what you're what the way that you're thinking then is not I have to build AI into this. It's it's more like

37:38 I have to make a piece of software that humans and AI want to collaborate on together. And That's hard. But it's Once you build it, it's a lot cheaper than

37:49 Uh assuming everyone's spending tokens. And Um I it's I think it's a I think it's a good business and and part of the reason I'm so bullish on SAS is A?

38:01 Everybody internally here is Uh Like I said, we've got all got agents and we're all using codecs and whatever, and we still pay for a ton of SAS and our SaaS spend is up year over year. And we're not like vibe coding every single like little thing, you know? And

38:19 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 ex insane spike in the amount of demand that they have because there's gonna be tons of agents using these products at like a very high volume. And like I said, that's a huge infrastructure challenge. There's a there's a lot of like interesting pricing challenges.

38:42 Right. Uh it It it makes me very bullish on SAS. I love that if anything else comes out of this conversation, Dan Shipper, SaaS is the future of AI. Be to be Sass.

38:56 Hashtag send tweet. I I love just yeah, this is uh quite contrarian and The other interesting piece is that The fact that you guys are hiring, that you doubled in

39:05 people in the past year. Which is not what people would have expected from a company that is so AI forward. Talk about what your experience there of just okay, we still actually need humans. Automation is a lie. Um In the sense.

39:19 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. And so

39:28 Um, you know, I wrote this piece a couple of years ago called the allocation about the al allocation economy, like the idea that the the way that humans are gonna work with AI is gonna is gonna be Like Like being a manager. And

39:40 The thing that you have to remember about managers is like managers actually spend a lot of time working. Mm. Most managers are not like on the beach. They're like checking in with their employees all the time and and and and trying to figure out, okay, 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

39:58 There's some differences between it being a human manager and being a model manager, but Um, fundamentally it r 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 Yeah.

40:15 Benchmarks make it look like AI is more autonomous than it is. And by autonomy I mean something specific by autonomy and I'm gonna try to express it. It's like a little hard to express, but I learned this for myself. 'Cause 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.

40:36 And I think part of the paradox Part of the paradox are to like resolve for me a little bit when I made my own benchmark. So I made this senior it's called a s the senior engineer benchmark and it's like how good is AI versus a human engineer?

40:51 And The way that I built it. Is Again. w have this app proof. I just vibe coded it on the side.

40:58 And uh 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 we u tested it internally.

41:12 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. Uh or really Codex is like, uh, I do know what's what's going on. I fixed it and then Yeah.

41:30 it would cause four other errors and then you're just going around in a circle and I wasn't sleeping and I I I vibe coded so hard I got brusitis on my elbow. So uh that's a there's a life lesson in there. Yeah. Mm. Um, so anyway, I got a I got

41:47 actually two different senior engineers to fix it independently. So I have two different rewrites. of the code base that Um Tells me how they did it.

41:58 Right? And so what I get to do is when we get new models I just give the new model a prompt. I say like This is vibe coded slap. If you wanted to rewrite it.

42:09 from first principles. How would you write it? Go do it. And All the models until GPT five point five got like a thirty out of a hundred. And senior uh like a human senior engineer gets like high eighties, low nineties out of a hundred. So there's a lot to go.

42:26 And then I tried GPT five point five and it got like a sixty two. And mind you, the sixty the sixty score was Um G five point five using an Opus four point seven plan. Opus four point seven plans are very good. GBD five point five is the only model though though that has the sense of agency and confidence to just like rip out old code and just like actually rewrite from first principles.

42:48 Other coding models. They kinda 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 five point five, there's like a Thirty point bump.

43:02 In the score. Sixty out of a hundred. It's like Very I it's very clear that in A year. Or less.

43:11 It's gonna be senior engineer level. Right. 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. And I can tell you that when we get to that Point.

43:23 I will be ver it will be very easy for me to change the benchmark to zero out the current model. So that gets a zero out of a hundred. And so for example Uh it seems like there's no skill or no thought into the prompt, which is this is Vob Cudislap, like fix it from first principles, but actually it took me a a while to get to a prompt that

43:45 Didn't give away the answer. But Uh Uh but

43:52 What it's capable of. And uh 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 waken up I'd I'd woken up in the morning.

44:06 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

44:19 Every coding model on the market. And I a s I am I'm pretty sure This here's a prediction. I'm pretty sure every coining model on the market will still do this in a year. Every coding model on the market will take that instruction seriously.

44:32 And if I tell it, here's a bunch of issues. Go fix it. They will just go try to fix the issues. What a actual human senior engineer does.

44:40 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 gonna have to like actually rewrite a lot of this. And it's gonna be hard and risky. I know you don't wanna hear that, but like we're gonna have to do that. And

44:54 If you asked the model Hey, like Should we do that? It'll it'll probably it it'll probably get there. But it's not gonna do it on its own. Um, and it and there's a lot of incentives pushing against it doing that. And even if it does that, there's a there's always a higher frame for us to go.

45:11 And so I think it's it's really important. Um when 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

45:28 Uh Mm. It can't be scored until you write it down, but the act of thinking to prompt it or write it down Um Is

45:35 Uh means that even if the benchmarks get saturated it doesn't mean the same thing as we you totally replace all senior engineers and it's and I think it's why even though the models are getting better at automation.

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46:54 And as a listener of this podcast, you get one thousand dollars off Vanta. That's vanta dot com slash Lenny. One thing I mentioned recently on the podcast, I heard that Speaking of the code that you have of like humans writing code. Uh

47:07 Data labeling companies are buying code that was written before twenty twenty one, twenty twenty two, before AI became a thing is like Very valuable. Data. Yeah, exactly. Exactly. That's exactly right. And it's so interesting that that's exactly the kind of code used to build this model.

47:23 Well what's interesting so I wanna I wanna 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 gonna work with them.

47:41 I don't really care. It's like it's sort of like Am I gonna erase? a human against a car. Like I probably wouldn't do that. But Um I would raise

47:49 a human in a car versus another human in a car and say which one's better. And in this case, uh what the the way the benchmark is structured is yeah, like uh these human engineers used AI, but They used in a way that I could not. 'Cause I didn't understand it and I didn't have time and I didn't really want to like 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

48:12 AI is a broadly distributed technology that any human can use. And when we Are benchmarking it against humans. AI against humans, we're actually really always talking about A one human using AI versus another human using AI because AI doesn't use itself. it it may be able to in this like slight somewhat recursive way, but there's

48:32 In any real use case there's always a human like pretty close to it making sure that it's working. Okay. I'm gonna try to wrap up our first bucket. There's so much to talk about. I made a little list of things that I think people uh should do based on

48:45 your predictions to be successful. We'll talk about this at the end too, but just a few things. Uh One is start using codex or claw code more and more for the work you're doing. And Especially the browser and use tools inside of it. Uh two is allow your age allow agents to be to use your products.

49:02 If you're building a SaaS tool. Make it easy for agents to be okay. A a user. Essentially. Uh three is

49:09 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, 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. Uh

49:24 Buy SAS stock ASAP. Ha ha Not investment advice. I think that's totally right. I will like my slight tweak is When you're thinking about

49:36 Okay. Building your software for agents. the current model is I'm building a CLI that an agent uses, but They're u using it in a sort of like I d they're de being I delegated a task to the agent and the agents using the CLI.

49:51 And what we what 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 and they're they both need to be In sync. And that is, I think, a new challenge that's really interesting. Awesome.

50:07 Anything else before we get to our next one? Uh category. Buy Sass. That's the title. Oh man. Okay.

50:16 The second uh Category of Predictions is around just The shape of the work that we're gonna be doing is gonna change. Uh

50:24 What do you predict? There's all this interesting stuff in terms of 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 Uh Async agents off that you delegate work to, then you've got your like codecs, quad code.

50:37 like work surface that 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.

50:50 You know, we have people, you know, in consulting or in ops roles or whatever who are or or edit editors just like making pull requests. Um And A that's really cool. And it's a very different shape of work where

51:04 You should you can expect. that a higher percentage of your company or your users are gonna be doing things that previously only technical users can do. And What that does. Is

51:17 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

51:27 There's a lot of interesting things that happen with that. Like so for example Um Uh like Open Claw, I mentioned that earlier. Pete? gets like thousands of pull requests a day on OpenClaw and then he has like and then he just spins up like fifty thousand codex instances and then sorts through them and then merges like

51:47 A thousand of them. It's really crazy. I actually think that that's going to be more and more common. Um There's like

51:55 It brings up a lot of really interesting questions around Um which Pull request shooting merge. And You know.

52:05 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

52:23 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 gonna happen on one side. There's a lot of um non technical people can do technical work and then technical people

52:39 are in charge of making sure that that work Gets into a product or into a process in a cohesive, coherent way. And also they're proud of people are gonna be doing that too. And I think that's

52:51 That's quite cool. Something I'm hearing from people is that Now that everyone can do everything. Night. Engineers can design.

52:58 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?

53:11 And it's just creating a lot of Confusion uncertainty. In the world. Just a little bit. For real.

53:17 And one of the things that I think it's 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. I think that'll probably settle down.

53:29 At some point and it'll feel more normal. Like marketing people are still gonna 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 for smaller companies. The The other thing that I think is interesting is there are definitely some new job roles.

53:48 That are a thing. And the thing that is becoming really clear. Is the whole for deployed engineer concept I think is for real. And it comes out of Every agent needs a human.

54:05 Uh e like you go to the big model companies, they have they they have these agents that run internally. They have like teams of people that run these agents. You know. And I I don't think those teams are going away. The models are gonna get more powerful, the agents are gonna get more powerful, and the number of agents is gonna grow, but people are still gonna manage them. And

54:24 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 p the people who are in charge of making sure your agents are working and doing the right thing. We also do consulting, so we we we lend that out to people and and I think that's a big Um, that's a big thing that that people want. And

54:45 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

54:56 And there's a specific type of engine that really loves You know, Natesh, who's one of our uh Who who fits this, he's an AI engineer and he fits the sort of forward deployed um category and he's on our team. He spends most of his time actually talking to one of our agents in Slack.

55:12 We have an agent internally called Claudie, which runs our whole consulting practice. And And he spends a lot of time in Slack. Like there's 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 Let's fix that.

55:29 You know? Um, and so there's 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 uh in the works in a workspace and it looks a bit different than More traditional building more traditional software. In your sense there is we're not gonna

55:48 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 kinda like the setup part. And then there's the maintaining it forever part. And it feels like both are important.

55:59 uh is what I'm hearing like this is gonna be a job for a long time. AI is not gonna get smart enough to just automate its You fully automated for a while. Yes, I am simultaneously extremely AI pilled. Extremely And very bullish on humans and the role of humans in making sure that AI is working well.

56:16 Interesting. Okay. So the two kind of buckets here that you're talking about. One is Um, like the way I think I hear what you described earlier is this the pace of shipping software and everything is just increasing, which also means

56:29 uh there's so much more work reviewing All this Sloppy output. I I was just talking to a data science friend and he was saying how His team is just his data science team is just

56:39 Their job used to be We'll 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 are is happening with engineers.

56:58 And it means that you need more Like you actually need that. engineers for this. And you need data scientists and It means that you haven't set up the appropriate systems.

57:09 or agents to help you with this. So like the way that it works inside of the big mono 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 Um is hooked up to their data warehouse that

57:24 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 they're there's a team that sets up this bot. all of the basic questions that people might want to ask that it sometimes gets w that it might get wrong.

57:40 that they're constantly making sure it's getting it right. And so the data science team doesn't have to answer all the like bullshit 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 exist, the data scientists would hate their lives. Yeah. It does though make the job maybe less fun'cause you're just sitting there

58:00 You know, gardening. People sloppy. work. Well that's what I think is like it it it can actually make the job better because for the data scientists you're now not dealing with all the silly requests. You're dealing with Um the deep the deeper questions that are harder for the the team who's dealing with all the basic requests and building an agent to do that.

58:21 Uh It's 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

58:31 product tech role is the least Changed. Now. So like engineers Hundred percent of code, AI now. It's like a completely different job.

58:41 Uh product management. A lot of the you know, PRDs are you don't have to write as much, you can ship code, you don't have to wait for people. design the whole design process uh dead. According to um recent guests, just like there's no time to do the whole design process, very different role. Data science, very different work now.

58:58 Um There's marketing, there's sales. So here's the question, what do you think is the least fundamentally ch changed role so far. Well one interesting thing

59:07 Is You know Yeah, I don't know if this counts, but like CEOs and investors, it seems still very very optional whether or not they use this stuff.

59:17 Mm-hmm. It seems that way. I I m 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

59:28 your company's only gonna 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. Uh,'cause you don't otherwise you don't have an intuition for it. But For a long time it has seemed like

59:40 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 Um And so I think if you're a CEO, you kinda can get away with your day looking very similar. Uh, I I think that will change rapidly at some point where it'll be like, Oh no, I'm like way behind. But for now

59:59 Because or maybe even middle managers like those kinds of people I think are are It's fairly similar. I think like Maybe sales. Because yeah.

1:00:12 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.

1:00:23 Um, and you can do it for sales, it's like re it's so useful to Um To uh like do research like My favorite codex, like one of my favorite codex experiences is

1:00:36 We're hiring Ahead of L and D. And I You know, we always put out a job post, whatever, but I was like

1:00:44 I feel like There's this company called General Assembly in New York. And they do it 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 who worked at general assembly and is now into AI would be really good.

1:00:59 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. It was like worked at General Assembly, was an instructor.

1:01:10 Um Uh 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 uh super valuable for sales for recruiting, all that kind of stuff.

1:01:27 Yeah. The sales is where my mind went. Like the top of funnel AI is helping a lot with. Sourcing and qualifying and things like that. It feels like the w the work of a salesperson is not fundamentally different. Yeah. And

1:01:39 Customer supports fundamentally changed. So it's interesting. Sales. So far so good for the for those folks. Yep. Okay. So Maybe just summarizing some of the predictions in this bucket of just like the shape of the work, how it's gonna change. What I'm hearing so far is there's gonna be a lot more reviewing of other people's output.

1:01:55 Is that part of the work? And then two, there's gonna be a lot of like almost babysitting of AI agents to make them do the thing you want them to do. gardening them along the way, make sure they continue to do their work.

1:02:08 Um 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

1:02:26 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 kinda like, you know, waiting for it to fuck up and then fixing it or whatever.

1:02:39 And You ca you ca 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

1:03:00 You know. the forward deployed engineering team is doing. but is like more generative and more new and and and you're you're dealing with harder questions. One other one last thing that I think is really interesting. Is

1:03:12 I think that we will be reading way more AI generated writing. And documents and emails. And we will like it. And I think we're we will alre we are already doing this. In coding where we read plan documents.

1:03:26 Like I don't want an engineer to hand write a plan document. That would be very silly. It would be it would be obviously silly. Um And I think the same is true, you know, when we did our

1:03:37 Uh uh quarterly planning for every at the end of twenty twenty five. 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.

1:03:51 And then we had Everybody in the company. Just Um Talked to an agent.

1:03:58 And it asked them about what happened last year. How did it go? What were your goals? What what 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 all I got these like incredibly good. AI generated. like strategy reports for uh or or plan like quarterly plans for for each part of the each team. And then I could go in and be like

1:04:21 Okay, 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. Um And uh, you know, who's which one of these is like ac like actually low quality or which one of these is high quality, like all that kind of stuff makes it it makes it a lot easier to process.

1:04:38 Um, and I see that all the time now. Like I I I consistently get AI generated stuff and There is a difference between an AI generated document that's slop and not. And the slap one Is

1:04:52 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

1:05:07 Big no no. Not allowed to do that. Um And I I think we have this This aversion

1:05:13 Two AI generated stuff that will go away. The kind of strategy document that GPT five point five can write when it's directed well by Sone 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 at writing their documents. The bar is low. Yeah. And

1:05:35 And same thing with email. Like I most of my email is written by GPT five point five and codecs right now. And I wouldn't I honestly would prefer it to say that it's coming from GPT five point five 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 this in an email to

1:05:53 um to one of our investors and I ask Codex like go do it. And usu like Codex knows to ask me and it usually does, but this time It didn't. And it just sent the email.

1:06:07 And I didn't look at it at all. And I was like, 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

1:06:23 It's just When you really think about it, most of your email is kinda It's not It's kind of rote. It's kind of prosaic. It's kind of

1:06:33 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 they do a lot. And this is coming from a writer, like I care a ton about writing. I think that Human writing is incredibly important. And I expect we only publish human writing. Well, actually we publish in the mix of human and AI writing, but we always label it. Um sometimes it's nice to have an AI co author on certain things. Um

1:06:59 I absolutely think that uh human writing is important and I think that the The the reaction or the aversion to AI writing is silly. It's such an interesting lens on that.

1:07:12 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 to your point. People already doing this?

1:07:26 You almost prefer it a lot of times'cause people are really bad. Yeah. 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.

1:07:42 And that's because like If you're writing a a huge informational thing, I mean you do this all the time. Um You in order to like really apply it, the best way to do that is just like have your agent ingest it. And

1:07:55 remember the next time I'm, you know, doing uh pricing to like remind me of this guide and we'll go through it together or whatever. It allows you to operationalize uh the idea is 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 the you can you talk to the human about the story and the stuff that matters and the core ideas and then the agent has all the details that it can then apply for you when you need it. Awesome.

1:08:22 Anything else? in this category before we get into our final category. No. Okay, let's do it. So the final bucket is just

1:08:30 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.

1:08:45 And I know that your audience will probably love that. Um, but w uh m my My anecdotal case that has convinced me of this is we have this guy internally his name is Marcus And

1:08:58 He runs Spiral, which is our writing app. Marcus Is a PM by training. He cr he previously ran Axios Axios's writing product.

1:09:10 And was it was a PM and had a big team and it got to, you know, tens of millions in of of revenue and ARR. And He took a year off. That job. And just got super AI pilled.

1:09:23 and just learned how to use cursor basically really well. Now I think he uses cloud code, but he was extremely cursor killed for a long time. And he's I would call him like lightly technical. um, 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 I we never could have hired him to do this job.

1:09:44 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.

1:09:53 With his really spiky product sense and sense for writing and sense for users. And it's like it's so dangerous. Like he ships faster than almost anyone on a 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

1:10:12 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

1:10:24 It's super impressive and it makes me very, very bullish on any PM who gets like really AI called. Music to my ears, Dan. Uh you're making a lot of very happy listeners here. Uh 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 Problems to solve.

1:10:45 Uh So I love that you're actually seeing this. Come to fruition. I I I really believe it. This could be the highest rated podcast episode of my whole podcast. Hell yeah. It's gonna be okay. Sass is back, PMs are back, you know. This is the most contrarian episode I've ever done. Oh my god.

1:11:05 So okay, so the other the other people that I think are gonna be like super, super power people. And I again I this is cause we see this internally is Full stack designers. If you're a designer And you're in these tools all the time.

1:11:19 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

1:11:34 They now feel so empower to like go build stuff because they're like, I have all these ideas to make things look amazing and these interesting interactions. And 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.

1:11:51 And what you see when we work with them internally is now they're just like They're just making pool 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

1:12:07 I think it'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'cause they can f they can make stuff now. And I think Designers are such creative people. And I think AI is like a Super tool for anyone like that.

1:12:23 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 clot design. They're like the creativity to your point is gonna it just feels like it's gonna be more and more valuable. To d

1:12:36 to stand out from all the sloth that people are shipping and launching all constantly. So I completely agree. It's it's interesting the 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.

1:12:54 Hm. That is really interesting. We'll see. Yeah. We'll see. We'll see. That that might be a way to predict this is are people hiring more designers? I don't know. That is interesting.

1:13:04 Yeah. All right. Uh So that's so PM designer. Thriving. Designer Friving.

1:13:11 Um, I also just think generally the A uh job pocalypse 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 l s 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 th, like coming and this is a good excuse.

1:13:31 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

1:13:50 A new model drop does or what models do in general is they make Yesterday's human competence cheap. So what I mean by that Is they ingest all this data of what What has happened already.

1:14:02 And they make it really cheap to deploy that in in whatever situation you want. As your o as your own. Right. Um And what happens then

1:14:12 Is Every this is a new this is a new power that everyone has. So it it gets adopted super rapidly. And it 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.

1:14:28 But what's interesting is because It's all from because it it's all coming from these models and everyone's using basically the same models. Uh It all looks the same if you use it in the in the most default basic

1:14:44 Way. And so That's it becomes commoditized, like it's not valuable anymore. And what humans do

1:14:51 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

1:15:05 because of the way the models work, because of the financial incentives of model of model companies to like make them Um uh compliant and aligned. Structurally. There are always going to be trailing behind those people who are taking taking the models.

1:15:19 and using them to make new expertise or or make new things that haven't been done that way before. for their very, very particular situation. And that stuff is gonna get incorporated into the models, but again, it will create room for people to Um to push further ahead. And

1:15:35 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?

1:15:52 And I think that's something that the benchmarks rising don't doesn't really capture and Uh It feels like a thing that will take a long time to change. People may be hearing in this. Uh

1:16:03 prediction here of just okay, the job pock lip's not gonna people are not gonna be all fired. There's gonna be human jobs remaining for quite a while. It may be Almost too comforting. Because you may

1:16:15 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. Yeah. And I think that is actually super important.

1:16:27 Um Mm 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.

1:16:42 If you're just doing that and when new models come out, you're trying them and figuring out, okay, how can I now they're new powers, how can I use them? Instead of just being like I'm gonna 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 Uh, if you ride on top of them.

1:16:59 They exp 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 Uh

1:17:11 we're going to need people doing that. For a very, very long time. I like this term ride the model. So that What's like saying new while it comes out. What do you think someone say working it? I don't know.

1:17:22 Salesforce. Say a PM and 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

1:17:40 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. And There there are there are certain things where I know it can't quite do it yet.

1:17:53 But when a new model comes out, I like always turn the rock over again to be like Can I do it now? You know? Um

1:18:01 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 at a sixty out of a hundred, which is like really good. Um So The way to ride the models is like not one specific thing'cause they're always changing, but it is to be

1:18:18 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. Uh because It may not work now. But

1:18:31 It may work eventually. It probably will work eventually. And The way that you use it matters. So What's really cool is that

1:18:41 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

1:18:55 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. And that that's it's like a new discovery. And I think that's why For example, we're in

1:19:15 We're in Brooklyn. But I I really think of us and I I think we are like quite far ahead of people in San Francisco because We just Use them for everything and um If people

1:19:28 Uh If people do that consistently, I think It's gonna be very hard to lose. Amaz amazing things about AI right now is

1:19:39 No matter how much money you have. or 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.

1:19:52 Maybe the only people that have an advantage are the people working at Open AI or enthropic. Um but otherwise it's just like available. I know I was at I was at uh their event with you, their code with clawed event with you last week and um or a couple weeks ago and They're they're like all using Mythos and I'm like

1:20:09 Okay. 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

1:20:21 A bajillion dollars and only like Good. 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.

1:20:32 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

1:20:44 Um, it means that Everyone has this broadly accessible tool that they can use and I think that's amazing. 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 they're smart.

1:21:01 Yeah. If I zoom out on the conversation, it's really interesting. There's kind of these two sites to the coin. One is Not a lot is actually. Like so much was not changing. SAS continues jobs not disappearing.

1:21:14 We're still emailing each other, we're still working on Slack. Like a lot of the work. Not changed. On the other hand, every role transformed. Engineers, don't write code. PMs Don't write PRDs. Uh design and design. You know, it's like

1:21:27 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 gonna 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 that's how I feel. Like I'm simultaneously so excited and it feels like everything has changed. And I'm so bullish on it and and the and the progress that we're gonna make and all that kind of stuff. And yeah, I just I feel like there are there are these things where

1:21:51 They're gonna 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

1:22:03 the intuitions that people had in the Middle Ages about Like what happened at the end of the horizon. You 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.

1:22:19 gonna happen over the horizon. And also that uh Some people are like, there's something incredible. It's going to change everything. We're going to all gonna be happy. It's 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

1:22:36 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'cause 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 gonna happen and this is gonna happen and whatever, because you're you're gonna tell a story that

1:22:56 Sounds like 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 once you get there, I think you're s you're sort of start starting to see like Oh yeah, this is a real thing.

1:23:11 Part of it is that the AI companies are very good at scaring us about what might m 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 the debate. I that PR strategy just does not make any sense to me. I I do think that it's Like genuine. But it's so Ineffective.

1:23:28 And um and I I think it's also wrong. Mm. How about we um end with maybe just like a few things listeners should do. to be successful over the next year. With the the way the world is moving.

1:23:42 R the models. I would Uh Try all of your workflows in

1:23:49 Codic. Or co work. and see how that works. And if your company doesn't let you do it on your own time. I would try out some of these um Agent products like

1:24:01 Open claw or Hermes or um for m less technical people, there's there's like Victor, we have one plus ones. I 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

1:24:17 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 Nickel Singall was on the podcast. And the way he described it is you gotta find your moment of joy.

1:24:36 With AI, once you find like wow, I can't believe AI did this for me. This is awesome. We're gonna keep building stuff. Yeah. I agree. If you haven't seen that yet, then it's just like try find try solving it. The thing I hear a lot is just find a problem in your life. More work.

1:24:49 And see if I I can do it. Go to Lovable, go to Like code get it reply. Just try to build a thing. And often it's like holy shit. This is so cool.

1:24:58 Dan is there anything else that we haven't covered? We've gone deep on so much. Is there anything else you want to share? Anything else you want to predict or just say? Before we get to a very exciting lightning round. Uh I think we covered it. We we did a lot. This is this is awesome and I'm very excited to see how well or poorly I do. uh in a year and I hope that you hold me to it.

1:25:19 We're gonna have an AI score us. How about that? Well great. Look at the world, look at Dan's predictions here, goes. Well, with that, Dan Shipper, 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

1:25:39 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

1:25:56 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. It'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've like really liked recently.

1:26:16 Um and and whenever I like something, I always just like tell everyone about it. So um I have recommended these a lot. Um I I have been I've been reading one of the things I I learned which 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 of history and memoir. And I think that's so cool because he was there, you know, he did it.

1:26:39 And there's something about what we do it everywhere. I I 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 History of World World War II is fantastic. I just finished the first volume, I'm on the second volume. The Nazis just invaded France. Very it's very captivating stuff. Um

1:27:00 So That's one. I also just I I've been on like a little bit of like a quantum physics Like Kick recently.

1:27:08 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 Um It's like a it's a history of ideas that relates Uh.

1:27:21 Heisenberg? who has the his uncertainty principle. Um, Borges, who's uh uh uh like a uh uh Argentinian uh fiction writer has w 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.

1:27:41 And Very cool. Like super mind blowing, lots of like interesting overlaps with AI stuff. And uh

1:27:50 Yeah, highly recommend. I feel like we can have a whole podcast episode about your reading and uh books you recommend. I know this is a a passion of yours. My current obsession is the power broker. I don't I think we talked about it when I was visiting you. It's just so good. It never ends, but it's uh Surprisingly compelling to read through the history of New York.

1:28:09 Okay, second question. Do what is a recent movie or TV show you really see recently enjoyed if you have time for TV? So I've been watching a lot of basketball, so that's one. Um I'm I became a Knicks fan like this this year, so Uh

1:28:22 That's really fun. But uh I recently watched this I guess it's like a It's like a mini series documentary called The Dark Wizard. About

1:28:34 This guy Dean Potter. Oh He was like Alex Honald before Alex Honold was Alex Honold. And Uh

1:28:42 He just has this like very extreme personality where he's like free soloing everything and then he's like You know. space jumping in in like a wing suit and stuff like that, and it's sort of exploring his psychology and what happened to him and Um

1:28:55 I I don't know. I I kinda like stuff like that. Like there's another one called Hundred Foot Wave where it's like about people who are trying to s like big wave surfers. There's something about that that sort of I guess just reminds me of founders or whatever, but um the Dark Wizard highly recommend. Is there a product you recently discovered that you really love? Codex. Okay. It's like it's the b it's really good.

1:29:16 It's really good. Do you have a favorite life motto that you often come back to? in worker 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.

1:29:31 And Uh and then there's There's this guy, Rob Burbea, who's like very um very popular in like you know, A the AI meditation like overlap discourse.

1:29:43 Which is also a big thing. Um, and who I also I really like him. He's dead, but uh 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.

1:30:03 What you wanna do is be able to relate to it. From a Position of spaciousness and strength. And

1:30:14 There is something I think really interesting and important in that. Like a lot of the meditation discourse are 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.

1:30:33 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 Um

1:30:42 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 it's and strength? And if not, can I like

1:30:55 Get there. because it will be much more productive for me to deal with it. From that place. And That has been very, very helpful for me.

1:31:04 Well I love that. Well, our final question. Uh just on the On the theme of this conversation.

1:31:13 An AI tool that you think is still kind of underrated, that you're just like recently uh I mean I I can say codex. I hate to say this, but I have to because like Any w anyone who knows me, like we were at this this conference recently, a private conference, and I'm like telling like Boris and Kat from Cloud Code, like you have to try codex. And

1:31:35 Um, it's it's just really good and the things that you can do with it are so different. Um Yeah, especially if you're using it with the in app browser to do things like your emails or check you check analytics or like anything like that. Um It it has completely transformed the way I work and

1:31:54 I would be doing you a disservice if I like was searching for something else because is that good. Tam. That's wild. Uh do you feel like Anthropic can catch up and or is this just like, well, they go No, yes, I think I think they can. I I like I like I said, I think it's gonna be a horse race and uh and different people will be ahead at at different at at at different times. But

1:32:15 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 couple of months, like six months or so, but I think they're back. Interesting. And uh And you switch if one I would.

1:32:29 I would. People people it's funny. People are like, Oh, are you like sponsored by Open Aya? And I'm like, No, I just like talk about what I like. I was super loud about Clawed 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. I I truly do still use Claude a lot. Yeah.

1:32:51 Such a big market. Well, Dan, we did it. We we went through so much. I can't wait to revisit this in a year slash uh Get this out so people can start planning for this next year. Um Two final questions, where can folks find you and every what should people know? And then how can listeners be useful to you?

1:33:07 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 dot T O. Every dot T O slash subscribe. How can listeners be useful?

1:33:20 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. And um, so the most useful thing you can do is like find ways to use it well in your life and share it. Dan, thank you so much for being here.

1:33:42 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 Lenny's Podcast dot com. See you in the next episode.

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