Category: business

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This Time, AI Doesn’t Have an Alibi

A poster on a display panel in a landscaped office park shows a hand-painted before-and-after: a tangled blue workflow on the left re-linked into a clean, redesigned one on the right, one step marked in mint.

The work AI is absorbing now is the work we offshored twenty years ago, for the same reason. The advantage won’t go to whoever swaps a human for a machine. It goes to whoever redesigns the work.

AI had an alibi for the vanishing entry-level job until 2025. It won’t have one for what comes next. The work most exposed to it in the short term is the one we spent the 2000s offshoring, from call centers to entry-level coding to the back office, because both waves seek the same thing: how much of a job is written down and how little of it is judgment. That work already left once. The mistake now would be to automate it in place, swapping a machine for a person and changing nothing else. The larger prize, including for the economies that first took this work, belongs to whoever is willing to rethink how it gets done at all.

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The SaaS Bill Just Split Into Two Meters

A watercolor illustration of two rising gauge dials, one small and capped, one large and still climbing, hung as a framed painting on the wall of a modern tech-startup meeting room in downtown San Francisco, with a blue pen and a laptop on the conference table below.

One meter counts how many people log in. The other counts everything the agents they authorized won’t stop doing, and the surviving architecture stacks both.

Your next software renewal will likely carry two prices, not one. One line still counts the people who log in. The new line counts everything the agents they turned loose did while nobody was watching a screen. Salesforce already reports it publicly: Agentforce revenue up 205% year over year, with the seat line holding right beside it. This is a stacking model, not a swap. It changes three things at once: what you sell, what you buy, and how fast a software bill you thought was fixed can run away from you.

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Every AI Answer Is a Bet Dressed as a Fact

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Leadership was always about judgment under uncertainty. Now the machine is uncertain, too, with every AI answer arriving as a single number in a confident voice. Re-attaching the odds is the skill LLMs quietly made critical.

Your AI sounds equally sure whether it is right or guessing. That flat confidence is not a quirk; the same training that makes a model agreeable also makes it overconfident. Leadership has always meant deciding before all the facts are in, and the tool used to be the one certain thing on the desk. It just stopped being certain. This is the fourth cognitive lens: reading every answer as one drawn from a distribution, and supplying the calibration that the machine cannot.

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Your AI Agent Will Never Question the Premise

It was built to reply with the “best” existing answer. The one move it can’t make is the one now worth the most.

Ask an agent to price your product, and it hands you a polished version of what your competitors already do. That’s not a bug; it’s the objective working perfectly. A model is a machine for the most likely next thing, which means it drifts toward the average answer, the one everyone else’s agent is also producing. The expensive human move runs the other way. SpaceX did it with a rocket and found that 98% of the cost was due to inherited habit. Here’s why that move is now the edge, and why a faster agent makes skipping it more dangerous, not less.

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When the Parts Got Cheap, the Connections Got Expensive

AI discounted all traditional knowledge work. The value didn’t vanish. It moved to the one place a model still can’t reach, and most leaders aren’t looking there.

AI has made the components of leadership work nearly free: analysis, draft, model, first-pass decision. The value didn’t evaporate. It relocated to the one place a model still can’t reach: the connections between the parts. That shift is why systems thinking, long filed under “nice-to-have,” is now the highest-return skill a leader owns. A nine-second corporate catastrophe this spring shows what it costs to keep watching the parts instead.

This is the second article in a short series on three cognitive lenses for thinking under pressure: tension, connection, and reduction.

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Your Hardest Problems Aren’t Problems

The ones that keep coming back to the same meeting were never yours to solve. In the age of AI agents, tensions move to the core of the job.

A company handed two-thirds of its customer service to AI, shed the equivalent of 700 support agents, and called it solved. A year later, the CEO said the words every leader dreads: We went too far. It was not an AI mistake. It was a category mistake, the same one that breaks reorgs, rewrites, and roadmaps. Many of your hardest calls were never problems with answers. These were tensions to be managed, and AI agents just turned spotting the difference into a survival skill.

This is the first article in a short series on three cognitive lenses for thinking under pressure: tension, connection, and reduction.

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AI Has an Alibi

Everyone blames it for the vanishing entry-level job. The timeline says otherwise.

The entry-level job is disappearing, and the headline has already named the culprit: AI. Here is the problem with the case. The sharp decline set in around late 2022, and AI was nowhere near good enough to replace anyone until late 2025. A cause cannot arrive three years after its effect. So who actually did it? The honest answer is a lineup, and the most interesting suspect never makes the headline: a feedback loop nobody had on a budget line.

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The most durable thing in your software isn’t in the code.

AI can clone a SaaS product in a week and walk an agent straight through a shallow integration. What survives are three things that compound into one another, plus the human judgment beneath them that no competitor can replicate.

A trader at Jefferies, not an engineer, named the “SaaSpocalypse” panic, which tells you what kind of event this is. Software shed close to $2 trillion from its October peak on the theory that anything can now be cloned in a week. The theory is right about features and wrong about moats. The error is picturing a moat as a wall, one thing you build once and stand behind. The durable defense is a loop, and underneath it sits the part no competitor can vibe-code, because it was never in the code: judgment.

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The blank canvas and the seasoned eye.

Ten years ago, streaming a movie over Starlink at 30,000 feet would have sounded like fantasy. We are about to be just as wrong about AI, and the people who get the next decade right will not be the ones you expect.

We are reliably bad at imagining ten years out, and we miss in one direction: we underestimate. AI is the next thing we are underestimating. The instinct in most rooms is that the young will lead and everyone else will catch up. That instinct is wrong twice over, and the answer to why is older than any of the tools.

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Your AI budget is already gone.

Editorial documentary photograph of a quiet finance executive's desk; a framed editorial illustration above the desk shows a small stack of token-shaped chips with one corner of the stack burning steadily and a thin trail of smoke rising upward, the rest of the stack intact but visibly being consumed.

Three Uber executives, three different seats, told the same story this spring. The cost category most boards govern quarterly is moving to hourly.

By April, Uber’s CTO had blown the AI budget he set in December. Three weeks later, the CEO said he was metering headcount and leaning further in. Two weeks after that, the COO asked aloud whether any of it was producing value. Three quotes, three seats, one cost category nobody had experience with. Here is why token spend breaks the quarterly cadence finance was built on, and the three questions a board should be asking by the next meeting.

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