For twenty-five years, "Googling" meant hunting. You typed a query, got a list of clues (ten blue links), and then you assumed the burden of reading, filtering, and synthesizing the answer. It was a librarian handing you a stack of books and saying "the answer's in here somewhere—good luck."
That era ended this spring. We don't search anymore; we ask. We don't want links; we want answers. The rise of synthesis engines—AI that reads the whole internet for you and writes a custom report—has inverted the relationship between user and information.
The Search Paradigm
Google's genius was to organize the world's information by relevance. Before Google, finding information online was nearly impossible—AltaVista and Yahoo gave you haystacks when you wanted needles. PageRank changed that by using the web's own link structure as a signal of importance. Suddenly, you could find what you were looking for.
But Google's model had a fundamental limitation: it could only point you to information, not deliver it. The search engine was a directory, not an oracle. You still had to click through, read multiple sources, resolve contradictions between them, and form your own synthesis. The labor of understanding remained with the user.
For simple queries—"What year was Lincoln born?"—this was fine. Google featured snippets and knowledge panels that gave direct answers. But for complex questions—"How do I negotiate a salary increase?" or "What's the best approach to treating my specific health condition?"—you were left to triangulate between dozens of sources, each with its own perspective and omissions.
The Synthesis Revolution
Synthesis engines are fundamentally different. When you ask a complex question, the AI doesn't give you a reading list; it reads the list for you. It ingests relevant sources, weighs their credibility, resolves their contradictions, and produces a coherent answer tailored to your specific question. The burden of synthesis shifts from the user to the machine.
This changes the nature of information access. Previously, the ability to synthesize information from multiple sources was a learned skill—researchers, analysts, and educated professionals were better at it than others. It was a form of cognitive labor that created value. Now that labor is automated. The person asking the question gets the same quality of synthesis whether they're a PhD or a high school student.
The implications for education are profound. "Research skills" traditionally meant knowing how to find sources and synthesize them. When the synthesis is automated, what remains? Critical evaluation of the AI's output, perhaps. The ability to ask the right questions, certainly. But the traditional skill of reading and summarizing—the bread and butter of academic work—becomes less central.
The Economic Disruption
This is better for the user, but it shatters the economic model of the web. For two decades, the internet ran on what we might call the "traffic economy." Publishers created content to attract visitors; visitors saw advertisements; advertisers paid publishers. Google sat in the middle, taking a cut for directing traffic. Everyone's incentive was to get you to click.
Synthesis engines break this chain. If the AI reads the website so you don't have to, who sees the ad? If the answer appears in the chat interface, why would you visit the source? The user gets a better experience, but the publisher—whose content made the answer possible—gets nothing.
This is the "aggregation problem" writ large. Publishers spent years complaining that Google's snippets reduced clicks; now the entire article is summarized without a visit. The web is transitioning from a collection of billboards to a training corpus—valuable as input to AI systems, but not necessarily as a destination for users.
The Value Migration
But the Technium finds a way. When one economic model fails, another emerges. We're already seeing the early signs of what comes next.
Attribution protocols that track which sources contributed to which answers, enabling some form of compensation. Micropayments for content, where the cost of synthesis is shared with the creators who made it possible. Information subscriptions, where users pay for access to high-quality synthesis rather than raw information. Premium content that's kept behind paywalls, invisible to free synthesis engines.
The value is still there; it's just moving from the distribution (the link) to the source (the insight). Publishers who create genuinely original analysis—not just aggregated information—will command premium prices. The commodity layer of "information about information" collapses; the value concentrates in primary sources and genuine expertise.
From Information to Knowledge
We are evolving from the Information Age to the Knowledge Age. These are different things. Information is raw data—facts, figures, observations. Knowledge is synthesized understanding—the integration of information into a coherent picture that guides action.
We've been drowning in information for decades. The internet made more data accessible than any individual could consume in a thousand lifetimes. But access to data didn't automatically translate to understanding. If anything, the flood of information made wisdom harder to find—important signals buried in noise, truth obscured by volume.
Synthesis engines are the spoon that helps us eat from the fire hose. They don't just find information; they transform it into knowledge. They don't just answer "what"; they explain "why" and "how." They make the accumulated wisdom of humanity accessible not just as data, but as understanding.
This is the promise that early internet visionaries imagined: a world where human knowledge is accessible to all, where the accident of which books you happened to read doesn't determine what you can know. We're finally getting there, though by a different path than anyone expected.