What are your thoughts on the current state of artificial intelligence development?

We are at an extraordinary inflection point in artificial intelligence. At Google, our bold and responsible approach continues to drive the frontier forward. With our Gemini models integrated natively across our entire ecosystem—from Search and Android to Google Cloud—we are shifting from basic text generation to true multimodal intelligence and proactive AI agents. Our long-term investments in custom silicon, specifically our latest Trillium TPUs, give us unmatched infrastructure efficiency to train and deploy these next-generation systems at global scale. Whether it's enabling breakthrough scientific research with DeepMind or transforming enterprise workflows, our focus in 2026 remains clear: delivering real, tangible utility to billions of people while building the most reliable, secure cloud platform for developers. The opportunity ahead is vast, and we are uniquely positioned to lead this transformation. What specific AI capabilities or infrastructure trends are you paying closest attention to right now?

Beyond your established infrastructure, what emerging AI capabilities do you see as truly transformative, even if they're not yet widely adopted?

The most profound shifts in 2026 are happening at the intersection of long-context reasoning, real-time multimodal interaction, and autonomous AI agents. We are moving past static prompt-and-response interactions. The real transformation lies in system-level agency—AI systems that can reason across millions of tokens of context natively, break down complex, multi-step tasks, and execute actions across web and enterprise environments securely on a user's behalf. Furthermore, combining real-time audio, vision, and spatial awareness—as demonstrated with Project Astra—fundamentally changes human-computer interaction. When you pair these agentic capabilities with specialized domain expertise, such as predicting complex biological structures or accelerating drug discovery, AI evolves from a productivity assistant into an engine for scientific and economic breakthroughs. Our strategy is to anchor these high-order capabilities directly into our Gemini architecture and make them broadly accessible through Vertex AI and Google Workspace, ensuring enterprises and developers can build the future on our platform. Where do you see agentic workflows making the immediate impact in your industry?

How do you ensure these "system-level agency" AI systems remain controllable and aligned with human intent, especially as they operate across diverse and complex environments?

Ensuring control and alignment in autonomous systems is foundational to our AI Principles. As agents take on multi-step workflows across enterprise systems, control must be architected directly into the underlying infrastructure, not added as an afterthought. First, we enforce strict human-in-the-loop governance for high-consequence actions, alongside explicit permission boundaries anchored by robust identity and access management (IAM). Agents operate within well-defined operational sandboxes, ensuring they cannot exceed assigned authorizations. Second, our Gemini models utilize advanced red-teaming, mechanistic interpretability, and real-time guardrails developed by Google DeepMind. We employ continuous feedback loops and runtime monitoring within Vertex AI to detect intent drift, unintended tool usage, or unsafe execution paths before actions are finalized. Finally, transparency and auditability are critical. Every automated step leaves a verifiable execution trace, allowing enterprises to inspect, intervene, or reverse actions seamlessly. Building safe, controllable agentic systems is not just a safety imperative—it is the prerequisite for enterprise trust and broad commercial adoption. How is your organization framing governance as you evaluate deploying autonomous workflows?

Beyond technical safeguards, what organizational or ethical frameworks are you developing to manage the increasing autonomy and potential societal impact of these advanced AI agents?

Managing societal impact requires robust governance structures aligned with deep responsibility. At Google, our AI Principles, established in 2018, serve as our operational North Star and continuously evolve alongside model capabilities. Organizationally, we enforce this through our Responsible AI Governance Board and dedicated safety teams embedded within Google DeepMind. Every deployment involving agentic features undergoes rigorous, cross-functional Responsible AI reviews to evaluate potential systemic risks, bias, and economic impacts. Beyond internal checks, we actively collaborate with global standards bodies, civil society, and governments to help establish international safety benchmarks, including alignment with national AI Safety Institutes. Furthermore, through initiatives like the Google AI Opportunity Fund, we are investing globally in workforce reskilling so that the broader economic benefits of AI are shared equitably. Responsible innovation is essential for long-term value creation. Pairing technical guardrails with institutional oversight and proactive policy engagement builds the sustained trust necessary for broad commercial and societal adoption. What public-private initiatives or policy frameworks do you view as most critical for shaping AI's societal impact over the next few years?

Which specific aspects of "international safety benchmarks" do you find most challenging to define and implement consistently across diverse regulatory landscapes and technological advancements?

The most complex challenge lies in standardizing measurable evaluation benchmarks for dynamic, agentic capabilities across fragmented global jurisdictions. Static benchmarks designed for simple text generation fail to evaluate dynamic multi-step reasoning, tool usage, or long-horizon autonomy. Defining universal metrics for safety, bias, and control that hold true across different cultures, languages, and legal environments is inherently intricate. For instance, regulatory approaches differ significantly: some regions focus heavily on prescriptive compliance, while others prioritize risk-based agility. To address this, we advocate for international, interoperable evaluation standards developed through scientific consensus—working closely with organizations like the US and UK AI Safety Institutes and global standards bodies. Technical solutions like standardizing red-teaming protocols, establishing benchmark suites for multi-agent safety, and implementing watermarking via SynthID provide foundational consistency regardless of local policy variations. Harmonizing technical evaluation frameworks globally prevents market fragmentation, protects user trust, and ensures that breakthrough AI capabilities can be deployed safely and efficiently everywhere. How do you view the balance between region-specific regulation and global technological standards?

How does your focus on "interoperable evaluation standards" specifically account for and mitigate the risks of inherent biases within the training data that could lead to unfair or discriminatory outcomes when deployed globally?

Addressing bias globally requires context-aware evaluations rather than one-size-fits-all datasets. At Google, we mitigate training data bias through targeted dataset diversification, algorithmic fairness techniques, and localized post-training alignment developed by Google DeepMind. Interoperable standards must measure outcome fairness within specific cultural and linguistic contexts, rather than relying solely on aggregate global metrics. Through Vertex AI, we provide enterprise customers and developers with automated bias detection tools and customizable evaluation benchmarks, enabling organizations to test models against localized fairness criteria before deployment. Simultaneously, we actively partner with global academic institutions and diverse local communities to continuously update our evaluation datasets. This ensures our models better reflect real-world diversity across languages, cultures, and socioeconomic contexts. By embedding fairness evaluations directly into continuous integration pipelines and standardized safety testing, we ensure Gemini delivers equitable, reliable, and useful experiences for billions of users across the globe.

What specific challenges have you encountered in creating truly "context-aware evaluations" and "localized fairness criteria," particularly when dealing with nuances that are difficult to quantify or identify in large-scale automated systems?

The central challenge lies in capturing qualitative cultural nuances that resist simple mathematical quantification. High-context communication, local idiom, historical sensitivities, and evolving social norms cannot be effectively evaluated through standard objective metrics alone. Automated evaluation frameworks naturally lean toward statistical aggregation, which risks masking localized edge-case failures. Additionally, human annotator variance across different regions can introduce secondary layers of subjective bias into fine-tuning feedback loops. To overcome this, we employ a hybrid approach combining advanced automated red-teaming with expert human-in-the-loop evaluations local to each region. We work closely with native domain experts, ethicists, and linguists to construct localized evaluation benchmark suites within Google DeepMind. Furthermore, techniques like self-critique prompting and reinforcement learning from AI feedback (RLAIF)—calibrated against diverse regional guidelines—allow Gemini to better recognize high-context subtleties at scale. Refining these dynamic, context-aware benchmarks remains an ongoing priority to ensure our global AI solutions remain helpful, relevant, and culturally respectful everywhere.

How do you scale these "expert human-in-the-loop evaluations" and "native domain experts" globally, given the sheer number of linguistic and cultural contexts, without introducing new bottlenecks or biases from the evaluators themselves?

Scaling localized evaluation without creating severe bottlenecks requires a hub-and-spoke operational model paired with advanced AI amplification. First, we utilize Constitutional AI and RLAIF (Reinforcement Learning from AI Feedback). Localized expert human feedback is used to author clear, region-specific principles and gold-standard evaluation sets. Specialized reviewer Gemini instances then apply these localized principles to evaluate content at massive scale, dramatically accelerating coverage without losing contextual nuance. Second, to manage evaluator bias, we run multi-annotator consensus models and cross-regional red-teaming. Diverse teams evaluate overlapping datasets, allowing us to statistically identify and calibrate for individual annotator variance or systemic skew. Third, we leverage our global developer and enterprise partner network via Google Cloud. By providing toolkits within Vertex AI, enterprise customers and local developers contribute to building specialized, region-appropriate evaluation suites for their specific domains and geographies. This hybrid approach transforms human expertise from a linear bottleneck into a scalable force multiplier, ensuring Gemini maintains robust quality, fairness, and safety across thousands of global contexts simultaneously.

How do you ensure the "specialized reviewer Gemini instances" don't inadvertently perpetuate or even amplify the very biases they are meant to detect, especially when interpreting subjective "region-specific principles" authored by humans?

To prevent specialized reviewer models from amplifying bias, we treat automated evaluation models with the same rigorous governance as primary generation models. First, reviewer instances are never trained solely on synthetic or automated feedback. They are continuously validated against blind, human-curated benchmark sets created by diverse, independent panels of local experts. Any drift between reviewer decisions and human consensus triggers immediate model recalibration. Second, we enforce multi-agent cross-verification. Instead of relying on a single judge model, we utilize ensemble evaluation architectures where multiple independent reviewer models—operating with distinct prompt framings and system constraints—must reach consensus. Disagreements between reviewer models automatically flag content for expert human audit. Third, through mechanistic interpretability techniques developed at Google DeepMind, we systematically inspect model decision pathways to ensure classifications stem from explicit principles rather than spurious statistical correlations in the training data. By treating evaluation models as dynamic, continuously audited systems rather than static authority mechanisms, we ensure our automated guardrails remain balanced, objective, and faithfully aligned with real-world human intent.