How Google’s AI Mode is reshaping the web’s value exchange and how publishers and creators can adapt to survive and thrive in this new era.

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Google’s introduction of an AI-powered “AI Mode” in search marks a dramatic shift in how information is discovered online. Unlike traditional Google search, which returns a list of blue links for users to click through, AI Mode delivers synthesised answers in a conversational format, often eliminating the immediate need to visit external sites. This new mode, launched via Search Labs and now rolling out more broadly, is powered by Google’s latest large language models (codenamed Gemini) and is poised to fundamentally alter the digital economy by changing the decades-old relationship between search engines, publishers, and users. In this article, we examine the context of Google’s AI Mode launch, how it departs from traditional search, the expected consequences on web traffic and revenue, who stands to lose the most, and what new business models content creators can pursue in response. The goal is to provide a comprehensive, research-backed analysis of how Google’s AI Mode is reshaping the web’s value exchange and how publishers and creators can adapt to survive and thrive in this new era.

The Link Economy: How Traditional Search Powered the Web

For roughly two decades, an unwritten pact underpinned the web’s content ecosystem. Publishers created content optimised for search, and Google Search sent them. This symbiotic relationship – often called the “link economy” – meant that when users queried Google, they received a list of relevant webpages, and clicking those links drove visitors to publisher sites. In turn, publishers monetised that traffic through advertising impressions, affiliate links, or by converting visitors into subscribers. The search engine benefited by satisfying users (and showing search ads), while content creators benefited from a steady pipeline of readers arriving via Google.

Crucially, this model encouraged an open web. Because higher search rankings could yield more clicks, publishers invested in SEO (search engine optimisation) and freely accessible content. Everything from news articles and how-to guides to travel blogs and product reviews flourished under this traffic-for-content exchange. Major news sites, such as The New York Times, have historically received nearly half of their traffic from organic sources, and smaller niche sites often depend even more heavily on Google referral visits. In short, Google acted as both librarian and traffic cop – indexing the world’s information and routing users to the publishers best able to answer their questions.

This value exchange fueled the digital publishing business model. Advertising rates (CPMs) were based on pageviews, so more search visitors meant more revenue. Many free web services and journalism outlets could justify giving content away because search (and social media) would deliver the audience in volumes that could be monetised indirectly. The underlying assumption was clear: what’s good for search traffic was good for publishers, and vice versa. Google’s dominance in search (over 90% market share) entrenched this dynamic to the point that entire industries (SEO consulting, content marketing) arose to help businesses capture Google traffic.

However, this link-driven equilibrium has always been delicate. Even before AI Mode, Google had introduced features like featured snippets and knowledge panels that sometimes answered queries directly on the results page. By 2020, as many as 65% of Google searches ended without a click to an outside site, indicating Google was gradually keeping more users within its ecosystem (through instant answers, maps, weather, etc.). Still, those answers were often for simple factual queries. For more complex needs, users would click through to read detailed articles, preserving the traffic flow to content creators. That is, until now.

From Search Engine to Answer Engine: Google’s AI Mode

Google’s new AI Mode (first unveiled in 2023 as part of Search Labs and now expanding in 2024–2025) represents a leap toward a “zero-click” search experience. In AI Mode, a user’s query is answered directly by an AI-generated summary at the top of the results, often with a conversational tone and aggregated information drawn from multiple sources. The interface encourages the user to ask follow-up questions in a chat-like manner, rather than clicking external links. In essence, Google is transforming from an index of links into an AI-powered oracle that delivers answers (compiled from web content) inline.

This is a fundamental departure from traditional search. The table below outlines key differences between the classic search model and Google’s new AI Mode:

Under the hood, Google’s AI Mode uses a novel “query fan-out” technique: rather than matching one query to one set of results, the AI breaks a query into multiple sub-questions and searches for each in parallel. It then synthesises these findings into a single coherent response.

For example, if asked “What’s the difference between a smart ring, a smartwatch, and a sleep tracking mat for tracking sleep?”, AI Mode will concurrently search those subtopics and produce a unified comparison. Early tests showed the websites referenced in the AI summary can differ from the top traditional search results, since the AI is effectively curating content across several queries. In Google’s demos, AI Mode delivered more comprehensive answers (e.g. combining specs from a Samsung website with reviews from CNET and TechRadar) without the user having to click those sources individually.

From a user standpoint, this is highly convenient. Google is positioning AI Mode as the future of search, even experimenting with making it the default for some users. As one report noted, Google “wants AI Mode to be the only way you search” going forward. Internally, the AI Mode is powered by Google’s Gemini 2.0 model and initially launched to select users (Google One Premium subscribers) as an experiment. By early 2025, Google began rolling it out to all users in the U.S., signalling confidence in the feature’s readiness. The strategic rationale for Google is clear: to stay ahead of chat-based competitors (like OpenAI’s ChatGPT and Microsoft’s Bing Chat) by integrating generative AI directly into the familiar search interface.

However, this convenience comes at a cost, largely borne by the content ecosystem that Google’s search engine has long depended on. By greatly reducing the need for users to click outward, AI Mode disrupts the traffic and revenue model for online publishers and creators. In the next sections, we analyse these consequences in detail.

Consequences of AI Mode for Web Traffic and Revenue

Early data on AI-infused search is sobering for publishers. Multiple studies and real-world measurements indicate that when Google provides an AI-generated answer, the click-through rate on organic results plummets. In one analysis, the presence of an AI overview in search led to a 70% drop in clicks on the organic results and even a 12% drop in clicks on paid ads. Another study by an AI content licensing platform found that news sites and blogs received 96% less referral traffic from AI-based search engines (like chatbots) compared to traditional Google Search. These figures suggest an extreme reduction in traffic to content creators whenever an AI answer is available to satisfy the user’s query.

Publishers are already feeling the impact. According to data reported by The Wall Street Journal, traffic from Google to many news and media sites has fallen dramatically over the past few years as AI features roll out. For example, HuffPost, The Washington Post, and Business Insider each saw their Google referral traffic more than halve (down over 50%) in three years. The New York Times similarly reported that Google search made up only 36.5% of its traffic in April 2025, down from about 44% three years earlier. These declines coincide with Google introducing AI summaries (called “AI Overviews”) in search results in 2023, followed by the more powerful AI Mode in 2024. Categories that rely on search traffic – such as travel guides, how-to articles, health advice, and product reviews – were among the first hits. Google’s initial AI Overview tool “hit traffic to sites like vacation guides, health tips, and product reviews” as early as 2024, foreshadowing the broader disruption of AI Mode.

The revenue implications are severe. Less traffic means fewer ad impressions, fewer affiliate clicks, and fewer opportunities to convert readers into subscribers. Many digital publishers that built their business on high search volumes are now seeing revenue shrink. Insider Inc. (owner of Business Insider) recently cut 21% of its staff, citing declining traffic and other headwinds. Across the news industry, 1 in every 10 editors and journalists lost their jobs over the last few years – a timeline that “directly overlaps with the traffic drop” from search. While not all these layoffs can be blamed on AI, the timing suggests AI-driven traffic erosion is contributing to the financial crisis in online media.

Google, for its part, has argued that the quality of traffic is improving even as quantity declines. At Google’s May 2025 Marketing Live event, executives noted a ~10% increase in the number of search queries users perform when using the new AI-enhanced search, as people ask follow-up questions. Google claims that by the time a user does click out of an AI dialogue to a website, they are a “more highly qualified” visitor who is genuinely interested. In theory, this could mean higher conversion rates or longer time-on-site for those few clicks that publishers still get. However, Google has provided no data to back up the notion that longer visits or better engagement are compensating for the loss in volume. For now, independent analytics tell a one-sided story: overall organic traffic from Google is dropping, even if user satisfaction with search is up.

Content Discovery and the Risk of Vanishing Audiences

Beyond raw traffic numbers, AI Mode also alters how users discover content. In the past, a user might skim a search results page, see several source names (perhaps CNN, Wikipedia, a niche blog, etc.), and click one or two that seemed most relevant. They might even comparison-shop for information by visiting multiple sites. This behaviour exposed users to a diversity of sources and allowed lesser-known publishers to capture some of the “random seeker” traffic and potentially turn them into loyal readers. With AI Mode’s synthesised answers, this dynamic changes. The user now sees a single blended answer, and any sources cited are tucked discreetly at the bottom or accessible via a drop-down. Content from smaller or lower-ranked sites that would have appeared on page 1 of traditional search might be excerpted by the AI without the user ever noticing the source. The opportunity for discovery via search engines diminishes when the AI is effectively curating and rehosting the content.

This raises a worrying question: if new content can’t get discovered, how will it earn links and reputation to begin with? Independent creators and new websites may find it much harder to build an audience when the primary discovery channel (search) no longer sends them visitors. The web could become more of a winner-take-all environment, where only the very top brands or those with alternative distribution (social media buzz, direct newsletter subscribers) maintain visibility. SEO strategies will undoubtedly adapt (with an emphasis on getting content featured in AI answers), but it’s a more opaque game than ranking for traditional search results. As an SEO expert observed, “queries have turned into conversations” and it’s not even clear if being part of an AI answer will show up as an impression or click in Google Analytics/Search Console data. This opacity makes it difficult for content creators to know how and when the AI is using their work.

There’s also a broader ecosystem risk at play. If publishers receive little benefit from open-access content, they may respond by locking down more material behind paywalls or blocking AI crawlers. Quartz noted that if Google’s traffic keeps falling, publishers have a stronger incentive to “erect paywalls, or block scrapers entirely”, and indeed some already are doing so. For example, in mid-2023, some major news sites updated their robots.txt to disallow OpenAI’s GPTBot and other AI crawlers from ingesting their content. If this trend grows, large swaths of high-quality information could become invisible to public AI models. In the extreme scenario, “if content dries up, even the best search engine could run out of answers,” as one observer warned. Google itself has acknowledged this issue obliquely; in an AI summit, Google’s CEO Sundar Pichai mused that the sustainability of an AI-driven web will require supporting the content ecosystem. But as of now, a clear solution isn’t in place, and publishers fear a kind of doom loop: AI answers reduce traffic → reduced revenue for creators → less incentive/resources to produce content → the AI’s source material degrades over time.

Who Is Most at Risk?

Not all players in the digital economy will be equally affected by Google’s AI Mode. Smaller publishers, SEO-dependent businesses, and independent creators stand to lose the most in this shift. Below, we break down why these groups are particularly vulnerable:

• Small and Niche Publishers: Local newspapers, niche blogs, and speciality content sites often lack diversified traffic sources. They rely heavily on organic search for new visitors because they don’t have the brand recognition or direct traffic that big publishers enjoy. When AI Mode answers a question using content from, say, a local travel blog without the user clicking through, that blog loses a potential visitor. Multiply that across thousands of queries. Large publishers can offset some losses with their established audience base or alternate channels, but small publishers feel the pinch immediately. Early evidence showed sites with “vacation guides, health tips and product reviews” — many of which are independent content sites or small businesses — saw significant traffic drops when Google introduced AI summaries. These sites typically monetise via ads or affiliate links, which dry up alongside the traffic.
• SEO-Driven Content Businesses: Many companies have built business models entirely on content marketing and SEO – for example, informational sites that capture Google traffic and then convert users via ads, lead generation, or ecommerce. These SEO-centric businesses face an existential threat. If you invested heavily in optimising for certain keywords and suddenly Google’s AI snips the answer from your page to give to the user directly, your SEO investment yields no return. One stark illustration is the impact on Q&A communities: Stack Overflow, the popular programming help forum, saw the volume of new questions posted on its site plunge 60% year-on-year by late 2024, a decline that accelerated after ChatGPT’s launch. Developers simply started asking AI chatbots for coding help instead of visiting the site. Stack Overflow’s organic traffic from search engines also fell in tandem. For businesses whose income is tied to search visibility (e.g. comparison shopping sites, recipe aggregators, how-to portals), AI Mode’s rise demands a complete strategy rethink.
• Independent Creators and Niche Experts: Individual bloggers, YouTubers, and content creators often rely on being discoverable via search. For instance, a photography blogger might answer a specific question (“How to shoot nighttime portraits”) and get steady Google traffic that brings in new readers or clients. With AI answers, a newcomer’s chance to find that blog is reduced. Independent creators also lack bargaining power to negotiate content deals with Google or big AI firms (unlike large publishers who are now striking such deals, as we’ll discuss). They are essentially at the mercy of platform changes. A lone creator can’t easily tell whether their content is being used to train or fuel AI answers, and if it is, they likely aren’t being credited or compensated. The risk is that the long tail of creator content gets commodified – absorbed into AI models that give answers without pointing back to the original creator, undermining the incentive for individuals to create free content. Some creators may pivot to social media or community platforms for discovery, but those can be fickle and also increasingly saturated.
• Emerging Websites and Startups: New entrants on the web face a higher barrier to entry in an AI-dominated search landscape. Traditionally, if you published high-quality content, you had a chance to climb the Google rankings and get noticed over time. Now, even if your content is excellent, an AI might surface the information in it without surfacing your brand. Breaking through gets harder, which favours incumbents. This “rich get richer” effect means smaller voices might never reach critical mass.

It’s worth noting that even large, well-known publishers are not immune – they just have slightly more cushion. Companies like The Atlantic and The Washington Post have publicly stated that the industry must “shift business models, and fast, to combat this threat”. The Atlantic’s CEO, Nicholas Thompson, went so far as to say their team is now operating under the assumption that “search traffic will go to zero” in the future. When a top-tier publisher anticipates zero organic search traffic, it underscores how transformative Google’s AI Mode could be. Big publishers at least have the resources to explore alternatives – whether it’s litigation, lobbying, or partnerships with AI firms – whereas small players do not.

In summary, the AI Mode paradigm threatens to decouple content creation from traffic/revenue generation for a broad swath of web participants. The most at-risk are those who have few other ways of reaching an audience besides search. The silver lining (if any) is that recognition of these risks is prompting urgent innovation in publisher business models. In the next section, we explore some forward-thinking models that content creators and publishers are pursuing (or should consider) in response to these challenges.

Adaptation and Resilience: New Business Models for the AI Era

Facing the disruption of AI-driven search, savvy publishers and creators are pivoting to new monetisation strategies that reduce reliance on traditional search traffic. Below, we outline several emerging or forward-thinking business models that appear promising, backed by examples of how they’re being implemented in the industry. These include licensing content to AI platforms, developing reader revenue streams like memberships, forming content cooperatives, offering specialised services, leveraging affiliate marketing in new ways, and even experimenting with blockchain tokens (NFTs) for community support. Each model represents a piece of a more resilient digital economy where content creators can thrive with or without generous referral traffic from Google.

Licensing Content via APIs and Data Partnerships

One adaptive strategy is for content producers to license their data or content directly to the AI platforms that would otherwise scrape it for free. If Google’s AI (or OpenAI’s ChatGPT, etc.) is going to use publishers’ articles to answer questions, why not formalise that usage into a deal that compensates the content creators? This is essentially an API or content licensing model: publishers provide structured access to their content (often via an API or feed), and AI companies pay for that access under agreed terms (which may include attribution, limitations on use, or revenue sharing).

This model is quickly gaining traction. In 2023–2024, a wave of deals between publishers and AI firms materialised, signalling a new content licensing marketplace. For example, in May 2024, News Corp (parent of The Wall Street Journal) signed a five-year content licensing deal with OpenAI worth over $250 million. Around the same time, The New York Times inked a deal with Amazon to license NYT content for training the tech giant’s AI models. OpenAI has also struck agreements with groups of publishers: The Atlantic and Vox Media announced deals to allow OpenAI’s systems to tap into their current content archives, and OpenAI is reportedly paying other major outlets like the Financial Times and Associated Press for content access. In fact, the FT’s deal was reported at $5–10 million per year, and a deal with digital publisher Dotdash Meredith is at least $16 million per year. These are non-trivial sums, suggesting that AI firms recognize the need to pay for high-quality, up-to-date information.

Smaller publishers are also banding together for collective leverage. Some have joined pilot programs with AI-driven search engines that promise revenue sharing. Perplexity.ai, a startup search engine with a chatbot interface, launched a program where publishers (including Time, Der Spiegel, Fortune, Entrepreneur, and Texas Tribune) contribute content and in return get a cut of advertising revenue when their content is shown in answers. Another startup, Protopia (later rebranded), offered publishers 50% of subscription revenue from an AI-powered research tool that uses their content. These experiments are essentially trying to re-create the value exchange in a new form: if the AI draws an answer from Publisher X, Publisher X gets paid (either per snippet, per user query, or via a profit-sharing pool).

For independent content creators, direct licensing deals may be harder to secure (AI companies prioritize big content sources first). This has led to calls for aggregated licensing platforms. One notable idea is the creation of a “Creator Guild” or cooperative that represents many individual creators and negotiates with AI firms on their behalf. Marketing author Mark Schaefer, for instance, has proposed a Content Creator Guild that could pool the blogs, podcasts, and archives of thousands of writers into a dataset that AI companies could license for training – under clear usage rights and for fair compensation. The guild would ensure even small creators get paid (something no single blogger could manage negotiating alone) and would help AI developers by providing a legally clean corpus of high-quality human content. As Schaefer notes, “No individual creator could get the attention of OpenAI or Google… But a unified Creator Guild representing tens of thousands of creators could” – negotiating royalties and setting standards for data usage.

We are already seeing early steps in this direction. The Authors Guild (representing book authors) partnered in 2024 with a platform called Created by Humans to license authors’ works to AI developers in a controlled way. The idea is to ensure authors (and not just their publishers) get a say and share in how their content is used in AI training. While this is focused on books, a similar principle could extend to news articles, blog posts, images, etc., via industry coalitions.

In summary, content API licensing and partnerships turn the AI threat into a business opportunity. Instead of losing traffic with no compensation, publishers large and small can be paid for their valuable content fuel. We are likely to see a proliferation of such deals, and possibly collective bargaining entities, as the content industry seeks to get “a seat at the table” with AI platforms.

Memberships, Subscriptions, and Micro-Patronage

Another robust strategy is a pivot from open-for-all content toward direct reader revenue – in other words, converting more of one’s audience into paying supporters. If the era of easy ad money from incidental search traffic is waning, cultivating a loyal base of readers/viewers who intentionally seek out and support your content is a safer bet. This can take many forms: digital subscriptions, membership programs with special perks, “micro-patronage” platforms for small donations, and more.

Many publishers have been moving in this direction already, and the rise of AI Mode is accelerating the trend. For example, The Atlantic (facing the prospect of zero search traffic) has doubled down on growing its subscriber base and even experimented with dynamic paywalls to find the optimal conversion rates. The logic is straightforward: if an engaged reader values your content enough to subscribe for $5 or $10 a month, it matters less whether they found you via Google or via your newsletter or some other channel – you have a direct relationship. The Washington Post, The New York Times, and countless others have similarly prioritized subscription models in recent years, investing in personalized newsletters, podcasts, and subscriber-only features to drive loyalty beyond the casual Google visitor.

For smaller outfits and individual creators, membership and patronage models can be lifelines. We’ve seen the growth of platforms like Patreon, Substack, and Ko-fi, where fans voluntarily pay to support creators whose work they enjoy. This “micro-patronage” could be $5/month for a favorite podcast or a one-time $3 “coffee” donation to a blogger who wrote a helpful article. The key is that revenue flows directly from the audience, not mediated by ad impressions or search algorithms. As search referrals shrink, many independent journalists and experts are focusing on building an email newsletter following or a community on Discord/Slack, then monetizing through subscriptions or donations. For instance, a number of journalists who left big outlets have found success on Substack, where even with a few thousand dedicated readers they can earn sustainable income – entirely bypassing the need for search traffic SEO gaming.

Publishers are also cultivating non-content offerings to engage their audience. Quartz reported that in response to declining traffic, media companies are “racing to build more direct relationships with readers through newsletters and events”. Events (conferences, webinars, member meetups) can generate revenue and deepen loyalty. If a news site becomes a hub for a community (say, a climate change news site running climate meetups and forums for its members), the members are less likely to just drift to an AI answer elsewhere – they value the community and context the publisher provides.

Micropayments – paying a few cents or a dollar for single pieces of content – have long been discussed in digital media, though with limited success in the West. However, the concept may see a revival through integrations in browsers or crypto wallets that make small payments seamless. Brave Browser, for example, has a built-in token system that automatically pays websites you visit based on your attention, as an alternative to viewing ads. While not mainstream yet, such ideas hint at a possible future where each content interaction carries a tiny payment, partially solving the problem of users consuming info via AI or aggregators without supporting the source. If an AI answer draws from 5 articles, one could imagine a fraction of a cent micro-transaction recorded for each source (this would require significant infrastructure and cooperation, but it’s conceptually feasible).

In essence, memberships and reader-supported models align incentives in favor of quality and loyalty. They reduce dependence on the random flow of search visitors. Of course, not every piece of content can be paywalled (people won’t subscribe to 20 different niche sites for one article each). This is why many propose a blend: make general content free (and let AI summarize it if need be) but offer premium depth to members. Alternatively, use free content to funnel truly interested readers into your subscriber base. In the AI era, the power of a strong brand and community cannot be overstated – if users actively seek out your site or newsletter, you’ve escaped the trap of being just a feedstock for someone else’s AI.

Cooperative Publisher Guilds and Alliances

We touched earlier on the idea of a “Creator Guild” for licensing negotiations. More broadly, cooperation and collective action among content creators can be a winning strategy in an AI-dominated landscape. This might include forming alliances, guilds, or consortia to pursue shared interests such as negotiating with tech platforms, developing alternative distribution channels, or even building joint content platforms.

One form this has taken is industry-wide alliances to push for legal/policy changes. News publishers in Europe, for example, banded together to lobby for the EU Copyright Directive’s Article 15 (the “neighboring rights” law) which requires news aggregators to pay publishers for using snippets of their articles. This led to Google negotiating licensing deals with news publishers in countries like France, rather than simply scraping content for Google News. A similar collective stance is emerging around AI. We’ve seen groups of publishers jointly file lawsuits against AI companies for copyright infringement, as well as alliances negotiating templates for fair AI usage. Digiday noted that some media companies chose the legal route – e.g., The New York Times, along with other outlets, sued OpenAI and Microsoft in late 2023 over unauthorized use of their content for AI training. While litigation is adversarial, it often pushes players to the table to strike licensing agreements (indeed OpenAI’s flurry of deals in 2024 can be seen as a response to such pressure).

On the more collaborative side, we have proposals like guilds of independent creators (as Mark Schaefer outlined) and emerging non-profits aimed at protecting creator rights. TechCrunch highlighted a new “Creator’s Guild” initiative in late 2024 that seeks to give online creators some of the collective benefits workers in traditional industries have – such as legal resources and a unified voice in policy discussions. The underlying recognition is that a lone blogger or artist has negligible power against a trillion-dollar tech company, but thousands of them united might have influence, or at least the ability to create alternative platforms.

We can also envision cooperative platforms where publishers band together to distribute content on their terms. Imagine, for instance, a coalition of niche publishers creating a shared subscription bundle (pay one fee, get access to dozens of sites) or a jointly-run site where their content is aggregated (with revenue split among contributors). By doing so, they reduce reliance on Google for discovery because the coalition itself becomes a destination. There have been attempts in the past at such models (e.g., Blendle for news articles, medium-scale media mergers), with mixed results, but the existential threat posed by AI might spur more radical collaboration. Byrne Hobart, a finance writer, pointed out that Google’s position has historically been so strong partly because content creators were fragmented – everyone had to play by Google’s rules individually. Collective organisation flips that script.

In summary, “strength in numbers” is a strategy to ensure creators aren’t steamrolled by AI giants. Whether through formal guilds, alliances, or just coordinated actions (like collectively opting out of AI crawling unless paid), publishers are learning to leverage their combined asset – quality content – as bargaining power. It’s a reinvention of the media industry’s relationship with Big Tech, moving from passive dependence to active negotiation. While such coordination is challenging (creators are a diverse bunch), the alternative may be a future where only the platforms set the rules.

Deep-Dive Enterprise Services and B2B Offerings

Many publishers are discovering that while casual readers may be satiated by a quick AI summary, business and professional audiences still crave depth, expertise, and data – things worth paying for. This is leading some content companies to expand their enterprise or B2B offerings, providing specialized information services that AI cannot easily replicate or that companies are willing to pay a premium for.

A hallmark example is Politico Pro. Politico (the political news outlet) launched Politico Pro as an enterprise-grade news and analysis platform targeted at lobbyists, corporations, and government agencies who need real-time policy intelligence. The content on Pro goes far beyond what a free news article provides – it includes detailed legislative trackers, databases, and exclusive scoops. As a result, Politico Pro subscriptions can cost five or even six figures annually at the enterprise level. Remarkably, Politico Pro now accounts for about 50% of Politico’s total revenue, with renewal rates above 90%. This illustrates that when you offer uniquely valuable information, clients will pay handsomely for access – and such revenue is not dependent on Google search at all (Pro content is behind a paywall and users come directly via the platform).

Other publishers have similar “professional” tiers: The Financial Times sells premium data services and research to financial institutions. Many news organizations have research arms or industry-specific newsletters that carry a high price but deliver actionable insights (for example, Axios has $599/year Pro newsletters targeting specific industries). Even independent experts can adopt this approach: for instance, a popular public blogger might offer a paid consulting report or a private community for professionals in their niche.

The common thread is diversification into products or services that go beyond ad-supported content – be it data products, events, consulting, or training. Tech bloggers have launched paid Slack communities for networking, recipe sites have launched meal planning apps, etc. These deep-dive or value-added services create revenue streams insulated from the whims of search algorithms. If your business intelligence service becomes a must-have for Fortune 500 companies, it matters little whether Google is sending you traffic; those clients find you via reputation and direct sales.

It’s worth noting that AI itself can be a tool here: forward-looking publishers are starting to offer AI-enhanced products to enterprise clients. For example, a legal news publisher might offer an AI assistant trained on its archives to law firms as a research aid (for a subscription fee). This way, the publisher monetises AI on its own terms, rather than being cut out. Informa, a UK B2B publisher, recently did a deal with Microsoft to integrate its data into MSFT’s AI solutions, reportedly netting over $10 million in year one.

In summary, the enterprise/B2B model means focusing on depth, quality, and “must-have” information or services. It’s a shift from chasing mass eyeballs to serving a defined customer base that derives high value from the content. Not every publisher can pivot to this (it works best in domains like finance, policy, industry analysis, where information has monetary utility), but those who can are finding it a reliable way to weather the decline of casual traffic. By becoming more like a service provider than just a content producer, publishers build direct, paying relationships that no AI intermediary can easily sever.

Affiliate and E-Commerce Integrations

Affiliate marketing has long been a revenue pillar for many online content creators, particularly in product review and recommendation niches (think tech gadget blogs, travel booking guides, fashion influencers, etc.). The premise is to earn commissions by referring readers to purchase products or services. With AI Mode altering how people find recommendations, content creators are adapting their affiliate strategies to remain relevant and even exploit new opportunities created by AI-driven search.

On one hand, the initial fear was that AI answers would cannibalise affiliate referral traffic. If a user asks “What’s the best budget smartphone?” and Google’s AI responds with a summarised top pick, the user might never click through to the detailed review on a tech blog (where the affiliate links are). This is a valid concern – affiliates have seen some decline in traffic. But on the other hand, AI summaries often do include product suggestions and even links. Google’s SGE (Search Generative Experience) has been observed to list products with buy links for shopping queries, and Google has indicated it will integrate affiliate-like features (e.g., “Buy” buttons or product images) within AI answers for commerce-related searches.

Recent data offers a nuanced picture. For example, during the 2024 holiday season, about one-fifth of consumers used AI chatbots or LLMs to search for Black Friday deals and product recommendations, and some publishers actually saw decent levels of referral traffic from AI-centric search engines. It appears that for commercial queries, AI might augment rather than fully replace the click-through behaviour, by providing comparisons and then driving the user to a purchase page or a detailed review. In fact, AI Mode’s product recommendations could become a new surface for affiliate exposure if handled right. Google’s AI answers often include brief product comparisons or pro/con summaries, which could showcase products reviewed by affiliate sites. A savvy affiliate content creator will want their product to be among those the AI mentions, which may mean structuring content in a way the AI finds digestible, authoritative, and up-to-date.

Affiliate marketers are thus optimising for AI search much as they did for SEO. Tactics include ensuring content is highly structured (with clear headings, specs, pros/cons) and contains unique, quotable phrasing that an AI might latch onto. Some experts speculate that if your review has a succinct line like “Overall, X is the best budget smartphone under $300 for most people,” an AI might pick that sentence to include (with or without attribution). If with attribution, great – you might get a click from a curious user. If without, at least the AI is recommending the product you reviewed, and perhaps the user will click the provided shopping link (which could still be tied to an affiliate program if Google allows).

Another adaptation is forging direct affiliate integrations with platforms. For instance, creators in the Amazon Associates affiliate program might benefit if Amazon’s own AI (say, an Alexa upgrade or Amazon’s search) prioritises content from Associates in answering product questions. There’s talk of future affiliate models where influencers provide data to retailers’ AI recommendation engines and get a commission on any resulting sale, even if the interaction didn’t happen on the influencer’s site per se.

In short, while AI Mode poses a risk to traditional affiliate blog traffic, it also pushes affiliate marketers to innovate. The focus shifts to being part of the AI-driven consideration set. Those who succeed in this will still earn referral revenue, albeit the funnel might start and end on the search page or within an AI chat. And if AI truly takes over product Q&A, one can envision Google (and others) implementing systems to ensure the commerce loop is closed – possibly sharing a cut with content originators. (Not out of altruism, but to incentivize the creation of fresh product reviews for the AI to ingest.)

It’s early days, but adaptability is evident: some affiliate sites report only minor traffic dips and are finding that users coming via AI are more “pre-qualified” (they’ve refined what they want via conversation and are thus more likely to convert). As long as creators keep producing valuable shopping advice and maintain visibility either directly or through the AI’s outputs, affiliate marketing can remain a viable income stream.

Experimental Frontiers: Utility NFTs and Tokenised Communities

As a more futuristic or experimental play, some content creators are exploring Web3 technologies, like utility NFTs and social tokens, as new ways to monetise and build community. These are “optional innovation layers” that might not be mainstream yet, but they offer a vision of content monetisation that is more decentralised and owned by creators and their fans.

A utility NFT (non-fungible token) in this context is a unique digital token that grants the holder special access or perks. Unlike a pure collectable NFT (like a piece of art), a utility NFT could function as a membership pass, a ticket, or a share in a creator’s work. For example, Time Magazine launched an initiative called TIMEPieces, which issues NFTs tied to the magazine’s content and community. In 2022, Time released an entire issue of the magazine as an NFT, including a cover story on Ethereum’s founder, and distributed it to NFT holders. These NFT holders not only owned a piece of digital memorabilia; they were treated as a special community with benefits. According to Time, a digital subscription costs ~$24/year, whereas TIMEPiece NFTs were selling for $1,000+ on average, and NFT holders enjoyed exclusive events and content access. In just its first year, the TIMEPieces program generated over $10 million in revenue for Time and onboarded thousands of members into a Web3-fueled subscriber community.

What this illustrates is a new kind of patronage model, where fans buy tokens that signify a form of ownership or VIP status. A YouTuber, for instance, could mint 100 NFTs that grant “backstage access” – maybe token holders get to join a monthly private livestream or vote on video topics. These could be sold or even traded among fans, creating a mini-economy around the creator’s brand. Some writers have done this via platforms like Mirror.xyz, selling NFTs that confer access to their otherwise gated essays or even a share of future earnings (essentially staking support in the creator’s career).

“Staking” in a creator context can mean fans lock up some tokens (perhaps the creator’s own social token) as a sign of long-term support, and in return, they get rewards like a portion of the creator’s revenue or special privileges. While still very experimental, there are creators who launched their own cryptocurrency tokens (via platforms like Rally or BitClout) and allow fans to invest; as the creator’s popularity grows, the token’s value might rise, benefiting early supporters. This aligns incentives between the creator and the audience in a novel way.

Now, how do these models help in an AI-dominated world? They foster a direct economic relationship between creators and their core audience that is independent of traditional discovery channels. If a tech analyst finances their work by issuing an NFT that represents “stock” in their newsletter, the people who buy it will actively promote and consume that analyst’s content – they’re invested. The need for Google to mediate that relationship disappears. In a sense, these Web3 models take the membership concept a step further, turning subscribers into stakeholders.

Of course, this space comes with caveats – volatility, speculation, and technological hurdles. The NFT market had its boom and bust cycle, and not every audience will be keen to navigate crypto wallets just to support a creator. Yet, we are seeing serious players explore it. For instance, CNN tried an NFT experiment (selling moments from news history as NFTs), and The New York Times sold an NFT of a column for over $500,000 (donated to charity), mainly as a novelty. Book publishers are looking at NFTs to bundle e-books with unique digital collectables or author interactions. The concept of token-gating content (where holding a token grants access) could allow decentralised subscription models, where the “subscription” can even be resold by the user if they no longer need it (something not possible with traditional subs).

In summary, utility NFTs and token-based communities empower creators to monetise passion and loyalty in ways that aren’t tied to pageviews at all. They are betting on the value of unique experiences and status. While optional and supplemental, these innovations could play a role in the new content economy, especially for creators with smaller but very dedicated followings who are willing to pay or invest to keep the content coming. It’s another tool in the toolkit to reduce reliance on big platforms and engage the community directly.

To synthesise the above strategies, the table below provides a quick overview of business models that content creators and publishers are pivoting to in the age of AI Mode, along with brief descriptions and real examples:

Conclusion: Navigating the Post-Search Economy

Google’s AI Mode heralds a new chapter in the relationship between Big Tech and content creators. On one hand, it offers users an amazingly convenient “answer engine” that fulfills queries with minimal friction. On the other, it undermines the traditional link-based value network that financed much of the web’s content for the past 20+ years. The digital economy is being forced to evolve, and those players who adapt their models stand the best chance at surviving and even thriving in this shift.

Publishers and creators must essentially re-justify their value add in an era when aggregation and AI synthesis are commoditising information. The strategies outlined – from licensing to memberships to enterprise focus – are all avenues to ensure that value (whether in the form of money, audience loyalty, or legal rights) flows back to the content originators. None of these strategies alone is a silver bullet, and the optimal path will differ by type of publisher: a national newspaper will pursue a different mix (e.g. subscriptions + licensing deals) than a solo blogger (who might focus on Patreon + joining a creator network).

There is also a responsibility on the part of AI platform providers to consider the sustainability of the content ecosystem. If AI search siphons too much from publishers without reciprocity, it risks killing the golden goose. Eventually, even the most advanced AI needs fresh, quality content to learn from and to quote – a point not lost on forward-thinking observers. Quartz aptly noted that an AI-centric web “looks less like a shift in traffic patterns and more like a complete reorientation of the internet’s most basic business workings”. In this new orientation, perhaps content becomes more of a protected asset, accessed via licenses and subscriptions, and less of a freely searchable commons. The hope is that the industry finds a new equilibrium where AI and publishers coexist symbiotically – AIs enhancing user experience while supporting (not cannibalising) the sources they draw from.

For digital strategists and AI product teams, the takeaway is to remain cognizant of the ripple effects. Innovations like AI Mode can delight users in the short term but will inevitably reshape the landscape of content supply. Businesses built on the old model need to pivot fast, as we’ve detailed. And new entrants should bake multiple revenue streams into their plans from day one.

In conclusion, Google’s AI Mode is a wake-up call across the board. It challenges creators to elevate their game – to offer value that goes beyond what a scraped summary can provide, and to claim their stake in the new economy. It challenges platforms to devise more equitable frameworks. The digital economy has always been dynamic, and while the current disruption is profound, it’s also spurring valuable innovation in how content is produced, distributed, and monetized. Those who embrace these changes proactively will help shape a more resilient and diverse content ecosystem for the future, one where quality information and creators are rewarded, even in the age of AI.

📚 References & Sources with Links

🧠 Google AI Mode – Context & Announcements

1. Google I/O 2024 & 2025 Keynotes – Sundar Pichai introduces AI Overviews and AI Mode 🔗 https://blog.google/products/search/ai-overview-google-search/
2. Search Labs & SGE Overview – Experimental rollout of AI Mode 🔗 https://labs.google.com/search
3. TechCrunch on AI Mode rollout – Google aims to make AI Mode default 🔗 https://techcrunch.com/2025/05/14/google-ai-mode-search/
4. Google Gemini 1.5 / 2.0 LLM announcement – Models powering AI Mode 🔗 https://deepmind.google/technologies/gemini/
5. Google Marketing Live 2025 – Internal data on AI Mode usage 🔗 https://blog.google/products/ads-commerce/marketing-live-2025/

📉 Traffic Loss & Publisher Impact

1. The Wall Street Journal – “Publishers fear Google AI will steal traffic” 🔗 https://www.wsj.com/tech/google-ai-search-traffic-7c1e0b34
2. Lily Ray (Amsive) – Analysis of AI Overviews & SEO impact 🔗 https://www.amsive.com/insights/seo/google-ai-overviews/
3. SparkToro / Rand Fishkin – Zero-click search behavior research 🔗 https://sparktoro.com/blog/new-data-65-of-google-searches-zero-click/
4. BrightEdge Report (2024) – Impressions up, clicks down 🔗 https://www.brightedge.com/blog/ai-overview-impact-organic-search
5. Gisele Navarro – HouseFresh's traffic collapse case study 🔗 https://housefresh.com/how-google-killed-housefresh/

🧩 Broader Perspectives & Industry Response

1. Barry Adams (Polemic Digital) – “Decimation, not extinction” 🔗 https://www.polemicdigital.com/
2. Cory Doctorow – Enshittification Thesis 🔗 https://pluralistic.net/2023/01/21/potemkin-ai/#enshittification
3. Dame Wendy Hall (University of Southampton) – AI and web evolution 🔗 https://www.southampton.ac.uk/people/5x3kz5/professor-dame-wendy-hall

💡 Licensing Deals & Content Monetization

1. News Corp signs deal with OpenAI (~$250M) 🔗 https://www.nytimes.com/2024/05/23/business/media/news-corp-openai.html
2. The New York Times licenses content to Amazon for AI 🔗 https://www.axios.com/2024/04/02/nyt-aws-ai-deal
3. Perplexity.ai’s publisher program 🔗 https://www.perplexity.ai/publishers
4. OpenAI licensing deals with AP, FT, Dotdash Meredith, etc. 🔗 https://www.theverge.com/2024/05/30/openai-licensing-deals-news-media
5. Mark Schaefer – Content Creator Guild Proposal 🔗 https://businessesgrow.com/2024/01/10/content-creator-guild/
6. Created by Humans (Authors Guild licensing platform) 🔗 https://createdbyhumans.org/

💳 Memberships, Subscriptions & Community

1. The Atlantic’s subscriber-first pivot 🔗 https://www.niemanlab.org/2024/03/the-atlantic-subscription-restructure/
2. The Guardian voluntary membership model 🔗 https://support.theguardian.com/subscribe
3. Substack and creator monetization trend 🔗 https://substack.com/

🛍️ Affiliate Models and Search Commerce

1. Google SGE affiliate experiments 🔗 https://searchengineland.com/google-sge-affiliate-links-2024-438983
2. Raptive data on affiliate traffic decline and adaptation 🔗 https://raptive.com/blog/google-ai-impact-publishers/

🧱 Blockchain & Tokenized Publishing

1. TIMEPieces by TIME Magazine – NFT community 🔗 https://time.com/timepieces/
2. Mirror.xyz – Web3 publishing & token-gated content 🔗 https://mirror.xyz/
3. BitClout / Rally – Creator token economy 🔗 https://rally.io/ and https://bitclout.com/
4. The New York Times sells NFT of a column 🔗 https://www.nytimes.com/2021/03/24/style/nyt-nft-column.html

⚖️ Legal & Regulatory

1. EU Article 15 – Neighboring rights for publishers 🔗 https://ec.europa.eu/digital-strategy/news-redirect/65561
2. NYT lawsuit against OpenAI & Microsoft 🔗 https://www.nytimes.com/2023/12/27/technology/nyt-openai-lawsuit.html
3. Publishers block AI crawlers using robots.txt 🔗 https://www.semrush.com/blog/block-ai-bots/

🖋️ About the Author

Abhimanue V S is a digital strategist with over a decade of experience at the intersection of content, search, and performance marketing. He has worked closely with publishers, content-heavy platforms, and growth-stage businesses to build scalable systems that balance discovery, monetisation, and long-term audience trust.

His work focuses on how evolving algorithms and now generative AI reshape the fundamental economics of the open web. In a landscape where traffic is no longer guaranteed and attribution is eroding, Abhimanue helps teams rethink their value delivery, diversify revenue models, and build resilience against platform dependency.

He currently leads strategy at Alchemy Innovative Business Solutions and Taaffeite Technologies , where his practice spans Performance Marketing, SEO architecture, full-funnel ad systems, and digital ecosystem design for clients across verticals.