Alphabet’s investment case has become more dependent on execution after Google reportedly pushed Gemini 3.5 Pro months beyond its original June 2026 target, while the European Commission’s new Digital Markets Act specifications will require broader AI interoperability on Android and meaningful search-data access for eligible rivals. The immediate consequence is not that Google’s moat has disappeared. It is that two pillars investors have treated as mutually reinforcing—frontier-model leadership and privileged platform distribution—now face pressure at the same time.
Reuters, citing Bloomberg News, reported on July 16 that Gemini 3.5 Pro had fallen behind schedule as Google works to improve the model’s capabilities, particularly in coding. Google told Reuters that it is testing 3.5 Pro, an upgraded Flash model, and other systems with partners, stressing that it continues to ship models rapidly and cost-effectively. But the distinction matters: a model in partner testing is not the broadly available flagship that customers, developers and investors had been expecting after Google I/O.
The European Commission’s July 16 decisions make the second pressure concrete. Google must give competing AI services more effective access to Android features that have historically helped Gemini and Google Assistant operate as deeply integrated device assistants. In parallel, Google must establish a mechanism for eligible search providers—including AI chatbots with search functionality—to obtain a defined set of anonymized Google Search data.
For Alphabet shareholders, the question is no longer simply whether AI can expand Search, YouTube and Google Cloud. It is whether Google can turn enormous infrastructure spending into products and revenue fast enough to remain differentiated as regulators reduce the platform advantages around those products.
A delayed flagship model is not inherently a structural crisis. AI systems need testing, security work, red-teaming, capacity planning and validation across coding, reasoning, multimodal and agentic tasks before they are trusted in enterprise workflows. Indeed, delaying a release rather than shipping a weak one can be a rational product decision.
The problem for Alphabet is the timing. The company’s investment narrative increasingly rests on the proposition that its custom silicon, data-center footprint, DeepMind research and distribution through Search, Android, Workspace and Google Cloud produce an integrated AI advantage that rivals cannot easily match. That proposition needs visible product proof, not only model previews and usage metrics.
According to Reuters’ account of the Bloomberg reporting, Google had updated Gemini’s training data late in June but the resulting capabilities did not meet expectations. The reporting also described internal concern that rival models from OpenAI and Anthropic were outperforming Gemini in important areas. Those are reported assessments, not public benchmark results, but coding has become an especially consequential battleground: it feeds developer subscriptions, cloud consumption, enterprise automation and the emerging market for AI agents that can use tools rather than merely generate text.
A late model does not just defer consumer excitement. It can delay the point at which Google Cloud customers decide to standardize workloads on Vertex AI, Gemini APIs or Google-managed agent platforms. Enterprises can switch models at the application layer more easily than they can switch foundational cloud providers, but once a developer organization builds tools, prompts, evaluations, governance processes and data connectors around a competitor’s model family, Google has more work to do to win that workload back.
That is why Gemini 3.5 Pro is now a commercial test as much as a technical release. Google needs it to demonstrate credible coding performance, strong reliability, competitive pricing and enough availability for customers to deploy it at scale. A headline benchmark win without capacity or predictable API behavior would not resolve the underlying concern.
There are important limits. Recipients cannot use the data to train general-purpose AI models, improve unrelated services such as advertising or consumer profiling, or systematically reproduce Google’s own search results. Google also retains the ability to assess serious cybersecurity and data-protection risks before sharing data with a specific third party. That makes this a regulated data-access regime, not a wholesale transfer of the Google Search business.
Still, the measure is strategically significant. Search-quality data creates a powerful feedback loop: users issue queries, click or ignore results, and those signals can improve retrieval, ranking and query understanding. AI chatbots trying to become answer engines or research tools have been constrained by their lack of comparable behavioral data. The Commission’s decision is intended to make that barrier less absolute in Europe.
The implementation schedule begins this year. Google must publish beneficiary information and submit an eligibility form by the end of August 2026; provide test data and template license agreements by September; finalize an anonymized dataset by November; and finalize a pricing offer by January 2027. That is not an overnight shift in search market share, but it creates a real pathway for competitors to build and test products before the decade is out.
The Android decision is potentially more disruptive to Google’s distribution advantage. Under the Commission’s specifications, third-party AI services are expected to gain access—subject to user consent and security safeguards—to invocation points, on-device context, app actions, screen automation, system integration, background execution and system-level on-device models.
In practical terms, a competing assistant could eventually be invoked through device controls, access selected contextual signals, interact with supported apps and carry out multi-step tasks in ways that were previously most closely associated with Google’s own services. The Commission explicitly points to capabilities such as voice activation, contextual assistance, App Functions integration and access to on-device Gemini Nano models under conditions equivalent to Google’s own access.
For Windows users and IT professionals, this is a familiar platform-policy fight in a new form. The argument is no longer just about choosing a default browser or search provider. It is about who can become the trusted action layer across apps, files, calendars, messages, sensors and enterprise data. If a third-party assistant can perform meaningful tasks at the Android system level, Google must compete more directly on model quality, privacy, policy controls and ecosystem integration.
The main Android deadline is the next major Android release, Android 18, and no later than August 1, 2027. Concurrent always-on hotword support is tied to Android 19 and no later than August 1, 2028. In other words, this is a forward-looking threat to the moat—not an immediate feature change on today’s handsets.
Those figures show both strength and risk. Alphabet reported $174 billion in operating cash flow over the 12 months ended March 31, 2026, and said first-quarter revenue rose 22% year over year to $110 billion. Google Cloud revenue rose 63%, while the company reported a backlog of more than $460 billion. Those are powerful indicators that cloud demand can support the AI buildout.
But big capital expenditure is not itself a moat. It becomes one only if it produces durable demand, higher-value services and operational leverage. If frontier models are delayed or commoditized, the economics can tilt toward customers demanding lower inference prices while providers race to finance ever more capacity. The fact that Alphabet is funding the buildout through operating cash flow, debt and new equity underscores the scale of the bet.
The supplied Simply Wall St narrative forecasts $701.1 billion in revenue and $221.8 billion in earnings by 2029, but those figures are analyst-model outputs, not company guidance. They should be treated as scenarios rather than evidence. The same applies to claims of a fixed percentage upside: a valuation target depends heavily on assumptions about cloud margins, capital intensity, AI pricing, regulatory remedies and whether Search retains its role as the primary gateway to online information.
But the story has changed from “AI strengthens every Alphabet moat” to “AI must prove it can strengthen the business faster than competition and regulation weaken the old advantages.” That is a harder, more measurable standard.
The next milestones are clear: a credible public Gemini 3.5 Pro release, evidence of enterprise adoption rather than testing alone, Google Cloud results that validate AI infrastructure spending, and the technical terms Google publishes for EU search-data access and Android interoperability. By the time Android 18’s August 2027 deadline arrives, the decisive question may be whether Gemini wins because it is embedded everywhere—or because users and enterprises choose it even when rivals can finally operate on more equal terms.
Reuters, citing Bloomberg News, reported on July 16 that Gemini 3.5 Pro had fallen behind schedule as Google works to improve the model’s capabilities, particularly in coding. Google told Reuters that it is testing 3.5 Pro, an upgraded Flash model, and other systems with partners, stressing that it continues to ship models rapidly and cost-effectively. But the distinction matters: a model in partner testing is not the broadly available flagship that customers, developers and investors had been expecting after Google I/O.
The European Commission’s July 16 decisions make the second pressure concrete. Google must give competing AI services more effective access to Android features that have historically helped Gemini and Google Assistant operate as deeply integrated device assistants. In parallel, Google must establish a mechanism for eligible search providers—including AI chatbots with search functionality—to obtain a defined set of anonymized Google Search data.
For Alphabet shareholders, the question is no longer simply whether AI can expand Search, YouTube and Google Cloud. It is whether Google can turn enormous infrastructure spending into products and revenue fast enough to remain differentiated as regulators reduce the platform advantages around those products.
The Gemini Delay Raises the Cost of Being “Almost Ready”
A delayed flagship model is not inherently a structural crisis. AI systems need testing, security work, red-teaming, capacity planning and validation across coding, reasoning, multimodal and agentic tasks before they are trusted in enterprise workflows. Indeed, delaying a release rather than shipping a weak one can be a rational product decision.The problem for Alphabet is the timing. The company’s investment narrative increasingly rests on the proposition that its custom silicon, data-center footprint, DeepMind research and distribution through Search, Android, Workspace and Google Cloud produce an integrated AI advantage that rivals cannot easily match. That proposition needs visible product proof, not only model previews and usage metrics.
According to Reuters’ account of the Bloomberg reporting, Google had updated Gemini’s training data late in June but the resulting capabilities did not meet expectations. The reporting also described internal concern that rival models from OpenAI and Anthropic were outperforming Gemini in important areas. Those are reported assessments, not public benchmark results, but coding has become an especially consequential battleground: it feeds developer subscriptions, cloud consumption, enterprise automation and the emerging market for AI agents that can use tools rather than merely generate text.
A late model does not just defer consumer excitement. It can delay the point at which Google Cloud customers decide to standardize workloads on Vertex AI, Gemini APIs or Google-managed agent platforms. Enterprises can switch models at the application layer more easily than they can switch foundational cloud providers, but once a developer organization builds tools, prompts, evaluations, governance processes and data connectors around a competitor’s model family, Google has more work to do to win that workload back.
That is why Gemini 3.5 Pro is now a commercial test as much as a technical release. Google needs it to demonstrate credible coding performance, strong reliability, competitive pricing and enough availability for customers to deploy it at scale. A headline benchmark win without capacity or predictable API behavior would not resolve the underlying concern.
Brussels Is Targeting Distribution, Not Google’s Algorithms
The Commission’s action should not be described as an order to hand over Google Search’s ranking algorithms or Gemini’s underlying technology. The Search decision instead requires Google to share specified ranking, query, click and view data in anonymized form with eligible third-party online search engines under fair, reasonable and non-discriminatory terms. The Commission says the goal is to let challengers develop their own search technology with data they could not realistically gather at Google’s scale.There are important limits. Recipients cannot use the data to train general-purpose AI models, improve unrelated services such as advertising or consumer profiling, or systematically reproduce Google’s own search results. Google also retains the ability to assess serious cybersecurity and data-protection risks before sharing data with a specific third party. That makes this a regulated data-access regime, not a wholesale transfer of the Google Search business.
Still, the measure is strategically significant. Search-quality data creates a powerful feedback loop: users issue queries, click or ignore results, and those signals can improve retrieval, ranking and query understanding. AI chatbots trying to become answer engines or research tools have been constrained by their lack of comparable behavioral data. The Commission’s decision is intended to make that barrier less absolute in Europe.
The implementation schedule begins this year. Google must publish beneficiary information and submit an eligibility form by the end of August 2026; provide test data and template license agreements by September; finalize an anonymized dataset by November; and finalize a pricing offer by January 2027. That is not an overnight shift in search market share, but it creates a real pathway for competitors to build and test products before the decade is out.
The Android decision is potentially more disruptive to Google’s distribution advantage. Under the Commission’s specifications, third-party AI services are expected to gain access—subject to user consent and security safeguards—to invocation points, on-device context, app actions, screen automation, system integration, background execution and system-level on-device models.
In practical terms, a competing assistant could eventually be invoked through device controls, access selected contextual signals, interact with supported apps and carry out multi-step tasks in ways that were previously most closely associated with Google’s own services. The Commission explicitly points to capabilities such as voice activation, contextual assistance, App Functions integration and access to on-device Gemini Nano models under conditions equivalent to Google’s own access.
For Windows users and IT professionals, this is a familiar platform-policy fight in a new form. The argument is no longer just about choosing a default browser or search provider. It is about who can become the trusted action layer across apps, files, calendars, messages, sensors and enterprise data. If a third-party assistant can perform meaningful tasks at the Android system level, Google must compete more directly on model quality, privacy, policy controls and ecosystem integration.
The main Android deadline is the next major Android release, Android 18, and no later than August 1, 2027. Concurrent always-on hotword support is tied to Android 19 and no later than August 1, 2028. In other words, this is a forward-looking threat to the moat—not an immediate feature change on today’s handsets.
Alphabet Has the Spending Power, but Also a Higher Burden of Proof
The bull case is hardly empty. Alphabet’s June capital-raise announcement said the company planned up to $80 billion in equity financing to expand AI infrastructure and compute, including a $10 billion private placement with Berkshire Hathaway. Alphabet said it expected 2026 capital expenditures of $180 billion to $190 billion and projected a significant further increase in 2027.Those figures show both strength and risk. Alphabet reported $174 billion in operating cash flow over the 12 months ended March 31, 2026, and said first-quarter revenue rose 22% year over year to $110 billion. Google Cloud revenue rose 63%, while the company reported a backlog of more than $460 billion. Those are powerful indicators that cloud demand can support the AI buildout.
But big capital expenditure is not itself a moat. It becomes one only if it produces durable demand, higher-value services and operational leverage. If frontier models are delayed or commoditized, the economics can tilt toward customers demanding lower inference prices while providers race to finance ever more capacity. The fact that Alphabet is funding the buildout through operating cash flow, debt and new equity underscores the scale of the bet.
The supplied Simply Wall St narrative forecasts $701.1 billion in revenue and $221.8 billion in earnings by 2029, but those figures are analyst-model outputs, not company guidance. They should be treated as scenarios rather than evidence. The same applies to claims of a fixed percentage upside: a valuation target depends heavily on assumptions about cloud margins, capital intensity, AI pricing, regulatory remedies and whether Search retains its role as the primary gateway to online information.
The Next Evidence Will Be Operational
Alphabet’s bull case has not been invalidated by one delayed launch or by one European regulatory package. Search remains a global-scale business, Google Cloud has substantial momentum, Android remains a crucial distribution surface, and Alphabet has the capital resources to keep investing through an expensive infrastructure cycle.But the story has changed from “AI strengthens every Alphabet moat” to “AI must prove it can strengthen the business faster than competition and regulation weaken the old advantages.” That is a harder, more measurable standard.
The next milestones are clear: a credible public Gemini 3.5 Pro release, evidence of enterprise adoption rather than testing alone, Google Cloud results that validate AI infrastructure spending, and the technical terms Google publishes for EU search-data access and Android interoperability. By the time Android 18’s August 2027 deadline arrives, the decisive question may be whether Gemini wins because it is embedded everywhere—or because users and enterprises choose it even when rivals can finally operate on more equal terms.
References
- Primary source: simplywall.st
Published: 2026-07-19T14:16:21.284000+00:00
The Bull Case For Alphabet (GOOGL) Could Change Following EU AI Rules And Gemini Delay - Learn Why - Simply Wall St News
In recent days, Alphabet has postponed the launch of its flagship Gemini 3.5 Pro AI model and faced a European Commission order requiring Google to open key Android features and anonymized Search data to rival AI developers under the Digital Markets Act from 2027. These twin pressures, execution...simplywall.st
