I’ve known Larry Lessig for more than 25 years, and throughout that time, I’ve looked to him for wisdom – and a bit of pique – when it comes to understanding the complex interplay of law, technology, and the future of the Internet. Lessig is currently the Roy L. Furman Professor of Law and Leadership at Harvard Law School. He also taught at Stanford Law School, where he founded the Center for Internet and Society, and at the University of Chicago. He is the author of more than half a dozen books, most of which have deeply impacted my own thinking and writing.
As part of an ongoing speaker series “The Internet We Deserve,” a collaboration with Northeastern’s Burnes Center For Social Change, I had a chance to sit down with Lessig and conduct a wide-ranging discussion covering his views on the impact of money in government’s role as a regulator of last resort. Lessig is particularly concerned about today’s AI-driven information environment, which he says has polluted public discourse and threatens our ability to conduct democratic processes like elections. Below is a transcript of our conversation, which, caveat emptor, is an edited version of AI-assisted output. The video can be found here, and embedded at the bottom of this article.
Vint Cerf is one of the most recognizable figures in the pantheon of Internet stardom – and as he enters his ninth decade of a remarkable life, one of its most accomplished. I had the honor of interviewing Dr. Cerf last month as part of the “Rebooting Democracy in the Age of AI” lecture series hosted by the Burnes Center for Social Change at Northeastern University. The conversation also served as the kick-off to my own Burnes Center lecture series, “The Internet We Deserve” where I’ll talk with notable business, policy, technology and academic leaders central to the creation of the Internet as we know it today (last week I spoke with Larry Lessig).
Universally recognized as one of “the fathers of the Internet,” Cerf’s many awards include the National Medal of Technology, the Turing Award, the Presidential Medal of Freedom, the Marconi Prize, and membership in the National Academy of Engineering. Dr. Cerf received his PhD from UCLA, where he worked in the famous lab that built the first nodes of what later became known as the Internet. He has worked at IBM, DARPA, MCI, JPL, and is now Chief Internet Evangelist at Google. Cerf has chaired, formed, and participated in countless working groups, governing bodies, and scientific, technological, and academic organizations.
As hype escalated around the debut of ChatGPT more than a year ago, I predicted that OpenAI and Microsoft would rapidly develop consumer subscription service models for their nascent businesses. Later that year I wrote a piece speculating that Google would inevitably follow suit. If Google was smart, and careful, it had a chance to become “the world’s largest subscription service.” From that piece:
Google can’t afford to fall behind as its closest competitors throw massive resources at AI-driven products and services. But beyond keeping up, Google finds itself in an even higher-stakes transition: Its core business, search, may be shifting into an entirely new consumer model that threatens the very foundation of the company’s cash flow spigot: Advertising.
Reading Ben Thompson’s coverage of Google’s earnings call this week, one thing jumps out, and simply can’t be ignored: Google CEO Sundar Pichai was asked a simple question, and, as Thompson points out, Pichai dodged it completely. A Merril analyst asked this question:
“Just wondering if you see any changes in query volumes, positive or negative, since you’ve seen the year evolve and more Search innovative experiences.”
2023 was a down year on the predictions front, but at least I’ve learned to sidestep distractions like Trump, crypto, and Musk. If I can avoid talking about the joys of the upcoming election and/or the politics of Silicon Valley billionaires, I’m optimistic I’ll return to form. As always, I am going to write this post with no prep and in one stream-of-conscious sitting. Let’s get to it.
The AI party takes a pause. The technology industry – and by this point, the entire capitalist experiment – is addicted to boom and bust cycles and riddled with blinkered optimism. In 2023 we allowed ourselves to dream of AI genies; we imagined trillions in future economic gains, we invested as if those gains were a certainty. In 2024, we’ll wake up and realize – as we did with the web in the early 2000s – that there’s a lot of hard work to do before our dreams become a reality. I’m not predicting an AI crash – but rather a period of digestion, with a possible side of Tums. Corporations will find their initial pilots less impactful than they hoped, and when told of the sums they must spend to course correct, insist on cutting back. Consumers will become accustomed to genAI’s outputs and begin to rethink their $20 a month subscriptions. Growth will slow, though it will not stagnate. Regulators around the world will take the year to move past Terminator nightmares and into the hard work of deeply understanding AI’s societal impact. IP holders – artists, newspapers, craftspeople – will press their lawsuits and infuse the market with uncertainty and hesitancy. In short, society will take a pause that refreshes. And that will be a good thing.
But Progress Continues… It may feel like a pause, but below the tech media scorekeeping narrative, a growing ecosystem of AI startups will make important strides in areas that will matter beyond 2024. AI is driven by data, and as a society we’re not particularly good at structuring, governing, or sharing data. It makes sense that big companies with access to unholy amounts of structured data pioneered the AI era. (Of course, if you’re not a big company, and you want access to massive amounts of data, it helps to just take it without asking permission). But the AI-driven startups that will make waves in 2024 will do so by structuring discrete chunks of valuable information on behalf of very specific customers. It won’t make many headlines, but taken collectively, it’s this kind of work that will lay the groundwork for AI becoming truly magical.
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As I do each December, it’s time to grade my own homework. And the past twelve months certainly started out well. But unless a certain fascistic presidential candidate has a change of heart in the next few days (he won’t), I’m afraid I didn’t break .500 this year (last year I was smokin’ hot, I must say).
I’ve been following the story of generative AI a bit too obsessively over the past nine months, and while the story’s cooled a bit, I don’t think it any less important. If you’re like me, you’ll want to check out MIT Tech Review’s interview with Mustafa Suleyman, founder and CEO of Inflection AI (makers of the Pi chatbot). (Suleyman previously co-founded DeepMind, which Google purchased for life-changing money back in 2014.)
Inflection is among a platoon of companies chasing the consumer AI pot of gold known as conversational agents – services like ChatGPT, Google’s Bard, Microsoft’s BingChat, Anthropic’s Claude, and so on. Tens of billions have been poured into these upstarts in the past 18 months, and while it’s been less than a year into since ChatGPT launched, the mania over genAI’s potential impact has yet to abate. The conversation seems to have moved from “this is going to change everything” to “how should we regulate it” in record time, but what I’ve found frustrating is how little attention has been paid to the fundamental, if perhaps a bit less exciting, question of what form these generative AI agents might take in our lives. Who will they work for, their corporate owners, or …us? Who controls the data they interact with – the consumer, or, as has been the case over the past 20 years – the corporate entity?
Those of you who’ve been reading for a while may have noticed a break in my regular posts – it’s August, and that means vacation. I’ll be back at it after Labor Day, but an interesting story from The Information today is worth a brief note.
Titled How Google is Planning to Beat OpenAI, the piece details the progress of Google’s Gemini project, formed four months ago when the company merged its UK-based DeepMind unit with its Google Brain research group. Both groups were working on sophisticated AI projects, including LLMs, but with unique cultures, leadership, and code bases, they had little else in common. Alphabet CEO Sundar Pichai combined their efforts in an effort to speed his company’s time to market in the face of stiff competition from OpenAI and Microsoft.
The buildings are the same, but the information landscape has changed, dramatically.
Today I’m going to write about the college course booklet, an artifact of another time. I hope along the way we might learn something about digital technology, information design, and why we keep getting in our own way when it comes to applying the lessons of the past to the possibilities of the future. But to do that, we have to start with a story.
Forty years ago this summer I was a rising Freshman at UC Berkeley. Like most 17- or 18- year olds in the pre-digital era, I wasn’t particularly focused on my academic career, and I wasn’t much of a planner either. As befit the era, my parents, while Berkeley alums, were not the type to hover – it wasn’t their job to ensure I read through the registration materials the university had sent in the mail – that was my job. Those materials included a several-hundred-page university catalog laying out majors, required courses, and descriptions of nearly every class offered by each of the departments. But that was all background – what really mattered, I learned from word of mouth, was the course schedule, which was published as a roughly 100-page booklet a few weeks before classes started.
Consisting of thousands class summaries organized by department, the booklet was the product of centuries of evolving information design. Its thin newsprint pages were easily navigated and steadfastly accurate. This unassuming information artifact was the only way that tens of thousands of students, faculty, and administrators could – quite literally – get on the same page. It did its job – even if the page itself consisted mainly of smudgy nine-point type.
Welcome to the hellscape of “Made for Advertising” sites
This past Monday NewsGuard, a journalism rating platform that also analyzes and identifies AI-driven misinformation, announced it had identified hundreds of junk news sites powered by generative AI. The focus of NewsGuard’s release was how major brands were funding these spam sites through the indifference of programmatic advertising, but what I found interesting was how low that number was – 250 or so sites. I’d have guessed they’d find tens of thousands of these bottom feeders – but maybe I’m just too cynical about the state of news on the open web. I have a hunch my cynicism will be rewarded in due time, once the costs of AI decline and the inevitable economic incentives that have always driven hucksters kick in.
Given 250 is a manageable number for a mere mortal, I decided to ask the good folks at NewsGuard, where I’m an advisor, for a copy of their listings. Nothing like a tour through the post-apocalyptic hellscape of our AI future, right?
What I found was…disappointing. Most of the sites were beyond shoddy – barely literate, obviously automated, full of errors and content warnings, and utterly devoid of any sense of organizational structure. The most common message, upon clicking on a story link, was some variation of an OpenAI violation:
Not exactly a compelling headline. The next most common experience was this:
This of course is evidence that the scammers are rotating URLs to avoid blacklisting, unburdened of any concern about building audience loyalty. Beyond OpenAPI warnings and 404s, there’s the browser warnings that the site you’re about to visit is, well, seedy:
When you do get an actual news experiences, it becomes clear that these publishers have little interest in passing as “real news sites,” IE publications a sane person might intend to visit. They are instead built as SEO chum in the hopes that Google’s indexes might favor them with some low quality traffic, or worse, as destinations for bot traffic destined for arbitrage inside the darker regions of the programmatic ad universe. The editorial decisions on the various home pages I visited were, well, hilariously inchoate:
Perhaps that’s what we should expect with the first phase of this particular genre, but I found their general awfulness depressing: Most reporters will look at these sites and dismiss them. But they shouldn’t.
Traditional “made for advertising” sites already control 21 percent of all programmatic advertising revenues, and these sites tend to dominate Google search results, enshittifying the open web with low-calorie crap that, one would hope, actually good AI might help us avoid. But the relatively low volume of AI sites indicates, at least anecdotally, that so far the economics of replacing human-built content with AI-driven drivel have yet to kick it. Put simply, it’s still too expensive to replace sites like Geeky Post or Explore Reference with AI. For now.
But when costs come down, I expect made for advertising sites will pivot to AI almost overnight. And I wonder if that’s a bad thing. Once the web’s worst sites all shift to AI-driven output, perhaps they’ll find themselves in a positive spiral of competition for actual human attention. If these sites start to create reasonably high quality content, and search and social start to reward them with real traffic that converts to revenue, perhaps we can simply automate away the shitshow that the open web has become.
