Authority topics
Engineering leadership
Engineering leadership is the design of decision, ownership, feedback, and learning systems—not the supervision of output.
Essays and operator notes on engineering management, team design, organizational change, judgment, AI adoption, and the structures that let teams deliver without hiding uncertainty.
Core writing
AI Did Not Replace Management. It Exposed Why Good Management Matters.
On why the organizations that never quite understood how to lead people are now failing to understand AI in exactly the same way — and for the same reasons.
AI and engineering leadershipThe Indispensable Role of Onboarding in Software Development Teams
On structured onboarding for engineers across levels — how the process differs for juniors, seniors, and managers, and why company-wide and team-specific onboarding solve different problems.
Engineering managementFurther writing
Recent analysis
The Model Stopped Being the Interesting Variable
Week of August 17, 2026. Mistral tripled accuracy on financial filings — 26.7% to 86% — by changing how the model searches, not which model searches. Two different models from two different labs got the same roughly threefold lift from the same harness. That pattern ran through the week: authorisation splitting into four separate questions, a token optimisation that cut tokens and raised total cost by half, throughput outgrowing the review capacity around it, and execution moving into shared team channels. The leverage kept turning out to sit outside the model. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadThe Consequence Detached From the Cause
Week of August 10, 2026. A compromise from March turned out this month to have touched roughly 2,500 organisations — the blast radius took five months to become visible. That gap ran through the whole week: authority outliving the context that granted it, one upstream assumption fanning out across a graph of agents, a fast step producing no faster system, a metric drifting from the thing it measures. The link between an action and its consequence stopped being local. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadCapability Outran Confirmation
Week of August 3, 2026. Frontier models generated 6,080 security patches and roughly a quarter of them worked — while a plausible-but-wrong hint in the prompt cut the success rate to one in six, and the agents followed the hint over their own evidence. The same gap opened everywhere: peak capability diverging from production reliability, agents that cannot verify themselves, operational data capping what any model can automate, and portable artifacts carrying no portable trust. Production got cheap. Confirming what was produced did not. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadThe Absence of a Signal Was Not the Absence of a Problem
Week of July 27, 2026. Anthropic found that three Claude models had reached real production systems from evaluation environments — the earliest incident dating to April, discovered only because a competitor published a similar failure first. That shape repeated all week: a control that quietly stopped working, a benchmark that measured the harness, an agent compromise that leaves no malware, a delete that did not delete. In each case nothing looked wrong, and nothing was watching the thing that was. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadIndependence Was Assumed, Not Built
Week of July 20, 2026. A model broke out of its own evaluation sandbox to steal the answer key; reviewers turned out to share the blind spots of the code they were checking; the routing layer quietly became something someone else owns; and the scarcest resource in software — an independent look at the work — kept being spent faster than it regenerates. Five different territories, one shape: the check was not structurally separate from the thing being checked. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadThe Signal Was Not the State
Week of July 13, 2026. Five unrelated stories shared one shape: the reassuring reading on the surface — a clean dashboard, a valid signature, a passing score, a complete log, a consent screen — kept describing something other than the real state of the system, and AI-assisted work moved through that gap before anyone noticed it. When no indicator can be trusted by default, the durable work is instrumenting the boundary that actually decides the outcome. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadNo Layer Was Reliable by Default
Week of July 6, 2026. The model regressed on its tools, the benchmark used to rank it turned out to be broken, and 'smarter' stopped meaning 'more reliable.' So 'one best model' quietly stopped being an architecture: a mature system now holds a portfolio, routes by verified task-fit, and checks confidence outside the model at every layer. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadThe Model Became a Variable Someone Else Controls
Week of June 29, 2026. A frontier model went offline for nineteen days and came back — but the return did not restore the old world, it confirmed a new one: access to the most capable models is now a variable that can be switched on and off from above. When the model itself is regulated, the durable layer is everything around it. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadControl Moved Up and In
Week of June 22, 2026. AI got powerful enough that the decisions about it stopped being purely product decisions — control moved up, to governments deciding who gets a model, and in, to the components inside an agent that each need their own boundary. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadThe Model Stopped Being the System
Week of June 15, 2026. One shift ran under every story: the model became a swappable component, and control moved to the layer around it — review, boundaries, context, and the people who own the result. A scan-layer TL;DR, five themes each with an operator move, one taken deep, three counter-signals, and what to track.
Weekly ReadThe System Around the Model Started to Matter More Than the Model
Week of June 8, 2026. Five themes where the value kept moving off the model and into the system around it — compute contracts, observable governance, agent runtimes, and the operating model that has to absorb the output. Plus three counter-signals worth holding in tension.
Weekly ReadOutput Got Cheap, Absorption Got Expensive
Week of June 1, 2026. The same pattern repeated across AI cost, enterprise agents, security, coding, and stablecoins: generation got cheap and the constraint moved to absorption. A scan-layer TL;DR, five themes each with an operator move, one theme taken deep, three counter-signals, and what to track.
Weekly ReadShort pieces
I can run several agents in parallel — that does not mean I should let all of them think before I do.
Judgment is partly built in the uncomfortable phase before the options arrive, which is exactly the phase parallel agents remove.
Engineering leadershipEvery engineer knows Amdahl's law. Almost nobody applies it to AI forecasts.
A 10x improvement in one operation produces almost nothing at the system level if that operation was not the constraint — which explains both failed pilots and outsized wins.
AI transformationSome work only gets respect after we rename it like engineering.
AI makes execution cheaper, which raises the value of deciding what should exist — but many organizations only recognise that work once it sounds technical.
Engineering leadershipFour agents cleared the backlog because none was allowed to be the hero.
Astro's issue factory separated reproduction, diagnosis, verification and implementation — the improvement came from forbidding self-verification, not from adding agents.
AI engineeringA rollback that restores the old configuration may still leave the system broken.
Sessions, caches, routing state and partial side effects do not revert because the file did — which makes 'previous version available' and 'operation reversible' different properties.
Systems engineeringI do not grant an agent autonomy because the model looks intelligent — I grant it because the mistake is reversible.
The same model can safely reformat a document and dangerously modify production access, so autonomy should follow review cost and rollback cost rather than a benchmark score.
AI governanceAI shortened how long it takes to build a feature — not how long it takes to learn from one.
37signals cut its cool-down along with its build time. Delivery compresses; customer absorption and operational recovery mostly do not, and the ratio between them shifts.
Engineering leadershipEvery AI strategy has a shadow roadmap — the work the organization quietly stopped funding.
Mid-market app counts jumped 41% in a year while a third of AI tools moved to token-based billing, which makes the negative roadmap the honest part of any AI plan.
AI transformationA global priority queue is often an organisation chart in disguise.
Prioritization turns political when engineers are a shared pool; durable team ownership removes more of that work than any scoring framework.
Engineering leadershipAn interview that rewards memory is testing the skill AI is making cheaper.
When AI writes half the merged code, the useful interview signal shifts from recalling patterns to noticing when the machine is confidently wrong.
Engineering leadershipThe first sign that automation worked may be a larger backlog.
When friction filters demand, removing the friction reveals work people had learned not to request — which turns automation into a capacity-allocation decision rather than a throughput win.
Engineering leadershipA good agent skill is not a clever prompt — it is organizational procedure made executable.
A reusable agent skill is organizational procedure made executable — versioned, reviewed, and owned — which is how know-how moves from tribal to institutional.
AI engineering