Enterprise AI, from go-to-market to governance.
I've spent four decades building software, architecting enterprise cloud, and leading teams. As Lead AI GTM Strategist at GitHub, I help organizations do far more than sell AI. I help them implement it, manage it, and govern it at enterprise scale.
Recent thinking
All articles →The Model Was Never Going to Be the Moat
In seven months, every major AI lab and cloud stood up an army of engineers to embed inside your operations—and, in doing so, quietly conceded that the model itself is no longer the moat. Here is what the deployment land grab is really about, and how to tell whether the offer on your desk leaves you more capable or more captive.
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Open-Weight AI in the Enterprise: What CISOs Need to Know After Amodei's Warning
Anthropic's CEO warned Congress that open-source AI is heading down a dangerous path. Here's what that actually means for enterprise security teams weighing self-hosted model deployments.
Read article →The Second Invoice: What AI-Generated Code Costs After the Meter Stops
The token bill is the cheap part. AI-generated code moves most of its real cost downstream—into review time, rework, failed builds, and the compute burned re-running pipelines. Here is how to find that cost before it finds your budget.
Read article →The Multi-Model Mirage: Governance, Cost, and Accountability When Every Developer Picks Their Own AI
Enterprise AI coding environments now support a dozen model choices per developer. That flexibility is real. So is the governance vacuum it creates—and the cost exposure that accrues quietly under uncapped usage-based billing.
Read article →Bringing AI into your enterprise and need it to actually land?
Advisory for leaders who need AI strategy, adoption, and governance to move as one.