Slow Takes Episode 3: Who Decides?

This is the third episode of Slow Takes, a weekly Substack Live I co-host with Leor from Exploring ChatGPT. The format is simple: we take the week’s AI news and react to it without hype, without predictions, and without pretending we have all the answers. We invite the audience to call us out when we get it wrong.The recording is above. What follows is not a summary. It is context that did not fit into 46 minutes, plus the sources so you can read further and form your own view.One thread ran through every story this week: who decides? In every case, the people affected had no say.What we covered1. DOGE used ChatGPT to cancel humanities grantsLawsuit discovery documents filed on 6 March revealed that DOGE fed NEH grant descriptions into ChatGPT, asked “Is this DEI?”, and used the yes/no answers to build a spreadsheet. That spreadsheet replaced the one created by actual NEH staff. Grants cancelled include a Holocaust documentary, Appalachian photography archives, and Native American language preservation projects.What we said on the live: A chatbot decided which humanities projects deserve to exist. Not expert review. A language model answering a binary question about a term it cannot define. This is what happens when AI replaces judgement: not with better judgement, but with no judgement at all. As Diamantino Almeida pointed out in the chat, this is also a way to avoid accountability. The moment somebody is held responsible, they will say, “It was the tool that made this decision.”The best AI governance has one rule at the top: AI should not be involved in decision making. You can use these tools to frame your thinking. The minute you outsource your actual judgement to a system we know to be biased, you have abdicated the responsibility you are paid to hold.What did not come up: The scale. More than $100 million in NEH funding was withdrawn. That is nearly half the agency’s annual budget. Some projects were forced to shut down entirely. The plaintiffs are the American Council of Learned Societies, the American Historical Association, and the Modern Language Association: three of the largest humanities organisations in the United States.Discovery also revealed that DOGE staff used Signal to communicate about the process, which likely violates the Federal Records Act. Two DOGE team members were deposed. Some grants were terminated despite NEH’s own staff concluding they did not conflict with the new policies. The chatbot overruled the humans who were paid to make the judgement.The deeper question is precedent. If a government agency can use a chatbot to make funding decisions about the humanities, the same method can be applied to healthcare, housing, criminal justice, and immigration. The technology is the same. The spreadsheet is the same. The absence of human judgement is the same.2. House of Lords: creative industries face “clear and present danger”A House of Lords committee report published on 6 March found that AI companies are training on copyrighted work without consent or payment. The committee wants a licensing regime, mandatory training data disclosure, stronger deepfake protections, and an end to the proposed text-and-data-mining opt-out. The government must publish its copyright report by 18 March.The numbers: UK creative industries are worth £124 billion and support 2.4 million jobs. The UK AI sector is worth £12 billion and supports 86,000 jobs.What we said on the live: The numbers tell the story. The government is being asked to sacrifice the larger industry for the smaller one. Those numbers should end the argument. There are existing models for compensating creators (music royalties, the Authors’ Licensing and Collecting Society). The proposed opt-out scheme puts creators in an impossible position: opt out and you protect your work but disappear from generative engine optimisation. Opt in and your work trains models without compensation. Elton John called the government a bunch of losers. Karen Brasch 🚁 raised the harder question in the chat: how do you identify who gets to claim new original work when so much is AI-generated and duplicative?What did not come up: The report contains 38 recommendations in total. Beyond copyright, it calls for stronger protections against unauthorised digital replicas and “in the style of” uses of creators’ work, highlighting that the UK has no robust “personality rights” protecting digital likenesses. It also recommends prioritising domestically governed AI systems so the UK is not reliant on “opaquely trained US-based models.”The 18 March deadline for the government’s copyright report is significant. If the government sides with AI companies over the Lords committee’s recommendations, it will set a precedent that projected future value outweighs realised present value, and that 86,000 jobs in a speculative industry justify undermining 2.4 million jobs in a proven one.A note on the numbers: the £124 billion figure is from 2023, the £12 billion from 2024. Gov.uk sources put the AI industry value higher ($23-53 billion) when projected economic contribution is included. The Lords’ report uses the more conservative, verifiable figure, which is the stronger basis for policy.3. OpenAI’s Pentagon deal: surveillance ban with loopholesEpisode 2 follow-up. OpenAI revised its Pentagon contract, adding a domestic surveillance ban. The EFF called it “weasel words.” Sam Altman admitted he cannot control how the Pentagon uses AI once deployed. Anthropic was blacklisted for refusing to allow bulk data analysis on Americans. The company that said no got punished. The company that said yes (with caveats) got the contract.What we said on the live: Actions speak louder than words. OpenAI’s ‘ban’ is a press release, not a technical constraint. Anthropic’s refusal was a technical constraint, and they lost the contract for it. The incentive structure is clear. Leor argued that blacklisting any US AI company is bad strategy: Project Genesis shows the government needs all its AI companies working together.Claude crashed twice last week from server demand as users left OpenAI. Back-of-the-envelope: Anthropic lost a $12 billion contract but may be close to recovering that through consumer subscriptions alone. They lost the contract and bought themselves goodwill.What did not come up: The contract clause exists because someone had to write it down. The Pentagon did not volunteer a surveillance ban. OpenAI inserted it, which means both parties knew the capability existed and the temptation was real enough to require a written prohibition.Whether that clause is enforceable, and what happens when it conflicts with a classified directive, is a question nobody in the room can answer. The EFF’s characterisation as “weasel words” is not about the intent. It is about the enforceability.4. The “Artificial Hivemind”: AI is making everyone sound the sameThe NeurIPS 2025 Best Paper award went to ‘Artificial Hivemind’ from the University of Washington. The researchers tested 70+ AI models on 26,000 open-ended queries and found systematic convergence: not just that each model repeats itself, but that different models produce strikingly similar outputs to each other. A separate Nature study found AI erasing cross-cultural differences in academic writing style.What we said on the live: The hivemind is not a metaphor. It is a measured effect across 70 models. The more people use these tools, the more they sound alike. Not just students. Everyone. Leor made the point that model distillation (smaller models learning from larger ones) makes this convergence unsurprising but no less concerning. The question of whether the simulated users in the study were themselves diverse enough is worth asking.Sam raised the language dimension: AI tools default to English. If everything defaults to English, the loss is not just linguistic. It is cultural. Caroline Bobby added in the chat that the issue is not just language repression but the domination of normative brains, and that AI tools are massively biased towards neurotypical people.What did not come up: Last week’s peer review story (one in five reviews at ICLR were fully AI-generated) is the downstream effect of this upstream problem. If the models producing research reviews all converge on the same outputs, peer review stops being a diversity of expert opinion and becomes a single opinion wearing multiple masks.The Nature study on cross-cultural writing differences is particularly significant for universities. If AI is flattening academic writing style across cultures, the students most affected will be those whose first language is not English, the same population already disproportionately flagged by AI detection tools.5. AI is already causing fatal accidents in gig workA UN/ILO webinar in March 2026 presented evidence that trade union monitoring has documented fatal accidents linked to couriers chasing impossible delivery targets set by algorithms. Workers affected are predominantly in the Global South. The ILO is calling for international regulatory frameworks for algorithmic management.What we said on the live: We spent the episode talking about grants, copyright, and contracts. This is the version with a body count. Algorithmic management is already killing people. Not hypothetically. Many workplace efficiency measures came from manufacturing environments and simply do not work for people. The algorithms do not account for road closures, weather, or the fact that a human being has limits.What did not come up: An important nuance: the fatal accidents claim comes from Evelyn Astor, Director of Economic and Social Policy at the International Trade Union Confederation, speaking at the ILO/ITU webinar. It is based on trade union monitoring, not a formal ILO study. The evidence is real but the source is advocacy, not peer-reviewed research.The strongest empirical evidence cited was a 2025 University of Cambridge study that found around two thirds of UK drivers and couriers reported anxiety caused by sudden schedule changes and unfair feedback from automated systems. More than half said they risk their health and safety at work.The gig economy operates in a regulatory gap. Workers are often classified as independent contractors: no employer to hold accountable, no union to represent them. The algorithm sets the target. The platform takes the margin. The worker absorbs the risk. When that risk turns fatal, there is no employer, no manager, and no AI to prosecute. The workers dying are predominantly in the Global South, delivering for platforms headquartered in the Global North. The people who designed the algorithm will never meet the people harmed by it.The threadA chatbot decided which grants to cut. A government is being asked to sacrifice creators for an industry a tenth the size. A corporation added a surveillance ban it cannot enforce. A paper proved we are all starting to sound the same. And couriers are dying chasing targets set by an algorithm.Who decides? Not us. Not yet.Every Monday, 8am ET. Go slow. 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