Everyone is wrong about Hank Green.
Everybody needs a personal AI policy. Just ask Hank Green.
How can we reap AI’s benefits without melting our brains in the process?
In case you missed the controversy: The veteran YouTube star, writer, and science comms entrepreneur was recently “canceled” after he acknowledged using AI for research.
“I have been relying too heavily on AI as a research aid,” he wrote in a statement on Reddit. “It can be very useful for this task, giving me access to a lot of papers I didn’t know existed really fast, but I think that has been to the detriment of my work because it has not given me the freedom to find all of my own ways into and around a topic.” Although Green wrote that the words in his videos are his own, his reliance on AI as a research aid still gave the finished work an ineffable “AI feel.” And his relationship with AI, he wrote, had become “not healthy for me or good for the world.”
Some of Green’s followers, known by the cheerfully dorky moniker “Nerdfighters,” turned on him for daring to use AI in any capacity. Just as quickly, that backlash produced its own backlash, aghast not at Green’s use of AI but at his prostration before an anti-AI mob — “self-canceling,” as some put it, over a legitimate use of the technology.
I think both of these camps are misguided and have flattened a complex issue into a set of binary extremes. And it surprised me that, despite robust societal debate on AI’s impacts on our ability to think, write, and produce original ideas, the debacle hasn’t prompted more thoughtful conversation about the limits of AI in creative work.
I felt this because I recognized myself in Green’s statement: the feeling that even using AI for research can start to take over your creative process, that it can become hard to know where your own brain ends and where AI begins, and that the technology can simply push you to work too fast. I don’t use AI to generate writing and would not do so — but its use need not rise to that level to raise profound questions about how much of our work to automate, and what happens to our ability to think for ourselves when we do.
In a follow-up video published late last week, Green laid out a new AI policy for his work. He wrote:
1. No portion of any script will be written, edited, or outlined by an LLM.
2. The thesis of a video will always originate with a human.
3. No image or music in a video will be generated by AI. If something is accidentally included, best efforts will be made to remove it.
4. LLM outputs are not trusted as a source.
These are all good ideas for any creator trying to avoid AI creep in their craft. But still, they raise a bigger, harder-to-answer question: The very structure of generative AI makes it hard to use without offloading human thought and judgment, which can lead to a widely discussed phenomenon known as “cognitive surrender.” And it pushes us toward uses — like synthesizing research, brainstorming, generating ideas and angles — that short-circuit the original thinking and discovery that we ought to be doing ourselves. What, then, can we even responsibly use AI for? How can we set guardrails that allow us to avail ourselves of its usefulness, without melting our brains in the process?
The most tempting uses of AI are precisely those best avoided
Remember late 2022, when ChatGPT first came out and everyone mocked its crappy research skills and propensity to hallucinate in every other sentence? I am so wistful for those days.
Many people who abstain from AI may not know it, but in the time since, and especially in recent months, large language models have gotten way smarter (especially the paid premium versions). It’s become unnervingly good at summarizing niche, complex research areas and debates, and producing ideas, often without being asked, for further research or writing on the same subject.
Whenever I have a research question these days (which is pretty much any time I’m working on a story), I’m more likely to fire up an LLM than a traditional search engine. If I ask, “Why are old-growth trees still being logged in North America?” it produces a synthesis of research, news, opinion, and whatever else its training absorbed on the subject: “We’re using an essentially nonrenewable ecological asset to smooth a temporary transition to a renewable timber resource,” it says. Probe it further, and it’ll suggest arguments for you: “Instead of conservationists having to prove that every old forest deserves protection, logging companies should have to demonstrate that cutting a centuries-old stand serves a need that cannot reasonably be met with second-growth or engineered wood.”
LLMs are designed to make cognitive work effortless, but that feels so icky because for it to be worthwhile at all, it has to be effortful.
These aren’t particularly smart or creative ideas — they’re perfectly replacement-level, which makes them plausible substitutes for the thoughts of most people. The AI can supply pat answers to every conceivable question and follow-up you might have while working on a project, relieving you of the need to mentally engage with the shape of a problem. Contrast that with Googling in the pre-AI overview days, which, while certainly not without its problems, at least used to send you to a list of sources that you then had to read and make sense of on your own.
Most of us who’ve engaged with LLMs know what this feels like. They make it easy for users to skate on the surface of a subject and feign understanding or insight, and in the process they can become involved in interpretive decisions that should be our own. In my experience, even more narrowly designed generative AI models don’t escape these problems. Google’s Gemini Notebook (formerly NotebookLM), for example, allows you to upload all of your sources for a project — books, reports, papers, audio and video recordings — and ask it questions based on what they contain, rather than searching the entire internet. It’s less prone to generating outright slop than general-purpose AIs. I use it for most stories I write — it’s an incredibly useful, time-saving tool. But it also enables me to engage with sources in a perfunctory, contextless manner: The AI can surface precisely the bit I need rather than forcing me to form the deeper connections that come from reading a text as a whole.
The best creative work (including not just art and writing, but also technological and medical breakthroughs) probably comes from having a wide range of background associations, and being able to combine them in unexpected ways. The French mathematician Henri Poincaré put this beautifully in his essay “Mathematical Creation,” where he wrote that it’s the tedious, sustained conscious effort that ultimately leads to flashes of insight.
I think this is what Green meant when he wrote that AI can prevent him from finding his “own ways into and around a topic.” LLMs are designed to make cognitive work effortless, but that feels so icky because for it to be worthwhile at all, it has to be effortful. This argument has already been made about AI-generated writing: Letting an LLM write for you defeats the point, because writing is thinking. But it can also be true, as Green’s example has shown, of using AI for the research that feeds the creative process.
If you use AI, consider creating a personal AI policy
Perhaps all these concerns are overblown — humans are hardly less prone to lazy and logically unsound thinking than AI. That’s absolutely true, but the point of doing our own thinking isn’t that we’re inherently good at it. To the contrary, it’s that we can only get better at reasoning by practicing it.
I don’t want to suggest that using AI for research is illegitimate. It’s too useful a tool to take off the table entirely, and we can’t put that genie back in the bottle. It can be extremely helpful with identifying the best sources that you wouldn’t find otherwise, but those very abilities can make it double-edged, foreclosing a slower, more open-ended exploration process. But AI’s greatest strength — its endless variety and flexibility — can be used to steer it away from the most tempting uses, especially those that ultimately harm us.
How to practice good AI hygiene
- Don’t use AI to form your thesis or core arguments.
- Use AI to find, not replace, sources, and avoid depending on AI-generated syntheses of sources. Read through source material yourself.
- Keep creative borrowing of AI-generated language microscopic, not much different from how you’d use a thesaurus.
- Watch out for compulsive chatbot use.
There are very obvious things that any LLM user should do to that end, like never assuming that a claim from an AI is accurate and always reading original sources. Beyond that, the necessary guardrails depend on your own use patterns, but above all, I think it’s helpful to avoid training ourselves to expect immediate answers to difficult questions.
One of my colleagues refrains from using it to brainstorm ideas entirely, instead using it to provide sources for narrow factual questions and to aid in the fact-checking process (emphasis on “aid”) after a story is written. To generalize from this, I think it’s a good idea to resist having AI do much synthetic work on a subject before you have drafted your project yourself. The less you do that, the less you will, to paraphrase Green’s recent video, see every problem as an “LLM-shaped problem,” and the less you’ll feel like you’re in the singularity where your brain is merging with AI.
One way that I like to use AI is as an enhanced thesaurus, to find the precise word or short phrase to express what I want to say in a sentence. When done right, I don’t find this harmful any more than using a traditional thesaurus; I find that it can enrich my working lexicon. But it must be used carefully and surgically, by setting a clear limit on the length of a phrase used from AI — like two or three words max — and avoiding sharing much of your writing with the tool at all, lest it start recommending extensive rewrites.
When interrogating the contents of specific sources or a body of work, or stress testing your own arguments, AI would be better for our intellectual development if it took a Socratic approach — pushing you to discover an answer rather than simply giving you one. It might say, for example, “there might be some relevant caveats to your idea on pp. 42-43 of the source.” LLMs can be directed to behave this way in their custom instructions. It also helps to simply touch grass — find the sources you need, and rather than interviewing the AI about what they say, just close the chatbot and read them from start to finish.
Configuring AI in a way that’s healthier for our brains would also make it less addictive — when you find yourself getting sucked into a long back-and-forth with an AI, that’s often a sign that something has gone amiss. Green evidently struggled to set that boundary, referencing the unhealthy “level of dopamine I’ve been getting from interacting with LLMs.” AI labs have very strong commercial incentives to want us to be addicted to their products, and unless they build different constraints into models themselves, it’s hard to expect the average person, who has far less autonomy over the terms of her work than Green does, to change these conditions on her own.
Although researchers at some AI labs are thinking about the societal risks of cognitive atrophy, it’s another matter to expect these companies, which compete on ease of use, to introduce friction into their models. We shouldn’t count on that happening soon — but we’re far from powerless against AI’s impacts. We can set our own personal AI use policies, and we can enforce social norms against AI-induced brain rot. Like, at bare minimum: Don’t send me your AI-generated writing. It’s rude!