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

The last time you were on this podcast, you had this hot take that people were sleeping on Claude Code. You were so unbelievably right. The premise of this

unbelievably right. The premise of this episode is we're going to go through what else you predict will happen.

The AI job apocalypse is not really a thing. I am super super bullish on PMs

thing. I am super super bullish on PMs and full-stack designers.

So, you guys are hiring doubled in 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 [music] very bullish on humans.

Automation is a lie. Every agent needs a human. We have so much automation, so

human. We have so much automation, so much AI, and I also work way more.

Creativity. It just feels like it's going to 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. What humans do is we go in there and we're like, "Yeah, we have all this frozen human competence from yesterday. How do I use this like

from yesterday. How do I use this like make something new and interesting?"

What are some predictions for how the way we work is going to change?

It's going to bifurcate in two main ways. One is everyone's going to have at

ways. One is everyone's going to have at least one agent that they talk to, that they can offload work to. Second is that most of the work that you do [music] is actually going to happen on your

computer in an environment like Codex or Claude Co-work.

What you're predicting here is the SaaS tools will run within Codex or Claude Code.

I think the SaaS apocalypse is dumb. I

would buy SaaS stocks right now. What

agents do is increase the number of users of SaaS, not get rid of it.

A lot of people are moving to CLI and trying to work from the terminal.

[music] We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over.

Today my guest is Dan Shipper, CEO and founder of Every. Dan and his team are building maybe the most AI forward startup out there. And as a result, are very much living in the future of how

work is going to look as AI becomes a bigger and bigger part of our day-to-day. Everybody at their company,

day-to-day. Everybody at their company, including every non-technical person, uses Codex and Co-work and Claude Code to get much of their work done. And this

is why, way before [music] anybody else, Dan saw the rise of cloud code and what is now Cohere, which he predicted almost a year ago when he was on the podcast last [music] time. So, I asked Dan to

come back on the podcast to share his current biggest predictions for how work is [music] going to change over the coming year for most people. We chatted

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 [music] 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 [music] expecting him to say, and we are going to revisit this conversation exactly a year from

today to see how much he got right.

Before we get into it, do not forget [music] to check out Lenny's Product Hunt dot 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

subscribers. With that, I bring you Dan Shipper.

[music] Dan, thank you so much for being here and welcome back to the podcast. Thanks

for having me. Always a pleasure to be with you. The last time you were on this

with you. The 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 cloud code and in particular cloud code for

non-engineering work, for just like fixing files, sorting your hard drive, just all these things that people hadn't thought about. Nobody was talking about

thought about. Nobody was talking about this. This was a year ago.

this. This was a year ago.

You were so unbelievably right about this. It's just like unreal what has

this. It's just like unreal what has happened since then. They built Cohere, which was this whole They built on this very specific idea using cloud code for non-technical work. A Codex is getting

non-technical work. A Codex is getting into this now. I imagine you've been seeing this. They're like leaning into

seeing this. They're like leaning into this non-technical use of basically coding agents.

I feel like this has also been a big part of Anthropic's success over the past year, just like how do non-technical people use this stuff?

Uh so, you were just so go on this stuff. I I I even wrote a newsletter

stuff. I I I even wrote a newsletter post building on this idea. I'm like,

"Hey, this is you interesting. I should

dig into this." I asked people, "How do you use cloud code for non-engineering work?" And I just had like so many

work?" And I just had like so many examples and it's like my second most popular post.

So, uh clearly you uh you you have a unique glimpse into where things are heading. So, the premise of this episode

heading. So, the premise of this episode is we're going to go through uh what else you predict will happen in the future, how things will change for people building products.

And I think it'd be helpful to start with giving people a brief glimpse into just how you operate and how your team operates that gives you this unique lens into where things are going. So, just

give us a sense of how you how you work.

Thank you. Um I I really appreciate the introduction. Um

introduction. Um and yeah, I think what 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 want to

do is prognosticate.

What do you What you want to do instead is um is just live in it together. So,

everybody at Every is an AI early adopter. We're almost 30 people now. I

adopter. We're almost 30 people now. I

think when when we did our interview we were 15, so we've doubled in size in the in the last year.

We're all early adopters and we have engineers, we have designers, we have writers, we have editors, we have um sales people, we have customer service people.

And everybody has a little bit of that um whatever that thing is where you're just like, "Oh, I like to explore. I like to experiment. I'm very curious and I'm

experiment. I'm very curious and I'm like super all in on AI."

And what I what that does, I think, is it creates this like little pocket of the future where we're all living in it together and we get to be a little bit further ahead cuz at any other company there's like a mix of people. There's

early adopters, there's like there's sort of like the middle of the pack people and there's people who are that like very anti.

And another thing that happens, which is really cool, is we get to because of our role um you know, reviewing models and and being a little bit of a tastemaker in AI, we

get access to stuff before it comes out.

So, we get to beta test and alpha test and kind of help help steer the direction of where things are going a little bit, which is very, very cool.

And so, when when I think about predicting the future, um it's actually when you create an environment like that, it's actually just about um noticing what's going on.

Um and and I think what a core part of it, too, is writing about it. I think

articulating what you're noticing, articulating the future, kind of brings it about in this way that um uh makes it real for you and your team and then anybody else who's like on the

internet who's reading it.

And so, the Claude Code thing, it was this is this very organic thing where for us, um we tried Claude Code when it came out.

That's sort of our job. We we we try all the new stuff from all the new all the new model all we tried all the new stuff from the model companies.

And at the time it was like a little bit early. But right around, I think like

early. But right around, I think like Sonnet 3.5 or Sonnet 3.7, we were testing that to do our vibe check on it.

And we're like, "Holy [ __ ] This is crazy. This is like really You can They

crazy. This is like really You can They got rid of the code editor." And so, from that point on, we just basically we we run At this point now we run like six products software products internally.

At 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.

Everybody was you know, talking to their computer in English using Claude Code in the terminal.

And so, I was able to see like, "Ooh, this is starting to happen."

Um and then be because my job is a little bit to just like push and play with stuff, I was like, "I wonder if I could use this for like my writing. Like, how

could I do that?" And then it just like starts to unfold and you're like, "Okay, this is not ready yet, but it's obviously useful for me. You know, my like one of the

for me. You know, my like one of the things that we talk about internally is what I call the reach test, which is like, do you just like, when you wake up in the morning, do you like reach for it organically? I love this combination of

organically? I love this combination of uh you're using the latest stuff, and I think this is, as you said, maybe an under underrated skill, you're you're good at uh being self-aware of here's what's weird

and new and different and interesting.

So, that's a really cool combination, partly cuz you have to write about it, and you write about it. So, I think that's like the perfect recipe for someone having a sense of where things are going.

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So, the way that I'm going to structure this conversation, there's going to be basically three buckets of predictions.

One is how the way we work is going to change in the coming years.

Two is how what the shape of the work we're going to be doing is going to look like and change. And then three is who is going to be most successful in this future / what should you be doing and

working on now to be successful in this future? Lenny, my only ask is we come on

future? Lenny, my only ask is we come on a year from now and then you score it. I

want to score it.

Okay, so this is a year from now. Okay.

Okay. So, is this let's actually is [clears throat] this like your predictions for in a year this is what it's going to look like or this is like the emerging future?

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 going to talk about will be pretty apparent within a year, but it probably it may it may take longer than that. But I think it will it should within at least a year be like

not obviously wrong. Like it it seem it could it should seem like it's moving in that direction to count. Okay. May of

2027, we will review your predictions.

Right. Amazing.

Confirmed. Okay, I love this. Okay, so

let's dive in.

What are some predictions for how the way we work is going to change in the coming year? One of my favorite

coming year? One of my favorite questions because I think if you look at the benchmarks, you're just looking at okay, like yeah, AI is going to just take all of our jobs basically, you know.

I 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 it

what's it called? Oh, like mythos preview the like big anthropic model that everyone's like so worried about it can do tasks of 17 hours at 50% accuracy. It's like holy [ __ ] that's

accuracy. It's like holy [ __ ] that's crazy. And I think it is real. It's true

crazy. And I think it is real. It's true

and 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.

Humans have a lot more work to do.

Even as models get better at doing work and there's like a really interesting paradox there.

And my prediction for the like how how work will Well, my my big prediction of how work will change or how you will be doing work in a year is it's going to bifurcate in this in

two main ways, how you how you use agents.

One is you're going to be doing I think like what we figured you would be doing like 5 years ago when we thought about how work with AI works, which is everyone's

going to have at least in their company at least one agent that they talk to that uh can do work, that they can offload work to. And we'll

talk about like what that looks like, but it's essentially like Open Claw.

The 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

Co-work that becomes the sort of operating system for it becomes the sort of operating system for how how you do all of your work, whether that's your email, the documents

you create, like all that kind of stuff.

It's going to be on that kind of a surface. It's that's becoming the the

surface. It's that's becoming the the clear competitive landscape.

Um so there's I want to go in order of those two.

Um so the first one is you're going to 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 going to be like for that. Um is

everyone going to have an agent? Uh is

every team going to have an agent? Is it

going to be like just one agent? Is it

like agent specializes? There's this

parallel shadow org chart.

And when Open Claw first came out, everyone internally at Every adopted it, and I was very convinced that it would be a everyone has their own

agent. And there's like some real really

agent. And 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

chart. Agents in that world sort of become little reflections of you, which is like really cool and really interesting. It's like if you ever Did

interesting. It's like if you ever Did you ever read The Golden Compass?

Um it's like having a little daemon on your shoulder. You know, it's a little

your shoulder. You know, it's a little part of your soul.

Um I I really think like that's sort of what it looked like was happening.

Um and so I was very into personal agents. And I have completely flipped.

agents. And I have completely flipped.

And I I really think that uh the the model for now is going to be a super agent, like one agent for the entire company. And I you you're

entire company. And I you you're starting to see this in some companies.

So like um Shopify very famously has one. Uh Ramp has one now. Um and and I

one. Uh Ramp has one now. Um 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 Open Claw. Everyone's like,

"I'm going to set it up. It's so cool."

or whatever. And then everyone realizes it's like way too much work. This thing

breaks all the time. I got to like fumble around with it. I got to be able to SSH into my server and like blah blah blah. And most people

blah. And most people to do work at least just don't want to spend that time or can't. Um

and the the like fundamental underlying thing that drives that is whether it's Open Claw or any other harness, in order for an AI agent to be useful right now,

it really needs a human who cares about it. It really needs it like a human

it. It really needs it 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, "Ah, like I don't I don't want to like maintain this like dumb Open Claw." is the minute the agent is like not really that useful anymore.

And that's why it I think it has started to shift to a more uh one agent per company model because for

now like the the the ideal is uh you you basically set up a forward deployed engineer or someone with that sort of profile who's responsible for making sure that that agent is working for the whole company. And then maybe you have

whole company. And then maybe you have some like some little team agents. Um

and I think as the models get better at being more independent, that will like shift down and you will it'll be more likely that we'll have more personal agents cuz we don't have to [ __ ] around with all the internals. But um

the model that I see working for us and for a lot of other companies, including the model companies, the model companies themselves are starting to see this, is when it comes to the sort of like async

agents, it's really a you know, you have one agent at the top that's like doing sometimes it's everything, a lot of times it's um a particular kind of job that you've decided that everyone in the company

needs an agent for like data requests.

And uh and then I think it will start to it will start at the top at at the top and then it sort of starts to trickle down where you make it more specialized agents and teams and and all that kind of stuff.

And the mechanism is agents need people who care about them. That is so interesting that point about you need to like garden your agent because there's context you have to keep adding to it.

There's like it breaks as you said and it's just like once it's just too much work, you're like, okay, forget this thing. I'm going to go back to CodeX or

thing. I'm going to go back to CodeX or Cloud or something like that. Exactly.

Okay, cool. So this is a cool opportunity. So the idea that So what

opportunity. So the idea that So what you're predicting here is uh companies will have this super agent that everyone can talk to. As you said, a Shopify's got River, I think it's called. What's

the Ramp one called? I can't remember.

Okay. It's probably got a fun name.

Okay. So uh So that's the prediction.

Okay.

That's that's the first prediction.

That's the first prediction.

um we will start with agents at the top that uh that are more general and are used by more people in the company and then it will start to kind of

grow down as the as people get more used to these use cases, they get more specialized and um agents become uh less

uh uh less fiddly. Like they just work better. And is this mostly going to be

better. And is this mostly going to be in Slack, do you predict? For work?

Yeah, it seems to make sense. I think

people I people love having the green bubbles on Open Claw. Um like it I'm sorry, the the blue bubbles on Open Claw. Like if you can use it with your

Claw. Like if you can use it with your iPhone, but I think there's this little thing in people's heads where they really like to keep their personal and work agents separate. Mhm. And

um I think there's a whole there's a whole territory Our our COO Brandon Gall calls this um computer errands. There's

like a this whole territory of using personal agents for your computer errands. It's like

errands. It's like order my groceries or whatever, and it's like there's so much of that that I think this is going to it's going to be huge for, but um I focus we focus mostly on the work stuff.

Um and I think that's going to happen mostly in Slack. Sweet. Go Slack. Should

we Do you want to talk about the uh the other work surface?

Absolutely. Codex co-work. Okay. This is

the last Let's do it. I'm so excited about this one. I think it's the coolest thing. So, basically what happened was

thing. So, basically what happened was Anthropic realized at some point that with Claude 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. It

uses the terminal, so it has like basically superpowered access to it. And

not only that, it really these agents really understand how to use the terminal cuz there's so much content online about about that.

And it it created this like superpowerful coding paradigm, which is um you know, Anthropic was really doing it first. OpenAI for a while was I I I

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. It's really

interesting.

Um but they were very early on this.

Um when people were still thinking about coding agents or coding models as being really pair programmers, they were among the first to be like, "No." And do it successfully. Like there

"No." And do it successfully. Like there

were people before them like Devin who I think had had a big had the big like cloud environment and and Open AI tried this too, but um but the the real

adoption seems to have happened when you uh put it on your computer.

So, they figured that out. And then I think they figured out um 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 want to do.

And people started just hacking Claude code essentially to do all of their work. So, Anthropic then built co-work

work. So, Anthropic then built co-work um which is you know, a little bit of a nicer wrapping around Claude code, but it's fun- fundamentally the same thing.

And then I think you know, I think Open AI 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. Like it

was a little hard to they they didn't quite get what you meant. They get ex- they got exactly

meant. They get ex- they got exactly what you said.

And 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, we get it.

Like it's um this model is fast. It's

like really good for general purpose knowledge work type tasks." And 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 co-work. And you can kind of see that in the tabs on the on the Anthropic

desktop app UI.

I think Open AI 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. Like it

they're going to have different positions. Um

positions. Um but I I think Codex right now is my daily driver. I like spend all all my

daily driver. I like spend all all my time in it basically. I 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 cuz I again I think it'll change.

Whoever is 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. It has an in-app browser. And when I'm writing a

browser. And when I'm writing a document, I just go into one of my uh one of my Codex threads which I have one thread for every project. And I just open the in-app browser. I go to the

document. I usually do it in Proof which

document. I usually do it in Proof which is this um online mark markdown editor that I built.

And then I just have Codex running and watching me in Proof. And Codex can see what I'm doing. I can see what Codex is doing. It's all kind of in one place

doing. It's all kind of in one place which is the an extension of the same thing that made Claude code work really well originally. And I basically feel

well originally. And I basically feel like I have this parallel work buddy that not only can it like respond and write in the document, but then it can go do research. It can go it

can use my computer to basically do anything that I can do on my computer.

And that's like incredibly powerful.

Um and I do this with everything. Like

I've been in I've been in inbox zero for like 10 days straight now which if you know me is crazy. I'm never like this. And that's because I literally

this. And that's because I literally just have Codex gather all my emails with Cora which is our email agent. And then um it it

renders a little page uh and I I think I showed you this at the Anthra- at the Anthropic event. It renders a little

Anthropic event. It renders a little page and I just like monologue into it and just talk at each email. I'm like,

"Okay, go go research this. Oh, here's a question from our lawyers. Can you go like collect all of the you know, documents from the last like four years and then put them into a report and send them?" And it just does it. And so all

them?" And it just does it. And so all the stuff that I would procrastinate on, I don't really procrastinate on anymore.

And so I feel like there's this For a long time, we thought I thought, too, that the optimal experience of AI was going to be take AI and put it in a

browser.

And I think the reverse is actually starting to happen and be like really, really valuable in a way that I did not expect, which is take the AI agent that you use all the time on your computer and put a browser

in it so it can see everything you're doing. And that is just like a magical

doing. And that is just like a magical combination that I think will be is very uncommon now. You can't even do this in

uncommon now. You can't even do this in cloud in cloud code um because they they don't let you browse external websites inside of cloud code. So it's very uncommon now, but I think it will be

super common in a year. This is more profound than it may even sound. What

I'm hearing is instead of AI being baked into SaaS tools, what you're predicting here is uh you will the SaaS tools will run within

Codex or Claude code.

That That is That is one uh really important uh second-order effect of this is um Okay so 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. And

the agent has access to the website, so it has access to everything that I have access to. And it has access to my whole

access to. And it has access to my whole computer. When I run the agent on that

computer. When I run the agent on that website, I'm using my tokens.

I'm not using the the vendor's tokens.

I'm not using the app's tokens. And so

it puts SaaS back in its place where yeah, you want to make it friendly for an agent. And everyone's got a CLI now.

an agent. And everyone's got a CLI now.

Um you want to make the HTML uh really uh really usable. You want to 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 going to be the thing that users use in the sense that you don't need to build an agent naturally into your product. I think you can and there's there's another really interesting bifurcation of this that

that that we should talk about which is that having two agents is better than one.

Um but I think for now there's this really cool thing where uh with proof for example, uh anyone who uses it, I don't pay for tokens because they're just bringing

they bring their AI to the to proof. And

so it changes what you build as a SaaS company uh and you build it now for both humans and agents to use at the same time and it changes your margins back to well, I don't really have to pay for tokens anymore cuz the user is going to

bring AI.

So I think this is a huge deal. So what

you're describing here is uh more and more work that we do, more and more professional work is it just going to happen within Codex or Cloud Code. Uh how where does Cursor fit into this? Is that one of the is is there potential there?

That's a good question. I think that Cursor sees a lot of the same stuff.

And they're and in some ways they have some of the same stuff but it's better.

Like I think that Cursor's cloud implementation is better than either it or when AI's or Anthropic's and is more advanced.

And I think that Cursor has at least so far more distinctly chosen a lane. Like

they're more distinctly choosing to be a for programmers.

And that may limit how far they get in here.

Like I think the definition of programmer is expanding enough that they'll have a big market, but I don't know that they're going to jump into like okay, use this to make a slide deck or whatever. But it is really clear

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 um

the model. And so where the where all

the model. And so where the where all the platforms are moving is to a world where you're not just doing prompt and response when you call the the model at

on on the open AI platform, the Anthropic platform, you are they are 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 and so you see, you know, Anthropic's got cloud managed agents.

Um open AI does not have a have a response yet, but I assume that that's going to happen and now Cursor uh was just essentially acquired by SpaceX.

It's not like a full acquisition, but it's close. So I think people are

it's close. So I think people are starting to realize like I can't just do the like model part of it. I have to have this like harness above it and 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 going to be a hard decision for them whether to stay just for coders or not.

So people building products that aren't open AI or Anthropic, if this proves to be true, the prediction here is they're going to be using your product over time inside of one of these agents.

Uh is there something you would do if you were one of those companies to prepare for that future?

I would I would just prepare for that.

So like, you know, for for example, um every more classic piece of productivity software, whether it's Slack or uh Word docs or PowerPoints or whatever,

it's really mostly meant for a human to use.

Um and now people are doing CLIs, so it's like meant for uh an agent to use independently of a human.

And we're moving into this new paradigm, I think, where the human and the agent are on the same piece of work together and they're both doing things and you need to have I need to have visibility into what the agent is doing. The agent

has to have visibility into what I'm doing. We have to go back and forth in

doing. We have to go back and forth in this sort of like seamless way.

And the kind of software that you make for that is going to be very different.

So, for example, um like there's a lot of stuff that Proof doesn't have. I don't have to have a lot

doesn't have. I don't have to have a lot of the like Word document kind of like formatting or page breaks or like, you know, making tables or whatever cuz the agent just does it. I don't need to worry about that. It can do all the formatting for me.

So, you can make the products a lot simpler and faster to start than the legacy products are.

And then there's all these other affordances that you need to start to have because the way agents interact with software is very different. So, for

example, agents can do a lot at once. They can

just do like a billion different things to your document or your slide deck or your code base or whatever. And how you display that to the user is going to be very different than the way you might

display a human being concurrent in your document and doing stuff. You need um you need like approval. You need a sort of inbox that sort of summarizes, here's all the stuff that's going to happen or

has happened. You need um you need logs

has happened. You need um you need logs and the ability to roll it back real quick.

So, there's all those kinds of considerations that um that change the actual product. And then

the underlying UX of it or the underlying infrastructure you need is different too because you know, agents can make a billion requests in like 3 seconds. So, how are you going to deal with that, right? Um

this is exactly why, you know, GitHub is having problems right now cuz because the the number of people using GitHub has is skyrocketing exponentially and it's really just people's agencies in GitHub.

So, I I think it's a this whole new world that is just starting you're just starting to see like a little peek of it. But, there's so many cool things

it. But, there's so many cool things about it. So, for example, in Proof

about it. So, for example, in Proof and some of our other products, too, uh when someone has a problem, they don't email support. Their agent

sends a bug report.

And an agent bug report is way better than a human bug report. Um it has like, here's exactly what I did, here's the exact repro steps, here's like Proof is open source, so here's what I think is

going in the code base. And then we just get that, it becomes a GitHub issue, and then we can just like send off an agent to fix it. And um you can't do that with everything, but it's so much better.

And you can see the like the glimmers of this this very fast like closed loop between I ran into something, a paper cut, a little feature I want, a little

bug, and my agent just goes off and talks to the company agent, and then the company agent just goes and fixes it. That I think is incredibly

fixes it. That I think is incredibly cool.

So, is there a part of this that you a lot of people are moving to a CLI and trying to work from the terminal? Is

part of this prediction that people shift away from that and back to actually you you acts with agents kind of running alongside them? CLIs are

over.

Um we we speed ran the CLI uh era. It

was nice while it lasted, but I think it's pretty it's pretty clear It's not that CLI Sorry. It's not that CLIs are going to completely go away. Obviously,

they've been around for the last like 30 years or 40 years or 50 years or whatever. They will continue to be

whatever. They will continue to be around. And I think there is this moment

around. And I think there is this moment when cloud code was like so popular and 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 a CLI, and I don't think that's what it is. And

when you move into an actual UI for this, you start to realize um we made GUIs for a reason.

And it's just nicer to be in a GUI. And you

can get all the same benefits inside inside of a GUI, especially for non-programmer 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.

I think a lot of programmers are still flipping into it every once in a while, but it's more or less they're using Codex, cloud code, cursor, um that kind of thing. Awesome. Okay. I I I would I

of thing. Awesome. Okay. I I I would I definitely wanted to make that part clear. So, coming back to kind of the

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. And then there's on

answer questions. And then there's on your computer running Codex or Cloud Code.

And within that all the work that you normally do kind of on your computer is now going to be living within Codex or Cloud Code or maybe some third-party that emerges that we're not not even aware of yet. Yes, and you're going to

use apps inside of the internal browser of those of those tools.

Wow, okay. Like

listening to you talk about it, it's like it may not feel as profound as it is cuz this is a big change to how we work. We don't currently have an AI that

work. We don't currently have an AI that we talk to regularly in Slack and we also don't work currently mostly in Codex or Cloud Code. So, this is actually a pretty massive shift. I think

so.

Is there anything else along these lines before we get into our next prediction?

Well, a few things. I'm definitely not an agent maximalist. Like I really think we're going to have a lot of different agents that we use. Seems pretty clear to me.

And I really do think that two agents are better than one.

So, what's a good example?

When 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.

And it can go back and forth talking about things that would take a long time for me to express directly to an agent that you get this like speed up effect when

you assume that your users are you are using Codex or Cloud Code or Co-work as their as their basic way they access your app.

And a really simple example, we have this um hosted open claw product which we we had it we had it on wait list. We actually

had to deposit because we started taking people off the wait list and open claw is just a very hard agent harness to to make work. It's like

make work. It's like it's moving so incredibly fast.

Uh and if you're like a platform for it, it just it's like it when things break you can't fix it. It's very hard. Um

but one of the things that we learned in that process is if you're let's say you're building an agent product um or or a new any new software experience, what you would assume, let's say to set

up an agent, is you need to build like a little like web interface or a little um slack workflow that ask people about, "Okay like who are you and um what are you going to

use this for and like what's your what's your ideal, you know, dream outcome?" Or what like whatever the things you are that you would put on an on onboarding checklist.

If instead you you just you just make a hard line of we are only going to service users who use Codex or Co-work.

Um what happens is you just paste something into you just paste a prompt into Codex or Co-work.

It goes and talks to the app and the app can be either just a regular server or it or it can be its own agent.

And Codex has so much information about you that it can just give it here's all the stuff I've been working on with Dan.

Um here's all the ways that you know, he might he might want to use this app and then bring it back to me and it's this very custom experience.

And also for a technical product like an agent, when something goes wrong, I can just tell Codex, "Go fix it." And

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 more things I want to touch on before we get started cuz there's like so much to talk about. Uh

one of you made this point about SAS tools not using like you can use tokens from the uh model companies basically when using a SAS tool. Talk a bit more about that cuz that may change the business

model for SAS companies in the future.

That feels like a big deal. Well, I

think it actually may 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 going to be the main way that I that people interact with me."

And I think that uh and that costs tokens, obviously. And

I actually think once I have once I have Codex or Co-work as my main work surface, I still want to use SAS. So, this is another good prediction. I would buy SAS stocks right now. Um I would I think the

SAS apocalypse is done and SAS stocks will be up majorly in the next couple years.

Not Not investment advice, but you know, I would buy SAS stocks.

Um So, So, uh so I think it saves your margin because now what you're what the way that you're thinking that is not I have

to build AI into this. It's It's more like I have to make a piece of software that humans and AI want to collaborate on together.

And that's hard, but it's once you build it, it's a lot cheaper than assuming everyone's spending tokens.

And um I it's I think it's a I think it's a good business. And And part of the

good business. And And part of the reason I'm so bullish on SAS is A, everybody internally here is uh like I said, we've all got agents and

we're all using Codex and whatever and we still pay for a ton of SAS and our SAS spend is up year over year.

And we're not like vibe coding every single like little thing, you know?

And I think that what agents do is increase the number of users of SAS.

Not get rid of it.

And so I think SAS companies are going to see like an insane spike in the amount of demand that they have because there's going to be tons of agents using these products at like a very high volume.

And like I said, that's a huge infrastructure challenge.

There's a There's a lot of like interesting pricing challenges, but uh it it it makes me very bullish on SAS.

I love that if anything else comes out of this conversation, Dan Shipper, SAS is the future of AI. This

[laughter] You to be SAS.

#sendtweet I I love just Yeah, this is quite contrarian and the other interesting piece is that the fact that you guys are hiring that you doubled in people in the past year, which is not what people

would have expected from a company that is so AI forward. Talk about what your experience there of just Okay, we still actually need humans. Automation is a lie.

Um 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. And so

um you know, I wrote this piece a couple years ago called the allocation about the allocation economy like the idea that the way that humans are going to work with AI is going to is going to be like like being a manager.

And the thing that you have to remember about managers is like managers actually spend a lot of time working.

Most managers are not like on the beach.

They're like checking in with their employees all the time and and and and trying to figure out Okay, how do we make this work good? How do we make it better? How's it doing? How's this

better? How's it doing? How's this

person doing? All that kind of stuff.

And I think there's there's some differences between being a human manager and being a model manager, but um fundamentally it it still requires a lot of time and attention.

And I think that we kind of miss that in the model discourse. And one of the reasons is benchmarks make it look like AI is more autonomous than it is.

And by autonomy, I mean something specific by autonomy, and I'm going to try to express this. It's like a little hard to express, but I learned this for myself because I've been feeling this paradox a little bit. I've been feeling the like

little bit. I've been feeling the like we have so much automation, so much AI, and I also work way more.

[snorts] And I think part of the paradox or 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 a the senior engineer benchmark, and it's like, "How good is AI versus a human engineer?"

engineer?" And the way that I built it is again, have this app proof. I just

vibe coded it on the side and I like while running the rest of every.

And when we launched it, because it was completely vibe coded, it just started going down and I couldn't fix it. And it

was very embarrassing. I had a lot of egg on my face. And like the product worked. We We tested it internally. We

worked. We We tested it internally. We

had a lot of beta testers, but like the day after launch, it was like just every like 10 minutes the servers would go down and people were looking at me and I'd be like, "I don't know what's going on." Like, "Codex, fix it." And

going on." Like, "Codex, fix it." And

Codex was like, "I don't know what's going on." Um or really Codex was like,

going on." Um or really Codex was like, uh "I do know what's what's going on. I

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.

So, uh that's a There's a life lesson in there. Vibe coder elbow.

there. Vibe coder elbow.

[laughter] Um so, 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 um tells me how they did it, right? And so,

what I get to do is when we get new models I just give the new model a prompt. I

say like, "This is vibe coded slop. If you wanted to rewrite it from first principles, how would you write it? Go do it.

And all the models until GPT 5.5 got like a 30 out of 100.

And senior like a human senior engineer gets like high 80s, low 90s out of 100.

So, there's a lot to go. And then I tried GPT 5.5 and it got like a 62.

And mind you, the 60 the 60 score was um GPT 5.5 using an Opus 4.7 plan. Opus

4.7 plans are very good. GPT 5.5 is the only model though that has the sense of agency and confidence to just like rip out old code and just like actually rewrite from first principles.

Other coding models, they kind of like try they like end up papering over the edges around the edges and they're like, "Oh, this is a big job. Like I'll just do a little patch." And you're like, "No, I like specifically told you not to."

to." So, GPT 5.5 there's like a 30-point bump in the score. 60 out of 100. It's like

very it's very clear that in a year or less, it's going to be senior engineer level, right? And that gives you a certain picture in your mind, especially based on how I named the benchmark, which I think a lot of

benchmarks do.

And I can tell you that when we get to that point, I will be very it will be very easy for me to change the benchmark to zero out the current model.

So, that gets a zero out of 100.

And so, for example, uh it seems like there's no skill or no thought into the prompt, which is this is vibe code is slop like fix it from first principles, but actually it took

me a a while to get to a prompt that didn't give away the answer, but uh uh but got the model to reveal what it's capable of.

And the original prompt I gave it was the original prompt that I gave it when uh when I was trying to fix the issue in production was going down, which is like I'd woken up I'd I'd woken up in the

morning and I was like, "Okay, we had four or five reported issues yesterday. I want you to go

issues yesterday. I want you to go through all the issues and then come to like a make a plan for how to resolve all of them and go do it, right?" And every coding

model on the market, and I 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.

Every coding model on the market will take that instruction seriously. And if

I tell it, "Here's a bunch of issues, go fix it."

fix it." They will just go try to fix the issues.

What a actual human senior engineer does is they go look at the code base and they're like, "This is a piece of [ __ ] This guy doesn't know what he's doing."

[laughter] And then then they say, "We're going to have to like actually rewrite a lot of this and it's going to be hard and risky. I know you don't want to hear

risky. I know you don't want to hear that, but like we're going to have to do that."

that." And if you asked the model, "Hey, like should we do that?" It'll it'll probably it it'll probably get there, but it's not going to do it on its own.

Um and it and there's a lot of incentives pushing against it doing that. And even if it does that, there's

that. And even if it does that, there's a there's always a higher frame for us to go.

And so I think it's it's really important uh when when we think about benchmark progress to think about it from that perspective, which is benchmarks rise on problems

that we've framed that we can articulate, that we can score.

And there's a lot of work that's human work that uh it it can't be scored until you write it down, but the act of thinking to prompt it or write it down um 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. And it's I think it's why

even though the models are getting better at automation, I still hire engineers.

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One 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 is like very valuable data.

Original human code. Yeah, exactly.

That's exactly right. And it's so interesting that that's exactly the kind of code used to build this model. Well,

what's interesting? So, I want to I want to clarify there. So, I did not have a human write the code all by hand. Because I

actually think that that's sort of it feels silly to me.

Like, I don't really care because I know if if an engineer is not using AI, like I'm not going to work with them. I don't

really care. It's like it's sort of like am I going to race a human against a car? Like, I probably wouldn't do that. But, um I would race a human in a car versus another human in a

car and say which one's better.

And in this case, what the the way the benchmark is structured is yeah, like these human engineers used AI, but they used it in a way that I could not cuz I didn't understand it and I didn't have time and I didn't really want to like go in and try to understand the code base,

to be honest.

And I think that's a really important thing when we think about benchmarks is 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 cuz AI doesn't use itself.

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

Okay, I'm going to try to wrap up our first bucket. There's so much to talk

first bucket. There's so much to talk about. I've made a little list of things

about. I've made a little list of things that I think people should do based on your predictions to be successful. We'll talk about this at

be successful. We'll talk about this at the end, too, but just a few things.

One is start using Codex or cloud code more and more for the work you're doing and especially the browser, use tools inside of it.

Two is allow your allow agents to be to use your products. If you're really going to SAS tool, make it easy for agents to be a user essentially.

Three is start thinking about some Slack bot that you can work with, like try out tools. Like I know Slack has their own

tools. Like I know Slack has their own Slack bot that I think is really good, too, and I haven't played with it, but people really like it. So, look for I guess a tool that could become the AI

agent within your company.

Buy SAS stock ASAP.

[laughter] Not investment advice.

I think that's totally right. I will

like my slight tweak is when you're thinking about building your software for agents, the current model is I'm building a CLI that

an agent uses, but they're using it in a sort of like they're being I delegated a task to the agent and the 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

app together. The agent's probably using the CLI, but you're using the web interface and they're they both need to be in sync.

And that is I think a new challenge that's really interesting.

Awesome. Anything else before we get to our next uh category? Bisas.

That's the title.

[laughter] Oh, man. Okay. So, the second uh

Oh, man. Okay. So, the second uh category of predictions is around just the shape of the work that we're going to be doing is going to change.

Uh what do you predict? There's all this interesting stuff in terms of in terms of the shape of work. Like once you're in this land where you've got, you know, these you've got async uh async agents off that you delegate work

to then you've got your like Codex cloud code like work surface that that starts to happen. So,

to happen. So, one thing that we see a lot internally and you also see this in the big model companies is the number of pull requests that you get is like skyrockets.

You know, we have people, you know, in consulting or in ops roles or whatever who are or or editors just like making pull requests. Um

pull requests. Um and 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 going to be doing things that previously only technical users could do.

And what that does is it creates all this pressure on the other end for the people who have to deal with all of the new code for how to deal with that.

And so, I think there's a lot of there's a lot of interesting things that happen with that. Like so, for example,

with that. Like so, for example, um uh like open claw, I mentioned that earlier. Pete

earlier. Pete gets like thousands of pull requests a day on open claw 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.

It's really crazy. I actually think that that's going to be more and more common.

Um there's like it brings up a lot of really interesting questions around um which pull request should you merge?

And you know, when you whenever you add capacity in one part of your process, like it breaks things. Um it

used to be really hard to build things, and now it's very easy. So,

the the point is not, can we build it?

It's like, would it make sense with the rest of what we've built? And how do we keep a like sense of a coherent whole?

And also, what do we delete? I think

Entropic does this really well. Like

they they delete a lot of stuff from Cloud Code to make sure that's not bloated.

So, I I think there's a there's a lot of that going to happen. On one side, there's a lot of um non-technical people can do technical work, and then technical people are in charge of making

sure that that work gets into a product or into a process in a cohesive, coherent way. And also, their product

coherent way. And also, their product people are going to be doing that, too.

And I think that's that's quite cool. Something I'm hearing from people is that now that everyone can do everything, like engineers can design, PMs can code, marketing people

can ship stuff.

There's just this like confusion about what the hell is my job anymore. Yeah.

What am I responsible for, exactly?

Like, am I supposed to be shipping stuff? Am I still a marketing person?

stuff? Am I still a marketing person?

And it's just creating a lot of confusion and certainty in the world. I

think I think that's for real, and one of the things that I think is special about every is everyone is sort of a generalist and really loves like having their fingers in a lot of different

pots, or whatever the metaphor is. I

think that'll probably settle down at some point, and it'll feel more normal.

Like, marketing people are still going to do marketing, even if they're touching the website. Like, that's just part of marketing now.

But I also think that you can get a lot further being a generalist now, and that's like really cool, especially for for smaller companies.

The The other thing that I think is interesting is there are definitely some new job roles that are a thing.

And the thing that is becoming really clear is the whole forward deployed engineer concept I think is for real.

And it comes out of every agent needs a human.

Uh even like you go to the big model companies, they have they they have these agents that run internally. They

have like teams of people that run these agents, you know? And I I don't think those teams are going away.

The models are going to get more powerful, the agents are going to get more powerful, and the number of agents is going to grow, but people are still going to manage them.

And so that looks like a very specific kind of person.

And you know, we have a couple of those people internally here, and it's like the the people who are in charge of making sure your agents are working and doing the right thing. We also do

consulting, so we we we lend that out to people, and and I think that's a big um that's a big thing that that people want and it's another one of those places where you're like, "Hmm,

automation was supposed to take away jobs, but it looks like it just created one or many."

[laughter] You know, um and there's a specific type of engineer that really loves, you know, Nitesh, who's one of our uh who who fits this. He's an AI engineer

and he he fits the sort of forward deployed um category, and he's on our team.

He spends most of his time actually talking to one of our agents in Slack.

We have an agent internally called Claudie, which runs our whole consulting practice.

And and he spends a lot of time in Slack.

Like there's there is code, and he is using Claud code and other things like that, but a lot of it is just talking to it and being like, "Why did you do this dumb thing? Like let's

let's fix that, you know?"

Um and so there's certain kinds of engineers that I think love that and love having their hands on the latest thing, and also love making this like being that's like in in the works in a work space and it looks a bit different than

more traditional building more traditional software.

And your sense there is we're not going to we're not near a place where these agents don't need a human. You 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. It feels like both are

forever part. It feels like both are important. Is what I'm hearing like this

important. Is what I'm hearing like this is going to be a job for a long time. AI

is not going to get smart enough to just automate it you fully automate for a while. Yes, I'm simultaneously extremely

while. Yes, I'm simultaneously extremely AI filled extremely and very bullish on humans and the role of humans in making sure that AI is working well.

Interesting.

Okay, so the two kind of buckets here that you're talking about one is like the way I think I hear what you described earlier is this the pace of shipping software and everything is just increasing which also means

there's so much more work reviewing all this sloppy output. I was just talking to a data science friend and he was saying how his team is just as data science team is just their job used to

be do analysis, answer questions, see if this experiment was a good was a was positive. Now it's just everyone's doing

positive. Now it's just everyone's doing that and they're sharing the results and they're and they're like no this is not correct and most of their job is now reviewing bad data science work. Which

is a problem and it means that and the same thing is happening with engineers and it means that you need more like you actually need data engineers for this

and you need data scientists and 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 mono companies for example like at least one of them

has literally a data science bot that every single person in the org can query that is hooked up to their data warehouse that knows who's who so that it knows at

the warehouse level like who has permission to access what.

And so all of the basic questions because they're there's a team that sets up this bot. All of the basic questions that people might want to ask that it sometimes gets that might get wrong that they're constantly making sure it's

getting it right. And so the data science team doesn't have to answer all of the like [ __ ] 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 scientist would hate their lives. Yeah.

It does though make the job maybe less fun cuz you're just sitting there you know gardening people's sloppy work versus what I think is like it it can actually make the job better because for the data

scientist you're now not dealing with all the silly requests. You're dealing

with the deep the deeper questions that are harder for the the team who's dealing with all the basic requests and building an agent to do that.

It's it's like filtering all that stuff out so you can focus. Here's a question I've been thinking about. I was not planning to talk about this but it's something that I've been thinking about.

So the question is which product tech role is the least changed now. So like

engineers 100% of code AI now. It's like

a completely different job.

Product management a lot of the you know PRDs are you don't have to write as much. You can ship code. You don't have

much. You can ship code. You don't have to wait for people. Design the whole design process dead according to recent guests just like there's no time to do the whole design process very different role. Data science very different work

role. Data science very different work now.

There's marketing there's sales. So

here's the question. What do you think is the least fundamentally changed role so far? Well one interesting thing is

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. Mhm. It seems that way. I I

this stuff. Mhm. It seems that way. I I

I think the opposite is actually true.

Like my experience we do a lot of this with senior executives and senior leadership teams. My experience is that your company's only going to go as far as your CEO goes in AI and it's not something you can delegate. You have to

have your hands in it cuz you don't otherwise you don't have an intuition for it. But for a long time it has

for it. But for a long time it has seemed like yeah, that's something that the people who are doing the work have to do but like I don't have to do that.

Like I'll just tell them what to do.

And And so I think if you're a CEO, you kind of can get away with your day looking very similar.

I I think that will change rapidly at some point where it'll be like oh no, I'm like way behind but for now because or maybe even middle managers, like those kinds of people I think are are

it's fairly similar.

I think like maybe sales because it's so so impersonal.

that's yeah, that's my vote. You know,

there it's sort of creeping up in the kind of BDR like we can deal with a lot of, you know, BDR type of type queries.

You're only talking to like people who actually want it. And you can do it for sales it's like it's so useful to to

like do research. Like my favorite codex, like one of my favorite codex experiences is we're hiring a head of L&D.

And I you know, we always put out a job post whatever but I was like I feel like there's this company called General Assembly in New York and they do like they've done really good technology

education for a long time. And so I was like I feel like someone who is into who who who worked at General Assembly and is now into AI would be really good and I just like literally typed it into

codex and then like went off and was doing something else and I came back and it found like this the perfect guy.

It was like worked at General Assembly, was an instructor like is super AI pilled and follows me on Twitter.

So I just DM'd him and then I had dinner with him. And it's like that's crazy.

with him. And it's like that's crazy.

You know, that would have taken so long before.

And super valuable for sales, for recruiting, all that kind of stuff.

Yeah, sales is where my mind went. Like

the top of funnel AI is helping a lot with sourcing and qualifying things like that. It feels like the the work of a

that. It feels like the the work of a salesperson is not fundamentally different. Yeah.

different. Yeah.

And customer support has fundamentally changed. So that's interesting sales. So

changed. So that's interesting sales. So

far so good for the for those folks.

Yeah.

Okay. So maybe just summarizing some of the predictions in this bucket of just like the shape of the work, how it's going to change. What I'm hearing so far is it's going to be a lot more reviewing of other people's output as a part of the work.

And then two, there's going to be a lot of like almost babysitting of AI agents to make them do the thing you want them to do for deploying and then just gardening them along the way, make sure they continue to do their work.

Um anything else before we get into our third bucket? I would sort of split it

third bucket? I would sort of split it into less babysitting agents and more your forward deployed team is trying to

build a whole system that makes it so that people who have less knowledge can use that system without like doing something dumb.

And that's like a really interesting engineering challenge.

I think babysitting kind of makes it feel like it's yeah, you're just kind of like, you know, waiting for it to [ __ ] up and then fixing it or whatever. And you can you can that can be the case, but I think a lot of it is just extremely interesting

engineering challenge of building a system for to enable everybody else in the 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.

One 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.

And I think we're we will already we are already doing this in coding where we read plan documents.

Like I don't want an engineer to handwrite a plan document. That would be very silly. It would be It would be

very silly. It would be It would be obviously silly.

Um and I think the same is true, you know, when we did our our uh quarterly planning for every at the end of 2025 we did it all with Notion agents. And we just had a bunch of

agents. And we just had a bunch of Notion agents and or we had really one Notion agent and then we had a top-level company strategy and 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 want to do this year, what are your metrics, it pushed back, and then

it was like, how does it How does this relate to the overall company idea? Like

all that kind of stuff. And then I got I got these like incredibly good AI generated like strategy reports or or plan like quarterly plans or for each part of each

team. And then I could go in and be

team. And 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? Um

each other? Um and uh you know, who's Which one of these is like 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.

Um and I see that all the time now. Like

I I I consistently get AI generated stuff and there is a difference between an AI generated document that's slop and not.

And the slop one is it took them less time to make it than it takes me to read it.

And they don't stand behind every line.

So my expectation is, if you send me an AI generated document, I think that's great. And

great. And if we talk about it and it's clear you have no idea what's in it, like big no-no. Not allowed to do that.

no-no. Not allowed to do that.

Um and I I think we this this aversion to AI-generated stuff that will go away because the kind of strategy document that GPT-5.5 can write when it's directed

well by someone on my team is way better than like them just like dinking and dunking like like their fingers on the keyboard.

Right. Like most people are really bad at writing strategy documents. So, the

bar is low. Yeah.

And and same thing with email. Like I most of my email is written by GPT-5.5 and Codex right now. And I would I honestly would prefer it to say that it's coming

from GPT-5.5 and I may change it to do that. But I had this I had this

that. But I had this I had this experience the other day where I had this I had to send an email to um to one of our investors and

I asked Codex like go do it and you like Codex knows to ask me and it usually does, but this time it didn't.

And it just sent the email.

And I didn't look at it at all. And I

was like, [ __ ] And so, I went to my sent and looked at it and I was like, oh, this is exactly what I would have sent. And so, it's like it's pretty

sent. And so, it's like it's pretty close to to that a lot of the time. Um

it can be like a little over formal and there's a couple things that that it's just when you really think about it, most of your email is kind of it's not

it's kind of wrote. It's kind of prosaic. It's kind of

prosaic. It's kind of I I definitely want to be the one to think about what it should say, like what what it should say, but the actual sentences don't matter that much to me

usually. Sometimes they do a lot. And

usually. Sometimes they do a lot. And

this is coming from a writer. Like I

care a ton about writing. I think that human writing is incredibly important.

And I expect we only publish human writing. Well, actually, we publish a

writing. Well, actually, we publish a mix of human and AI writing, but we always label it. Um sometimes it's nice to have an AI co-author on certain things. Um

things. Um I absolutely think that uh human writing is important and I think that the the the reaction or the

aversion to AI writing is silly. It's

such an interesting lens on that 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. And to your to your point,

written. And to your to your point, people are already doing this. You

almost prefer it a lot of times cuz people are really bad Totally. We have

this too for external stuff. Like we

publish all these guides and the guides are often agent they're agent assisted and the agent is a co-author and they're intended to be read both by humans and by agents. And that's because

like, if you're writing a huge informational thing, I mean, you do this all the time.

Um, in order to like really apply it, the best way to do that is just like have your agent ingest it and remember the next time I'm, you know,

doing pricing to like remind me of this guide and we'll go through it together or whatever.

It allows you to operationalize uh, the ideas much better and it allows you to go much deeper because agents can read like 10,000 pages in like a second.

And so you 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.

Awesome.

Anything else in this category before we get into our final category? No.

Okay, let's do it. So, the final bucket is just who will be successful in this AI future that we are approaching {slash} what should people be working on to be successful in this next year or

two?

I am super super bullish on PMs. And I know that your audience will probably love that.

Um but my my anecdotal case that has convinced me of this is we have this guy internally his name is Marcus and he runs Spiral, which

is our writing app.

Marcus is a PM by training.

He He previously ran Axios Axios's writing product and was it was a PM and had a big team and it got to, you know, tens of millions in of revenue and ARR.

And he took a year off that job and just got super AI pilled.

And just learned how to use cursor basically really well. Now, I think he uses cloud code, but he was extremely cursor pilled for a long time.

And he's I would call him like lightly technical.

Um like knows what a database migration is. Like if he has to look at the code,

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.

But the coding models have gotten good enough that he can pair the kind of the technical knowledge 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. Like he

ships faster than almost anyone on the team and he has such a eye for every single user, every single conversation, like what does it mean and how do we collect it into a story about

like where we want to go next and what are the issues we need to fix and like all that kind of stuff. And I think that he feels liberated cuz he doesn't have to organize a whole team of people to do

that. He can just do it. And it's super

that. He can just do it. And it's super impressive and it makes me very, very bullish on any PM who gets like really AI pilled. Music to my ears, Dan. You're

AI pilled. Music to my ears, Dan. You're

making a lot of very happy listeners here.

I've been saying this for a long time, too. It's just like the skills you need

too. It's just like the skills you need to build are the things like the building out is done for you. What do

you need to be good at? Figuring out

what to build, figuring out if it's great, figuring out what problems to solve.

So, I love that you're actually seeing this come to fruition. I I I really believe it.

This could be the highest rated podcast episode of my whole podcast. There is

going to be like [laughter] Hell yeah. It's going to be okay. Stats

Hell yeah. It's going to be okay. Stats

is Stas is back, PMs are back, you know.

This is the most contrarian episode I've ever done.

[laughter] Oh my god.

So, okay, so the other the other people that I think are going to be like super super power people and I again I this is cuz we see this internally is full stack designers.

If you're a designer and you're in these tools all the time, you're so used to um okay, I make this beautiful interaction and the engineer like just doesn't want to do it or it doesn't like happen the way I think it

should happen or you know, there's all this stuff and I see so many designers for us internally or externally where they now feel so empowered to like go build stuff cuz 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 live coding because it just all looks the same so it all looks like slop and they can make stuff that

looks so different and now they can actually build it. And what you see when we work with them internally is now they're just like they're just making pull pull requests.

Like they don't they don't need to hand it off as much.

Sometimes they do but like a lot of times they just make pull requests and it's like the thing is built and that's it and I think that's incredible for the way that companies work but it's also there's a huge opportunity for those people to become much better and like

start their own thing cuz they can they can make stuff now and I think designers are such creative people and I think AI is like a super tool for anyone like that.

I so agree. Even though there is cloud design, there's all these AI designing tools, like once you see it, you're like that's definitely cloud design. And

they're like the creativity to your point is it just feels like it's going to be more and more valuable to do to stand out from all the slop that people are shipping and launching all constantly. So, I completely agree. It's

constantly. So, I completely agree. It's

It's interesting that designer roles I do I do research on the job market and interestingly designer roles have not grown in a while. So, I'm waiting to see if that becomes a big trend just like we

need more designers. Hm, that is really interesting. We'll see.

interesting. We'll see.

Yeah.

We'll see. We'll see. That that might be a way to predict this is are people hiring more designers? I don't know.

That is interesting. Yeah.

All right.

Uh so, that's so PM designer thriving.

PM designer thriving.

Um I also just think generally the AI job apocalypse is not really a thing.

Absolutely, we see companies starting to reorganize and I think that makes a lot of sense.

I I think to be honest a lot a lot of the reorganization you can say it's AI, but it's like we over hired and like the company's not doing as well and all that kind of it was like coming and this is a good excuse.

But the like mass unemployment thing I think that like some AI CEOs are talking about like I think that's not going to 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. So,

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?

Um and what happens then is every this is a new this is a new power that everyone has. So, it gets adopted super rapidly and it's and

suddenly that stuff is everywhere. It's

like suddenly anyone can make a landing page, there's new landing pages everywhere. Suddenly everyone can write,

everywhere. Suddenly 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, uh

it all looks the same if you use it in the in the most default basic way.

And so, that's it becomes commoditized. Like it's not valuable anymore. And what humans do is

valuable anymore. And what humans do is we sort of go in there and we're like, "Yeah, we have all this like frozen human competence from yesterday.

How do I use this like make something new and interesting?"

And I really think that structurally, because of the way the models work, because of the financial incentives of model of model companies to like make

them um uh compliant and aligned, structurally, there are 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.

And that stuff is going to 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. Suddenly, everyone's an

engineers. Suddenly, everyone's an engineer. That doesn't mean we fire the

engineer. That doesn't mean we fire the engineers. There's like way more demand

engineers. There's like way more demand for engineers cuz you need the engineers to like figure out, "Okay, this is all slop. How does this actually How should

slop. 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 uh it feels like a thing

capture. And uh it feels like a thing that will take a long time to change.

People may be hearing in this uh prediction here of just, "Okay, the job apocalypse is not going to People are not going to be all fired. There's going

to be human jobs remaining for quite a while." It may be almost too comforting

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 you have any sense of just like, "Here's what you

need to do to not be one of these layoffs?" Yes. And I think that is

layoffs?" Yes. And I think that is actually super important. Um

the only thing you need to do is ride the models.

And that means use them for whatever it is that you do.

You know, we've talked about how Codex and Co-work are becoming the a of standard operating system for work. If

you're just doing that and when new models come out, you're trying them and figuring out, okay, how can I now they're new powers, how can I use them instead of just being like, I'm going to like try to ignore it cuz it like makes me afraid, which I think is honestly

it's rational, it's a reasonable response. And also uh if you ride on top

response. And also uh if you ride on top of them, they ex- extend your powers in a way that doesn't leave you behind.

Like you you're you're you're part of the future and part of the way work happens and I think that uh we'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 saying you all comes out, what do you think someone say working at I don't know, Salesforce. Say a PM at Salesforce, what should they do to ride the model? Well, one of the things

the model? Well, one of the things that's really interesting is a lot of companies like handicap their employees from even doing this because like I don't know what model I don't know if you can use the latest models at Salesforce, you know, like a lot of

times you have to wait or it's, you know, whatever. So,

know, whatever. So, maybe you have to do it on your in your off time. But, the thing that I really

off time. But, the thing that I really like to do with new models is play.

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

can it do it now? You know, um so, it you know, it could not do the senior engineer benchmark last time and I turned it over turned the rock over again and now it's out of 60 out of 100, which is like really good. Um

so, the way to ride the models is like not one specific thing cuz 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, it probably will work eventually and the way that you use it matters. So,

what's really cool is that I think people think of the edge of AI as being in San Francisco.

And I actually don't think that that's where it is. I think the edge of AI is wherever AI meets like a real human doing something.

Because the people in San Francisco, they're making it, but they don't actually know a lot about how to use it.

They don't know or at least they don't know everything about how to use it.

They need to see how other people use it. And so you whenever new model comes

it. And so you whenever new model comes out, you get to be one of the first person one of the first people in the world to discover what it might be useful for. And like that's it's like a new discovery. And I think

that's why, for example, we're in we're in Brooklyn. But I I really think of us and I think we are like quite far ahead of people in San Francisco because

we just use them for everything. And um

if people uh if people do that consistently, I think it's going to be very hard to lose.

That is one of the amaze most amazing things about AI right now is no matter how much money you have or little money you have, you have access to the most advanced AI model. Like it's

not free, so you need some money.

Uh uh but like and you can get it immediately when it comes out. Maybe the

only people that have an advantage are the people working at OpenAI or Anthropic.

Um but otherwise, it's just like available. I know I was at I was at uh

available. I know I was at I was at uh their event with you their code event with you last week and um or a couple weeks ago and they're they're like all using mythos and I'm like, "God damn it."

it." So annoying. [laughter]

So annoying. [laughter] But I I think that's totally true. Like

that is if IBM had invented AI, you can bet it would not be like this.

And it would be like a bajillion dollars and only like the top companies could use it and they would be using it in the in the weirdest, most uninteresting ways.

And I think there's it's there it's really important that AI was built in America and in the Silicon Valley culture that's like we want to make intelligence too cheap to meter. Like

that's not the default stance.

And um it means that everyone has this broadly accessible tool that they can use and I think that's amazing. It's such a good point

that's amazing. It's such a good point and interestingly it's also created the most fastest growing companies in history, the biggest companies in history. That's

true.

Not the way to Those Silicon Valley guys, they're they're smart.

If I zoom out on the conversation, it's really interesting. There's a kind of

really interesting. There's a kind of these two sides to the coin. One is not a lot is actually like so much is not changing. SaaS continues, jobs not

changing. SaaS continues, jobs not disappearing. We're still emailing each

disappearing. We're still emailing each other. We're still working in Slack.

other. We're still working in Slack.

Like a lot of the work not changed. On the other hand, every

not changed. On the other hand, every role transformed. Engineers don't write

role transformed. Engineers don't write code. PMs don't write PRDs. Design and

code. PMs don't write PRDs. 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.

It's interesting that people think it's going to be this whole new world, but in many ways it's okay. It'll continue the way it is with a lot of stuff around the edges. That's that's how I feel. Like

edges. That's that's how I feel. Like

I'm simultaneously so excited and it feels like everything has changed. And

I'm so bullish on it and and the and the progress that we're going to make and all that kind of stuff. And yeah, I just I feel like there are there are these things where they're going to be pretty similar to how they are and that's probably good.

And I think generally our intuitions about the future the the model that I have of what our intuitions are about the future is the intuitions that people had in the Middle

Ages about like what happened at the end of the horizon, you know, it's like are there dragons? Like does it drop off

there dragons? Like does it drop off into nothingness or whatever? You know,

like a lot of people have a lot of deep intuition that there's something terrible going to happen over the horizon. And also that uh some people

horizon. And also that uh some people are like there's something incredible.

It's It's to change everything. We're

going to all all going to be happy as a utopia. And what happens is you get

utopia. And what happens is you get there and you're like, there's some really cool things, there's some not cool things, and it's just another horizon. And I think that's that's the

horizon. And I think that's that's the way to think about the future. And until

you get to that place where you're starting to see it, and I think we get to see it cuz we get to see it internally all the time, it's important not to let your your mind get away from you and being

like, this is going to happen and this is going to happen and whatever cuz you're you're going to tell a story that sounds sounds so real in the moment, but um later on you're like, actually it's much more complex than that and somewhere

it's sort of a both everything has 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, this is a real thing.

Part of it is that 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 that they've realized maybe we should not freak everybody out about the dangers.

I that PR strategy just does not make any sense to me. I I do think that it's like genuine, but it's so ineffective and um and I I think it's also wrong.

Mhm.

How about we um end with maybe just like a few things listeners should do to be successful over the next year with the the way the world is moving. Write

the models.

I would uh try all of your workflows in Codex or Co-work and see how that works. And if your

company doesn't let you do it on your own time, I would try out some of these um agent products like Open Claw or Hermes or um for less technical people there's

there's like Victor, we have 1 + 1's. I

I would get comfortable with both of those ways of working.

And try to like try to have fun. I think there's too much of I'm doing this because I have FOMO like it might I might lose my job or like I might miss out on this big thing or whatever and

the best way to actually figure out interesting useful things to do with AI is to like do something enjoyable.

We had a um Nikhil Singhal was on the podcast and the way he described it is you got to find your moment of joy with AI. Once you find like, "Wow, I can't

AI. Once you find like, "Wow, I can't believe AI did this for me. This is

awesome. I'm going to keep building stuff." Yeah, I agree.

stuff." Yeah, I agree.

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. Go to loveabull, go to clockcode, go to replit. Try to build the thing and often it's like, "Holy [ __ ] this is so cool."

Dan, is there anything else that we haven't covered? We've gone deep on so

haven't covered? We've gone deep on so much. Is there anything else you want to

much. Is there anything else you want to share? Anything else you want to predict

share? Anything else you want to predict or just say before we get to our very exciting lightning round?

Uh I think we covered it. We we did a lot. This is This is awesome and I'm

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.

We're going to We're going to have AI score us. How about that? We'll Look

score us. How about that? We'll Look

look at the world like a dance prediction series.

Well, with that Dan Shipper, we've reached our very exciting lightning round. I've got five questions for you.

round. I've got five questions for you.

Are you ready? I'm ready.

What are two or three books that you find yourself recommending most to other people? Um obviously Annie Dillard.

people? Um obviously Annie Dillard.

Um I Everyone at Every has to read The Writing Life.

Like when you join you get a copy and you have to read it. Uh you only have to read the last chapter though. I think

the last chapter is incredible and it is at the intersection of writing technology and the future and it's like it's

relationship to the future and to time and I think that's like it's it's everything about every like wrapped up into like a very tight chapter. It's so

good and I think Annie Dillard just generally is fantastic.

What else do I recommend? I'll just I'll just tell you a couple things that I've read that I like really liked recently um and and whenever I like something I always just like tell everyone about it.

So, um, I have recommended these a lot.

Um, I I've been I've been reading One of the things I I learned, which I didn't know, is Churchill's a really good writer.

And he has a whole history of World War II that he wrote, and it's like a combination history and memoir. And I

think that's so cool because he was there, you know, he did it. And there's

something about what we do at everywhere. I I feel some like sort of

everywhere. I I feel some like sort of kinship with that of like we're building stuff, we're writing stuff, and it's very rare to find people that also do that. And and so, Churchill's history of

that. And and so, Churchill's history of World War II is fantastic. I just

finished the first volume. I'm on the second volume. The Nazis just invaded

second volume. The Nazis just invaded France. Very It's very captivating

France. Very It's very captivating stuff. Um,

stuff. Um, so, that's one. I also just I I've been on a like a little bit of like a quantum physics like kick recently. AI is very

actually very good for quantum physics if you get into it. And there's this book called The Rigor of Angels that I just finished, which is um, it's like a it's a history of ideas that

relates uh, Heisenberg, who has the his uncertainty principle, um, Borges, who's uh, uh,

uh, like a uh, uh, Argentinian uh, fiction writer is wrote a bunch of great short stories that are actually starting to get like a lot of play now cuz they're very AI-related, and um, and Kant.

And very cool, like super mind-blowing. Lots

of like interesting overlaps with AI stuff. And uh,

stuff. And uh, yeah, highly recommend.

I feel like we could have a whole podcast episode about your reading and uh, books you recommend. I know this is a a passion of yours.

My current obsession is The Power Broker. I don't I think we talked about

Broker. I don't I think we talked about it when I was visiting you. It's just

never ends, but it's uh, surprisingly compelling to read through the history of New York.

Okay, second question. Do you What is a recent movie or show you really see recently enjoyed if you have time for TV? So, I've been watching a lot of

TV? So, I've been watching a lot of basketball, so that's one. Um

I'm I became a Knicks fan like this this year, so uh that's really fun. But

uh I recently watched this I guess it's like a it's like a mini-series documentary called The Dark Wizard about this guy Dean Potter who he

was like Alex Honnold before Alex Honnold was Alex Honnold.

And 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 and um I I don't know. I I kind of like stuff like that. Like there's another one called 100 Foot Wave where it's like about people who are trying to like big wave surfers. There's something about

wave surfers. There's something about that that's sort of I guess it just reminds me of founders or whatever, but um The Dark Wizard, highly recommend. Is

there a product you recently discovered that you really love? Codex.

It's like it's the best It's really good.

It's really good. Do you have a favorite life motto that you often come back to in work or in life? Yes, I have several.

Um the the like the core one that I wrote for myself in college was um do things worth writing about and write things worth reading.

And uh and then there's there's this guy Rob Brezsny who's like very um very popular in like you know, the the

AI meditation like overlap discourse, which is also a big thing.

Um and who I also I really like him.

He's dead, but I think he's amazing.

And I listened to like so many of his talks and there's like this one talk that he gives where it's just like one sentence, but he just talks about like when you're

dealing with stuff that's hard, what you want to do is be able to relate to it from a position of spaciousness and strength.

And there is something I think really interesting and important in that. Like

a lot of the meditation discourse or just generally like how do you deal with hard things? It's like a little bit more

hard things? It's like a little bit more of like the David Goggins like you just got to like just got to like go for it kind of and like just um and sometimes that sometimes that can

work.

And also I think sometimes when you're dealing with things so for example when you're dealing with I'm super afraid of like how AI is going to um um

you know, change my job.

It is it has been very helpful for me to be like am I coming at this from a vantage point of spaciousness and strength and if not can I like get there? Because it will be

much more productive for me to deal with it from that place.

And that has been very very helpful for me.

Wow.

I love that.

Well, our final question uh 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 uh I mean are sleeping on. I I I I Codex. I hate to say this but I have to

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 at at an Anthropic conference and I'm like telling like Boris and Kat from Claude Code like you have to try

Codex. And

Codex. And um it's it's just really good and the things that you can do with it are so different.

Um uh especially if you're using it with the Anthropic browser to do things like your emails or check your check analytics or like anything like that. Um

it 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. Uh do you feel like Anthropic can catch up and or is this just like well No, yes, I think I think they can. I I like like I said, I think

they can. I I like like I said, I think it's going to be a horse race and and different people will be ahead at different at different times, but I think right

now Open AI has like has has gotten back the mandate of heaven a little bit. It's

been It was a rough couple a couple months, like 6 months or so, but I think they're back. Interesting. And and you'd

they're back. Interesting. And and you'd switch if one became more I would. I would. People People It's

I would. I would. People People It's funny. People are like, "Oh, are you

funny. People are like, "Oh, are you like sponsored by Open AI?" And I'm like, "No, I just like talk about what I like." I was super loud about Claude

like." I was super loud about Claude Code when that was the thing I really liked. And I'll just say what I like

liked. And I'll just say what I like when when it happens, you know? And to

your point, people like there's a lot of value in using both for different things. So people There is. I I switch

things. So people There is. I I switch back and forth. Like I I truly do still use Claude a lot. Yeah.

Such a big market.

Well, Dan, we did it. We We went through so much. I can't wait to revisit this in

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.

Um two final questions. Where can folks find you and every what should people know and then how can listeners be useful to you?

You can find me on X at Dan Shipper, s h i p p e r, and you can subscribe to every. Please subscribe to every,

every. Please subscribe to every, every.to.

every.to.

every.to/subscribe.

How can listeners be useful?

You know, have fun with AI. Like

seriously, it's it's super fun. There's

like a lot of It's not necessarily useful to me, but like it's it makes it I think it makes everything better when people put their hands in it and just like start figuring it out together rather than like arguing about

it. And so the most useful thing you can

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

Thank you.

Thank you so much for listening. If you

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