An explorable essay in seven acts
The Lemon Market: Dating
Why dating feels broken for almost everyone, why the people still single are so often the ones you don't want, and what actually moves your odds.
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Share of US women married, by age, for each decade of birth
Every generation marries later and less. Hover to compare.
Census data by birth cohort: 1950s drawn by hand, 1960s-2000s real data, no extrapolation.
Dating discourse dominates the attention sphere for a reason: it sucks for both sides. The median male experience is no swipes, no dates, and a lot of wasted time. The median experience for women is being flooded with low-effort messages, and being able to find dates but not commitment. Meanwhile, pairing, sex and marriage are going down every year. We have a sex recession and a marriage recession.
Why? Dating has become a lemon market, just like used cars and hiring. The people who are easiest to find are, disproportionately, the ones nobody chose to keep, and the tools that made finding people easy made it worse.
This essay shows that this is true, why it is true, lets you play with the numbers yourself, and ends with what actually moves your odds. The switch here sets whose side you're reading from; most of the numbers are different for women and men.
Act I · The shift
Everyone moved online
In twenty years, apps went from nothing to the way most couples meet. Every other channel shrank.
How heterosexual couples met, by the year they met
Share of couples. The last online point is the newest HCMST wave: couples who met in 2020-22. Bar or restaurant leaves out couples who met online and had their first date at one. Hover to compare years.
Rosenfeld, Thomas & Hausen (2019), PNAS, How Couples Meet and Stay Together 2009 and 2017, read from the published chart, with couples coded both bar/restaurant and online taken out of the bar line (the paper notes they are its whole post-2010 rise); that overlap and 2020-22 computed from the public HCMST 2017-2022 file (v2.2), weighted.
In 2017, 39% of new couples met online. Among couples who met in 2020-22 it was 59%. That last number rests on only 49 couples (give or take 17 points) and years when the bars were shut, but the direction isn't in doubt. Meeting through friends, the top channel for half a century, fell from a third of couples to a fifth. Family, school, church and neighbors all collapsed.
It's not hard to see why. Apps give you the deepest bench that has ever existed. On a good day out in the world you might cross paths with a handful of single people your age. On an app you can see hundreds before lunch, every one of them apparently single and in your range.
And with hundreds on tap, standards rise to match. Here is what "not being picky" does to the bench, from the author's Dating Calculator, which counts real Americans from census data:
Single men matching each set of standards, starting from the whole country (bars on a log scale).
Single women matching each set of standards, starting from the whole country (bars on a log scale).
Dating Calculator: ACS persons with NHANES-imputed body measures.
That is the first half of the problem: everyone is in the same giant market, looking at the same apparently endless supply, with standards set by the most attractive profiles they've seen. The second half is what that market does to attention.
Act II · The attention market
A power law in likes
More men than women, women who like a sliver of what they see, and a feed that shows popular profiles more. Put together, a few men get most of the attention.
A year on the apps, for single women 22-35
From everyone single, to the women who actually date from the apps, to the ones who pair off. The hatched bar is a range.
Single women: ACS. App use: Pew 2022 (25% of adults under 30 in the past year). Actually dating: NSFG 2022-23, single women who had sex with someone they met online, scaled by how often an active dater does. Casual men: model. Paired off: census first-marriage rates two years on, times the share of couples who met online (HCMST: 39% in 2017, 59% in 2020-22) and through a dating app or site (54% named one, up to 82%), less the recent fall in couple formation.
Share of the profiles each person likes
Distribution across users, 1-point bins.
Paul, "What really happens inside a dating app" (2025), digitized.
Share of the women shown a man's profile who like him
Distribution across men. The median man is liked by about 1 in 20.
Same source, digitized. For women the same measure centers on 38%.
Share of all likes going to the top men and women
Hinge's figures.
How much of the likes the top 5% get
Today, and if things changed. Model, calibrated to the app data above.
Start with who's on the apps. Among 18-to-29-year-olds using them, 73% are men (Pew, 2022). That's 2.7 men for every woman.
Over a whole year the ratio is gentler, about 1.5 to 1, because women try the apps and leave faster. But at any moment, the men far outnumber the women.
And most of the women on the apps aren't really there to date. About 38% of single women 22-35 used a dating app in the past year, but only about one in five of those actually went on dates from it: 7.8% of single women. The rest browse, swipe and chat, for the ego boost or in case someone great turns up.
Of the ones who are dating, 75% spend months of that year on at least one man who was only ever there for something casual. And about 18% to 41% of them pair off on a path to marriage. The apps look like a market of millions of eligible women; in a year it's about 1.2 million who actually date, each shown more men than she could ever see.
Women's likes follow a power law, and the median woman likes very few men: only 4.5% of the profiles she's shown. The median man likes 26%.
Put those together with the unbalanced gender ratio and the average woman gets about a hundred likes a day. If she has time for five conversations, she can answer the top 5%. Any woman with strict filters ("only men 6 feet or above with grad degrees") set them purely to cut down the flood. Her standards didn't get crazy for no reason; they reflect the overwhelming amount of attention women get on the apps.
This is what it looks like from the men's side. The median man is liked by about 5% of the women who see him. A long right tail of men is liked by 20%, 30%, more.
The tail falls off like a power law: the density drops with roughly the 2.2 power of the like rate.
For women the same chart is a broad hump centered on 38%. Men like broadly; the median woman is liked by more than a third of the men who see her.
On Hinge, the top 5% of men get 41% of all likes, the top 10% get 58%, and the bottom half of men share 4%.
About half of the inequality is taste; half is the feed showing popular profiles more. If every profile were shown equally, the top 5% of men would get 18% of likes. Offline, the same shape holds: among men 25-45, the top 5% by lifetime partners account for 40% of all partners (GSS).
It isn't that women all want the same man. It's that there are too many men.
Men actually agree more about which women are attractive than women do about men (0.56 vs 0.48), and the top 5% of women still only get 31% of men's likes. The top 5% of men get 41% of women's.
If women agreed far less about which men are attractive, the top 5% would still get 27%. If apps held to one man per woman, a woman with time for the same five conversations could like 2.7 times as many profiles, and the top 5% would get 27%. The problem is supply and demand.
Try it yourself
Build an attention market
Change how many men there are per woman, how much women agree about who's attractive, and how hard the feed pushes popular profiles. Watch how much of the attention the top men get.
Likes received by men, from most to least liked
Share of all likes held by the top X% of men. The diagonal would be perfect equality.
Today: 2.7 (Pew, 18-29-year-olds). With fewer men per woman, each woman can like more of the profiles she's shown.
0 = everyone has their own taste; 1 = everyone agrees. Fitted: 0.48.
How much more often popular profiles are shown. 0 = everyone shown equally; 0.74 fits Hinge.
Act III · The funnel
Most men get no dates at all
Likes aren't dates. Women have high standards, most women on the apps aren't going on a lot of dates, and those who are largely choose the same men.
Straight single men 18-35 (NSFG)
Two waves of the National Survey of Family Growth.
Chance a man on the apps gets no first date all year
By his appeal (what a swipe measures) among all men. Model, calibrated to NSFG.
First dates a year, by his appeal
Model. Bars are shares of all first dates; labels are dates per man. Bands are percentiles among all men; the top 10% of all men are only about 5% of single men.
One man's Hinge funnel, three years
A 35-year-old, 5'10" startup founder in New York.
Lana Li, "The Infinite Pussy Glitch: the data".
Start from the end of the funnel. In 2022-23, 47% of straight single men aged 18-35 had no sexual partner at all in the past year, up from 28% in 2017-19.
Only 6.1% had sex with anyone they met online. Those who did averaged 3.4 partners, against about one for everyone else.
Work backwards and the funnel falls out. Each woman who is actively dating has about a dozen first dates a year and gives them to the best-looking of her matches. But only about 1 in 5 women on the apps is actually dating in a given year; the rest browse, swipe and chat.
The result: 60% of men on the apps get no first date in a year. For the bottom half of men by appeal it's 84%.
Calibrated so the model reproduces NSFG: women with any app sex average 2.8 partners, men 3.4.
The dates that do happen go to the top. The top 10% of men on the apps get 66% of all first dates, and the top 20% get 82%.
That's the experience of most men on the apps: not rejection after dates, but no dates to be rejected from.
For a sense of the best case: a man above average in looks, a founder with a name-brand degree, in New York. Over three years he received 2,031 likes and matched with 345 women. That became 57 meetup attempts, about 19 a year (Lana Li).
He is in the best market that exists for a man looking for educated women in their twenties, he is a top-tier guy, and he still got barely any dates. One every other week! Now adjust that down: most men are not in strongly gender-biased markets, and have less going for them. What is a realistic expectation there? Let's find out in the next section.
Try it yourself
Your year on the apps
How many likes you get a week, and the woman you're hoping for. A match needs you both to like each other.
How many likes you get a day, and the man you're hoping for. A match needs you both to like each other.
Your year, from likes to first dates
Model (the funnel above). Heights on a square-root scale; every stage is labeled with its count, and the colored count below each column is the part at your target or better.
About the 50th percentile.
Appeal percentile, or better.
Act IV · The lemon market
Who's left
The men everyone swipes on are the ones least likely to still be single for a reason you'd want. Nobody has to be lying for this to happen.
Share of men on a dating app looking for something casual
By how attractive women rated them, least to most (8 groups).
Dots: data from a real app (blog.luap.info). Line: what you get if every man is equally likely to be casual and only who pairs off differs.
Odds he'd commit to a median woman, by his appeal
A serious man of 30 who has dated a woman of 28 for months: his decision alone. Blue: a median woman; pink: a top-10% woman. His appeal here is his overall value as a partner (status, looks, social skills, height), not just his swipe appeal. Model.
Chance a man is still single after N first dates
Model, by his appeal, against a naive 40% chance per woman.
Share of men who are casual, not looking to stay
All men, and the men you actually meet. Model, fitted to the app data and the census.
Desire for something casual scales directly with attractiveness. This is based on data from a real dating app, and it shows that the more attractive women rated a man, the more likely he was on the app for something casual: about 23% of the least attractive men, 52% of the most attractive.
You don't need hot men to be different people to get this. Serious men pair off when they meet someone they want; casual men mostly don't, and are the ones still on the app. The more matches a man gets, the faster a serious one gets taken off the market, and as we've seen, the most attractive men get most of the matches. That cashes out to the men on the app being fairly adversely selected in terms of what women want, and more adversely selected the more appealing they are.
Even the serious ones rarely commit, because abundance raises the bar. A man who can open the app and find someone new in an hour has every reason to wait for someone better. Chipped nail polish, doesn't go to the gym enough, doesn't know who Heidegger is: out.
A 90th-percentile man would commit to a median woman he has dated for months only about 0.4% of the time. A median man would, 47% of the time. And even when both would, it still has to work out (timing, chemistry, everything else), which in the model happens about 77% of the time.
People commit to partners near their own level. The model is calibrated so couples sort as tightly as real ones: husbands' status and their wives' families' status correlate 0.76 (PSID, 17,000 couple-years). It also matches who never marries (59% of the lowest-earning fifth of men in their forties have ever married, against 91% of the top fifth), how fast single women marry (census), and how often the most attractive women and men marry: about 90% by their late thirties, the same for both sexes (Add Health).
So he goes on first date after first date. If each woman had a 40% chance of being the one, a man would be off the market within a handful of dates. In the model, a first date ends in a relationship both people commit to about 4% of the time for a man between the 50th and 75th percentile of looks, and 1% for the top 5%.
After twenty first dates, most men are still single, and the more desirable the man, the likelier he is.
Dating apps aren't driven by bad people doing bad things. They're driven by incentives and structure.
Men who are worse partners than they seem either don't commit at all, or get left sooner and cycle back into the apps. Men who are a good catch and want commitment leave the app almost instantly, because they find it right away. What's left, at any moment, is tilted toward exactly the men you'd want to avoid.
Try it yourself
Run the commitment filter
How many men are casual, and how fast serious men pair off. See who's still single, by appeal.
Share of single men who are casual, by appeal
Grey line: the share among all men.
Grounded in the app data: the least attractive men pair off slowest, so their share on the app (23%) is close to the share among all men. Casual men pair off at a tenth of the serious rate (fitted).
For the median man, relative to how fast relationships end. Fitted: 1.65.
Act V · The clock
The clock runs on both sides
Women peak early and fast. Men who stay single long enough get selected, too.
Share of the opposite sex interested, by age, relative to the peak
OkCupid, digitized.
Rudder, OkTrends / Dataclysm (2014).
Fecundity used and WHR by age
Share of lifetime fecundity used since 20 (Geruso et al. 2023), and share of women at WHR ≤ 0.74.
Never-married men, by age
Model fitted to the census never-married curve and the marriage gap by earnings: share casual, and average value as a partner (percentile among all men).
A median woman's odds within five years, by the age she starts
A man who commits, and one as good as her or better. Model.
Men of every age, from 20 to 50, find women in their early twenties the most attractive (OkCupid). Interest in women peaks at 22. By 30 it's at about 65% of the peak, by 35 38%, by 40 23%.
This is the Wall, measured. Men's curve peaks later, at 26, and falls more slowly.
The two things men respond to first line up with fertility. Counting from 20, a woman has used about 69% of her lifetime fecundity by 30 and 87% by 35 (Geruso et al. 2023).
And waist-to-hip ratio, the other strong signal, goes the same way. Only about 5% of women at 20 have a WHR of 0.74 or lower; by 30 it's 2%, by 40 about 1%. Nearly half of women in their twenties are already at 0.85 or above.
For men the clock runs through selection. Each year, the men women want most get taken first. Among never-married men, the casual share rises from 23% at 25 to about 56% at 45, and their average value as partners falls from the 45th to the 27th percentile.
The census agrees: never-married men in their forties earn a median $36k against $67k for men who married, and fewer have degrees. A man in his forties who has never married usually has a reason, and women know it.
Put her clock and his selection together. A median woman's odds of a man who commits within five years fall from 41% if she starts at 22 to 28% at 30 and 21% at 36. Her odds of a man as good as her or better fall much faster: from 34% to 11% and 4%. The clock takes who even more than whether.
Five years on the apps, open to men up to two years older, two men properly dated a year. Fitted to the census rate at which single women marry from 25 to 30; in their thirties the model runs below the census, since real women that age also meet men through friends, work and remarriage.
Try it yourself
Your clockYour odds
How old you are, how many likes you get, and how much older you'd go. Five years of looking.
Start from what the apps tell you, add what they don't, and pick the ages you're looking at. Five years of searching, two women properly dated a year.
About the 50th percentile for your age.
About the 50th percentile on looks.
About the 50th percentile for your age.
Act VI · The search
The luck and the rarity
Even at the very top, you can only evaluate a handful of people, you can't see who they are up front, and good traits don't come bundled.
Even if you're at the very tippy top of the pyramid, a one-in-a-million candidate, you won't be able to find somebody comparable to you, because of the limits of search and the time it takes to surface the information that really matters.
You want your husband to be tall, high-earning and a go-getter, but also sensitive, smart, conscientious and kind. You want somebody good with kids, from a family you can get along with, with similar goals and the same way of resolving conflict. But finding all this out takes months to years for each person! Over the decade most people spend single and looking before they marry, you can only properly evaluate somewhere between 4 and 20 people.
Let's catapult you to the top. You're a "666" man: six feet, six figures, six-pack, one of the few men who can swipe on anyone and get dates three times a week. Whatever you are, you are rare.
Now try to find someone as rare as you. "Wife" is a much higher bar than "would." You want someone attractive, but also smart, conscientious, kind, good with kids, from a good family, compatible on how you fight and what you want. Finding out takes months to years per person. Over the decade most people spend on this, you can properly evaluate somewhere between 4 and 20 people.
Good traits barely travel together, either. In measured data on American men (NLSY97), height and income correlate at 0.06, fitness and income at 0.02, IQ and height at 0.09. Only IQ and income move together much (0.38). So "top 10% on three things" is about 1 in 1,000, and each thing you add multiplies the rarity.
Moreover, you can't determine this up front. Strangers judge personality with an accuracy of about 0.17; after a first date, maybe 0.25; friends who've known someone for years, about 0.47 (Connelly & Ones, 2010). A profile shows you looks, not character.
Chance that 20 people evaluated from the top 1% of what you can read up front include a 1-in-N partner
By how well the up-front read tracks the qualities you care about. "Perfect" is the author's original arithmetic.
Model: true quality and the up-front read are jointly normal with correlation r (Gaussian copula, checked against NLSY97's measured joint tails).
Say you can somehow winnow your dating candidates down so that you know they are all top 1% or better before you ever even go on a date with them. An extraordinary feat! But even with that level of selection and a perfect read, you find a 1-in-10,000 partner only 18% of the time. With a realistic read of 0.5, your odds fall to 7%.
And the person you want has to want you back. People commit to partners near their own level, so the rarer they are, the less likely they are to commit to anyone below them. Put it all together:
Try it yourself
Your odds over five years
How many likes you get a day. Five years of looking from 25, open to men up to two years older.
How many likes you get a week. Five years of looking from 30, at women 22-30, with everything else about you at the median.
Your odds
Model. "As good as you or better": his standing among men at least your standing among women your age (or the reverse). Top 10% of men: by standing now, among all men 22-55.
About the 50th percentile.
Where you start is where you end up. A median woman finds a committed man as good as her or better in about 25% of five-year searches from 25, and a top-10% man almost never. Appeal buys attention from everyone, but commitment only from men near your own level.
For men, the apps decide whether you're in the game at all, and your value as a partner decides who stays. On the apps, a median man finds a committed woman in only about 9% of five-year searches from 30: the few women who date him are also dating better-looking men, and they keep seeing those instead. Off the apps it's 65%.
Settling isn't optional. Pick the three things that matter most to you, accept average elsewhere, and decide early.
Act VII · What to do
What actually moves your odds
Five years of searching, starting from the baseline: not looking till 27, open to men up to two years older. Odds are for a man who commits, one as good as her or better, and one in the top 10 / 5% of men.
- 1Start early. Your pull and your pool are both at their best in your early twenties. Starting at 23 instead of 27 raises your odds of a committed man, and your odds of one as good as you or better by much more.
18% → 31%a man as good as you or better within five years, for a median woman, starting at 27 → 23 (any committed man: 32% → 39%)
- 2Widen the age range upward. Fewer women consider older men, but your age lets you reach more successful, higher-tier men. For a top-20% woman of 23, being open to men 15 years older takes her odds of a top-10% man from under 1% (0.4%) to about 8%: few men her age are top-10% today, however promising. For brides 18-22, a 10+ year gap makes a top-10%-income husband 4.4 times as likely (ACS).
0.4% → 7.6%a top-10% man within five years, for a top-20% woman of 23, open to men 2 → 15 years older
- 3If you're already top 20%, GLP-1s and the gym. Going on GLP-1s and a year of glute work in the gym multiplies your chances of landing a top-10% man nearly five times, and more than doubles them if you're also open to older men. It raises your odds of any committed man too (43% → 48%). Below the top 20% it helps less: fat loss shrinks the hips too, so waist-to-hip ratio moves only about a third as far as the waist.
0.2% → 0.9%a top-10% man within five years from 27; open to men 15 years older, 5% → 9%
- 4Give more men a real chance. Months of dating with three men a year instead of two.
32% → 50%a committed man within five years, for a median woman, dating 2 → 3 men a year
Everything together: a top-20% woman going for a top-10% man
Each step added to the one before. Bars are her odds of a committed top-10% man within five years; under each, her odds of any committed man and of one as good as she started out or better. The age gap costs a little of "anyone" and buys most of the shot at the top.
Why doesn't starting at 23 help on its own? Men do value those four years, but the men who are top-10% today are mostly older: among single men 22-25 fewer than 1 in 100 are, against 2 in 100 at 30 and 4 in 100 at 38. A 23-year-old dating men her own age is mostly meeting men who haven't made it yet. Starting early buys her many more men as good as her (18% to 40%); to turn her youth into a top-tier man she has to be open to older ones.
How to land a millionaire husband
The author has argued before that attractive women 20-29 all have a realistic chance at a millionaire husband, if they are willing to entertain age gaps and put some time into optimizing their waist-to-hip ratio. This section goes through those levers.
Count the women at each WHR tier and the men at each income and wealth level. Single women 22-29 at WHR ≤ 0.74 are about 1 in 25, and there are about as many of them as single men 28-42 earning $160k+ or worth $1.1M+. Under assortative matching on market value, that's who they can expect to reach.
What GLP-1s and the gym do for her odds of a millionaire
By where a woman 20-29 starts on waist-to-hip ratio: her odds of a committed millionaire husband within five years from 25, before and after a GLP-1 and a year of glute work, open to men 2 or 15 years older. Millionaire: worth $1M+, 8% of men 22-55 (0.5% at 22-26, 17% at 47-55). The first row is for scale: the same woman's odds of a top-5% man, a rarer catch than a millionaire.
NHANES women 20-29, measured (n = 923), each given simulated responses: weight loss from the trials (semaglutide 2.4 mg: −15%; tirzepatide 15 mg: −21%; averaged), scaled down for women with less fat to lose; waist falls 0.84× as fast as weight; hips follow the waist at the population slope (0.66 in logs, from the author's WHR notebook), so WHR moves about a third as far as the waist; glute work adds an inch of hip.
The age gap buys reach
Share of husbands in the top 10% of men by income, in marriages from the last five years, by the wife's age at marriage.
ACS 2024. Husband income is measured now, so older husbands have had more time to earn; part of what a gap buys is an established man. Wives in recent top-1% marriages are 82-90% college graduates. The author's analysis of millionaires' over-representation in 10+ year age-gap marriages.
Where millionaires outnumber qualifying women
Single women 22-29 at WHR ≤ 0.74, per single man 28-42 worth $1M+, in the largest metros. At 0.4 there are about five single millionaires for every two qualifying women; below 1, you have the leverage.
Women's body measures are imputed from NHANES by demographics in the Dating Calculator, not measured city by city.
The recipe: get to WHR ≤ 0.74 if you're close, finish a degree, start looking early, be open to a man 10 or more years older, and look where the millionaires outnumber you.
For men, value as a partner is mostly status. The author's dating power equation: 0.5 status + 0.2 social skills + 0.2 attractiveness + 0.1 height. Everything here is five years for a median 30-year-old man looking at women 22-30, moving one thing at a time, on the apps and off them (approaching one woman a month in person).
- 1Get off the apps. On the apps you are competing with the top 10% of men for a dozen first dates a year per woman. In person she isn't flooded, and she sees more of you than a photo. Approaching one woman a month puts you in the top quarter of single men, and three in four women 18-30 say they want to be approached (Date Psychology). Of the men who do approach, 60% got a date within the year and 13% a long-term relationship. Flirting is implying, not saying.
9% → 65%a committed woman within five years, apps → approaching one woman a month in person
- 2Lose the weight, get fit, then get abs. Looks decide whether you get dates at all, and your body is the half of looks you control. Six in ten single men 25-35 are overweight or obese; only 5% are fit (body fat of 20% or less) and 2% have strict abs (17% or less), so each rung takes you past most of the competition. Fitness only moves your value as a partner a little (from the 50th to the 65th percentile); what it moves is how many women say yes to a first date, from under one a year to six. GLP-1s work where diets fail; style and grooming do the rest (light stubble rates 0.65 points higher on a 7-point scale than clean-shaven, Neave & Shields 2008).
9% → 43%a committed woman within five years on the apps, fit (with strict abs: 58%)
- 3Status is half of it, off the apps. Income, career, education, and the cheap signals of them: good shoes and a good haircut are the highest-return spend there is. Outwork your peers; the bottom fifth of men work about 20 hours a week. On the apps status barely shows (a job line under the photos), and his dates still come from his photos, so 90th-percentile status takes a median man only from 9% to 28% there, against 65% to 85% in person. Where women can see it, it's the biggest lever on who you end up with; this is how top earners marry at 90% rates.
2.8% → 38%a 75th-percentile woman or better within five years, in person, with 90th-percentile status (a committed woman: 65% → 69%)
- 4Be polarizing. Women agree about men only half as much as men agree about women (0.25 vs 0.50, Roth et al. 2023), and on OkCupid a profile some people love and some dislike got far more messages than one everyone found fine. Lean into what's distinctive.
~3×messages for polarizing profiles (OkTrends)
- 5Stack them. Each moves a different thing: your body and getting off the apps get you dates, status and social skills decide who stays. A median man of 30 who gets fit, gets to the 75th percentile on status and social skills (together, about the 88th percentile as a partner) and meets women in person:
<0.1% → 55%a 75th-percentile woman or better within five years (a committed woman: 9% → 71%)