The argument in one line.
AI automation does not reduce human work -- it restructures it, because every agent needs a human steward, and models only commoditize yesterday's competence while leaving new expertise perpetually one step ahead.
Read if. Skip if.
READ IF YOU ARE…
- You are a product manager, designer, or technical generalist figuring out where to invest your skills in the next 12 months.
- You are building a SaaS product and wondering whether AI agents will make your category obsolete.
- You manage or advise a team and want a concrete framework for which roles are changing most and least.
- You are skeptical of the AI job apocalypse narrative but have not heard a rigorous counter-argument.
SKIP IF…
- You want a technical deep-dive on how Claude Code or Codex work under the hood -- this is strategic, not technical.
- You need predictions with specific probability estimates or timelines; these are directional bets, not forecasts.
The full version, fast.
Dan Shipper argues the AI paradox resolves once you understand that agents need dedicated humans to function well, and models only make yesterday's competence cheap while new expertise always stays one step ahead. Work will bifurcate into async company-wide super-agents in Slack and desktop environments like Codex or Claude Code that become the operating system for all knowledge work. SaaS will not die -- agents become power users, and the bring-your-own-tokens model improves SaaS margins. The roles that will dominate are PMs and full-stack designers who can now ship without a full team. The only survival strategy is to ride the models.
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Create a free account →Who's talking.
00:00hostLenny Rachitsky
01:33guestDan Shipper
Where the time goes.
00:00 – 02:55
01 · Introduction
Lenny frames Dan's track record on Claude Code predictions and sets up the three prediction buckets.
02:56 – 09:17
02 · Living in the AI future
Every as a 30-person AI-forward company; the reach test; writing as mechanism for articulating the future.
09:17 – 16:38
03 · How we work will change
Bifurcation into async super-agents (Slack) and on-computer work surfaces (Codex/Claude Code). Personal agents have stalled; company-wide super-agents are winning.
16:39 – 33:33
04 · Codex and Claude Code as the new work OS
Browser-inside-agent unlocks all knowledge work. Dan's inbox-zero via Codex. CLIs are over.
33:34 – 39:00
05 · Two agents are better than one
Agent-to-agent communication passes context a human could not type. SaaS onboarding redesigned around agent users.
36:22 – 46:59
06 · Why Dan is bullish on SaaS
Agents increase SaaS users rather than replacing them. SaaS spend at Every is up year over year despite heavy AI use.
39:01 – 47:59
07 · Why automation does not reduce human work
Allocation economy framework. Senior engineer benchmark: GPT 5.5 at 62/100 vs humans at 88/100.
48:00 – 1:02:23
08 · Recap and the shape of work changing
Non-technical people making PRs. Technical people becoming coherence-keepers. Forward deployed engineers as a permanent new role.
58:17 – 1:02:24
09 · Which roles are least changed
Sales least disrupted so far. CEOs and middle managers have been able to opt out but that will change.
1:02:25 – 1:08:27
10 · We will read more AI-generated writing and like it
Slop distinction: bad when sender does not stand behind it. Quarterly planning at Every done entirely with Notion agents.
1:08:28 – 1:13:10
11 · PMs and designers will dominate
Marcus at Every: PM background plus AI ships faster than most engineers. Full-stack designers can now build what they design.
1:13:11 – 1:20:00
12 · The AI job apocalypse will not happen
Models make yesterday's competence cheap, creating room for new expertise. Engineers are not being fired; they are maintaining coherence.
1:20:01 – 1:24:43
13 · How to ride the models
Turn over the same rocks with each new model drop. The edge of AI is wherever a real person applies it to something specific.
1:24:44 – 1:34:06
14 · Lightning round
Books: Annie Dillard, Churchill, The Rigor of Angels. Favorite product: Codex. Life motto: do things worth writing about.
Lines worth screenshotting.
- Every agent needs a human who cares about it -- the moment that connection severs, the agent stops being useful.
- Benchmarks rise on articulated problems. The labor of knowing what to ask goes unmeasured and stays human.
- The edge of AI is not in San Francisco -- it is wherever a real person applies the latest model to something specific they do.
- Models make yesterday's competence cheap, which commoditizes it. New expertise always stays one step ahead because humans generate it first.
- SaaS stocks are a buy: agents increase the number of SaaS users rather than replacing them, and the bring-your-own-tokens model improves SaaS margins.
- The senior engineer benchmark: GPT 5.5 scores 62/100 vs human senior engineers at 88/100. The gap is in recognizing when to rewrite rather than patch.
- CLIs are over. The benefits of terminal-first work survive inside a good GUI; the GUI's advantages do not survive inside a terminal.
- A PM with strong product sense plus AI tools ships faster than most full engineering teams.
- Full-stack designers who can build what they design are becoming the scarcest high-leverage people in tech right now.
- The AI job apocalypse is not about jobs disappearing -- automation creates new review, coherence, and stewardship work immediately.
- Two agents talking to each other pass more context than a human can type, making agent-to-agent communication a genuine performance multiplier.
- The right frame for the future is not utopia or apocalypse -- it is another horizon.
The only thing that keeps you ahead is riding the models.
WHAT TO LEARN
Agents do not replace the humans who manage them -- they multiply the surface area that needs managing, which means the scarce resource is still people who care about making things work.
03How we work will change
- Work bifurcates into two surfaces: a Slack-accessible company super-agent and an on-computer agent environment like Codex or Claude Code.
- Personal agents stalled because they need maintenance most people are unwilling to do; company-wide super-agents with a dedicated steward actually work.
04Codex and Claude Code as the new work OS
- The real unlock is the browser inside the agent, not AI baked into a browser -- the agent can see and act on everything you can access.
- The bring-your-own-tokens model means SaaS vendors no longer need to pay for AI inference, improving their margins without reducing utility.
07Why automation does not reduce human work
- Benchmarks rise on problems already articulated; the labor of knowing what to ask goes unscored and stays human.
- GPT 5.5 at 62/100 on the senior engineer benchmark means the gap is real -- and it is mostly in judgment about when to rewrite versus patch.
08How the shape of work is changing
- Non-technical people are now submitting pull requests, which creates a coherence and review burden on technical staff.
- Every agent needs a forward deployed engineer: someone responsible for making sure it keeps working, stays on task, and does not do dumb things.
11PMs and designers will dominate
- A PM with strong product intuition who learns to use AI tools ships faster than most engineering teams -- the skill gap is now in product judgment, not coding.
- Full-stack designers can now build what they design without handoff, which removes the most common friction point in product development.
12The AI job apocalypse will not happen
- Models commoditize yesterday's competence but cannot generate tomorrow's expertise -- that perpetually stays one step ahead in human hands.
- More AI output means more demand for humans who can maintain coherence, judge quality, and know when to throw out work and start over.
Terms worth knowing.
- Super-agent
- A single AI agent deployed company-wide, typically accessible through Slack, that every employee can delegate work to. Contrasted with personal per-employee agents, which require too much individual maintenance to be practical today.
- Forward deployed engineer
- A technical role whose primary job is maintaining and improving the company AI agent -- ensuring it has current context, catches mistakes, and continuously improves. Distinct from traditional software engineers who build products.
- Bring your own tokens (BYOT)
- A usage model where users bring their own AI API access into a SaaS product rather than having the SaaS vendor pay for AI inference on their behalf. Improves SaaS margins and removes the token-cost pressure from vendors.
- Allocation economy
- Dan Shipper's framework for how humans work with AI: acting as managers who allocate, direct, and review AI work rather than doing the work themselves. Managers still work hard; they just spend time on oversight rather than execution.
- Senior engineer benchmark
- A benchmark Dan built using real production code rewrites: a human senior engineer scores ~88/100, GPT 5.5 scores ~62/100, and earlier models score ~30/100. Designed to measure actual production software judgment, not coding ability on clean isolated problems.
- Reach test
- Dan Shipper's internal heuristic for tool adoption: does a team member spontaneously reach for the tool when they wake up in the morning? If not, it has not been genuinely adopted.
Things they pointed at.
1:25:00bookThe Writing Life (Annie Dillard)
1:25:05bookThe Second World War (Churchill)
1:25:10bookThe Rigor of Angels
Lines you could clip.
14:47
“Every agent needs a human.”
46:16
“I would buy SaaS stocks right now.”
32:33
“CLIs are over. We speed ran the CLI era.”
1:20:00
“What models do in general is they make yesterday's human competence cheap, and so it becomes commoditized.”
1:22:15
“The edge of AI is wherever AI meets a real human doing something.”
Where the conversation goes.
09:17 – 33:33denseHow agents and work surfaces will change
33:34 – 47:59denseSaaS economics and tokens
47:00 – 47:59denseAutonomy limits and senior engineer benchmark
50:09 – 58:17denseShape of work changing
58:17 – 1:20:00denseRoles winning and losing
1:20:01 – 1:24:43steadyHow to stay ahead
Word for word.
metaphoranalogy
00:00The last time you're on this podcast, you had this hot take that people were sleeping on Claude code. You were so unbelievably right. The premise of this episode is we're gonna go through what else you predict will happen.
00:10The AI jobpocalypse
00:12is not really a thing. I am super, super bullish on PMs and full stack designers.
00:18You 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.
00:30Every agent needs a human. We have so much automation, so much AI, and I also work way more. Creativity.
00:35It just feels like it's gonna be more and more valuable to stand out from all the slop that people are shipping and launching constantly. What models do in general is they make yesterday's human competence cheap, and so it becomes commoditized. It's not valuable anymore.
00:47What humans do is we go in there and we're like, yeah. We we have all this frozen human competence from yesterday. How do I use this to, like, make something new and interesting?
00:54What 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.
01:05Second is that most of the work that you do is actually going to happen on your computer in an environment like Codex or Cloud Cowork. What you're predicting here is the SaaS tools will run within Codex or Cloud Code. I think the SaaS apocalypse is dumb.
01:19I would buy SaaS docs right now. What agents do is increase the number of users of SaaS, not get rid of it. A lot of people are moving to CLI and trying to work from the terminal.
01:26We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over.
01:33Today, 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.
01:48Everybody at their company, including every non technical person, uses Codecs and CoWork and Cloud Code to get much of their work done. And this is why way before anybody else, Dan saw the rise of Cloud Code and what is now CoWork, which he predicted almost a year ago when he was on the podcast last time. So I asked Dan to come back on the podcast to share his current biggest predictions for how work is going to change over the coming year for most people.
02:13We 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.
02:39Before we get into it, do not forget to check out lennysproductpass.com for a free year of the hottest and most well crafted AI products in the world available exclusively to Lenny's newsletter subscribers. With that, I bring you Dan Shipper.
02:56Dan, thank you so much for being here, and welcome back to the podcast. Thanks for having me. Always a pleasure to be with you.
03:03The last time you were on this podcast, you had this kind of it was almost like an offhand hot take that people were sleeping on Claude code, and in particular Claude 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.
03:20This was a year ago. Uh, you were so unbelievably right about this. It's just, like, unreal what has happened since then.
03:28They built Cowork, which was this whole they built on this very specific idea using Cloud Code for nontechnical work. Codex is getting into this now.
03:36I imagine you've been seeing this. They're, like, leaning into this nontechnical use of basically coding agents. I feel like this has also been a big part of Anthropic success over the past year, just like how do nontechnical people use this stuff.
03:48So you were just so ahead on this stuff. I I I even wrote a newsletter post building on this idea.
03:53I'm like, hey. This is interesting. I should dig into this.
03:56I asked people how to use Cloud Code for non engineering work, and I just had, like, so many examples, and it's, like, my second most popular post. So clearly, you you you have a unique glimpse into where things are heading.
04:08So the premise of this episode is we're gonna go through 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.
04:23That gives you this unique lens into where things are going. So just give us a sense of how you how you work. Thank you.
04:29I I really appreciate the introduction.
04:33And, yeah, I think one one of the things about predicting the future or or the way that we think about predicting the future at every is that you what you don't wanna do is prognosticate. What do you what you wanna do instead is is just live in it together.
04:50So everybody at Every is an AI early adopter. We're almost 30 people now. I think when when we did our interview, we were 15.
04:56So we've doubled in size in the in the last year. We're all early adopters, and we have engineers. We have designers.
05:03We have writers. We have editors. We have salespeople.
05:07We have customer service people. And everybody has a little bit of that.
05:13Whatever 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.
05:20And 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 there's sort of, like, the middle of the pack people, and there are people who are the, like, very anti.
05:38And another thing that happens, which is really cool, is we get to because of our role, you know, reviewing models and and being a little bit of a pacemaker in AI, we get access to stuff before it comes out.
05:50So 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, it's actually when you create an environment like that, it's actually just about noticing what's going on.
06:08And 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 makes it real for you and your team and then anybody else who's, like, on the Internet who's reading it.
06:24And so the Claude code thing, it was this it's this very organic thing where, for us, we tried Cloud Code when it came out.
06:34That's sort of our job. We 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.
06:45But right around, I think, like, Sonnet three five or Sonnet three seven, we were testing that to do our vibe check on it, and we were like, holy shit. This is crazy. This is, like, really you can they got rid of the code editor.
06:58And 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 a world where everybody was no one was looking at the code.
07:13Everybody was, you know, talking to their computer in English using Cloud Code in the terminal. And so I was able to see, like, oh, this is starting to happen.
07:24And then 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?
07:32And then it just, like, starts to unfold, you're like, okay. This is not ready yet, but it's obviously useful for me.
07:40You 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, when you wake up in the morning, do like, reach for it organically? I love this combination of
07:50you are using the latest stuff, and I think this is, as you said, maybe an underrate underrated skills. You're you're good at being self aware of here's what's weird and new and different and interesting.
07:59So 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. This episode is brought to you by our season's presenting sponsor WorkOS.
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09:17So 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 is gonna look like and change.
09:31And 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? Lenny, my only ask is we come on a year from now and then you score it. I wanna score.
09:45Okay. So this is a year from now. Okay.
09:48Okay. So is this that's actually
09:51is this, like, your predictions for in a year, this is what it's gonna look like, or this is, like, the emerging future? I think 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 I'm gonna talk about will be pretty apparent within a year, but it it probably it may it may take longer than that.
10:07Okay. But I think it will it should within at least a year be, like, not obviously wrong.
10:12Like, it it seem it could it should seem like it's moving in that direction to count. Okay. May
10:172027, we will review your predictions.
10:22Amazing. Okay. I love this.
10:23Okay. So let's dive in. What are some predictions for how the way we work is gonna change in the coming year?
10:29One 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.
10:37You know? Meter has this really cool benchmark where it's like, it measures how long it can like, the newest models can do tasks autonomously, and it's like, oh, it's like, it can what's it called?
10:50Oh, myth like, mythos preview, the, like, big anthropic model that everyone's, like, so worried about. It can do tasks of seventeen hours at 50% accuracy. It's like, holy shit.
10:59That's crazy. And I think it is real. It's true.
11:02And and and the the progress, like, model progress is going up exponentially. And my experience and my feeling is that we will look back in a year and say, we actually have a lot more work to do.
11:17Humans have a lot more work to do even as models get better at doing work. And there's, like, a really interesting paradox there.
11:24And my prediction for the, 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.
11:41One 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 how work with AI works, which is everyone's gonna have at least in their company, at least one agent that they talk to that can do work, that they can offload work to.
12:01And we'll talk about, like, what that looks like, but it's essentially like OpenClaw. 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 Cowork that becomes the sort of operating system for it becomes a sort of operating system for how you do all of your work, whether that's your email, the documents you create, like, all that kind of stuff.
12:29It's gonna be on that kind of a surface. It's that's becoming the the clear competitive landscape.
12:36So there's I wanna go in order of those two. So the first one is you're gonna have agents you delegate to probably in Slack, but, 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.
12:52Is everyone gonna have an agent? Is every team gonna have an agent? Is it gonna be, like, just one agent?
12:59Is it, like, do agent specialize? Is there this, like, parallel shadow org chart? And when OpenCloud first came out, everyone internally at every adopted it, and I was very convinced that it would be a everyone has their own agent.
13:15And there's, like, some real really interesting things about that world of, you know, a parallel a parallel org chart agents in that world sort of become little reflections of you, which is, like, really cool and really interesting.
13:30It's like if you ever did you ever read the Golden Compass? It's like having a little demon on your shoulder. You know?
13:35That's a little part of your soul. Yeah. I I really think like, that's sort of what it looked like was happening.
13:42And so I was very into personal agents, and I have completely flipped. And I I really think that the the model for now is going to be a super agent, like, one agent for the entire company.
13:56And I you're you're starting to see this in some companies. So, like, Shopify very famously has one.
14:02Ramp has one now. And and I think there's some, like, really interesting reasons for that. I actually still think that the personal agent thing is coming, but what we found is there's all this hype with OpenClaw.
14:16Everyone's like, I'm gonna set it up. I'm it's so cool or whatever. And then everyone realizes it's, like, way too much work.
14:22This 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.
14:28And most people, to do work at least, just don't wanna spend that time or can't.
14:38And the the, like, fundamental underlying thing that drives that is whether it's OpenClaw or any other harness, in order for an AI agent to be useful right now, it really needs a human who cares about it.
14:52It really needs, 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 that it's useful for people. And the minute you, like, sever that connection so the minute someone's like, ugh.
15:04Like, 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 one agent per company model because for now, like, the the the ideal is 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.
15:31And then maybe you have some, like, some little team agents. And I think as the models get better at being more independent, that will, like, shift down, and you'll it'll be more likely that we'll have more personal agents because we don't have to fuck around with all the internals. But 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, 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.
16:03A lot of times it's a a particular kind of job that you've decided that everyone in the company needs an agent for, like, data requests. And and then I think it will start to it starts top at at the top, and then it sort of starts to trickle down where you may get more specialized agents and teams and and all that kind of stuff.
16:20And the mechanism is agents need people who care about them. That is so interesting, that point about you need to, like, garden your agent 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.
16:35Forget this thing. I'm gonna go back to Codex or Cloud or something like that. Exactly.
16:39Okay. Cool. So this is a cool opportunity.
16:41So the idea so what you're predicting here is companies will have this super agent that everyone can talk to. As you said, Shopify has got River, I think it's called. What's the ramp one called?
16:49I can't remember. Okay. It's probably gotta fund it.
16:52Okay. So so that's the prediction.
16:55Okay.
16:56Use it. That's the first prediction. That's the first We will start with agents at the top that 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 agents become less less fiddly.
17:21Like, they just work better. And is this mostly gonna be in Slack? Do you predict?
17:25For work? Yeah. It seems to make sense.
17:27I think people people love having the green bubbles on OpenClaw. Like sorry. The the blue bubbles on OpenClaw, like, if you can use it with your iPhone.
17:34But I think there's this little thing in people's heads where they really like to keep their personal and work agents separate. Mhmm. And I think there's a whole there's a whole territory.
17:45Our our COO, Brandon Gell, calls this computer errands. There's, like, a this whole territory of using personal agents for your computer 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 I focus we focus mostly on the work stuff, and I think that's gonna happen mostly in Slack.
18:07Sweet. Go Slack. Should we do you wanna talk about the, uh, the other work surface?
18:12Absolutely. Codex, co work? Okay.
18:14This is the Let's do it. I'm so excited about this one. I think it's the coolest thing.
18:18So, basically, what happened was Anthropic realized at some point that with Cloud Code, if you put an agent on your computer and it runs on your computer, it has everything it has access to everything that you have access to.
18:34It uses the terminal, so it has, like, 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 about that.
18:47And it it created this, like, super powerful coding paradigm, which is, you know, Anthropic was really doing it first. OpenAI for a while was, I I I, in my opinion, like, very, very behind on this, and then in my opinion has surpassed them recently.
19:01It's really interesting. But they were very early on this.
19:07When 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.
19:17Like, there are people before them like Devin who, I think, had had a big had the big, like, cloud environment and and OpenAI tried this too, but the the the real adoption seems to have happened when you put it on your computer.
19:31So they figured that out. And then I think they figured out, along with their community, that once you have a coding agent on your computer that can build anything, it's actually really good for any kind of work you wanna do.
19:43And people started just hacking Cloud Code essentially to do all their work. So Anthropic then built Cowork, which is, you know, a little bit of a nicer wrapping around Cloud Code, but it's fundamentally the same thing.
19:56And then I think, you know, I think OpenAI made a couple of different bets, but their main bet on a programming agent was the the the the earlier version of the codex were, like, very technical, and they were, like, super smart, but they were, like, a little bit autistic.
20:12Like, it was a little hard to they didn't quite get what you meant. They get they got exactly what you said.
20:19And I think maybe, like, three or four months ago around the time that they launched, uh, 5.3, they started to move in this direction of, oh, no.
20:29We get it. Like, it's, um, this model is fast. It's, like, really good for general purpose knowledge work type tasks.
20:35And then they launched the Codex desktop app. And I think the Codex desktop app takes if you look at all the lessons that, like, Anthropic learned, they went from Claude code to Cowork, and you can kinda see that in the tabs on the on the Anthropic desktop app UI.
20:52I think opening out was just like, we 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.
20:59Like, they're gonna have different positions. But I I think Codex right now, it's my daily driver. I, like, spend all all my time in it, basically.
21:08I flip the card 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, 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 in, uh, in the app.
21:32It has an in app browser. And when I'm writing a document, I just go into one of my one of my codex threads, which I have one thread for every project. And I just open the in app browser.
21:43I go to the document. I usually do it in proof, which is this online mark markdown editor I built. And then I just have Codex running and watching me in proof, and Codex can see what I'm doing.
21:54I 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.
22:12It can go it can use my computer to basically do anything that I can do on my computer, and that's, like, incredibly powerful. Um, and I do this with everything.
22:21Like, 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.
22:30And that's because I literally just have codex, gather all my emails with Quora, which is our email agent, and then it it renders a little page.
22:41And I I think I showed you this at the in at the Anthropic event. It renders a little page, and I just, like, monologue into it and just talk at each email.
22:49I'm like, okay. Go go research this. Oh, here's a question from our lawyers.
22:53Can 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 does it. And so all the stuff that I would procrastinate on, I don't really procrastinate procrastinate on on anymore.
23:04And so I feel like there's this for a long time, we thought I thought too that the optimal experience of AI was gonna be take AI and put it in a browser.
23:17And 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.
23:35You can't even do this in cloud in Cloud Code 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.
23:45This is more profound than it may even sound. What I'm hearing is
23:49instead of AI being baked into SaaS tools, what you're predicting here is, uh, you will the the SaaS tools will run within Codecs or Cloud Code.
24:01That that is that is one, uh, really important, uh, second order effect of this is okay.
24:10So yeah. Like, I'm I'm using Proof or or really any website, maybe Posthog or whatever, and I'm doing it inside of my agent.
24:19And 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.
24:28I'm not using the the vendor's tokens. I'm not using the app's tokens. And so it puts SaaS back in this place where, yeah, you wanna make it friendly for an agent.
24:37And everyone's got a CLI now. Um, you wanna make the HTML, uh, really, uh, really usable.
24:43You 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 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.
25:03I 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. Um, but I think for now, there's this really cool thing where, with Proof, for example, anyone who uses it, I don't pay for tokens because they're just bringing they bring their AI to to Proof.
25:26And so it changes what you build as a SaaS company, and you build it now for both humans and agents to use at the same time. And it changes your margins back to, well, I don't really have to pay for tokens anymore because the user is gonna bring the AI.
25:39So 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 Codex or Cloud Code?
25:48Where does Cursor fit into this? Is that one of the is is there a potential there? That's a good question.
25:53I think that Cursor
25:55sees a lot of the same stuff. And there and in some ways, they have some of the same stuff, but it's better. Like, I think that Cursor's cloud implementation is better than either it or OpenAI's or Anthropix and is more advanced.
26:08And I think that Cursor has, at least so far, more distinctly chosen a lane.
26:16Like, they're more distinctly choosing to be for programmers, and that may limit how far they get in here.
26:24Like, 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. 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 the model.
26:43And so where the where all the platforms are moving is to a world where you you're not just doing prompt and response.
26:51When you call the the model at at on on the OpenAI 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 of the model, they need to offer that.
27:07And so you see, you know, Anthropic's got cloud managed agents.
27:13OpenAI does not have a have a response yet, but I assume that that's gonna happen. And now Cursor was just essentially acquired by SpaceX. It's not, like, a full acquisition, but it's close.
27:23So I think people are starting to realize, like, I can't just do the, like, model part of it. I have to have this, like, harness above it.
27:30And 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 OpenAI or Anthropic, if this proves to be true,
27:48the prediction here is they're gonna be using your product over time inside of one of these agents. Is there something you would do if you're one of those companies to prepare for that future?
27:59I I would just prepare for that. So, like, you know, for for example, every more classic piece of productivity software, whether it's Slack or Word docs or PowerPoints or whatever.
28:12It's really mostly meant for a human to use. And now people are doing CLI, so it's, like, meant for an agent to use independently of a human.
28:25And we're moving into this new paradigm, I think, where the human and the agent are on the same piece of work together, and they're both doing things. And you need to have I I need to have visibility into what the agent is doing.
28:36The 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.
28:46So for example, like, there's a lot of stuff that Proof doesn't have. I don't have to have a lot of the, like, Word document kinda, like, formatting or page breaks or, like, you know, making tables or whatever because the agent just does it.
29:00I 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.
29:08And then there's all these other affordances that you need to start to have because the way agents interact with software is very different. So, for example, agents can do a lot at once.
29:19They can just do, like, a billion different things to your document or your slide deck or your code base or whatever. And 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.
29:33You need you need, like, approval. You need a sort of inbox that sort of summarizes here's all the stuff that's going to happen or has happened. You need you need logs and the ability to roll it back real quick.
29:45So 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.
29:58So how are you gonna deal with that? Right? Um, this is exactly why, you know, GitHub is having problems right now because because the the number of people using GitHub has is skyrocketing exponentially, and it's really just people's agencies in GitHub.
30:10So 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. But there's so many cool things about it. So, for example, in proof, in some of our other products too, uh, when someone has a problem, they don't email support.
30:27Their agent sends a bug report, and an agent bug report is way better than a human bug report.
30:36Um, it has, like, here's exactly what I did. Here's the exact repro steps. Here's, like proof is open source.
30:41So here's what I think is going on in the code base. And then we just get that. It becomes a GitHub issue, and then we can just, like, send off an agent to fix it.
30:48And you can't do that with everything, but it's so much better. And you can see the, like, the glimmers of this this very fast, like, closed loop between I ran into something, a paper cut, a little feature I want, a little bug, and my agent just goes off and talks to the company agent, and then the company agent just goes and
31:10fixes it. That, I think, is incredibly cool. So is a part of this that you a lot of people are moving to CLI and trying to work from the terminal.
31:17Is the part of this prediction that people shift away from that and back to actual UX with agents kind of running alongside them? CLIs are over. We we speed ran the CLI, uh, era.
31:29It was nice while it lasted, but I think it's pretty it's pretty clear.
31:33It's not that CLI 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.
31:42They will continue to be around. And I think there is this moment when Cloud Code was, like, so popular, uh, or or when when Cloud Code was really starting to gain in popularity that people were like, the the thing that's working is the fact that it's the CLI, and I don't think that's what it is.
31:58And when you move into an actual UI for this, you start to realize we made GUIs for a reason, and it's just nicer to be in a GUI.
32:09And you can get all the same benefits inside inside of GUI, especially for nonprogrammer work. But I would I would estimate that definitely the majority of the technical people inside of every are not using CLIs anymore as their main work surface.
32:26I think a lot of programmers are still flipping into it every once in a while, but it's more or less they're using
32:32codecs, Cloud Code, cursor, um, that kind of thing. Awesome. Okay.
32:36I 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 super agent within a company that you chat with through Slack, most likely, that can go off and do work and answer questions.
32:52And then there's on your computer running Codex or ClockCode. And within that, all the work that you'd normally do kind of on your computer is now gonna be living within Codex or ClockCode or maybe some third party that emerges that we're not even aware of yet. Yes.
33:06And you're going to use apps inside of the internal browser of those,
33:11uh, of those tools.
33:14Wow. Okay. Like, listening to you talk about it, it's like, it may not feel as profound as it is, because this is a big change to how we work.
33:22We don't currently have an AI that we talk to regularly in Slack, and we also don't work currently mostly in Codex or GlarCode. So this is actually a pretty massive shift. I think so.
33:34Is there anything else along these lines before we get into our next prediction?
33:38Well, 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.
33:44Seems pretty clear to me. And I really do think that two agents are better than one. So that's a good example.
33:53When I have codex interact with another agent, it can give so much more context about me and what I want than I would be able to type.
34:05And it can go back and forth talking about things that would take a long time for me to express directly to an agent that you get this, like, speed up effect when you assume that your users are are using Codex or Cloud Code or Cowork as their as their basic way they access your app.
34:24And a a really simple example, we have this hosted OpenClaw product, which we we had it we had on waitlist.
34:33We actually had to pause it because we start taking to all the waitlist. OpenClaw is just a very hard agent harness to to make work.
34:40It's like, 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.
34:49It's very hard. But one of the things that we learned in that process is if you're let's say you're building an an agent product or or a news any new software experience, what you would assume, let's say, to set up an agent is you need to build, like, a little, like, web interface or a little Slack workflow that ask people about, okay.
35:10Like, who are you, and what are you gonna use this for?
35:14And, 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 onboarding checklist.
35:23If instead, you you just you just make a hard line of we are only going to service users who use Codex or Cowork.
35:34Um, what happens is you just paste something into you just paste a prompt into Codex or Cowork. It goes and talks to the app, and the app can be either just a regular server or or it can be its own agent. And Codex has so much information about you that it can just give it here's all the stuff I've been working on with Dan.
35:56Here's all the ways that, you know, he might he might wanna use this app and then bring it back to me. And it's this very custom experience. And, also, for a technical product like an agent, when something goes wrong, I can just tell Codex, go fix it.
36:09And Codex will go talk to the app and figure out what's going on for me. And so I think the whole paradigm starts to change when you assume that everyone's got an agent, and those agents are talking to other agents in this, like, really magical and important way. There's a couple of more things I wanna touch on before we get started because there's, like, so much to talk about.
36:26One is you made this point about SaaS tools not using like, you can use tokens from the, uh, model companies, basically, when using a SaaS tool. Talk a bit more about that because that may change the business model for SaaS companies in the future.
36:39That feels like a big deal. Well, I think it actually
36:42may, uh, save their margins. 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.
36:57And I think that, uh, and that cost tokens, obviously.
37:01And I actually think once I have once I have codex or co work as my main work surface, I still wanna use SaaS. So this is another good prediction.
37:09I would buy SaaS stocks right now. I would I think the SaaS apocalypse is dumb, and SaaS stocks will be up majorly in the next couple years.
37:19Not not investment advice, but, you know, I would buy SaaS stocks.
37:25So so so I think it saves your margin because now what you're what the way that you're thinking then is not have to build AI into this.
37:36It's it's more like I have to make a piece of software that humans and AI wanna collaborate on together. And that's hard, but it's once you build it, it's a lot cheaper than assuming everyone's spending tokens.
37:52And 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 SaaS is, a, everybody internally here is like I said, we have all got agents, and we're all using codecs and whatever.
38:08And we still pay for a ton of SaaS, and our SaaS spend is up year over year. And we're not, like, vibe coding every single, like, little thing. You know?
38:18And I think that what agents do is increase the number of users of SaaS, not get rid of it.
38:26And 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,
38:42but, uh, it it it makes me very bullish on SaaS. I love that. If anything else comes out of this conversation, Dan Chipper, SaaS is the future of AI.
38:52This b to b SaaS.
38:56Hashtag send tweet.
38:58I 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 people in the past year, which is not what people would have expected from a company that is so AI forward.
39:11Talk about what your experience there of just, okay, we still actually need humans. Automation is a lie,
39:16um, in the sense that every time you automate something, in order to make sure the automation is working well, you need a human on top of it, like, making sure that it's working well.
39:26And so, you know, I wrote this piece a couple years ago called the allocation about the allocation economy, like, idea that the way that humans are gonna work with AI is gonna is gonna be, like, like, being a manager.
39:39And the thing that you have to remember about managers is, like, managers actually spend a lot of time working. Most managers are not, like, on the beach.
39:48They're, like, checking in with their employees all the time and and and and trying to figure out, How okay. Do we make this work good? How do we make it better?
39:54How's it doing? How's this person doing? All that kind of stuff.
39:57And I think there's there's some differences between being a human manager and being a model manager, but, um, fundamentally, it still requires a lot of time and attention. And I think that we kind of missed that in the model discourse.
40:12And one of the reasons is benchmarks make it look like AI is more autonomous than it is. And by autonomy I mean something specific by autonomy, and I'm gonna try to express this.
40:25It's, like, a little hard to express, but I learned this for myself because I've been feeling this paradox a little bit. I've been feeling the like, we have so much automation, so much AI, and I also work way more.
40:36And I think part of the paradox part of the paradox started to, like, resolve for me a little bit when I made my own benchmark. So I made this senior it's called the the senior engineer benchmark.
40:47And it's like, how good is AI versus a human engineer? And the way that I built it is, again, have this app proof.
40:56I just vibe coded it on the side and like, while running the rest of every. And when we launched it, because it was completely vibe coded, it just started going down, and I couldn't fix it. And it was very embarrassing.
41:07I had a lot of egg on my face. And, like, the product worked. We we tested it internally.
41:12We had a lot of beta testers, but, like, the day after launch, it was, like, just every, like, ten 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.
41:22Like, Codex, fix it. And Codex is like, I don't know what's going on. Or, really, Codex was like, I do know what's what's going on.
41:28I fixed it, and then it 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 bursitis on my elbow.
41:39So that's a there's a life lesson in there. Vibe coder elbow.
41:43Yeah.
41:45So, anyway, I got a I got actually two different senior engineers to fix it independently. So I have two different rewrites of the code base that tells me how they did it.
41:58Right? And so what I get to do is when we get new models, I just give the new model a prompt.
42:04I say, like, this is Vibe coded slap. If you wanted to rewrite it from first principles, how would you write it? Go do it.
42:12And all the models until GPT 5.5 got, like, a 30 out of a 100, and senior like, human senior engineer gets, like, high eighties, low nineties out of a 100.
42:24So there's a lot to go. And then I tried GPT 5.5, and it got, like, a 62. And mind you, the 60 the 60 score was GPT 5.5 using an OPUS 4.7 plan.
42:36OPUS 4% the plans are very good. G b d 5.5 is the only model though that has the sense of agency and confidence to just, like, rip out old code and just, like, actually rewrite for first principles.
42:48Other 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.
42:56I, like, specifically told you not to. So g p t 5.5, there's, like, a 30 bump in the score, 60 out of a 100. It's, like, very it's very clear that in a year or less, it's gonna be senior engineer level.
43:13Right? 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, I will be it will be very easy for me to change the benchmark to zero out the current model.
43:28So that gets a zero out of a 100. And so, for example, it seems like there's no skill or no thought into the prompt, which is this is why I could've slapped, like, fix it from first principles.
43:41But, actually, it took me a a while to get to a prompt that didn't give away the answer, but, uh, but got the model to reveal what it's capable of.
43:54And the original prompt I gave it was the original prompt that I gave it when, uh, when I was trying to fix the issue when production was going down, which is, like, I'd woke waking up I'd I'd woken up in the morning, and I was like, okay.
44:08We had four or five reported issues yesterday. I want you to go through all the issues and then come to, like, make a plan for how to resolve all of them and go do it.
44:18Right? And every coding model on the market and I I am I'm pretty sure this here's a prediction. I'm pretty sure every coding model on the market will still do this in a year.
44:28Every coding model on the market will take that instruction seriously. And if I tell it, here's a bunch of issues. Go fix it.
44:35They will just go try to fix the issues. What a actual human senior engineer does is they go look at the code base, they're like, this is a piece of shit. This guy doesn't know what he's doing.
44:45And then 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 if you ask the model, hey.
44:56Like, should we do that? It'll it'll probably it'll probably get there, but it's not gonna do it on its own. And it and there's a lot of incentives pushing against it doing that.
45:07And even if it does that, there's there's always a higher frame for us to go. 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.
45:25And there's a lot of work that's human work that, uh, it it can't be scored until you write it down, but the act of thinking to prompt it or write it down is, uh, is something that you can't measure, but, like, kind of means that even if the benchmarks get saturated, it doesn't mean the same thing as we you totally replace all senior engineers.
45:45And it's I think it's why even though the models are getting better at automation, I still hire engineers.
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47:00One thing I mentioned recently on the podcast, I heard that speaking of the code that you have of, like, humans writing code, uh, data labeling companies are buying code that was written before 2021, 2022, before AI became a thing.
47:13It's, like, very valuable data. Archezonal human code. Yeah.
47:17Ex That's exactly right. And it's so interesting that that's exactly the kind of code used to build this model.
47:23Well, what's interesting so I wanna I wanna clarify there. So I did not have a human
47:29write 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. I don't really care.
47:42It's like it's sort of like, am I gonna race a human against a car? Like, I probably wouldn't do that.
47:48But I would race a human in a car versus another human in a car and say which one's better. And in this case, what the the way the benchmark is structured is, yeah, like, these human engineers use AI, but they use it in a way that I could not because I didn't understand it, I didn't have time, and I didn't really wanna, like, go in and try to understand the code base, to be honest.
48:07And I think that's a really important thing when we think about benchmarks is AI is a broadly distributed technology that any human can use. And when we are benchmarking against humans, AI against humans, we're actually really always talking about one human using AI versus another human using AI because AI doesn't use itself.
48:27It it may be able to in this, like, slightly somewhat recursive way, but there's
48:32in any real use case, there's always a human, like, pretty close to it making sure that it's working. Okay. I wanna try to wrap up our first bucket.
48:39There's so much to talk about. I made a little list of things that I think people, uh, should do based on your predictions to be successful.
48:47We'll talk about this at the end too, but just a few things. Uh, one is start using Codex or ClawCode more and more for the work you're doing, and especially the browser, use tools inside of it. Two is allow your a allow agents to be to use your products.
49:01If you're building a SaaS tool, make it easy for agents to be a a a user, essentially. Three is start thinking about some Slack bot that you can work with, like try out tools.
49:13Like, 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. Buy SaaS stock ASAP, not investment advice.
49:31I think that's totally right. I will like, my slight tweak is when you're thinking about building your software for agents, the current model is I'm building a CLI that an agent uses, but they're you're using it in a sort of, like, I'd they're debt being I delegated a task to the agent.
49:49The agent's using the CLI. 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.
50:02And that is, I think, a new challenge that's really interesting.
50:06Awesome. Anything else before we get to our next, uh, category? By SaaS.
50:12That's the title. Oh, man. Okay.
50:16So the second, uh, category of predictions is around just the shape of the work that we're gonna be doing is gonna change.
50:24What 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 async agents off that you delegate work to, then you've got your, like, Codex Cloud Code, like, work surface, that that starts to happen.
50:40So one thing that we see a lot internally, and you also see this in the big model companies, is the number of pull requests that you get is like, skyrockets.
50:50You know, we have people, you know, in consulting or in ops roles or whatever who are or or edit editors just, like, making pull requests.
50:59And, hey. That's really cool, and it's a very different shape of work where 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.
51:14And what that does is it creates all this pressure on the other end for the people who have to deal with all of the new code for how to deal with that.
51:25And so I think there's a lot of there's a lot of interesting things that happen with that. Like, so for example, like, OpenClaw.
51:35I 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, 50,000 codex instances and then sorts through them and then merges, like, a thousand of them.
51:49It's really crazy. I actually think that that's going to be more and more common. There's like it brings up a lot of really interesting questions around which pull request should you merge.
52:02And, you know, when you whenever you add capacity in one part of your process, like, it breaks things. It used to be really hard to build things, and now it's very easy.
52:13So 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?
52:22And, also, what do we delete? I think Anthropic does this really well. Like, they they delete a lot of stuff from Cloud Code to make sure that it's not bloated.
52:31So I I think there's a there's a lot of that gonna happen on one side. There's a lot of nontechnical people can do technical work, and then technical people are in charge of making sure that that work gets into a product or into a process in a cohesive, coherent way.
52:47And, also, their product people are gonna be doing that too. And I think that's
52:51that's quite cool. Something I'm hearing from people is that now that everyone can do everything, like, engineers can design, PMs can code, marketing people can ship stuff. There's just this, like, confusion about what the hell is my job anymore.
53:05Yeah. What am I responsible for exactly? Like Yeah.
53:08Am I supposed to be shipping stuff? Am I still a marketing person? And it's just creating a lot of confusion and uncertainty in the world just like that.
53:15For
53:16real. And one of the things that I think is special about every is everyone is sort of a generalist and really loves, like, having their fingers in a lot of different pots or whatever the metaphor is. I think that'll probably settle down at some point, and it'll feel more normal.
53:31Like, 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.
53:44The the other thing that I think is interesting is there are definitely some new job roles that are a thing.
53:51And the thing that is becoming really clear is the whole forward deployed engineer concept, I think, is for real.
54:00And it comes out of every agent needs a human. You like, you go to the big model companies, they have they they have these agents that run internally.
54:09They have, like, teams of people that run these agents. You know? And I I don't think those teams are going away.
54:15The 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 so that looks like a very specific kind of person.
54:28And, you know, we have a couple of those people internally here, and it's like the the people who are in charge of making sure your agents are working and doing the right thing. We also do consulting.
54:38So we we we lend that out to people, and and I think that's a big that's a big thing that that people want. And it's another one of those places where you're like, automation was supposed to take away jobs, but it looks like it just created one or many.
54:54You know? And there's a specific type of engineer that really loves you know, Nitesh, who's one of our, uh, who who fits this.
55:02He's an AI engineer, and he he fits sort of forward deployed, um, category, he's on our team. He spends most of his time actually talking to one of our agents in Slack.
55:12We have an agent internally called Claudia, which runs our whole consulting practice. And and he spent a lot of time in Slack.
55:19Like, 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?
55:27Like, let's let's fix that. You know?
55:30And so there are certain kinds of engineers that I think love that and love having their hands on the latest thing and also love making this, like, being that's, like, in in the works in a workspace, and it looks a bit different than more traditional building more traditional software.
55:45And your sense there is we're not gonna we're not near a place where these agents don't need a human. You've said that so many times now that agents need a human, and there's kind of, like, the setup part, and then there's the maintaining it forever part.
55:57And it feels like both are important 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 be fully automated for a while?
56:06Yes. I'm simultaneously
56:08extremely AI pilled, extremely, and very bullish on humans and the role of humans in making sure that AI is working well. Interesting.
56:17Okay. 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 just the pace of shipping software and everything is just increasing, which also means, uh, there's so much more work reviewing all this sloppy output. Uh, was just talking to a data science friend, and he was saying how his team is just his data science team is just their job used to be do analysis, answer questions, see if this experiment was a good was a was positive.
56:45Now 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.
56:53Which is a problem, and it means that and the same thing are is happening with engineers. And it means that you need more like, you actually need that engineers for this, and you need data scientists.
57:05And it means that you haven't set up the appropriate systems or agents to help you with this. So, like, the way that it works inside of the big model companies, for example, like, at least one of them has literally a data science bot that every single person in the org can query that, um, is hooked up to their data warehouse that knows who's who so that it knows at the warehouse level, like, who has permission to access what.
57:30And so all of the basic questions because there there's a team that sets up this bot. All of the basic questions that people might wanna ask that it sometimes gets that might get wrong, that they're constantly making sure it's getting it right.
57:42And so the data science team doesn't have to answer all the, like, bullshit questions because there's another team building an agent that that that is set up to do that really well. But if the team didn't exist, the data scientists would hate their lives.
57:56Yeah. It does, though, make the job maybe less fun because you're just sitting there, you know, gardening people's sloppy work where Well, that's what think is, like critical. It it can actually make the job better because
58:08for the data scientists, you are 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 agent to do that.
58:22It'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, uh, I've been thinking about.
58:29The question is which product tech role is the least changed now?
58:36So, like, engineers, 100% of code AI now. It's like a completely different job.
58:41Uh, product management, a lot of the, you know, PRDs are you don't have to write as much. You can ship code.
58:46You 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.
58:54Very different role. Data science, very different work now. Um, there's marketing.
58:59There's sales. So here's the question. What do think is the least fundamentally changed
59:04role so far? Well, one interesting thing is, you know, 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.
59:17Mhmm. It seems that way. I I I think the opposite is actually true.
59:22Like, my experience and we do a lot of this with senior executives and senior leadership teams. My experience is that your company is 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, because you don't otherwise, you don't have an intuition for it.
59:37But for a long time, it has seemed like, yeah, that's something that the people who are doing the work have to do, but, like, I don't have to do that. Like, I'll just tell them what to do. And and so I think if you're a CEO, you kinda can get away with your day looking very similar.
59:53I I think that will change rapidly at some point where it'll be like, oh, no. I'm, like, way behind. But for now, because or maybe even middle managers, like, those kinds of people, I think, are are it's fairly similar.
1:00:05I think, like, maybe sales because That's exactly what person.
1:00:11That that's yeah. That's my vote. You know?
1:00:13There it's sort of creeping up in the kind of BDR. Like, we can deal with a lot of, you know, BDR type type queries. You're only talking to, like, people who actually want it.
1:00:23And you can do for sales, it's, like, re it's so useful to to, like, do research.
1:00:31Like, my favorite codex like, one of my favorite codex experiences is we're hiring a head of l and d.
1:00:39And I you know, we always put out a job post, whatever, but I was like, I feel like there's this company called General Assembly in New York, and they do like, they've done really good technology education for a long time.
1:00:51And 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. And I just, like, literally typed it into Codex and then, like, went off and was doing something else, and I came back.
1:01:04And it found, like, this the perfect guy. It was, like, worked at general assembly, was an instructor, like, is super AI pilled and follows me on Twitter.
1:01:17So I just DM'd him, and then I had dinner with him. And it's like, that's crazy. You know?
1:01:20That would have taken so long before
1:01:23and, uh, super valuable for sales, for recruiting, all that kind of stuff. Yeah. Sales is where my mind went.
1:01:29Like, the top of funnel AI is helping a lot with sourcing and qualifying things like that. It feels like the the work of a salesperson is not fundamentally different. Yeah.
1:01:38And and customer support's fundamentally changed. So that's interesting.
1:01:42Sales. So far, so good for the for those folks. Yep.
1:01:45Okay. So maybe just summarizing some of the predictions in this bucket of just, 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 as a part of the work.
1:01:57And 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 for deploying and then just gardening them along the way, make sure they continue to do their work. Anything else before we get into our third bucket?
1:02:11I would sort of split it into
1:02:14less babysitting agents and more your forward deployed team is trying to build a whole system that makes it so that people who have less knowledge can use that system without, like, doing something dumb.
1:02:28And that's, like, a really interesting engineering challenge.
1:02:32I think babysitting kinda 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.
1:02:39And you you that can be the case, but I think a lot of it is this extremely interesting engineering challenge of building a system for to enable everybody else in your organization to do what used to be a technical job. And then if you're not one of those people, like, you're the data scientist or whatever, you can go a lot deeper with AI into, like, really important questions that eventually probably filter into the work that the, you know, the forward deployed engineering team is doing, but is, like, more generative and more new and and and you're you're dealing with harder questions.
1:03:09One other one last thing that I think is really interesting is I think that we will be reading way more AI generated writing in documents and emails, and we will like it.
1:03:19And I think we're we will we are already doing this in coding where we read plan documents. Like, I don't want an engineer to handwrite a plan document.
1:03:29That would be very silly. It would be it would be obviously silly. And I think the same is true.
1:03:36You know, when we did our our, 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're we had really one Notion agent, and then we had a top level company strategy.
1:03:51And then we had everybody in the company just, um, talked to an agent, and it asked them about what happened last year, how did it go, what were your goals, what what do you wanna do this year, what are your metrics?
1:04:05They pushed back, and then it was like, how does it how does this relate to the overall company idea? Like, all that kind of stuff. And then I got I got these, like, incredibly good AI generated, like, strategy reports or or plan like, quarterly plans for for each part of each team.
1:04:19And then I could go in and be like, 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?
1:04:29And, you know, who's which one of these is, like, at 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.
1:04:39And 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.
1:04:48And the slop one is it took them less time to make it than it takes me to read it, and they don't stand behind every line.
1:04:58So my expectation is if you send me an AI generated document, I think that's great. And if we talk about it and it's clear you have no idea what's in it, like, big no no.
1:05:08Not allowed to do that. And I I think we have this this aversion to AI generated stuff that will go away because the kind of strategy document that g p t five point five can write when it's directed well by someone on my team is way better than, like, them just, like, dinking and dunking, like like,
1:05:27their fingers on the keyboard. Right. Like, most people are really bad at writing their documents.
1:05:31The bar is low. Yeah.
1:05:33And and same thing with email. Like, I most of my email is written by g p t five point five and Codex right now.
1:05:39And I would I honestly would prefer it to say that it's coming from g p t 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 to send an email to to one of our investors, and I asked Codex to go do it.
1:05:59And use like, Codex knows to ask me, and it usually does. But this time, it didn't.
1:06:05And it just sent the email, and I didn't look at it at all. And I was like, fuck.
1:06:10And so I went to my sent and looked at it, and I was like, oh, this is exactly what I would've sent. And so it's like, it's pretty close to to that a lot of the time. It can be, like, a little over formal, and there's a couple of things that that it's just when you really think about it, most of your email is kinda it's not it's kind of rote.
1:06:31It's kind of prosaic. It's kind of I I definitely wanna 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.
1:06:43Sometimes I do a lot. And this is coming from a writer. Like, I care a ton about writing.
1:06:47I think that human writing is incredibly important, and I expect we only publish human writing. Well, actually, we publish in the mix of human and AI writing, but we always label it.
1:06:56Sometimes it's nice to have an AI coauthor on certain things. Um, I absolutely think that, uh, human writing is important, and I think that the
1:07:05the the reaction or the aversion to AI writing is silly. It's such an interesting lens on that because when people think about AI writing, I think about social media and videos. And your point is internally, if you're just, like, working on planning and documents and email and things like that, like, that is much less scary that it's AI written in your to your point, people are already doing this.
1:07:26You almost prefer it a lot of times because people are really bad doing this anyway. We have this too for external stuff. Like, we publish all these guides, and the guides are often agent
1:07:35they're agent assisted, and the agent is a coauthor. And they're intended to be read both by humans and by agents. And that's because, like, if you're writing a huge informational thing mean, you do this all the time.
1:07:49In order to, like, really apply it, the best way to do that is just, like, have your agent ingest it. And remember the next time I'm, you know, doing pricing to, like, remind me of this guide, and we'll go through it together or whatever.
1:08:02It allows you to operationalize the ideas much better, and it allows you to go much deeper because agents can read, like, 10,000 pages in, a second. And so you you can you talk to the human about the story and the stuff that matters and the core ideas, and the agent has all the details that it can then apply for you when you need it.
1:08:20Awesome.
1:08:22Anything else in this category before we get into our final category? No. Okay.
1:08:27Let's do it. So the final bucket is just who will be successful in this AI future that we are approaching slash what should people be working on to be successful in this next year or two?
1:08:39I am super,
1:08:42super bullish on PMs. And I know that your audience will probably love that, but my my anecdotal case that has convinced me of this is we have this guy internally.
1:08:56His name is Marcus, and he runs Spiral, which is our writing app. Marcus is a PM by training.
1:09:04He he previously ran Axios Axios' writing product and was a was a PM and had a big team, and it got to, you know, tens of millions in of of revenue in ARR.
1:09:17And he took a year off that job and just got super AI built and just learned how to use cursor basically really well. Now I think he uses Cloud Code, but he was extremely cursor built for a long time.
1:09:30And he's I would call him, like, lightly technical. Like, knows what a database migration is. Like, if he has to look at the code, I think he can understand it, but he's like, I we never could have hired him to do this job even a year ago.
1:09:46But the coding models have gotten good enough that he can pair the kind of the technical knowledge that he does have with his really spiky product sense and sense for writing and sense for users, And it's, like, it's so dangerous.
1:10:01Like, he ships faster than almost anyone on the team, and he has such a eye for every single user, every single conversation. Like, what does it mean, and how do we collect it into a story about, like, where we wanna go next, and what are the issues we need to fix, and, like, all that kind of stuff.
1:10:16And 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.
1:10:23And it's super impressive, and it makes me very, very bullish on any PM who gets, like, really AI built. Music to my ears, Dan. Uh, you're making a lot of very happy listeners here.
1:10:33Uh, 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?
1:10:41Figuring out what to build, figuring out if it's great, figuring out what problems to solve. So I love that you're actually seeing this come to fruition. I I I really believe it.
1:10:50This could be the highest rated podcast episode of my whole podcast. They're on I love it. Hell, yeah.
1:10:54It's gonna be okay. SaaS is SaaS is back. PMs are back.
1:10:58You know? This is the most contrarian episode I've ever done.
1:11:04Oh my god. So okay. So the other the other people that I think are gonna be, like, super superpower people, and I again, I this is because we see this internally, is full stack designers.
1:11:15If you're a designer and you're in these tools all the time, you're so used to, okay, okay, I make this beautiful interaction, and the engineer, like, just doesn't wanna do it, or it doesn't, like, happen the way I think it should happen, or you know, there's all this stuff.
1:11:29And I see so many designers for us internally or externally where they now feel so empowered to, like, go build stuff because they're like, I have all these ideas to make things look amazing and these interesting interactions. And that's the exact thing that it's really hard to do with vibe coding because it just all looks the same.
1:11:46So it all looks like slop, and they can make stuff that looks so different. And now they can actually build it. And what you see when we work with them internally is now they're just, like they're just making poor poor requests.
1:11:57Like, they don't they don't need to hand it off as much. Sometimes they do.
1:12:01But, like, a lot of times, they just make poor requests, and it's like, the thing is built, and that's it. And 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 because they can they can make stuff now.
1:12:15And I think
1:12:17designers are such creative people, and I think AI is, like, a super tool for anyone like that. I so agree. Even though there's cloud design, there's all these AI design y tools, like, once you see it, you're like, that's definitely cloud design.
1:12:31They're like the creativity, to your point, is gonna it just feels like it's gonna be more and more valuable to to to stand out from all the slop that people are shipping and launching constantly. So I completely agree.
1:12:42It's it's interesting that designer roles I do I do research on the job market. And interestingly, designer roles have not grown in a while.
1:12:50So I'm waiting to see if that becomes a big trend. Just like we need more designers.
1:12:54That is really interesting. We'll see. Yeah.
1:12:58We'll see. We'll see. That that might be a way to predict this is are people hiring more designers?
1:13:02I don't know. That is interesting. Yeah.
1:13:05Alright. Uh, that's so PM designer thriving.
1:13:09Kilometers designer thriving. Um, I also just think generally the AI jobpocalypse is not really a thing.
1:13:16Absolutely. We see companies starting to reorganize, and I think that makes a lot of sense. I I think, to be honest, a lot a lot of the reorganization, you can say it's AI, but it's like we overhired and, like, the company's not doing as well and all that kind of it was, like, coming, and this is a good excuse.
1:13:31But the, like, mass unemployment thing, I think that, like, some AI CEOs are talking about, like, I think that's not gonna happen. The the pattern that I see so far and, again, I don't have a total crystal ball, but I I do feel like we've seen enough of the new model drops to, like, have some sense of how this is going is that what a new model drop does or what models do in general is they make yesterday's human competence cheap.
1:13:56So what I mean by that is they ingest all this data of what what has happened already, and they make it really cheap to deploy that in in whatever situation you want as your as your own. Right?
1:14:10And what happens then is every this is a new this is a new power that everyone has, so it gets adopted super rapidly.
1:14:18And it's and suddenly that stuff is everywhere. It's like, suddenly, anyone can make a landing page. There's new landing pages everywhere.
1:14:25Suddenly, everyone can write. There's, like, slop tweets everywhere. But what's interesting is because it's all from because it's all coming from these models and everyone's using basically the same models, it all looks the same if you use it in the in the most default basic way.
1:14:45And so that's it becomes commoditized. Like, it's not valuable anymore.
1:14:50And what humans do is we sort of go in there, and we're like, yeah. We we have all this, like, frozen human competence from yesterday. How do I use this, like, make something new and interesting?
1:15:00And I really think that structurally, because of the way the models work, because of the financial incentives of model of model companies to, like, make them, uh, compliant and aligned, structurally, they're always going to be trailing behind those people who are taking taking the models and using them to make new expertise or or make new things that haven't been done that way before for their very, very particular situation.
1:15:27And that stuff is gonna get incorporated into the models, but, again, it will create room for people to, um, to push further ahead. And I think that you see this in a small way in, like, pretty much all the jobs is, like, engineers.
1:15:41Suddenly, 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.
1:15:47This is all slap. How does this actually how should this actually go in our code base? And I think that's something that the benchmarks rising don't doesn't really capture, and,
1:15:57uh, it feels like a thing that will take a long time to change. People may be hearing in this prediction here of just, okay. The jobocalypse not gonna people are not gonna be all fired.
1:16:08There's gonna be human jobs remaining for quite a while. It may be almost too comforting because you may you probably have to change the way you operate to still have a job in the future. Do have any sense of just, like, here's what you need to do to not be one of these layoffs?
1:16:25Yes. Yeah. And I think that is actually super important.
1:16:28The only thing you need to do is ride the models, and that means use them for whatever it is that you do. You know, we've talked about how Codex and Cowork are becoming the sort of standard operating system for work.
1:16:42If you're just doing that and when new models come out, you're trying them and figuring out, okay. How can I now there are new powers? How can I use them?
1:16:48Instead 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, if you ride on top of them, they ex extend your powers in a way that doesn't leave you behind.
1:17:04Like, you you're you're you're part of the future and part of the way work happens. And I think that
1:17:11we're going to need people doing that for a very, very long time. I like this term, ride the model. So the what's like say New Wallet comes out.
1:17:20What do you think someone say working at, I don't know, Salesforce?
1:17:23Say APM at Salesforce. What should they do to ride the model? Well, one of the things that's really interesting is a lot of companies, like, handicap their employees from even doing this because, like, I don't know what model.
1:17:34I 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.
1:17:40So 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 there are certain things where I know it can't quite do it yet, but when a new model comes out, I, like, always turn the rock over again to be like, can I do it now?
1:17:59You know? 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 60 out of a 100, which is, like, really good. So the way to ride the models is, like, not one specific thing because they're always changing, but it is to be curious and playful to apply the model, the new model to whatever it is that you care about, whether that's your job or something outside of your job, and to keep turning over rocks, uh, because it may not work now, but it may work eventually.
1:18:33It probably will work eventually. And the way that you use it matters.
1:18:37So what's really cool is that I think people think of the edge of AI as being in San Francisco, and I actually don't think that that's where it is.
1:18:48I think the edge of AI is wherever AI meets, like, a real human doing something. Because the people in San Francisco, they're making it, but they don't actually know a lot about how to use it.
1:18:58They don't know or at least they don't know everything about how to use it. They need to see how other people use it. And so you whenever a new model comes out, you get to be one of the first person one of the first people in the world to discover what it might be useful for.
1:19:10And that that's it's like a new discovery. And I think that's why, for example, we're in we're in Brooklyn. But I I really think of us, and I think we are, like, quite far ahead of people in San Francisco because we just use them for everything.
1:19:25And if people if people do that consistently,
1:19:32I think it's gonna be very hard to lose. That is one of the most amazing things about AI right now is no matter how much money you have or a little money you have, you have access to the most advanced AI model.
1:19:45Like, it's not free, so need some money. But, like and you can get it immediately when it comes out. Maybe the only people that have an advantage are the people working at OpenAI or Anthropic.
1:19:57But, otherwise, it's just, like, available. I know. I was at I was at their event with you their Code to Cloud event with you last week and, um, or a couple weeks ago, and they're they're, like, all using Methodos.
1:20:08And I'm like, god damn it. It's so annoying.
1:20:11But I I think that's totally true. Like, that is if IBM had invented AI, you can bet it would not be like this. And it would be, like, a bajillion dollars and only, like, top companies could use it, and they would be using it in the in the weirdest, most uninteresting ways.
1:20:29And I think there's it's there it's really important that AI was built in America and in the Silicon Valley culture that's like, we wanna make intelligence too cheap to meter.
1:20:41Like, that's not the default stance. And, um, it means that
1:20:47everyone has this broadly accessible tool that they can use, and I think that's amazing. That's such a good point. And interestingly, it's also created the most fastest growing companies in history, the biggest companies in history.
1:20:57That's true.
1:20:58A way to Those Silicon Valley guys, they're they're smart.
1:21:02If I zoom out on the conversation, it's really interesting. There's kind of these two sides to the coin. One is not a lot is actually like, so much is not changing.
1:21:11SaaS continues. Jobs not disappearing. We're still emailing each other.
1:21:15We're still working in Slack. Like, a lot of the work, not changed.
1:21:19On the other hand, every role transformed. Engineers don't write code. PMs don't write PRDs.
1:21:25Uh, design and design. You know, it's like it's so interesting how much has changed, how much has not changed. I don't know.
1:21:30It'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.
1:21:39Like, I'm simultaneously so excited, and it feels like everything has changed.
1:21:43And I'm so bullish on it and and the and the progress that we're making, all that kind of stuff. And, yeah, I just I feel like there are there are these things where they're gonna be pretty similar to how they are, and that's probably good.
1:21:54And I think, generally, our intuitions about the future the the model that I have of what our intuitions are about the future is the intuitions that people had in the middle ages about, like, what happened at the end of the horizon.
1:22:08You know? It's like, are there dragons? Like, does it drop off into nothingness or whatever?
1:22:13You know? Like, a lot of people have a lot of deep intuition that there's something terrible gonna happen over the horizon.
1:22:21And, also, that, uh, some people are like, there's something incredible. It's gonna change everything. We're gonna all gonna be happy as a utopia.
1:22:28And 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 . And I think that's that's the way to think about the future.
1:22:38And until you get to that place where you're starting to see it, and I think we get to see it because we get to see it internally all the time, it's important not to let your your mind get away from you and being like, this is gonna happen and this is gonna happen and whatever because you're you're gonna tell a story that sounds sounds so real in the moment, but later on, you're like, actually, it's much more complex than that in somewhere.
1:23:03It's sort of a both. Everything's changed and nothing has. Um, and once you get there, I think you're you're sort of start starting to see, like, oh, yeah.
1:23:10This is a real thing. Part of it is the AI companies are very good at scaring us about what might might happen in the future, and I think that's actually shifting. I think they've realized maybe we should not freak everybody out about the behaviors.
1:23:20That PR strategy just does not make any sense to me. I I do think that it's, like, genuine, but it's so ineffective, and, um, and I I think it's also wrong.
1:23:31How 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?
1:23:42Buy the models.
1:23:44I would try all of your workflows in Codecs or Cowork and see how that works.
1:23:54And if your company doesn't let you do it on your own time, I would try out some of these agent products like OpenClaw or Hermes or for less technical, there's there's, like, Victor.
1:24:06We have one plus ones. I I would get comfortable with both of those ways of working and try to, like, try to have fun.
1:24:15I think there's too much of I'm doing this because I have FOMO. Like, it might I might lose my job or, like, I might miss out on this big thing or whatever.
1:24:23And the best way to actually figure out interesting useful things to do with AI is to, like, do something enjoyable.
1:24:31We had a, um, Nikhil Singhal was on the podcast, and the way he described it is you gotta find your moment of joy with AI once you find, like, wow. I can't believe AI did this for me. This is awesome.
1:24:41I'm gonna keep building stuff. Yeah. I agree.
1:24:43If 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 or work and see if AI can do it. Get a lovable, get a Cloud Code, get a replete.
1:24:53Just try to build the thing. And often it's like, holy shit. This is so cool.
1:24:57Dan, is there anything else that we haven't covered? We've gone deep on so much. Is there anything else you wanted to share, anything else you want to predict or just say before we get to a very exciting lightning round?
1:25:08Uh, 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.
1:25:19And we're gonna we'll have AI score us. How about that? Well Great.
1:25:21Look look at the world like a dance prediction. Here it goes. Well, with that, Dan Shiffer, we've reached a very exciting lightning round.
1:25:28I've got five questions for you. Are you ready? I'm ready.
1:25:31What are two or three books that you find yourself recommending most to other people? Um, obviously,
1:25:37Annie Dillard.
1:25:40I 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.
1:25:47I think the last chapter is incredible, and it is at the intersection of writing and technology and the future and its, like, its relationship to the future and to time.
1:26:00And 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 Dillard just generally is fantastic.
1:26:10What else do I recommend? I'll just I'll just tell you a couple things that I've read that I'd, like, really liked recently. And and whenever I like something, I always just, like, tell everyone about it.
1:26:19So I have recommended these a lot. I I've been I've been reading one of the things I I'd learned, which I didn't know, is Churchill's a really good writer. And he has a whole history of World War two that he wrote, and it's like a combination history and memoir.
1:26:35And I think that's so cool because he was there. You know? He did it.
1:26:39And there's something about what we do at everywhere. 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.
1:26:48And and so Churchill's history of World War two is fantastic. I just finished the first volume. I'm on the second volume.
1:26:54The Nazis just invaded France. Very it's very captivating stuff. Um, that's one.
1:27:01I also just I I've been on, like, a little bit of, like, a quantum physics, like, kick recently. AI is very actually, very good for quantum physics if you get into it.
1:27:11And there's this book called the rigor of angels that I just finished, which is it's like a it's a history of ideas that relates Heisenberg, who has the his uncertainty principle, Borges, who's, like, a Argentinian fiction writer, has wrote a bunch of great short stories.
1:27:35They're actually starting to get, like, a lot of play now because they're very AI related and, um, and Kant. And very cool, like, super mind blowing.
1:27:45Lots of, like, interesting overlaps with AI stuff. And, yeah, highly recommend.
1:27:51I feel like we gotta 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, and I think we talked about it when I was We did.
1:28:02Visiting you. It's just So good. Never ends, but it's surprisingly compelling to read through the history of New York.
1:28:09Okay. Second question. Do you what is a recent movie or TV show you really see enjoyed if you have time for TV?
1:28:15So I've been watching a lot of basketball, so that's one.
1:28:18I'm I became a Knicks fan, like, this this year, so, uh, that's really fun. But, uh, I recently watched this I guess it's like a it's like a miniseries documentary called The Dark Wizard about this guy, Dean Potter, who he was like Alex Honnold before Alex Honnold was Alex Honnold.
1:28:40And, uh, he just has this, like, very extreme personality where he's, like, free soloing everything, and then he's, like, you know, base jumping in in, like, a wingsuit and stuff like that. And it's sort of exploring his psychology and what happened to him.
1:28:53And I I don't know. I I kinda like stuff like that.
1:28:58Like, there's another one called 100 foot wave where it's, like, about people who are trying to like, big wave surfers. There's something about that that sort of, I guess, just reminds me of founders or whatever, but, um, the dark wizard, highly recommend. Is there a product you recently discovered that you really love?
1:29:12Codex.
1:29:13Okay. It's like it's the it's really good. It's really good.
1:29:17Do you have a favorite life motto that you often come back to in work or in life? Yes. I have several.
1:29:24The the, like, the core one that I wrote for myself in college was, um, do things worth writing about and write things worth reading. And, uh, and then there's there's this guy, Rob Rubea, who's, like, very, um, very popular in, like, you know, a the AI meditation, like, overlap discourse, which is also a big thing, um, and who I also I really like him.
1:29:47He's dead, but I think he's amazing. And I've listened to, like, so many of his talks, and there's, like, this one talk that he gives where it's just, like, one sentence, but he just talks about, like, when you're dealing with stuff that's hard, what you wanna do is be able to relate to it from a position of spaciousness and strength.
1:30:13And 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?
1:30:21It's, like, a little bit more of, the David Goggins. Like, you just gotta, like, just gotta, like, go for it kind of and, like, just and sometimes that sometimes that can work.
1:30:33And, 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, you know, change my job.
1:30:45It is it has been very helpful for me to be like, am I coming at this from a vantage point of spaciousness and strength?
1:30:53And if not, can I, like, 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.
1:31:04Wow. I love that. Well, our final question, just on the on the theme of this conversation, curious if there's just, like, an AI tool that you think is still kind of underrated that you're just, like, recently
1:31:18I mean people have just know about. I I I I'll say codex. I hate to say this, but I have to because, like, any anyone who knows me like, we were at this this conference recently and then private conference.
1:31:30I'm, like, telling, like, Boris and Kat from Cloud Code, like, you have to try Codex. And, um, it's it's just really good.
1:31:37And the things that you can do with it are so different, especially if you're using it with the in app browser to do things like do your emails or check check analytics or, like, anything like that.
1:31:50It it has completely transformed the way I work, and I would be doing you a disservice if I, like, was searching for something else because it is that good. Damn. That's wild.
1:32:01Do you feel like Anthropic can catch up and or is this just like, well, they get No. Yes. I think I think they can.
1:32:06I I like like I said, I think it's gonna be a horse race, and and different people will be ahead at at different at at at different times. But I think right now, OpenAI has, like, has has gotten back the mandate of heaven a little bit.
1:32:20It's been it was a rough couple a couple months, like, months or so, but I think they're back. Interesting. And and you'd switch if one became I would.
1:32:28I would. People people it's funny. People are like, oh, are you, like, sponsored by OpenAI?
1:32:32And I'm like, no. I just, like, talk about what I like. I was super loud about Cloud Code when that was the thing I really liked, and I'll just say what I like when when it happens.
1:32:41You know? And to your point, people like, there's a lot of value in using both for different things. There is.
1:32:46I I switch back and forth. Like, I I truly do still use Quad a lot. Yeah.
1:32:51Such a big market.
1:32:53Well, Dan, we did it. We we went through so much. I can't wait to revisit this in a year slash, get this out so people can start planning for this next year.
1:33:02Two final questions. Where can folks find you and every what should people know? And then how can listeners be useful to you?
1:33:07You can find me on x at Dan Shipper, s h I p p e r, and you can subscribe to every. Please subscribe to everyevery.toevery.to/subscribe.
1:33:18How can listeners be useful? You know, have fun with AI. Like, seriously, it's it's super fun.
1:33:23There'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.
1:33:39Dan, thank you so much for being here. Thank you.
1:33:43Thank 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.
1:33:51Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.
The Hook
The bait, then the rug-pull.
A year ago, Dan Shipper said people were sleeping on Claude Code for non-engineering work. He was right. Now he has twelve more predictions -- and the most surprising one is that AI is not taking your job, it is burying you in work.
How they asked for the click.
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