AI Strategy
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
- Uncategorized (32)
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Why AI Assistant Memory Matters More as Models Open Up
A product strategy for owning assistant memory, preserving user trust, and using open-weight models without locking your product to one vendor.
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AI Product Pricing and Packaging: A Practical Playbook
A practical method for choosing an AI pricing model, defining the value metric, validating willingness to pay, and testing real unit economics.
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A Product Leader’s Playbook for Every New AI Model Release
A practical system for deciding when a new AI model deserves testing, how to evaluate it, and when to adopt, watch, or walk away.
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AI-Driven Learning and Development: A Practical Operating Model
A practical operating model for using AI to speed learning production, personalize pathways, protect quality, and prove workforce capability gains.
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AI Agent Economics: How to Plan Capacity Before You Scale
A practical framework for measuring accepted agent output, calculating its full unit cost, routing models, and deciding when added capacity pays.
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Frontier AI Scaling: A Product Leader’s Agent Economy Playbook
A practical framework for turning abundant frontier-model capacity into trusted, measurable agent work without mistaking supply for demand.
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Claude 5.5 Task Economics: Price Outcomes, Not Tokens
A practical framework for testing Claude 5.5 on real work and measuring model spend, correction time, retention, and cost per accepted outcome.
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Planning Product Strategy Around Rapidly Falling AI Costs
A practical framework for modeling AI unit economics, sequencing cost-sensitive bets, pricing usage, and building defensible products as inference gets cheaper.
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Jev Decision Model Performance: Accuracy, Cost, and Fit
A practical guide to interpreting Jev benchmarks, designing a safe cascade, and measuring whether its fast decisions improve real outcomes.
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How to Build an AI Strategy as Costs Fall and Tools Multiply
A practical framework for selecting, integrating, governing, and retiring AI tools as capability prices fall faster than planning cycles.
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Why Better AI Models Aren’t Making Your Team Much Faster
A practical operating model for turning faster AI-assisted work into shorter team lead times without burying reviewers or top operators.
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Fei-Fei Li and the Product Foundations of Computer Vision
Use ImageNet’s history to decide where a vision product really needs investment: data, taxonomy, evaluation, compute, or model design.
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How to Make Your Brand Discoverable and Selectable by AI
A practical framework for correcting AI’s view of your brand, earning trust in a focused category, and measuring whether assistants choose you.
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Why AI-Era Expert Teams Still Need Expert Leaders
A practical guide to replacing low-value management work with expert judgment, clear decision rights, stronger coaching, and real accountability.
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How Private Equity Firms Can Measure AI-Driven Advantage
A practical scorecard for separating AI activity from verified economic value, repeatable execution, defensibility, and managed risk in private equity.
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How to Make Frontier AI Evaluations Independent and Credible
A practical framework for frontier AI evaluations that preserves access, limits provider influence, controls observer effects, and earns trust.
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When AI Agents Need World Models: A Product Leader’s Guide
A practical framework for deciding when an AI agent needs a world model, designing its control loop, and testing consequences before granting autonomy.
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AI Music Search Starts With Metadata, Not a Chatbot
A product playbook for building AI music search on structured metadata, rights-aware retrieval, rigorous evaluation, and durable strategy.
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How to Review Your AI Architecture and Career Story
A practical method to expose production tradeoffs, strengthen AI system design, and turn the same evidence into a credible career story.
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How to Evaluate SpaceX’s AI and Orbital Compute Claims
A decision framework for testing SpaceX’s revenue, capacity, customer, and orbital-compute claims before they enter your AI strategy.
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Browse topics
- AI Strategy (429)
- Generative AI (113)
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- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
- Uncategorized (32)
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