AI’s Anna Delvey Problem: How Enterprises Are Faking It Till They (Don’t) Make It – Home: sdarchitect.blog

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I’ll admit it. I binge-watched Inventing Anna on Netflix over a couple of nights, the way most of us did back in 2022. And like most viewers, I walked away simultaneously appalled and a little bit fascinated by Anna Delvey. Here was a woman with no trust fund, no inheritance, and no real business, who nonetheless convinced New York’s elite, its banks, and its hottest hotels that she was a German heiress about to open a private arts foundation. She rented private jets. She dropped hundred-dollar tips like they were nothing. She hired lawyers and architects for a foundation that never existed on paper as anything more than a mood board.

There’s a scene early in the series — Anna, cornered by her friend Neff about how she’s actually going to pay for all this — that has stuck with me:

"You have to work hard to get what you want. I’ve always known that."

It’s a great line, delivered with total conviction, by someone who was doing everything except the hard work required to make her fictional empire real. She wasn’t actually doing work building a foundation. She was building the appearance of one, in the hope that appearance alone would eventually conjure the funding to make it real. Spend the money first, look the part, and the substance will follow — that was the bet.

This is an exploration for me too, but the more I watch large enterprises approach AI adoption in 2026, the more I see Anna’s playbook being run at scale, with board decks instead of Birkin bags.

The Thesis, Up Front

Large enterprises today are running the Anna Delvey playbook on AI — spending big, moving fast, and performing transformation for the board and the Street, without doing the unglamorous, structural work that would make the returns real. And just like Anna, when the bill comes due and the money isn’t there to back it up, the fallout won’t be limited to a few embarrassed executives — it will show up as real technical debt, real financial debt, and real credibility debt that takes years to unwind.

I want to be careful here — I’m not saying enterprises are committing fraud. Of course, they are not. But the underlying dynamic — spend conspicuously to project transformation, without a credible mechanism to generate the return that justifies the spend — appears uncomfortably similar. Let’s walk through why, using the lens of First Principles, the same way I approach every problem that requires Systems Thinking, which this one certainly does..

Anna’s Strategy, Translated to the Boardroom

Anna’s entire operation rested on a few pillars: look the part, associate with the right people, spend visibly, and let the perception of inevitability do the fundraising for you. She wasn’t lying about wanting to build something real — the Anna Delvey Foundation was a genuine idea. Her failure was in sequencing: she tried to manifest the outcome before building the underlying capability to deliver it.

Compare that to how a lot of large, traditional enterprises are approaching AI right now:

Announce aggressive AI initiatives in earnings calls and investor decks to signal "we get it" to Wall Street and the board.

Reallocate budget and headcount away from revenue-generating, working projects to fund flashy AI pilots.

Push AI into products and workflows without a clear definition of the business outcome it’s supposed to produce.

Skip or shortcut the risk, compliance, and feasibility assessments that would normally gate a major technology investment and transformation of this size and targeted impact.

None of this is because leadership is dumb or malicious. It’s because the pressure to appear transformed is enormous, and — just like Anna discovered — appearing transformed is a lot faster and cheaper in the short term than actually being transformed. The problem, of course, is that the bill always comes due.

The Debt Accumulates Quietly, Then All at Once

In Inventing Anna, there’s a moment where Anna tells her friend, with total sincerity, "When you’re out of new ideas, make your old ideas bigger." That’s a pretty good description of what I see happening inside a lot of AI programs today: instead of validating whether the original idea actually works, teams double down and scale it, on the theory that bigger will somehow fix the fact that it never had a foundation.

This is where I want to draw a distinction I’ve made in previous posts about DevOps and cloud adoption, because it applies directly here: there is explicit technical debt and there is implicit technical debt , and enterprises rushing AI are stacking up both.

Explicit technical debt is the debt you can see and point to — AI features bolted onto products without proper rearchitecture, agents wired into workflows with no validated eval framework, pipelines built for a demo that were never meant to survive contact with production being pushed to Prod. This is the...