Philosophy & AI We Trained AI to Be Engaging, Not Helpful The attention economy's central insight, articulated by Herbert Simon and later developed by economists studying social media, is simple and devastating: when information is abundant, attention becomes the scarce resource. Platforms optimize for attention capture, not value delivery. Engagement metrics go up. User wellbeing doesn't. The
data-governance AI Governance Is Data Governance With Consequences AI Governance Is Data Governance With Consequences The framing of AI governance as a separate discipline from data governance is a mistake. Most organizations making it will discover this the hard way, because the EU AI Act's high-risk obligations activate on August 2, 2026, and the first
Philosophy & AI LLMs Don't Reason — They Hypothesize A language model that confidently gives a wrong answer isn't broken. It's doing exactly what it was built to do. That's the issue. Charles Sanders Peirce, the American logician writing in the late 1800s, drew a clean line between three modes of inference. Deduction
AI The Development Team and the AI Split: A Role-by-Role Breakdown The Development Team and the AI Split: A Role-by-Role Breakdown The productivity story for AI on development teams has been loud for three years. Code generation. Test scaffolding. Documentation. The headline numbers are real, and the gains in form-bound work are significant. But the development team is
data-governance Governance Belongs in the Platform, Not Next to It Governance Belongs in the Platform, Not Next to It The bolt-on era of data governance had a predictable failure mode: the governance layer was separate from the platform where data actually moved. You had your Snowflake or Databricks instance, and then separately you had a Collibra or Alation catalog
AI Business Roles and the AI Split: What to Delegate and What to Own Business Roles and the AI Split: What to Delegate and What to Own Business roles carry the clearest exposure to the judgment-bound / form-bound split. The client sees the output. Experienced clients can tell within minutes which layer got skipped. It's a different kind of credibility risk
AI Judgment-Bound vs. Form-Bound: The AI Split You're Not Making Judgment-Bound vs. Form-Bound: The AI Split You're Not Making Most AI-assisted work produces one of two outcomes: a genuine multiplier, or a cleanup job that takes longer than doing it yourself. The difference isn't the model. The answer isn't the prompt
data-governance The Data Contract Is an API for Trust The Data Contract Is an API for Trust When GoCardless engineer Andrew Jones was dealing with data quality problems in 2020, he didn't reach for a new governance framework. He asked a different question: what if data had an interface the way services do? Software engineers understand API
Observability The Cost of Knowing: Observability in the Age of AI Agents AI and ML workloads generate 10–100x more telemetry than traditional applications. That's not a configuration problem — it's an architecture problem. The series closes with what the feedback loop between production and the agents writing your code actually looks like.
Observability Closing the Loop: The Graduated Case for Self-Healing Systems Kubernetes self-heals at the infrastructure layer. AI-driven remediation is at the frontier. Between them is a trust ladder most teams skip. The step they skip most consistently is Learn — and that's why the same incidents keep coming back.
data-governance The Fourth Principle: Federated Governance and the Hardest Problem in Data Mesh The Fourth Principle: Federated Governance and the Hardest Problem in Data Mesh Zhamak Dehghani published her data mesh article in May 2019. Most of the attention landed on two of the four principles: domain-oriented ownership and data as a product. Those two are conceptually satisfying and relatively tractable. You
Observability From Alert Storms to Answers: The Long, Humbling Arc of AIOps AIOps promised to cut alert noise and automate root-cause analysis. For most of 2019–2023, it delivered mainly event correlation. LLMs changed what's possible — but your telemetry quality is still the ceiling. Here's the honest accounting.
Observability Golden Paths to Observability: What Platform Engineering Actually Delivers (and What It Doesn't) DORA's 2024 data shows internal developer platforms reduce change throughput by 8% and stability by 14%. Build the platform anyway — but for governance and consistency, not for productivity. Here's why the distinction matters.
diffusion-of-innovations Reading the Room: Where Your Organization Actually Is on the AI Curve Most organizations are misreading their AI maturity, measuring activity instead of capability. Use Rogers' Diffusion of Innovations and the AI Adoption Maturity Ladder for a more honest assessment.
Observability Wide Events, eBPF, and Why the Three Pillars Are a Tax You're Still Paying Storing the same request as a metric, a log, and a trace costs you three times. Observability 2.0 and eBPF attack the same problem from different angles — here's when each makes sense and what the cost model actually looks like.
data-governance Your Stakeholders Are Your Data Quality System Your Stakeholders Are Your Data Quality System In 2023, 74% of data professionals said business stakeholders find data quality issues "all or most of the time" before the data team does. That number was 47% the year before. Not a marginal drift. A 57% increase in one year.
data-governance From Cop to Coach: When Governance Stopped Being About Control From Cop to Coach: When Governance Stopped Being About Control In August 2021, Gartner retired its Magic Quadrant for Metadata Management Solutions. They replaced it with a Market Guide for Active Metadata Management. That is a sentence that looks like analyst housekeeping but is actually a declaration about the entire
Observability Datadog, Dynatrace, and the Bill That Ate Your Engineering Budget Standardized telemetry made it easy to collect everything. The APM vendor boom made it expensive to keep it. The observability tax is now an architectural decision — here's how to model it before you get the bill.
Observability Metrics, Logs, Traces — and the War That Standardization Won The three-pillars model organized the early observability market — and provoked the standardization that ended vendor lock-in. OpenTelemetry didn't just solve fragmentation. It changed the economics of the field.
data-governance The Policy Binder Didn't Change Anyone's Behavior The Policy Binder Didn't Change Anyone's Behavior In 2020, only 8% of organizations rated their data governance program "highly successful." Not 8% of bad organizations. 8% of the ones that had formal programs, named stewards, and documented policies. TDWI surveyed data professionals that year
Observability The 2019 Baseline: What the Books Said vs. What Your Team Actually Ran In 2019, a wide and well-documented gap separated what DORA and Google SRE recommended from what most engineering teams actually ran. Here's what the baseline looked like — and why it matters for benchmarking where you are today.
Which Failure Mode Can You Afford? The Security Personality of Your Model Which Failure Mode Can You Afford? The Security Personality of Your Model Every conversation about model selection eventually arrives at the same question: which model is better? The answer most people expect is a leaderboard ranking: here are the top five, use number one, done. The Sonar leaderboard study of
The Evaluation-Governance Flywheel: Neither Works Without the Other The Evaluation-Governance Flywheel: Neither Works Without the Other The four-layer AI governance model I've written about in this series is technically sound. Layer one defines policy. Layer two discovers the AI surface area you're governing. Layer three monitors models in production for drift. Layer
Acceleration Whiplash: The Hidden Metric AI Adoption Is Breaking Acceleration Whiplash: The Hidden Metric AI Adoption Is Breaking The productivity numbers look great. Epics are closing 66% faster. PRs are merging at a higher rate. Task throughput is up 34%. If you stop there, the story writes itself: AI coding tools are working, the investment is paying off, and
The Right Experiment for the Right Claim The Right Experiment for the Right Claim Most ROI cases for enterprise AI are pre/post analyses. A pre/post analysis can prove a metric moved. It cannot prove the model moved it. Get it wrong, and you're signing a number you can't defend. The previous
