TL;DR: A company's own job postings tell you whether its AI transformation is real. Most senior AI roles hire people to coordinate other people around the technology: a new layer of human go-betweens to manage the thing that removes the need for go-betweens. That is automating the status quo, not rebuilding. A real rebuild works differently: the knowledge that lives in the heads of the middle (why each control exists, which invisible correction is required) has to be captured into the AI systems first, and headcount falls only as the capture completes. Doing that requires people who understand why things work, not just how the forms move; the technology can reproduce every form for free, so understanding is the only thing left worth paying for. And it requires the firm to redesign itself, a decision no delegated department can make, yet firms absorbed every previous technology wave precisely by delegating it to a department, so demolition-and-rebuild is not in the repertoire. Watch what gets deleted, not what gets hired.
Contents: What the Job Postings Reveal · The Rebuild Is an Encoding, Not a Deletion · Syntax People, Semantic People · A Firm-Level Problem Wearing an IT Badge · The Missing Playbook · The Test That Matters
The clearest evidence of whether a company has started its AI transformation is sitting in public view: its own senior AI job postings. The last post argued that real transformation runs shrink, rebuild, expand, and that the shrink comes first, because you cannot rebuild an organization's information flows while thousands of people maintain the old ones. No shrink, no transformation. This post applies that diagnostic to where organizations actually are today. Almost everyone is automating the status quo rather than rebuilding, and the nibbling is not stupidity. It is the predictable output of how these firms are led, what they are incentivized to do, and what their management's careers never taught them.
If the cycle is real, you should be able to see where organizations are in it just by reading their job postings. So I did: three senior "AI" roles at three of the most sophisticated institutions in the same market, posted in the same period. Three postings are an illustration, not a survey; what they illustrate is the same assumption, three times over.
The first: "Director of AI Products." The verbs are align, coordinate, translate strategy into enablement plans, drive communication, lead through influence across matrixed teams: a chief of staff for AI activities. The role doesn't build anything. It routes knowledge about AI between the people who do. This organization is responding to AI by constructing a brand-new middle layer: hiring a human router to manage the technology that eliminates human routing.
The second: an "AI Technology Director." Closer to the edge: the posting wants an owner, someone who writes requirements and reviews code, takes prototypes to production. Better. But the job itself is to establish the software development lifecycle, CI/CD handoff processes, and reusability standards, and to coach juniors to execute within established frameworks. Every one of those exists to coordinate many human developers. The role optimizes the delivery pipeline, and delivery itself is the old paradigm: a producing team ships software to a consuming business.
The third: a senior AI architecture role. Ten-plus years of enterprise architecture, strength in stakeholder management, and the giveaway phrase: "review and govern solution designs through architecture checkpoints." A checkpoint is a coordination gate: it exists because downstream human teams can't be trusted to stay within constraints on their own. This is the translation-and-gatekeeping layer, staffed and formalized.
Three institutions, three senior AI roles, and not one describes the roles the future actually needs. All three assume the stratified model: a software team delivers, a business consumes, and humans in between route, gate, and translate.
What's missing? In the rebuilt organization, the central technology team doesn't deliver finished applications at all. It delivers primitives (data access, security guardrails, identity, compute, cost controls), a paved road. The working software is then grown and continuously evolved at the edge, by the people who own the outcome, directing AI to do it. Software stops being a product that ships and becomes a living practice. "Production" as a sacred, gated state stops making sense when the owner can regenerate an application as easily as editing a document. Governance doesn't disappear. It moves out of meetings and into the platform, enforced by the substrate instead of by checkpoints.
We spent thirty years treating this pattern (business users building their own tools) as a pathology called shadow IT, because unaided humans built fragile, unauditable things. AI changes what edge users can build. The correct response flips from prohibition to paving the roads.
So the test, for any AI job posting including the ones at your own company: does the role coordinate humans around software delivery, or does it build the substrate on which edge owners evolve their own software? By that test, all three postings (one obviously, two subtly) are hiring for the organization they have, not the organization the technology implies.
The test says the middle layer goes away. But that phrase is exactly where the rebuild gets misunderstood, because the middle knows things, and the rebuild lives or dies on where that knowledge goes.
The shrink gets imagined, sometimes even by its advocates, as "delete the middle layer and hope AI figures it out." It is not that. The AI substrate that replaces the middle is the middle's knowledge, encoded: the tribal know-how, the exception handling, the invisible error-correction, the compliance instincts that currently live in people's heads. Anyone who thinks the organization gets rebuilt without capturing that knowledge is describing a different project, a doomed one. Companies have run that experiment before: cut the experienced "process defenders" for efficiency, then spend a decade discovering which of their invisible corrections was central. The shrink in this cycle is not that blunt cut. It is a role conversion: the people who hold the knowledge stop performing the routing and start teaching it to the substrate, and headcount falls as the encoding completes, not before.
The regulated-industry objection resolves the same way. Yes, a bank cannot let edge owners regenerate applications unsupervised, but compliance knowledge encoded as machine-enforced, fully logged guardrails is something a regulator should prefer to human checkpoints, which fail silently, vary by reviewer, and leave no trace. The encoding doesn't remove the controls. It makes them consistent and auditable for the first time.
But who can do the encoding?
The dividing line is not the org chart. It is the difference between people who understand the organization's syntax and people who understand its semantics. Syntax people know the form: which template, which approval chain, which forum, how a request must be phrased to move through the system. Semantic people know the meaning: why the control exists, what risk it actually guards, what the customer actually needs, what good looks like independent of how it is currently expressed.
When the organizational grammar changes, syntax knowledge doesn't just depreciate. It misleads. The syntax person's only move is to re-impose the old forms on the new system, which is why AI initiatives so often produce agents that faithfully reproduce every legacy approval step. Semantic knowledge, by contrast, is portable across grammars by definition. Encoding the middle into the substrate is exactly a translation task: extracting the meaning from processes where it is tangled up with form, and re-expressing it in the new grammar (prompts, guardrails, evaluations) instead of forms and sign-offs. Only semantic people can do that translation. And AI itself is the ultimate syntax machine, which is the point: when syntax becomes free, semantics becomes the only scarce thing left to pay for.
The line cuts across seniority, not along it. A thirty-year compliance veteran who knows why each control exists is exactly who the encoding needs. A young manager fluent in every ceremony but unable to say why the ceremony exists is pure syntax. A simple field test, a cousin of Chesterton's fence (the old rule that you don't remove a fence until you know why it was put up): ask someone why a process step exists. The semantic person explains the risk it manages and what could replace it. The syntax person says "that's the process." The second answer is the hindrance, whatever the title on the door. Syntax fluency was a rational investment for decades, because organizations paid for navigating forms, not for understanding meanings; the people who over-invested in it made the correct bet in the world as it was. The world is changing what it pays for.
One design problem follows directly: encode-then-shrink requires organizations to retain and motivate exactly the people who suspect they are training the layer that replaces their old role. Getting the semantic people to stay and do the translation, through incentives that give them a stake in the rebuilt organization rather than a severance date, may be the single hardest piece of execution in the whole cycle. And it is not a problem IT can solve, because incentives, equity, and reporting lines are not IT's variables to set.
The pattern in those three postings is not a coincidence, and it is not stupidity. It is scope.
When AI transformation is delegated to IT, those three roles are roughly the best possible outcome, because IT can only optimize within the organizational design it has been handed. An enablement center, a better delivery pipeline, an architecture gate: each is the maximum change available without touching reporting lines, headcount logic, or how the P&L is carved up. Those variables belong to the firm as a whole, not to any department within it. Block, the payments company, didn't hand AI to its technology function; it redrew the organization around humans at the edges. That is an act of corporate design, not technology procurement.
So why do most firms nibble instead? First, legibility: when the board asks "what's our AI strategy," a Director of AI Enablement is a legible answer; a demolished middle layer is not. Second, incentives: refounding means betting the existing revenue engine on an unproven organizational design, and public markets punish that asymmetrically: a failed transformation ends careers, slow decay is survivable for years. Third, and most human: a firm's management rose through the very coordination hierarchy in question. Asking the firm to treat its org as demolition material is asking management to declare its own formative skills obsolete.
The third reason may be the deepest: management has never had to transform a firm because of technology. Most senior leaders came up through the 90s and 2000s, and every technology wave of that era (ERP, the corporate website, e-commerce, outsourcing, cloud) was successfully handled by delegation and containment. You hired a CIO, created a digital division, ran a program with consultants, moved a cost line. Even the internet, the biggest wave of those careers, was absorbed by most incumbents as a channel bolted onto the existing organization, not a redesign of it. Management's experiential library contains exactly one playbook for technology, and it is the delegation playbook. The nibbling isn't just rational under today's incentives. It is the faithful execution of every lesson those careers taught.
Management has handled enormous change in last few decades. But those were external shocks to be absorbed, where success meant restoring the firm to how it functioned before and assimilating the external changes while keeping status-quo. AI demands the opposite: internally chosen demolition of a firm that still works. Absorbing shocks and refounding are different strategies with absorption as a default. The nibbling is rational. It is also exactly why the electrification lag lasted thirty years: the incumbents knew about distributed power for decades and still couldn't rebuild the factory, because the factory was them.
The presence of AI job titles is not evidence of AI transformation. It may be evidence of its absence. Real transformation shows up in the org chart, the compensation structure, and what gets deleted, not in what gets hired. If a company's AI story is a new function inside IT, it is automating the status quo. If the AI story is the firm redesigning how it works (fewer layers, ownership at the edges, a central team whose product is the substrate), that is the paradigm shift.
For individuals: your syntax knowledge of the current organization is a depreciating asset; your semantic knowledge (why things exist, what good looks like) is the appreciating one, and it is what the encoding will need. For firms: stop measuring importance in headcount, and stop delegating to IT a transformation that only the firm as a whole can execute, especially when its management's careers never included this kind of change. Watch the deletions, not the hires.
In the next post: what the rebuilt will looks like and what is needed to support it.