Principle
Models make yesterday''s human competence cheap and commoditized
Each model release commoditizes yesterday''s human competence, so durable value moves to the frontier where humans turn cheap capability into something new.
Because everyone uses the same models, default output all looks the same — slop tweets, identical landing pages. The financial incentive to keep models compliant and aligned means they structurally trail the humans pushing past them.
Do not compete on what the model already does — compete on what you build on top of it.
“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, we have all this frozen human competence from yesterday. How do I use this? Like make something new and interesting.”Dan Shipper
Principle
Automation is a lie — every agent needs a human on top of it
Every automation you deploy requires a human on top of it to keep it working, so automation creates supervisory work rather than eliminating labor.
Shipper frames automation as management: managers are not on the beach, they check in constantly. The same holds for model management — it takes real time and attention. This is why an AI-forward company still hires humans.
Budget human attention for every agent you deploy — the labor moves, it does not disappear.
“Automation is a lie 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.”Dan Shipper
Principle
Benchmarks rise only on problems we can frame and score
Benchmark saturation measures only framed, scorable work, so it never equals full human replacement — the framing itself stays human.
Shipper notes he could trivially rewrite his own benchmark to zero out the newest model, because there is always a higher frame to move to. Benchmark progress is real but partial.
Distrust the leap from "benchmark saturated" to "job automated" — the unframed work is where humans stay.
“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 it, it can''t be scored until you write it down, but the act of thinking to prompt it or write it down is, is something that you can''t measure”Dan Shipper
Principle
Ride the models — using each new release is the durable job-security move
To stay employed through AI progress, ride the models: apply each new release to whatever you do.
Riding the models is not one fixed action because they keep changing — it is staying curious and playful, applying every new model to your job or life and re-testing what it can now do.
Make trying each new model against your real work a standing habit.
“The only thing you need to do is ride the models and that means use them for whatever it is that you do.”Dan Shipper
Principle
Predict the future by living in it, not by prognosticating
Foresight comes from building a pocket of the future you live in and then noticing, not from abstract prediction.
Every staffs entirely early adopters and reviews models, giving them alpha/beta access and a lived vantage point. Writing about what they notice both crystallizes it and makes it real for others.
Stop forecasting; build a team that lives in the future and report what you see.
“what you don''t wanna do is prognosticate what do you, what you wanna do instead is, is just live in it together.”Dan Shipper
Principle
The edge of AI is wherever it meets a real human doing something
The frontier of useful AI is at the point where a model meets a real human task, not where models are built.
Shipper argues Every in Brooklyn is "quite far ahead of people in San Francisco" on usage, because whoever applies a new model first discovers what it is good for — a genuine discovery available to anyone.
Being first to apply a new model to a real task is a form of discovery open to everyone.
“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.”Dan Shipper
Principle
A company only goes as far in AI as its CEO does — it is not delegable
AI fluency at the top is a hard ceiling on the whole company — the CEO cannot delegate their way to intuition.
It currently looks like a CEO can get away with an unchanged day, but Shipper predicts that reverses rapidly into "I''m way behind." Senior leadership hands-on use is the differentiator.
If you lead, put your own hands in the tools — you cannot delegate the intuition.
“your company''s only gonna go as far as your CEO goes in AI and it''s not something you can delegate. You have to have your hands in it ''cause you don''t, otherwise you don''t have an intuition for it.”Dan Shipper
Principle
AI writing is good if you stand behind every line; slop is what you don''t
Judge AI-generated work not by its origin but by whether the author stands behind every line — that is the slop line.
Shipper welcomes AI-generated documents but bans sending one you cannot discuss; a well-directed GPT-5.5 strategy doc beats most hand-written ones because the bar for human strategy writing is low.
Send AI-assisted work only if you can defend every line of it.
“there''s a difference between an AI generated document that''s slop and not, and the slap 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.”Dan Shipper
Principle
Generalists can go much further now, especially at small companies
The AI era rewards generalists, who can now execute across functions that used to need specialists — a boon for small companies.
Every deliberately hires generalists who love touching many areas. Shipper expects roles to re-settle over time (marketing people still do marketing), but generalist reach is a durable new advantage.
Hire and cultivate generalists — the tools now let one person cover ground that took several.
“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.”Dan Shipper
Principle
The human and the agent are now on the same piece of work together
Work is shifting from delegating to agents to collaborating with them live on the same surface, with mutual visibility.
Shipper''s Codex-plus-Proof setup embodies this: Codex watches him write, he watches Codex, both act in one place. Software built only for human use or only for agent use misses this middle.
Build tools where human and agent see each other and act together in real time.
“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.”Dan Shipper
Principle
Be simultaneously AI-pilled and bullish on humans
The winning stance is to be maximally AI-adopting and maximally human-bullish at the same time.
This resolves the paradox of an AI-forward company doubling headcount: heavy automation increases, not decreases, the need for human judgment and supervision.
Reject the framing that AI enthusiasm and hiring humans are in tension.
“I''m simultaneously extremely AI pilled extremely and very bullish on humans and the role of humans in making sure that AI is working well.”Dan Shipper
Principle
Agents increase the number of SaaS users, they do not replace SaaS
Agents multiply SaaS demand instead of destroying it, because agents become high-volume users of the same tools.
Shipper reports Every''s own SaaS spend is up year over year despite heavy internal agent use, and would "buy SaaS stocks right now." The SaaS apocalypse is "dumb."
Do not short SaaS on AI fears — agents are new customers.
“I think that what agents do is increase the number of users of SaaS, not get rid of it. And so I think SaaS companies are going to see like an insane spike in the amount of demand”Dan Shipper