The AI Middleman Effect: AI’s Impact on Content Quality

by Imed Bouchrika, PhD

Co-Founder and Chief Data Scientist

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When you search for something on Google, do you still click on a link? If you’re like most people, you probably already rely on AI overviews for your answers.

Where users once had to open multiple tabs to find key information, they now ask an AI to create a single, unified response. But how will the rise of AI search impact the quality of our web content?

Already, AI search is causing a dramatic shift in how content is found, created, and valued. As content creators adapt to the new environment, content quality is likely to thrive or become diluted.

Let’s break down what’s driving these shifts, how the industry is reacting, and what this all means for the future of content quality. 

What Is AI Search?

The classic list-based results you’re used to are slowly being replaced by AI-generated answers. AI models pull information from multiple sources across the web, presenting it as a single, paraphrased response.

You’ve probably noticed a Google AI overview already—they’re the snippets of information presented above any website links. The chances are that the answer you’ve been searching for is neatly presented in this overview, eliminating the need to click through any links.

Classic Search vs. AI Search

In a classic search, users had to evaluate multiple sources, comparing viewpoints and findings. Most of the time, you had to click through various websites to find your answer. AI search is already removing most of that friction, making the search engine experience more convenient.

Unlike traditional search methods, AI search interprets the intent of the search and retrieves the most probable answer to the query. Rather than directing the user to the best result, AI search attempts to generate the best answer. However, original sources aren’t always cited, leading to a lack of exposure and credibility. In addition to that, AI search consumes more energy. Estimates state that a simple ChatGPT search ranges from 0.3 watt-hours (wh) to around 3Wh.

For users, AI search feels efficient, empowering, and useful. Yet, for content creators and researchers, this represents a major shift in how information is retrieved, credited, and authenticated.

The AI Middleman Effect: How AI Search Is Replacing Clicks

To understand how AI search will influence future content quality, we must look at the cause for this shift. For now, we’ll call it the AI Middleman Effect. 

Just like a middleman in business, users no longer deal directly with the source. Instead, your trusty AI middleman is tasked with consuming information on behalf of the user. This results in huge decreases in clicks and organic website traffic, and the potential for misinformation.

AI Search Relies on Content While Robbing It of Exposure

The early cost of the AI Middleman Effect is already showing. New research from Bain & Company reveals that 60% of searches that trigger an AI overview end without the user clicking on a single link. Consequently, AI overviews on search engine pages are causing many content pages to see less traffic?

Ironically, the content that AI models rely on is the very same content that is losing exposure, courtesy of AI search. So while AI requires high-quality web content to train, it ultimately reduces the traffic that funded that content to begin with. This may cause a severe lack of high-quality, up-to-date information in the future.

Long story short:

• AI models depend on high-quality, human-generated content for accurate results.
• Yet, AI search intercepts the traffic that funds this content.
• As exposure drops, creators lose incentive to produce highly researched content.
• Without quality content, future AI models may fail to provide accurate responses.

Extraction of Knowledge Without Proper Attribution

A rising ethical consideration for academics using AI search is the lack of proper attribution. This source blindness reduces the credibility of information, as users aren’t always able to verify the claims made by AI search.

Additionally, search engines personalize results, often leading to a generalized view to suit the user’s search profile. Academics and researchers should refrain from using the majority of AI-powered search engines for serious research purposes. 

TIP: In order to minimize personalized search results, researchers can use a VPN paired with an incognito browser.

There are already some emerging AI-powered search engines designed specifically for academic purposes. The current frontrunner is Consensus, which uses an evidence-based approach to avoid hallucination and ensure proper citation. However, all claims made by any AI search still need to be verified via source material, even if the platform is designed to avoid inaccuracies.

Middleman Effects in the Digital World

The AI Middleman Effect mirrors the same patterns seen throughout the digital landscape. Some of the biggest software companies made their name as middlemen, inserting themselves between the creator and the audience. This works time after time because users want streamlined tech experiences.

We’ve all but forgotten about how the taxi industry was upended by the introduction of ride-sharing apps like Uber. Streaming giant Netflix made the entire DVD rental industry redundant. Independent retailers continue to struggle against the overwhelming weight of e-commerce titan Amazon. 

While these middlemen sell convenience, they also destabilize existing ecosystems. Value flows toward platforms and away from creators. E.g., streaming platform Spotify was revolutionary for the music industry, but over time, independent artists continue to see fewer royalties, even when their fanbases increase.

The Shifting Tides of Online Content Strategies

Due to AI search, the classic Search Engine Optimization (SEO) playbook is steadily losing importance, making way for Generative Engine Optimization (GEO). Let’s explore how the content industry is reacting to this shift.

Death of the SEO Content Mill…

It seems like only yesterday that you’d have to wade through pages of filler content before finding key information. SEO mills were pumping out content at an astounding rate, focused on search engine rankings over high-quality insights.

Now, with AI search soaring in popularity, the old SEO rules have to be redefined. The classic tricks like keyword stuffing and boilerplate articles no longer attract the viewership they once did.

Users are also tired of the fluff associated with SEO content, preferring the concise, information-rich results produced by AI. According to SEMrush, around nine out of ten searches that trigger an AI overview are informational. Clearly presented informational content will take precedence as AI search grows.

…And the Birth of GEO

While SEO is being shown the door, GEO is on the fast track to taking its crown. Content is no longer being optimized for search engine rankings, but for AI retrieval and categorization.

Essential GEO rules include:

• Clearly structured data and tables;
• Schema/semantic markup;
• Conversational, human-written content;
• Proven expertise rather than website authority;
• Multi-modal content.

Some forward-thinking websites have already jumped on the GEO bandwagon, producing content intended to be quoted by AI. The importance of GEO will likely lead to less fluff and more concise, expert-based content with clarity and authority at the forefront.

Experience-based Content Is Gaining Traction

On the other hand, while SEO articles are getting snubbed, experiential content is gaining value like never before. It’s much harder for AI to mimic real-life experience: unique opinions, case studies, failure stories, and firsthand insights.

As a result, personal expertise will become key to content exposure. Verified and proven experience will trump any SEO tricks—original ideas, emotional experiences, and field research are about the only things AI search can’t do.

Previously, experiential content often took the backseat to high-functioning, keyword-stuffed SEO content. This marks a huge positive for content creators making experience-based content like travelogues, primary research, and opinion pieces. 

Does AI Search Improve or Impair Content Quality?

We’ve explored the structural shift in online content through The AI Middleman Effect and how the industry is responding to these changes. Now, we can begin to answer the question: Is AI search improving content quality or impairing it?

On one side, content creators are being empowered like never before to produce high-quality, relevant content faster than ever. On the flip side, content creators are losing the incentive to produce high-quality content, leading to more generalized and simplified viewpoints.

Pro: AI Search Is Empowering Content Creators

High-quality content has always been sought after—but in-depth research and the ability to communicate complex findings take a lot of time. As SEO rose to prominence, the value of high-ranking articles became more important than that of high-quality ones.

Now, it’s easier and quicker to produce high-quality content with AI search:

• Faster research: Key research takeaways are presented in simple AI overviews.
• Quick drafting: AI tools help creators with idea generation and structure.
• AI-assisted workflows: Editing, proofreading, and fact-checking can be streamlined with AI search.
• Improved digestibility: AI can help creators to simplify complex topics into straightforward explanations.
• More visibility for niche experts: AI search can uncover insights from low-ranking pages that you’d never find.
• Lower requirements for entry: New creators without resources can produce high-quality content, regardless of location.

Con: The Dilution of Information Through AI Search

Given this information, one would assume that future content will only increase in quality—the reality is much more complicated. It’s crucial to remember that AI doesn’t have opinions and can reflect biases from its training data. So the viewpoints it presents are averaged, leading to flat and repetitive content.

Creators producing GEO-focused content will tend to offer suitable opinions—that being, none at all. This creates somewhat of a simplification loop:

1. AI simplifies web content.
2. Users publish AI-friendly, simplified content.
3. AI models train on simplified content.
4. As steps 1-3 repeat themselves, overall content quality erodes.

Academics and serious researchers will need to be wary of the ethical ramifications of AI search. The use of AI in any academic work will require complete transparency to avoid issues of plagiarism and the over-reliance on AI search.

Pro: AI Search as a Quality Filter

Despite its risks, AI search may not degrade content quality, but rather act as a filter for quality. Rather than reward over-optimized content, the most relevant and insightful content will shine through. 

The SEO era has been characterized by several excesses, such as:

• Keyword-stuffed filler;
• Low-effort SEO content;
• Optimization over usefulness;
• See-through affiliate marketing;
• Quantity over quality;
• Spammy link building.

In theory, AI search rewards content that provides clarity, factual accuracy, and relevance. Content that is structured, written well, and offers genuine insights is easier for AI systems to interpret and present. This could push content creators away from SEO manipulation tactics and towards value-driven content creation.

However, it’s important to note that AI’s ability to filter for clarity does not mean that it can also filter for truth. While AI search has the potential to improve the baseline quality of web content, it risks promoting overly simplified viewpoints and potentially inaccurate information.

Con: Implications for Academics and Researchers

AI search presents a new conflict for academics and researchers. While its added convenience can quickly thrust research in the right direction, it also represents risks surrounding attribution, fact verification, intellectual property, or “hallucinating” responses.

AI overviews tend to omit or use secondary sources, making it hard to fact-check presented information. This source blindness will represent a significant ethical dilemma in future research. Ideas can appear authoritative without any clear citations.

In an academic context, source blindness may weaken:

• Effective peer review;
• Reproducibility of findings;
• Scholarly accountability.

AI search also runs the risk of producing a false consensus effect in research. AI systems equalize perspectives, which can flatten viewpoints. This effect could undermine individual interpretation, which is crucial in certain studies like the social sciences.

Monetization Collapse: Who Pays for Knowledge in an AI Web?

At the sweet, chocolatey center of the AI Middleman Effect lies a simple, but unanswered question: When clicks disappear, who will pay for new knowledge?

For decades, the exchange of information has operated on a simple idea—creators publish content, users visit their sites, and monetization occurs through ads, subscriptions, affiliate links, or sponsorships.

AI search is slowly removing the need for users to visit original sites. Without this primary step, how future content gets funded is unknown. As traffic declines, so does revenue, causing traditional methods of content monetization to potentially become obsolete. 

High-quality content can no longer be funded through simple exposure. So, how could creators monetize their content going forward?

How Content May Be Monetized With AI Search

Several alternatives to content monetization are being explored, as traditional methods will dry up in the coming years. The following predictions explore ways in which web content can still be funded when exposure disappears:

• Direct audience ownership: Monetization is made through email lists, member-only content, and private forums.
• Paywalls on primary sources: High-quality findings, datasets, scientific reports, and empirical research hide behind paywalls, forcing AI to pay up.
• Subscription-based knowledge hubs: Creators develop knowledge platforms on subscription-based payment schemes.
• Experience-based products: Knowledge is sold through courses, workshops, and live seminars.
• Sponsored thought leadership: Brands fund original research and analysis in exchange for association instead of SEO.
• Attribution-based revenue sharing: Creators receive micro-payments when their work is cited in AI overviews.

Ushering In An AI Content Hierarchy

Looking to the future, the content that we consider valuable will shift, and AI will be the judge, jury, and executioner. Let’s take a quick look at what types of content will be considered top, middle, and low tier.

Top-tier content:

• Opinion pieces from proven experts;
• Firsthand experiences (tech reviews, travel, etc.)
• Data-driven articles;
• Investigative and academic work.

Mid-tier content:

• Well-written and structured content;
• Simple tutorials and procedures;
• Insightful, step-by-step guides.

Low-tier content:

• Pure ranking-focused content;
• Generic informational blogs;
• Reused news pieces;
• Over-simplified “Top 10 X” listicles.

This hierarchy is developing by the day and will guide content creators on where to focus their energies. Individuals with AI degrees will become an asset to content marketing agencies, and original, experience-backed data will become a precious commodity.

How Creators Can Get a Leg Up on the AI Content Hierarchy

Right now, we can only make predictions, but as this hierarchy solidifies, creators will need to adapt to stay relevant. Ideal content will be focused on top-tier areas that are difficult for AI to replicate.

This will require producing content that is:

• Founded on proven expertise;
• Rich in information and originality;
• Fueled by firsthand experience;
• Not reliant on keyword stuffing and SEO tricks.

Content formats that show undeniable authority—case studies, expert opinions, data analysis, interviews, etc.—are difficult for AI to flatten and simplify. 

Repetitive, unoriginal content can be summarized into AI overviews. But it’s the pieces rooted in professional experience, clear references, and direct involvement that will soak up the remaining spotlight.

Conclusion

AI search isn’t just revolutionizing the way we find information—it’s reshaping what’s worth writing in the first place. The old, overused SEO tricks are losing their magic, as original expertise is taking the forefront.

The future of content quality will depend on:

• The incentives offered to content creators.
• What AI models choose to surface.
• Proper attribution on AI overviews.
• Spotlights offered to niche experts.
• Slowly evolving GEO principles.

If content becomes more original, experiential, and researched, AI’s influence could actually improve overall content quality. However, if attribution and monetization remain weakened, content quality is likely to become diluted.