I still find myself running into conversations where people are skeptical about the use cases, benefits, and wisdom of using Generative AI. I don’t think this is a case where we can ignore a technology (kudos to you if you completely sat out Web3 NFTs), but I find myself thinking about the assumption that AI is coming, no matter what…
The Marketplace of Ideas, Crashed
In the mid-2000s, as blogs rose to prominence, I was an absolutist about free speech and a true believer in the Marketplace of Ideas.
Social media followed, and I set aside my concerns about its potential harm, focusing instead on its promise of facilitating good.
When I worked in the largest newspaper company in the country, I carried Facebook’s banner on behalf of the division’s leaders, though I would’ve happily done so myself, making it easier for journalism to be shared in the digital public square, and making it much easier for the public to express themselves about the stories and the issues they covered.
When that same digital public square was turned into a weaponized pipeline for misinformation, I groaned. This transformation, fueled by algorithms optimizing for engagement over truth, wasn’t just predictable – it was inevitable.
The years leading up to the 2016 election rattled my core belief that the Marketplace of Ideas would naturally lift the best, most truthful, most compelling content up to a larger audience, while the worst falsehoods would ungraciously sink to the dark corners of the Internet, and, in a worst case scenario, be found by the worst sorts of actors.
I was wrong about the Marketplace of Ideas, or at minimum, I was wrong about how closely a News Feed algorithm optimized for user growth and retention would embody John Stuart Mill’s theoretical vision.
Enter Generative Artificial Intelligence
That experience, lived over a dozen years, has made me skeptical of new technology billed as a radical leap forward in human expression, or the human condition.
It was easy to tag the Web3 NFT jpeg salesfolk as snake oil purveyors, though there are a few useful cases for blockchain technology.
It’s still easy to call Bitcoin and its descendants a network of pyramid schemes, each middleman layer feeding the next above it, though some of its principles are surely intriguing as an alternative to “traditional” banking, excluding the massive consumption of energy required to generate it, which is a cruel joke.
But Generative AI is something different.
The more I use it, and the more I see what other people build on top of it, the more I am convinced we are – as the venture capitalists pouring hundreds of millions of dollars into it are prone to say – living through an inflection point right now, and maybe the biggest technological change since the original dot-com boom.
If you listen to the people spending the most time with this new technology, they are, yes, deterministic about its utility, and at times, its destiny to change humanity for the better.
There are meaningful applications for science and medicine being built on top of new AI models. Those are mostly positive effects. The military use cases are coming on quickly. I can’t help but think those will have negative effects.
And yes, of course, AI and ML have been part of lots of software for lots of purposes for a long time – please understand that I understand that, and I am not talking about those models just like I am not talking about Clippy or the average scripted software onboarding experience as being the same as an AI Agent in 2024 tuned to a particular product and customer type.
If you listen to the people saying Generative AI is not that impressive, they will emphasize that the parlor trick of “next word prediction” is not “original thought” or even “intelligence.” I know, because, sometimes, I’m one of them.
Generative AI is an advanced technology, not magic, and the two are, in fact, distinguishable.
But the fact that GenAI is programmable and repeatable and adjustable is a feature, not a bug. While science fiction is sometimes predictive about the negative effects of new technologies, we might have more fun if we focus on TARS from Interstellar instead of HAL from 2001.
Cooper: Hey TARS, what’s your honesty parameter?
TARS: 90 percent.
Cooper: 90 percent?
TARS: Absolute honesty isn’t always the most diplomatic nor the safest form of communication with emotional beings.
Cooper: Okay, 90 percent it is.
Manifest Destiny Model
As a society, we are rarely comfortable putting genies back in bottles. A new invention, potentially enabling a giant leap in efficiency and profit (again, never mind the energy consumption and cost of building foundational models for the moment) is not something we can expect the marketplace to walk away from.
Where things get dangerous is when we allow any experiment to flourish by default, whether or not anyone has stopped to think about whether it should flourish, to paraphrase a different science fiction movie.
It’s difficult to believe, given our rapidly changing political state, that GenAI will be regulated anywhere outside California, and even their regulations may prove unenforceable – or simply ignored.
And so, Generative AI is in its Manifest Destiny era.
It’s subject to the unrelenting drive to advance until there’s nowhere left to go. The technologists behind these systems aren’t stopping until they metaphorically “reach the sea,” and there’s no clear end in sight.
This is a metaphor. In reality, it’s hard to see an “end” to the advancement of AI without looking to science fiction, where we often find unpleasant outcomes.
The companies working on Generative AI today talk about “Artificial General Intelligence” as a goal, wherein an AI would be able to reason and act on its own without much human intervention required.
It’s hard to trust the people building AI today to make sure AGI is used responsibly.
Technological Determinism
Which brings us to the big question:
How will this AI boom change humanity?
Technological Determinism tells us a new form of communication, and information retrieval, and knowledge delivery, will inevitably change the world and how we live in it.
This theory conveniently reminds me of all my grad school work on mass communication, disintermediation, and yes, blogs.
When we look at the newspaper, the telegraph, the radio, the television, and various earlier iterations of the internet, we can come back to the most pop of popular media theories: “The Medium is the Message.”
I do not happen to have Professor McLuhan right here, but I asked ChatGPT what he might think of Generative AI. Here’s the short summary it provided after a pleasantly long and well argued series of points:
McLuhan would likely approach generative AI as a profound extension of human communication and creativity, while simultaneously warning of its numbing effects and potential cultural homogenization. His insights would challenge us to remain aware of the transformative power of this “medium” and to critically evaluate its societal impact.
I am less concerned about cultural homogenization, and more concerned about shrinking filter bubbles. The not-too-distant-future that keeps me up at night isn’t Idiocracy, or Wall-E, though it’s easy to see them reflected in the present, but Neal Stephenson’s 2019 Fall; or, Dodge in Hell which imagines a world where everyone has personal editors curating their feeds of information.
We have long been subject to personalization and targeted content, advertising, and marketing, but the ease with which it’s now possible for someone to tell an AI companion selecting content to never surface anything that contradicts their worldview is staggering.
So how will AI change humanity? I think that’s going to be up to the people who use it to create products, applications, and networks. And I don’t think we can expect meaningful regulation, rules, or policies to protect ourselves from the dangers of our own creations anytime soon.
The lesson I’ve learned from watching the Marketplace of Ideas falter is this: progress is not inherently good, and innovation is not inherently right. If we want AI to reflect the best of us, we must resist the deterministic thinking that says these technologies will improve humanity simply because they exist.
Instead, we must ask harder questions about who benefits, what is lost, and why we’re building these systems in the first place. It’s not enough to marvel at what AI can do; we must demand better answers about what it should do – and provide those answers ourselves. If we don’t, we risk building tools that look intelligent but fail to make us wiser.
Yes, I used Generative AI to help edit this post, and to create an image. See, it’s not so bad. It’s fine. It’s not writing this part. Honest.
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