The Washington edition: the city writing AI policy for the rest of the country still can't get AI to solve its own dating math
No city has a stranger relationship to AI than the one currently deciding how the rest of the country is allowed to use it. Washington doesn't just adopt artificial intelligence — it regulates, funds, and procures it at a pace that outstrips almost anywhere else in America. Federal AI spending grew from $355 million to $7.2 billion between 2024 and 2026, a 966% increase, and the number of federal agencies holding active AI contracts climbed from 17 to 28 over the same stretch. As of this spring, federal agencies had logged 3,611 individual AI use cases government-wide, including 445 classified as high-impact. This is, without much competition, the most AI-saturated square mile of policymaking on Earth.
And yet Washington's dating math hasn't moved an inch in the direction all that automation might suggest. If anything, it's the most stubbornly unautomatable dating market in this entire series.
Start with the number DC is most associated with, because unlike the citywide statistics in New York, Los Angeles, and Chicago — which all flatten out or reverse once narrowed to the actual dating-age population — Washington's doesn't. Census Bureau data has identified DC as having the lowest ratio of unmarried men to unmarried women of any major U.S. metro, at roughly 80 unmarried men for every 100 unmarried women. A separate SmartAsset analysis of 119 major cities put the figure at closer to 86 to 100, tying Baltimore for the most female-skewed ratio among large cities studied — and on the city's west side specifically, one widely cited Census mapping found roughly 1.4 college-educated single women for every college-educated single man in the same age range. Every independent data source examining this city's dating-age population points the same direction, at a similar magnitude. It isn't a viral statistic that falls apart under scrutiny. It's one of the rare cases where the scrutiny makes it worse.
None of that gap has been closed by better matching technology, despite this being a city that runs almost entirely on optimized decision-making in every other domain of daily life. Government, law, consulting, and policy work all reward exactly the kind of structured, data-driven judgment AI is good at — and DC professionals apply that instinct to everything from legislative strategy to, per local dating coverage, treating their own dating life like "due diligence." What that instinct hasn't produced is a matching algorithm that actually closes a demographic gap this well-documented. A gender imbalance isn't a discovery problem, which is the kind of problem AI is genuinely built to solve — it's an arithmetic problem, and no model, however sophisticated, can algorithm its way around a shortage that Census data confirms is real.
Compare that to what Washington-adjacent AI is actually excellent at, which is logistics — precisely the category the federal government has been racing to automate. Nationally, Phocuswright found 56% of U.S. leisure travelers used AI to plan a trip this year, and 78% of people who tried it booked something based primarily on its recommendation. Dulles and Reagan National sit inside a metro area that produces some of the highest business-travel volume in the country, run by professionals who are, per the KPMG-style adoption data seen across federal and private sectors alike, already comfortable letting AI compare flights, hold fares, and reroute a canceled connection. That's a clean, bounded, well-instrumented problem, the same category the federal government's own AI use cases skew toward: internal operations, service delivery, structured workflows with clear inputs and outputs.
Dating in DC isn't that kind of problem, and the industry's clearest cautionary tale makes the point without needing a local example. Justin McLeod, who built Hinge into the country's most dominant dating app, left to start an AI-first matchmaking service backed in part by Match Group — and has said plainly that even his new venture treats AI as a coach, not a decision-maker. Three Day Rule, fifteen years into human-run matchmaking before adding an AI layer, drew early criticism for a specific failure: both sides of a match receiving identical AI-generated opening lines, leaving the actual humans as spectators in their own conversation. In a city this fluent in reading a canned talking point, that's a hard thing to get away with twice.
None of this argues AI has no role in DC dating — coaching tools that sharpen a profile or catch a self-defeating pattern remain a legitimate, evidence-backed use case, distinct from trying to automate the judgment call itself. But Washington may be the single clearest example in this series of where the line actually sits: a city that has spent two years accelerating AI adoption harder than any other American metro, government-wide, and one where the dating math still requires something no dataset can supply — someone who has actually met the people involved. That's the case for Luvo Matchmaking's approach here, sourcing introductions from real communities and events across the district rather than a database that can quantify the gender ratio but can't do anything about it. Washington will keep getting faster at automating everything a model can actually see. Finding the right person is still, even in the most AI-governed city in the country, a job for someone who's in the room.