Mollie, a 10-month-old pet hamster in the Netherlands, probably runs a lot more than you. 

We know this because, a few weeks ago, a Dutch physicist named Thijs — who owns Mollie — rigged up the rescued hamster’s running wheel to Strava, a popular app that runners use to log their miles. Thijs found that Mollie typically runs around six miles every night.

“He’s beating me,” Thijs told the CBC. “Almost 70 kilometres a week, which I think many people doing marathon training would be happy to hit.”

The internet loves a quirky animal story, and this one has gotten plenty of attention on Reddit and X. Mollie even got a recent profile in Runner’s World, and he now has over 9,000 followers on Strava. Mollie’s Strava updates are headlined with cute puns like “No Rest for the Whiskered,” and “The Fast and the Furriest.”

I get why so many people are cheering on Mollie and his commitment to going the distance: Hamsters are adorable, and the idea of one logging miles on Strava is extra charming. But the light-hearted virality of the story masks a fundamental problem with owning small pets like hamsters. Mollie evolved to run in an open and diverse ecosystem, but he lives in a small cage and doesn’t have much to do besides circle endlessly in a simple wheel.

Big problems with small pets

Most pet hamsters are Syrian hamsters, native to that country’s Aleppinian plateau — steppe terrain in the northern part of the country. In the wild, they regularly traverse the landscape to forage for food, consuming a wide sampling of seeds, fruits, insects, and green vegetation. They store their haul in their cheeks while journeying back to their two-foot deep burrows. 

But, as pets, they’re largely fed a monotonous diet of grain pellets and are confined to tiny cages where they can’t engage in their natural behaviors, like building proper burrows, traveling for food, and exploring their turf. Confining a dog or cat in a kennel for all or most of their lives would no doubt be a severe form of cruelty, yet we’ve normalized doing so for small animals whom we’re less likely to view as capable of suffering, even though there’s no scientific basis for that assumption.

And while you might think it could be nice for an animal to not have to work for their food, research has found that many species don’t like to freeload (cats might be one exception). On top of all of that, according to some research, these smaller pets are less likely to receive veterinary care to address their ailments and injuries than cats and dogs. 

Their tedious and confined existence can contribute to “stereotypic” behavior: repetitive motions, like gnawing on cage bars, that are indicators of stress. Some animal welfare researchers have proposed that wheel use could also be a sign of stress — or that it could indicate an urge to run or escape. One other study suggested it might help reduce other stereotypic behaviors. 

“I think that the welfare of these animals is worse than anybody else’s,” Jessica Pierce, a bioethicist and author of several books on the ethics of pet keeping, told Vox for a previous story. She was talking about small pets in general — not just hamsters, but also species like snakes, fish, and birds, most of whom are still much more wild than our domesticated cats and dogs (who also might not be as happy as we think). 

Thijs, for his part, is sympathetic to these concerns, which he told me over email were fair. “I hope the attention that Mollie got will not result in people impulsively buying hamsters (and then forgetting about them after it turns out their hamsters just sleep 80% of the time),” he said.

He adopted Mollie from someone who could no longer take care of him and who kept him in an even smaller enclosure. “[Mollie] showed some behaviour that really concerned us back then (climbing, biting the fence, etc),” Thijs said over email. So, within the first week, he upgraded Mollie to a larger cage and a larger wheel and got him some toys, which seemed to improve his mood.

“I think some behavioural aspects (more running, digging tunnels in the bedding, ‘foraging’ if we hide food in the bedding) make his behaviour closer to natural, but it’s, of course, never like in the wild,” he said. Thijs also wonders whether his data-gathering might provide insight into how certain actions — like when he cleans Mollie’s bedding or when he stays up late watching Netflix — affects Mollie’s running behavior. 

“People often talk about pets being ‘happy,’ but, especially with small animals, that’s difficult to assess objectively,” he told me. “I’m curious whether long-term activity data might give us useful clues about changes in his behaviour or wellbeing!”

Inside a “hamster mill”

While Thijs adopted Mollie, there’s also the question about where many of these pets come from. A 2024 undercover investigation by the nonprofit Last Chance for Animals into a massive US “hamster mill” documented thousands of rodents being bred and raised in overcrowded, small, barren cages. Some of the animals were dehydrated, diseased, and suffered from wounds. Individual animals weren’t provided with veterinary care, and breeding hamsters whose production dropped — along with litter runts — were gassed to death.  

Investigations into other rodent mills have found similar problems. Such investigations, combined with veterinarians’ concerns with keeping small animals as pets, has driven some cities — including Cambridge, Massachusetts, and Washington, DC — to prohibit pet stores from selling rodents and other small pets.  

I don’t mean to single out Thijs for his interesting experiment, especially considering he adopted Mollie and immediately improved his life. But animal ethicists like Pierce argue that however much effort one puts into giving these small pets a better life, it likely doesn’t ever add up to a good life, considering the constraints of cage confinement and other common care issues. 

To Pierce and some of her colleagues, the sizable gap between what small pets need and what they often get reveals a largely hidden but serious crisis of animal neglect and suffering. And that’s rarely mentioned, if at all, in viral social media posts or news articles about these critters. 

If we take a moment to pause and see Mollie’s life for what it is — and examine the industry that breeds these small animals for profit — all those miles he’s logging on Strava seem more like daily posts saying that he’s striving for a far different life with no way to get there. 

“This entire ‘track his runs on Strava’ thing started off more as a geeky side-project, I never expected it to get anywhere near this much attention,” Thijs told me. “But it’d be nice if you can use it as a way to educate people (including me!) about the ethical considerations!”

A version of this story originally appeared in the Future Perfect newsletter. Sign up here!

Everyone is wrong about Hank Green. 

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.

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!