AI is killing the old web, and the new web struggles to be born

In recent months, the signs and portents have been accumulating with increasing speed. Google is trying to kill the 10 blue links. Twitter is being abandoned to bots and blue ticks. There’s the junkification of Amazon and the enshittification of TikTok. Layoffs are gutting online media. A job posting looking for an “AI editor” expects “output of 200 to 250 articles per week.” ChatGPT is being used to generate whole spam sites. Etsy is flooded with “AI-generated junk.” Chatbots cite one another in a misinformation ouroboros. LinkedIn is using AI to stimulate tired users. Snapchat and Instagram hope bots will talk to you when your friends don’t. Redditors are staging blackouts. Stack Overflow mods are on strike. The Internet Archive is fighting off data scrapers, and “AI is tearing Wikipedia apart.” The old web is dying, and the new web struggles to be born. 

The web is always dying, of course; it’s been dying for years, killed by apps that divert traffic from websites or algorithms that reward supposedly shortening attention spans. But in 2023, it’s dying again — and, as the litany above suggests, there’s a new catalyst at play: AI. 

The problem, in extremely broad strokes, is this. Years ago, the web used to be a place where individuals made things. They made homepages, forums, and mailing lists, and a small bit of money with it. Then companies decided they could do things better. They created slick and feature-rich platforms and threw their doors open for anyone to join. They put boxes in front of us, and we filled those boxes with text and images, and people came to see the content of those boxes. The companies chased scale, because once enough people gather anywhere, there’s usually a way to make money off them. But AI changes these assumptions.

Given money and compute, AI systems — particularly the generative models currently in vogue — scale effortlessly. They produce text and images in abundance, and soon, music and video, too. Their output can potentially overrun or outcompete the platforms we rely on for news, information, and entertainment. But the quality of these systems is often poor, and they’re built in a way that is parasitical on the web today. These models are trained on strata of data laid down during the last web-age, which they recreate imperfectly. Companies scrape information from the open web and refine it into machine-generated content that’s cheap to generate but less reliable. This product then competes for attention with the platforms and people that came before them. Sites and users are reckoning with these changes, trying to decide how to adapt and if they even can. 

In recent months, discussions and experiments at some of the web’s most popular and useful destinations — sites like Reddit, Wikipedia, Stack Overflow, and Google itself — have revealed the strain created by the appearance of AI systems.

Reddit’s moderators are staging blackouts after the company said it would steeply increase charges to access its API, with the company’s execs saying the changes are (in part) a response to AI firms scraping its data. “The Reddit corpus of data is really valuable,” Reddit founder and CEO Steve Huffman told The New York Times. “But we don’t need to give all of that value to some of the largest companies in the world for free.” This is not the only factor — Reddit is trying to squeeze more revenue from the platform before a planned IPO later this year — but it shows how such scraping is both a threat and an opportunity to the current web, something that makes companies rethink the openness of their platforms. 

Wikipedia is familiar with being scraped in this way. The company’s information has long been repurposed by Google to furnish “knowledge panels,” and in recent years, the search giant has started paying for this information. But Wikipedia’s moderators are debating how to use newly capable AI language models to write articles for the site itself. They’re acutely aware of the problems associated with these systems, which fabricate facts and sources with misleading fluency, but know they offer clear advantages in terms of speed and scope. “The risk for Wikipedia is people could be lowering the quality by throwing in stuff that they haven’t checked,” Amy Bruckman, a professor of online communities and author of Should You Believe Wikipedia? told Motherboard recently. “I don’t think there’s anything wrong with using it as a first draft, but every point has to be verified.” 

Stack Overflow offers a similar but perhaps more extreme case. Like Reddit, its mods are also on strike, and like Wikipedia’s editors, they’re worried about the quality of machine-generated content. When ChatGPT launched last year, Stack Overflow was the first major platform to ban its output. As the mods wrote at the time: “The primary problem is that while the answers which ChatGPT produces have a high rate of being incorrect, they typically look like they might be good and the answers are very easy to produce.” It takes too much time to sort the results, and so mods decided to ban it outright.

The site’s management, though, had other plans. The company has since essentially reversed the ban by increasing the burden of evidence needed to stop users from posting AI content, and it announced it wants to instead take advantage of this technology. Like Reddit, Stack Overflow plans to charge firms that scrape its data while building its own AI tools — presumably to compete with them. The fight with its moderators is about the site’s standards and who gets to enforce them. The mods say AI output can’t be trusted, but execs say it’s worth the risk.

All these difficulties, though, pale in significance to changes taking place at Google. Google Search underwrites the economy of the modern web, distributing attention and revenue to much of the internet. Google has been spurred into action by the popularity of Bing AI and ChatGPT as alternative search engines, and it’s experimenting with replacing its traditional 10 blue links with AI-generated summaries. But if the company goes ahead with this plan, then the changes would be seismic.

A writeup of Google’s AI search beta from Avram Piltch, editor-in-chief of tech site Tom’s Hardware, highlights some of the problems. Piltch says Google’s new system is essentially a “plagiarism engine.” Its AI-generated summaries often copy text from websites word-for-word but place this content above source links, starving them of traffic. It’s a change that Google has been pushing for a long time, but look at the screenshots in Piltch’s piece and you can see how the balance has shifted firmly in favor of excerpted content. If this new model of search becomes the norm, it could damage the entire web, writes Piltch. Revenue-strapped sites would likely be pushed out of business and Google itself would run out of human-generated content to repackage. 

Again, it’s the dynamics of AI — producing cheap content based on others’ work — that is underwriting this change, and if Google goes ahead with its current AI search experience, the effects would be difficult to predict. Potentially, it would damage whole swathes of the web that most of us find useful — from product reviews to recipe blogs, hobbyist homepages, news outlets, and wikis. Sites could protect themselves by locking down entry and charging for access, but this would also be a huge reordering of the web’s economy. In the end, Google might kill the ecosystem that created its value, or change it so irrevocably that its own existence is threatened. 

But what happens if we let AI take the wheel here, and start feeding information to the masses? What difference does it make?

Well, the evidence so far suggests it’ll degrade the quality of the web in general. As Piltch notes in his review, for all AI’s vaunted ability to recombine text, it’s people who ultimately create the underlying data — whether that’s journalists picking up the phone and checking facts or Reddit users who have had exactly that battery issue with the new DeWalt cordless ratchet and are happy to tell you how they fixed it. By contrast, the information produced by AI language models and chatbots is often incorrect. The tricky thing is that when it’s wrong, it’s wrong in ways that are difficult to spot. 

Here’s an example. Earlier this year, I was researching AI agents — systems that use language models like ChatGPT that connect with web services and act on behalf of the user, ordering groceries or booking flights. In one of the many viral Twitter threads extolling the potential of this tech, the author imagines a scenario in which a waterproof shoe company wants to commission some market research and turns to AutoGPT (a system built on top of OpenAI’s language models) to generate a report on potential competitors. The resulting write-up is basic and predictable. (You can read it here.) It lists five companies, including Columbia, Salomon, and Merrell, along with bullet points that supposedly outline the pros and cons of their products. “Columbia is a well-known and reputable brand for outdoor gear and footwear,” we’re told. “Their waterproof shoes come in various styles” and “their prices are competitive in the market.” You might look at this and think it’s so trite as to be basically useless (and you’d be right), but the information is also subtly wrong.

To check the contents of the report, I ran it by someone I thought would be a reliable source on the topic: a moderator for the r/hiking subreddit named Chris. Chris told me that the report was essentially filler. “There are a bunch of words, but no real value in what’s written,” he said. It doesn’t mention important factors like the difference between men’s and women’s shoes or the types of fabric used. It gets facts wrong and ranks brands with a bigger web presence as more worthy. Overall, says Chris, there’s just no expertise in the information — only guesswork. “If I were asked this same question I would give a completely different answer,” he said. “Taking advice from AI will most likely result in hurt feet on the trail.”

This is the same complaint identified by Stack Overflow’s mods: that AI-generated misinformation is insidious because it’s often invisible. It’s fluent but not grounded in real-world experience, and so it takes time and expertise to unpick. If machine-generated content supplants human authorship, it would be hard — impossible, even — to fully map the damage. And yes, people are plentiful sources of misinformation, too, but if AI systems also choke out the platforms where human expertise currently thrives, then there will be less opportunity to remedy our collective errors. 

The effects of AI on the web are not simple to summarize. Even in the handful of examples cited above, there are many different mechanisms at play. In some cases, it seems like the perceived threat of AI is being used to justify changes desired for other reasons (as with Reddit), while in others, AI is a weapon in a struggle between workers who create a site’s value and the people who run it (Stack Overflow). There are also other domains where AI’s capacity to fill boxes is having different effects — from social networks experimenting with AI engagement to shopping sites where AI-generated junk is competing with other wares. 

In each case, there’s something about AI’s ability to scale — the simple fact of its raw abundance — that changes a platform. Many of the web’s most successful sites are those that leverage scale to their advantage, either by multiplying social connections or product choice, or by sorting the huge conglomeration of information that constitutes the internet itself. But this scale relies on masses of humans to create the underlying value, and humans can’t beat AI when it comes to mass production. (Even if there is a lot of human work behind the scenes necessary to create AI.) There’s a famous essay in the field of machine learning known as “The Bitter Lesson,” which notes that decades of research prove that the best way to improve AI systems is not by trying to engineer intelligence but by simply throwing more computer power and data at the problem. The lesson is bitter because it shows that machine scale beats human curation. And the same might be true of the web.

Does this have to be a bad thing, though? If the web as we know it changes in the face of artificial abundance? Some will say it’s just the way of the world, noting that the web itself killed what came before it, and often for the better. Printed encyclopedias are all but extinct, for example, but I prefer the breadth and accessibility of Wikipedia to the heft and reassurance of Encyclopedia Britannica. And for all the problems associated with AI-generated writing, there are plenty of ways to improve it, too — from improved citation functions to more human oversight. Plus, even if the web is flooded with AI junk, it could prove to be beneficial, spurring the development of better-funded platforms. If Google consistently gives you garbage results in search, for example, you might be more inclined to pay for sources you trust and visit them directly. 

Really, the changes AI is currently causing are just the latest in a long struggle in the web’s history. Essentially, this is a battle over information — over who makes it, how you access it, and who gets paid. But just because the fight is familiar doesn’t mean it doesn’t matter, nor does it guarantee the system that follows will be better than what we have now. The new web is struggling to be born, and the decisions we make now will shape how it grows.