For more than two decades, web discovery was shaped by search engine optimization. The deal was imperfect, occasionally annoying, and very good at producing blog posts with sixteen nearly identical headings, but the basic goal was still human: help people find a page worth reading.
AI search changes that deal. Search results now include AI Overviews, answer engines summarize pages directly, and agents are beginning to browse, compare, and act on behalf of users. That does not mean the human web is dead. It means the web has a new reader sitting between your page and the person you hoped to reach.
That reader is a machine. It is impatient, literal, and allergic to your cleverest buried point.
The practical lesson is simple: websites now need to be useful to humans and legible to machines. Not one or the other. Both.
GEO is mostly a new name for an old discipline
Generative Engine Optimization, or GEO, is the attempt to make content more visible in AI-generated answers. The term has academic backing: the GEO paper accepted to KDD 2024 studied ways content could improve visibility in generative engine responses and found that some methods could increase visibility by up to 40% in their benchmark.
That sounds shiny, so the marketing industry immediately did what it does best: put a new acronym on an old ladder and start selling climbing lessons.
There is a real signal underneath the sludge, though. AI answer systems need sources they can understand, cite, and summarize. Pages with clear structure, direct explanations, factual support, and identifiable entities are easier to use than pages built from vague claims, thin summaries, and vibes wearing a sport coat.
Google’s own guidance for AI features in Search is not “throw away everything and worship the robot.” It says the same foundational Search practices apply: make helpful, reliable, people-first content that Google can access and understand. AI features may use query fan-out and supporting links differently than classic blue links, but the starting point is still a crawlable, useful web page.
So yes, GEO matters. But the useful version is not magic. It is clarity with receipts.
Being cited is different from being ranked
Classic SEO cared a lot about ranking position and click-through. AI search shifts part of the value toward citation: whether your page becomes one of the sources an answer system uses when it explains something.
That is a different game. A page can rank well for humans and still be a weak citation if the useful facts are buried, unsupported, or surrounded by filler. A page can also be less flashy and still be useful to an AI system because it clearly states what it knows, why it knows it, and where the reader can verify it.
This is one reason I am increasingly skeptical of sites that treat “content” as a paste-colored SEO substrate. AI summaries punish mush in a different way than human readers do. Humans skim past it. Machines may summarize it confidently, which is somehow worse.
If you want to be cited accurately, make the claim easy to find. Put the answer near the relevant heading. Link to primary sources when a claim needs support. Use names, dates, product names, organizations, and definitions consistently. Do not make the reader excavate your point from seven paragraphs of throat-clearing.
I made a related argument in The Dead-End Web: answer engines change the publishing contract. If the machine consumes the page and the human never clicks, attribution and accuracy become more important, not less.
AEO is real, but the hype is early
Agent Engine Optimization, or AEO, is the related idea that websites should be understandable to AI agents that research, compare, book, buy, or complete workflows for users.
There is a real trend here. Gartner has predicted that by 2028, up to 90% of B2B purchases could be agent-intermediated, with enormous spending flowing through workflows where AI systems help evaluate options. That is worth paying attention to.
But “agent-intermediated” does not mean humans vanish and your next customer is a faceless procurement bot sipping electrons in a server closet. In many cases, it means AI tools help people search, compare, summarize, negotiate, or prepare a decision. The human may still approve the purchase. The agent just does more of the legwork.
That distinction matters because it keeps the advice sane.
If you sell something, publish the information a serious buyer or agent needs: pricing model, capabilities, limitations, availability, return policy, support terms, documentation, contact path, and proof that your organization is real. Put that information in normal HTML, not only in images, PDFs, or JavaScript tricks that require a tiny ritual sacrifice to render correctly.
Clean APIs and data exports can matter, especially for larger commerce and procurement systems. But most websites do not need to cosplay as a Fortune 500 supply-chain endpoint by Friday. Start by making the public site understandable. If humans cannot find the answer, the agent probably will not become enlightened by your accordion menu either.
Machine-readable does not mean human-hostile
The older version of this argument framed the future as a two-tier internet: a human-facing layer on top and a shadow web of JSON, metadata, and machine-to-machine feeds underneath.
There is some truth there. Structured data matters. Metadata matters. Stable URLs matter. Schema can help machines understand what a page is about and how entities relate to each other. The Schema.org Organization type and sameAs property, for example, can help connect a site to official profiles and other identity signals.
But “machine-readable” does not require making the human-readable page worse. Good HTML is already machine-readable. Clear headings are machine-readable. Tables with actual text are machine-readable. A plain explanation with source links is machine-readable. The boring web primitives still work. Shocking development: standards were useful before a vendor renamed them.
The better mental model is not a shadow web. It is a clear web.
A clear web page tells humans what they need to know and gives machines enough structure to avoid mangling it. It has readable headings, stable URLs, descriptive titles, useful internal links, honest metadata, and source links where claims need evidence. It does not hide the important stuff in screenshots, carousels, decorative PDFs, or marketing copy that sounds like it escaped from a SaaS pricing page during a thunderstorm.
The practical checklist is boring, which is good
If you run a website, the useful response to AI search is not panic. It is maintenance.
- Write clear titles and headings. A machine should be able to tell what the page is about without guessing.
- Answer the obvious question directly. Do not bury the useful answer below a warm-up lap of context nobody asked for.
- Cite sources for factual claims. Especially for numbers, studies, standards, legal claims, security issues, and product behavior.
- Use structured data where it fits. Organization, Article, FAQ, Product, LocalBusiness, and other Schema.org types can help clarify entities and page purpose when used honestly.
- Keep important information in accessible HTML. Do not trap core facts in images, PDFs, popups, or scripts that make the page harder to parse.
- Maintain trust pages. About, contact, privacy, editorial policy, author info, and update history are not glamour work. They are credibility plumbing.
- Link internally with purpose. Help readers and machines understand how related ideas connect.
- Do not optimize for robots by becoming unreadable to people. That way lies schema confetti and sadness.
This is also where AI trust gets tangled with web structure. In AI Tools Repeat Consensus. That Is Not the Same as Truth., the point was that models are good at repeating patterns, not magically discovering reality. A well-structured page with clear sourcing gives those systems less room to improvise nonsense.
Agents still need boundaries
The agent side of this is not just about discoverability. It is also about safety.
As more AI systems browse and act for users, websites become part of an agent’s operating environment. That raises practical questions: Can the agent find the right information? Can it distinguish official policy from user comments? Can it tell a documentation page from an ad? Can it perform a consequential action without confirmation?
That is why browser agents need better boundaries. Agents may be reading the same web pages humans read, but they bring different failure modes. A confusing page is not just a bad user experience anymore. It can become bad input for an automated workflow.
For site owners, the fix is not to build a secret robot-only internet. The fix is to make the normal web less ambiguous. Label actions clearly. Separate documentation from marketing. Make prices, policies, and requirements explicit. Keep forms predictable. Do not make critical information depend on hover states, vague icons, or a sales funnel designed by someone who thinks friction is a personality.
The human web is still the point
AI search changes discovery. It may reduce some clicks. It may make citations more valuable. It may force site owners to care more about structure, entity clarity, and source quality. Those are real shifts.
But the goal is not to surrender the web to machines. The goal is to build pages good enough that machines can represent them accurately and humans still want to read them when they arrive.
That is the practical future of GEO and AEO: not a dead human web, not a fully autonomous agent economy where typography goes to die, and not a fresh excuse for SEO grifters to sell panic in quarterly installments.
It is just better publishing discipline.
Write clearly. Structure honestly. Cite what matters. Make your site trustworthy enough for people and parseable enough for the machines helping them look.
That is less dramatic than “the death of the web.” It is also more useful, which is generally a good trade.