Will AI Populism Decide the 2028 Election? | Jasmine Sun

The conversation delves into the burgeoning political phenomenon of "AI populism," defined as a worldview where AI is seen not just as a technology but as an elite political project to be resisted. Jasmine Sun, a writer on AI, technology, and politics, explains that while AI populism isn't a primary force in U.S. politics yet, it's rapidly gaining traction. Polling indicates AI ranks 29th out of 39 issues, but it's the fastest-rising issue in salience. This is attributed to AI being perceived as intertwined with core voter concerns like affordability, economic mobility, and geopolitics. Politicians like Bernie Sanders are heavily investing in AI populist messaging because these issues are linked. The cost of living, economy, corruption, inflation, and healthcare remain the top concerns for voters. If AI is blamed for exacerbating these issues, it becomes a potent political tool. Sun suggests this is also opportunistic, with politicians using AI as a new reason to push pre-existing agendas, such as increased speech regulation or stronger safety laws for tech platforms. The discussion draws a parallel to the anti-crypto sentiment, exemplified by Elizabeth Warren's "anti-crypto army" campaign. However, Sun argues AI differs significantly. AI is a far larger part of the economy, with data centers appearing in neighborhoods and AI tools like ChatGPT experiencing rapid adoption, making it more relevant to "normal people" than crypto ever was. Furthermore, unlike the crypto industry, AI leaders themselves are vocal about the risks, including job displacement, lending credibility to populist concerns. The rapid rise of AI's political salience is evident in local town hall meetings protesting data centers, a phenomenon that seems more significant than AI's 29th ranking. This grassroots activism, coupled with the industry leaders' pronouncements about societal transformation, galvanizes public reaction. The specter of AI competition, akin to the "China threat" narrative in recent years, is used as a broad justification for various policy actions. Looking towards the 2028 election, Sun anticipates AI populism will accelerate as a campaign theme. Potential candidates on both sides are already incorporating AI into their platforms, using it as a galvanizing issue to justify their unique leadership. AI's adaptability allows it to be distorted to fit diverse political plans. This trend aligns with pre-existing populist sentiments of distrust towards institutions, elites, and billionaires. The immense wealth of AI billionaires and the scale of AI investments make them easy targets for anti-billionaire and anti-corporate sentiment. A stark contrast is drawn between the AI industry's attempts at political influence and that of the crypto industry. While the crypto industry's Super PAC, Fairshake, effectively lobbied for pro-crypto legislation by targeting anti-crypto candidates, the AI industry's "Leading the Future" PAC has seen the opposite effect. Endorsing or attacking candidates has backfired, making politicians seem more appealing by association with anti-AI billionaire sentiment. This suggests that AI industry backing can be a political liability due to existing populist sentiment. The conversation touches on the disturbing trend of political violence, with the attacks on Sam Altman's home and the murder of a healthcare CEO being examined. Sun distinguishes these acts, attributing the AI-related attacks to specific fears about existential risk, while other assassinations stem from a broader disillusionment with societal systems. Both, however, are linked to young, "very online" individuals from niche online communities who develop extreme beliefs and feel political violence is their only outlet. This reflects a growing prominence of political violence in the U.S., with a significant portion of the public believing assassination attempts can be justified. The core of the issue, Sun argues, is a growing nihilistic politics where individuals no longer believe in the democratic system's efficacy. Lacking other channels for their voice, they resort to direct action, including violence, to express discontent and halt perceived negative societal changes. This is also seen on a smaller scale with protests against data centers, where ordinary citizens have limited avenues to influence AI development or policy. The discussion then moves to the economic implications, specifically the debate around mass unemployment driven by AI. While figures like Dario Amodei predict significant job losses, critics like Marc Andreessen invoke the "lump of labor fallacy" and Jevon's paradox, arguing that increased productivity and lower costs will create new jobs and demand. Sun presents Amodei's counterargument: AI's potential to automate labor without human involvement breaks the historical link between productivity and human employment. This is particularly concerning for cognitive jobs like software engineering, where AI is rapidly improving. Sun offers a more moderate perspective, expecting near-term labor disruption in easily automatable sectors like software engineering, digital marketing, and copywriting. However, she believes physical world jobs and those protected by regulation will take longer to automate. Retraining is often overestimated, and displaced workers may struggle to adapt, leading to political resentment. This resentment could manifest as left-wing populism, blaming AI billionaires. She also worries about a declining labor share of the economy, even with full employment, leading to wealth inequality and resentment. The concept of a "capitalism end game" where labor becomes obsolete is explored, leading to a permanent underclass. While acknowledging this extreme scenario, Sun believes human labor will remain scarce in certain sectors, particularly relational ones like personal trainers and party hosts. She also highlights that wealth inequality itself can reduce consumer demand, limiting the growth of the services economy. Sun expresses skepticism about the immediate feasibility of full automation, suggesting significant challenges remain before robotics can fully replace human labor. She believes the focus should be on near-term scenarios and planning for potential disruptions. Addressing the gap between private concerns and public optimism within the tech elite, Sun notes that many express extreme concern about labor market impacts privately but become optimistic publicly. This reluctance to speak openly stems from a desire to avoid backlash and being labeled the "bad guys." She asserts that figures like Amodei genuinely believe their predictions of job displacement, and these statements are not merely marketing ploys. The conversation shifts to policy recommendations. Sun suggests increased taxes on corporate and capital gains, expanded unemployment insurance, and a reevaluation of education systems to focus on apprenticeships and national service programs. She also touches on universal healthcare and shorter work weeks as potential solutions for a changing economy. The idea of a "grand bargain" between technological progress and societal benefit is proposed, drawing parallels to the 20th century's successful integration of automation through strong unions and federal welfare expansion. Sun believes a similar bargaining mechanism is needed today, where AI executives and policymakers negotiate how the gains of technology will be shared with affected workers. This could involve wage guarantees, tying wages to productivity, and investing in training programs. Finally, Sun emphasizes the importance of humans finding their comparative advantage against AI, focusing on what AI *cannot* do yet, such as building trust, experiencing physical environments, and engaging in complex conversations. She advocates for continuous experimentation with AI to identify its limitations and invest in uniquely human skills. The conversation concludes with a call for proactive policymaking and a balanced approach to technological advancement, ensuring its benefits are broadly shared.