The £20 Billion Handshake: Backend Deals Reshaping Your Search Bar
The smartphone in your pocket contains a curious paradox. Apple, one of the world's most valuable companies, builds its own chips, designs its own operating system, and controls every aspect of its ecosystem with obsessive precision. Yet when you tap Safari's search bar, you're not using an Apple search engine. You're using Google. And Google pays Apple a staggering $20 billion every year to keep it that way.
This colossal payment, revealed during the US Department of Justice's antitrust trial against Google, represents far more than a simple business arrangement. It's the visible tip of a fundamental transformation in how digital platforms compete, collaborate, and ultimately extract value from the billions of searches and queries humans perform daily. As artificial intelligence reshapes the search landscape and digital assistants become genuine conversational partners rather than glorified keyword matchers, these backend licensing deals are quietly redrawing the competitive map of the digital economy.
The stakes have never been higher. Search advertising generated $102.9 billion in revenue in the United States alone during 2024, accounting for nearly 40 per cent of all digital advertising spending. But the ground is shifting beneath the industry's feet. AI-powered search experiences from OpenAI's ChatGPT, Microsoft's Copilot, and Google's own AI Overviews are fundamentally changing how people find information, and these changes threaten to upend decades of established business models. Into this volatile mix come a new wave of licensing deals, platform partnerships, and strategic alliances that could determine which companies dominate the next generation of digital interaction.
When Search Was Simple
To understand where we're heading, it helps to grasp how we got here. Google's dominance in search wasn't accidental. The company built the best search engine, captured roughly 90 per cent of the market, and then methodically paid billions to ensure its search bar appeared by default on every device that mattered. Apple, Samsung, Mozilla, and countless other device manufacturers and browser makers accepted these payments, making Google the path of least resistance for billions of users worldwide.
The economics were brutally simple. Google paid Apple $20 billion annually, representing roughly 21 per cent of Apple's entire services revenue in 2024. In exchange, Google maintained its dominant position in mobile search, where it captured nearly 95 per cent of smartphone searches. For Apple, this represented essentially free money, high-margin revenue that required no product development, no customer support, no operational complexity. The company simply collected a 36 per cent commission on advertising revenue generated from Safari searches.
Judge Amit Mehta, in his landmark August 2024 ruling in United States v. Google LLC, described this arrangement with clinical precision: “Google is a monopolist, and it has acted as one to maintain its monopoly.” The 277-page opinion found that Google's exclusive contracts violated Section 2 of the Sherman Act, maintaining illegal monopoly power in general search services and text advertising markets.
Yet even as the legal system caught up with Google's practices, a more profound transformation was already underway. The rise of large language models and generative AI was creating an entirely new category of digital interaction, one where traditional search might become just one option among many. And the companies positioning themselves for this future weren't waiting for courts to dictate the terms.
When Assistants Get Smart
Apple's June 2024 partnership announcement with OpenAI marked a watershed moment. The integration of ChatGPT, powered by GPT-4o, into iOS, iPadOS, and macOS represented something fundamentally different from the Google search deal. This wasn't about directing queries to an existing search engine; it was about embedding advanced AI capabilities directly into the operating system's fabric.
The deal's structure reveals the shifting economics of the AI era. Unlike the Google arrangement, where billions of dollars changed hands annually, the OpenAI partnership reportedly involves no direct payment from Apple to OpenAI. Instead, OpenAI gains exposure to over one billion potential users across Apple's device ecosystem. Users can access ChatGPT for free without creating an account, and premium ChatGPT subscribers can connect their accounts to access advanced features. For OpenAI, the deal represents a potential path to reaching one billion users, a scale that could transform the company's trajectory.
But here's where it gets interesting. Apple didn't abandon Google when it partnered with OpenAI. The Google search deal continues, meaning Apple now has two horses in the race: traditional search through Google and conversational AI through OpenAI. Siri, Apple's long-struggling digital assistant, can now call upon ChatGPT when it encounters queries beyond its capabilities, whilst maintaining Google as the default search engine for web searches.
This dual-track strategy reflects a crucial truth about the current moment: nobody knows exactly how the search and assistant markets will evolve. Will users prefer traditional search results with AI-generated summaries, as Google is betting with its AI Overviews feature? Or will they migrate to conversational AI interfaces that provide direct answers without traditional web links? Apple's strategy is to cover both scenarios whilst maintaining optionality.
Microsoft, meanwhile, had moved earlier and more aggressively. The company's multi-billion dollar investment in OpenAI, first disclosed in January 2023, gave it exclusive rights to integrate OpenAI's technology into its products. Bing, Microsoft's perennial search underdog, became the first major search engine to integrate GPT-4 directly into search results. The new Bing, announced in February 2023, promised to “reinvent search” by combining traditional web results with AI-generated summaries and conversational interactions.
The Microsoft-OpenAI arrangement differs fundamentally from the Apple-Google model. Rather than simply paying for default placement, Microsoft invested billions directly in OpenAI, reportedly securing 49 per cent of the company's profits until Microsoft recoups its investment. This structure aligns incentives more closely: Microsoft succeeds if OpenAI succeeds, and vice versa. The partnership granted Microsoft exclusive access to OpenAI's models for integration into commercial products, including not just Bing but also Office applications, Windows, and Azure cloud services.
Yet despite the technological leap, Bing's market share remains stubbornly low. Even with AI superpowers, Google's dominance barely budged. Google's search market share dipped below 90 per cent for the first time since 2015 in October 2024, but the company still controlled the vast majority of queries. This stubborn reality underscores a crucial lesson: technological superiority alone doesn't break entrenched defaults and user habits.
The Economics of Digital Gatekeeping
The financial mechanics behind these deals reveal the extraordinary value of controlling access points to digital information. Google paid a total of $26.3 billion in 2021 across all its default search placements, with $20 billion going to Apple alone. To put this in perspective, that's more than the entire annual revenue of many Fortune 500 companies, paid simply to remain the default choice.
These payments work because defaults matter enormously. Research on user behaviour consistently shows that overwhelming majorities never change default settings. When Google is the default search engine, around 95 per cent of users never switch. This makes default placement extraordinarily valuable, justifying multi-billion dollar payments that would seem absurd in a genuinely competitive market.
The business model creates what economists call a two-sided market with network effects. On one side, users generate queries. On the other, advertisers pay for access to those users. Google's dominance in search made it the essential platform for digital advertising, and that dominance was maintained partly through ensuring its search bar appeared everywhere users might look for information.
US search advertising revenues surged 15.9 per cent to reach $102.9 billion in 2024, according to the Interactive Advertising Bureau and PwC annual Internet Advertising Revenue Report. Google captured the lion's share, with search spending on Google rising 10 per cent year-over-year in the fourth quarter of 2024 alone. The average cost per click increased 7 per cent, demonstrating that even as queries grew, the value of each search remained robust.
But the AI revolution threatens to disrupt these economics fundamentally. Generative AI search tools experienced an astonishing 525 per cent revenue growth in 2024, albeit from a small base. More concerning for traditional search, studies found that Google search results featuring AI Overviews saw 34.5 per cent lower clickthrough rates compared to traditional results. When users get their answers directly from AI-generated summaries, they don't click through to websites, which undermines the entire advertising model built on those clicks.
Research firm SparkToro found that roughly 60 per cent of Google searches now end without a click to any website. Gartner predicted that traditional search engine volume will decline by 25 per cent by 2026 due to AI chatbot applications. If these trends continue, the entire economic foundation of search advertising could crumble, making those multi-billion dollar default placement deals look like investments in a declining asset.
This creates a fascinating strategic dilemma for companies like Google. The company must integrate AI features to remain competitive and meet user expectations for more sophisticated answers. Yet every AI-generated summary that satisfies a user's query without requiring a click potentially destroys a small amount of advertising value. Google is essentially forced to cannibalise its own business model to prevent competitors from doing it first.
New Street Research estimated that AI Overviews advertising would account for just 1 per cent of Google's search advertising revenues in 2025, growing to 3 per cent in 2026. But this gradual integration masked deeper uncertainties about long-term monetisation. How do you sell advertising against conversational AI interactions that don't involve clicking on links? Google's experiments with embedding ads directly in AI-generated summaries provided one answer, but it remained unclear whether users would accept this model or whether advertisers would pay comparable rates for these new formats.
The Regulatory Hammer Falls
Into this already complex landscape came regulators, wielding antitrust law with renewed vigour. Judge Mehta's August 2024 ruling that Google maintained an illegal monopoly in search triggered a lengthy remedies process, culminating in a May 2025 trial to determine how to restore competition.
The Department of Justice initially proposed aggressive remedies. The DOJ called for Google to divest Chrome, its web browser, and to end exclusive distribution agreements with device makers like Apple and Samsung. The department argued that only structural separation could prevent Google from using its control over key distribution channels to maintain its search monopoly.
Apple moved to intervene in the case, filing motions to defend its “contractual interests” in the Google relationship. The company argued that the Justice Department's efforts would harm consumers and stifle innovation, particularly in artificial intelligence. The filing revealed Apple's dependence on this revenue stream; analysts at J.P. Morgan estimated Apple faced a potential $12.5 billion annual revenue hit if courts forced Google to stop making payments.
The eventual ruling, delivered in September 2025, split the difference. Judge Mehta prohibited Google from entering or maintaining exclusive contracts relating to search distribution but stopped short of requiring Chrome's divestiture. Critically, the ruling allowed Google to continue making payments to partners, just not under exclusive terms. Apple and other partners would need to offer users genuine choices, but they could still receive payments for making Google one available option.
The ruling represented a partial victory for Apple and Google's business relationship whilst establishing important guardrails. As Judge Mehta noted, “Cutting off payments from Google almost certainly will impose substantial, in some cases, crippling, downstream harms to distribution partners.” Mozilla, maker of the Firefox browser, had revealed that search engine royalties totalled $510 million against total revenue of just $594 million in 2022, illustrating the existential dependence some companies had developed on these payments.
Across the Atlantic, European regulators took a different approach. The Digital Markets Act, which came into force in March 2024, designated six companies as “gatekeepers”: Alphabet, Amazon, Apple, ByteDance, Meta, and Microsoft. These companies faced strict obligations to enable interoperability, prohibit self-preferencing, and provide fair access to their platforms.
The European Commission opened non-compliance investigations against Alphabet, Apple, and Meta in March 2024. The Commission expressed concern that Alphabet's search preferenced its own vertical services, such as Google Shopping and Google Hotels, over rival offerings. By March 2025, the Commission had informed Alphabet that Google search treated the company's services more favourably than competitors, a violation of DMA provisions.
The DMA's approach differed from US antitrust enforcement in important ways. Rather than requiring proof of market harm through lengthy litigation, the DMA imposed ex ante obligations on designated gatekeepers, shifting the burden to these platforms to demonstrate compliance. Penalties could reach 10 per cent of global annual revenue for violations, or 20 per cent for repeated infringements. The Commission fined Apple €500 million and Meta €200 million in April 2025 for non-compliance.
Critically, the DMA required gatekeepers like Google to share data useful for training search models, potentially lowering barriers for alternative search engines. This provision acknowledged that in the AI era, access to training data mattered as much as access to users. A search engine couldn't compete effectively without both the scale to attract users and the data to train increasingly sophisticated AI models.
The Small Players' Dilemma
For smaller search engines and AI model providers, these backend deals and regulatory interventions created a complex and often contradictory landscape. Companies like DuckDuckGo and Ecosia had built businesses around privacy-focused search, capturing small but loyal user bases. DuckDuckGo held a 0.63 per cent worldwide market share, whilst Ecosia claimed 0.11 per cent.
But these alternative search engines faced a fundamental problem: they didn't actually operate their own search infrastructure. DuckDuckGo sourced its main search results from Bing and Yahoo. Ecosia's search content and advertisements came from Bing. This dependence on larger tech companies for backend infrastructure limited their ability to truly differentiate and left them vulnerable to changes in these upstream relationships.
The barrier to entry for building a competitive search index was immense. Google had spent decades and tens of billions of dollars crawling the web, indexing pages, and refining ranking algorithms. Microsoft's Bing represented a similar massive investment. Smaller players simply couldn't match this scale of infrastructure investment and ongoing operational costs.
In November 2024, Ecosia and Qwant announced a partnership to build a European search index, explicitly aiming to reduce dependence on US technology companies. The initiative acknowledged that the Digital Markets Act's requirement for Google to share data provided an opening, but it would take years and substantial investment to build a competitive alternative index.
The shift towards generative AI created additional barriers for smaller players. Training large language models required not just vast amounts of data but also expensive computing infrastructure. Smaller AI firms often faced 12 to 18-month wait times for GPU delivery, whilst well-capitalised hyperscalers secured priority access to scarce H100 and next-generation G100 accelerators through billion-dollar pre-purchase contracts.
Cloud infrastructure dependency compounded these challenges. Smaller AI companies weren't just running on the cloud; they were locked into it. Big Tech companies structured deals to ensure that partner rollouts were routed through their cloud infrastructure, creating additional revenue streams and control points. A startup building on Amazon's Bedrock platform or Microsoft's Azure AI services generated ongoing cloud computing fees for these giants, even if it charged end-users directly.
Yet open-source models provided some countervailing force. Over 50 per cent of foundation models were available with open weights, meaning an AI startup could download a state-of-the-art model and build on it rather than investing millions training from scratch. Meta's Llama models, Mistral's offerings, and numerous other open alternatives lowered barriers to entry for application developers, even if training truly frontier models remained the province of well-funded labs.
The Apple-OpenAI deal illustrated both the opportunities and limitations for AI startups in this environment. On one hand, OpenAI's access to over a billion Apple devices represented extraordinary distribution that no startup could hope to match independently. On the other, the deal didn't provide OpenAI with direct payment from Apple, relying instead on the assumption that exposure would drive premium subscriptions and enterprise deals.
For smaller AI model providers, securing similar distribution deals appeared nearly impossible. Anthropic, despite raising billions from both Amazon and Google, took a different path, focusing on enterprise partnerships with companies like Cognizant, Salesforce, and Palantir rather than pursuing consumer platform deals. Anthropic's strategy reflected a pragmatic assessment that without Apple or Google-scale consumer platforms, the path to scale ran through business customers and cloud marketplaces.
Amazon's $4 billion investment in Anthropic, completed in March 2024, illustrated the deepening vertical integration between cloud providers and AI model developers. The investment gave Anthropic capital and guaranteed compute access through Amazon Web Services, whilst Amazon gained a competitive AI offering for its cloud customers. Similar dynamics played out with Google's investments in Anthropic and Microsoft's OpenAI partnership.
These investment structures created a new kind of gatekeeping. If the major cloud providers each had preferred AI partners, smaller model developers might struggle to secure both the computing resources needed for training and the distribution channels necessary for reaching customers. The market appeared to be consolidating into a handful of vertically integrated stacks: Microsoft-OpenAI, Google-Anthropic-Google's own models, Amazon-Anthropic, and Apple's multi-partner approach.
Search Monetisation in the AI Era
The transition from traditional search to AI-powered experiences raised fundamental questions about monetisation. The old model was straightforward: users entered queries, search engines displayed results along with relevant advertisements, and advertisers paid per click. This generated enormous revenues because queries signalled clear intent, making search advertising uniquely valuable.
AI-powered interactions threatened to disrupt this model in multiple ways. When a user asked ChatGPT or Claude a question and received a comprehensive answer, no advertisement appeared, and no advertiser paid anyone. The AI companies were essentially providing information services without a clear revenue model beyond subscription fees and enterprise licensing.
Google faced this challenge most acutely. The company had begun rolling out AI Overviews, which used generative AI to provide summaries at the top of search results. These summaries answered many queries directly, reducing the need for users to click through to websites. Studies found that clicks for URLs included in AI Overviews decreased by 8.9 per cent compared to when they appeared as normal search result links.
For publishers and websites that relied on search traffic, this was potentially catastrophic. If AI systems summarised content without driving clicks, the entire ecosystem of ad-supported content faced an existential threat. This explained the wave of licensing deals between AI companies and publishers throughout 2024.
OpenAI signed content licensing deals with News Corp (reportedly worth over $250 million over five years), The Atlantic, Condé Nast, and Hearst. Microsoft signed deals with the Financial Times, Reuters, Axel Springer, and USA Today Network for its Copilot Daily feature. Google signed its first publisher deal with the Associated Press in January 2025. Amazon courted publishers for its reinvented Alexa, securing a deal with The New York Times.
These deals typically involved two components: one-off payments for training rights to historical content, and ongoing variable payments for featuring current content with attribution. Axel Springer's $25 million deal with OpenAI, for instance, included both a training payment and backend fees based on usage.
The licensing deals served multiple purposes. They provided AI companies with high-quality training data and current information to improve model accuracy. They gave publishers new revenue streams to offset declining search traffic and programmatic advertising revenue. And they began establishing a new economic model for the AI era, where content creators received compensation for their contributions to AI training and operation.
But the deals also raised competitive concerns. If only the largest, best-funded AI companies could afford expensive licensing arrangements with major publishers, smaller model providers faced yet another barrier to competing effectively. The cost of content licensing could become a significant moat, favouring incumbents over startups.
Moreover, these deals didn't solve the fundamental monetisation challenge. Even with licensed content, AI companies still needed business models beyond subscriptions. ChatGPT Plus cost $20 per month, whilst enterprise deals commanded higher rates, but it wasn't clear whether subscription revenue alone could support the massive computing costs of running large language models at scale.
Advertising remained the obvious answer, but integrating advertisements into conversational AI experiences proved challenging. Users had grown accustomed to ad-free interactions with ChatGPT and Claude. Introducing advertisements risked degrading the user experience and driving users to competitors. Yet without advertising or equivalently robust revenue models, it wasn't clear how these services could achieve sustainable profitability at massive scale.
Google's experiments with advertising in AI Overviews represented one potential path forward. By embedding contextually relevant product recommendations and sponsored content within AI-generated summaries, Google aimed to preserve advertising revenue whilst providing the enhanced experiences users expected. But clickthrough rates remained lower than traditional search advertising, and it remained to be seen whether advertisers would pay comparable rates for these new formats.
The average ad spending per internet user in the Search Advertising market was estimated at $58.79 globally in 2025. For AI-powered experiences to generate comparable revenue, they would need to capture similar or greater value per interaction. This seemed plausible for high-intent commercial queries but much harder for informational searches where users simply wanted answers without purchase intent.
Collaboration, Competition, and Consolidation
The deals between platform owners and AI providers, search engines and publishers, and cloud providers and model developers painted a picture of an industry in flux. Old competitive boundaries were dissolving as former rivals became strategic partners whilst ostensibly collaborating companies competed in adjacent markets.
Apple's dual strategy with Google and OpenAI exemplified this complexity. The company maintained its lucrative search deal with Google whilst simultaneously partnering with Google's primary AI competitor. This hedging strategy made sense during a transition period when the ultimate shape of user behaviour remained uncertain. But it also created tensions: how would Apple balance these relationships if Google's search and OpenAI's ChatGPT increasingly competed for the same queries?
The regulatory environment added further complexity. The September 2025 ruling allowed Google to continue making payments whilst prohibiting exclusivity, but the practical implementation remained unclear. How would Apple, Samsung, and other partners implement genuine choice mechanisms? Would users face decision fatigue from too many options, leading them to stick with familiar defaults anyway?
The European Digital Markets Act's more prescriptive approach demanded specific interoperability and data-sharing requirements, but enforcement remained challenging. The Commission's investigations and fines demonstrated willingness to punish non-compliance, yet the underlying market dynamics favouring scale and integration proved hard to counteract through regulation alone.
For smaller companies, the landscape appeared increasingly difficult. The combination of infrastructure barriers, data access challenges, capital requirements, and distribution bottlenecks created formidable obstacles. Open-source models provided some relief, but the gap between open models and the capabilities of frontier systems from OpenAI, Google, and Anthropic remained substantial.
The venture capital funding environment for AI startups remained robust, with billions flowing into the sector. But increasingly, strategic investments from cloud providers and large tech companies dominated financing rounds. These investments came with strings attached: compute credits tied to specific cloud platforms, distribution channels through investor platforms, and expectations about technology stack choices. The apparent abundance of capital masked a reality where meaningful independence from the major platforms became harder to maintain.
Industry consolidation appeared likely to continue. Just as the cloud infrastructure market concentrated into three major players (Amazon, Microsoft, and Google), the AI model and digital assistant markets seemed headed towards a similarly concentrated structure. The economics of scale in training, the advantages of vertical integration between models and distribution, and the network effects from user data all pushed towards consolidation.
Yet genuine innovation remained possible around the edges. Specialised models for specific domains, novel interaction paradigms, privacy-focused alternatives, and open-source collaboration all represented paths where smaller players could potentially carve out sustainable niches. The challenge was whether these niches could grow large enough to represent genuine alternatives to the dominant platforms.
The New Digital Divide
The backend deals reshaping search and digital assistants represent more than business arrangements between wealthy corporations. They reflect and reinforce a fundamental divide in the digital economy between companies with platform power and everyone else. Those controlling the devices people use, the operating systems running on those devices, and the default experiences presented to users wield extraordinary influence over which technologies succeed and which fail.
The $20 billion annual payment from Google to Apple isn't just a revenue stream; it's a tax on search monetisation that Google pays to maintain access to Apple's users. The multi-billion dollar investments in OpenAI and Anthropic aren't just capital allocations; they're defensive moats ensuring that Microsoft, Amazon, and Google maintain positions in whatever AI-powered future emerges.
For users, these deals often bring genuine benefits: better integrated experiences, more sophisticated capabilities, and services they can access without explicit payment. Apple users gained ChatGPT integration without monthly fees. Google users received AI-enhanced search results at no additional cost. The major platforms competed partly by giving away AI-powered features that would have seemed miraculous just years earlier.
Yet this largesse came with less visible costs. Competition constrained by billion-dollar barriers to entry was less vigorous than it might otherwise be. Innovation from smaller players struggled to reach users trapped behind platform gatekeepers. And the concentration of power in a handful of companies created systemic risks and governance challenges that societies were still learning to address.
The regulatory response, whilst increasingly aggressive, struggled to keep pace with market evolution. By the time courts ruled on Google's search monopoly, the market was already transitioning towards AI-powered experiences that might render traditional search less central. The remedies imposed risked fighting the last war whilst the next one had already begun.
Looking forward, the competitive dynamics for digital assistants and search monetisation will likely reflect broader patterns of platform power and vertical integration. Success will depend not just on building superior technology but on securing access to users, training data, computing infrastructure, and content licensing. The backend deals determining these access points will shape which companies thrive and which struggle to compete.
The market isn't winner-take-all, but neither is it a level playing field where merit alone determines outcomes. Platform power, network effects, capital resources, and strategic partnerships create strong advantages for incumbents and favourably positioned challengers. Smaller players can succeed, but increasingly only in partnership with or in niches uncontested by the major platforms.
For regulators, the challenge will be balancing the genuine benefits of integration and scale against the competitive and innovation harms from excessive concentration. Neither the US antitrust approach nor the EU's ex ante regulatory framework has yet found the right balance, and both will likely require continued adaptation as markets evolve.
The billion-dollar handshakes between platform owners and AI providers aren't ending anytime soon. They're evolving, becoming more sophisticated, and extending into new areas as the technological landscape shifts. Understanding these deals and their implications matters not just for industry insiders but for anyone concerned with how power, innovation, and value are distributed in the digital economy. The search bar on your phone isn't just a tool for finding information; it's a battleground where the future of digital interaction is being determined, one lucrative partnership at a time.
Sources and References
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Tim Green UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk