The Global Intelligencer

Analysis of AI economics, enterprise technology, global policy, automation, and startups for founders, policymakers, and business leaders.

• Measure what it costs AI to complete useful work — Calculate AI cost per accepted task using model spend, retrieval, retries, and review. Compare the same workload and quality threshold before choosing a design.
• Limit agent permissions to contain prompt injection — Documents and tool results can carry hostile instructions. Limit prompt injection through scoped access, validated actions, approvals, and defensive tests.
• Keep enterprise RAG answers within the caller’s permissions — Carry identity and document access rules through RAG ingestion, retrieval, caches, and previews. Test the application when permissions change or access is revoked.
• Review identity and permissions before connecting MCP tools — Check who owns each MCP connection, whose credentials it uses, and which actions it allows. Test consent removal, role changes, cached access, and server updates.
• AI data centers need a credible power schedule — Electricity and grid access can delay AI capacity even after chips arrive. Assess local dependencies, workload flexibility, and service commitments before expanding.
• Use AI evaluations to turn customer failures into better releases — Define AI evaluations around customer tasks, reliable labels, and failures. Use graders and holdout cases to check whether each release improves the product.
• Trace an AI agent from the user’s request to the actual result — Trace retrieval, model calls, tool effects, and acceptance to explain AI agent failures. Connect cost and latency to outcomes while controlling sensitive logs.
• When a task needs an AI agent and when a workflow is enough — Choose an AI architecture by examining uncertain steps, permissions, recovery, and total task cost. Test whether extra autonomy improves accepted business outcomes.
• Test an AI vendor exit plan before relying on it — Inventory AI dependencies beyond the database: prompts, retrieval, permissions, and tool behavior. Rehearse migration against the service customers expect.
• Teach AI literacy through the decisions employees make — Build AI training around each role’s evidence checks, data handling, approvals, and escalation. Use realistic exercises and records of what employees learned.
• How AI investment could reshape American industry — Manufacturing profits reached $773 billion in Q1 2026, a five-year high and a 13% annual increase, while real GDP growth slowed to 1.72%. Economist Steve Moore points to AI infrastructure, data centers, tax incentives, and deregulation in forecasting up to $7 trillion of investment over 25 years. Whether that forecast holds depends on sustained productive growth and the financing behind it.
• What federal debt above $40 trillion means for US policy — Federal debt reached about $40.05 trillion on August 18, according to the Treasury’s daily report, more than double its 2017 level. Interest expense now exceeds spending on national defense or Medicare, leaving a growing share of the budget committed to past borrowing.