A 500-word summary of Jessica Tillipman’s article Buying Blind: Corruption Risk and the Erosion of Oversight in Federal AI Procurement, examining how rapid federal AI adoption, weakened oversight, contractor lock-in, opaque systems, and reduced auditability create corruption and integrity risks in public procurement, and why governance is essential to sustainable innovation.

Buy versus Build an LLM: A Decision Framework for Governments by Lu, Xu, Tjhi, Li, Bosselut, Koh, and Kankanhalli. This article explains why government LLM decisions involve sovereignty, security, cost, and lifecycle planning—and why federal contractors must adapt by offering secure, auditable, hybrid-ready AI solutions aligned to public-sector procurement priorities.

Summary of Tarkowski & Sieker’s 2026 policy brief proposing “European Public AI” as public digital infrastructure—open, mission-driven, and democratically governed. Explains risks of AI market concentration, Europe’s cloud/model dependencies, and a full-stack strategy spanning compute, data commons, open-source models, and purposeful deployment.

Congress is modernizing its data infrastructure for the AI era through GPO’s new Model Context Protocol, open legislative datasets, and AI-driven constituent engagement. For federal contractors, these initiatives signal a shift toward interoperability, verified data access, and new standards for AI-based tools supporting the U.S. legislative branch.

State governments are moving from AI pilots to structured governance, reshaping expectations for vendors. This post explains how the IBM Center’s “AI in State Government” report signals new requirements—and opportunities—for contractors selling AI-enabled solutions to federal and state agencies.

GAO’s 2025 report on property technology for homebuying examines how AI-driven platforms, automated valuation models, underwriting systems, and e-closings reshape mortgage lending. This blog analyzes their benefits, risks to fair lending and privacy, and FHFA’s evolving oversight of Fannie Mae and Freddie Mac.

This article summarizes Kathrin Frauscher and Kaye Sklar’s Open Contracting Partnership analysis on how governments are buying AI and explains why these shifts in off-the-shelf tools, centralized procurement, and “shadow AI” are strategically significant for federal government contractors.

A New America/RethinkAI report urges governments to move beyond AI “efficiency” toward an ALT framework—Adapt, Listen, Trust. For federal contractors, that means proposals must forecast demand surges, build institutional context, and prove trustworthiness with measurable public outcomes, aligning solutions to tightening state guardrails and CIO-led enterprise adoption.

Public AI—shared, government-aligned AI infrastructure—is moving from idea to policy. Here’s what it means for federal contractors: multilateral frameworks (GPAI, G7 Hiroshima), compliance-first engineering, and capture strategies that emphasize interoperability, governance, and measurable public value.

GSA’s strategy for implementing OMB M-25-21 details how federal agencies will scale AI through a tiered use-case model, USAi shared services, FedRAMP “20x,” CAIO-led governance, and public AI inventories. For contractors, it foreshadows evaluation artifacts, telemetry expectations, and acquisition pathways (e.g., OneGov) that will shape requirements, compliance, and competitive advantage.

World Bank’s 2025 “Building Trustworthy AI” translates global AI ethics into operational tools—explainability, fairness, privacy, and governance—tailored to public programs. For U.S. federal contractors, it foreshadows RFP requirements, audit artifacts, and performance controls. Use its checklists and PETs guidance to strengthen proposals, MLOps, and compliance.

Apertus, a Swiss “public AI,” signals a shift toward sovereign, open, and auditable AI infrastructure. For U.S. federal contractors, OMB M-24-10 and the NAIRR pilot point to solicitations that reward transparent data, reproducible evaluation, security, and governance. Prepare for contracts where compliance and lifecycle stewardship rival model performance.

This blog explores how state and local governments can responsibly integrate AI by grounding initiatives in public values, engaging communities, adopting tiered governance, collaborating across jurisdictions, building internal capacity, and ensuring continuous oversight—guided by the 2025 consultation by Ghani, Langston, McNeese, and Venkatasubramanian.

GAO’s July 2025 report reveals how federal agencies are rapidly adopting generative AI, with use cases surging ninefold in one year. The report outlines key benefits, challenges in privacy, procurement, and workforce, and agency efforts to implement responsible policies under evolving OMB mandates.

This blog post is a summary of the article “How AI Can Unlock Public Wisdom and Revitalize Democratic Governance” by Rahmin Sarabi, published by the Carnegie Endowment for International Peace on July 22, 2025. The views expressed herein are those of the original author and do not necessarily reflect the views of Carnegie or its affiliates. This summary is provided for informational purposes only and should not be construed as legal, policy, or investment advice.

The OECD’s June 2025 AI Paper explores how privacy-enhancing technologies (PETs) support secure AI model sharing and co-creation. The report outlines use case archetypes, real-world applications, and policy strategies to ensure responsible AI innovation across sectors.

This article explores why PeaceTech—the use of technology for conflict prevention and peacebuilding—must become the next major focus for innovation and investment, arguing that it holds multibillion-dollar potential and could reshape global security.

This blog post explores “The Agentic State” whitepaper by Luukas Ilves, which outlines how agentic AI will transform ten core functions of government. The report envisions a paradigm shift toward AI-driven public value, with implications for policy, leadership, and public services.