FinOps
- AI Strategy (315)
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- Leadership (76)
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- Product Management Leadership (306)
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Designing AI Products for Chip and Platform Volatility
A practical framework for choosing AI infrastructure, testing provider claims, and building exit paths before platform shifts reach customers.
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How to Measure Enterprise AI Agent Performance and ROI
A practical framework for routing agent work, setting hard budgets, measuring accepted outcomes, and proving enterprise AI ROI against a credible baseline.
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Cheaper AI Models: Measure the Cost of Accepted Results
A practical framework for routing AI work, controlling agent loops, and measuring whether lower token prices reduce cost per accepted business result.
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Enterprise AI Spend Is Concentrating. Manage It as a Portfolio
A practical operating model for separating AI activity from value, attributing costs correctly, and scaling proven workflows without rewarding waste.
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The Real Economics of Self-Hosting Frontier AI Models
A practical break-even framework for deciding when token volume, model quality, platform staffing, and utilization justify owning AI infrastructure.
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Financing AI Compute Without Giving Up Strategic Control
A practical framework for matching AI infrastructure financing to workload demand, capacity rights, product economics, and a credible exit path.
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How to Benchmark AI Models for Cost, Quality, and Risk
A practical framework for choosing AI models by cost per accepted result, workflow reliability, failure severity, and production economics.
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SpaceX Orbital AI: How to Evaluate the Revenue Case
A practical framework for separating SpaceX’s reported AI revenue from the technical and economic proof required to scale orbital compute.
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How to Give Your AI Strategy Financial Staying Power
A practical framework for sequencing AI investment, testing unit economics, preserving optionality, and surviving timelines you cannot control.
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Codex Token Cost Optimization Without Losing Useful Context
A practical model for trimming Codex context, preserving decisions, and proving token savings without creating more retries or review work.
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Cost-Aware AI Model Selection: Pay for Accepted Work
A practical framework to compare AI models by accepted-result cost, test quality, choose hosting, and route production work with safe fallbacks.
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AI Inference Economics: Optimize for Value, Not Cost
A practical framework for balancing AI inference cost, latency, and quality against conversion, retention, support demand, and revenue.
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Built for Your Biggest Days: How We Engineer Fair, Reliable Scale Without Downtime
Enterprise teams ask sharper questions about scale—and they should. I share how we handle 150k+ requests/sec, shard our source-of-truth data with Vitess and PlanetScale, and reshape…
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From Internal FinOps Agents to Customer-Embedded Optimization
Learn how to turn internal FinOps agents into governed, customer-embedded workflows that improve cloud cost-to-value and product learning.
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Browse topics
- AI Strategy (315)
- Generative AI (106)
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- Leadership (76)
- Product Management (344)
- Product Management Leadership (306)
- Uncategorized (32)
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