Podcast Episode

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

Lenny's Podcast: Product | Career | Growth

2 clips•May 24, 2026•1 hr 34 min•

About

Dan Shipper is the co-founder and CEO of Every, a media and software company that’s become a living laboratory for the future of work. Everyone at his company of about 30 people is an AI early adopter; from editors to ops people, they use AI to do much of their work, giving Every a unique lens into where the world is heading. A year ago on this show, Dan predicted that people were sleeping on Claude Code for nontechnical work, which proved to be remarkably prescient. Today he’s back with another set of calls: the SaaS apocalypse is dumb, CLIs are over, the forward deployed engineer is the most valuable new hire, and the only thing you need to do to stay employed is ride the models. Dan’s predictions: 1. The future of work will happen inside Codex or Claude Code. 2. Every company will have one “super-agent” inside their Slack that every employee talks to regularly. 3. SaaS is not dead—in fact, Dan is bullish on SaaS stocks. His contrarian take: “I would buy SaaS stocks right now.” 4. SaaS economics will shift: users will bring their own AI tokens into apps, which actually improves SaaS margins. 5. PMs will thrive in the AI era. 6. Full-stack designers will become superheroes. 7. The AI job apocalypse is not happening. 8. Forward deployed engineer is the new most essential role. 9. CLIs are over. 10. Automation is a lie. 11. We will read way more AI-generated writing and we will like it. 12. We’ll be building software for humans and agents to use together. — Brought to you by: WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lenny Vanta—Automate compliance, manage risk, and accelerate trust with AI: https://vanta.com/lenny — Episode transcript: https://www.lennysnewsletter.com/p/the-ai-paradox-dan-shipper — Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0 — Where to find Dan Shipper: • X: https://x.com/danshipper • LinkedIn: https://www.linkedin.com/in/danshipper/ • Podcast: https://every.to/podcast • Website: https://danshipper.com — Where to find Lenny: • Newsletter: https://www.lennysnewsletter.com • X: https://twitter.com/lennysan • LinkedIn: https://www.linkedin.com/in/lennyrachitsky/ — In this episode, we cover: (00:00) Introduction to Dan Shipper (02:56) Dan’s unique position living in the AI future (09:17) How the way we work will change in the coming year (16:39) The case for general agents (18:08) Codex and Claude Code as the new operating system for work (25:39) How Cursor fits in (27:42) How this changes what SaaS companies should build (31:13) Why CLI is already over (33:34) Two agents are better than one (36:22) Why Dan is bullish on SaaS stocks (39:01) Why automation doesn’t reduce human work (47:00) The value of human-written code (48:36) Quick recap (50:15) How work is changing (56:17) Why data scientists are drowning in bad analysis (58:24) Which product/tech roles are least changed by AI (1:02:17) We will read way more AI-generated writing and we will like it (1:08:28) Why product managers will dominate the AI era (1:11:05) Full-stack designers are the other big winners (1:13:11) The AI job apocalypse won’t happen (1:16:00) How to “ride the models” to stay relevant (1:21:02) Final predictions and advice (1:25:24) Lightning round — References: https://www.lennysnewsletter.com/p/the-ai-paradox-dan-shipper — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com. — Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

AI Summary

Dan Shipper argues that AI is reshaping work in a more human, not less human, direction: teams will rely on general-purpose agents, especially inside tools like Codex and Claude Code, while Slack becomes home to a company-wide super-agent. He also pushes back on doomier takes, saying SaaS is far from dead, automation won’t eliminate work, and the real winners will be product managers, full-stack designers, and forward-deployed engineers. The episode explores how software will increasingly be built for both humans and agents, and why adapting to the models matters more than chasing one fixed workflow.

Clips

15:12

“The future starts with company-wide agents, then trickles to personal ones.”▲ Hide transcript

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I think it has started to shift to a more one agent per company model, because for now, the ideal is you basically set up a forward deployed engineer or someone with that sort of profile who's responsible for making sure that that agent is working for the whole company. And then maybe you have some little team agents. And I think as the models get better at being more independent, that will shift down and you will it'll be more likely that we'll have more personal agents because we don't have to fuck around with all the internals, but the model that i see working for us and for a lot of other companies including the model companies - the model companies themselves are starting to see this - is when it comes to the sort of like async agents it's really, you know, you have one agent at the top that's like doing, sometimes it's everything, a lot of times it's a particular kind of job that you've decided that everyone in the company needs an agent for, like data requests and then i think it starts top at the top and then it sort of starts to trickle down where you may get more specialized agents and teams 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 Claude, or something like that”. Exactly.

Captured: Sep 7, 2026⏱ 86sSource: file-groq-sc

56:29

“The bot handles basic questions so data scientists tackle harder problems.”▲ Hide transcript

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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, his data science team is just, their job used to be do analysis, answer questions, see if this experiment was a good, was positive. Now it's just, everyone's doing that and they're sharing their results and they're like, no, this is not correct. And most of their job is now reviewing bad data science work. Which is a problem. And it means that, and the same thing is happening with engineers. and it means that you need more, like you actually need that engineers for this and you need data scientists. 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 model companies, for example, like at least one of them has literally a data science bot that every single person in the org can query that is hooked up to their data warehouse that knows who's who so that it knows at the warehouse level, like who has permission to access what? And so all of the basic questions, because there's a team that sets up this bot, all of the basic questions that people might want to ask that it sometimes gets that might get wrong, that they're constantly making sure it's getting it right. And so the data science team doesn't have to answer all the like bullshit questions because there's another team building an agent that is set up to do that really well. but if the team didn't exist the data scientists would hate their lives yeah it does though make the job maybe less fun because you're just sitting there you know gardening people's sloppy work well that's what i think is like it it can actually make the job better because for the data scientists you are now not dealing with all the silly requests you're dealing with um the deep the deeper questions that are harder for the the team who's dealing with all the basic requests and building an agent to do that. It's like filtering all that stuff out so you can focus.

Captured: Sep 8, 2026⏱ 115sSource: file-groq-sc

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