The AI Resistance is Forming
Cal Newport talks with author Brad Stulberg, who spent months genuinely trying ChatGPT and Claude as writing tools and then quit almost entirely. Stulberg lays out a three-plank case for a "humanist resistance": AI removes the effort and struggle that make work meaningful, it homogenizes output toward a soulless average, and it quietly crowds out the human connection and learning that come from doing things the hard way. Both argue the AI conversation should shift from existential risk and productivity hype to a simpler question — does this make your individual life deeper and better? — and that it's fine to wait until a tool is obviously, easily useful before adopting it.
- Stulberg tried paid ChatGPT and Claude subscriptions as a writing "thought partner," but found within six weeks that it gave the same shallow suggestions and mostly reassured him his work was good enough; he now uses it only for occasional search results.
- Plank one of his argument: meaning comes from effort, and AI's core promise is eliminating effort — the "click of a button economy" — which he compares to taking a gondola to the top of a mountain and having nothing to say about the climb.
- Plank two: LLMs homogenize creative work around an average, so AI "will make you average if you're bad, keep you average if you're average," and should be kept away from any domain where you want mastery.
- Plank three: constant frictionless use atrophies the skill of exerting effort itself, so you can't handle involuntary difficulty when it arrives.
- Newport extends the critique to ordinary office work: because knowledge work uses visible activity as a proxy for productivity, AI has reduced the cost of producing visible work activity to zero, amplifying "email swirl" into "AI swirl" without improving what a company actually produces.
- Both note the inverted consumer relationship — AI labs are still in a pre-IPO posture proving their tech is impressive rather than proving it's useful, leaving individuals to feel like losers if they don't do the R&D themselves.
- Stulberg's practical heuristics: treat AI like ultra-processed food, take it off your phone and browser plugins and sign out to add friction, and ask of any use whether it diminishes meaningful effort, whether it makes work better or just more average, and whether it contributes to an excellent life.
- Newport draws the parallel to email and social media — technologies adopted without intention that broke work and attention — and argues the winning move is to work backward from what makes a life meaningful rather than debating whether AI is dangerous or utopian.
Stulberg's clearest image for why outsourcing effort produces a hollow result, paired with the sharp distinction that unlike a car, AI is a brain-car you can carry everywhere.
You can take a gondola to the top of a mountain and still have an incredible view at the top, but if someone asks you what the mountain was like and what you learned climbing the mountain, you're not going to have any good answers.
Newport's most concrete structural claim: AI doesn't fix pseudo-productivity in knowledge work, it turbocharges it.
The cost of producing visible work activity has been reduced to zero... my workload has never been higher. They've doubled the amount of projects they expect me to work on, and working on these projects just means slinging AI slop back and forth to be consumed by other AIs.
Nobody wants their gravestone to read: he was efficient.
We think that we're using the machine, but like a virus, the virus uses you. The more you use the machine, the more you start to resemble the machine.
If you suck at something, AI is great because it's going to make you average. If you're average at something and you keep using AI, it's going to keep you at average. If you are great or you want to be great, you should probably resist using AI in most cases.