
The Broken Yardstick: AI in Frontier Model Evaluation
As model capability accelerates, evaluation has to move beyond static leaderboards into scenario testing, risk signals, and business-grounded checks.
Trends and analysis in AI and technology

As model capability accelerates, evaluation has to move beyond static leaderboards into scenario testing, risk signals, and business-grounded checks.

Private data collaboration needs a defined output, threat model, minimized inputs, secure computation, quantified privacy, and accountable release controls.

Carbon AI should trace each disclosed figure to boundaries, activity data, emission factors, estimates, approvals, and revisions—not guess through gaps.

A governance-focused guide to using AI for evidence, scenarios, dissent, board briefs, and decision follow-up without outsourcing executive judgment.

How legal teams can use AI for clause evidence, draft comparison, obligation tracking, and retrieval while preserving authority, privilege, and review.

How food teams can connect AI-assisted anomaly detection and traceback to lot identity, preventive controls, cold-chain evidence, and recall readiness.

Payment AI works best when identity proofing, authentication, fraud scoring, authorization, and customer redress remain distinct controls.

A resilience-planning guide to bounded AI for damage reports, demand forecasts, and procedure search that still works when data, networks, cloud, or models fail.

AI can widen design options and accelerate prototypes, but credible product decisions require real users, practical tasks, accessibility, and observed behavior.

How enterprises can build permission-aware AI search with governed sources, provenance, measurable retrieval quality, controlled answers, and useful feedback.

Procurement AI should strengthen evidence and scenario analysis while award criteria, conflicts, negotiations, and supplier decisions remain accountable.

How hotels can combine demand forecasting, governed pricing, guest records, service recovery, and bounded personalization without covert profiling.