· ai · 2 min read
AI is killing the old web, 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 this article, James Vincent, a senior reporter at The Verge, explores how AI is killing the old web and the new web struggles to be born. He covers the following topics and examples:
- The signs and impacts of AI scraping and generating content on websites like Reddit, Wikipedia, Stack Overflow, and Google, and how they affect the quality, reliability, and openness of the web.
- The debate and experiment at Google over whether to replace its 10 blue links with AI-generated summaries that would rank higher in search results, and what this would mean for the web’s economy and content.
- The comparison and contrast between human and AI-generated content in terms of accuracy, expertise, and influence, using examples from a hiking report and a chatbot.
- The implications and challenges of AI scale and abundance for the web’s platforms, users, and creators, and how it challenges the assumptions and models that underlie the web’s value and economy.
- The lesson from the bitter lesson of machine learning research, which shows that the best way to improve AI systems is not by engineering intelligence but by throwing more computer power and data at the problem, and how this applies to the web as well.
He concludes by asking whether the web as we know it will change in the face of artificial abundance, and what this means for the web’s future.
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