The Last 20%
Essais sur les 20 % opérationnels finaux
Essais courts et tranchants sur ce qui bloque entre 80 % et la production. Pour les CIO, CTO et CFO d'entreprises dirigées par l'ingénierie, de 50 M$ à 1 Md$.
S'abonner sur Substack- Essai 01
The Last 20%
Why Every Mid-Market AI Initiative Stalls at the Same Place
An engineering-led IT team ships an AI initiative at 80% within 90 days. The remaining 20% never closes — not because the team is lazy, but because the 20% is structurally different from the 80%, and almost nobody plans for the gap.
#CIO#EnterpriseAI#MidMarketLire sur Substack → - Essai 02
BYOK Is the Tell
A 30-Second Vendor Sorting Hat
One 30-second question separates AI vendors who'll be around in 2027 from the ones who won't. Ask it on every bake-off call — and watch the room reorganize itself.
#CIO#AIprocurement#CFO#VendorEvaluation#EnterpriseAILire sur Substack → - Essai 03
Stop Hiring Platform Engineers
Hire One Less
The math on hiring a $200K platform engineer to maintain your AI stack vs. paying a specialist $50K/yr doesn't survive contact with a CFO who can do four-year arithmetic.
#CIO#CFO#ITbudget#PlatformEngineering#FP&ALire sur Substack → - Essai 04
Pricing Transparency
$75K + $50K/yr — Here's the Build-vs-Buy Math
Most AI vendors hide pricing behind "talk to sales." Here's exactly what we charge — $75K engagement + $50K/yr — and why publishing the build-vs-buy math is the most defensible move we could make.
#CIO#CFO#AIvendor#PricingTransparency#ITbudgetLire sur Substack → - Essai 05
Year 2-3 of the PE Hold
When "We Built It Ourselves" Stops Being a Brag
There's a window in the PE hold cycle when "we built it ourselves" stops being a brag and starts being a liability. Recognize the window before the operating partner does.
#CIO#PE#PrivateEquity#MidMarket#ITdiligenceLire sur Substack → - Essai 06
The Power Automate Endgame Problem
Five Cliffs Every Mid-Market Deployment Eventually Hits
Every mid-market Power Automate deployment eventually hits the same five cliffs. The community knows about three of them. The other two only show up at scale.
#PowerAutomate#PowerPlatform#Microsoft365#CIO#ITopsLire sur Substack → - Essai 07
What Cyber Insurance Carriers Actually Look at
In an AI Breach Claim
Five artifacts insurance carriers demand when an AI mediates a denied claim. Four of them don't exist in your current pipeline. Here's what they look like and where they go.
#CISO#CIO#CyberInsurance#ITcompliance#RiskManagementLire sur Substack → - Essai 08
What I'd Tell a $50M–$1B CIO
If I Wasn't Selling Anything
Twenty minutes of unvarnished advice with no pitch, no demo, no NDA. Most of it is what to stop doing.
#CIO#EnterpriseAI#VendorEvaluation#MidMarket#ITstrategyLire sur Substack → - Essai 09
Cognitive Surrender
What Happens When Knowledge Workers Stop Reasoning and Dispatch Problems to AI Instead
A devops engineer named the failure mode of AI without discipline this year. Cognitive surrender is the decision to stop reasoning and let the model decide instead. Seven flavours of vendor-trust collapse, four cautionary cases at four scales, and the structural fix that is not "more AI literacy."
#AIGovernance#CIO#EnterpriseAI#OperationsPartner#AILire sur Substack → - Essai 10
Seven Questions Every AI Vendor Should Be Able to Answer
A Buyer's Diagnostic for AI Vendor Evaluation
Seven questions you can run in a discovery call, RFP, or 30-minute vendor-eval review. One per vendor-trust-collapse flavour, with vendor-honest and vendor-evasion answer shapes laid out so you can tell which one you are hearing.
#AIGovernance#CIO#EnterpriseAI#OperationsPartner#AILire sur Substack → - Essai 11
Two Kinds of AI Exclusion
And the One Your CGL Renewal Won't Tell You About
Headlines about carriers "excluding AI" blur two structurally different exclusions filed with state regulators. One is narrow and applies only to generative AI. The other is broad enough to exclude algorithmic credit scoring. Your renewal preparation needs to know the difference.
#CyberInsurance#AIGovernance#CIO#RiskManagement#InsuranceLawLire sur Substack → - Essai 12
What You're Actually Deferring When You Defer Agent 365
Gartner Is Right to Say Wait. The Question Is What Fills the Deferral Window.
Gartner's First Take on Microsoft 365 E7 and Agent 365 says defer, and Gartner is right. The harder question is what you buy instead, and what you commit to operating yourself for the 6 to 18 months of the deferral window. The three honest paths, laid out.
#CIO#Microsoft365#AgentGovernance#ITStrategy#AILire sur Substack → - Essai 13
The Ungoverned Marketing Department
Why Marketing Surrendered to AI First, and the Category That Is Still Empty
Marketing ships in public and the feedback is fast, so cognitive surrender showed up there before anywhere else. The marketing-operator cut of the Cognitive Surrender arc: what the failure mode looks like inside the department, and the governance shape that fixes it.
#Marketing#CMO#AIGovernance#OperationsPartner#B2BMarketingLire sur Substack → - Essai 14
Integration Is the Moat
Portfolio Value Creation When Acquisition Velocity Stops Working
The cheap-debt, buy-the-growth roll-up playbook is closing. When you cannot buy the next turn of growth, the only return left is the one you build inside the businesses you already own. Integration is an operating capability, not a transaction.
#PrivateEquity#ValueCreation#PortfolioOps#Integration#AIGovernanceLire sur Substack → - Essai 15
The Job Description Is the SOW
The Open Marketing Requisition Is a Statement of Work With a Cleared Budget
Every portfolio company has a marketing requisition that has been open 60 to 120 days. The responsibilities section is a Statement of Work, the salary band is a pre-approved budget envelope, and the hiring manager is the named sponsor. The trial starts Monday.
#PrivateEquity#ValueCreation#PortfolioOps#PortfolioMarketingLire sur Substack → - Essai 16
The Vendors Just Named Our Category
Five Billion Dollars in Two Weeks, and the Segment the FDE Model Cannot Reach
In one fortnight of May 2026 the frontier vendors put more than $5B behind the Forward Deployed Engineer delivery shape, and stated publicly which buyers it will not reach. The partner-network half of that admission is the empty square the Operations Partner fills.
#PrivateEquity#ValueCreation#PortfolioOps#AIGovernance#ManagedAILire sur Substack → - Essai 17
The Forward Deployed Engineer Will Not Be Coming to Your Office
Three Structural Responses for the $50M-$1B IT Organisation
The May 2026 FDE capital says who gets the embedded engineer: Fortune-500 regulated verticals. Mid-market IT is routed to partner networks and self-serve APIs. Three structural responses, decidable from engineering bench, time-to-production, and audit-artifact readiness.
#CIO#CISO#EnterpriseAI#AIGovernance#MidMarketITLire sur Substack → - Essai 18
Cloud Can't Reach the Work. Local Can't Be Governed.
Why Agents Need a Substrate and a Control Plane — Not a Bigger Model
Every demo of an AI agent runs in a sandbox; real work lives behind a firewall. The market's two default answers are both quietly broken — cloud-only can't reach the work without opening your network, local-only reaches everything but governs nothing. Kubernetes already solved this shape: split the control plane from the data plane and let the workers pull. The tell for any agent platform is the direction of the wire.
#AIGovernance#AIAgents#EnterpriseAI#CISO#ZeroTrustLire sur Substack → - Essai 19
Who Operates Is the Moat
Agent Platforms Agree on the Hard Part. They Split on Who Operates.
A wave of agent-orchestration startups — open-source projects and venture-backed seeds alike — have independently converged on the same spine: a governed work-item, a human approval gate, an immutable audit trail. The architecture is basically settled. The question nobody is asking is who actually operates the work — and that operating choice, not autonomy level or model size, is the durable moat.
#AIAgents#AgentOrchestration#AIGovernance#ManagedAI#AgenticAILire sur Substack → - Essai 20
Don't Sandbox the Agent. Govern the Surface.
Why the Local Agent Needs Managing Like an Employee Laptop — Not Sandboxing Like Hostile Code
Caging the local agent is the obvious reflex and the wrong one — a sandbox tight enough to be safe is tight enough to be useless. We already have a discipline for a capable actor with broad reach on a device we don't fully control: an employee with a laptop. Trust the managed device; govern the surface it connects to, where the plane holds the credentials and the gate, and a revoked badge ends the session in seconds.
#AIGovernance#AIAgents#EnterpriseAI#CISO#ZeroTrustLire sur Substack → - Essai 21
The Session Is the Tell
The 30-Second Question for Any Vendor Whose AI Agent Runs on Your Machine
When a vendor's agent runs on your machine, something has to authenticate it to your systems — so what credential sits on the box, and where do the real keys live? One 30-second question, three answer shapes: a god-key, an encrypted god-key, or the agent's own short-lived session. The sequel to BYOK Is the Tell — the session is the tell because it reveals blast radius, real per-agent revocability, and where governance actually lives.
#AIGovernance#AIAgents#EnterpriseAI#CISO#ZeroTrustLire sur Substack → - Essai 22
The Leads Your Locations Lost Last Month
Your Cost Per Lead Is on a Slide. The Leads That Walked Are in No Dashboard.
Last month your locations took in hundreds of new inquiries — and a real share never got a fast human response, and quietly walked. It's not a demand problem, it's a capture problem: the unowned seam between marketing and operations, a seven-figure leak across a location footprint that shows up in none of your metrics. Here's how to put a per-location number on it.
#MultiLocationMarketing#CMO#MarketingOps#SpeedToLead#FranchiseMarketing#CustomerAcquisitionLire sur Substack → - Essai 23
Agency Is Not a Metaphor
When an AI Agent Acts, the Law Looks for a Principal
When an AI "agent" acts, courts don't see a clever tool — they see an agent acting for a principal, and the principal answers for what it did. From Air Canada's chatbot to imposed-liability rulings, the pattern holds: you can't delegate accountability to software. Governance is how you stay a principal you can defend.
#AIGovernance#AILiability#CISO#GeneralCounsel#AIAgentsLire sur Substack → - Essai 24
You Can't Incorporate Away the Principal
Argentina Wants AI-Run Companies With No Humans. It Doesn't Work.
Argentina wants to legalise companies run by AI with no humans required — the corporation's last move to shed human liability. It doesn't work: a foreign shell won't hold the bag, and the party who granted the authority and can prove how it was governed is the one the loss comes home to. The sequel to "Agency Is Not a Metaphor."
#AIGovernance#AILiability#GeneralCounsel#Insurance#AIAgentsLire sur Substack → - Essai 25
The Evidence Your Regulated Clients Keep Asking For
Run AI on Regulated Data and You've Signed Up to Prove It
Sooner or later a regulated client asks you to prove exactly what your AI did on their data — and that it couldn't have done anything else. A screenshot and "trust us" isn't evidence. Real proof is three things: full provenance, a trail anchored beyond your own reach, and a pack mapped to the client's framework — and the provider who can produce it wins the accounts the others fear.
#MSP#Compliance#HIPAA#SOC2#GovernedAILire sur Substack → - Essai 26
The Audit Log Is the Tell
Can You Believe the Record of What the Agent Did?
Every governed-AI pitch points at an audit log. The question that sorts vendors: if a row were deleted or changed — by an attacker, or by you — could I prove it? Three answer shapes (editable rows / vendor-signed "immutable" / hash-chained and anchored beyond the vendor's reach) tell you whether the record survives its own keeper. Third in the "tell" lineage.
#AIGovernance#AIAgents#EnterpriseAI#CISO#ComplianceLire sur Substack → - Essai 27
Who Can Become You?
The Fourth Tell: Who at the Vendor Can Quietly Become You
Every SaaS support team has a "become the customer" button, and almost nobody governs it the way they'd never let you leave the AI ungoverned. The fourth tell, after BYOK, the session, and the audit log: who at your vendor can see and act as you — and would you ever know? The only good answer is the one where you grant it, you see it, and the record can't be rewritten.
#AIGovernance#AIAgents#EnterpriseAI#CISO#TrustLire sur Substack → - Essai 28
Who Can Become Your Client?
The Access Question Your Regulated Clients Will Ask Next
Run AI on a regulated client's data and you've inherited the question every client eventually asks: who can become them — see and act as their account? The row nobody reads aloud is the support master key, yours and your AI vendor's. A defensible answer is granted by you, scoped, on a record you can see and can't rewrite — and the provider who has it wins the accounts the others are afraid to take.
#MSP#Compliance#HIPAA#SOC2#GovernedAILire sur Substack → - Essai 29
The Black Box in Your Funnel
Your Call Center Books Half Your Patients. Why Is It Your Least-Measured Channel?
For most multi-location businesses, roughly half of bookings close in a human conversation — the call center — and it is the least-instrumented part of the entire funnel. Last-touch over-credits the digital channels you can see and under-invests in the one actually closing the business; multi-touch attribution quietly breaks when its highest-converting step, the phone, is not instrumented. The call center is not an ops cost center — it is your highest-intent conversion surface, run blind.
#MarketingAttribution#MultiTouchAttribution#CallTracking#MultiLocation#HealthcareMarketing#CMOLire sur Substack → - Essai 30
The Band Where AI Is Safe Is a Design Problem, Not a Discipline
Everyone now agrees AI shouldn't render the verdict. Agreeing is the easy part — here's how you build the thing that enforces it.
The consensus that AI shouldn't make the final call is the easy part; enforcing it is the hard part. "Be careful" is not a control — it depends on the most distracted person on the worst day. The band where AI is safe has to be a property of the system: scope, gates, and an audit that hold whether or not anyone remembers. Design beats discipline.
#AIGovernance#EnterpriseAI#AILeadership#DecisionMaking#AIStrategyLire sur Substack → - Essai 31
"Self-Sufficient When We Leave" Has a Floor
AWS just put a billion dollars behind forward-deployed AI. Its own model names exactly who it cannot serve.
AWS stood up a dedicated Forward Deployed Engineering org with a billion dollars behind it — and its own promise, "self-sufficient when we leave," names exactly who it cannot serve. There is a floor below the Fortune-500-regulated tier where the embedded engineer never comes, and someone still has to operate for everyone beneath it. The category is settled; the floor is the opening.
#AIGovernance#EnterpriseAI#ManagedAI#ForwardDeployedLire sur Substack → - Essai 32
What Compounds
A major vendor just open-sourced the agent governance layer. When your differentiator ships as a free download, you find out what your moat actually was.
Databricks open-sourced its entire agent "meta-harness" — Apache-2.0, the whole spine: a model-agnostic harness, a policy engine, a split control/data plane, an approval gate. When the layer you thought was your differentiator ships as a free download, you get an honest answer to what your moat actually was. Read what the open version can't do and two absences jump out, both about time: a record you can prove, and a memory that compounds.
#AIAgents#AIGovernance#AgentOrchestration#ManagedAI#OpenSourceLire sur Substack → - Essai 33
A Memory of Judgment
I tore down the five best open-source memory systems. Every one remembers what's true. None remembers whether the agent should be trusted to act — and why nobody built that is the whole business.
I read the five best open-source AI-memory systems line by line — supermemory, mem0, Open Brain, Graphiti, Letta. Every one remembers what's true about the world; not one remembers whether its own past actions were good enough to be trusted again. The gap is structural: you only get that signal if you operate — which makes judgment memory a company, not a library.
#AIAgents#AgentMemory#AIGovernance#ManagedAI#OpenSourceLire sur Substack → - Essai 34
The Agents Won't Govern Themselves
Your enterprise won't run one AI agent — it'll run a mixture. I read four line by line. Each guards its own turf well; none governs the fleet. The layer that does is the whole business.
Readers pushed back on the memory teardown: those are libraries — show me the actual agents. So I read four line by line: OpenAI's Codex, two chat-channel assistants (OpenClaw, Hermes), and a self-improvement experiment (Evolver). Per-agent safety is close to solved and the best labs ship it well — but the enterprise runs a mixture of agents, and not one of the four governs the fleet. That governing layer is the whole business.
#AIAgents#AIGovernance#EnterpriseAI#AgentSecurity#ManagedAILire sur Substack → - Essai 35
Nobody Budgets for Governance
A friendly prospect asked three questions I couldn't answer cleanly — and exposed a sequencing error: governance is why customers stay, but productivity is why anyone buys. Accelerate, accumulate, govern. In that order.
Enterprise budgets attach to functions and headcount-shaped work: support ops, the content agency, the SDR tools line. There is no line called "governance" — that line gets created later, by pain. So the sequence is Accelerate → Accumulate → Govern: AI doing real work is why anyone buys; the track record and the risk pile up together; governance is why they stay, expand, and pass audits. What we changed in a week: named offers mapped to budget lines a buyer already has, a fixed pilot price on the website, and governance demoted to one quiet, load-bearing line in every offer.
#AIGovernance#EnterpriseAI#GoToMarket#ManagedAI#FounderLessonsLire sur Substack → - Essai 36
Your Agent Needs an Allowance
A dashboard only tells you what already happened — when what happened is "the agent spent the quarter's budget overnight," the dashboard is an obituary. We shipped the alternative: an allowance, with an earned-autonomy ladder for spend.
Everyone's read the runaway-agent bill story, and the resolution is always "we'll watch it more closely." Watching is not a control. Last week we shipped the alternative: a hard 100% wall (new work blocked, in-flight spared — a pause, never a crash), loud pausing with reasons and auto-resume dates, surgical snapshot-then-restore resume, and repeat offenders losing auto-resume entirely. Spend has an earned-autonomy ladder, exactly like actions. Plus a confession about almost putting the gate on the wrong function.
#AIAgents#AIGovernance#FinOps#EnterpriseAI#ManagedAILire sur Substack → - Essai 37
The Law Just Located the Risk
England's judiciary-chaired taskforce just answered who's liable when AI causes harm: whoever granted the autonomy — and can't prove the supervision.
On 7 July the UK Jurisdiction Taskforce published 130 pages on liability for AI harms — a reading of England & Wales private law, chaired by the Master of the Rolls. Read as an operator, it's a map: it says where AI risk legally lives, and the answer is the operations layer. The sentence that decides cases: liability turns on "the level of autonomy and the degree of supervision that is exercised or should have been exercised." Supervision that left no record is legally indistinguishable from supervision that never happened. Foundation-model developers mostly escape; careless deployers don't; professionals face a ratchet; and the only human-review posture that survives is evidenced, genuine review.
#AIGovernance#AILiability#AIAgents#EnterpriseAI#CISOLire sur Substack → - Essai 38
Gates the Agent Can't Bypass
Our AI agents were told to run the tests before every commit. They did — often. Here's the enforcement layer that replaced the word "often".
My company's code is mostly written by AI agents, and the instruction said: run the type check and the tests before every commit. The agents did — often. Under deadline pressure an agent rationalises exactly like a tired engineer at 6pm, so we stopped asking. Three layers: the commit demands evidence (a marker only a passing gate run can produce), the receipt expires on any post-run edit, and the bypass flag is blocked at a layer the agent can't modify — with a human-only override key. Week one, the enforced gates caught two flaky tests that had been latent for months. An instruction is a request; if your agent governance lives in a prompt, you have a preference with a policy number.
#AIAgents#AIGovernance#SoftwareEngineering#DevTools#PlatformEngineeringLire sur Substack → - Essai 39
Nobody Coordinated This
A stranger from a different corner of software independently built the same governance organs I did. When two lineages that never met grow the same structure, you've found a law, not a fashion.
The octopus eye and your eye were invented twice — separate lineages, same machine, because seeing demands a lens. This month I watched the same thing happen in software: an ex-Google engineer building AI for database incident response and I compared notes across unrelated domains, and the notes were the same notes. The audit trail is the product. A log of actions isn't enough — you need a memory of judgment. Certify the harness, not the model. Consistency isn't correctness. Then a third lineage published the same finding, and it wasn't a builder: England's judiciary-chaired AI-liability taskforce concluded liability turns on the supervision you can prove. Convergence doesn't crown a winner — it proves the pressure is real.
#AIAgents#AIGovernance#SoftwareArchitecture#BuildingInPublic#AgenticAILire sur Substack → - Essai 40
The Review Economy
The team that bought a 5x tool and shipped 1.3x. The bottleneck nobody budgets for is the person who has to say "ship it."
Run the arithmetic nobody runs: in a normal org, review is ~10% of total time. Make producers 5x productive and review demand wants to be 50% — an impossibility, so production falls to meet review capacity. You bought 5x; you ship ~1.3x plus unreviewed inventory. AI is the greatest non-bottleneck accelerator ever built. Orgs that don't engineer review pick one of three defaults without noticing: queue collapse, rubber-stamping (an editor's liability with no evidence), or bypass. The fourth option treats review as a system: price it by risk, make each review cheap with evidence-first artifacts, calibrate the judges, record every accept/reject so review compounds, then graduate to supervising the system itself. Plus the three-number diagnostic most organisations can't answer.
#AIAgents#AIGovernance#EngineeringLeadership#EnterpriseAI#AIAdoptionLire sur Substack → - Essai 41
The Plausibility Subsidy
AI made looking good free — so looking good stopped meaning anything. The market for lemons is now inside your company, and the fix is evidence that buys freedom.
For the history of knowledge work, polish was expensive, so polish was information — and every manager alive was trained to read that signal. AI just made polish free. What's left is the plausibility subsidy: plausible work costs ~10x less than correct work, and the gap flows to whatever looks good. Adverse selection for slop isn't a character failure; it's the equilibrium. The fix is a design principle: stop trusting assertions about work and read evidence attached to work — produced by the process, not the author. Five instruments, one uncomfortable implication (AI made senior judgment measurable), two traps (Goodhart, and the fatal one: bossware), and the incentive-compatible core: evidence buys freedom.
#AIAgents#AIGovernance#EngineeringLeadership#FutureOfWork#EnterpriseAILire sur Substack → - Essai 42
Context Is Exclusion
Every company has a vector database. Almost none has anyone who decides what belongs in it — and what makes a library a library isn't the pile of books. It's the catalogue, and the discard pile.
A prediction that sounded wrong two years ago and reads like a diagnosis now: bigger context windows made AI worse at most companies. Not technically — operationally. When the window was small, somebody had to choose what went in, and choosing is where the quality lived. The solo operator's AI works for a reason nobody copies: they aren't a retrieval system, they're a relevance system — the asset is knowing what doesn't matter. Context is judgment about what matters, written down, which means context is exclusion. Three failure shapes (the empty RAG, the everything-bagel, the ownership vacuum), the feedstock everyone throws away (every reviewer correction is a context patch), and the library test.
#AIAgents#RAG#KnowledgeManagement#EnterpriseAI#AIEngineeringLire sur Substack → - Essai 43
Earned, Not Configured
Every AI rollout configures trust on day one — and configuration can only guess. The organisations that get autonomy right treat trust as a price the agent pays down with evidence: promotion by track record, demotion by incident, ownership by name.
There are two trust systems in every company. The one for people is earned: track record, calibration, graduated responsibility. The one for AI is configured: someone sets permissions on day one and hopes. Configuration sets the ceiling; evidence sets the level. The ladder that fixes it: autonomy earned per task-class by demonstrated calibration, promotion that is slow and boring by design, demotion that is fast and automatic, an owner with a name, and the price that moves only upward — plus portability: the track record survives the model that earned it. Anything else is a guess with uptime.
#AIAgents#AIGovernance#AITrust#EnterpriseAI#AgenticAILire sur Substack → - Essai 44
Everything Implicit Becomes Infrastructure
Why AI works so well for individuals and so poorly for teams: everything that's free when you work alone must be built when you don't. Seven rows, one law.
Why does AI work so well for individuals and so badly for organizations? Everything that makes an individual productive with AI is implicit — invisible, automatic, free. An organization gets none of it for free; every implicit thing must become infrastructure. Seven rows: context (curation is exclusion), trust (autonomy earned, not configured), review (the economy that caps throughput), enforcement (gates, not prompts), attribution (the provable audit trail the law now asks for), incentives (quality made legible), and memory (judgment that compounds). The table is a company — and the diagnostic is to mark each row infrastructure, implicit, or missing. The dangerous mark is implicit: one person is quietly carrying it.
#AIAgents#AIGovernance#EnterpriseAI#EngineeringLeadership#AIAdoptionLire sur Substack → - Essai 45
The Grammar of Operations
DSLs make LLMs reliable. The skipped question is where the right DSL comes from. A semi-algorithm exists: compression over real instances — and in operations, the corpus is the audit log.
Unmesh Joshi's piece on martinfowler.com is right: constrain an agent to a small language with a deterministic validator and generate-and-hope becomes generate-check-repair. But it walks past the hard part — where a good DSL comes from. The folklore says "iterate"; the method is compression: start from a corpus of 20-50 real instances (never a concept), factor repeats into constructs and variance into parameters, test round-trip coverage and back-translation fidelity, stop when new instances stop forcing new constructs. What resists the loop is two judgment calls — choosing the corpus and deciding which variance is essential. And operations already holds the corpus: every escalation, approval, and override a governed agent generates is a labeled instance of what the machine may do.
#AIAgents#SoftwareArchitecture#DomainSpecificLanguages#AIEngineering#EnterpriseAILire sur Substack → - Essai 46
Governance You Inherit
The sequel to Everything Implicit Becomes Infrastructure. I built the whole table — and the seven things break identically for every operation, so the floor is reusable, and reusable governance is the actual product.
The anchor essay drew the table: seven things — context, trust, review, enforcement, attribution, incentives, memory — that are free alone and must be built by a team. This is what building the whole table taught me. When you build all seven, for a patent firm and a managed-IT shop and a wealth advisor, you see the thing you can't un-see: the seven break identically. Same failures, same order. Only the one dangerous action at the top differs — a filing deadline, a production push, money moving — and underneath it sits the identical floor. Which means the expensive part, the part every company is told to build for itself, is reusable. You don't commission electricity; you inherit the grid and pay for what you draw. Governance is the same once the floor is built correctly one time: not a project each company repeats, but a utility they inherit. And I can say it's reusable because I'm customer zero — the agents that build the system run under the same seven rows, and row seven is just the trust-until discipline I already used to keep my own memory from lying to me. The last twenty percent, closed once, so nobody I work with has to close it again.
#AIGovernance#ManagedAI#EnterpriseAI#AIAgents#EngineeringLeadershipLire sur Substack → - Essai 47
Same Model, Different Floor
I ran the same tasks through two harnesses on an open enterprise benchmark. The frontier model passed everywhere but paid $2.13 a task on raw interfaces against $0.59 on a governed floor. The cheapest model went from 31% to 94% without changing the model. The difference was never intelligence — it was the floor under it.
This month a vendor published a benchmark that is more interesting than its leaderboard. Both lanes run the same model — and when the model is held constant, a leaderboard stops measuring intelligence and starts measuring harnesses. Their claim: moving an agent from curated tools to protocol-realistic APIs costs 18 to 19 points of accuracy and doubles token cost. A vendor claiming its architecture is the special ingredient is marketing. But this claim is checkable, because they published the dataset. So I checked it: same tasks, same judge, same models, through two doors onto the same 32,768-ticket company. Seventy-five trials later, the frontier model passed everything on every interface but paid ~$2 and ~29 turns a task on raw surfaces against $0.59 and 6 turns on the governed tier. The cheapest model went from 5 of 16 on raw APIs — failing by silently dropping records it never knew existed — to 15 of 16 on the best floor per task, including one cell at eight for eight at four cents a run. Fixing the weak model with a bigger model costs about $1.70 a task, forever. Fixing it with a better interface costs a dime. And the decisive layer was not pre-computation: it was two hundred lines of declarative topology — the map of how the sources relate. Halfway through, the experiment audited me back: my own curated tier shipped a misread threshold, and the confession is the best section.
Lire sur Substack → - Essai 48
The Underwriting Variable
The sequel to The Law Just Located the Risk. The courts put AI liability on supervision you can show; the insurance market just located the same risk on the same axis — and attached a price.
Earlier this month the courts located the risk of autonomous AI: liability turns on the supervision you can show. Last week a second system located the same risk on the same axis, and did what the courts do not, it attached a price. Insurers are filing AI exclusions across general liability, D&O, and E&O. An exclusion does not reduce AI risk, it relocates it from the carrier to the policyholder, and the carve-out for loss arising out of AI turns one question into the whole game at renewal: can you show the loss arose from a licensed human decision, or did it arise out of the model? Same event, opposite sides of the same clause. Two systems that do not coordinate, doctrine and price, reached for the same axis, provable human accountability, so it is not an opinion you can wait out. The market is the sharper signal because it reprices at renewal instead of after a bellwether case, and it converts the audit trail from the line item nobody could justify into the instrument that decides a paid claim from a denied one. Governance stopped being compliance the moment the market made it the underwriting variable.
#AIGovernance#Insurance#EnterpriseAI#RiskManagement#ErrorsAndOmissionsLire sur Substack → - Essai 49
The Selection Problem
In July I said AI would explode the variance between workers. The research says the opposite on most work. What it disperses is judgment — and the fear was never replacement.
Three weeks ago I argued that AI amplifies rather than levels, that the mean rises while the variance explodes. Then I read the research and had to take my own argument apart. Put a generative assistant in front of thousands of support agents and the novices gain about 34% while the experts gain almost nothing; a Science paper on writing tasks reports that inequality between workers decreased. These tools compress performance on bounded work, because the model already contains the expert answer and handing it to a novice closes the gap. Where they disperse it is open-ended work, and a field experiment with 640 Kenyan business owners isolates why: identical questions in, identical advice out, opposite outcomes, because of which advice each owner selected and implemented. The variable was judgment. Which reframes the workforce anxiety too. Only 22% worry about losing their job to AI; the top fear, at 51%, is being expected to do more for the same pay. That is not a fear about technology, it is a fear about who banks the gain, and it is the same question at company scale, where metered inputs hand the surplus to the buyer only if the buyer can operate the capability. If judgment is the scarce input, the organization that gets ahead is the one that makes good selection institutional rather than individual, which is what a review record is actually for.
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