platform scalability
- AI Strategy (429)
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Prioritizing AI Across Product Value and System Design
A practical framework for ranking AI features and platform work through user outcomes, evaluation readiness, information budgets, and measured bottlenecks.
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How to Design AI Agents for Large-Scale Problem Solving
A practical operating model for decomposing agent work, controlling tools and memory, recovering from failure, and scaling only when outcomes hold up.
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The Real Cost of AI-Assisted Software Product Delivery
A practical way to price AI-assisted delivery across prototyping, production engineering, reliability, launch, operations, and maintenance.
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How to Scale AI Agent Autonomy Without Losing Control
A practical framework for expanding agent permissions, isolating execution, verifying real outcomes, and scaling without multiplying failures.
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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 AI Engineering Leaders Should Run Competing Bets
A practical model for running parallel AI engineering bets, comparing them fairly, choosing on evidence, and converging without political fallout.
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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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Frontier AI Updates: A Product Leader’s Adoption Playbook
A practical framework for turning model, inference, open-weight, agent, and multimodal releases into measured product and architecture decisions.
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How to Evaluate and Optimize Open Models for Production
A practical framework for deciding whether an open model is production-ready, then improving quality, latency, cost, and reliability.
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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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Durable Product and Platform Leadership Beyond the Launch
A practical framework for turning product momentum into lasting platform value through governance, customer trust, disciplined growth, and AI readiness.
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Beyond Black‑Box Scores: Custom AI That Elevates Trust & Safety Without Burnout
Off-the-shelf moderation scores miss nuance and force teams into painful manual review. In this episode, I explore how Musubi blends traditional ML with LLMs, policy optimizers,…
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Engineering MCP Agents as a Reliable Product Platform
A practical framework for building MCP agent platforms around controlled context, safe actions, measurable reliability, and governed scale.
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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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How to Scale Session Replay Without Sacrificing Privacy
A practical operating model for scaling session replay with strict capture controls, performance budgets, targeted sampling, and measurable governance.
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From 70 Employees to Dominance: My Playbook for Hypergrowth, Focus, and Top-Down Goals
Scaling an atoms-based marketplace demands a different kind of product leadership. In this first-person playbook, I share how I apply a simple “plate spinning” framework to…
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How to Build a Trusted AI Product Platform That Scales
A practical operating model for building AI platforms that earn trust through governed data, verifiable outputs, controlled actions, and measurable outcomes.
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Commercial vs. Internal Products: Hard Truths, High Leverage, and How I Make the Call
Internal products quietly drive customer experience, margin, and speed—yet they’re often underfunded. Commercial products expand scope with positioning, pricing, and GTM complexity, making them harder to…
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Never Stop Disrupting: Why the Fin API Platform Signals a New Era for Agentic AI
Disruption favors teams that move from features to agents—and the Fin API platform accelerates that shift. With a customer agent platform resolving over 2M issues weekly…
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Browse topics
- AI Strategy (429)
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