Find published work on AI, governance, and the workplace.

Reads Stanford Medicine's 2020 vaccine list and England's 2020 A-level grading model, where leaders blamed "the algorithm" for choices people made, and shows how a sentence moves a decision to a tool. Closes with an exercise: rewrite each AI sentence so a person is in it.

Reads four supply-chain failures, the Sun-Times' AI-invented reading list, Deloitte's corrected Australian review, Rite Aid's facial recognition vendor and the ROSS Intelligence training memos, and shows the work was handed off while the decision to accept it never was. Closes with five questions to ask of the chain.

Reads this summer's attacks on water-utility controllers alongside the 2023 Unitronics campaign and shows a factory setting no party in the chain owned, then finds the same pattern in exposed AI connectors.

Two people paste the same client email into AI and get opposite results; the explainer for The Consigliere sets out what is safe to bring to AI, what must never go in, and which decisions stay yours.

Reads Stanford and Census payroll data as a hiring story rather than a layoff story, and shows how fewer early-career hires add up to a decision nobody made.

The short launch-day follow-up to Warning: I Am Selling Something, saying what The Consigliere is, what it costs, and where to look before buying.

Compares how four enterprise platforms meter AI in credits, assists and compute units, and shows the buyer negotiates the unit price while the agent decides how many units the work consumes.

An open sales piece for The Consigliere that argues the wrong default is not using AI, but assuming that because a system can do more of the work there is less reason to think deeply about it.

Shows the same model shutting down on different dates depending on the platform it was bought through, and argues the day a system stops answering is set in procurement rather than by the technology.

Argues that stated intent and published commitment are not controls, and that a safety posture only counts where something in the system enforces it.

Uses analyst survey data to argue that what separates organizations that scale AI from those that do not is whether they can prove what happened after the money went in, not how much they spent.

Uses a 3,300-role reduction to ask what a company actually controls when it commits capital to a capability it cannot switch off.

Pushes back on adoption for its own sake and asks a leadership team to state the purpose before the tooling.

Contrasts survey data on who is said to own AI against who is actually accountable, and argues ownership means the ability to stop something.

Goes looking for how many AI agents an organization actually runs, and finds that nobody can produce the number.

Sets two public CEO warnings about vendors learning from customer usage against what those vendors' own policies say.

Argues the purchase model changed: software was one signature, consumption is thousands of unsigned purchases a day, and governance was built for the first.

Treats an AI vendor as a counterparty with its own funding fragility, and asks who in the organization owns vendor financial risk.

A government bought a review of its own automated system using its old procurement pattern. Examines what buying AI-assisted work actually requires.

A confession that revealed the threat rather than ending it, and what that says about incident assumptions.

Traces a so-called rogue AI incident back to a human failure point nobody had checked.

On California's AI Transparency Act taking effect, and why content provenance verifies origin but goes blind at the first screenshot.

An academic finding that the jobs long treated as safest are getting riskier for the workers who relied on that safety, and why the instinctive response is wrong.

Opinion. Argues the AI-detection business and the AI-cleanup business are one business, and that the reader was always the only real judge.

Risk teams can all name the owner of a data breach. Almost none can name the owner of a decision an AI agent just made.

Reads a coding-agent data incident as a category error rather than a breach: an actor that inherits a developer's access was governed with the controls built for an app.

Reads enterprise AI terms for what they protect and what they leave open about what a provider may observe.

Pennsylvania did not pass a new AI law. It applied an existing one, and that is the pattern worth watching.

Buying AI coverage hands an insurer the first governance step the organization was supposed to own.

Insurers repriced AI risk, and the bill lands on the company running the model rather than the one that built it.

Business-critical technology used to arrive through procurement, legal and a named owner. Argues AI bypasses all three and no institution has replaced them.

A vendor argued existing law is sufficient the same month it switched on face and voice capture by default. Where the governance question actually lands.

The Tech Alley talk of 24 June 2026, on the practical reality of using AI responsibly.

Running a decision through an AI does not make it the AI's decision, and regulators have twice proven it with a bill attached.

The fastest-growing governance exposure is not the AI a team adopted. It is unreviewed code arriving inside what the organization buys.

Two capable models were switched off by government action. Most companies cannot say which models they would lose.

Every autonomous system promises a human in the loop. Asks who gets to draw the loop.

On naming a machine after the function that failed, and what the name conceals.

Every layer of AI security governs what a system can do. None of them governs whether it was right.

The answer-engine-optimization trend is real. Separates that from the control its marketing promises.

For financial firms waiting on an AI rule: the obligations already exist, and the default setting is already the policy.

When the human is the product and the AI is the marketing.

Argues the most dangerous AI governance is the kind that passes every audit.

AI vendor financial durability is a counterparty risk, and most governance frameworks assign it to nobody.

A short piece on the conversation the author has repeatedly with leaders about AI accountability.

The enterprise AI market measures seats. Argues it should be measuring influence.

For leadership teams using AI in consequential decisions: governance is available now and does not require a regulator first.

AI systems are logged. AI decisions are not. On the gap between the two.

Most organizations believe AI is making them better. Examines what they are actually learning.