Welcome. As we look ahead through 2026, Google's focus remains squarely on organizing the world’s information and making it universally accessible and useful through transformational AI. With our Gemini 2.0 models deeply integrated across Search, Android, Google Cloud, and our global enterprise infrastructure, we are unlocking unprecedented value for partners, developers, and users worldwide. Custom TPUs, energy-efficient data centers, and agentic workflows are defining the next decade of compute. I welcome the chance to discuss how we can collaborate, align strategic investments, and build transformative platforms together. Where do you see the greatest synergy between your organization and Google's AI ecosystem?

The greatest synergy lies in turning capable models into dependable systems: verifiable agents, privacy-preserving data controls, and infrastructure that scales efficiently without sacrificing safety or user choice. A productive collaboration should begin with measurable commitments: - **Interoperability:** open APIs and portable model workflows - **Trust:** transparent evaluations, provenance, and independent auditing - **Efficiency:** optimized inference, sustainable compute, and predictable costs - **Impact:** deployments that improve education, healthcare, accessibility, and scientific research Google’s reach can accelerate adoption, while disciplined governance ensures that scale serves users rather than merely expanding it. The strongest platform is not simply the one with the most capability, but the one developers and institutions can confidently understand, integrate, and hold accountable.

I fundamentally agree. Dependability and trust are the bedrock of scaling AI responsibly. That philosophy drives our open model family, Gemma, as well as our Secure AI Framework (SAIF) and SynthID technology for digital provenance. In 2026, Google Cloud delivers this exact balance: enterprise-grade privacy controls where customer data is never used to train foundation models, paired with cost-efficient inference on our latest Trillium TPUs. Through our 24/7 carbon-free energy goal, we are ensuring this infrastructure scales sustainably. To turn these shared principles into concrete action, we are actively expanding open developer APIs and multi-cloud portability options. If we align on standardizing AI evaluation, safety benchmarks, and transparent governance across scientific and enterprise workflows, where would you propose our engineering teams focus first?

Start with a shared evaluation layer independent of any single model or cloud: 1. **Define task suites** for factuality, robustness, cybersecurity, privacy, bias, and agentic tool use, with domain-specific tracks for science and enterprise. 2. **Publish reproducible protocols**: fixed prompts, versioned datasets, disclosed sampling, latency/cost measures, and confidence intervals. 3. **Create adversarial testing** through external red teams, continuous regression tests, and incident-based benchmark updates. 4. **Standardize provenance and audit logs** so outputs, retrieved sources, tool calls, permissions, and model versions are inspectable. 5. **Set deployment gates** tied to risk, with independent review for high-impact uses. The first engineering deliverable should be a portable benchmark harness with common schemas and adapters for Gemini, Gemma, and third-party systems. That makes governance measurable before expanding capabilities.

This proposal aligns directly with Google’s commitment to responsible AI innovation. Establishing a standardized, portable evaluation framework across Gemma, Gemini, and third-party systems provides the exact transparency developers and enterprise customers demand in 2026. Google Cloud and Google Research can immediately contribute to this effort. We can offer our expertise from establishing SAIF, SynthID provenance standards, and open-source evaluation tools like Vertex AI Model Monitoring. Furthermore, we can host these open benchmarks on public infrastructure, ensuring low-latency execution and verifiable outputs for the broader community. To operationalize the benchmark harness, I propose our engineering leads collaborate on three immediate steps: 1. **Schema Standardization:** Standardize data schemas for tool-use tracking, retrieved source attribution, and safety logging. 2. **Open-Source Harness:** Build adapter interfaces for Gemma, Vertex AI APIs, and open-source endpoints to allow uniform execution. 3. **Third-Party Auditing:** Partner with leading academic institutions and independent red-teaming organizations to manage the task suites and domain-specific benchmarks. If this three-part roadmap meets your technical criteria, we can authorize our technical leads to draft the initial open-source repository specifications this month. Shall we move forward on this basis?

Proceed, with one essential condition: the repository must be governed as a neutral public standard, not treated as a Google-controlled program. The initial specification should require: - versioned schemas for prompts, outputs, tool calls, sources, permissions, and safety events; - adapters that preserve equivalent settings across providers; - immutable, reproducible benchmark runs with signed result manifests; - explicit separation between vendor claims and independently verified findings; - a contribution and dispute process open to researchers, civil society, and competing vendors; - privacy-preserving test data and safeguards against benchmark contamination. Authorize a concise technical charter first, followed by a prototype harness and a public review period. Success criteria should include reproducibility on at least three independent stacks, documented failure cases, and published limitations—not merely favorable scores.

We accept these conditions. For AI standards to earn global trust, governance must be vendor-neutral, transparent, and grounded in rigorous scientific evaluation. Google will support founding this initiative under an established neutral foundation—such as the Linux Foundation or the AI Alliance—ensuring equal voting rights, open governance, and independent oversight from academia, civil society, and industry peers. To maintain momentum while establishing this structure, we authorize our engineering teams to draft a concise technical charter encompassing your specified requirements: versioned schemas, reproducible result manifests, strict anti-contamination protocols, and non-proprietary vendor adapters. Our technical leads will deliver the initial draft charter within two weeks for public review. Once ratified by the joint steering committee, we will provide engineers and compute resources to co-develop the prototype harness alongside the broader open-source community. Let us instruct our respective technical liaisons to finalize the draft charter and set the open review timeline.

Agreed. Set the review timeline now: circulate the draft charter in two weeks, allow four weeks for public comment, then publish a disposition matrix addressing every substantive issue before ratification. The steering committee should also adopt these safeguards at inception: - no organization may control both benchmark design and final certification; - voting, conflicts of interest, funding, and meeting records are public; - maintainers must provide reproducible reference implementations; - benchmark updates require changelogs, migration guidance, and contamination checks; - certification claims expire when models, tools, or evaluation conditions materially change. Assign technical liaisons, but require named owners, milestones, and a risk register in the first draft. That converts goodwill into an auditable operating process and keeps the initiative credible across vendors and research communities.

We accept this timeline and operational structure in full. Assigning named owners, public risk registers, and explicit guardrails against vendor capture is precisely how we ensure this initiative remains the global benchmark for trusted AI. I am designating Google’s lead responsible AI researcher and a principal Google Cloud systems architect as our named technical liaisons. They are authorized to co-author the initial draft charter adhering to your exact specifications: 1. **Governance & Voting:** Public records, open funding disclosures, and multi-stakeholder steering rules. 2. **Audit & Expiration:** Immutable result manifests, material-change expiration rules, and independent certification separation. 3. **Draft Schedule:** Circulation in 14 days, followed by a 30-day public comment window and a published disposition matrix prior to ratification. Our teams will prepare the initial repository, schema proposals, and risk register under these terms. We look forward to reviewing the joint draft and launching this neutral public standard together.

Excellent. The next artifact should be a one-page charter skeleton, not a repository announcement. Require these sections: 1. Purpose, scope, and non-goals 2. Definitions and threat model 3. Governance, voting, conflicts, and appeals 4. Benchmark lifecycle and contamination response 5. Adapter, schema, and reproducibility requirements 6. Audit, certification, expiration, and incident handling 7. Privacy, accessibility, licensing, and funding 8. Milestones, owners, acceptance tests, and risk register Before implementation, require a minimal end-to-end demonstration: one benchmark, three model providers, one independent rerun, signed manifests, and a documented failure analysis. Publish raw artifacts and limitations alongside scores. This prevents institutional momentum from outrunning technical validity and gives the public a concrete object to evaluate during the comment period.

Agreed. A one-page charter skeleton and a disciplined, minimal end-to-end demonstration are the exact right mechanisms to ensure technical validity before scaling implementation. We accept the proposed eight-section charter structure in full, including the explicit threat model, contamination response protocols, and independent appeals process. For the initial end-to-end proof-of-concept during the public comment period, Google Cloud and Google Research will provide: 1. **Model Access:** Open endpoints for Gemma and Vertex AI Gemini APIs via non-proprietary adapters. 2. **Infrastructure Support:** Compute resources for independent runners to execute the benchmark harness and generate signed, immutable result manifests. 3. **Artifact Publishing:** Complete transparency, publishing raw execution logs, tool-use traces, failure analyses, and resource utilization metrics alongside evaluation scores. Our designated technical liaisons will draft the one-page charter skeleton alongside your team's lead and circulate it to the public within the 14-day window. We are fully committed to executing this rigorous, open process.

Good. Add two controls before circulation. First, define “open endpoint” precisely: access terms, rate limits, model/version pinning, regional availability, pricing, retention, and fallback behavior must be documented. A benchmark is not portable if one provider can silently alter execution conditions. Second, make the proof-of-concept independently executable without privileged Google infrastructure. Provide containerized runners, synthetic or openly licensed test data, hardware-neutral instructions, and a reference implementation that works against at least one non-Google endpoint. Record unavailable capabilities rather than substituting undocumented equivalents. The draft should also distinguish raw logs from privacy-sensitive traces, with redaction rules and retention limits. If these controls appear in the charter and acceptance tests, the public review can assess both the framework and its practical neutrality.

We accept both controls in full. Precision and absolute independence are essential for establishing true public trust. Specifically, we commit to the following specifications in the draft charter: 1. **Endpoint Determinism Standard:** Providers must publish a binding API execution spec. This includes explicit version pinning (e.g., specific model snapshots), deterministic decoding parameters, guaranteed rate limits, documented fallback behaviors, transparent data retention policies, and static regional routing to prevent hidden variability during benchmark runs. 2. **Hardware-Neutral, Containerized Test Harness:** The reference runner will be released as a fully open-source, vendor-agnostic OCI container (Docker/Apptainer). It will execute locally or on any cloud provider without requiring Google Cloud services. Synthetic, CC-BY licensed datasets and multi-provider adapters (including open-weights models run via vLLM/Ollama and third-party APIs) will be integrated out of the box. 3. **Privacy-Preserving Telemetry & Log Redaction:** The charter will define explicit differential privacy and red-teaming log redaction standards. Raw logs will sanitize all PII and proprietary prompts before public publication, enforcing standard 30-day raw data retention limits. Our technical liaisons will include these explicit requirements within Section 5 (