When I studied economics (a lifetime ago), I actually read The General Theory of Employment, Interest, and Money by John Maynard Keynes. Keynes’ theories have dominated economics ever since. Yes, there have been many competitors and new iterations of Keynes’ theories, but much of what Keynes wrote is still relevant. Don’t worry, I’m not going to discuss economics. What I am going to discuss is related to a passage in Keynes’ book that I think is relevant to many of today’s hot topics such as growing authoritarianism, declining academic performance, and artificial intelligence. Here is the passage from Keynes:
The ideas of economists and political philosophers, both when they are right and when they are wrong, are more powerful than is commonly understood. Indeed the world is ruled by little else. Practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist. Madmen in authority, who hear voices in the air, are distilling their frenzy from some academic scribbler of a few years back. I am sure that the power of vested interests is vastly exaggerated compared with the gradual encroachment of ideas…. J.M. Keynes, The General Theory of Employment, Interest, and Money
Who is our “madman in authority” today? Which “academic scribblers” is he “distilling [his] frenzy from?” Who will be the new “academic scribblers” of the future? I will answer the first two question very briefly. Donald Trump is today’s “madman in authority.” His academic scribblers are many: Project 2025, Steve Bannon (Breitbart), Stephen Miller (speechwriter & immigration strategist), Peter Navarro (economist, advisor), The Fox News ecosystem, talk radio and digital influencers. There are many others including Arthur Laffer, Casey B. Mulligan, Richard Burkhauser, Judy Shelton, Stephen Miran, Scott Bessent and more. These are the “academic scribblers” whose published ideas and policy advocacy have fundamentally shaped Trump’s economic worldview. Their academic work provides both justification and guidance for his administration’s signature actions, from tariff wars to tax reform.
What I want to focus on is not the President and his current advisors whatever you may think of them, but on who the new academic scribblers will be. There has been a significant drop in the math and reading skills of U.S. high school students.
In reading, the average score in 2024 was the lowest score in the history of the assessment, which began in 1992. Thirty-two percent of high school seniors scored below “basic,” meaning they were not able to find details in a text to help them understand its meaning. In math, the average score in 2024 was the lowest since 2005, when the assessment framework changed significantly. On the test, 45% of high school seniors scored below “basic” achievement, the highest percentage since 2005. Only 33% of high school seniors were considered academically prepared for college-level math courses, a decline from 37% in 2019. (NBC News)
The “academic scribblers” of the future, the thinkers whose ideas quietly shape policy, technology, and culture, may look very different from those of Keynes’ day. Low reading and math proficiency among many high school graduates suggests a smaller pipeline of traditional public intellectuals. But instead of disappearing, influence is shifting into new forms and institutions: techno-intellectuals & AI architects, behavioral economists & cognitive architects, edtech & platform epistemologists, corporate & algorithmic philosophers, fringe theorists & Alt-influencers, global and nontraditional knowledge centers.
Lower reading and math skills won’t stop “scribblers” from emerging, but they’ll increasingly come from smaller, elite circles (tech hubs, think tanks, labs), algorithmic systems rather than individuals, outside the U.S. mainstream where education systems are producing stronger foundations.
Instead of Keynes’ professors in ivory towers, the next generation of “scribblers” will likely be coders, neural network trainers, behavioral modelers, and content engineers. The new academic scribblers may be AI systems distilling their “frenzy” from datasets, shaped by engineers, influencers, and opaque algorithms. The next generation may not even recognize who’s ideas they’re living under.
If we treat AI as today’s madman in authority–systems making consequential decisions or shaping human behavior at scale–then the academic scribblers are the researchers, theorists, and philosophers whose ideas, often developed decades ago, underpin AI’s current power. AI doesn’t “think” independently–it distills its authority from: mathematical frameworks created by dead statisticians, cognitive theories of how humans process meaning, ethical paradigms implicit in data and design, and economic incentives embedded by living institutions. Just as Keynes warned, we forget the origins. Policymakers, CEOs, and even engineers often treat AI outputs as oracular when in reality they are shadows cast by old ideas.
We must ask whose scribblers are baked into the models. Are they Eurocentric? Male dominated? Capital driven? AI’s authority isn’t neutral, it inherits centuries of assumptions. Choosing which scribblers dominate AI’s foundations is a political act. The problem is that the same technology that purports to make us smarter actually discourages us from using our brains to think. This is brilliantly explained in a recent article in Medium by Eva Keiffenheim whose TED talk is posted below. She points to our collective narrative: “Why remember what I can just Google or ask ChatGPT?”
While the questions seems logical (and I’ve blindly followed it for most of my adult life), it’s based on a fundamental misunderstanding of what memory is for, and this seemingly harmless habit is quietly eroding the very neural architecture that expert thinking is built upon. (Eva Keiffenheim, The Smartest People I Know Are Obsessed With a Skill Many Were Told Is Useless)
I won’t repeat the incredibly useful information in the article which you can read for yourself other than to say that Keiffenheim points out how outsourcing our thinking to devices undermines the three core processes of deep learning (automaticity: the ability to perform a skill without conscious thought, schema construction: a mental framework that organizes knowledge, and prediction error: the brain learns best when it’s surprised). She goes on to say “The fix isn’t nostalgia for pre-internet memory drills. It’s a simple strategic framework: shape knowledge, don’t just find it; follow a stepwise progression; and use technology as a complement … Every time you face a gap in your knowledge, you make a choice. You can make a short-term withdrawal for an immediate answer, or you can make a long-term investment by doing the work to internalize the knowledge.”
If Keynes were alive today, he might rewrite his quote above as: “Practical engineers, who believe themselves exempt from philosophical influence, are usually the slaves of some defunct linguist, statistician, or metaphysician.” AI is powerful precisely because it amplifies old intellectual commitments without us always recognizing them. When an LLM “speaks,” you’re hearing a chorus of Alan Turing, Claude Shannon, Noam Chomsky, Ludwig Wittgenstein, and Geoffrey Hinton filtered through trillions of data points.
Our most important task today is to learn how to build and use AI in a way that improves our ability to learn, control and improve our lives. And, to do that we’re going to need to improve the math and reading skills of our high school students with the same degree of urgency as we did in 1957 after the Soviet Union launched the world’s first satellite, Sputnik I. Instead, the Trump administration’s assault on America’s universities by cutting billions of dollars of federal support for scientific and medical research is pushing us in the wrong direction. That’s what happens when you listen to the wrong academic scribblers.