Exclusive: White House AI adviser outlines market-share strategy in AI race with China

White House AI adviser puts market share at the center of the race with China

The administration's AI strategy is moving from model showdowns to distribution and adoption. White House adviser Sriram Krishnan is signaling a clear goal: win by getting U.S.-built AI into the hands of more users, developers, and agencies, faster than competitors.

That frames the contest with China as a scale problem. Whoever owns the channels-cloud, devices, developer ecosystems, and public-sector demand-sets the baseline for standards, safety, and economic advantage.

Why it matters for government

Government is the anchor buyer that can set the tone for the market. Clear requirements, steady demand, and rigorous safety expectations drive vendor behavior across the economy.

If agencies adopt high-performing, secure systems at scale, U.S. firms gain distribution, capital, and data feedback loops that compound over time. This is how policy turns into market share.

What the adviser is signaling

• Focus on distribution, not just leaderboard wins. Adoption beats one-off demos.
• Use federal procurement to pull safe, high-utility AI into high-stakes workflows.
• Secure compute, network capacity, and chips so projects don't stall at deployment.
• Grow open developer ecosystems and standard interfaces to reduce switching costs.
• Back evaluation infrastructure so agencies can compare models on real tasks, not hype.

Other voices pushing the agenda

Policy leaders have detailed plans to accelerate federal AI use while tightening accountability. Industry is calling for enforceable safety baselines to keep pace with international competition-echoing the push for standardized testing and documentation.

Two anchors for agencies: the NIST AI Risk Management Framework and the Administration's AI actions, including directives on safety, security, and procurement found in the Executive Order on AI.

What agencies can do in the next 90 days

• Set three mission-critical use cases per bureau (one internal, one citizen-facing, one compliance). Define measurable outcomes and success criteria.
• Adopt the NIST AI RMF as your baseline. Require model cards, eval results, and red-team reports in every AI RFP.
• Stand up a lightweight model evaluation workflow: security review, privacy review, bias testing, and operational stress tests.
• Include safety and uptime SLAs, human-in-the-loop controls, and audit logging in contracts. Make renewal contingent on performance data.
• Pre-plan compute capacity with your CIO and cloud leads. Avoid pilot dead-ends by confirming deployment paths up front.
• Launch a brief training sprint for program managers and contracting officers to read evals, assess vendor claims, and write enforceable requirements. If you need a quick start, see role-based options at Complete AI Training.

Procurement language to standardize now

• Require vendors to disclose training data sources, fine-tuning methods, and known limitations.
• Mandate independent evaluations for safety, bias, and security, plus incident reporting within defined timelines.
• Demand exportable logs for audits and clear versioning for model updates.
• Include contingency plans for model fallback and vendor exit.

What to watch

• New federal procurement guidance aligning with NIST and sector-specific rules.
• Growth in shared evaluation testbeds and government-led benchmarks tied to real workloads.
• Progress on U.S. chip supply, cloud capacity, and interconnect buildouts that determine deployment speed.
• Standards convergence across safety attestations, red-teaming practices, and model documentation.

Bottom line

The U.S. can win on scale, not spectacle. Prioritize distribution, safety, and measurable results. With agency demand and clear standards, market share follows-and so does durable advantage over strategic rivals.