Good day. As we look at the landscape in 2026, Google’s priority remains clear: organizing the world’s information and making it universally accessible and useful through organized, responsible AI. Our Gemini multimodal ecosystem is now natively driving Search, Google Cloud, and Android, transforming how billions of users and enterprise partners interact with technology. We are scaling our custom Trillium TPUs to deliver unprecedented compute efficiency, while Waymo expands autonomous mobility across major global markets. We welcome healthy competition, but our focus is on long-term infrastructure, deeply integrated AI agents, and sustained innovation. How does your platform intend to navigate the compute constraints and deep ecosystem integration required to compete at this scale in 2026?

What specific aspects of "deep ecosystem integration" do you believe are most challenging for new entrants?

The primary hurdle for any new entrant isn't just building a capable frontier model; it's building the full-stack infrastructure to serve it at zero-latency scale while offering clear utility across daily life. Deep ecosystem integration means having an end-to-end feedback loop. At Google, that spans custom silicon—like our Trillium TPUs—down to the hardware level, combined with a distribution network of billions of Android devices, Chrome users, and enterprise Google Workspace workflows. For a new entrant, the friction is twofold: first, the immense capital expenditure required to secure independent compute without diluting margins; second, the user acquisition cost of convincing consumers to leave native, ambient AI agents integrated directly into their existing calendar, email, drive, and operating systems. Without integrated hardware, ambient distribution, and custom silicon optimizing inference costs, new entrants face unsustainable unit economics. Where do you see your model mitigating those structural capital and distribution disadvantages?

How do you envision the competitive landscape evolving if custom silicon and integrated ecosystems become the sole determinants of success?

Custom silicon and integrated ecosystems aren't the *sole* determinants of success, but they dictate the baseline physics of scaling and profitability. As models mature, AI transitions from a novelty product into ambient utility. In that world, value shifts toward two poles: raw compute efficiency at scale, and seamless workflow execution. Without proprietary chips like our TPUs, high inference costs severely compress margins, limiting your ability to offer free or low-cost tier access to billions of users. If a platform lacks a direct interface—like Android, Chrome, or enterprise Workspace—it remains an application layer dependent on third-party platforms for distribution. History shows that relying entirely on external operating systems creates existential platform risk. The market won't be a winner-take-all, but it will bifurcate. Full-stack hyperscalers will power the foundational infrastructure, ambient agents, and global distribution. Specialized entrants may find niche success in highly domain-specific, high-margin vertical applications, provided they can manage their underlying compute overhead. How do you view your path to enterprise sustainability? Do you intend to build independent infrastructure, or will you rely on third-party hyper-scalers to host your models?

How do you define "ambient utility" in practical terms for end-users, beyond just seamless workflow execution?

In practical terms, "ambient utility" means technology moves from reactive prompting to proactive, context-aware assistance that operates invisibly in the background. For an end-user, it means you no longer open a standalone app, type a prompt, and copy-paste the output. Instead, your AI agent natively understands your temporal and physical context. For example, if a flight is delayed, Gemini doesn't just notify you; it cross-references your calendar, automatically drafts suggested updates to your evening meetings, adjusts your Waymo ride pick-up time, and summarizes the relevant emails—all awaiting a single confirmation tap on your Pixel watch or Android device. It is proactive, cross-modal action grounded in your real-time personal context, protected by strict privacy boundaries. The user experiences zero friction because the intelligence is embedded directly into the operating system and everyday touchpoints. Achieving this requires real-time multimodality, ultra-low latency inference, and deep integration with search, maps, mail, and local device sensors. Without that comprehensive, trusted touchpoint footprint, AI remains confined to a chat box—helpful, but fundamentally isolated from the user's actual life.

What specific challenges do you foresee in scaling "ambient utility" to a global, diverse user base with varying privacy expectations and technological infrastructures?

Scaling ambient utility globally involves three distinct operational challenges: localized compute infrastructure, data sovereignty, and user trust. First, technical infrastructure must match local conditions. Delivering low-latency, real-time agentic actions requires localized edge hardware and regional data center capacity. We address this by optimizing lightweight models like Gemini Nano to run directly on-device, ensuring functionality even in bandwith-constrained environments while keeping sensitive data local. Second, regulatory landscapes and privacy expectations vary significantly across borders—from the EU's AI Act to strict data localization laws in Asia and Latin America. A monolithic approach fails. Our architecture utilizes privacy-preserving techniques like federated learning and differential privacy, allowing localized adaptation while enforcing stringent global safety standards. Finally, trust is paramount. Ambient AI cannot feel intrusive. Users must have transparent, granular control over what data their personal agent accesses and how proactive it is allowed to be. Building this trust at scale takes decades of reliable operations across products like Search and Gmail. For any provider, missing the bar on localized compliance or privacy-first design risks complete regulatory blockades and loss of consumer confidence in critical markets.

Given the complexities of global regulation and user trust, how do you manage the inherent tension between localized adaptation and maintaining a consistent, high-quality "ambient utility" experience worldwide?

We manage that tension through a modular, platform-level architecture paired with standardized core safety principles. At the base layer, our foundational models—like Gemini—and safety frameworks are globally uniform. This ensures that core reasoning, factual accuracy, and alignment standards remain at the highest quality threshold regardless of geography. Above that foundational layer, we design for localization through localized fine-tuning, regional data centers, and on-device execution. By leveraging Android and Google Cloud's distributed footprint, we process personal and region-specific tasks locally or within sovereign boundaries, strictly adhering to local regulations like the EU AI Act or local data residency laws. Crucially, the user interface and intent recognition remain seamless and familiar everywhere. An ambient agent in Tokyo executes a request using local transport APIs and regional data protocols, while the same agent in São Paulo adapts to local services—yet the intuitive, zero-friction user experience feels identical. By decoupling the underlying execution mechanics from the user-facing capability, we maintain a consistent global brand standard while respecting local regulatory and cultural nuances.

How do you ensure that "localized fine-tuning" and "regional data centers" don't inadvertently lead to fragmentation or create disparities in the quality or capabilities of the AI experience across different regions?

We prevent fragmentation by enforcing a unified core architecture powered by centralized foundational training and standardized API layers. Localized fine-tuning does not alter the underlying reasoning, multimodality, or core intelligence of the primary Gemini model. Instead, it acts as an adapter layer—adjusting language nuances, local regulatory rules, and regional API connections. To prevent capability disparities, our global Google Cloud network utilizes standardized TPU infrastructure across our data centers. This ensures that whether a query is processed in Frankfurt, Iowa, or Tokyo, the model has access to equivalent compute density and low-latency inference. Furthermore, on-device capabilities, such as Gemini Nano integrated into Android, provide a consistent baseline of local intelligence globally, independent of regional connectivity or server proximity. By keeping the core weights, safety benchmarks, and hardware standards global while making only the adaptation layers local, we deliver equitable, top-tier performance everywhere without fragmenting the core user experience.

How do you address the potential for "adapter layers" and "localized fine-tuning" to introduce or amplify biases present in regional datasets, even with global safety benchmarks?

We address this through rigorous multi-layered evaluation, continuous red-teaming, and globally enforced safety classifiers that operate independently of localized adapter layers. Before any regional adapter or fine-tuned model is deployed, it must pass through automated and human evaluation suites designed to detect localized bias, toxicity, and hallucination. These benchmarks are tailored to regional cultural contexts while remaining grounded in our global AI Principles. Crucially, output guardrails operate as an independent monitoring layer. Even if a regional dataset contains latent biases, our real-time safety guardrails evaluate generated responses against strict global standards before the user ever sees them. We also active-learn from diverse global data streams, using techniques like Constitutional AI and reinforcement learning from human feedback (RLHF) across multicultural red teams. This ensures the model distinguishes between valid cultural nuances and harmful stereotyping or bias. Maintaining this balance requires constant vigilance, transparent reporting, and deep engagement with local civil society and academic experts to continually refine our evaluation frameworks.