Blog
Product operations thinking
Frameworks, insights, and perspectives for VP/Heads of Product who want to move beyond engineering metrics.
The Translation Gap: why your team score and your product score diverge
Median 23-point gap between team maturity (F1) and product AI-nativeness (F3). It's the most important number most teams never measure.
Why strategy and operations must converge
The traditional divide between strategy and execution is collapsing. AI-native teams that merge both will outperform those that don't.
Why most maturity scores are wrong (and what it takes to fix them)
Maturity models are everywhere but most are dangerously simplistic. What rigorous cross-function scoring actually requires.
The rise of the CPTO and the unified product team
CPTO demand surged 110% in 2024. AI is collapsing the boundary between product and engineering. Why unified product teams with shared outcomes are the future.
The vital signs of your product organization
Every framework tells you how to organize. None tells you whether it's working. The missing measurement layer for product operations.
The decade that built product operations (and the one that will break it)
Product ops has been solving the same problem for ten years. The frameworks were right about the problems. AI changes what's possible to solve. The fourth pillar is intelligence.
The measurement gap beyond engineering
Engineering teams have DORA metrics. The rest of the product org is flying blind. Here's why that matters and what to do about it.
From score to action: how the five DAC layers work
DAC-framework. DAC-score. DAC-intelligence. DAC-coach. DAC-diagnostics. Each layer builds on the last. Here's what you get at each step and why the progression matters.
Why Faster Shipping Makes Decision Intelligence More Important, Not Less
Velocity trap: 2-3x faster shipping expands the decision surface, not shrinks it. 27% increase in new decisions. Decision infrastructure needed.
F3: What it actually means for a product to be AI-native
27 dimensions across architecture, economics, trust, and competitive moat. A field guide to measuring product AI-nativeness, and why it's different from what your product team calls AI.
From gut feel to data-driven product leadership
Product leaders still rely too heavily on intuition. Data-driven leadership isn't about dashboards. It's about measuring what was previously unmeasurable.
The compound effect of operational intelligence
Point-in-time diagnostics are snapshots. Continuous operational measurement creates a compounding advantage that's nearly impossible to replicate.
Bolt-on vs AI-native: a framework for thinking
Most companies think they're building AI-native products. They're not. Here's a framework for honest self-diagnostic and a path forward.
Anthropic Just Published the Case for Decision Intelligence (and Did Not Realize It)
Anthropic research on AI transforming work maps directly to decision intelligence. 27% net-new work, supervision paradox, mentorship decline. The data makes the case.
Agent-led growth and the end of the traditional software funnel
AI agents are becoming the buyer. When machines research, evaluate, and recommend software, the entire GTM playbook changes.
Human judgment at AI speed
The new operating model for product leaders. The bottleneck has shifted from execution capacity to decision quality. This is what that looks like from the inside.
The invisible layer that makes a product real
The difference between a demo and a product is trust infrastructure. Auth, billing, RBAC, documentation, and compliance are invisible when they work and catastrophic when they don't.
What happens when a 20-year product veteran has 100 hours and no team
I gave myself 7 days to build an AI-native product from scratch. Just Claude Code and two decades of product experience. This is the story of that bet.
73 files and the judgment that wrote them
A production Next.js application in days. Here's what twenty years of product experience actually did when paired with AI execution speed.
Start with the thinking, not the code
When AI can generate anything, the differentiator is knowing what to generate. Why the first thing built wasn't code but the scoring frameworks.
No designer, no framework, no problem
How tight constraints and zero build dependencies produced a more coherent design system than most committees manage.
The parts AI can't do alone
Pricing, positioning, competitive framing, and an investor thesis. The business layer is where twenty years of experience earns its highest return.
Why DORA is necessary but not sufficient
Engineering metrics cover 1 of 6 functions. Here is what they miss, and why product leaders need a fuller diagnostic.
The 6 functions every product team should measure
Most teams measure engineering output. Here are the six functions that determine whether your product team is actually effective.
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