A New Vision for Human Work After Automation

Thesis: 2027 is made possible by our founding partners, Lovable and Notion. Lovable is the fastest way to turn ideas into software people love: https://link.lovable.dev/every Notion is the collaborative AI workspace where teams and agents work side by side: https://www.notion.com/?utm_source=youtube&utm_campaign=thesis-2027&utm_content=every-261111-video What does great human work look like after automation? It’s a question we try to answer every day at Every, and the one we built our conference, Thesis, around. In his closing keynote, Dan Shipper argues that as AI makes intelligence cheap and widely available, what matters most is human perspective. It’s your “non-consensus” point of view, plus your responsibility for a particular corner of the world, that helps you spot the insight no one else would. On this week’s episode of The Every Podcast, we’re sharing Dan’s full talk at Thesis: 2027. Dan explains why benchmark charts make the future of work look bleaker than it is, why picking the right idea still takes a human, and how we should really measure AI models. Subscribe to Every (free for 14 days): https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign= (https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode)thesistalkepisode Links to resources mentioned in the episode: Thesis: https://every.to/thesis-2027 Dan Shipper: https://x.com/danshipper OpenAI’s GDPval benchmark: https://openai.com/index/gdpval/ Timestamps: 0:00 Introduction 1:51 Work after automation 5:00 1 person vs 5 jobs 10:18 Why perspective matters 17:55 Better AI, more valuable humans

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Hey, welcome back to the Every Podcast, where we talk about the future of work. I'm Natalia Quintero, and at Every, our team of writers and builders use AI every day, so we can tell you how the frontier is shifting, what actually works, and what you need to know. So, last week at Every, we hosted our first-ever conference, Thesis. It was a full day of talks in Brooklyn, featuring speakers from Anthropic, OpenAI, Notion, Lovable, Ode, and many more. If you missed it, we'll have all of the talks available at every.to/tv. We built the whole day around one question: What does great human work look like after

• 0:44Speaker 00

  automation? Dan Shipper, Every's co-founder and CEO, closed the conference with his answer, which you'll hear later in this episode. He took our quarterly customer survey results and built a custom CRM, then a video game, and then personally reached out to a pretty vast number of our customers. In pre-AI terms, he did the work of about five people: a data scientist, a game developer, a marketer, an engineer, and a salesperson. But instead, he did all of that work with a single Claude subscription over the course of a single week. And if you ask Claude what it sees in the same survey, you'll probably get a generic answer that anyone could get. And that's actually Dan's point.

• 1:29Speaker 00

  Great work is the perspective you bring to the model. So, for the podcast this week, Dan's closing talk from our conference, Thesis. Enjoy.

• 1:51Dan Shipper?

  Thesis! Did everyone have a good time?

• 1:59Dan Shipper?

  Well, we gathered here to answer one question, which is, what is the future of great human work after automation? I hope you got some good answers today. Did you get some good answers? Yes? Yes? All right. I want to add my take, hopefully I can help bring it together a little bit. So, I really, I love this question. And, obviously, because we built the conference around it, it's really the question that we try to answer every day at Every. And what's really interesting about this question is, you're really trying to predict the future. And there's two, I think, real methods for predicting the future. One is what I'll call the aggregate, where you gather lots and lots of data, and then

• 2:44Dan Shipper?

  you extrapolate a trend line. And the other is called the intimate, where you zoom in on one real case or real situation, and then you extrapolate from there. And in our culture, we tend to emphasize the aggregate. The aggregate is objective, it's quantitative, and the intimate kind of feels a little bit anecdotal. And it's also, the aggregate's also extremely powerful. The reason we have AI is, the aggregate is, for example, scaling loss. We found that if you put more data and compute on one end of the system, you get more capability out.

• 3:29Dan Shipper?

  And all of us here are here because we're riding that curve that you can see in the aggregate. We also use the aggregate to answer this question that we have come here for: What does work look like after automation? A good example is GDPVal from OpenAI. It's a benchmark which measures how good models are at doing economically valuable tasks. And the way it works is, they have a bunch of experts pick tasks from their job, real tasks, and they turn it into a prompt. They give the prompts to a model, and they give a prompt to a human that's not using AI.

• 4:10Dan Shipper?

  And then blind experts pick which one is better, the human version or the AI version. And the prompts are very, very detailed. They look something like this. And in 2024, with GPT-4O experts, or AI won against a human expert 12.4% of the time. In 2025, it was 47.6. And in 2026, it was, it's about 83%. And this is with GPT-5.4, so I'm sure it's higher now. I'm sure this benchmark is saturated. And you look at that, and AI is beating a human 83% of the time, according to blind human experts, at expert work.

• 4:52Dan Shipper?

  And you're like, and I can like, I can also see this in my work, in the way that I work. If we look at my week, last week, we ran our quarterly customer survey. And in the cracks of my calendar, in the middle of all of the things that I had going on, I analyzed the survey data in a way that once would have required a data scientist or a growth person to do it. Like, I asked a ton of different questions and got a ton of different reports and outputs. I made a video game for the team to play. So, we played in our all-hands game to compete to see who knew our customers best. It was a live, real-time video game. I realized that there's actually a really interesting opportunity to sell Every to teams from all of this work.

• 5:37Dan Shipper?

  And then, as one does, I vibe-coded my own CRM, and started using that to reach out to people. You may have gotten an email from me last week, if so, apologies. But it's kind of stunning. It's amazing. But if you look at it, and compared to human, like, human work, this is, in 2020 terms, like, a data scientist, a game developer, a product marketer, an engineer, a BDR, and like, 200, maybe 200K. And I got all of this for free as part of my Claude subscription. And you think about that, you really think about that, you look at a table like this, and you are like, we are so fucked. We're so fucked.

• 6:20Dan Shipper?

  And to me, it's, it does this thing that I've been calling, it creates an agency rupture. Because we identify ourselves with the things that we do, with the work that we do. And if you see a model doing work like you do, it strikes at our sense of self. And I also think that while the aggregate is a very useful lens, especially when it comes to new technology, it also misses things. It can be misleading. And you can only see what's missing if you get up close. So, let's do that.

• 7:03Dan Shipper?

  So, again, we've got these two different ways of predicting the future, the aggregate. Let's go into the intimate. Let's see how things look in the intimate, where you zoom in on one real case, really understand all the messiness of it, and all the different interconnected variables, and then try to extrapolate from there. So, if you look back at this table, and you think about my week last week, doing all of this, which would have taken five people in 2020, and I'm doing all of that work with a team of agents in the cracks of my week while I'm doing a totally other, separate set of tasks. I think that this is actually how a lot of people in this room are working already,

• 7:48Dan Shipper?

  and probably, if you're not, you will be in the near future. And it feels almost normal to work this way. Like, it's not even that remarkable that I analyzed my survey, or built the vibe-coder to see a master, send some cold emails with my agents. But this is actually really not normal, even a few years ago. So, think about in 2022, like, before ChatGPT came out, we only had GPT-3. And there were a lot of people looking at this chart, this aggregate chart, and saying, this is not going to continue, it's just a stochastic parrot. And a lot of other people were being like, this is the end of all jobs already. Like, we're not going to have any work to do in two years, it's all over.

• 8:31Dan Shipper?

  You know, go to the bunker in New Zealand, that's it. And for me, and for Every, we take this intimate perspective. We try to be in, we try to be up close. And so, in 2022, I was using GPT-3, and I realized that it felt like, if I extrapolated out from using that tool...

• 8:57Dan Shipper?

  It would feel like using a team of people in a couple of years just by using an AI. So I wrote that rather than replacing individuals, recent advances in AI will empower them to make an impact on scale matching some of the biggest businesses, research labs, and creative organizations of today. And I really think that that's true now. That's true, and you could see it if you if you put your hands in GPT-3 and used it and extrapolated. And so I want to try to do that again today here with all of us to be like, if we take this and how we're working today, what does that mean for the future? And then what does that mean for the future of great human work? And are we really fucked? And what's really interesting is, again, if I look at this table in a vacuum, you might

• 9:42Dan Shipper?

  assume like, why would I need a team? I have a team. I've got the agents right here. But there's must be something missing from this table because I do have a team. We are 30 people. And by the way, the team here did such a fantastic job in this conference. Can we just give them a hand? This incredible, incredible group of humans and their Claude's, and their Claude's, but mostly the humans. And so I think we can answer this. If we answer this question, it's going to tell us a lot about this larger question that we're trying to answer. And I want to zoom in even more on this particular customer survey that I ran.

• 10:25Dan Shipper?

  So when we ran this customer survey, this Q4 customer survey, I gave it to the team. So everybody had access to the data and to do their own analysis. And what I got out of it was there's actually a really cool opportunity to sell every for teams. And I made my whole custom CRM and I started emailing people. But what Yash got out of it, and Yash is our go-to-market engineer, what Yash got out of it, he cares, he's a monomaniac about growth, about growing paid subs. And what he got out of it was a lot of first-time subscribers to every churn after the first month because we send too much email. And what Douglas, who is our head of marketing and put together this conference, what he

• 11:08Dan Shipper?

  got out of it was actually, a lot of our readers feel lonely. There is this interesting psychographic of people in this room where we all feel like we love AI, we're using it for all these things, but everyone else at our work and in our lives kind of like looks at us like we're crazy. And a lot of people in this room and a lot of people on our list come to us because we're the only place where we're like, yeah, you can actually do really great stuff with this. And that's what Douglas found. And Jack, who is a senior editor, he made a claymation Family Feud game show explaining the results.

• 11:49Speaker 02

  It's time for Survey Showdown. Here's your host, Dan Shipper.

• 11:58Speaker 03

  Thank you, thank you. Tonight, the vibe chips take on the context windows. Okay, the vibe chip?

• 12:04Dan Shipper?

  So extremely informative, but also very fun. And I want to reiterate, they use AI to do all this. We're using the same survey, we're using the same Claude, but different people on the team found very, very different and important things that they emphasized. And that's kind of interesting because if you look at what Claude says, because anyone can hand Claude this survey, what Claude says about the survey with just a default prompt, which is just like, what do you see in the data? It says, the writing is the business, the bundle is what makes people stick, the risk is focused, the risk is focused and too much volume. And all of this is actually, it's true. Like, these are real insights.

• 12:46Dan Shipper?

  But everybody has access to this now, and so it's cheap. And so not a single person on the team just submitted a response to this survey that looked like the default response. They all did something very different. And I think that's really, really interesting to pull on. And another really interesting part of this is for each of these people on the team, they did all of this analysis while getting prepared for this event. And everybody knows if you've ever put together a conference, it is like an all-consuming thing. And still, Yash and Douglas, for example, who are intimately involved in every part

• 13:30Dan Shipper?

  of this, did their own analysis and came up with interesting things. And Yash, for example, he did the analysis and he wrote a PR to make the change, to move the number that he wanted to move. And while he was doing that, he was also helping build the livestream experience for this event, which is crazy. If you have not seen this, we livestreamed the entire thing with this custom video player that like thousands and thousands of people were watching all day. It's like really amazing, and he did that, I think he had some help, but like he did a lot of that, and he did it while, you know, doing this analysis that took him to do.

• 14:11Dan Shipper?

  And I know what you may be thinking is, well, couldn't I just have prompted Claude for what Yash and Douglas and Jack found? And I think that's a really interesting question, and the answer is yes. We've actually experimented with this. So Mike Taylor, who's our head of evals, tried to predict whether he could, tried to figure out if he could predict what I would say in Slack. And the answer is yes, he can predict what I would say in Slack. But there's like, there's an interesting caveat, which is he can only do it, he can do it 93% of the time, but there's an interesting caveat, which is he can only do it if Claude can give 400 answers for each question.

• 14:54Dan Shipper?

  So Claude knows when it's trying to predict something in Slack, if Claude can give 400 answers at the same time, one of the things I'm going to say is in there. But it can't rank them. If you ask Claude to rank them, the thing that I say is only in the top 10%, like 60% of the time. So it's like, it's kind of a coin flip. And that's really interesting because it means we're getting into this world where, yeah, I could, Claude can predict what I would do, or I could get the output that Yash got, or Jack got, or Douglas got. Generation is really cheap, but ranking is hard. I actually wouldn't be able to find it in all of the noise that I'd have to sort through

• 15:39Dan Shipper?

  in order to like get the thing that they would get. And that should tell you something really important about why teams are valuable in AI, and ultimately why humans are valuable in AI. If you're on a team, and you're a human, and all of us are humans, you have a responsibility for a particular area of the world that helps you narrow down what you look for, like how you prompt the model. And then you have your own perspective. You have a perspective that determines what you pick out of that world and call important, and call important enough to take responsibility for or take action on.

• 16:22Dan Shipper?

  And I think if you want to look at it this way, you know, for all of us who analyzed this survey last week, there were millions and millions of different paths available in the data, millions and millions of different insights we could have pulled out. And our responsibilities, the things that we are responsible for in the business, help to narrow down where we look or where they looked. And our perspective lights up a few of those. It helps us to rank those things. And then you pick one and you take it forward. And you've gone from this, you know, millions and millions of latent possibilities to one

• 17:06Dan Shipper?

  thing that you're the only one who would rank that because it's you, it's the thing that lights up for you. And if you zoom out, you know, we just went through one person doing that, a team allows you to explore a much, much larger space of possibilities, where each person has a perspective that allows them to rank and filter the parts of the world that they care about based on their responsibility and their perspective. And that's much more powerful than whatever the default answer I would get from a default prompt. And that's why it's really valuable to have Yash, or Douglas, or Jack, or anybody else on the team accessing the survey data and pulling insights out rather than just being

• 17:51Dan Shipper?

  like, what would Yash think of this? And I know what you're thinking, okay, Claude can't rank now, but give it a year, it probably will be able to rank. And I think that that is probably true. It probably will get a lot better at ranking. And if we ask that question, we probably would set it up something like this. 2027 Claude's can be a lot better at ranking. How is 2026 Yash going to fare versus 2027 Claude? And I think if you really look at this, both sides of this equation are totally rigged. So let's like, let's take it step by step.

• 18:36Dan Shipper?

  First of all, why are we comparing 2026 Yash to 2027 Claude? In 2027, Yash is going to be 2027 Yash with a whole bundle of new experiences and perspectives, and he's going to be using 2027 Claude, I have no doubt. It might be ChatGPT, it might be Gemini, but like, he's going to be using a model. And so now we're like, okay, 2027 Yash versus 2027 Claude versus 2027 Claude. And if you look at the 2027 Claude on the right side, someone had to set that Claude up. I don't care if it's like a persistent agent, or it's like, you know, you spun up some harness and it's off doing its thing. Someone had to start it.

• 19:20Dan Shipper?

  There's no like immaculate conception of Claude happening in 2027, I don't think. And so what you're really saying in this equation is we're comparing 2027 Yash with 2027 Claude versus probably the default prompt of 2027 Claude. And if you do that, now you start to realize, well, we've got 2027 Claude on both sides of this equation, we could just cancel them out. And that's how math works in the intimate. I don't make the rules. But this is, like, seriously, this is for real. If you think about this, you actually are really comparing what I'd rather have, 2027

• 20:02Dan Shipper?

  Yash, who's using this model, or just the default prompt. And Yash has a responsibility to a particular area of the world and a perspective, and the default prompt embodies the average answer, which we've already said, everyone has access to, so it's not that valuable. And my answer is I'd pick Yash every day. I'd pick Yash every day, every time.

• 20:24Dan Shipper?

  And I think that this, if you ask me why, the way that I would talk about this is we have to go back to when everyone has access to the model, whether it's 2026 Claude or 2027 Claude or 2028 Astra or whatever, it's going to be broadly distributed. You've got Claude on both sides of the equation. And because of that, that raises the floor for everyone. And the default answers you get from the model, they look impressive to us right now, but because everyone will have them, they become not that valuable. They're just, they're status quo, they're the baseline.

• 21:05Dan Shipper?

  And yes, the floor keeps moving, but the floor keeps getting distributed to everybody. And so what's valuable, the alpha, is where you push the model using your perspective and your responsibility for a particular area of the world. And yes, that may get consumed into, that may get subsumed into tomorrow's floor as capabilities increase. But if you're continuing to use the model, you're not frozen back in 2026 when you're using the old version of the model, you're using that new model yourself. And so those new capabilities rapidly become cheap. And what is actually expensive and rare is how you, how you use them using your perspective.

• 21:47Dan Shipper?

  And so now we've kind of started to get back to the question that we, that we started with, which is if you remember the aggregate, from the aggregate alone, all you see is this like line going up that looks like it's going to replace all of us. And it looks like human work is going to zero. And if you, if you look at the aggregate from the intimate, what we've started to find is that we have the same line, but like humanity, human perspective, the way that, the way that we use the models rides on top. And, and I think that, that is a more accurate view of, of how work will happen in, in the

• 22:32Dan Shipper?

  future with these models than just saying, oh, we're just going to get, we're going to get replaced because look at this chart. And this is what I've been talking and writing about for several years now, this, this way of working and being in the economy called the allocation economy. I wrote this article in 2024 where I wrote, "You won't be judged on how much you know, but instead on how well you can allocate and manage the resources to get work done, the resources of intelligence. You'll be allocating work to AI models and making sure the work gets done well." And there are some really, really interesting implications if you, if you believe this.

• 23:13Dan Shipper?

  We're going to be allocating work to models and the default capability is distributed to everyone. So the, the responsibility and the perspective that you bring to the model is the thing that's valuable. The first thing is, the first interesting flip is it should flip how we measure AI. Right now, when we measure, when we measure AI capabilities, we measure them based on consensus. Is it good at problems that we all agree are worth solving? So like math problems are easy because they're, they're, they're, we can validate them. Program problems are easy because we can validate them. But again, by this logic, those capabilities are now distributed, they're no longer as valuable.

• 23:55Dan Shipper?

  And what we should be measuring instead is how good is it at doing my work from my perspective? And this is like kind of a weird flip. It's a weird cultural inversion because I think for all of us, we have this feeling that getting to a settled answer or being able to use a settled answer or know a settled answer is where all the value is. Like we're thinking about math and science and computer programming where your perspective doesn't actually matter. And my view is that stuff has become much less valuable because it's, it's commoditized.

• 24:37Dan Shipper?

  And now the value comes from, from you having a non-consensus perspective that you can use to push the model to make something that you think is good. And if you, if you, if you internalize that, then you start to, you start to think of things, think of model progress, not as something that's going to replace you, but something that works for you, for your ends. And, and as it gets better, so, so do you. And I think this is why the intimate matters.

• 25:22Dan Shipper?

  When you look at what's going on in, in the aggregate versus the intimate, the aggregate has to freeze you. It has to, in order to, in order to measure model progress, it has to freeze you, freeze humans in 2020 and compare it to, to humans in 2020. And I think the intimate empowers you to see what you might build and who you might become. And we have all lived that perspective today. This, this whole conference, this whole day was about getting intimate with what it is like to, to work with these tools and with, and work with other people, be with other people who are working with these tools.

• 26:07Dan Shipper?

  And, and you all agree, you all know how valuable that is because if you could just look at a capability chart and know everything you need to know, you wouldn't have come here. And I think now that the people in this room understand, understand this and feel this, it's our job to go and spread it to the rest of the world. Thank you. [applause] [music]