Happy Friday! It's deputy editor Izzie Ramirez here, with a quick update before Kelsey dives into the possibility of AI taking your job. ⁉️ Does Trump know he doesn't have a foreign policy? 📈 We're barely keeping track of this growing climate problem. 🙃 How America is failing its rural hospitals.
👀 Our senior reporter Sigal Samuel wants to know if you have a moral quandary she can help you navigate in the next Your Mileage May Vary column. If you do, just hit reply to this email!
!We'll see you Wednesday. Have a great weekend! |
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| Kelsey Piper is a senior writer for Future Perfect. She writes about science, technology, and progress. You can read more of her work here and follow her on X. |
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| Kelsey Piper is a senior writer for Future Perfect. She writes about science, technology, and progress. You can read more of her work here and follow her on X. |
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Hey readers, In 2023, one popular perspective on AI went like this: Sure, it can generate lots of impressive text, but it can't truly reason — it's all shallow mimicry, just "stochastic parrots" squawking. At the time, it was easy to see where this perspective was coming from. Artificial intelligence had moments of being impressive and interesting, but it also consistently failed basic tasks. Tech CEOs said they could just keep making the models bigger and better, but tech CEOs say things like that all the time, including when, behind the scenes, everything is held together with glue, duct tape, and low-wage workers. It's now 2025. I still hear this dismissive perspective a lot, particularly when I'm talking to academics in linguistics and philosophy. Many of the highest profile efforts to pop the AI bubble — like the recent Apple paper purporting to find that AIs can't truly reason — linger on the claim that the models are just bullshit generators that are not getting much better and won't get much better. But I increasingly think that repeating those claims is doing our readers a disservice, and that the academic world is failing to step up and grapple with AI's most important implications. I know that's a bold claim. So let me back it up. |
Ying Tang/NurPhoto via Getty Images |
"The illusion of thinking's" illusion of relevance The instant the Apple paper was posted online (it hasn't yet been peer reviewed), it took off. Videos explaining it racked up millions of views. People who may not generally read much about AI heard about the Apple paper. And while the paper itself acknowledged that AI performance on "moderate difficulty" tasks was improving, many summaries of its takeaways focused on the headline claim of "a fundamental scaling limitation in the thinking capabilities of current reasoning models." For much of the audience, the paper confirmed something they badly wanted to believe: that generative AI doesn't really work — and that's something that won't change any time soon. The paper looks at the performance of modern, top-tier language models on "reasoning tasks" — basically, complicated puzzles. Past a certain point, that performance becomes terrible, which the authors say demonstrates the models haven't developed true planning and problem-solving skills. "These models fail to develop generalizable problem-solving capabilities for planning tasks, with performance collapsing to zero beyond a certain complexity threshold," as the authors write. That was the topline conclusion many people took from the paper and the wider discussion around it. But if you dig into the details, you'll see that this finding is not surprising, and it doesn't actually say that much about AI. Much of the reason why the models fail at the given problem in the paper is not because they can't solve it, but because they can't express their answers in the specific format the authors chose to require. If you ask them to write a program that outputs the correct answer, they do so effortlessly. By contrast, if you ask them to provide the answer in text, line by line, they eventually reach their limits. That seems like an interesting limitation to current AI models, but it doesn't have a lot to do with "generalizable problem-solving capabilities" or "planning tasks." Imagine someone arguing that humans can't "really" do "generalizable" multiplication because while we can calculate 2-digit multiplication problems with no problem, most of us will screw up somewhere along the way if we're trying to do 10-digit multiplication problems in our heads. The issue isn't that we "aren't general reasoners." It's that we're not evolved to juggle large numbers in our heads, largely because we never needed to do so. If the reason we care about "whether AIs reason" is fundamentally philosophical, then exploring at what point problems get too long for them to solve is relevant, as a philosophical argument. But I think that most people care about what AI can and cannot do for far more practical reasons. AI is taking your job, whether it can "truly reason" or not I fully expect my job to be automated in the next few years. I don't want that to happen, obviously. But I can see the writing on the wall. I regularly ask the AIs to write this newsletter — just to see where the competition is at. It's not there yet, but it's getting better all the time. Employers are doing that too. Entry-level hiring in professions like law, where entry-level tasks are AI-automatable, appears to be already contracting. The job market for recent college graduates looks ugly. The optimistic case around what's happening goes something like this: "Sure, AI will eliminate a lot of jobs, but it'll create even more new jobs." That more positive transition might well happen — though I don't want to count on it — but it would still mean a lot of people abruptly finding all of their skills and training suddenly useless, and therefore needing to rapidly develop a completely new skill set. It's this possibility, I think, that looms large for many people in industries like mine, which are already seeing AI replacements creep in. It's precisely because this prospect is so scary that declarations that AIs are just "stochastic parrots" that can't really think are so appealing. We want to hear that our jobs are safe and the AIs are a nothingburger. But in fact, you can't answer the question of whether AI will take your job with reference to a thought experiment, or with reference to how it performs when asked to write down all the steps of Tower of Hanoi puzzles. The way to answer the question of whether AI will take your job is to invite it to try. And, uh, here's what I got when I asked ChatGPT to write this section of this newsletter: |
Is it "truly reasoning"? Maybe not. But it doesn't need to be to render me potentially unemployable. "Whether or not they are simulating thinking has no bearing on whether or not the machines are capable of rearranging the world for better or worse," Cambridge professor of AI philosophy and governance Harry Law argued in a recent piece, and I think he's unambiguously right. If Vox hands me a pink slip, I don't think I'll get anywhere if I argue that I shouldn't be replaced because o3, above, can't solve a sufficiently complicated Towers of Hanoi puzzle — which, guess what, I can't do either. Critics are making themselves irrelevant when we need them most In his piece, Law surveys the state of AI criticisms and finds it fairly grim. "Lots of recent critical writing about AI…read like extremely wishful thinking about what exactly systems can and cannot do." This is my experience, too. Critics are often trapped in 2023, giving accounts of what AI can and cannot do that haven't been correct for two years. "Many [academics] dislike AI, so they don't follow it closely," Law argues. "They don't follow it closely so they still think that the criticisms of 2023 hold water. They don't. And that's regrettable because academics have important contributions to make." But of course, for the employment effects of AI — and in the longer run, for the global catastrophic risk concerns they may present — what matters isn't whether AIs can be induced to make silly mistakes, but what they can do when set up for success. I have my own list of "easy" problems AIs still can't solve — they're pretty bad at chess puzzles — but I don't think that kind of work should be sold to the public as a glimpse of the "real truth" about AI. And it definitely doesn't debunk the really quite scary future that experts increasingly believe we're headed toward.
—Kelsey Piper, senior writer |
This veteran health official watched Americans lose trust in science. How do we get it back? |
Sarah Silbiger/AFP via Getty Images |
Francis Collins, a former director of the NIH, has overseen some of the most revolutionary science of the last few decades. But nothing in his years leading biomedical research for the US government could have prepared him for the disruption at NIH over the past few months. Noam Hassenfeld spoke to Collins on Vox's Unexplainable podcast about how so many Americans lost trust in science. Read the transcript here, and listen to the episode here.
More on this topic from Vox: |
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We're producing more food than ever before — but not for long |
Even America's richest farmlands can't outrun climate collapse. That's bad news not just for farmers, but for everyone — especially as it becomes harder and more expensive to feed a more crowded, hungrier world, according to a new study published in the journal Nature. Correspondent Umair Irfan explains what might happen to global agricultural production as the planet continues to warm.
More on this topic from Vox: |
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📢 What ethical questions do you have? Talk to us! Your Mileage May Vary is senior reporter Sigal Samuel's advice column offering you a framework for thinking through your philosophical questions every other Sunday right here in your inbox. |
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It was Father's Day this past Sunday, and like any father who just turned 47, I spent much of the day reading about World War II. (What — did you think I was going to spend the entire day with my family?) So let me offer a summer book recommendation, for dads and for others. David Benioff's City of Thieves follows a 17-year-old boy living on his own and a Red Army deserter with a flair for the poetic as they navigate the hellscape that is the Nazi siege of Leningrad, where hundreds of thousands of people died during one of the most barbaric episodes of WWII. Despite the conditions, the novel is a hoot, when the narrative isn't dancing on a knife's edge. (If Benioff's name sounds familiar, it's because he's the co-creator of the HBO show Game of Thrones. But don't worry — the novel actually has a good ending.) — Bryan Walsh, senior editorial director The most important thing happening in AI policy in the US right now, by a decent margin, is the progress of the RAISE Act. The law passed in the New York state legislature June 12 and requires developers of advanced models to write and obey "safety and security protocols" that reduce the risk of the model causing grievous harm. It sailed through the state House and state Assembly, and now only awaits Gov. Kathy Hochul's signature. But this was exactly the position SB 1047 was in when California Gov. Gavin Newsom vetoed it, and the same industry pressure that killed SB 1047 is coming to bear on RAISE now. The writer Zvi Mowshowitz has the best rundown of what the bill actually does. TL;DR: It's not going to prevent disaster on its own, but it's an important first step, and the alternative is a regime where these systems go wholly unregulated even as they grow in power exponentially. — Dylan Matthews, senior correspondent European tourists could be forgiven for believing most of the continent's food is produced on rolling Tuscan hills and the picturesque French countryside — the image of the pastoral family farm and the importance of agricultural heritage are both prominent in European marketing and media. But a massive new investigation across Europe shows that mega factory farms — especially raising pigs and poultry birds — have proliferated across the continent, notably in Spain, Italy, France, Germany, Poland, the Netherlands, and the UK. See the Guardian's write-up on the investigation here. — Kenny Torrella, senior reporter A lot of us here on Team FP are cat people, but after a weekend catsitting for my partner's parents, I opted for reading Shiba Inu Rooms. It's a manga about a girl named Kori who moves into a haunted apartment building. Except the ghosts are hundreds of shibas. The apartment building was a former puppy mill, and so the dogs who passed away there can't ascend to the afterlife until they have felt happiness. Some dogs will ascend for something as simple as being petted too much, but Kori's shiba doesn't want to ascend because Kori won't be there. So. What constitutes a good life for a pet? That's the question our main character has to figure out to find the right balance, and discover herself in the process. — Izzie Ramirez, deputy editor |
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Today's edition was produced by Izzie Ramirez and edited by Bryan Walsh. |
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