What we got wrong in “AI went MAGA”
Nik and I got some stuff wrong. If you would like the full list starting with Nik choosing the wrong study partner for the science fair in 1st grade and botching his chance and placing, let us know. For now we are going to keep it confined to a piece we wrote for Inside Higher Ed in February of 2025 titled “AI has gone MAGA.” We still like the piece and think we shared some smart insights, but the scope of the piece does not seem accurate. Broadly, we theorized that AI would become a right-wing-coded technology due to strong associations between the new (at the time) Trump administration and Silicon Valley elites. The strongest evidence for this was a press conference announcing a partnership between Softbank, OpenAI, and the administration. We were never under the impression this would materialize (which it has not), but we thought this would signal the right-wing-coding of a technology the way early social media was left-wing coded. Here is what we got wrong.
AI has not been drafted into the culture war. Enthusiasm for AI is still quite low with a whole 1/10 Americans being more excited than concerned about AI in daily life–that is lower than the approval rating for congress. Somehow, anxiety about AI is a bipartisan issue. Even among the elite political class you see aisle crossing. The data center moratorium is a collaboration between Bernie Sanders and AOC while one of the loudest AI skeptics in the country is Florida governor Ron DeSantis. This could still sort out around partisan lines, but what seems more likely now is that politicians have found a “winning” issue in opposing AI and will continue to pile in from the left and the right.
In some ways, this is a continuation of an existing trend. For years Silicon Valley companies and products have been a political punching bag despite their material successes. Politicians from the right have complained ad nauseum about liberal bias on social media while those on the left are often upset about not having enough censorship or about the deleterious effects of social media on social life. Much of this was encapsulated in a The Weeds podcast from 2019 entitled “Why everyone hates big tech.”
AI has been politically coded, but in reference to China. We have been outspoken in our skepticism of AI as a new front in a great power competition like the space race. Briefly, these technologies are being developed by private companies, not governments, and the companies themselves are incredibly multi-national. To this end when politicians talk about “American AI” they are actually referring to companies which may have corporate headquarters in the US, with a CEO and/or board of international leaders, using chips made in Taiwan, stacked in data-centers all over the world, powered by energy that may have been imported from the Persian Gulf. This is not necessarily bad, but it is definitely not American. I digress, what has unified the political elite of the left and right is concern about falling behind Chinese AI. From Chuck Schumer (D) drafting a document stating “urgent action is required for the U.S. to stay ahead of China” to Ted Cruz (R) similarly saying “America has to beat China in the AI race.” China hawks are prominent in both parties. This position tends to be a winning issue for most Americans so again, even for those in favor of AI development, it does not sort neatly along partisan lines.
Nik and I also speculated about what the right-coding of AI would mean for colleges and universities. Rather than right-coded this has become leadership/elite coded. Many rank-and-file faculty feel as though the technology is being foisted on them by Silicon Valley elites and university leaders. So while opposition to and anxiety about the technology continues to be quite high it tends to break along elite/non-elite lines rather than traditionally political ones.
This could all change in a hurry in ways that are difficult (obviously) to predict. The midterm political season may see this technology sorted along more traditional lines, but for now we wanted to own up to a set of predictions that did not turn out.