
The agent problem nobody budgeted for
THE SO WHAT
The real cost of agentic AI isn’t just compute — it’s governance, monitoring, and failure handling that nobody put in the 2024–2025 budgets. If you’re piloting agents, assume you’ll need a control plane, audit trail, and rollback strategy long before you scale usage.
READ THE SOURCE
MORE FROM THE WIRE
Applied AIThe AI Copyright Lawsuits Have Finally Produced an Actual Payout
$1.5B moving from an AI lab to rights holders turns copyright risk from theoretical to priced-in. If your models or data vendors touch copyrighted corpora, you now have a reference number for downside exposure — legal, PR, and cash — and boards will start asking for that line item.
Applied AIGoogle releases three new Gemini models — but no 3.5 Pro
Google doubling down on 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber — and still holding back 3.5 Pro — says the near-term fight is on cost, latency, and specialization, not max IQ. If you’re building on Gemini, design for a portfolio of cheap, task-tuned models rather than betting your roadmap on a single flagship that keeps slipping.
Applied AIAnthropic’s $1.5 billion book piracy settlement approved by judge
Judicial approval of a $1.5B author settlement cements a template for how training-data disputes might be resolved at scale. If you’re training or fine-tuning models, expect counterparties to benchmark against this outcome when they price licenses, indemnities, and audit rights.
Applied AIGoogle shipped three cheap Gemini models and a Mythos rival, but not the one that matters
Google’s answer to being outrun is a fleet of low-cost Gemini Flash models and a Mythos competitor — not a new Pro flagship — ahead of earnings. If you’re choosing a foundation partner, assume Google’s near-term edge will be on price-performance and verticalized variants, not on having the single strongest general model.