AI Summaries

Technology: The Comprehension Gap

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To stay abreast of technology and remain relevant in the sales and marketing industry I‘ve spent years mastering Web 3.0, the Metaverse, Cryptocurrency, and more recently Generative Engine Optimisation and AI Summary citations. The problem? These skills are useless if few people understand them. Businesses can’t adopt what they don’t grasp and in straightened times most won’t invest the time, money, or mental energy required to bridge that gap. The technology stalls and my consultancy stalls with it.

I’ve called it The Comprehension Gap. Given the title, I’ve attempted to be as concise as possible.

Every few years the internet reinvents itself.

Web3. The metaverse. Now AI. Each wave promises to change everything. Yet the same problem repeats: most people don’t really understand what it’s for.

That’s not a failing – it’s the point.

Mass adoption doesn’t happen because technology is clever. It happens when an average person can see the value without needing it explained. When the benefit is obvious, usage follows. When it’s not, adoption stalls.

Meta’s metaverse rebrand this decade is the sharpest example. Since 2020, it has burned more than seventy billion dollars building a future most couldn’t grasp. Not because the tech failed, but because the value wasn’t clear enough to justify the effort.

Most people didn’t reject it. They simply couldn’t answer a basic question: Why would I use this?

Web3 hit the same wall. Decentralisation, tokens, secure wallets, NFTs, blockchains. Technically clever, but hard to understand beyond enthusiasts. For most, the cost of understanding outweighed any benefit.

Here’s the uncomfortable truth:

  • If people need to learn new language before seeing value, adoption will be slow.
  • If using the tech feels like work, most will opt out.
  • If the benefit can’t be explained simply, it won’t spread.

This isn’t dumbing down. It’s how adoption works – remember Malcolm Gladwell’s description in The Tipping Point?.

The early internet succeeded because it didn’t ask people to change how they thought. Email felt like post. Search felt like asking a question. Social networks felt like a conversation. You didn’t need to understand the system. You just used it.

Web3 and the metaverse flipped this. They asked people to understand a fairly complicated premise first, then participate, then maybe benefit later. That order guarantees failure outside niches.

This gap between what technology can do and what average people grasp is expensive.

Big platforms pay for it. When users don’t “get it”, marketing becomes education, support becomes consultancy. Growth slows. Costs rise. Confidence evaporates.

Agencies pay too. Clients want to move forward but don’t understand what they’re signing up for. Strategy becomes explanation. Delivery becomes risk. Fees stop matching effort. Everyone feels busy; no one feels certain. An easy to confirm first page ranking becomes a ‘possible’ AI summary citation if the wind is in the right direction.

More truths:

  • When clients don’t understand the tech, they don’t trust outcomes.
  • When they don’t trust outcomes, they don’t commit long term.
  • When they don’t commit, adoption never scales.

Now AI and AI-driven search risk the same gap.

AI feels familiar. It fits on top of what we already do: write, search, research, plan. But the systems beneath are opaque. Rankings disappear. Attribution blurs. Decisions come from models that don’t explain themselves. The decision requires a leap of faith and businesses don’t run on faith.

For business leaders, that’s unsettling. Old rules vanish; new ones aren’t clear. Understanding lags behind capability.

When that happens, behaviour shifts. People hesitate. Budgets freeze. Vendors promising certainty gain power. Bad decisions follow.

This isn’t an innovation problem. It’s a comprehension problem.

Technology reshapes society only when it becomes boring enough to feel obvious. When people stop asking what it is and start asking how to use it. That moment won’t come if capability runs ahead of understanding.

The lesson from Web3 and the metaverse: the ideas weren’t wrong, but they weren’t simple enough for mass adoption.

AI won’t escape that fate unless it crosses the same line. The next internet phase won’t be won by the smartest systems. It will be won by the ones that make sense to non-experts.

Because mass adoption happens only when average comprehension is enough. Anything needing more will stay niche, no matter how much money pours in.

AI SEARCH: The Science Behind GEO

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Generative Engine Optimisation Isn’t a Buzzword. It’s a Research Discipline.

For the past year or so, *Generative Engine Optimisation* (GEO) has been talked about as if it were simply “SEO for ChatGPT”. That shorthand does it a disservice.

“GEO isn’t a marketing fad dreamed up in a boardroom. It is grounded in serious academic work on how AI-powered search systems actually operate – and that distinction matters if you want your content to be visible in the next generation of search.”

The scientific foundation was laid in 2023 by researchers from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi, in a paper later accepted to KDD 2024. This wasn’t speculative thought leadership; it was empirical research into how large language models retrieve, synthesise, and present information.

From Search Engines to Generative Engines

The research introduces a critical shift in thinking: we are no longer optimising for *search engines*, but for what the authors call *generative engines*.

Traditional search engines retrieve and rank documents. Generative engines do something fundamentally different. They retrieve information from multiple sources, synthesise it using large language models, and generate an original response – often with inline attribution.

That difference breaks many of the assumptions SEO has relied on for two decades.

GEO sits at the intersection of computational linguistics, cognitive science, and machine learning. Instead of focusing on keyword placement or ranking signals, it addresses how language models recognise patterns, how they use their context window, and how probability distributions shape what ultimately appears in a generated answer.

In short: it’s not about being number one on a list. It’s about being *included* in the synthesis.

How GEO Was Tested (And Why That Matters)

One of the strengths of the research is its methodology. The authors didn’t rely on anecdote or tool screenshots. They built GEO-bench: a large-scale benchmark of 10,000 queries spanning multiple domains, each tagged by intent, difficulty, domain, and expected answer format.

The experimental setup was deliberately realistic. First, top sources were fetched from Google Search. Then GPT-3.5-turbo was used to generate answers with inline citations, mirroring how generative search systems work in the wild.

Crucially, the researchers didn’t measure success using traditional rankings. They introduced new visibility metrics designed specifically for generative engines, including:

  • Position-adjusted word count (how prominently a source appears in generated responses)
  • Subjective impression scores, measuring relevance, influence, uniqueness, and likely user engagement

Using these metrics, they tested nine distinct optimisation methods.

The results were telling.

What Actually Improves Visibility in AI Search

Certain GEO techniques consistently outperformed others. Adding clear citations, quotations, and concrete statistics increased visibility in generated responses by as much as 40 per cent.

Meanwhile, many familiar SEO habits barely moved the needle. Keyword stuffing, in particular, showed minimal impact on generative engines – a finding that should give pause to anyone still writing for algorithms rather than understanding.

The research also highlights that GEO is not one-size-fits-all. Effectiveness varies by domain:

  • An authoritative, declarative tone works best for debate and historical topics
  • Citation-rich content performs strongest for factual queries
  • Structural clarity matters more than keyword density

This reinforces an uncomfortable truth for some marketers: optimising for AI means writing better, not trickier.

Why This Changes Content Strategy

GEO forces a rethink of what “optimised” content looks like. If your material can’t be easily understood, trusted, and recomposed by a language model, it risks invisibility – regardless of how well it ranks today.

“What the research makes clear is that generative visibility is earned through clarity, evidence, and structure. AI systems reward content that behaves like a good academic source or a solid piece of journalism: well-sourced, precise, and unambiguous.”

That may feel less glamorous than chasing hacks. But it’s a more durable advantage.

As generative engines continue to replace blue links with synthesised answers, GEO will stop being a niche concern and become a baseline competency. Those who treat it as a discipline – grounded in how these systems actually work – will be the ones whose voices are carried forward.

The rest will simply be paraphrased out of existence.

AI SEARCH: Red Alert. GEO Is Now Critical

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For more than two decades, organic search followed a broadly predictable pattern. Rank higher, earn more clicks. Position one hoovered up attention, position two fought over the scraps, and by page two you were effectively invisible. Entire SEO strategies, pricing models and business forecasts were built on that curve.

In 2025, that curve has broken.

The widespread rollout of Google’s AI Overviews has fundamentally altered how users interact with search results. The most important statistic to understand this shift is not impressions, not rankings, and not even traffic. It is click-through rate by position.

And the change is not subtle.

The 2025 CTR Shock

Multiple large-scale studies now show that when an AI Overview appears, organic click-through rates drop sharply at the very top of the page.

Position one, historically responsible for roughly 27 to 30 percent of clicks, now often sees figures closer to 18 to 20 percent when an AI Overview is present. Position two has been hit even harder, with CTR reductions approaching 40 percent in some verticals. Positions three to five also decline, though less dramatically.

This is not seasonal noise or algorithmic wobble. It is structural.

“The reason is simple. Users are no longer starting their journey with organic listings. They are starting with a machine-written synthesis that sits above everything else.”

For many informational queries, the search ends there.

The Rise of the No-Click Result

AI Overviews represent the most aggressive expansion of the zero-click search model Google has ever deployed. Featured snippets were short. Knowledge panels were limited. AI Overviews are comprehensive, contextual and designed to resolve intent directly on the results page.

“In 2025, a majority of informational searches that trigger an AI Overview now result in no click at all.”

This matters because it breaks a long-standing assumption in digital strategy: that visibility inevitably leads to traffic. It no longer does.

A page can rank first, be technically sound, well written, and perfectly aligned with search intent, and still receive a fraction of the traffic it would have earned two years ago.

Why Lower Rankings Are Not the Answer

Some commentators have pointed out that positions six to ten sometimes see a relative increase in CTR when AI Overviews are present. This is true, but it is also misleading.

Those positions are benefiting from a smaller group of users who scroll deliberately to validate or explore sources after reading the summary. They are not outperforming top positions in absolute terms, and they are not a growth strategy.

This is not a reshuffling of clicks. It is a contraction of them.

The Real Divide in 2025 Search

The meaningful distinction in modern search is no longer between page one and page two. It is between content that is cited by AI systems and content that is merely indexed.

Being cited inside an AI Overview changes the equation. It restores relevance, trust and visibility at the point where the user’s attention actually is. It turns a passive summary into a gateway rather than a dead end.

Businesses that are cited consistently tend to see stronger branded searches, higher downstream engagement, and better conversion quality, even if raw organic traffic volumes are lower than historical peaks.

Businesses that are not cited experience something worse than a ranking drop. They experience quiet irrelevance.

Why Traditional SEO Is Now Failing Businesses

“In 2026 most SEO strategies are still built for a search landscape that no longer exists. They optimise for rankings rather than references, keywords rather than concepts, and pages rather than entities.”

AI systems do not think in keywords. They synthesise from sources they consider authoritative, current, structured and reliable. If your content is not written, structured and positioned to be used as a source, it is invisible to the most important layer of modern search.

This is why many businesses report stable rankings alongside falling traffic and weakening lead quality. The strategy is technically succeeding while commercially failing.

The Cost of Inaction

Choosing not to adapt is still a choice, but it is an expensive one.

If your content is not being cited, you are training AI systems to answer questions without you. Every un-cited article reinforces competitors as default sources. Every missed summary compounds future invisibility.

In 2025, search visibility compounds in two directions. Upwards if you are referenced. Downwards if you are not.

The Strategic Shift Required

The implication for business is clear.

“SEO is no longer about chasing clicks. It is about earning inclusion in machine-generated answers. That requires a shift toward Generative Engine Optimisation, whether or not that label is used internally.”

Content must demonstrate expertise clearly, answer questions directly, and be structured in ways AI systems can parse, trust and reuse. Authority signals matter more than ever. So does clarity, accuracy and topical depth.

Ranking still matters, but it is no longer the end goal. Being used as a source is.

The Bottom Line

The dramatic change in organic CTR by position is not a temporary anomaly. It is the clearest measurable signal that search behaviour has crossed a threshold.

Businesses that continue to optimise as if blue links are the primary interface are optimising for the past. Businesses that understand how AI systems select, summarise and cite sources are building visibility where it actually exists.

In 2025, search success is not about being first on the page. It is about being present in the answer.