After AI

The larger question now is what becomes of us.

Hi everyone,

I admit to being less than eager to jump into discussions about AI these days, and as I’ve mentioned before, a large part of my foot-dragging has been the simple fact that I was lead author on a co-authored book that will be published by MIT Press (it looks like now in January). The book is good, and we were contacted that a donor nominated it as a “book to read” for the lineup (again, now in January).

But you get to this place where you feel like you’ve said all you’ve said, and so I wish to move on to different ideas, adjacent ideas than the endless exhortations and grumblings about the latest algorithm sweeping the planet from Silicon Valley.

The two innovations clearly not directly tied to Moore’s Law (increases in compute power and memory storage) are on the one hand decades old—backpropagation, or the idea of reducing error by incremental adjustment—and the much newer idea that “attention is all you need,” as the now famous paper from the now famous researchers, the “Google Eight” put it. They were removing a constraint imposed by previous methods like recurrent neural networks, which processed sequences step by step and therefore made it harder to capture long-range dependencies in language. Attention allowed the model to consider relationships among words across a sequence all at once.

This proved decisive, as it enabled the learning to range up and down the token sequences (sentences) and pick up or “pay attention to” the important tokens in the sentence such that the model could reproduce not only good English (or other language) grammar but—as we all now know—impressively relevant and informative responses to prompts, or questions put to it by a human user.

The pivot to alignment concerns, wiping out jobs for people, the usual patter about the Singularity being near, and justifiable worries about deskilling and stunting human development ensued, predictably.

I tried to write—it’s co-authored, but I am the lead author responsible for the writing—a book that was worthy of my first, The Myth of Artificial Intelligence: Why Computers Can’t Do What We Do (Harvard University Press, 2021). It’s called Augmented Human Intelligence: Empowering Minds in the Age of AI, and it attempts to show the true capabilities and limitations of the current systems, how they work, why they work, but also more importantly how the expected hype is obscuring the even more pertinent role now for human smarts. We’re not going away.

In truth, the two books I just mentioned contain most all of what I wish to say about “AI” for now anyway, circa 2026. The resistance movement is impressive and well-meaning, and I’ve reviewed the books: The AI Con: How to Fight Big Tech’s Hype and Create the Future We Want by Emily Bender and Alex Hanna, Brian Merchant’s Blood in the Machine: The Origins of the Rebellion Against Big Tech, and many others (I reviewed The AI Con for The Los Angeles Review of Books. You can read it here).

I’m sitting in the Barnes and Noble bookstore (it’s still weird for me to see my own book on the shelves), and I see evidence of a vigorous push-back against Big Tech’s version of AI that we’re all happily using now. U.S. Senators have Big Tech pushback books on the shelves, too, like Missouri Senator Josh Hawley’s The Tyranny of Big Tech (2021). And very good journalists like Karen Hao dedicated many good pages, in her excellent Empire of AI to interviewing the principles at OpenAI and tracing the contradictory evolution of that new denizen of Big Tech. It’s a story of greed and futurism and crocodile tears about job loss and not being the smartest thing on the planet anymore (I had given up this conceit by my first graphic calculator in the 1990s.).

And hear I fear that I’ll let my readers down. I had assumed that getting to the “smart power” of our current crop of frontier models, in natural language conversation and problem solving and other central historical concerns of AI as a field of research would be a more interesting and, what? conceptual path. But it turns out that we accomplished a quantum jump—yes, even with the maddening black box problems including the problem of errors in generative systems—in AI capability, and in my area of research known as natural language processing no less, by eliminating constraints and allowing gobs of compute and the attention mechanism be all we need. That was a nice insight—but it shouldn’t have changed the world, like say special relativity did. It was, really, a tweak. A relaxing of a constraint, a question as to why we need sliding windows and other restrictions when training.

OpenAI beat everyone, including Google, the company that had sponsored the research and whose researchers wrote the now famous 2017 paper “Attention Is All You Need.” They beat everyone by throwing obnoxious amounts of VC-funded compute at an equally obnoxious capture of the human writing and communication on the Internet. I should know, as I am part of a class action lawsuit against Anthropic, the company chasing OpenAI that also hoovered up my first (nonfiction) book, The Myth of AI, and then impressively went about answering the hard natural language questions I’d offered in the book as notorious challenges for AI.

Now, after decades of exploratory research, the recipe we have is: (a) get a power grid for training (b) beg, borrow, or steal every human scrap of communicative meaning on the web, and (c) wait a few months while mass gets turned into energy, or, if that’s too obscure and hifalutin, as human written language gets captured and patterned so as to be appropriately regurgitated later, to the edification of all.

This is a let down to me. We didn’t learn anything interesting about ourselves, about how we solve problems or where our intelligence comes from (hint: it’s not like a frontier model). Cognitive science—to the extent that it still exists today as an interdisciplinary field—has not advanced or benefitted. Whether, following Bender, we’ve succeeded in creating super simulating stochastic parrots, or what Wharton professor Ethan Mollick has called an “alien intelligence” in his Co-Intelligence: Living and Working with AI, it’s clear to this observer anyway that we’ve created yet another massive obstacle to my fight here, which is exploring what it means to be human in an age of data (the tagline to this Substack). I find the Resistance Movement against Big Tech to be, as Merchant explores, another round of Luddites reacting to very real threats to human security and flourishing. I also think that nothing short of revolution will stop Silicon Valley from proceeding on the path it’s chosen, as it’s also the path we all joyfully stepped on.

In my admittedly unscientific observations, it seems that pretty much everyone who writes emails or Substacks or papers for scientific journals or fiction books about romance or anything else involving language is now using LLMs to some degree or other. In this scenario there’s really no point in taking a resistance movement seriously, in the dire sense of say, the French Resistance during the Nazi occupation of Paris. Or, say, the resistance movement that tells everyone to stop drinking sugared sodas, while not banning them and continuing to advertise them. Or what have you. Another way to see why this well-meaning and smart group of resistance fighters won’t get anywhere is to simply roll time back to the last great Silicon Valley creation. Let’s skip past all the deep neural networks inspired social media of the 2010s, leapfrog over web 2.0 in the 2000s, and end up back at Stanford, where a couple of graduate students were worrying Stanford’s brass using its servers to index the web and make it searchable using a recursive technique that came to be called “PageRank.”

The writer and cultural critic Nick Carr wrote persuasively about the growing problem with Google searches and human agency, in his piece in The Atlantic, “Is Google Making Us Stupid?” But who would take up that fight today? Carr himself seems to have come to a sort of begrudging acknowledgement in his latest, Superbloom: How Technologies of Connection Tear Us Apart. (I reviewed Superbloom on Colligo. You can read it here.)

I fear that the same gradual amnesia and indifference to today’s AI will set in. I don’t believe that we’ll overthrow Silicon Valley in the sense that regulation or public pressure or anything else will ever amount to anything more than token restraints to satisfy lawmakers and get everyone except the few true believers to get on with their day now, no doubt using a frontier model to make it easier.

I’m not sure what large topics I want to take up in the scope of AI proper. Not yet. What I see out on the web and in the institutions and think tanks and all the hand wringing and hyping is, right now, a lot of smoke and not much light.

The era—I mean today, or the 2020s—seems to me to be decelerating, consolodating power, condensing, shrinking possibility and ultimately agency. In these circumstances, things will get worse gradually amongst our grumblings and triumphs, or there will be some decisive moment in history that sets us on a different path.

And three’s a third way, too, thank fully. We might just innovate again.

Administrative note: Karen Hao’s Empire of AI is, as I mention above, quite good and well-researched, and gives readers a behind-the-scenes look at the rise of OpenAI. I intend to review it soon, and I’ll post that review at my other Substack, Larson Reviews.

I want readers here to benefit from those reviews as well, so I’ll cross-post reviews from Larson Reviews to Colligo. On Colligo, they will usually be paywalled; on Larson Reviews, I’ll make them open to all.

I may review a book a month, or perhaps every other month. I’m also considering another online project at Larson Reviews, one that looks at the history of “big ideas,” or even a kind of “big history” beginning with the Big Bang. In that larger endeavor, I can bring in the various books I’ve written, and the many books I’ve read, that have put the Legos together for me to write about, well, big ideas.

So if you’d like the free reviews, and also want access to any larger paid projects I develop there, please subscribe to Larson Reviews as well.