Slow Takes Ep. 5: Who Gets the Gains?

This is the fifth 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.Slow Takes is also available on the YouTube channel: Exploring ChatGPT.The recording is above. What follows is not a summary. It is context that did not fit into the live, plus the sources so you can read further and form your own view.One thread ran through every story this week: distribution. AI will create more jobs than it destroys. That claim is technically surviving in the aggregate. But when you drill into who gains and who loses, the picture is brutal. Five stories about who keeps the benefits of AI and who pays the cost.What we covered1. The Harvard labour market studyHarvard Business Review published a study by Ana Elena Azpúrua analysing most US job postings from 2019 to early 2025. AI is cutting about 17% of roles in automation-heavy sectors while increasing demand by approximately 22% in positions that benefit from human-AI collaboration. The headline number looks balanced. The distribution does not.The losses are disproportionately hitting women, older workers, the highly educated, and the well-paid. Workers aged 22 to 25 in AI-exposed occupations have seen a 13% employment decline since 2022. There is now a 4.5x income multiplier between workers with AI skills and those without.What we said on the live: The jobs that young workers have been training for over five, seven, nine years of education are disappearing before they arrive. The best time to have started using AI was 2022. The second best time is now. But not blindly. I suggested an exercise: make two lists. Everything in your job that AI could technically do. Everything it could never do. The second list is always the same: human interaction, emotional intelligence, knowing which members of your team work together, knowing the details of a client you have worked with for twenty years. Those invisible elements are the ones that cannot be automated. When that conversation comes about redundancies, you want that list ready.Leor made the point that we cannot imagine the job creation on the other side of this. The same was true of the internet. Except this is a bigger shift.What did not come up: The study is behind a paywall, which limits its reach at exactly the moment it should be widely read. Only about 10% of the global population is using AI in any capacity, as one audience member pointed out. That means the labour market disruption documented here is being driven by a technology most of the world has not yet touched. The dislocation will accelerate as adoption spreads.The 4.5x income gap is particularly striking because it suggests a bifurcation in the labour market that predates AI but is being accelerated by it. Workers with AI skills are not just earning more. They are pulling away. The policy question is whether workforce development programmes can close that gap, or whether AI literacy becomes the new digital divide.The gendered distribution of losses deserves more attention than it received. Women are overrepresented in administrative, coordination, and communication roles, precisely the categories most susceptible to automation. This is not a new pattern. But AI is compressing the timeline.2. AI made you faster. Your employer kept the time.AES, an energy company, compressed a 14-day audit into one hour using AI tools. That compression ratio is staggering, and probably an edge case. But the broader pattern is real: AI is drastically reducing the time required for technical tasks. The question is who benefits from the time saved.What we said on the live: Parkinson’s law states that work expands to fill the time available for its completion. If you can do eight hours of work in one hour and your employer knows it, you will be given another seven hours of work. The productivity gain goes to the company. The worker gets more output expected at the same pay.I raised Keynes, who predicted in the 1930s that a 15-hour work week would be possible by 2030. He was right about the capability. He was wrong about the willingness. We have the technology to free people from the burden of routine work. The question is why we are not exploring that, and instead filling reclaimed time with more work.Leor was honest about his prediction: he does not think we are heading towards more free time. He thinks companies will use AI to achieve a multiplier effect per employee. People will work the same hours doing more. Capitalism will absorb the gains.What did not come up: The mental health implications are significant and underreported. It is not just the fatigue of overwork. It is the fatigue of uncertainty: the constant worry about whether your role is next. The Gen Z shift towards solopreneurship and multiple revenue streams is partly a response to this. If the employer is going to capture all the productivity gains, the rational move is to become your own employer. Substack, Fiverr, and freelance platforms are not just lifestyle choices. They are hedges against a labour market that rewards companies for extracting more from fewer people.The four-day working week conversation should be louder than it is. The pandemic demonstrated that many jobs could be done in less time. AI has made that even more obvious. The obstacle is not technological. It is structural.3. Hollywood’s copyright letter to the White HouseOver 400 A-listers, including Ron Howard, Paul McCartney, and Cate Blanchett, signed a letter to the White House arguing that their work should be protected from AI training without consent. This came in response to OpenAI and Google submitting formal recommendations to the White House explicitly requesting copyright exemptions for training data. Their argument: if you do not do this, China will beat us to better models.The creative industries employ 2.6 million people in the United States.What we said on the live: I found out about the OpenAI and Google letter through the protest, not the other way around. The request for copyright exemptions was made quietly. The response was not.Leor raised fair use, and the word ‘research’ specifically. One of the terms in fair use law is research. That word sounds respectable. But research is broad enough to cover ‘we are going to research which artist we can steal from to make more money.’ I called it disingenuous. It is strategically vague.We discussed what happens when artists die. Who owns the rights then? If copyright exemptions pass, what stops someone feeding Paul McCartney’s entire back catalogue into an AI tool and monetising the output on Spotify? The question is not hypothetical. Record labels under 360 deals own artists in perpetuity. And as Leor pointed out, artist deaths often increase sales. The incentive structure is grotesque.What did not come up: The Paul McCartney and Michael Jackson story is worth telling. In the 1980s, McCartney advised Jackson that buying other people’s music rights was a good investment. Jackson took the advice and bought the Beatles’ catalogue. The friendship never recovered. McCartney eventually bought it back. The point: ownership disputes in the creative industries predate AI by decades. AI is a catalyst, not a cause. But it compresses the timeline and scales the exploitation.The China argument is doing a lot of work in Washington right now. It was used to justify the copyright exemption request. It is being used to justify deregulation more broadly. It functions as a permission structure: we would prefer not to do this, but the enemy will do it first. That framing should be examined every time it appears. It is not wrong that geopolitical competition exists. It is wrong to use it as a blanket justification for policies that would otherwise be indefensible.4. The White House AI legislative frameworkOn 20 March, the Trump administration released a national AI legislative framework outlining six priority areas. Some are genuinely good: child safety is listed first, and the framework argues that ratepayers should not foot the bill for data centre energy costs. But the centrepiece, and likely the real purpose, is federal preemption: blocking states from passing their own AI laws.What we said on the live: California, Colorado, and Illinois have built AI regulations because their residents needed them. Overriding that from Washington is centralisation dressed as coordination. I pushed back on the assumption that governance restricts innovation. It does not. Good governance funnels innovation in the appropriate directions.Leor agreed that states should be able to regulate independently, but made the pragmatic point that it may not matter: companies can simply move to states with less regulation. Google does not have to stay in California. That mobility makes state-level regulation porous by design.We both acknowledged the good elements. Child protection as the top priority matters. The ratepayer protection matters: energy costs driven by data centres are becoming unaffordable for ordinary people.What did not come up: The framework’s language about an ‘AI race’ is revealing. The first sentences frame AI policy as a competition to be won, not a technology to be governed. That framing shapes everything that follows. If you are in a race, regulation is friction. If you are building infrastructure, regulation is engineering.The data centre energy point deserves expansion. AI model training and inference are driving unprecedented energy demand. If that cost is externalised to ratepayers rather than absorbed by the companies generating the demand, it is a subsidy. A subsidy paid by people who may not be using the products those data centres power.The lobbying dimension was implied but not stated. Federal preemption benefits a small number of very large companies. State-level regulation creates complexity for companies operating across jurisdictions. Removing that complexity is presented as good governance. It is also, precisely, what the largest AI companies have been lobbying for.5. The UK reverses on AI copyrightThe UK government reversed its position on AI and copyright after sustained pressure from artists, musicians, and writers. The original proposal would have made all creative work available for AI training unless the creator opted out. The reversal shifts the default to opt-in: your work cannot be used unless you give permission.Elton John called the original proposal’s supporters a bunch of losers. It was effective.What we said on the live: This was the hopeful story. When people say collective action does not work, this is the counter-evidence. The creative industries mobilised, made a fiscal argument as well as an ethical one (the UK creative sector employs hundreds of thousands of people and is worth billions of pounds), and won.I made the point that the opt-out model was structurally unfair. At the highest level, an Elton John can afford to opt out. At the lowest level, an independent artist uploading to Spotify has neither the knowledge nor the resources to navigate the process. The burden falls on the people least able to bear it.We discussed whether existing systems could handle licensing at scale. The UK has ALCS (Authors’ Licensing and Collecting Society), which tracks photocopying and library use and distributes royalties. The infrastructure for compensating creators exists. AI companies have chosen not to use it because paying the fine afterwards is cheaper than building the system beforehand.What did not come up: The UK reversal happened the same week as the Hollywood letter to the White House. Two countries. Two creative industries. The same fight fought in different ways. The UK won through sustained organising. The US fight is still open.The opt-in versus opt-out distinction matters more than it appears. Opt-out systems place the burden of action on the person whose rights are being used. Opt-in systems place it on the person seeking to use the rights. The difference is not administrative. It is philosophical. It answers the question: who does the default protect?The fair use question Leor raised about poetry is instructive. In the UK, the rough convention is that you can reproduce up to 10% of a work without permission. For a novel, that is chapters. For a haiku, that is three words. Effective governance, as I said on the live, is not about getting into the absolute miniature of what you can and cannot do. It is broad terms that benefit everybody, that are flexible, and that enable actual fair use.The threadA Harvard study shows AI creating more jobs than it destroys, but the people losing their jobs are not the people getting the new ones. An energy company compresses two weeks into one hour, and the employer keeps the time. Hollywood fights for its copyright while Google quietly asks the White House to abolish it. The White House framework protects children and ratepayers in the same document that strips states of the power to protect their own residents. The UK reverses a copyright policy because enough people refused to accept it.The thread is distribution. Who gains. Who loses. Who decides.AI does not distribute benefits. People do. Institutions do. Governments do. The technology is not the problem. The choices about who it serves are.Every Monday, 07:45 ET / 11:45 GMT.Go slow. Get full access to Slow AI at theslowai.substack.com/subscribe