What Margaret actually knew
So what did Margaret know?
Let me ask you this - how many companies have you worked in where there is an expensive Enterprise Resource Planning system or a Customer Relationship Management System, and there was often one person who knew how to assemble the ‘right’ information for the management meetings?
I have consulted for organizations that bought Salesforce, Oracle, and SAP, and then built an invisible second infrastructure out of Excel files, email chains, and one person who could pull it all together by Tuesday morning.
That person was sometimes a manager, sometimes an admin, sometimes whoever happened to be organized and patient enough to do the work nobody assigned. I will call her Margaret, because every company I have ever worked in had someone like that.
Margaret pulled data from four systems that did not talk to each other, reconciled the numbers, and built a summary on the schedule that reflected how decisions were actually made. The VPs who received her Monday email had no idea how the file worked. They just knew that when Margaret was on vacation, the week felt wrong.
Nobody called this anything. It was just how the week got done. But it had the shape of a quiet trade. Margaret’s salary on one side, the value of what she held in her head on the other, and a company sitting comfortably in the gap between them.
These gaps are everywhere
A law firm billing 10 hours for 2 hours of thinking and 8 hours of research is running a business built on the historical cost of finding the right precedent. A consulting team spending six weeks on a deliverable the client could assemble in a day is running on a gap in information distribution. The three-person agency charging $4,000 a month for social media management, the client does not know now comes from a freelancer with AI tools at a fifth of the price, running on a gap in access. These gaps are not bugs. They are what most of the economy is built on, from seven-figure ERP contracts to a dentist’s office on a Tuesday morning.
The question is: what happens when those gaps start closing faster than anyone can adjust to?
What I watched happen before
I have watched this before, in slower motion.
In 1979, VisiCalc shipped on the Apple II, and within five years, the spreadsheet had rewritten how accounting work actually happened. The gap it closed was arithmetic: the manual adding, reconciling, and cross-checking that had been a trained clerical skill for generations. The profession did not vanish. It got pushed upmarket. Routine ledger work shrank. Analytical work, the interpretation of what the numbers meant, expanded to fill the room that automation opened up.
The people who survived were not the ones who added fastest. They were the ones who understood what the numbers meant: the ones who knew which line item to question, which variance signaled a real problem and which was a rounding artifact, which summary the VP actually needed, and which one just looked complete. The tool replaced the arithmetic. It could not replace the judgment that made the arithmetic useful. The survivors crossed over. The people whose whole value was in arithmetic did not.
Here is the irony the piece has been building toward: the spreadsheet that Margaret uses is itself the product of an earlier gap closure.
The tool that holds her shadow infrastructure together is the scar tissue from the last time this happened, and the Margarets who survived that wave were the ones who learned to treat the new tool as a platform for judgment rather than a replacement for skill.
The travel agency business tells a similar story on a later clock. The profession ran almost entirely on information advantage: agents knew fares, routing options, and seat availability that customers simply could not access on their own. That gap sustained an entire industry. When Expedia, Orbitz, and direct airline booking made the database available to anyone with a browser, the commodity end of the business collapsed. Book-a-flight-to-Denver work went away. What survived was the high end: complex international itineraries, corporate travel management, luxury bookings where the client was not buying information but buying judgment, a relationship, and someone to call when the flight was canceled.
The gap closed. It did not close flat. It relocated upmarket, into the hands of firms large enough to absorb the freed-up capacity, and only the people who moved with it or were already inside those firms found a place to stand.
Both transitions played out over roughly a decade. The people caught in the middle of each one, too experienced in the old model to retrain cheaply, too young to retire, had a rough several years. Some crossed over. Most did not.
Here is what is different now: that decade is compressing to something between weeks and a couple of years, depending on which gap you are standing in.
The speed of closure
AI is automation on the next level. The old automation added columns of numbers and guessed which movie you might want next. The new automation makes the small judgment calls you used to make without thinking: which invoice has an error, which email can wait, which part of your Tuesday you forgot was even work. It reaches out and finishes something so repetitive you stopped noticing you were doing it.
The real traction is not in the enterprise transformations everyone writes about. It is showing up in a family-owned HVAC shop where the dispatcher used to spend Friday afternoons reconciling next week’s schedule against technician availability and customer callbacks, and now leaves at four instead of six thirty. It is showing up in a three-person real estate law office, where the paralegal used to type client intake forms from voicemails on Monday morning, and now opens her laptop to find forms already drafted.
These are not transformations. They are small gap closures, and a dozen of them add up to someone’s evening back. Once you start seeing these closures, you see them everywhere.
At the other end of the scale is Polymarket. It is a prediction market, a site where people put money on what they think will happen. In late 2025, one developer reportedly built a bot with Claude in about forty minutes, deployed it on Polymarket, and turned $313 into nearly half a million dollars in a month. The bot was not “smarter” in any mystical sense. It did not foresee the future. It moved faster than human traders to close tiny pricing gaps.
That example sounds like a triumph of democratized access. It is not. On the same platform, 92.4% of wallets lost money. That is not an unfortunate side effect. It is a reminder that these systems are built so that most participants do not win. The developer’s bot did not abolish the house edge. It found a way to operate inside it more efficiently than ordinary humans could. His AI system could spot small pricing errors and place bets faster than human traders could, and that speed was the source of its advantage.
That is the broader pattern. Access to AI tools is becoming nearly universal. The ability to turn that access into a consistent advantage is not. The gap that matters is no longer between people who have the tools and people who do not. It is between people who can convert automation into leverage and people who merely stand inside systems already optimized against them.
This decade’s real divide may not be access, but extraction.
The spreadsheet gap, and what lies beneath it
Come back to Margaret, because what she represents is bigger than the spreadsheet itself.
Every mid-size company I have worked in runs a shadow infrastructure. Every time mention this in private or in a presentation, I hear a nervous laugh. We call that a shadow’s laugh; people know this, but often don’t talk about it.
The official systems, the ones on the IT budget, capture transactions and generate reports. The shadow infrastructure, built in Excel and held together by institutional memory, captures everything the official systems miss: the context, the exceptions, the “we tried that in 2019, and it broke the billing system” knowledge that lives in one person’s head.
This is not stupidity. This is the gap between what software can be configured to do and what a business actually needs to know. Margaret’s spreadsheet is a bridge across that gap, and her labor is the cheapest possible material for that bridge.
When AI closes the spreadsheet gap, it does not just replace Margaret’s file. It makes the underlying systems conversationally queryable. A VP types “show me accounts where order frequency dropped 20% last quarter” and gets a direct answer without waiting for Tuesday morning.
But here is the part that most coverage of AI in business misses. Margaret’s spreadsheet was never just data. It was tacit knowledge made visible. She knew which numbers to pull because she understood which decisions the VPs actually made. She knew which exceptions to flag because she had watched those exceptions cause problems before. She knew the rhythm of the business in a way no system documentation captured.
Cory Doctorow calls this [https://pluralistic.net/2026/04/08/process-knowledge-vs-bosses/]process knowledge [https://pluralistic.net/2026/04/08/process-knowledge-vs-bosses/]: the hard-won operational understanding workers accumulate on the job, which management systematically undervalues because it cannot be measured or patented. When a team quits, the copyrights stay. The process knowledge walks out the door.
The trade was never between Margaret and the software. It was between Margaret’s salary and the value of what she held in her head. That gap was her employer’s margin, booked as nothing, paid for with her patience. The company that underpriced Margaret’s work for fifteen years did not make a mistake; it made a business model. When AI captures what Margaret knew, the question is not whether the capture happens. It is who owns the file.
Closing the spreadsheet gap with AI creates a real opportunity: the tacit knowledge can finally be encoded into a system that outlasts any one employee. But the question that determines whether this is progress or extraction is simple. If the captured knowledge belongs to the company that never paid for it, nothing has changed except the arrangement's durability. If it belongs to the workers who built it, the gap has actually closed. This is the difference between closing a gap and learning from what filled it.
The consulting gap, and who survives it
I have been on both sides of consulting engagements: inside the company, wondering why we hired these people, and outside the company, trying to deliver something useful before the contract ended. The gap is not intelligence. It is information distribution.
A Big Four firm sends four people for six weeks: two gathering data, two on analysis, two rehearsing the presentation. AI compresses the first two phases from weeks to days. Six weeks becomes three.
But the analysis was almost never the hard part. The hard part was organizational politics: which VP needed to hear which finding first, whether the CFO would accept a recommendation that threatened her direct report’s budget, and whether the finding would survive a meeting where someone with more tenure and less data had a different opinion.
I have watched correct analysis die in rooms where the presentation was competent and the data were solid because nobody understood who needed to save face. AI will not close that gap. Not because it is technically impossible, but because the people who control the budget do not want it closed. Political opacity is not a bug in organizations; it is how power maintains itself.
The consultants who survive this rotation are not the ones who gathered data well. They are the ones who understood that a finding is useless until it lands in the right conversation at the right moment. That skill is harder to automate than data gathering and harder to teach.
What the pattern reveals
In every example, the pattern holds. A gap arising from information asymmetry, labor costs, or coordination complexity is closed by AI. A new gap opens around judgment, context, relationships, or the quality of questions.
I should be honest about something. Every new gap I have described is conveniently human-shaped. Judgment. Politics. Tacit knowledge. The ability to know which question to ask. These are all things humans do well, and AI does poorly, today.
It is possible the next model release closes the judgment gap too, or that organizational politics becomes legible to a system trained on enough internal communications. Each new gap could be smaller than the last until it is too narrow for a human career to stand in. I am not confident that the new gaps are opening as fast as the old ones are closing.
What is different now is the speed: a decade for bookkeeping, a decade for travel agents, weeks on Polymarket. I’ll add in my media experience: it used to take a small team 3-5 days to create, edit, and post a video podcast; now, with AI tools, it can be done in a day or less by one person.
What this means for you
Nate Jones, whose analysis prompted this piece [https://youtu.be/BiqG3it0gY0?si=dTCC1gd7iRX4ZA9b], offers three diagnostic questions worth applying to whatever you do for a living.
* Where does your current work rely on information moving more slowly than it could?
* What would collapse if your customers had the same tools you do?
* Which parts of your work exist only because something else is hard to access or expensive to coordinate?
These are good questions. They will tell you where your exposure is.
I would add a fourth, drawn from every IT department and consulting engagement I have worked in:
* What does your organization know that is not written down anywhere?
That knowledge is both your greatest vulnerability and your greatest opportunity. Vulnerability, because when those people leave, the knowledge leaves with them. Opportunity because AI, for the first time, gives you a way to capture it: not in a dusty wiki nobody reads, but in a system that can answer questions in context.
Companies that use AI solely to close efficiency gaps will save money. The companies that use AI to capture what Margaret actually knew, to make the tacit explicit and the personal institutional, will build something more durable than a spreadsheet.
And the people who understand this, the ones who see that their value was never the spreadsheet but the knowledge that made the spreadsheet useful, are the ones who will find the next gap to stand in.
I have been watching gaps close and open for forty years. The pattern holds, so far. What is new is not the compression. It is the default destination of the compressed value.
The old gaps closed locally. A bookkeeper retrained in analysis, a travel agent moved upmarket, and an old school plant operator learned to read a screen. The freed capacity stayed roughly in the economic neighborhood of the workers it displaced.
The new gaps close inside platforms, and the value does not stay with the people who did the work. It moves upward, by design, to the firms that own the infrastructure. The HVAC dispatcher who leaves at four is more productive, but the margin she freed up shows on someone else’s balance sheet.
The compression is not a storm we are weathering. It is a redistribution of who captures what a decade of productivity used to fund. The real question is not whether we have time to learn. It is whether the people doing the learning will own what they build, or whether the value they unlock moves, by default, to the platforms engineered to collect it. That is a question about who gets to write the defaults.
What matters most now is the trade between the people doing the work and the people who, in advance, arranged to own the output. That is the gap worth standing in.
Reference - Nate B. Jones A Polymarket Bot Made $438,000 In 30 Days. [https://youtu.be/BiqG3it0gY0?si=dTCC1gd7iRX4ZA9b]Your Industry Is Next. Here’s What To Do About It [https://youtu.be/BiqG3it0gY0?si=dTCC1gd7iRX4ZA9b].
Let’s continue this conversation in the ‘Comments’ area. I want to share more of my own experiences related to this post!
T
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