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    "lede": "The models now come up with testable hypotheses faster than we can check them, and checking them needs people. reasonmonkey made that point to me in a Reddit thread, and I think testing and validating from anywhere is one of the answers to office jobs going away.",
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  "markdown": "By Vlad Cealicu, 3 November 2026. 955 words, about 4 min to read.\n\n- 2 days: for Google's Co-Scientist to suggest five explanations, the top one matching a lab's own hypothesis\n- 30,000+: contractors a week paid by Mercor, over $2 million a day\n- 19%: below expected employment for 22 to 25 year olds in the jobs most exposed to AI\n\nComing up with ideas, and checking them. Sources [5], [6] and [8].\n\nOn Tuesday 3 November I was answering comments on r/accelerate under the link to [my post about GPT-6 Astra editing a small model's weights](/blog/editing-the-weights), and we got into what the singularity is. reasonmonkey thinks of it as technological progress so fast that no human can keep up with it, over any time period, and I think it's when we can't understand why something works any more, even when a model explains it to us [[1]](#ref-1). I said you could argue we've been through his version before, because the industrial revolution must have felt like that to most of the people living through it (I wrote about [where their jobs went](/blog/where-the-jobs-went) the day before), and he agreed.\n\nBy my definition we're not there yet, we can still understand the why and it just takes us a long time. On 27 October OpenAI said its models had produced three results, one in maths, one in physics and one in computer science, and the computer science one is a proof that the Exponential Time Hypothesis is false [[4]](#ref-4). Roughly, that hypothesis says a well-known hard problem can't be solved without the time it takes growing exponentially as the problem gets bigger. OpenAI asked a group of experts, including the scientists who proposed the hypothesis, to check and explain the proof [[4]](#ref-4), and I haven't seen anyone confirm it yet.\n\nThen reasonmonkey wrote, \"Verification is where a ton of the jobs are going to be. Reality can't be fully simulated. You need to ask it.\" [[2]](#ref-2) In a longer comment he said the models can come up with far more testable hypotheses than we can test, and the maths in them holds up, but someone still has to test them. \"So we need trained scientists to sift through the gunk and then testers to validate / falsify the useful ones.\" [[3]](#ref-3)\n\nGoogle DeepMind made much the same argument to science funders in July. Don Wallace, Conor Griffin, Sean O'Neill, Thang Luong and Owen Larter wrote that AI agents make ideas and candidate solutions \"abundant and relatively cheap\", while testing them in the physical world is still slow and expensive, and Luong warned that AI could produce results faster than people can review them [[5]](#ref-5). José Penadés at Imperial gave their Co-Scientist a question his team had been working on, and within two days it suggested five possible explanations, and the top one was the hypothesis his team \"had spent so long working to prove\". Gary Peltz at Stanford chose two existing drugs to try against liver fibrosis and the agent chose three, and when they were tested on live human liver cells neither of his worked and two of the agent's did [[5]](#ref-5).\n\nSo our workload doesn't only come from society any more, we now have a magic machine that gives us work, and I don't dislike it. I don't think it'll only be science either, because the same models can come up with psychological theories and new philosophy, and those would need testing with real people too.\n\nThere are a lot of people who could do this work. We have the smartest and most mature generations since the start of humanity coming up after us, and some of them are struggling to find work. Brynjolfsson, Chandar and Chen went through ADP payroll records and found employment for 22 to 25 year olds in the jobs most exposed to AI about 19% below where it would have been [[8]](#ref-8).\n\nI think testing and validating from anywhere is one of the answers to office jobs going away. If you know a field you can check a proof, review code, compare a hypothesis with what's already been published or design the experiment to test it, all from home, though running the experiment still needs a lab, a clinic or someone on site, and the DeepMind paper asks funders for more public labs and automated labs [[5]](#ref-5). Some of this work exists already, Mercor said in May that it pays out over $2 million a day to more than 30,000 contractors a week [[6]](#ref-6), experts in fields like physics and finance in dozens of countries, and by June it was making over $2 billion a year in gross revenue selling expert data to AI labs to train their models [[7]](#ref-7), which isn't the same as testing ideas in the real world.\n\n## What I'm not sure about\n\nMaths might not stay a job for people for long. DeepMind says maths is the exception, because a computer can often check a proof [[5]](#ref-5), so checking maths could be automated first and the jobs that last would be the ones that need testing in the real world. I also don't know who pays for the testing, because a lab pays to test its own ideas and an AI company pays to grade its own model, and I don't know of anyone paying to test the ideas a model comes up with on its own that nobody asked for. And you need to know a field to check work in it, and DeepMind warns that agents could stop young researchers developing that judgement in the first place [[5]](#ref-5).\n\nIf reasonmonkey reads this, thanks, I hadn't thought of checking as a job before that thread. I think there's a good company in this, one that takes the hypotheses the models come up with, finds people who can test them, remotely where possible and in a lab where not, and pays them whether the hypothesis turns out right or wrong. Who wants to build this company?\n\n---\n\nMore in [Opinion](https://www.anchorterminal.com/blog/opinion/index.md)\n\n- Older: [Everyone says this is the singularity, and I think we're far from it](https://www.anchorterminal.com/blog/editing-the-weights.md)\n- All posts: https://www.anchorterminal.com/blog/index.md\n\n## References\n\nRead on 3 November 2026. Quotes are as published.\n\n[1] Reddit, r/accelerate, 3 November 2026, the comment thread under my link to \"Everyone says this is the singularity, and I think we're far from it\". Source for reasonmonkey's definition of the singularity (technological acceleration ending in progress so fast no human can keep up with it, over any time period), mine (when we can't understand the why, even when an LLM explains it), and the exchange about the industrial revolution. https://www.reddit.com/r/accelerate/comments/1xmi9qe/\n\n[2] reasonmonkey, 3 November 2026, a comment on r/accelerate. Source for \"Verification is where a ton of the jobs are going to be. Reality can't be fully simulated. You need to ask it.\" https://www.reddit.com/r/accelerate/comments/1xmi9qe/comment/19s27n5qbpxt1/\n\n[3] reasonmonkey, 3 November 2026, a comment on r/accelerate. Source for the longer version of the idea, models generating more scientifically and mathematically sound hypotheses than can be checked, and \"So we need trained scientists to sift through the gunk and then testers to validate / falsify the useful ones.\" https://www.reddit.com/r/accelerate/comments/1xmi9qe/comment/19s28ithgkuaq/\n\n[4] Engadget, 27 October 2026, \"OpenAI announces new discoveries in mathematics, computer science and physics\". Source for OpenAI's three results (Sogge's local smoothing conjecture, a proof that the Exponential Time Hypothesis is false, and results on quantum field theories in four dimensions), and \"The scientists who proposed ETH are now part of the group OpenAI has asked to examine and explain the results.\" https://www.engadget.com/2297941/openai-new-discoveries-mathematics-computer-science-physics/\n\n[5] Don Wallace, Conor Griffin, Sean O'Neill, Thang Luong and Owen Larter, July 2026, \"Conjecture Machines, AI agents and the new validation bottleneck in science\", Google DeepMind public policy. Source for \"conjecture machines\" and ideas \"abundant and relatively cheap\", testing staying slow and costly, Luong's \"proof indigestion\", the Penadés and Peltz examples, maths being the exception because outputs can often be checked computationally, the call for public facilities and automated labs, and the warning that agents could de-skill trainees before they develop judgement. https://deepmind.google/public-policy/conjecture-machines-ai-agents-and-the-new-validation-bottleneck-in-science/\n\n[6] Mercor, 7 May 2026, \"When you go from $2 million a month to $2 million a day\". Source for paying out \"over $2 million every day to more than 30,000 weekly active contractors across the world\". https://www.mercor.com/blog/when-you-go-from-2-million-a-month-to-2-million-a-day/\n\n[7] Dealroom, 6 July 2026, \"Mercor doubles to $2B gross revenue run rate as AI labs buy expert data\", citing The Information. Source for over $2 billion in gross annualised revenue in June 2026, 60% to 70% of it paid out to contractors, and contractors with expertise in fields such as physics and finance in dozens of countries. https://dealroom.co/news/137121-mercor-doubles-to-2b-gross-revenue-run-rate-as-ai-labs-buy-expert-data/\n\n[8] Erik Brynjolfsson, Bharat Chandar and Ruyu Chen, 12 August 2026, \"Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence\", Stanford Digital Economy Lab, on ADP payroll data. Source for employment of workers aged 22 to 25 in the most AI-exposed occupations standing about 19% below its expected level as of June 2026. https://digitaleconomy.stanford.edu/news/canariesaug26/\n",
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    "description": "Vlad Cealicu on an idea from a Reddit thread, credited to reasonmonkey. The models now come up with more testable hypotheses than anyone can check, from OpenAI's claimed disproof of the Exponential Time Hypothesis to Google's Co-Scientist, and testing them in the real world needs people. I think testing and validating from anywhere is one of the answers to office jobs going away, for the young people struggling to find work, and someone should build the company that does it.",
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