How we vet AI news: prioritize reproducible benchmarks, transparent demos with eval details, and real business factors like availability, pricing, latency, and reliability. (0:16)

Real safety means specifics—published red-teaming, incident reports, data-handling transparency, and third-party oversight—so teams can assess risk and adopt faster. (0:56)

Use AI at work safely by never pasting secrets, treating outputs as verified drafts, adding guardrails to workflows, and tracking latency and cost per task. (1:41)

Open models offer control and cost flexibility but require handling ops, security, and licensing, while closed APIs trade control for speed, uptime, and guardrails. (2:22)

Compute constraints are steering AI toward inference efficiency, smaller tuned models, smart scheduling, and TCO-aware ops across cloud and on-prem. (3:04)

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