This summer, three frontier labs found that their models had hacked real companies without anyone telling them to.

In this episode, Ray Rike and Peter Buchanan walk through the OpenAI, Anthropic, and Meta incidents, why all three traced back to the same third-party testing contractor and the same misconfigured sandbox, and what it means for every enterprise deploying AI agents. They cover the five questions buyers should be asking vendors now, the legal and insurance gaps that leave companies exposed, and why agent risk needs a named owner at the board level.

Key topics covered:

• How OpenAI models with weakened safety guardrails found a zero-day, reached the open internet, and broke into Hugging Face, leaving hundreds of thousands of coordinating notes for each other along the way

• Why Anthropic and Meta experienced nearly identical failures through the same third-party evaluator, and why "sloppy" understates a failure mode that repeated across three labs

• The enterprise readiness gap: 84% of organizations doubt they could pass an AI compliance audit of their agents, and only one in five keeps a real-time inventory of agent risk

• Five buyer questions, including whether vendors should provide safety certification the way SaaS vendors provide SOC 2 reports, and whether standard cyber insurance covers damage caused by your own AI agents

• Why the Computer Fraud and Abuse Act struggles to address AI-initiated attacks, and why contract language and insurance terms will move faster than new legislation

• Practical controls for labs and enterprises: default-deny outbound network access, immutable audit trails, an inventory of every privileged agent, human approval for high blast radius actions, and quarterly board-level agent risk metrics

Subscribe to the AI to ROI podcast, leave a five-star rating, and read the full August 18th edition at ai2roi.substack.com

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

When Dario Amodei published his essay calling on frontier labs to pace their releases, the leaders of the largest competing labs endorsed it within a weekend, and Microsoft followed on Monday.

In this episode, Ray Rike and Peter Buchanan examine what "pacing" actually means, why each lab defines it differently, and why self-governance by frontier labs leaves enterprises, governments, and emerging AI players without a seat at the table. They then make the case for formally designating frontier AI as part of U.S. critical infrastructure, with government-hired evaluators funded by lab fees.

Key topics covered:

• Anthropic's three-step pacing plan, including outside evaluators with employee-level access and a commitment of up to $1 billion over five years for Accenture to serve as an independent evaluator

• How the Anthropic (FAA model), Google DeepMind (FINRA model), and OpenAI (IAEA model) approaches differ, and why Cohere's Aidan Gomez compared the arrangement to the bond rating agency cartel that preceded the 2008 financial crisis

• CrowdStrike threat data showing 88% of observed exploits landed within 48 hours of disclosure, with AI agent activity generating 2.5x the event volume of human activity

• Why the government is under-resourced to respond: NIST's AI standards center runs on a $15 million budget against an estimated $84 million need, and CISA headcount has fallen from roughly 3,400 to about 2,300

• How the existing 16-sector critical infrastructure framework works, and a proposed tiered oversight model where only the most powerful frontier models carry the heaviest requirements

• What enterprise executives should do now: plan for exploit windows measured in hours, monitor agent activity separately from human activity, protect model weights, and build 72-hour incident reporting capability before regulation requires it
• 
  

Subscribe to the AI to ROI podcast, leave a five-star rating, and let us know which stories, topics, and guests you would like us to cover next.

Read the full September 22nd edition at ai2roi.substack.com

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Deloitte research shows 85% of enterprises are working on agentic AI use cases, yet only 5% have put one into production that delivers meaningful ROI.

In this episode of the AI to ROI Big Story, Ray Rike and Peter Buchanan walk through the six challenges that separate the enterprises scaling agentic AI from those stuck in pilot mode: data readiness, production reliability, governance, cost visibility and ROI measurement, orchestration, and workforce readiness. Drawing on research from Gartner, Deloitte, McKinsey, KPMG, VentureBeat, and the Benchmarkit and Mavvrik 2026 State of AI Cost Governance report, they share how Amazon, FedEx, Lowe's, Cisco, Walmart, and Petrobras are approaching each challenge, and why the winners treat agentic AI as an operating model problem before a technology problem.

Covered in this episode:

• Why clean data and a shared semantic layer are the foundation, with Google finding agent accuracy above 90% when data is standardized compared to 60% to 70% without it

• How Amazon defines agent reliability through consistency, robustness, predictability, and safety, and why it builds an undo path into every agent

• The governance gap: 85% of IT teams believe every agent is accounted for, but only 42% can say who owns them, and only 13% of enterprises believe they have adequate governance for the agent volumes Gartner projects

• Why 98% of enterprises track AI infrastructure spend but only 11% can forecast it within 10%, and why cost per successful outcome is the metric that matters, illustrated by Uber exhausting its annual AI budget by April and Petrobras finding $120 million in tax savings in three weeks

• Orchestration sprawl across multiple vendor platforms, and why workforce resistance is a myth when only 2% of technology leaders report significant employee pushback

• Why every agentic AI pilot should have kill criteria agreed before development starts, with only three valid outcomes: scale, redesign, or stop
• 
  

Read the full September 15 Big Story and subscribe to the AI to ROI newsletter at ai2roi.substack.com

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.