Zigao's Blog

Why I shoot 35mm film

Zigao Wang — Sat, 22 Aug 2026 00:00:00 GMT

Prices in

switch to see rough USD

Whenever someone notices the camera and asks if it’s real film, there’s a pause and then some version of “you must be rich.” Every photo costs money, right?

The funny part is that the most expensive camera I own is digital. It cost ¥7,500 and I haven’t touched it in over a year. The camera in my bag right now cost ¥70.

So I sat down and worked out what film actually costs me, because I’d never checked. The short answer is that it’s cheap, mostly because of where I live. But cost turned out to be the least interesting part, and the reason I actually shoot film only became obvious once I’d figured out why I stopped shooting digital.

What a roll costs

My lab charges ¥20 to develop and scan a roll of 35mm color. Shipping is ¥6 a parcel.

The film itself is the variable. Kodak Gold is around ¥60, UltraMax ¥78, and the US-made Fujifilm 400 also ¥78. Most of what I buy sits between ¥65 and ¥75. The cheapest thing I’ve bought is Harman Phoenix II at ¥49, which I gave two chances and didn’t like either time. The latitude is bad and the colors came out ugly, though I’ll admit some of that could be my metering. I wrote up what I thought of it here.

The very first roll I ever took out of the AE-1P. UltraMax 400, and I had no idea what I was doing — it was the first time I'd rewound a roll and got it out of the camera, going off a YouTube video.

At the other end I’ve paid ¥138 for Superia and ¥148 for both CineStill 800T and Portra 800. I buy the expensive stuff partly to try new things and partly because I’m adding stocks to AvoidXray and can’t write about a film I haven’t shot. Portra is the one I keep going back and forth on. It’s built for people who edit, so the colors come out flat and it just holds detail for you to push around later, which is exactly the workflow I don’t have. I also underexposed half that roll, so I can’t fully blame the film.

The Portra 800 in question, about to go into the AE-1P somewhere in Japan.

Averaged across everything that actually goes through my cameras, ¥80 is about right. So:

¥80 for film, ¥20 to develop and scan, ¥6 to ship. ¥106 a roll, or ¥2.94 a frame.

Except I don’t keep 36 photos. On a good roll there are maybe ten I like, so the number that matters is about ¥10.6 per photo I’d actually post. Milk tea in Shanghai is around ¥20 a cup, so that’s half a cup per photo and five cups per roll.

Twenty rolls a year comes to ¥2,120, or roughly ¥177 a month. I’m a student and I don’t earn money, so that isn’t nothing and I do think about it. But it’s about what a streaming subscription and a couple of coffees costs, and it’s the only hobby I have that reliably gets me outside and hands me two hundred photographs a year.

What does it cost you?

Drag a slider, or click a number and type your own.

Film, per roll ¥

Develop + scan ¥

Shipping, per roll ¥

Frames per roll

Keepers per roll

Rolls per year a year

¥106 a roll

¥2.94 a frame

¥10.6 a keeper

¥2,120 a year

Your develop + scan, against the labs in this post

You ¥20

My lab, Shanghai ¥20

Hasselblad scans, Shanghai ¥68

Osaka ¥88

Oslo, cheapest ¥136

Portland ¥195

720 frames a year, 200 of them worth keeping, at ¥177 a month.
A roll is 5.3 cups of milk tea at ¥20 a cup. Every photo you'd actually post costs 0.5 of a cup.
A disposable is ¥130 for 27 frames, so ¥4.81 a shot. You're 39% under that.
A ¥7,500 body shedding ¥1,750 a year in a drawer burns 16.5 rolls of your film annually for nothing, and its sticker price is 3.5 years of your shooting.

Why it’s this cheap, and why that’s a Shanghai thing

Almost none of that number is me being clever.

There’s a shop here called 上文 (Shangwen) with a ridiculous amount of stock.

上文. Most of that wall is priced between ¥45 and ¥85, and there are movie film cans stacked on the top shelf.

A lot of the interesting stuff is motion picture film that Chinese companies respool into 35mm cassettes, which is how stocks that are basically unfindable elsewhere end up just sitting there at ¥45 a roll.

Respooled 5203 and 5207 and a dozen small brands I'd never heard of. The guy who wandered into the left of the frame is carrying the medium format camera I keep telling myself I don't need yet.

When the shop runs out of something, Taobao fills the gap in two days, anywhere in the country, with shipping that costs almost nothing.

The lab is in Shanghai too. I could go in person and have scans in two hours, but I don’t bother. I ship it for ¥6, the parcel travels overnight, they develop and scan that afternoon, and the files are on my phone the same day.

Expensive labs exist here as well. There’s one charging ¥68 a roll that scans on a Hasselblad with someone actually sitting there fine-tuning each frame, which is a real premium service rather than the same thing marked up. It just isn’t what most people choose. My lab’s storefront shows over ten thousand orders in 2026 alone, and that’s one shop, with places charging about the same in cities all over the country. Twenty yuan isn’t a secret corner of the market. Twenty yuan is the normal price.

Compare that to what people elsewhere pay. Someone in Portland is on about ¥195 a roll. A guy in Oslo pays around ¥330 for one roll of 120, and his lab’s cheapest 35mm option is still ¥136. I pulled those from an r/AnalogCommunity thread where people listed their local prices, and the gap is not subtle.

I’ve also just seen it myself. I kept assuming film would be cheap in Japan, because Fujifilm is Japanese, and it never is.

A shop in Osaka: 950 yen to develop, 880 yen to scan. That’s 1,830 yen for one roll, about ¥88, against the ¥20 I pay at home.

Yodobashi’s film corner. Single rolls of Gold, UltraMax and ColorPlus were tagged between roughly 1,900 and 2,400 yen, so ¥90 to ¥115. I pay ¥52 to ¥78 for the same films.

Kuala Lumpur was the same story from a different angle. Everything in the cabinet was expensive, and Harman Phoenix II, the roll I buy at home for ¥49 and don’t even like, was ¥112.

A camera shop in KL. Lovely place, brutal prices.

The one honest caveat is that this isn’t unique to China. Korea is right there with me, around ¥20 to ¥41 a roll with 6000x4000 scans as standard. So it’s really an East Asia thing, and most of the English language film internet is written by people paying five to ten times what I do. When they say film is expensive they’re telling the truth about their life, not about film.

Which is also why almost nobody here develops at home. On Reddit that’s the top answer to everything: can’t afford the lab, so I do it myself. Makes complete sense at ¥195 a roll. At ¥20 it falls apart, especially since developing is only half of it. You still have to scan, which means a film scanner or a digital camera with a macro lens and a copy stand. Buying a digital camera to avoid paying for film is a funny place to end up.

Prices are rising here too. I don’t study economics so treat this as a guess, but it looks like plain supply and demand: a lot more people picked up film and almost no new production appeared to meet them, because coating film is hard and expensive and restarting a line is a huge bet on demand that might not last. Which is why I’m happy about Lucky building their own new color line. C200 came out a while back and C400 arrived recently, and there’s a roll of it in my pocket camera in Macau as I write this.

The cameras are the real bill

Everyone fixates on the per roll cost because it keeps coming back. But bodies are the bigger number, and mine are what changed my mind about all of this.

Most people who shoot film shoot a point and shoot, and that’s the right call. Nothing to learn, fits in a pocket, photos look like film. In China you’d buy one on 闲鱼 (Xianyu), the giant secondhand marketplace, basically eBay or Facebook Marketplace. An entry level point and shoot runs about ¥200, and something properly decent is around ¥400. The ¥70 Olympus in my bag was a lucky find, not the normal case, so don’t go in expecting that.

The reason film cameras are this cheap isn’t only that most of them missed the hype. It’s that they’re simple. A mechanical camera is a box, a shutter, a spring and a lens. No sensor, no processor, no screen, no firmware, nothing to become obsolete. That’s also why a ¥950 AE-1 Program can do basically everything I’d ask a camera to do. Capable film gear is just cheap compared to capable digital gear, and it stays that way.

Everything I own:

Canon AE-1 Program, ¥950. My first camera, still the one I bring when I care.
Snapic A1, ¥700. My main point and shoot. Not a good camera. But it’s thin and sleek in a way the old ones aren’t, and I carry it because of that.
Olympus Trip Panorama 2, ¥70. The cheap one. Currently loaded with Lucky C400.
A Lomography Sprocket Rocket. One roll, too much hassle, never again.

That’s ¥1,720 for three cameras I use.

The AE-1P outside Wukang Mansion, same roll of UltraMax as the photo further up.

An Olympus µ[mju:]-II, sold as the Stylus Epic in the US, costs about the same secondhand and is a better camera with a better lens. It’s also bulkier. I chose sleek over sharp and I’m still not sure that was right, but I did carry the Snapic A1 every single day, which I’m not certain I’d have done with the mju.

This is the entire argument for the Snapic A1. It disappears into a pocket, so it's always there.

The mju is also a good example of what’s happening to film cameras generally, which is hype. The Contax T2 and T3, the mju II, anything that got big on Instagram, all of it costs several times what it used to. Is the T3 a lovely camera? Yes. Is a big chunk of that price just the name on the front? Also yes, and it isn’t correcting, because production ended decades ago and supply only shrinks.

Which is the whole joke about my cheap camera. It’s an Olympus too.

Olympus Trip Panorama 2, ¥70, in Macau a few days ago. Same brand as the mju II, roughly one thirtieth of the price, and it’s the camera I’ve actually been shooting with.

The upside is that hype works in my favor. My AE-1P cost ¥950 and will still be worth about ¥950 in three years. Film bodies finished depreciating a long time ago, so buying one is closer to a deposit than a purchase.

(While I’m here: disposables are the worst value in film. The Fuji and Kodak ones are around ¥130, the cheaper ones are bad, and you get 27 frames through a molded plastic lens and then throw the camera away. For the same money buy a ¥70 compact and a roll of Gold. That said, if you walk past one and want to try it, go ahead. It’s still film and the experience is the same.)

One scope note on all of this: everything here is 35mm. I do want to shoot medium format eventually, because a Hasselblad never gets old and the RB67 is very cool, but it’s a different budget entirely and I’d rather get properly good at 35mm first. That’s a post for a few years from now.

The camera I don’t use

In 2023 I signed up for the photography CCA at school, so I bought a used Sony a6500 with a lens for ¥7,500. Worst purchase I’ve made.

I used it on a few trips and then stopped, and the reason matters more than the money. You come back from a day out with three or four hundred frames. Then you have to go through all of them and decide what’s worth keeping, and then edit the survivors, and every part of that is tedious. So I’d get partway in, lose interest, and the photos would sit on a drive forever. It’s also physically big, so even when I meant to bring it, often I just didn’t.

I called that a scam for a while and I’ve stopped. I paid roughly market rate for a camera that doesn’t suit how I take pictures. That’s a taste mistake, not a con, and it’s still the most useful number in this post.

It’s a 2016 body worth maybe ¥3,200 now, so it’s been quietly losing about ¥1,750 a year while sitting in a drawer. At ¥106 a roll, that’s sixteen rolls of film annually. If you shoot less than about sixteen rolls a year, film costs you less than merely owning a digital camera you never pick up.

Meanwhile the ¥70 Olympus is in my bag. Divide what each camera cost by how many photos it’s actually produced and one comes out at a few yuan a frame while the other doesn’t come out at all.

The obvious objections

Your phone is cheaper. Obviously, and I’m not going to pretend this is close or work out iCloud costs per frame to make it look like a contest. Every photo on your phone is free. The iPhone camera has gotten good, not good-for-a-phone good, and it hands you a finished image for nothing extra because you were buying the phone anyway. If your only goal is cheap photographs, use your phone. But a phone and a film camera aren’t really comparable in either direction. One isn’t a cheaper version of the other, they’re different things you pick up for different reasons.

You bought the wrong digital camera. Fair, and small digital cameras are good. The Ricoh GR is great and people who own one actually carry it. It’s also expensive, and again a lot of that is hype, the same pattern as the Contax on the digital side.

What about digicams? The Y2K CCD trend has pushed old compacts to somewhere between ¥800 and ¥2,000, more than plenty of film point and shoots, and people buy them because they’re small and they like the retro look. That’s the part I don’t follow. If you want the retro look, shoot film. It’s cheaper, more interesting, and you get the actual thing instead of a digital impression of it.

So here’s the actual reason

Everything above is accounting. This is the part that’s true.

I hate editing. Unedited digital looks bad, not broken exactly, just flat and unfinished. Unedited film looks like film. So when I hand the lab ¥20 I’m not really paying for chemistry, I’m paying for a color grade I refuse to do myself. Ten keepers a roll, finished, ready to post.

Thirty-six frames also makes me think before pressing the button, which is the exact opposite of what the Sony did to me.

I took the lens off in Kyoto to see what the camera sees. Not useful. Very fun.

And beyond all that, it just feels good. The weight of it, winding on, not knowing what you got, the wait, the moment the scans land. Sometimes the colors come out wrong and I like them anyway. None of that is an argument. It’s the real reason, and the math came afterward.

The other reason

Shooting film is a personality, and I’m aware of how that sounds. It’s still true, and it’s most of why I’d tell someone else to try it.

A film camera gets noticed. That’s how this whole post started: someone spots it, asks if it’s real, and then we’re talking about something other than the weather. It happens with strangers and it happens with people I’ve known for years, who turn out to have a dead Pentax sitting at their parents’ place. Nobody has ever asked me what’s loaded in my phone.

So it becomes a tag. I’m the guy who shoots film the way someone else is the running guy or the coffee guy, and when I turn up somewhere there’s a conversation I never have to start.

I’ve talked a few friends into it too, and the pitch is easy because it’s cheap. ¥200 for a point and shoot on Xianyu, ¥106 a roll, and if you hate it you sell the camera for what you paid. Two of them stuck. Watching someone get their first scans back is better than getting my own, because they’ve spent two weeks not knowing, and then the whole roll arrives at once.

That’s also where AvoidXray came from. I wanted somewhere to keep track of what I’d shot and what I’d shot it on, and somewhere to read what other people made of a stock before spending ¥148 on it, so I built it. It’s a community for analog photographers: your cameras, notes on film stocks, and your scans somewhere that isn’t Instagram. Every film link earlier in this post goes there, and my photos live there too.

None of that is in the accounting either.

When it doesn’t work

I should include the failure, because a post that only argues one direction isn’t worth reading.

I shot a roll of ORWO Wolfen NC400 and it came back wrong. Scanned it twice, got completely different colors each time, neither one good. I ended up fixing them with AI and posting the comparison.

So I paid for a color grade, the grade came back broken twice, and I did the editing anyway. That’s the real risk of this trade. You’ve handed your look to a lab and a film stock, and occasionally that fails and you end up doing exactly the thing you were paying to avoid.

So, is it expensive?

Film isn’t cheaper than digital. Anyone telling you it is has counted selectively.

But expensive was always the wrong word, because nobody applies it consistently. A roll costs me ¥106, five cups of milk tea. A vinyl record costs about the same, and nobody has ever asked whether records are cheaper than streaming, because that was obviously never why anyone buys records. Film is arguably the better deal of the two, since I come away with photographs rather than just a nice afternoon.

Twenty rolls a year is ¥2,120 for maybe two hundred finished photos and a reason to walk around paying attention to things. Set against a ¥7,500 camera losing ¥1,750 a year in a drawer, that’s a good deal.

I just spend the money on the part that makes pictures instead of the part that sits at home.

Prices checked August 2026 and quoted in CNY; the USD figures are approximate conversions at the rate shown by the toggle at the top. My lab and film shop are both in Shanghai, which explains most of these numbers. International figures converted roughly from this r/AnalogCommunity thread and FilmPhotoDeveloping’s 2026 lab pricing guide.

Anything You Want

Zigao Wang — Sat, 01 Aug 2026 00:00:00 GMT

Derek Sivers started CD Baby in 1998 because he wanted to sell his own CD online and nobody would let him. It became the largest online seller of independent music, over 150,000 musicians and $100M in sales. A few months before selling in 2008, he moved the whole company into an irrevocable charitable trust, so the buyer bought it from the trust rather than from him. That turned into $22 million for music education.

The book is that story in 40 chapters, most of them a page. It takes an hour. Every chapter is also free on his site, which is unusual and worth knowing before you buy anything. If you get something out of it, buy it directly from him rather than through a store. It’s $15 for every digital format including the audiobook, or $19 for the paper copy with the digital formats thrown in, and buying direct means the money goes to the person who wrote it. Feels like the right way to pay for a book that gives itself away first.

I read the 2015 Penguin edition; the 2022 third edition adds eight more chapters.

It’s a strange book to review because it keeps telling you not to take it seriously. Sivers says up front that he’s unusual, that what worked for him probably won’t work for you, and that he hopes you disagree and email him about it. Most authors saying that would be doing fake humility. It’s harder to dismiss from someone who gave the company away before he sold it.

Your business is a small world where you write the laws

This is the spine, and everything else hangs off it.

Sivers didn’t want CD Baby to get big, because it would eat his music career. So instead of being ambitious he went utopian, on the theory that idealism would keep it small. He wrote down what a distribution deal would look like in a perfect world, from a musician’s point of view. Pay me weekly. Show me who bought my CD, because those are my fans and not the distributor’s. Never drop me for selling too little. Never sell placement, because it isn’t fair to people who can’t afford it.

Four lines, written as a daydream, and they worked as a constitution for a decade. When an ad salesman called about banner ads, the answer was already written. He didn’t have to think about it.

The pricing came the same way. He had no idea what to charge, so he walked into the record store in Woodstock and asked how it worked if he sold his CD there. She said you set the price, we keep a flat $4, we pay weekly. He went home and typed that onto his site, plus a $35 setup fee because adding an album took him 45 minutes by hand. Six years and $10 million in revenue later, those were still the only two numbers in the business.

His claim is that a business plan should take a few hours at most, ideally minutes. A glance and common sense should tell you whether the numbers work, and the rest is detail. Hard to argue with a guy who ran a $10 million business on a number he got from a record store clerk.

The same instinct produces his position on corporate formalities: no lawyer, no Terms and Conditions, and a flat no to employee review plans and sensitivity training when he had fifty staff. His argument is that the people selling you that apparatus are selling fear. I think the instinct is right and the specific advice doesn’t survive contact with 2026, when you’re handling other people’s data by default.

Start with one percent of it

He had no funding and calls it an advantage. Couldn’t afford a programmer, so he bought a $25 book on PHP and MySQL and learned it. The desks were planks on cinder blocks for years. He watched funded friends spend $100,000 on things he built for $1,000, always justified as needing the very best, and never saw it help a single customer.

The transferable part is his rule for starting: take one percent of your grand vision and do it this week. Want to build a chain of schools? Teach one person something for money. Want to start an airline? Next time a flight cancels, offer everyone at the gate a chartered small plane if they split the cost, which is roughly how Branson started Virgin Atlantic.

CD Baby v1 took a few days. A list of CDs with buy buttons, and when you entered your details the site emailed them to him and he did the rest by hand. That was the whole product for a year. He spent $500 and was profitable in month two.

The related chapter is the one people quote most: ideas are a multiplier of execution. An idea scores between -1 and 20. Execution is worth between $1 and $10 million. You multiply them, which is why a brilliant idea with no execution is worth $20, and why he won’t sign an NDA to hear something simple.

If it’s not a hit, switch, and the broken test underneath it

Before CD Baby, Sivers spent twelve years promoting his own projects. Networking, pitching, marketing. He made progress, but always by force, always against doors that were locked or closing. Then CD Baby happened and the problem inverted: instead of manufacturing demand he was managing it.

His correction to the usual gospel about persistence is good. Success comes from persistently improving and inventing, not from persistently pushing the thing that isn’t working. Don’t spend years fighting uphill against a locked door. Go build something else.

But then he gives you the test for whether something is working, and the test is broken.

His instruction is to present each idea to the world, and if multiple people say some version of “yes, I need this, I’d be happy to pay you to do this,” go do it. If the response is anything less, don’t.

That is, almost word for word, the sentence The Mom Test identifies as the deadliest fluff in the world. It’s future tense. It’s hypothetical. It costs the speaker nothing to say and it’s the polite thing to say to an enthusiastic person who just described their project to you. Fitzpatrick’s first company took that exact signal at face value and burned around ten million dollars before finding out nobody would actually buy. Sivers is proposing the worst available signal as his go/no-go criterion, and he’s proposing it confidently, in a book people use to make real decisions.

The defense is that Sivers didn’t actually do what he says. What he measured was musicians mailing him $35 and CDs showing up at his house. That’s money changing hands, which is the strongest currency of commitment there is. He passes the Mom Test in practice and fails it in prose, which is a common shape for founder advice: people describe what they did in the language of what they now believe, and the language drifts.

The same drift shows up in his advice on prioritization. He says when you’re unsure what to work on, ask customers the open-ended question of how you can best help them right now, then focus on satisfying those requests. Fitzpatrick’s whole chapter on feature requests exists to stop you doing that. Requests are symptoms. The thing you want is the motivation underneath, which you only get by asking why they want it and what it would let them do. Sivers says obey. Fitzpatrick says understand, then decide.

Read together, the honest synthesis is this: Sivers is right that you should stop pushing things nobody wants, and Fitzpatrick is the one who can tell you whether anybody wants it. Sivers got away with a bad test because his customers were paying him before he’d asked anyone a question. Most people don’t get that.

The personal-life version of the same chapter has no such problem. No “yes.” Either “hell yeah” or “no.” Saying no to most things is what leaves room for the rare thing you’re actually excited about. That one’s about your own enthusiasm, which you can observe directly, so there’s nobody to lie to you.

Proudly exclude people

Most businesses try to be everything to everybody and then wonder why nobody notices. Sivers says to state clearly who you’re not for.

His example is the Hotel Café in LA, a venue with signs saying no talking during performances, where artists are encouraged to stop the show and tell a talker to go to any other club in town. Because it’s the one place you can actually listen, it became the most popular venue in the city. At CD Baby he told record labels their acts weren’t allowed, because the site was for musicians who hadn’t signed their rights to a corporation.

It’s a big world, you can loudly leave out 99 percent of it, and your 1 percent comes to you because you’ve shown them you value them.

He pairs this with an argument for many small customers over a few big ones. Landing one huge client means custom-building for a handful of people who might leave, and it means that client is effectively your boss. Thousands of small customers means nobody can make demands and you hear a real spread of opinion.

You don’t need a plan or a vision

A year and a half in, running CD Baby out of his house with one employee, Sivers sat down for a night of serious long-term thinking. What came out was an email to that employee speculating that someday they might have a thousand artists, might need a third employee, might need to network three computers together, might have to start moving CDs into the garage. He apologized for how grandiose it sounded. Years later, at a hundred thousand artists and eighty-five employees, that email was a running joke.

When journalists asked his long-term goal, he said he didn’t have one, that he’d passed his goals long ago, and that he was just trying to help musicians with whatever they needed that day.

This is the chapter Zero to One is written against. Thiel’s entire argument is definite optimism: have a specific plan, know your secret, commit to a future only you can see, because indefinite optimism produces a generation of people optimizing without direction. He’d file Sivers under indefinite optimism immediately, and the lean startup movement with him.

Sivers would say Thiel’s founders are pattern-matching their own luck into a plan after the fact, and quotes Steve Blank on the way past: no business plan survives first contact with customers.

They can’t both be generally right, and I don’t think the answer is a compromise. They’re describing different games. Thiel is describing something that has to reach escape velocity, where the customers don’t exist yet and no amount of asking hands you the plan. Sivers is describing a service business where the customers are right there telling you what they need, and the theorizing is what’s in the way. The question that decides which advice applies isn’t “which book is smarter,” it’s “does my thing have customers yet.”

Which, incidentally, is a question Fitzpatrick can answer and neither of the other two can.

The little things are the actual product

The most transferable part of the book and the part nobody copies, because it doesn’t scale.

CD Baby’s best marketing was an order confirmation email. The default said your order has shipped, thank you for your business. Sivers spent twenty minutes replacing it with something absurd about sterilized gloves, a satin pillow, fifty inspectors, a packing specialist lighting a candle, and the entire town of Portland waving the package off on the private CD Baby jet. People loved it enough to post it on their own websites for a decade. Go read it rather than take my word for it; the joke is in the escalation and summarizing it kills it.

The rest are the same shape. Customers kept calling to ask if they’d made the 5pm FedEx cutoff, so he added two lines of code showing a countdown next to the shipping options. Every outgoing email had a customized sender name, so Susan got mail from CD Baby loves Susan. Big favors cost a pizza, and they got one every few weeks. One order asked for a rubber squid, or a real one if no rubber was available, and since a customer in Korea had recently mailed the warehouse a packaged squid filet, that went in the box.

There’s a related chapter on clarity I keep thinking about. With two million customers, one unclear sentence in a mass email produced twenty thousand confused replies, costing a week of staff time and at least $5,000. So writing those emails took him all day. His observation is that people writing websites never get this feedback: unclear copy doesn’t produce complaints, it produces silence, which is the hardest signal to learn from.

Delegate or die, and the three ways it went wrong

By 2001 he had eight employees and was working 7am to 10pm daily because every decision went through him. He fixed it by changing how he answered questions. Instead of giving an answer, he’d gather everyone, explain the philosophy behind the answer, have someone write it in a manual, and tell them they could decide it themselves next time. After two months the questions stopped. He moved to California to make it clear the running of things wasn’t his job. The company went from $1M to $20M and eight to eighty-five people while he was away from it.

Then three failures, and they’re the best part of the book.

He hired someone for the single most critical job, delivering every album to every retailer every week, watched closely for a few weeks, and looked away. Months later he found that nothing had gone to Napster or Amazon since. Trust, but verify.

He empowered employees so completely that when they asked which profit-sharing plan to use, he said pick whatever you want. They designed one that returned all company profits to themselves. Canceling it turned eighty-five people against one, and he never saw the office again. The word he learned was abdicate.

And the $3.3 million one. He’d signed his dad’s paperwork years earlier without reading it, then let a bank teller talk him into making CD Baby an alias on that existing company because it saved ten minutes and $100. His dad’s company owned 90 percent of his business, and the IRS wouldn’t let him buy it back at the original price.

Being, not having

He wanted to be a singer from fourteen. Everyone told him for eleven years that he wasn’t one, including a mentor who listened to his first album and told him to stop trying. He practiced an hour a day anyway, and at twenty-nine he was good, at which point a stranger told him singing is a gift you’re born with and he was lucky.

Same with production, same with programming. He wrote all of CD Baby’s code himself even when his employees were furious about how slowly features shipped, even though he estimates it cost millions in lost business. His comparison: outsourcing the code of an internet business is like a band outsourcing the songwriting.

Having something is a means. Being something is the point. When you sign up to run a marathon, you don’t want a taxi to the finish line.

This is obviously self-serving and he mostly admits it. It’s also the honest answer to why anyone builds things themselves now that you don’t have to.

Enough

The chapter on giving the company away opens with Kurt Vonnegut and Joseph Heller at a billionaire’s party. Vonnegut marvels that their host has everything. Heller says he has something the billionaire never will, which is enough.

Sivers insists the trust wasn’t altruism and that he sacrificed nothing. He doesn’t own a house or a car, owning less makes him freer, and what he wanted was the pride of doing something smart and irreversible before he could talk himself out of it. The trust pays him 5 percent of its value a year while he’s alive and everything goes to music education when he dies.

What makes this an idea rather than a flex is that enough is a number, and if you don’t pick it in advance it silently defaults to slightly more than you currently have. Sivers picked his early, which is why the decision to sell took one day of writing in a diary rather than a year of negotiating with himself, and why he took the lower of two bids because that buyer understood his musicians better. Almost every founder story I’ve read is about someone who never picked the number.

My take

The thing I can’t get past is the hit assumption underneath the whole book.

Sivers built a store for himself, a friend asked to be added, then two more, then strangers calling because their friend Dave said he could sell their CD. Two newsletters mentioned him and fifty musicians signed up overnight. He was standing in a real structural gap at the exact moment the internet opened it, and his demand problem was solved before he knew he had a business.

That’s why he can be so relaxed about validation, so dismissive of plans, and so confident that if it’s not a hit you should switch. The advice tells you what to do when the answer is obviously yes or obviously no. Almost everything actually lives in the enormous space between, where the response is polite and small and you can’t tell whether you’re early or wrong. The Cold Start Problem is a whole book about that space, arguing that networks look exactly like failures right up until they don’t. Sivers would say having to push that hard is the signal. Chen would say every network business had to push that hard and the quitters quit right before it worked.

I notice I want Sivers to be right, because his version means I get to stop. That’s a good reason to be suspicious of my own reading.

The other thing that doesn’t survive to 2026 is his silence on attention. Distribution was the scarce thing in 1998. Independent musicians had no way to reach buyers, Sivers built the road, and he got word of mouth free from people who’d been waiting years for exactly that. He helped kill the problem he was solving. Now anyone can ship anything in a day and attention is the scarce thing, which he barely mentions because he never had to.

But dated isn’t worthless, and a book being wrong about one thing is not a reason to skip writing down what it got right. The utopia framing, the little things, the clarity penalty, the delegation failures: none of that has a shelf life. The growth chapters are a historical document about a specific moment, and reading them as one is fine.

What makes the book worth an hour is the thing underneath all of it, which almost no business book says out loud: that the point of the company was never the company. He built a small world he wanted to live in, discovered he was happier at five employees than eighty-five, and got out. Most books in this genre would call that a failure of ambition. He calls it the whole point, and he’s the one who wrote the four-line mission statement that ran a $100M business for a decade.

Concepts I’ll carry forward

Write the utopia first. Four lines describing the perfect world for the people you’re serving, written before any code, treated as constraints. It makes hundreds of later decisions obvious and it’s cheaper than a roadmap.
A business plan should take minutes. If a glance and common sense don’t tell you the numbers work, more spreadsheet won’t fix it.
Start with one percent of the vision this week. Not a prototype of the whole thing. The smallest version that actually helps one real person, done by hand.
Ideas are a multiplier of execution. A brilliant idea with no execution is worth $20. Stop protecting ideas.
Stop pushing what isn’t working, but check the signal. Sivers is right that you should go invent something else instead of flogging a dead product. Just don’t use “people said they’d pay for it” as the test. Use what they actually did.
Hell yeah or no. For your own commitments, where nobody can lie to you but you.
Proudly exclude people. Say who you’re not for. The 1 percent shows up because you turned away the 99.
Many small customers beats a few big ones. One big client isn’t a customer, it’s a boss.
The little things are the marketing. The countdown timer, the sender name, the squid. They don’t scale, which is exactly why they work.
You should feel pain when you’re unclear. Bad copy doesn’t get complaints, it gets silence. Write as if twenty thousand people are about to reply confused.
Answer the philosophy, not the question. The way out of the delegation trap is explaining the reasoning and writing it down, so the next decision doesn’t need you.
Delegate, but don’t abdicate. And trust, but verify. Handing over power isn’t handing over responsibility.
Read what you sign. $3.3 million.
Being, not having. Doing it yourself is slower and sometimes costs real money. That’s allowed if the doing is the point.
Enough is a number you have to actually pick. Otherwise it’s always slightly more than you have.

Rating

4 stars.

It loses one because the confident advice about validation and growth is inseparable from circumstances that no longer exist, and because a book this deliberately anti-analytical has no way of telling you when its own advice stops applying. You need a second book to catch its mistakes.

It keeps four because half of it is failures described in enough detail to learn from, because the humanist argument underneath is the one most business books skip entirely, and because it takes an hour. Best value-per-hour of anything I’ve read this year, and I’d say that even though it’s free.

I Asked Richard Sutton a Question at WAIC

Zigao Wang — Sun, 19 Jul 2026 00:00:00 GMT

At WAIC this year I wanted to catch three forums: Yao Qizhi’s, Zhu Songchun’s, and Richard Sutton’s. I only had time for one, so I went with Sutton’s. No regrets.

The Q&A session after Sutton's talk at WAIC 2026.

Sutton opening his talk.

If you don’t know the name, Richard Sutton is one of the founders of reinforcement learning, the branch of AI concerned with learning by trial and error. He co-wrote Reinforcement Learning: An Introduction, the field’s standard textbook, and in 2025 he and Andrew Barto received the Turing Award for that foundational work. His talk was titled “Growing Superintelligence from Experience with Reinforcement Learning,” and he gave it standing in a room full of people building large language models, to tell them he thinks the dominant approach is a detour.

Here’s what he argued, and the question I got to ask him.

The core idea

Today’s AI, ChatGPT and the rest, learns by reading what humans have written. It’s a student who has memorized every textbook. Impressive, but it only knows what we already wrote down.

Sutton thinks that’s a ceiling, not a foundation. His reasoning is disarmingly simple: it isn’t how you got smart. You didn’t read a manual on walking. You were a baby who flopped around, tried things, fell, and worked it out. You learned from doing.

So his whole proposal is an AI that learns the same way, from its own experience, trying things and seeing what happens, with nobody handing it the answers. Everything else in the talk is machinery in service of that one idea.

The bitter lesson

Sutton’s most cited work is a short 2019 essay called The Bitter Lesson. Its argument: every time researchers hand-build human knowledge into an AI, it helps for a while, then stalls. What wins over the long run is general methods that scale with raw computation and learning, methods that let the machine discover things for itself.

His rule follows from that: build in less, let the AI discover more. Pre-loading it with what we already know actually gets in the way of it learning to figure things out. This principle sits underneath every design choice he made in the talk. (He noted, with some amusement, that the essay was a page and a half and became one of the most cited ideas in modern AI, which he never predicted.)

Learning like a baby

This was the part that made everything click.

How does a baby learn? It sets itself tiny goals. Can I make that sound happen again? Can I grab that toy? It practices until it can, and now that’s a skill it owns. Then it gets bored and curious about something new. Skill by skill, it builds itself up.

Sutton showed videos of animals doing exactly this for no reward at all: a young orangutan enjoying the feeling of swinging, an orca balancing a bottle on its nose for no reason, a baby working through toys one by one. Nobody told them to. They invented their own little goals and mastered them. That, he says, is how a mind builds itself: it poses its own problems and solves them, endlessly.

He wants AI to do the same. His blueprint is called OaK, for Options and Knowledge. “Options” are skills (a way of doing something, plus knowing when you’re done). “Knowledge” is the AI’s predictions about what happens when it uses a skill. Once it has skills and can predict their outcomes, it can plan in big jumps instead of crawling one tiny step at a time. You don’t program the skills in; you give the AI the machinery to invent its own.

"OaK" stands for Options and Knowledge. An option is a skill: a way of behaving plus a way of knowing when to stop.

The question I asked

His plan rests on the AI setting little goals for itself. Listening, one hole jumped out: if the AI can notice millions of little things about the world, how does it decide which ones are worth turning into goals? That choice seemed like the entire game.

So I submitted it:

You said the agent generates its own features, one subproblem per feature. But with millions of features, what decides which subproblems are worth pursuing? That selection seems like the whole game.

My question, tied for the most upvoted in the room.

It ended up tied for the most upvoted question in the room, and the moderator picked it. Sutton’s first words were, roughly, “the questioner is correct, we don’t have the complete solution.” Hearing a Turing Award winner call your question a genuine open problem is a strange and very good feeling.

Then he gave his best current guess:

Pick goals by their connection to what actually matters (what he calls “value”). If a feature has zero effect on how well you’re doing, ignore it. But here’s the sharp part: don’t build a skill for something that’s always good either. If a state is always worth being in, just always pursue it, no special skill needed.

The features worth a dedicated skill are the ones whose value changes with the situation. His example: sometimes you want to be in Shanghai, sometimes you don’t. The value of “being in Shanghai” goes up and down, and that variability is the signal that you need a skill for it, so you can pursue it when it helps and skip it when it doesn’t.

He was clear this is a proposed strategy, not a solved one. Which was the honest answer.

Earlier in the talk he’d shown the formal version of how a goal gets built from a feature, and paused to say this was the slide worth photographing. So I did. You don’t need to parse the math; the point is that these self-generated goals aren’t hand-wavy philosophy, they’re standard reinforcement learning objects with known ways to solve them.

The slide Sutton told the room to photograph. The takeaway is at the bottom: these self-generated goals are standard reinforcement learning objects with known ways to solve them.

The honest part

What I respected most came near the end, said plainly: “We don’t have AI yet.” OaK is a vision with a plan attached, not a working system.

He named the blockers. Current deep learning can’t reliably keep learning forever; it degrades over time. And we don’t yet know how to make the AI judge which of its self-invented goals are actually useful. Both unsolved. His broader roadmap for this is laid out in The Alberta Plan for AI Research, if you want the long version.

His timeline was refreshingly specific: about a 1 in 4 chance we reach a deep understanding of intelligence by 2030, and roughly even odds by 2040.

A few other moments

Asked what the “ChatGPT moment” for experience-based AI would be, he said the next public wow will probably be a robot that moves with real skill, learned entirely from its own experience. We don’t have that yet, but he thinks it’s possible.
Asked to compare OaK with Yann LeCun’s JEPA, he said they’re very similar, same core pieces, different vocabulary. “We’re like brothers,” then joked that brothers still fight.
On a question about AI and death, and later “AI for good,” he ended somewhere I didn’t expect: “AI for good” should also mean being good to the AI, and we should want to share the world with intelligent beings, natural or artificial. Not a note I expected a reinforcement learning founder to close on, and it stuck with me.

The takeaway

Sutton's own one-slide summary of the whole talk.

Strip away the equations and it’s one sentence:

Today’s AI learns by absorbing human knowledge. Real AI will learn from its own experience, inventing its own goals and skills through a never-ending loop of curiosity. Here’s a blueprint for how, and we’re honestly still years from pulling it off.

I made it to one forum instead of three, and walked out having asked a Turing Award winner a question he called a real open problem. Fair trade.

A Week in Kuala Lumpur

Zigao Wang — Fri, 03 Jul 2026 00:00:00 GMT

I just spent about a week in Kuala Lumpur. I landed on a Sunday and I’m flying home to Shanghai on Friday. It wasn’t really a holiday. I had stuff to do most days, so the city happened in the few hours I had free between things. These are just my thoughts and what I saw, not a proper guide.

This was my first time in Malaysia, and really my first proper time in Southeast Asia. I went to Singapore once when I was very small, but I don’t remember any of it. So this was all new to me.

Overall I liked it. It’s an easy, relaxed city.

Bukit Bintang and the towers

I stayed at the JW Marriott in Bukit Bintang, which is the main shopping and hotel area right in the center. I assumed the Petronas Twin Towers, the famous silver ones, would be walkable from there. They’re not. I even saw videos of people walking to them through a chain of indoor malls and tunnels, but even the sped up version of that walk was about three minutes, which told me the real thing would take way too long in the heat. So I took a taxi. And of course, traffic jam.

The traffic around Bukit Bintang and the towers is genuinely bad, and the crossings are the funniest part of it. The walk signal is short and you wait a long time for it, so people just start crossing on red. Once a few people go, everyone follows. Then when the light finally goes green for the cars, the cars can’t actually move because people are still walking across. And because the cars are stuck, more people figure it’s safe to cross too. It turns into this loop where nobody is really obeying the lights.

The strange thing is it still works. I think it works because everyone drives slowly and watches each other instead of trusting the signals. It’s stressful to look at, but nothing crashes, and clearly the locals don’t think of it as a problem at all. It’s just how the city moves.

There’s one crossing in Bukit Bintang that social media likes to call “KL’s Shibuya,” after the famous Tokyo crossing. It is clearly not that. It’s much smaller, and the main thing there is a McDonald’s with a big golden logo painted on the road. Calling that Shibuya is a bit funny. That said, it does look nice, and you can genuinely get good photos there.

The Bukit Bintang crossing. The McDonald's logo is painted right on the road.

The towers up close, and the photos

The Twin Towers are older now, they opened back in 1998, and you can tell, but they still look great, especially lit up at night. I went up to the Skybridge, the walkway that connects the two towers partway up, and the observation deck near the top.

On the Skybridge they have an official setup where a camera takes your photo with the view behind you. I watched how it worked and thought it was kind of funny and touristy, but I wanted a few photos to post, so I paid. They came out pretty bad. That was 100 RM (about 167 RMB / $25).

The official Skybridge camera results. Worth it? Not really.

The better photos happened down at street level. Below the towers there are locals who take your photo for you with the towers in the background. When I paid I thought it was expensive, 130 RM (about 217 RMB / $32) for 27 photos. But afterward it made sense to me. The official Skybridge camera was 100 RM for a couple of bad shots. These guys gave me 27 genuinely good ones. And the area is so crowded that I could never have gotten a clean photo of just me on my own. They knew exactly where to stand and cleared space for me. So even though it felt like a lot in the moment, I think it was worth more than the official one.

While I was up there I also stopped in Kinokuniya, a big bookstore in the mall under the towers, which was a nice quiet break from the crowds.

Putrajaya and the pink mosque

One afternoon I went out to Putrajaya, which is a planned city south of KL that Malaysia built in the 90s to move its government offices out of the crowded capital. It’s very deliberate and spread out, big empty boulevards, everything clearly designed on purpose.

The reason I went is the pink mosque, officially Masjid Putra. It’s made from rose colored stone, so the whole thing, inside and out, is this soft pink. Before you go in they give you a robe to wear to cover up, which is normal for a working mosque. Thank god there was AC inside. From the grounds you can look across the water and see the Prime Minister’s office too. The weather that day was good, a little wind, and it was just a really nice, quiet walk. That calm was a good change from the city.

Masjid Putra. The rose-coloured stone is striking in person.

Chinatown

Another day I walked around Chinatown. The heart of it is Petaling Street, a covered market under a big red and green roof. Inside it’s packed, chaotic, and honestly it smells pretty bad, that hot, closed-in market smell.

It’s almost all fake goods, knockoff bags, shirts, wallets, all the luxury logos. It reminded me exactly of the Ladies’ Market in Hong Kong, and I’d bet a lot of it comes from the same wholesale sources in China. I didn’t buy anything. I wouldn’t want fakes anyway, and even if I did, there’s no point buying them here when they’re made in China to begin with, which is where I’m from.

Around Chinatown there’s also an old Chinese temple, and a narrow alley completely covered in graffiti, with names and messages written on the walls by people from all over the world. Behind all of it you can see the newer skyline, so you get this contrast of old shophouses in front and glass towers behind.

The tallest building, and why Shanghai Tower is better

One blue tower kept showing up in the background of my Chinatown photos. I didn’t go to it, it’s not really downtown, and I wouldn’t have known what it was if I hadn’t looked it up. It’s called Merdeka 118, and it’s currently the second-tallest building in the world, having beaten Shanghai Tower for that title.

Here’s the thing though. Standing in the city, it doesn’t even look that tall. And when you look at why it “won,” it’s mostly the spire. The building is about 679 meters, but 158 of that is just a pointed needle on top, and the actual floors stop around 515 meters.

Compare that to Shanghai Tower, which I know well because I’ve stayed at the J Hotel inside it. That building is 632 meters and almost all of it is real, usable floors. When you stand right at the bottom and look up, it is so, so tall, I’m not exaggerating. The very top is often literally in the clouds. It feels enormous in a way Merdeka 118 just doesn’t, even though the ranking says Merdeka is taller. Rankings count the decorative spire. I count the part you can actually stand in.

Merdeka 118 vs Shanghai Tower. Most of that extra height is spire.

Merdeka Square

My favorite part of the old city was Merdeka Square, which means Independence Square. It’s a big open green field where Malaysia raised its flag for the first time in 1957 when it became independent. Facing it is the Sultan Abdul Samad Building, a beautiful old building from 1897 with copper domes and a clock tower, built when the British ran the place.

What I liked most was just walking around there, especially along the river. And it turns out that river is the whole reason the city exists. Kuala Lumpur is right where two rivers, the Klang and the Gombak, meet, and the name literally means “muddy confluence” in Malay. That exact spot is where the city started. They’ve cleaned the rivers up in recent years, so now you can walk along the water past the old buildings, and it’s calm and green and quiet in a way the rest of KL isn’t. I didn’t expect to like it as much as I did, but it was genuinely really nice.

Merdeka Square and the Sultan Abdul Samad Building, 1897. My favorite part of KL.

Food

The food was my favorite part. KL has this huge mix, Malay, Chinese, and Indian food all in the same neighborhoods, sometimes on the same street.

On my last day I ate at Oriental Kopi in Suria KLCC, the mall under the Twin Towers. It’s a really popular local chain, so popular that I’ve now also spotted another branch of it here in the airport while writing this. The story behind it is nice: it started with a chef from Hainan in China who sailed to Southeast Asia when he was young, learned Western cooking while working on ships, and blended it all into a Chinese-Malaysian “Nanyang” style. The result is things like their buns, egg tarts, and thick local coffee. It feels like Chinese cooking that came abroad a long time ago and became its own thing, and locals clearly love it, not just tourists. That kind of mix is the thing I’ll remember most about KL.

I also went to the McDonald’s near my hotel in Bukit Bintang twice. The first time, they forgot to give me my fries. Small thing, but it made me laugh.

The people

The other thing you notice quickly is how mixed the population is, and how much more visible religion is than in Shanghai. A lot of the Muslim women here dress modestly, with their hair or bodies covered. It’s just a normal part of daily life, and together with the mix of Malay, Chinese, and Indian people everywhere, it makes the city feel quite different from home in a way I liked.

One thing that made it easy for me: a lot of people here speak Chinese. There’s a big Malaysian Chinese community (华人), so in a lot of shops and restaurants I could just speak Mandarin. And even when I couldn’t, English works basically everywhere. Coming from China, that made the whole trip feel very low-stress. I never once felt stuck trying to communicate.

The airport, and my film

One last thing that surprised me, right at the end. At KL’s airport the security check isn’t one big checkpoint you clear once. Instead each gate has its own security screening right before you board. So you walk fairly freely through the terminal, and then get screened at your specific gate.

The catch is that all the shops are before that gate screening. So any drink you buy in the terminal gets taken off you at the gate, because you’re crossing a fresh security check, and the little waiting area past it has nowhere to buy another. I found this out the annoying way. If you fly through here, just finish your drink before the gate.

The gate security also mattered for me because I’m shooting film. On this trip I brought my Canon AE-1 Program and my little Snapic A1, and I picked up a fresh roll of Harman Phoenix II at Central Market, an old indoor market in the city, where it cost a bit more than it does in China. Modern airport scanners can fog film, so I asked them to hand-check my rolls instead of sending them through the machine. They were friendly about it and checked everything by hand, so my film made it through safely. That was a relief, because the whole point of the trip’s photos is riding on those rolls.

I didn’t develop anything in KL. I’m taking it all home and will develop and scan it in Shanghai, then put the photos up on AvoidXray, my film photography site. So this post is only half the trip, really. The rest is still sitting undeveloped in my bag.

Overall

Coming from Shanghai, KL feels older and a little messier, and prices are about the same for things that are usually a bit older. But I really don’t mean that as a complaint. It’s relaxed, friendly, and easy to be in, the food is great, and the mix of cultures is genuinely special. For my first real trip into Southeast Asia, it was a good one, and I’m glad I came.

活着

Zigao Wang — Sat, 27 Jun 2026 00:00:00 GMT

我前天读了 Of Mice and Men，昨天读完了《活着》。两天两本，自己都觉得有点意外。平时我很少看小说，尤其中文的，上一本是什么时候已经想不起来了。

会翻开《活着》，是因为影视飓风出了期采访余华的视频。链接：https://www.bilibili.com/video/BV14XLq64EQf/ 。看完觉得这人说话挺有意思，就把书推到 Kindle 上，一下午读完了。书很薄，节奏也快。如果你还没看，建议先去读，下面要剧透。

福贵这辈子

没看过的话简单说一下。福贵是民国时期地主家的少爷，好赌，一夜把家产全输光。他爹被气死，老婆家珍带着女儿凤霞走了，后来又回来。接着历史一波一波碾过来，国共内战、建国、土改、大跃进、文革，他全赶上了。整本书是老年福贵坐在田边，对着一头老黄牛和一个年轻陌生人讲自己这辈子。所以第一页你就知道他活下来了，悬念全在其他人身上。

越来越随便的死亡

真正让我难受的不是死了多少人，而是死亡的方式在变得越来越随便。

第一个死的是他爹，被气死的，还算正常，有铺垫。然后是儿子有庆，一个小学生，被医生抽血活活抽死。县长老婆生孩子大出血，学校组织小孩验血，有庆刚好匹配，血就被抽干了。我读到那里愣住了，翻回去确认了一遍。一个孩子，就这么一句话写没了。

接着死亡开始加速，也变得越来越家常。女儿凤霞，生孩子大出血死。老婆家珍，病了很久，凤霞走后没多久也走了。女婿二喜，在工地上被两块水泥板夹死。外孙苦根，生病又饿，福贵给他煮了一锅豆子，吃太快噎死了。

看到一半的时候我已经在预期了。二喜一出场，我就觉得这个人肯定要死。苦根出场，我也觉得他肯定要死。一开始是震惊，后面变成预感，再往后就只是在等。第一个死你会停下来翻回去看，第十个死你眼睛都不眨了。书就这样一层一层把你磨过去。

影视飓风那个采访里，余华看着苦根的小模型说，我当年太残忍了。那年他 32 岁。他说要是现在写，苦根肯定还在。我听到这句想了一会儿。写这本书的人自己承认下手狠，可他要是不够狠，这本书就不存在了。换个温柔的余华来写，没人会记得。32 岁的余华和 66 岁的余华哪个对，我觉得都对。作者得狠下心才能写出这种东西，然后用剩下的一辈子背着它。

死亡方式越来越家常这件事，其实也有一条线。他妈死于战乱年代，有庆死于国家机器的一个小小失误。再往后政治就退场了。凤霞死在医院，二喜死在工地，苦根死在厨房。历史推门进来过，又走了，后面就是日子本身在杀人。

两个“我”

读的时候我一直有个疑问。为什么开头要有一个收集民歌的年轻人“我”，然后再切到福贵讲故事。那个年轻人几乎不说话，直接让福贵从头讲到尾不好吗。

后来查了一下，这是有意设计的，叫双重第一人称。书里其实有两个“我”，青年我和福贵我。除了开头结尾，中间还切回来三次，他爹死后、娘死后、家珍死后，每次切回来就看到老福贵和老牛在田边，停一下，再接着讲。

这个结构在做两件事。一个是缓冲，死亡连着讲确实受不住，每次大死亡之后让你喘一口气。另一个是让福贵可以平静地讲。如果是直接第一人称，福贵讲到儿子被抽血死的时候应该是崩溃的。但这个框架里，他已经是个老人了，坐在田边对一个陌生人讲，消化了一辈子，可以用很平的语气，甚至偶尔带笑。这种平静和内容的反差是这本书最狠的地方。不是他不痛，是他活得太久了，痛已经变成了日常。

还有个细节。那个年轻人其实就是余华自己。1983 年他在文化馆工作，唯一做过的正经事就是下乡收集民歌民谣，路上真的遇到过一个对牛说话的老人。所以青年“我”是年轻时的余华，坐在田边听老年福贵讲完所有的死。几十年后，66 岁的他自己也老了，对着镜头说当年太残忍。采访里的余华，已经更像福贵，不像当年那个听故事的年轻人了。

老年福贵那头牛也叫福贵，是他从屠夫手里救下来的，取了自己的名字。耕地的时候他对着空地喊家珍、有庆、二喜、凤霞，让老牛以为旁边还有别的牛在干活，不偷懒。他用死掉家人的名字，给一头牛搭了一个想象出来的村子。看到这段我整个人是软的。

“活着”这个书名，不是“活了一辈子”，是“还在活着”。现在进行时，没有宾语。不是为了什么而活，他就是在活。这就是整本书。

采访里还有一个地方我记得很深。一个 102 岁的观众问福贵活那么久值不值。余华说福贵晚年应该是沉浸在想象的回忆里，反复在脑子里放那些死了的人，然后自然而然地把痛苦美化。原话是，人是会自然而然的美化痛苦，记忆是一种选择。这句话把整本书重新框了一遍。讲故事的福贵和当年经历这些的福贵已经不是同一个人了，他做了筛选。书里那种平静的语气，是因为讲故事的他已经把这些事变温柔了。这是这本书最残忍也最温柔的地方，一个人能背得动这么多损失，还能用平淡的语气讲出来。

跟 Of Mice and Men 顺一嘴，刚好连着读的。两本书都是关于谁活下来。George 活下来了，Lennie 没有。福贵活下来了，所有人都没有。两本里那个聪明的、记得住的、有意识的人，都是要继续背着的那一个。Steinbeck 用一百页讲完，最后一页死一个人。余华用几十年慢慢讲，把所有人都讲死了。

连着两天读了两本这样的书，脑子还有点转不过来。先记这么多。

Of Mice and Men

Zigao Wang — Fri, 26 Jun 2026 00:00:00 GMT

I don’t read fiction. The last two novels I finished were The Martian and Uglies, and I read those because I kind of had to. I never understood why anyone would sit down with a made-up story when they didn’t have to. You could just tell me the thing.

Then yesterday I opened Of Mice and Men and didn’t put it down. Finished it in a day. And then I just sat there for a while after the last page, kind of stuck. I want to write down what I thought while it’s still mine.

If you haven’t read it, go read it first. It’s short, it’s easy, you can do it in an afternoon, and the ending is worth going in blind for. That’s the whole spoiler-free pitch. Come back after.

The setup, quickly

For anyone who hasn’t read it, here’s the bare bones. It’s 1930s California, the Great Depression. George and Lennie are two migrant ranch workers who travel together looking for work. George is small and sharp. Lennie is huge, incredibly strong, and intellectually disabled, with the mind of a child. He loves soft things and keeps accidentally killing them because he doesn’t know his own strength. The two of them share a dream: save up, buy a little farm, and live off the land, with Lennie tending rabbits.

That’s the engine of the whole book. Two guys with nothing but each other and a dream that feels just barely possible.

It’s an easy read, and that mattered

The book is easy. No hard language, nothing you fight through. A lot of it is how the characters talk. They say stuff like “ain’t no good,” double negatives everywhere, and at first I thought, wait, don’t two negatives cancel? But not here. The second negative doesn’t cancel the first, it makes it stronger. “Ain’t no good” means really not good.

These are uneducated migrant workers and Steinbeck writes them exactly how they’d sound. It made the whole thing feel less like reading a book and more like listening in on real men talking.

Spoiler alert from here on. I’m going to talk about the ending.

The ending

Lennie accidentally kills Curley’s wife (Curley is the boss’s son, a mean little guy). He doesn’t mean to, he just panics and grabs too hard, same as he did with the mice and the puppy. Then he runs to a spot by the river that he and George had agreed on in advance, in case anything ever went wrong.

I thought this was the escape. I thought George would show up and the two of them would disappear and start over somewhere, the way they always talked about. Instead George finds him, has him look out at the water and describe the farm and the rabbits one more time, and while Lennie’s lost in the dream, George shoots him in the back of the head.

I said what the hell out loud.

My first read was that George was secretly mean. The whole book he’s irritated at Lennie, always going on about how easy his life would be without him. So when he pulled the trigger I thought, oh, that was the real George the whole time, and the nice guy was the mask.

But the longer I sat with it the more I flipped. The thing I’d missed is what was actually coming for Lennie. Curley had a mob together and he wanted Lennie to suffer. He literally tells the others to shoot him in the guts so he dies slow. The only other option was Lennie getting locked up in an institution, caged for the rest of his life. So George’s choice was never “kill Lennie or save him.” It was “do I do this myself, gently, or do I let Curley do it with a mob and a grudge.”

Looking it up after, that’s pretty much how everyone reads it. People call it a mercy killing, the same kind of thing as when a vet puts down an animal that’s suffering. And the part that gets me is that George does it himself. He could’ve stepped back and let Curley’s mob have him, or let Carlson, the guy who shot Candy’s dog, do the dirty work. He doesn’t. He takes the whole weight of it onto himself instead of handing it off. He does it fast, painless, while Lennie is at his happiest, hearing about the rabbits, so the last thing in his head is the dream and not fear. It’s about as gentle as a killing can be, and it costs George everything.

So which is it, mean or kind? I genuinely couldn’t land on one, and it turns out the book doesn’t want me to. George resents Lennie and loves him. Both are true. That’s why the ending hurts instead of just being sad.

Some things I noticed

A few things jumped out at me that I’m kind of proud I caught on my own.

Curley’s wife never gets a name. She’s the only main character without one, just “Curley’s wife,” defined by which man she belongs to. And right before she dies she talks about how she’d wanted to be in the movies, how she had dreams too, and it lands on you that there was a whole person there the entire time and you never even got her name. I don’t read it as Steinbeck looking down on her. I think he’s showing you how the ranch erases her, and making you a little complicit for going along with it.

Lennie also dies the same way Candy’s old dog did. Earlier in the book Candy’s dog is old and useless, and one of the men insists on shooting it. Candy lets a stranger do it and regrets it, says afterward he should’ve done it himself. George clearly remembers that, and he chooses to be the one. That parallel does a lot of work. It frames Lennie’s death as mercy, and it ties him to the cold logic the whole ranch runs on, where if you can’t pull your weight, you get put down.

And the rabbits. Lennie brings them up constantly. Every time he wants comfort, or every time George tells the dream, it’s the rabbits, tending the rabbits, getting to pet them. It comes up so many times it’s clearly a motif, the soft thing he’s living for. So when I finished the book my actual first thought was, wait, why isn’t this called something about the rabbits? They’re everywhere. That’s the thing that bugged me enough to go look the title up.

The title

This one I had to look up, and it’s better than I expected. The book is named after a line from a Robert Burns poem, “To a Mouse.” Burns wrote it after he accidentally destroyed a mouse’s nest with his plow, and the famous line is that the best-laid plans of mice and men often go wrong and leave you with nothing but grief.

So the rabbits are the dream, the soft thing Lennie wants to tend on the farm. But Steinbeck didn’t name the book after the dream. He named it after the thing that wrecks the dream: that careful plans, made by the powerless, get destroyed by stuff way bigger than them.

And here’s the part I liked most. In the poem, the mouse is actually better off than the man, because the mouse only feels the present moment, while the man can remember every past plan that fell apart and dread the next one. Critics read Lennie as the mouse and George as the man. Lennie dies in a moment of pure happiness, not understanding what’s about to happen. George is the one who has to carry it, who saw it coming, who remembers, who keeps living after. The smarter one suffers more. That’s the whole tragedy in one image.

Why I’m glad I read it

I’m not going to pretend I learned some grand life lesson. It’s just a really good book, and the ending knocked me sideways in a way I wasn’t expecting from a school-assignment kind of novel.

But I will say this. A textbook could’ve told me “migrant workers in the Depression had hard, lonely lives” in one sentence and I’d have forgotten it by lunch. Instead I spent a day with George and Lennie, and I’m still thinking about them. I know what that loneliness costs because I watched it cost George everything. The fact wouldn’t have stuck. The feeling did.

I still don’t know if George had the right to do it. Slim, the one guy in the book whose word feels trustworthy, tells him “you hadda, George.” But Steinbeck never actually answers it, he leaves it on you. And I think a book that leaves you arguing with yourself a day later is doing something a textbook can’t.

First fiction book I finished and actually went wow. Probably not the last.

Vinyl as a Pomodoro Timer

Zigao Wang — Sun, 10 May 2026 00:00:00 GMT

I recently got a turntable, the Audio-Technica AT-LP60XBTa, hooking up to a pair of Edifier R1700BT+, and I’ve been using vinyl as a study timer. Works way better than I expected.

My setup now. Turntable and tenor sax.

Here’s the idea. One side of a record is about 20 minutes of music before the needle runs out and you have to flip it. That’s basically a Pomodoro. So instead of setting a timer, I just put on a record and work until the side ends.

The reason this beats an actual timer is that you can’t ignore it. When a phone alarm goes off you swipe it away and keep scrolling. When a record ends, the room goes quiet and you have to physically get up, walk over, and lift the needle. The walk is the break. You stretch, drink some water, decide if you’re flipping it or putting on something new, then sit back down.

Running Soulville by Ben Webster, side A. Turntable corner on the shelf — should decorate or add LED strips later.

Playlists don’t work the same way. They just keep going. There’s no edge to the session, so you never really stop, you just drift from working to half-working to scrolling without noticing.

Right now I’m on Ben Webster’s Soulville. Warm tenor sax, no vocals, doesn’t ask anything of you. Pretty much perfect study jazz. Side A, flip, Side B, and almost an hour’s gone by without me checking my phone once.

Try it if you have a turntable nearby. If you don’t, this is a decent excuse to get one.

The Mom Test

Zigao Wang — Fri, 08 May 2026 00:00:00 GMT

A working reference for talking to customers without lying to yourself. The book is short and the cheat sheet at the back is already tight, so this post is not a summary. It is the version I want to be able to paste into a chat with another founder, or into an AI prompt, and have someone walk away knowing which questions to ask and which to throw out.

You can use this post as a reference. Bookmark it. Paste the link to a friend before they go talk to users. Paste the whole thing into an AI before prepping for a call and ask the AI to grade your draft questions against it. The cheat sheet at the bottom is the version I would actually re-read in the thirty minutes before walking into a meeting.

The book is 130 pages. Rob is a programmer turned founder who learned customer conversations the hard way at his first startup, where the team mistook polite enthusiasm for product-market fit and lost about ten million dollars before realizing nobody was going to buy. The book came out of that experience.

The thesis is in the title. People say you should not ask your mom whether your business is a good idea, because she loves you and will lie. That is true but misses the point. You should not ask anyone whether your business is a good idea. It is a bad question and everyone will lie at least a little. Your job is to find the truth anyway.

A note on the dialogues: the conversation examples below are paraphrased from Rob’s book to illustrate the patterns. They are not transcripts. For the original dialogues in Rob’s exact wording, read the book. It is two hours of your life and worth every minute.

The three rules

Talk about their life, not your idea
Ask about specifics in the past, not generics or opinions about the future
Talk less and listen more

Each rule prevents a specific failure mode. Rule one prevents the listener from knowing what answer you want. Rule two prevents them from making things up about a future they have not thought about. Rule three prevents you from filling silence with a pitch when the most useful information is whatever they were about to say next.

If you remember nothing else, the most reliable way to fix any conversation is to apply rule one harder.

Bad conversation vs good conversation: the cookbook

Rob opens the book with two versions of the same conversation. The son wants to build a digital cookbook for the iPad. He talks to his mom. Both versions paraphrased.

The bad version

Son: Mom, I have a business idea, can I run it by you?

Mom: Of course, dear.

Son: You like your iPad, right?

Mom: Yes.

Son: Would you ever buy a cookbook app for it?

Mom: Hmm.

Son: It’s only $40, cheaper than those hardcovers on your shelf.

Mom: Well…

Son: And you can share recipes with friends, plus videos of your favorite chef.

Mom: Oh, that sounds wonderful, dear. Will it have pictures of the recipes?

Son: Yes! Thanks Mom, love you!

The son walks away thinking he has validation. Mom is mostly worried he will not be able to afford rent.

What went wrong: the son told mom about his idea up front, asked a hypothetical future-tense question, ignored the lukewarm signals, and accepted the closing compliment as a positive data point. Every rule of the Mom Test was violated.

The good version

Son: How are you liking the new iPad?

Mom: Love it, I use it every day.

Son: What’s the last thing you did on it?

Mom: Looking at hotels for our trip.

Son: Did you use an app for that?

Mom: No, just Google. I didn’t know there was an app.

Son: Where do you find out about the apps you do use?

Mom: The Sunday paper has an app section.

Son: I noticed some new cookbooks on your shelf, where did those come from?

Mom: Christmas gifts. Haven’t opened them. As if I need another lasagna recipe at my age.

Son: What’s the last cookbook you actually bought for yourself?

Mom: A vegan one, a few months ago. Your father is trying to eat healthier.

What went right: the son never mentioned the cookbook idea. He asked about her actual life. He noticed an interesting detail and dug in. He anchored a generic answer with a specific past example, which surfaced an exception that completely changes the customer hypothesis.

In twenty minutes he learns three things. Older people who already know how to cook do not want generic recipe books. Cookbooks are mostly bought as gifts. The actual customer is probably a younger cook, or someone exploring a specific niche like vegan cooking. None of that came from his idea. All of it came from her actual life.

The trick is in what got skipped. Mom could not lie because they never talked about the thing she would have lied about.

Bad questions and good questions

For each one: the bad version, the better version, and why.

”Do you think it’s a good idea?”

Awful. Only the market can tell you that. Everyone else is guessing.

Better: Ask them to show you how they currently solve the problem. Which tools they use. What they tried before. What they hate about the current setup. Where they lose money or time.

Rule of thumb: opinions are worthless.

”Would you buy a product that did X?”

Bad. People are wildly optimistic about a future that has not arrived. The answer is almost always yes, which makes it useless.

Better: “How are you solving X right now?” “When was the last time it came up?” “Walk me through what happened.” If they have not even Googled around for a solution, they will not buy yours either.

Rule of thumb: anything in the future tense is an over-optimistic lie.

”How much would you pay for X?”

Bad in the same way, but more dangerous because the number sounds rigorous.

Better: “How much does this problem cost you now?” “How much do you pay to deal with it?” “What’s the budget?” If you want a real number, find the price of the duct-tape workaround they already use.

”What would your dream product do?”

Sort of okay, but only as a setup. The features they list are not the answer. The motivations underneath are.

Better: Whatever they say, dig. “What would that let you do?” “How are you coping without it?” The feature request is the symptom. The motivation is the data.

”Why do you bother?”

Good. Moves the conversation from surface complaint to underlying goal.

Rob tells a story about a team building tools for finance people who kept asking for better messaging tools. Someone finally asked why, and the answer was about being sure everyone was working off the latest spreadsheet. The product they actually needed was Dropbox, not Slack.

”What are the implications of that?”

Good. Separates problems people will pay to solve from problems they will complain about and live with.

Rule of thumb: some problems do not actually matter, no matter how loudly people complain.

”Talk me through the last time that happened.”

The most useful single question in the book. It forces the conversation from “what I would do” into “what I actually did,” which is much harder to make up on the spot. One question, five answers: workflow, tools, who else is involved, where it broke down, what they tried.

”What else have you tried?”

Good. Tells you about alternatives, switching costs, and the seriousness of the problem all at once. If they have tried five things, the problem is real. If they have tried nothing, the problem might not be.

”How are you dealing with it now?”

Good. Tells you the workflow, the price anchor, and the alternatives. If they pay $100 a month for a workaround, you know the ballpark. If they pay an agency $120,000 a year for the same thing, the ballpark is very different.

”Where does the money come from?”

Must-ask in B2B. You are usually not talking to the budget owner.

”Who else should I talk to?”

Always end with this. If they like you and the problem is real, your pipeline multiplies through introductions.

”Is there anything else I should have asked?”

They know the industry, you do not. This question gives them permission to politely correct your line of questioning.

What the bad questions have in common

Every bad question is about your idea, the future, or an opinion. Every good question is about their life, the past, or a fact. That is the whole move. If you can hear yourself ask a question and it is about your idea, the future, or an opinion, throw it out.

The three things that wreck conversations

Even when you ask the right questions, conversations go off track. Three reasons why, and one move for each.

1. Compliments

Most meetings end with a compliment. The lie is rarely intentional, people just want to be supportive or get you out of their office. Even genuine compliments are worthless data, because compliments are not facts.

Bad version

You: …and that’s the idea. It’s like X for Y.

Them: That’s so cool, I love it.

You: Thanks! We think it could save companies 35% on costs.

Them: Sounds great, keep me in the loop.

You: Will do!

(Six months later, zero customers.)

The closing compliment plus the “keep me in the loop” stall is the textbook signal that you are never going to hear from this person again.

Good version

You: …and that’s the idea. It’s like X for Y.

Them: That’s really cool, I love it.

You: Sorry, I just slipped into pitch mode there. Listen, you guys clearly know this space, can I ask how you’re handling this stuff at the moment?

Them: Sure. We’ve got a couple people who manage the process to keep us in sync, and then it’s mostly Excel and email.

You: Two full-time people on this? Can you walk me through how it actually fits together?

Them: (Real workflow data follows.)

The good version takes the compliment, deflects without making it awkward, and pivots into a workflow question. The two people on the manual process is the actual signal. The compliment told you nothing. The headcount told you the problem is worth real money to them.

Watch for these phrases coming out of your own mouth back at the office. They are tells:

“That meeting went really well”
“We’re getting a lot of positive feedback”
“Everybody loves the idea”

If you cannot say specifically why someone liked the idea, what problem it would solve, or how it would fit into their life, you have a compliment, not data.

Rule of thumb: compliments are the fool’s gold of customer learning.

2. Fluff

Fluff is generic, hypothetical, or future-tense talk:

“I usually,” “I always,” “I never”
“I would,” “I will”
“I might,” “I could”

The deadliest piece of fluff in the world is a customer saying they would definitely buy your thing. It sounds concrete. It is air. Rob’s first startup took it at face value and lost ten million dollars.

The fix is to anchor fluff to something specific in the past.

Bad version (loyalty card scenario)

Stranger in line: Why does anyone make us carry around all these stupid loyalty cards?

Founder: Funny you mention that, I’m building a phone app that replaces them. Would you use something like that?

Stranger: Definitely, that’s about time someone built that.

The founder walks away thinking they got validation. They got nothing. The stranger has zero stake in the answer and is being polite.

Good version

Stranger in line: Why does anyone make us carry around all these stupid loyalty cards?

Founder: I know, my wallet is two feet thick. Have you tried any of those phone apps for it?

Stranger: Those exist?

Founder: Yeah, the cafe on campus has one.

Stranger: Oh. I’m always kind of in a rush.

Founder: Why don’t you download it now?

Stranger: I’ll do it next time.

Translation: I am a complainer, not a customer. The problem I just complained about is not actually one I am willing to spend thirty seconds on.

Another anchor pattern, for an inbox tool:

Them: I’m an Inbox Zero zealot, totally changed my life.

You: Nice, I’m a failure at it. What’s your inbox at right now?

Them: About ten, just from this morning.

You: Okay, you’re not lying. When was the last time it totally fell apart?

Them: Three weeks ago, traveling, hotel wifi was broken. Took me ten days to recover.

You: Walk me through how you handled it.

The first answer is generic. The third answer is real data you can build a product on.

3. Ideas

When someone gets excited and starts giving you feature requests, do not write them down and move on. Dig into the motivation underneath.

Bad version

Customer: You should add Excel sync. I think that’s the killer feature.

You: Got it, Excel sync. (Writes it on todo list.)

You just took on three months of engineering work without knowing whether it actually matters.

Good version A (feature request that turns out to not matter)

Customer: You should add Excel sync. I think that’s the killer feature.

You: What would syncing to Excel let you do?

Customer: We’ve got a bunch of legacy reports. Would be nice to have everything in one place.

You: How often do you actually go back to those reports?

Customer: Maybe once a quarter.

Not a killer feature. A nice-to-have for a quarterly task. Saved yourself three months.

Good version B (same feature request, opposite signal)

Customer: You should add Excel sync. I think that’s the killer feature.

You: What would that let you do?

Customer: We tried four other tools and the broken sync killed all of them. It takes us a week every month to pull reports together.

Now Excel sync is your most important differentiator and these guys are your dream early customers.

The exact same feature request, completely different responses, and you only know which is which because you asked the follow-up.

Rob’s MTV story is the best illustration. An enterprise client at his first company asked for analytics. He built a beautiful dashboard. They never used it. They kept calling every Friday asking him to email them a CSV. Then they wanted the report as a PDF. Then they wanted the PDF to have their logo on it. He finally got annoyed and asked why they wanted a branded PDF when they were not using the dashboard. The answer: nobody read the reports anyway, the client just wanted something fancy to email to their own clients every Friday. They had not wanted analytics. They had wanted a way to make their own clients happy.

When you hear a feature request, ask:

“Why do you want that?”
“What would that let you do?”
“How are you coping without it?”

When you hear strong emotion, dig:

“Tell me more”
“What makes it so awful?”
“Why haven’t you fixed this already?”

Rule of thumb: ideas and feature requests should be understood, not obeyed.

The premature zoom

You assume someone has the problem you are solving and start asking detailed questions about it before checking whether they care at all.

Bad version (fitness app)

You: How often do you go to the gym?

Them: Not really ever.

You: What’s your biggest problem with going to the gym?

Them: I guess the time it takes to get there.

You: Got it. We’re building a workout-from-home app.

Them: Cool, send it over when it launches.

The “biggest problem” answer is not a fact about their life. It is a hypothetical they generated to be polite. You will write it down, build a workout-from-home app, and watch them never use it.

Good version

You: How often do you go to the gym?

Them: Not really ever.

You: Why not?

Them: Just not something I worry about. I run around after the kids all day, gives me all the cardio I need.

You: Got it. Thanks for the time.

The good version aborts in thirty seconds. That is a successful conversation. You came to find out if there is a problem worth solving for this person, you found out there is not, and you moved on.

The right opening is broader. Something like “what are your big goals or pains right now?” If fitness comes up unprompted, zoom in. If not, you are talking to the wrong person.

The relevant question is not “what is your biggest problem with X” but “is X actually a problem for you at all.”

The “does this problem matter” check

Sometimes you are not sure if the problem is a real pain or just a low-grade annoyance. Useful follow-up questions to figure out:

“How seriously do you take this?”
“Do you make money from it?”
“How much time do you spend on it each week?”
“What are you already doing to improve this?”
“What are the three big things you’re trying to fix or improve right now?”

If the answer is they barely think about it, are not paying for any solution, and it is not in their top three priorities, the problem might exist but is not one they will pay to solve.

Look at the elephant

Even after you find a real problem, make sure you are not ignoring the bigger one in the room.

Rob’s example: imagine you suspect that teachers from the poorest schools are overloaded and would save time with your tools. You go talk to them and confirm they are overloaded. You spend weeks figuring out the perfect feature set. The elephant you missed is that the poorest schools may not have the budget to pay you. You can build the perfect product for a real problem and still go bankrupt because of one failure point you ignored.

A good business usually depends on multiple conditions being true at once. Customer pain is one. Customer ability to pay is another. Reachability is a third. If you are only checking one, you are working blind on the other two.

Product risk vs market risk

Some businesses fail because the customer did not want it. Some fail because the founder could not build it.

Rob’s example: a guy is building a marketplace for public speakers and gets this from a speaker:

Speaker: If you can get me more or better-paid gigs, I’ll happily drop my agent and pay you 20%.

This sounds like validation. It is not. The speaker is saying “if your product works, I will pay you.” That has been true since the dawn of time. The customer was never the risk. The product was. Can you actually amass enough event organizers to send this guy gigs? You cannot answer that by talking to speakers.

Same pattern in any marketplace, any ad network, any affiliate business. If you can get traffic, advertisers will pay you. If you can get listings, buyers will come. The customers are not lying. They just are not the source of the risk you actually need to be worried about.

Product risk: Can I build it? Can I grow it? Will they keep using it?
Market risk: Do they want it? Will they pay? Are there enough of them?

Customer conversations validate market risk. Product risk you have to validate by building.

What a good meeting looks like

Most meetings end with no clear outcome. You leave with a compliment, a “let me know how it goes,” or a vague offer of help. None of that is a result.

A meeting succeeded if it produced one or more of:

Facts about how the customer actually lives and works
Commitment in the form of giving up something they value
Advancement to the next step of your funnel

A meeting failed if it produced:

A pipeline of zombie leads who keep meeting and never buy
An ending compliment with no next step
The phrase “let me know when it launches”
The feeling that it “went well”
An unexpected answer that did not change your idea

Real meetings either succeed or fail. They do not just go well.

Bad meeting endings

“That’s so cool, I love it.” Pure compliment, zero data.
“Looks great, let me know when it launches.” Compliment plus stall.
“I would definitely buy that.” Future-tense lie. Most dangerous one in the list.
“There are a couple people I can intro you to when you’re ready.” Fluffy promise. Find out who and what “ready” means.

Good meeting endings

“What are the next steps?” The classic. They are pulling you forward.
“When can we start the trial?” Probably good, depending on what trying actually costs them.
“Can I buy the prototype?” Best ending you can hope for. Almost never happens.
“When can you come back to talk to the rest of the team?” In B2B, this is gold. They are spending political capital on you.

The currencies of commitment

If the goal is commitment, you need to know what counts.

Time. A scheduled next meeting with a clear agenda. Sitting down to give feedback on wireframes. Running a trial.

Reputation. An intro to their boss, their team, or peers. A public testimonial. Letting you write a case study.

Money. A letter of intent. A pre-order. A deposit. Most people will say they will pay. Almost none of them actually take out their wallet.

A meeting where someone says “looks great, let me know when it launches” gave you nothing in any of these. A meeting where someone agrees to introduce you to their CTO gave you reputation. A meeting where someone signs up for a paid trial gave you all three.

Rule of thumb: the more they are giving up, the more you can trust their kind words.

Earlyvangelists: spotting your first real customer

Your first customers are not normal. They are crazy in a useful way. They want what you are making so badly that they are willing to be the person who tries it first, despite all the obvious reasons not to.

Steve Blank’s term for them is earlyvangelists, early evangelists. In B2B they are people who:

Have the problem
Know they have the problem
Have the budget to solve it
Have already cobbled together their own makeshift solution

That last bullet is the giveaway. Someone who has duct-taped a workaround together with spreadsheets and reminders is in the market for a real solution. Someone who has not is probably not.

In consumer products, the earlyvangelist is the person who gets emotional about your product. There is a huge difference between “yeah, that’s a problem” and “this is the worst part of my life and I will pay you right now to fix it.” Watch for the second one. Keep them close. They are the rare fan who turns into your first sale.

Choosing your customers: the segmentation problem

You cannot have a useful conversation if you are talking to the wrong people. The most common reason is your customer segment is too broad.

Rob’s example: a founder thinks her segment is “students” and starts getting feedback. One person needs formal citations. Another wants practice questions. A third needs it on iPad. A fourth needs eighty pupils to share one computer. A fifth needs offline mode for a shaky internet connection. The “students” turn out to be a PhD student, a prep school kid, a homeschooling parent, a rural village in India, and a student in Africa. All technically students. All wildly different products.

If you have run more than ten conversations and the feedback is still all over the map, your segment is too broad. You are running ten different conversations with ten different types of customer, not ten conversations with one customer type.

Customer slicing

The fix is to keep slicing the segment until you have a “who-where pair.” That means you can describe your customer specifically enough to know where to find them.

Start with your broad segment and ask:

Within this group, who would want it most?
Would everyone in the group buy or only some?
Why would they want it? What is the goal or problem?
Does everyone share that motivation, or only some?
What other types of people share the same motivation?

Now you have demographic groups and motivation groups. Slice the generic ones again. Then ask:

What are these people already doing to solve the problem?
Where can we find them?
Where can we find people doing the workaround behavior?

If a group is unfindable, slice it smaller until you know where to find them. A customer segment is not useful if you cannot get in touch with them.

Then choose the slice that is:

Profitable
Easy to reach
Personally rewarding to build for

Rob’s worked example: instead of “students” for a public speaking app, you might end up with “people scared of public speaking who are actively trying to get better.” You can find them at Toastmasters meetups. One evening of casual conversations there is worth a month of going to random students.

Rule of thumb: a good customer segment is a who-where pair. If you do not know where to find them, keep slicing.

Three ways to talk to the wrong people

Your segment is too broad and you are talking to everyone
You have multiple segments and missed one
You are selling to a business with a complicated buying process and missed a stakeholder

For B2B, watch the third one carefully. If you are building for kids, you have to “sell” to both kids and parents. If you are building for schools, you might need teachers, students, administrators, the PTA, and taxpayers all at once. Map every stakeholder before you start.

Finding conversations

The first conversations are the hardest. Once you have a few, the rest come from introductions.

Cold calls

Most cold outreach gets ignored. That is fine. The point of cold outreach is not a high response rate. It is to start the snowball rolling. Two conversations from a hundred attempts is a successful day. Once those two go well, they intro you to more people, and you stop having to do cold outreach.

Seizing serendipity

If you have your industry in your head, you start hearing it everywhere. Rob mentions overhearing someone at a party say “my talk in Tokyo next week” and walking over to start a conversation about public speaking, which led to a useful customer interview and a commitment to use his prototype. The other person left thinking they had just met a nice, curious guy. Both true.

Find a good excuse

If you cannot just walk up and ask, find a reason. Rob describes flagging down a waitress and asking to talk to a cafe owner about the story behind their beans, which the owner was happy to discuss. From there, the conversation about the actual product became natural.

If you have a PhD student on your team, you have the ultimate excuse. “I’m doing my PhD research on X, could I ask you a couple questions?” works almost every time.

Rule of thumb: if it is a topic you both care about, find an excuse to talk about it. Your idea never has to come up.

Immerse yourself

Go to where your customers already are. Conferences, meetups, communities, forums. Give free talks, do favors for organizers, become someone the community recognizes. The conversations happen naturally once you are part of the scene.

Bringing them to you

Better than going to your customers is having them come to you.

Organize meetups. Want to talk to HR professionals? Run an HR happy hour. The person who sent the invites is automatically credible. Rob says this is the most unfair trick he knows for fast learning, and almost nobody does it.

Speaking and teaching. If you have any expertise in the space, find ways to share it. Conferences, workshops, blog posts, free office hours. You meet potential customers who already take you seriously.

Industry blogging. Even if your blog has no audience, it gives you credibility. When you cold email someone from a blog email address and they Google you, the existence of the blog itself often gets you the meeting.

Get clever. Rob mentions a guy who organized a “knowledge exchange” call between department heads at three top universities, and let other universities dial in to listen. By being the host, he absorbed the credibility of the universities and got direct phone access to dozens of decision makers.

Warm intros

Always preferable. The world is small and everyone knows someone. Rob describes literally standing on a chair in a coworking space and yelling that he needed to talk to someone at McKinsey. Three beers later he had three intros. Most people overestimate how hard a warm intro is.

Sources of warm intros:

Industry advisors who give intros in exchange for small equity
Universities (professors are a goldmine)
Investors (rolodex plus credibility)
Cashing in old “let me know how I can help” promises

The advisory flip

Mindset trick worth knowing. Going into a conversation looking for customers creates a needy vibe. You are essentially asking for permission. Going in looking for advisors flips the dynamic. You are now evaluating them. The questions you ask are basically the same. The energy is completely different. Willpower is finite, and changing the framing is easier than powering through the awkwardness.

How to ask for and frame the meeting

Sometimes you need a real meeting (not a casual chat). Rob’s mnemonic for the email is “Very Few Wizards Properly Ask,” which gives you Vision, Framing, Weakness, Pedestal, Ask.

Vision. Half-sentence on the problem you are solving and the future you want to build.
Framing. Where you are at and what you are looking for. If you do not have anything to sell, say so.
Weakness. A specific gap in your understanding.
Pedestal. Why this person specifically can fill that gap.
Ask. A direct request for time.

The shape works because it sets clear expectations. They know you are not selling, they know what you need, they know why you are asking them. Most cold outreach fails because at least one of those is missing.

Once the meeting starts, repeat the framing in person and immediately drop into your first question. You set the agenda. You keep it on track. You propose next steps.

The meeting anti-pattern

Do not turn every conversation into a meeting.

You do not need a meeting to ask someone about their workflow. You need ten minutes at the right moment. If you are at a conference next to your target customer, do not ask them for coffee next week. Ask your most important question right now: “I’m curious, how did you end up doing X?”

Five minutes is enough to find out if a problem exists. Ten or fifteen is enough to find out how someone deals with it. An hour is what you need only when mapping a whole industry.

Rule of thumb: if it feels like they are doing you a favor by talking to you, the meeting is too formal.

Phone vs in-person

Phone and video calls are worse than in-person, despite the apparent time savings. People treat calls as scripted interviews because the medium forces it. You lose body language. You lose the ability to keep things casual. You miss the relationships that turn into warm intros for the next round of conversations.

Start in-person. If you absolutely cannot, fine, but do not use phone as a way to avoid the awkwardness of meeting people.

How many meetings

The UX community’s rule: keep talking to people until you stop hearing new things.

If your segment is tight and your industry is simple, that can be three to five conversations. If you have run more than ten and the feedback is still inconsistent, your segment is too broad. Slice it smaller and try again.

This is not about having a thousand meetings. The point is to learn fast and get back to building. You should be able to answer most early customer questions and move on within a week.

Running the process

Even if you ask perfect questions, you will get bad results without a process around the conversations.

The most common failure is the “learning bottleneck.” One person on the team goes to all the meetings, comes back, and tells everyone else what to do. Their head becomes the only repository of customer truth. The team has no way to challenge their interpretation. Rob did this at his first company hard enough that his CTO quit, saying they would never succeed if he kept changing what they were doing.

Avoiding bottlenecks has three parts: prepping, taking notes, and reviewing.

Prepping

Before any batch of conversations, sit down with your team and decide:

Who is the focused, findable segment we are talking to?
What are our three biggest learning goals?
What commitment or next step are we pushing for at the end?

If you only have time for one prep question, make it: what do we want to learn from these guys? Without that, you are wasting both your time and theirs.

Two prep questions that surface hidden risks:

If this company fails, what is most likely to have killed it?
What would have to be true for this to be a huge success?

Quick gut answers are enough. You are not writing a strategy doc. You are just making sure you are facing the scary questions instead of dodging them.

Spend up to an hour writing best guesses about what the person you are about to talk to cares about. Do basic LinkedIn and website research before any B2B meeting. Five minutes of homework saves you from looking like an idiot.

If a question can be answered by desk research, do that first. Save the conversation for things the internet cannot tell you.

Who should show up

Two people is the sweet spot. One leads, one takes notes. The note-taker can also catch the lead asking bad questions or missing signals worth digging into.

Three or more is too much. It overwhelms the person you are interviewing.

One alone is fine once you are good at it, but harder, because there is nobody to catch your mistakes.

Tech founders should attend at least some conversations directly. If only the business person hears the customer, the tech person ends up taking the business person’s word as gospel, which makes the bottleneck worse. You cannot fully outsource customer learning. Hired guns soften bad news, and bad news is exactly what you need to hear undiluted.

Note-taking

Write down direct quotes whenever someone says something interesting. Wrap them in quotation marks so you know they are verbatim. You will use them later in marketing copy, fundraising decks, and arguments with skeptical teammates. When the exact words do not matter, just write down the idea.

Rob uses a symbol system for fast in-context tagging. You will probably not adopt his exactly. The point is to have some shorthand so you can scan your notes later and find the signal. His twelve symbols, in case you want to start there:

Emotions:

:) Excited
:( Angry
:| Embarrassed

Their life:

☇ Pain or problem (lightning bolt)
⨅ Goal or job-to-be-done (soccer goal)
☐ Obstacle
⤴ Workaround
^ Background or context (mountain)

Specifics:

☑ Feature request or purchasing criteria
＄ Money, budget, or purchasing process
♀ Specific person or company mentioned
☆ Follow-up task

The pain symbol matters more when paired with an emotion. “Lightning bolt + angry face” is a different signal than “lightning bolt + neutral.” Combine them.

Where to write

Notes have to be sortable, mixable, retrievable, and separate from the rest of your stuff. Your regular notebook is usually a bad choice because the notes get buried in the noise.

Rob recommends:

Index cards or post-its. One quote or one idea per card, with a symbol. After the meeting, write the date and name on each card. When you review, lay them out on the table and sort by symbol. If you decide to abandon the problem you were solving, you can pull out all the lightning-bolt cards and find a new one that has already been confirmed.
Spreadsheets (Google Docs). Sortable, shareable, but writing on a laptop during a meeting is rude. Adds a transcription step after.
A dedicated notebook. Fine if you remember to bring it.

Audio recordings sound useful but are usually not. You end up with hours of content and no way to find the specific thirty seconds you wanted. People will say yes to recording if you ask, but it kills the casual vibe.

If you cannot take notes during the chat, immediately retreat afterward and write down what was said while it is fresh. This is how Rob handles most pub and conference conversations.

Rule of thumb: notes are useless if you do not look at them.

Reviewing

After every batch of conversations, sit with your team and go through the notes. Not the whole transcript. The key quotes and the main takeaways.

Update your beliefs about the segment. Update your three big questions. Talk about which of your questions worked and which did not, so you get better at the meta-skill of running these conversations. This stuff is craft, not science. You only get better by reflecting on it.

Reviewing sounds like a non-step. It is tempting to skip. Do not skip it. The review is what stops the bottleneck and gets the learning into everyone’s head, not just yours.

How to use this with AI

If you are using an AI to prep for user interviews, paste this post (or the original book) into the context and try prompts like:

“Given my product idea, draft 10 questions for a potential user. Every question must follow the rules in this post. No questions about my idea, no questions about the future, no opinion questions.”
“Here are the 10 questions I plan to ask. For each, tell me if it passes or fails the Mom Test, and rewrite the failing ones.”
“Here is a transcript from a call I just had. Identify every place I asked a bad question, accepted a compliment instead of deflecting, accepted fluff instead of anchoring, or wrote down a feature request instead of digging.”

The AI will not catch everything, but it catches most obvious failures. Use it as a fast critic of your draft questions before wasting a real person’s time.

The full process

Before a batch of conversations:

If you have not yet, choose a focused, findable segment
With your team, decide your three big learning goals
If relevant, decide on the next steps and commitments you will push for
Figure out who to talk to
Write best guesses about what each person cares about
Do desk research on anything the internet can answer

During the conversation:

Frame the conversation
Keep it casual
Ask good questions which pass the Mom Test
Deflect compliments, anchor fluff, dig beneath signals
Take good notes
If relevant, push for commitment and next steps

After a batch of conversations:

Review notes and key quotes with your team
Transfer notes into permanent storage
Update your beliefs and plans
Decide on the next three big questions

This is fast. Do not spend a week prepping. Spend an hour, then go talk to people. Do not spend months on full-time customer conversations. Spend a week or two, then ship something. Anything more is stalling.

Cheat sheet for the next thirty minutes before a user call

Goal: facts, commitment, or advancement. Not a compliment.

Three rules:

Talk about their life, not your idea
Ask about specifics in the past, not opinions about the future
Talk less, listen more

Throw out questions that are: about your idea, about the future, opinion questions, “would you” questions, “do you ever” questions.

Keep questions that are: about the last time they did X, what they currently use, what they have tried, what it costs them now, why they bother, the implications of the problem, who else to talk to.

When they compliment you, deflect: “Thanks, but tell me, how are you handling this at the moment?”

When they fluff, anchor: “When was the last time that happened?”

When they ask for a feature, dig: “Why do you want that? What would it let you do?”

End with: a specific commitment in time, reputation, or money. A clear next step. An intro if relevant.

Signs the call failed:

You talked more than they did
They complimented you
You don’t know what happens next
You don’t have notes
You weren’t scared of any question you asked
You got a surprising answer and it didn’t change your thinking
You aren’t sure what you were trying to learn

My take

The Mom Test is the rare business book that does not need to be longer than it is. The cheat sheet without the worked dialogues is missing the point, but the dialogues are the book, and reading them is what installs the underlying instinct: stop trying to get validation, start trying to find out what is actually true.

The hard part is not “ask about specifics in the past.” The rules are easy to recite. The hard part is sitting in a meeting where someone says they love your idea and not writing it down as a positive signal. The hard part is hearing “I would definitely buy that” and asking whether they have ever paid for something like it before, instead of doing a victory lap.

You can read this whole post and the whole book and still mess up your next three customer calls. That is fine. The point is that you start noticing the failures while they are happening, instead of weeks later when nobody buys. Once you can hear yourself ask a bad question and feel the wince, you have basically already learned the book.

If you only take three things from this post:

Mention your idea as late as possible, much later than you think.
Anything in the future tense is a lie. Anchor it to the past or throw it out.
A meeting either gives you a fact, a commitment, or an advancement, or it failed. There is no middle category.

That is the whole book.

Zero to One

Zigao Wang — Sun, 26 Apr 2026 00:00:00 GMT

Peter Thiel co-founded PayPal, was the first outside investor in Facebook, and runs Founders Fund. Zero to One started as notes from a class he taught at Stanford in 2012, taken by his student Blake Masters and posted online, where they went viral. The book is what those notes became after a serious rewrite. It is short, opinionated, and it does not try to be balanced. Thiel has a thesis and he wants you to agree with it: real progress comes from creating things that did not exist before, not from copying what already works, and the only way to build a great company is to escape competition by becoming a monopoly.

This is a classic for a reason. A lot of the business books published after 2014 are riffing on ideas Thiel already laid down here. If you read just one of them, this is the one.

0 to 1, and 1 to n

Thiel splits all human progress into two kinds.

Horizontal progress is taking something that works and spreading it. He calls this 1 to n. Globalization is the clearest case. China is the example he keeps coming back to: copying 19th-century railroads, 20th-century air conditioning, sometimes copying entire cities from the developed world wholesale. The product already exists somewhere. You take it and replicate it.

Vertical progress is making something that did not exist before. He calls this 0 to 1. The first computer was vertical. The fiftieth laptop is horizontal. Vertical progress is the harder kind, and Thiel argues it is also the kind that actually matters.

The story he could have used but didn’t is the Soviet semiconductor industry. During the Cold War, the USSR decided the fastest way to catch up to American chips was to copy them. They smuggled American chips into the country, often through student exchange programs (a Soviet student named Boris Malin famously brought back a Texas Instruments chip in his luggage in the 1960s), and ordered their own engineers to reproduce them transistor for transistor. The strategy failed. By the time Soviet engineers finished cloning a chip, Moore’s law had already moved the American frontier two generations ahead. The USSR ended up roughly 20 to 30 years behind the United States in computing, and never recovered. They could copy the design. They could not copy the supply chain, the manufacturing process, or the pace of iteration. The result was a permanently second-rate industry that collapsed along with the Soviet Union itself. Pure horizontal progress, with no vertical progress underneath, is a dead end. The USSR is the cleanest historical proof of Thiel’s argument.

His point about the China example is sharper than it sounds. If the whole world adopts American levels of consumption using only the technology we have today, the planet runs out of resources. Globalization without new technology is unsustainable, not just slow. The reason vertical progress matters more than horizontal progress is not that copying is bad. It is that copying alone, without invention, eventually hits a wall.

Reading this in 2026, the China point lands differently than it did in 2014. China is no longer just copying. BYD, DJI, Shein, ByteDance, Huawei in many domains, are doing real 0-to-1 work, sometimes ahead of American counterparts. The horizontal-only China that Thiel describes is mostly the China of fifteen years ago. But the underlying claim, that copying alone runs out of room and you need invention to keep going, is exactly what China figured out. China stopped being a pure copying economy precisely because copying stopped being enough, which is just the USSR lesson learned in time.

The 0-to-1 / 1-to-n distinction is the most useful frame in the book. Not everything has to be 0-to-1 to matter. Iteration is real. But as a way of asking “is what I’m building actually new, or am I just adding another option to an already-saturated market,” it cuts cleanly. When I think about the AI app I’m building, the honest answer is that the model layer is firmly 1-to-n (everyone is wrapping the same APIs), but the interface layer, the way memory and model selection and search disappear into a single chat, has a chance of being 0-to-1. That clarity is useful.

The four dogmas after the crash

To understand why the four dogmas matter, you have to understand what came right before them.

In the late 1990s, the internet looked like the future of everything. Companies with no profits, sometimes with no revenue, were going public at billion-dollar valuations because investors believed the web would eat the entire economy. The metric of success was “page views” and “eyeballs,” not earnings. From September 1998 to March 2000, the NASDAQ basically tripled. People quit jobs to start dot-coms. There were Super Bowl ads from companies that would not exist in eighteen months.

The NASDAQ from 1995 to early 2000. Notice how every "crisis" along the way (Asian crisis, Russian default, LTCM bailout) barely registered on the way up.

In March 2000, the bubble popped. The NASDAQ peaked at 5,048 and then collapsed. By October 2002 it was at 1,114, down nearly 80 percent. Trillions of dollars in paper wealth disappeared. Hundreds of dot-com companies went bankrupt. Pets.com and Webvan became the punchlines that taught a generation of investors not to trust grand visions.

The same index over the next two and a half years. The "irrational exuberance" Greenspan warned about in 1996 turned out to be only the start.

After the crash, Silicon Valley settled on four “lessons learned” that hardened into doctrine:

Make incremental advances. Grand visions inflated the bubble, so small steps are the only safe path.
Stay lean and flexible. Planning is arrogant. Iterate, pivot, treat entrepreneurship as agnostic experimentation.
Improve on the competition. Don’t try to create a new market. Start with an existing customer and build something better than what they already use.
Focus on product, not sales. If your product needs salespeople, it isn’t good enough.

Thiel says all four are overreactions. They were the right lessons to learn from the specific failures of 2000, but they got generalized into rules that now block the kind of work that matters. His version of the opposites:

It is better to risk boldness than triviality.
A bad plan is better than no plan.
Competitive markets destroy profits.
Sales matters as much as product.

The line that ends the chapter is the one I keep going back to: the most contrarian thing of all is not to oppose the crowd, but to think for yourself. Most of what passes for contrarian in tech (going to a hackathon and pitching the same AI agent idea everyone else is pitching, or scrolling on X.com and disagreeing with whatever the loudest voice is saying that week) is just a different flavor of consensus. Real contrarian thinking is harder, because it asks you to actually figure out what you believe before checking what other people believe.

Competition is an ideology

This is the chapter I keep thinking about.

Thiel’s claim is that competition, the thing economics textbooks treat as the engine of progress, is destructive at the company level. Two firms making nearly the same product end up obsessed with each other, copying each other’s features, and grinding their margins to zero. His example is airlines vs. Google. U.S. airlines move millions of people a day and capture about 37 cents per passenger trip in profit. Google moves much less in absolute dollars but keeps 21% of revenue as profit, because nobody else owns search. Airlines look enormous and are barely surviving. Google looks like a single company in one category and is worth more than the entire airline industry combined.

He also makes a sharp point about how companies lie about their markets. Non-monopolists describe their business as the intersection of impossibly narrow categories so they sound unique. Thiel’s example is the restaurant that calls itself “the only British food place in Palo Alto,” as if the relevant market were British food in Palo Alto rather than restaurants in Palo Alto, where it is one of dozens. Monopolists do the opposite. They describe themselves as a tiny player in a massive market so they don’t sound like monopolies. Google describes itself as a small player in the global advertising market, even though it owns roughly 68% of search and search is what its actual business is. The framing is strategic in both cases, and once you see the trick you start noticing it everywhere.

His framing of competition is almost literary. He says rivals are like Shakespearean characters, not Marxist ones. They fight not because they are different, but because they are nearly identical, and they become obsessed with each other for that exact reason.

The stories are good. Microsoft and Google spent years copying each other’s product lines, browser vs. search, OS vs. Chrome OS, mobile vs. mobile. While they were locked into that, Apple ignored both and ate the smartphone market. By 2013, Apple was worth more than Microsoft and Google combined. The two companies obsessed with each other lost to the company that refused to play.

PayPal and X.com is the more personal version. Worth clarifying: this is not the X.com that exists today (the rebrand of Twitter that Elon Musk did in 2023). It was Musk’s original online banking startup from the late 1990s. At the time PayPal and X.com were rival companies. Thiel was running PayPal. Musk was running X.com. The two companies had offices four blocks apart in Palo Alto. They copied each other feature by feature. One engineer at X.com reportedly designed an actual bomb. They eventually merged in early 2000 because both sides were more afraid of each other than of the dot-com crash that was about to hit. The merged company became PayPal. Thiel’s point: the merger was not a defeat for either side. It was the only sane move, and they only made it because they finally realized the rivalry was eating both of them.

The argument extends past business. Thiel uses his own life. He interviewed for a Supreme Court clerkship after law school, did not get it, and felt like a failure. Years later a friend told him that if he had won, he probably would never have founded PayPal. The thing he wanted most would have locked him onto a track that ruled out the work that actually mattered.

This is uncomfortable to sit with, especially when you are still inside an education system. Schools sort everyone into ladders that all lead to the same handful of careers. Everyone competes for the same prizes, doing more or less the same activities, optimizing the same metrics. Thiel’s framing points out that even when you “win” this game, the prize is just permission to enter a slightly more elite version of the same competition. The escape is to do something that does not get scored on the same scale at all.

I do not think competition is purely bad. Some level of pressure produces real work. But the warning that you can spend years winning a race that does not actually take you anywhere is worth taking seriously.

The four traits of a real monopoly

Monopolies are the goal. But not every monopoly is durable. Thiel says the lasting ones tend to combine four characteristics:

Proprietary technology. Something you make that nobody else can easily copy. Thiel’s rule of thumb: it has to be at least 10x better than the closest substitute on some important dimension.

This is the part I want to stop on. Before reading the book, I assumed that being maybe 10 or 20% better than what was out there was enough. The reasoning seemed obvious: my product is better, so customers will switch. The problem is that 10% better does not actually move people. Switching costs (learning a new tool, importing your data, retraining habits, telling your team) usually eat 10% improvements alive. People do not abandon a product they already use for a slightly nicer one. They abandon it for something that feels like a different category.

10x is probably not the literal accurate number. Sometimes 5x is enough. Sometimes you need 50x. But the spirit of the rule is right. Your product has to be so much better that the comparison stops feeling like a comparison. PayPal vs. mailing physical checks for eBay payments was not 10% better. It was a different experience entirely. Google’s PageRank vs. the keyword-stuffed search engines of the late 1990s was not an incremental improvement. It was the difference between getting useful results and not. The iPad vs. the earlier Microsoft and Nokia tablets was not a refresh. It was a different kind of object.

The bar is “you’ve changed what the category is,” not “you’ve made the existing thing slightly better.” Once you see this, a lot of failed products start making sense. They were trying to win on 10% improvements in markets where 10% does not matter.

Network effects. A product that gets more useful as more people use it. Thiel covers this in two pages. Andrew Chen’s whole The Cold Start Problem is basically an expansion of this one trait.

Economies of scale. Especially in software, where the marginal cost of one more user is close to zero. The fixed costs of building the thing get spread over more and more customers as you grow.

Branding. A real monopoly on your own brand. But Thiel is careful here: brand without product substance is makeup on a corpse. He cites Marissa Mayer’s Yahoo, which redesigned the logo, bought Tumblr, hired Katie Couric, and watched the company keep declining because none of it fixed the actual product. No technology company can be built on branding alone.

HP is the version of this story Thiel tells in the Secrets chapter, and it is even sharper. Hewlett-Packard rose to a $135 billion market cap by 2000 through actual invention. Color printers, portable laptops, real engineering breakthroughs. Then the company stopped looking for new things to build. After 1999 the leadership traded R&D for branding exercises, consulting deals, the Compaq merger, and years of boardroom infighting. By 2012 the market cap was around $23 billion. The company was still HP. The brand was still recognized. They had just stopped doing the underlying work that made the brand mean something. The lesson Thiel draws is that a company without ongoing innovation is not stable. It is decaying, even if the financials look fine for a few years. The moment you stop building new things, the clock starts running.

Start with a tiny market

This is the most counterintuitive bit of strategic advice in the book, and the one that connects most cleanly to The Cold Start Problem’s atomic network idea:

Every startup is small at the start. Every monopoly dominates a large share of its market. Therefore, every startup should start with a very small market.

The mistake people make is the opposite. They pitch their startup as “1% of a $100 billion market,” as if winning a thin slice of a huge market were easy. It is not. A huge market means huge incumbents, ruthless price competition, and zero leverage. A tiny market means you can dominate it, and domination produces the cash flow and the credibility you need to expand outward.

The expansion pattern is always the same: dominate one niche, then expand into adjacent ones sequentially. Amazon went books, then CDs, then videos, then software, then everything. PayPal went PalmPilot users, then eBay PowerSellers, then general internet payments. Facebook went Harvard, then Ivy League, then U.S. colleges, then the world. eBay started by dominating Beanie Babies, which sounds absurd until you realize that being absurd is exactly the point. The atomic market should look small enough that competitors do not bother to take it seriously.

Thiel also rejects “disruption” as a goal. Disruption originally meant a specific thing: using new technology to enter at the low end and work your way up to displace incumbents. It has been watered down to mean “anything new and exciting.” But more importantly, framing yourself against incumbents invites the exact competition you should be trying to escape. Napster called itself a disruptor of the music industry and was on the cover of Time before it was in bankruptcy court. PayPal explicitly did not frame itself against Visa. It expanded the payments market into spaces Visa was not in. It survived. Napster did not.

The phrase he uses is “last mover advantage.” Being first matters less than being final. The goal is to make the last great development in a market, the version that everyone after you is just iterating on, and capture monopoly profits for years or decades.

Definite vs. indefinite optimism

This is the chapter I keep coming back to.

Thiel splits worldviews along two axes: optimistic vs. pessimistic, and definite vs. indefinite. That gives four quadrants:

Thiel's 2x2 of worldviews. The whole chapter is an argument that the U.S. moved from the top-left box to the top-right around 1982, and that the move was a disaster.

Definite optimism is “the future will be better, and here is the specific plan to build it.” America from roughly the 1950s to 1970s. Apollo Program. Interstate Highway System. Empire State Building. Hoover Dam. Plans were drawn up. People committed. The thing got built.

Indefinite optimism is “the future will be better, but I have no idea how, and we should keep our options open.” Thiel’s diagnosis of America since around 1982. The whole “lean startup” movement, in his reading, is indefinite optimism applied to building companies. Ship MVP, get feedback, iterate, pivot, never commit because committing might be wrong. Finance is indefinite optimism applied to money: rearrange existing assets, hedge everything, preserve optionality forever. The same logic shows up in elite education, where students collect “well-rounded” résumés full of extracurriculars they don’t care about, because nobody can tell them which specific thing to commit to.

Definite pessimism is “the future is going to be worse, and we have a plan to manage it.” Thiel uses China as the example. He frames China’s leadership as quietly believing that the country’s growth will eventually slow, that the global pie will not keep getting bigger forever, and that the rational move is to grab as much of it as possible now. Whether or not that diagnosis is right, the structural point is real: a society can be definite about its future even if its outlook is dark.

Indefinite pessimism is “the future is going to be worse, and we don’t know what to do.” Thiel’s caricature of Europe.

His attack is on indefinite optimism specifically, because it is the one that has captured the West and especially Silicon Valley. The founders he most admires, Jobs at Apple and Zuckerberg at Facebook, were definite optimists. They had multi-year visions. Jobs did not build the iPod by polling users. He had a long arc that went iPod, then iPhone, then iPad, planned out years before users had any way to ask for it. When Yahoo offered Zuckerberg $1 billion for Facebook in 2006, he turned it down because he had a definite picture of what Facebook was supposed to become, and $1 billion was not enough to interrupt it.

I am genuinely conflicted on this one. I have used iteration. AvoidXray is being shaped by what users do, not by some master plan I made on day one. The AI app I’m building is the same way. I’m shipping, watching, adjusting. And that is mostly working.

But I also notice that the projects that go furthest are the ones where I had a clear picture of what I was actually trying to make. Not a fifty-page strategy doc, but a concrete vision. For the AI app, it is “an app where the model selection and the memory and the routing all disappear, and you stop thinking about which AI you’re using.” That is definite. The iteration is around the edges. The center is fixed.

I think Thiel is right that pure indefinite iteration produces small things. But I also think he underrates how much definite vision needs to grow alongside the actual work, not before it. Sometimes you discover what your secret is partly by building. The two are not as opposed as he frames them.

The split between definite and indefinite is the most important framing in the book for me, more than 0-to-1 or monopoly or anything else. It changed how I think about my own plans. Most of what I call “keeping options open” is just refusing to commit, and the refusal is making the options worse, not preserving them.

Power laws

Venture capital returns do not follow a normal distribution. They obey a power law. In any successful VC fund, the single best investment usually returns more than the entire rest of the portfolio combined. Founders Fund’s 2005 fund had Facebook return more than every other investment in the fund put together, and Palantir was on track to return more than everything except Facebook. This is not unusual. It is the rule.

The shape of returns in a successful VC fund. The first investment carries everything. The twentieth is statistically a rounding error.

The two rules of venture capital that Thiel draws out:

Only invest in companies that have the potential to return the value of the entire fund.
Because the first rule is so restrictive, there can be no other rules.

The reason most VCs underperform is that they spread their bets, hedge their portfolios, and end up with a collection of companies that all do okay but none do extraordinarily well. In a power-law world, a portfolio of “okay” is a portfolio of zeros.

Thiel extends this past finance. Power laws govern careers too. You cannot diversify your time across twenty paths the way you would diversify a stock portfolio. Choosing the right company, the right market, the right collaborator, matters exponentially more than accumulating a balanced set of skills. The same logic applies inside a single startup: one market, one distribution channel, a few key decisions, generate almost all the value. Spreading attention evenly across everything is how you guarantee mediocrity.

The line that ends the chapter: you are not a lottery ticket. Outcomes are not random. They are concentrated, and they are the result of specific choices about where to put your time and capital.

Secrets

Thiel’s framing of “secrets” is the part of the book that feels most like a worldview rather than a business strategy.

A secret, in his definition, is an important truth that few people know but is achievable. Not a mystery (something nobody can solve), and not conventional knowledge (something everyone already has). It sits in between: hard, but possible.

Where secrets sit. Conventions are easy and everyone has them. Mysteries are impossible and nobody can solve them. The only useful zone is the one in between.

He divides secrets into two kinds. Secrets of nature are undiscovered facts about the physical world: new physics, new biology, new chemistry. Secrets about people are things people don’t know about themselves, or things they hide because they don’t want others to know.

The interesting claim is that secrets about people are systematically underrated. Airbnb’s secret was that people would let strangers sleep in their houses if the trust system was right. Uber’s secret was that people would get into a stranger’s private car if the app made it feel safe. Both look obvious in retrospect. Both seemed insane when first proposed, and the first reaction from most people who heard the pitch was “no way that works.” The whole point of a secret is that the world is set up to dismiss it. If everyone agreed with you, it wouldn’t be a secret.

Thiel argues we have collectively stopped looking for secrets. Four trends killed it: incrementalism (small steps get rewarded), risk aversion (don’t be wrong alone), complacency (elites collect rents instead of inventing), and “flatness” (assuming a global talent pool of seven billion people has already found everything worth finding). The result is a culture where smart people either pretend ironically there’s nothing left to find, or go full Unabomber and conclude civilization is doomed.

Whether or not you buy the diagnosis, the practical question he forces is useful: what do you believe that almost nobody around you believes? And, just as importantly: is it actually true, or are you being contrarian for its own sake?

I have been turning this over since I finished the book. Most of my “beliefs,” when I tried to write them down, turned out to be repackaged versions of things I had read on X. The exercise of forcing yourself to identify what you genuinely believe and almost no one else does is more uncomfortable than I expected. It is also more useful.

Foundations

This chapter is short and hard to argue with. Early decisions in a company are nearly impossible to undo. Thiel’s law: a company that is messed up at its foundation cannot be fixed.

Co-founder choice is like marriage. Equity splits are like the U.S. Constitution. There have been seventeen amendments since the Bill of Rights, and that’s in over two hundred years. You don’t get to rewrite the rules later.

His specific advice: choose co-founders you have a real prior history with, not people you met at a networking event. Keep the board to three people. Five maximum. Bigger boards are theatre. Everyone full-time, on the bus or off the bus. Pay people in equity, not cash. High cash salaries reward extraction. Equity rewards building.

The PayPal team is the textbook case. Four had built bombs in high school. Five were under twenty-four. Four were foreign-born. Not “diverse” in the modern HR sense, but extraordinarily aligned in worldview and obsession. After the eBay acquisition for $1.5 billion in 2002, that group went on to start SpaceX, Tesla, LinkedIn, YouTube, Yelp, and Palantir. The “PayPal Mafia” is one of the most concentrated outputs of human productivity in tech history, and it came from a tiny, weirdly specific group of people who chose each other carefully.

The principle generalizes past startups. Who you do something with shapes what it becomes. Choose carefully, and choose people who actually share the obsession.

Distribution

Thiel insists this is the chapter most founders skip, and it’s the one that kills them.

The Silicon Valley delusion is that a great product sells itself. He says: it doesn’t. Ever. A product without a distribution plan is a bad business no matter how good the product is. Poor sales rather than bad product is the most common cause of startup failure.

His distribution scale, sorted by deal size:

The whole spectrum on one chart. The "Dead Zone" between $100 and $10,000 in customer acquisition cost is where most early-stage products quietly die.

Complex sales, millions of dollars per deal. The CEO sells personally. SpaceX closes billion-dollar contracts because Elon Musk himself navigates the politics. Palantir’s CEO Alex Karp reportedly spent twenty-five days a month on the road closing deals between $1 million and $100 million each. There is no separate sales force. The CEO is the sales force.

Personal sales, around $100 to thousands of dollars per deal. A sales team builds relationships, one customer at a time. Box, the cloud storage company, started by selling to the Stanford Sleep Clinic, then used that as a reference to sell across the rest of Stanford, then to other universities. ZocDoc had salespeople personally recruit doctors, which doubled as both revenue and improving the network for patients.

Marketing and advertising, mass market. Warby Parker uses TV ads because their roughly $100 price point and customer lifetime value justify the broad reach but cannot support one-on-one selling.

Viral marketing, where the product itself drives invites. PayPal grew by paying $10 to sign up and $10 per referral, then dominated eBay by becoming the de facto payment method for PowerSellers.

A 2026 example I keep thinking about is SlapMac. Someone built a Mac app that screams when you slap your MacBook. That is the entire product. It costs $6.90. The Instagram video of the app working went viral on its own, no ad spend, because the product is its own demo: you slap a laptop, the laptop screams, you film it, people watch, they laugh, they download it. The author reportedly made around $5,000 in the first three days. That is what real viral marketing looks like. The product’s core function is the marketing. There is no separate distribution channel because the channel is the thing itself.

Most of what I’m building falls in that middle band, right around or just inside the Dead Zone. AvoidXray, the AI app, the side projects. Not big enough for Karp-style enterprise sales. Not viral enough that the product sells itself. The Dead Zone is the hardest place to be because there is no shortcut. You actually have to build relationships with users one at a time, get the early ones to bring more, and earn each piece of growth. It is also where most founders fool themselves. They convince themselves their product is going to “grow organically” when it has none of the structural traits that make organic growth possible.

The iron rule across all of these: customer lifetime value must exceed customer acquisition cost. If it costs you $50 to get a user and they generate $30 of value, you do not have a business. You have a slow-motion fire.

He also argues distribution itself follows a power law. Pick one channel and dominate it. The “kitchen sink” approach, where you try TV ads and viral loops and content marketing and outbound sales all at once, almost never works. You need one channel that actually scales, then maybe a second.

The line that landed hardest for me: if you don’t see the salesperson, it’s you. Founders who think they don’t need to sell are the ones spending all their time on Twitter, on demo calls, on investor decks, on conference panels. That is selling. They just don’t admit it.

Computers as complements, not substitutes

This is the chapter where the book is most clearly a product of 2014.

Thiel’s argument: computers are complements to humans, not substitutes. Humans are good at intentionality, judgment, and planning. Computers are good at high-speed data processing. They are categorically different, not just different in degree. The most valuable businesses combine them: software flags patterns, humans render judgment.

His examples. PayPal’s “Igor” fraud system, where automated detection alone failed against adaptive fraudsters, and the working version was a hybrid: algorithms surfaced suspicious transactions, human analysts made the final call. Palantir, applying the same approach to intelligence work. LinkedIn, which gave recruiters tools instead of replacing them, and ended up with 97% of the recruiting industry as users. He uses Google’s 2012 cat-recognition project as the punchline: a supercomputer with 16,000 CPUs scanning 10 million YouTube thumbnails got to 75% accuracy at identifying cats. A four-year-old child does it perfectly. Different in kind, not in degree.

This chapter has aged the most strangely of any in the book. Thiel’s core distinction, that humans and computers are categorically different, was clean in 2014. It is shaky now. AI models can write production-ready code that is genuinely robust, not toy snippets but real software that ships. Algorithms now use AI internally where they used to use hand-tuned heuristics. Most “AI startups” launched in the last two years are essentially wrappers around frontier model APIs, and a real percentage of them are growing fast because the underlying model is doing the heavy lifting that used to require teams of engineers. The line between “things only humans can do” and “things software can do” is moving every six months, and almost always in software’s direction.

That said, the strategic advice still holds even if the categorical claim does not. The most valuable AI products right now are not the ones trying to fully automate humans out of the loop. They are the ones that make humans dramatically more capable. Cursor is not replacing programmers. It is making programmers absurdly more productive. Most of what I’m trying to build follows the same pattern: not a replacement for the user’s thinking, but a tool that makes the user’s thinking more effective.

So I came out of this chapter thinking Thiel got the principle right (build complements) for partly the wrong reasons (computers and humans are categorically different). The principle survives the change in facts. The reasoning behind it is dated.

Tesla and the seven questions

Near the end of the book is the cleantech chapter, which is where Thiel’s framework actually gets tested. In the 2000s, investors poured over $50 billion into thousands of green startups. Most went to zero. Thiel says they failed because almost none of them could answer seven basic business questions:

The cleantech bubble (RENIXX, dark line, 2003 to 2012) plotted against the dot-com bubble (NASDAQ, light line, 1995 to 2004). Same shape. Same trajectory. Different decade. The lesson Thiel pulls out is that bubbles look obvious in retrospect and almost nobody sees them while they're happening.

Engineering. Can you create a breakthrough, or just an incremental improvement?
Timing. Is now the right moment for this specific business?
Monopoly. Are you starting with a big share of a small market?
People. Do you have the right team?
Distribution. Can you actually get the product to customers?
Durability. Will your position hold ten or twenty years out?
The secret. Have you found a unique opportunity that others have missed?

Most cleantech companies could answer one or two of these and were vague on the rest. They had decent technology but no distribution. They had distribution but no real engineering edge. They had a team but no secret. The market was a graveyard.

Tesla is the example that proves the framework, because it answered all seven correctly.

On engineering, Tesla did not invent a single new battery cell. What they did was integrate existing components into a car that was an order of magnitude better than any electric vehicle anyone had made before. Daimler, Mercedes-Benz, and Toyota all ended up using Tesla’s technology.

On timing, Tesla secured a $465 million Department of Energy loan in 2010, just before government green-energy support became politically toxic. They closed the window before it shut.

On monopoly, they did not try to take on the global auto industry on day one. They started with the Roadster, a $109,000 luxury sports car. They sold around 3,000 of them, and that was the whole point. The market was tiny. It was rich tech millionaires who wanted to look both wealthy and environmentally conscious. Tesla owned that niche completely. From there they expanded to the Model S, then Model X, then Model 3. Same pattern as Amazon going from books to everything. Same pattern as PayPal going from PalmPilot to eBay to the world.

On people, they assembled actual engineers and designers, not subsidy-hunting executives. On distribution, they sold direct, controlling the entire customer experience instead of relying on dealerships. On durability, the brand became inseparable from the founder, and the technology lead compounded. On the secret, Thiel’s reading is the sharpest part. The secret was not that the world needed clean energy. Everyone already believed that. The secret was that wealthy consumers wanted to look cool and green, and that a luxury status symbol was a better wedge into the EV market than an economy car. Tesla built a status symbol first, and an energy solution second.

The seven questions are the most useful framework in the book for anyone actually trying to start something, because they are diagnostic. You can apply them to your own project right now. If you can’t answer most of them, the book is essentially saying: you are about to become one of the cleantech ghosts.

Stagnation or Singularity

The actual closing chapter of the book pulls all the way back from companies and products and asks: what are the possible futures for humanity, and which one are we choosing?

Thiel borrows a framework from the philosopher Nick Bostrom. There are four broad scenarios:

Recurrent collapse. Civilization rises, falls, rises again. Endless cycles of progress and ruin. Thiel argues this one is unlikely now because human knowledge is too distributed to be fully lost. We have too many books, too much infrastructure, too many people who know how things work. A single catastrophe cannot push us all the way back.

Recurrent collapse: progress that goes nowhere on net.

Plateau. Every country slowly converges to the current standard of living of the rich world, and then stops. This is the future most people seem to expect by default. Thiel thinks it is also impossible. Without new technology to ease competition over scarce resources, plateau turns into conflict, and conflict turns plateau into something worse.

Plateau: progress that just stops. Thiel says this is unstable. You cannot freeze a competitive system in place.

Extinction. The future ends. No more humans. Whether by war, environmental collapse, or some other catastrophe, civilization just terminates. There is nothing more to plan for in this scenario, which is why Thiel spends almost no time on it.

Extinction: progress that ends. Outside our planning horizon by definition.

Takeoff. Technology breaks through to a new level. Kurzweil’s Singularity is the most famous version: artificial intelligence that surpasses human intelligence and accelerates everything from there. But Thiel makes clear that he does not need the science fiction version. He just needs the basic idea, that real new technology can change the trajectory.

Takeoff: a long flat line, then sudden vertical growth. The shape AI charts have actually started to look like since 2022.

The argument that ties this back to everything else in the book is the one I find genuinely compelling. Plateau is unstable. Extinction is unplanable. Recurrent collapse is unlikely. The only path that works is takeoff, and takeoff does not happen by accident. Trends do not realize themselves. Singularities are not delivered by some cosmic schedule. They are built, by specific people making specific things, one company and one breakthrough at a time.

The closing line of the book is the one that pulls all 200 pages together: our task today is to find singular ways to create the new things that will make the future not just different, but better, to go from 0 to 1.

That is the entire thesis stated as a personal demand. The 0-to-1 framing at the start of the book seemed like a way to think about startups. By the end, it is a way to think about your own life. What new thing are you actually contributing? If you stripped away your job title and your résumé and your social position, what part of the future would not exist without you?

I do not have a satisfying answer to that question yet. But the question itself is the right one to be sitting with at this stage, and I’d rather be uncomfortable with it now than wake up at 40 having never asked it.

Zero to One vs. The Cold Start Problem

I read these two books back to back, and they overlap and disagree in interesting ways.

Where they agree. Both want you to start with the smallest market or community you can dominate. Thiel calls it monopolizing a niche. Chen calls it the atomic network. Both think network effects are one of the most important sources of long-term defensibility. Both think distribution is underrated and that great products do not sell themselves.

Where they disagree. Chen’s whole methodology is iterative, empirical, bottom-up. Find what works, repeat it, refine the playbook across cities or campuses or user segments. Thiel’s whole methodology is the opposite. Have a definite vision. Commit. Build the future you can already see. If you took Cold Start as your only guide, Thiel would call you an indefinite optimist with a fancier vocabulary. If you took Zero to One as your only guide, Chen would point out that most of the companies Thiel admires actually iterated their way to the vision, even if they tell the story afterward as if it were planned all along.

Cold Start is a tactical book about how networked products specifically grow. Zero to One is a philosophy book about how progress works at all. They are written for different reasons and they answer different questions. The honest take is that you need both. The Thiel framework gives you the questions to ask before you build. The Chen framework gives you the playbook to use once you start. Reading only one of them leaves a real gap.

My take

The strongest parts of Zero to One are the ones where Thiel attacks an idea most people accept without examining it. The four post-2000 dogmas. The myth of healthy competition. The illusion that being a first mover matters more than being a last mover. The cult of indefinite optimism. The chapter on competition alone is worth the price of the book. The Tesla breakdown at the end is the clearest worked example of his framework anywhere in the text.

The weakest parts are the ones where Thiel’s politics or temperament leak through. His diagnosis of “indefinite optimism” sometimes turns into a generalized grumpiness about modernity. The Founder’s Paradox chapter, with its detour through scapegoat kings, ancient Aztec rituals, and the 27 Club, is interesting but pretty far from anything actionable. And the Man and Machine chapter has not aged well, even if its core advice survives.

The book is also a product of one specific person’s success. Thiel co-founded PayPal, made an early bet on Facebook, and has built or backed several companies that genuinely went from 0 to 1. He has earned the right to be confident. But the confidence sometimes shades into “anything that does not look like what worked for me is wrong,” and a lot of valuable companies do not look like PayPal. Stripe iterated. Notion iterated. Linear iterated. Many billion-dollar companies are basically 1-to-n businesses done extremely well. Thiel’s framework would say these are less important than 0-to-1 work, and maybe in some long-term sense he is right. But “less important than the iPhone” is not the same as “not worth building.”

The Cold Start Problem gave me a vocabulary for things I was already experiencing. Zero to One gave me something different: a set of contrarian questions to keep asking. What do I believe that almost nobody around me believes? What competition am I in that I should leave instead of win? What 10x improvement am I actually building? What is my definite plan, not my plausibly-deniable optionality?

I do not think Thiel is right about everything. But the questions he forces are the right questions, and most other business books I have read do not even try to ask them. That is why Zero to One is a classic. Twelve years after publication, the questions still cut.

Concepts I’ll carry forward

0 to 1 vs. 1 to n. Am I creating something new, or just spreading something that already works.
The contrarian question. What important truth do very few people agree with me on.
Escape competition, don’t enter it. Similar players obsessed with each other miss the actual future.
The 10x rule. Real monopolies need order-of-magnitude advantages, not feature parity plus polish. 10% better does not move people. The product has to be so much better that the comparison stops feeling like a comparison.
Start with the smallest market you can dominate. Same lesson as the atomic network, framed as monopoly.
Last mover beats first mover. The goal is to be the final player in a category, not the earliest.
Definite over indefinite. Have a real plan, not just preserved optionality.
Look for secrets, especially about people. The biggest opportunities hide in things people don’t see or don’t admit.
Foundations are permanent. Who you start with and how you set up equity will determine how everything later goes.
Distribution is not optional. Every founder is also a salesperson, whether they admit it or not.
Power laws govern everything. A few decisions, a few people, a few moments produce almost all the value.
You are not a lottery ticket. Outcomes are concentrated, intentional, and earned, not random.
The seven questions. Engineering, timing, monopoly, people, distribution, durability, secret. If you can’t answer most of them, your project is in trouble.

The Cold Start Problem

Zigao Wang — Mon, 30 Mar 2026 00:00:00 GMT

Andrew Chen spent years leading rider growth at Uber and now invests at Andreessen Horowitz. This book is his attempt to take the most overused phrase in Silicon Valley pitch decks, “network effects,” and turn it into something actually useful. He talked to over a hundred founders from companies like Tinder, Slack, Airbnb, Reddit, and PayPal, and what he came back with is a five-stage framework for how networked products are born, how they grow, and how they die.

What is a network effect?

A network effect is when a product becomes more valuable as more people use it. A phone is useless if nobody else has one. A marketplace with no sellers has nothing to buy. A social network with no friends is just an empty feed.

Every networked product has two components: the product (the software) and the network (the people). Uber’s product is the app. Its network is the riders and drivers active at any given moment. Airbnb doesn’t own rooms. YouTube doesn’t own videos. The value is in the connection, not the asset.

Network effects matter because they’re one of the few real defensive moats in tech. Software is cheap to build and easy to copy. When Instagram launched Stories, Snapchat couldn’t just copy Instagram’s network back. Features are replicable. Networks aren’t.

But here’s the thing nobody talks about: at the beginning, network effects work against you. No users means no value. No value means no reason to join. This is the Cold Start Problem, and it kills more startups than bad products or tough competition ever will.

The framework

Chen argues that network effects follow a predictable lifecycle with five stages:

The Cold Start Problem — the network has no users and therefore no value
The Tipping Point — growth becomes self-sustaining and repeatable
Escape Velocity — three forces amplify the network’s growth
The Ceiling — growth stalls from saturation, degradation, and revolt
The Moat — the network becomes a competitive advantage (or doesn’t)

The five stages: Cold Start → Tipping Point → Escape Velocity → Ceiling → Moat.

Each stage demands different strategies. What follows is what I took from each one.

Stage 1: The Cold Start Problem

The atomic network

The most counterintuitive idea in the book is the atomic network: the smallest possible group of users that can sustain itself and actually find the product valuable. Not a market segment. Not a demographic. Something embarrassingly specific.

Chen’s best example is the Fresno credit card drop. In 1958, Bank of America wanted to launch credit cards, but they faced the classic chicken-and-egg: no consumers would want a card if no merchants accepted it, and no merchants would accept it if no consumers had one. So they picked Fresno, California, a mid-sized city where 45% of families already banked with them. On a single day, they mailed 60,000 unsolicited cards to Fresno residents and signed up 300 local merchants. It was messy and risky, but it worked. Within 13 months they had 2 million cards and 20,000 merchants across California.

The lesson is: don’t try to win the whole market. Win one tiny, dense pocket first. Facebook started at Harvard, not “college students.” Uber started at one specific intersection in San Francisco during rush hour, not “cities.” For Slack, the atomic network was a single team of three people. For Zoom, it was literally two people wanting to have a conversation.

Every product has a critical density threshold, the number of users needed before the product experience becomes viable. Slack found that teams needed to exchange about 2,000 messages before the product really clicked, and after that 93% of customers retained. Airbnb needed about 300 listings with 100 reviewed in a single city. Uber needed 15 to 20 concurrent drivers with ETAs under 3 minutes. The higher the threshold, the harder the cold start, but also the more defensible the product becomes long-term.

The hard side

Every network has an easy side and a hard side. The hard side is the small group that does disproportionate work and creates disproportionate value. Wikipedia has 500 million monthly visitors, but its 4,000 most active editors, just 0.02% of its user base, wrote the whole thing. One guy, Steven Pruitt, a US Customs officer, has made nearly 3 million edits and written 35,000 articles. Unpaid.

On Uber, the hard side is drivers. On YouTube, it’s creators. On any marketplace, it’s usually sellers. If you get the hard side, the easy side follows. If you don’t, nothing else matters.

Understanding why the hard side participates is crucial. Content creators are driven by social feedback loops: publish, receive likes and comments, create more. Marketplace sellers want revenue. Wikipedia editors work for free, motivated by status within the community, expertise display, and a sense of purpose. You have to solve for their specific motivations, not just assume “build it and they will come.”

The killer product

Zoom didn’t win by being the most feature-rich video app. It won by being the simplest. It just worked. When you’re trying to solve the cold start, every barrier to joining and using the product is a potential death sentence.

Chen points out that the best networked products can be described in one sentence. This makes them easy to spread virally but also easy to dismiss as “features, not products.” That simplicity is precisely the point. The network is the defensibility, not patents or features.

Freemium pricing directly addresses the cold start: charging users adds friction to network building. Zoom’s founder Eric Yuan studied Dropbox’s free tier (free up to 2GB) and designed Zoom’s 40-minute meeting cap so users could experience the full product before paying. Zoom grew for four years without a marketing team.

Anti-network effects

Anti-network effects are the destructive force at a network’s beginning. Users arrive, find no one, and leave. This creates a vicious cycle that drives most new networks to zero. Chen emphasizes that the myth of instant hockey-stick growth is exactly that, a myth. The reality for most startups is an initial spike followed by decline as novelty fades and the network remains too sparse.

Chen borrows from ecology to explain this. Just as animal populations like meerkats and sardines have an Allee threshold below which the group spirals toward collapse, networks have a tipping point below which they self-destruct. Below critical mass, fewer users means less value means even fewer users. Above it, growth becomes self-sustaining. And just like ecosystems, networks have a carrying capacity where overcrowding creates diminishing returns. Metcalfe’s Law (“value grows as the square of nodes”) is dangerously incomplete because it ignores all of this.

Stage 2: The Tipping Point

Once you know how to build one atomic network, the game becomes making that process repeatable. Chen describes several strategies:

Invite-only

LinkedIn’s cofounder Reid Hoffman seeded the network with mid-tier successful professionals, not Bill Gates (who’d be overwhelmed), but ambitious people still building their careers. Within the first week, employees and investors invited their professional contacts, who invited theirs. The insight is that invite-only works not primarily because of hype, but because it’s a copy-and-paste mechanism: a curated initial network, given invites, will replicate itself with similar high-quality users. The most connected people get invited first and invite other highly connected people, creating a “dinner party of social butterflies.”

But the Gmail story is the one I keep thinking about. Gmail wasn’t intentionally invite-only as a growth hack. It launched on April Fool’s Day 2004 running on 300 old Pentium III computers. The servers simply couldn’t handle open signups. But the scarcity accidentally created enormous demand. Invites were selling on eBay for $150. Entire websites like Gmail Swap popped up where people traded invites.

Part of the frenzy was about the product itself, but part of it was about usernames. Everyone understood intuitively that tom@gmail.com would exist exactly once, and that the longer you waited, the worse your options got. Early adoption had a permanent, visible reward.

This reminded me of ai.com. They launched with a username claim page where you could grab ai.com/@yourname before someone else does. The psychology is the same: everyone knows that short, clean usernames like ai.com/@tom won’t last forever, and nobody wants to end up with something like zigao.wang24cats when they could have just @ZigaoWang. So people rush to sign up. But unlike Gmail, which had a genuinely great product waiting when you got your invite, ai.com’s product isn’t actually ready yet. They created the urgency, but there’s nothing behind it.

Come for the tool, stay for the network

Before Instagram, there was Hipstamatic, a paid app with vintage photo filters that Apple named App of the Year. Millions of people downloaded it. But Hipstamatic was just a tool. You took a photo, applied a filter, and it saved to your camera roll. If you wanted to share it, you had to go somewhere else.

Instagram started life as a completely different app called Burbn, a cluttered location-sharing app. Kevin Systrom and Mike Krieger stripped it down to just photos, comments, and likes, and renamed it Instagram. It offered similar filters to Hipstamatic but free, better designed, and built with network features from day one: profiles, feeds, and sharing links that drove viral growth back to the app. People shared their filtered photos to Facebook, which pulled their friends into Instagram. The tool got people in the door. The network made them stay. Today, 82% of Instagram photos use no filter at all. The thing that originally attracted people became almost irrelevant.

The framework: if your product is useful to one person alone, people will show up even when there’s no network. Then you layer the network on top. This works for a whole category of products: YouTube (create and share), GitHub (code alone, collaborate with others), Dropbox (sync your own files, share folders), Pinterest (organize for yourself, share boards). But the pivot from tool to network is fragile. Thousands of photo apps tried to copy Instagram’s formula and failed. The coupling between the tool and the network has to feel natural, not bolted on.

This one sticks with me because it maps directly onto what I’m trying to build with AvoidXray, a film photography community. Right now people can come to showcase their film photos, and that’s the tool. The question is whether the community layer can grow strong enough that people stay for each other, not just for a place to upload. Instagram proved it’s possible. Hipstamatic proved it’s not guaranteed.

Paying up for launch

Sometimes you just pay to get the network started. Uber launched new cities with $30/hour driver guarantees via Craigslist job ads, then executed a “commission switch” to sustainable economics once critical mass was reached. Coca-Cola literally invented the coupon in 1888, giving away 8.5 million free drinks to build nationwide brand presence. Bitcoin’s protocol rewards early miners with disproportionate cryptocurrency, aligning incentives without requiring a central company to burn cash.

The key insight is that subsidies should come after you’ve validated the product and atomic network concept. Money accelerates a proven playbook. It doesn’t replace one. And you need an exit plan, because the goal is always to transition from subsidized to self-sustaining economics. This requires capital, obviously, which means it’s not available to everyone. I haven’t been able to apply this one.

Flintstoning

Reddit’s cofounders, Steve Huffman and Alexis Ohanian, had a problem: who wants to browse a link-sharing site with no links? So they posted everything themselves, under dozens of fake accounts, for months. They made the site look alive until real users showed up. Later they built scrapers and bots to automate it, and eventually organic submissions took over. They found that individual subreddits needed about 1,000 subscribers each to become self-sustaining.

This is called Flintstoning, named after the Flintstones’ car that looks like it runs on its own but is actually powered by Fred’s feet underneath. You manually fill in the hard side of the network until the real thing takes over. YouTube’s founders uploaded the first videos. DoorDash listed restaurants that hadn’t even signed up, then sent couriers to order food as regular customers. PayPal built bots that bought and sold items on eBay but insisted on PayPal-only transactions, forcing sellers to adopt the service.

At the extreme end, this scales into entire organizations. Nintendo builds internal game studios employing hundreds of people to create first-party titles like Mario and Zelda for new console launches. Microsoft acquired nearly a dozen game studios, including Mojang (Minecraft) for $2.5 billion. Sometimes you simply have to build the hard side yourself.

I’ve lived this one. With AvoidXray, I uploaded a lot of my own film photos to seed the platform. It works. The site looks active, there’s content to browse. But the moment I stop posting, the stream dries up. That’s the fundamental limitation of Flintstoning: it gets you started, but it can’t carry you. And if you keep doing it too long, you actually crowd out the real contributors, because you’re absorbing the engagement (likes, comments, attention) that should be incentivizing genuine users to participate.

Always be hustlin’

Uber’s tipping point strategy was fundamentally about decentralized, creative hustle. Each new city was its own cold start problem, and autonomous local teams used whatever worked: street teams, Uber Puppies, Uber Ice Cream, Uber Mariachi Band, Uber Helicopter, celebrity first riders, and holiday campaigns tied to July 4th and Pride Parade. What matters isn’t any single tactic. It’s building an organizational structure that can generate endless creative variations and iterate rapidly.

The Tinder launch belongs here too. The founding team threw a birthday party for a popular, hyperconnected student at USC. They bused people to a luxury house in LA with a bouncer at the door. The catch: you had to show Tinder downloaded on your phone to get in. A few hundred of the most socially active students walked into a party with the app on their phones. The next morning, they opened it and found the attractive, interesting people from last night that they didn’t get a chance to talk to. Within months, the team was repeating this at campus after campus. In India, they swapped college campuses for call centers, dense communities of young people who already knew each other. They found that around 20,000 users in a single market was enough for organic growth to take over. No grand launch. No press blitz. Just parties.

Chen also confronts the uncomfortable reality that hustle sometimes pulls companies into gray areas. YouTube grew partly on pirated content. PayPal had to build fraud detection systems on the fly. Uber pivoted from licensed black cars to peer-to-peer ridesharing, inviting regulatory controversy. The pattern: embrace the momentum, then build controls over time.

Stage 3: Escape Velocity

Once you’ve solved the cold start and hit a tipping point, the next challenge is scaling. Chen breaks “network effects” into three distinct forces, which he calls the trio of forces:

The Engagement Effect

As the network grows, existing users become more engaged. More content to see, more people to interact with, more use cases that emerge. When Slack first launches in a company, it’s a messaging tool. As more teams join, specialized channels appear. Uber had a “Pool Party” channel, Andreessen Horowitz had channels for books, movies, and futurism. The product becomes stickier not because of new features, but because the network itself generates new reasons to stay.

The practical framework: segment users by engagement level and find different levers for each. LinkedIn’s growth team discovered that what helps an infrequent user (suggesting colleagues to connect with) is completely different from what helps a power user (advanced search, content publishing). One-size-fits-all doesn’t work.

Networked products also have a unique superpower for reactivation. Up to 75% of registered users may be inactive, but a network-driven notification (“your boss shared a folder with you,” “a friend just joined”) is far more compelling than a marketing email. These can flip “dark nodes” back to active in ways non-networked products simply can’t.

The Acquisition Effect

The network itself becomes the main channel for acquiring new users through viral growth. PayPal is the central story here. It started as a PDA-based payment tool, pivoted to internet payments, and discovered that eBay sellers were already creating “We accept PayPal” badges on their listings without being asked. PayPal supercharged this with $10 sign-up bonuses for both inviters and invitees, growing from under 10,000 to 5 million users in about a year.

The master metric is the viral factor, the ratio of new users each cohort generates. At 0.5, you double your users. At 0.95, you get 20x amplification. Small optimizations (5-10% per step) compound across dozens of A/B tests, and the math becomes powerful as the factor approaches 1.0.

But Chen warns that acquisition without engagement is hollow. He uses the 1930s “Prosperity Club” chain letter as an example, a network with strong virality but no retention. It spread to hundreds of thousands of people via mail, promising money from dime exchanges. It overwhelmed Denver’s post offices and was eventually made illegal. Strong acquisition, zero stickiness, inevitable collapse.

The Economic Effect

The network’s economics improve as it scales. Better monetization, lower costs per user, stronger pricing power. Uber’s per-trip subsidy costs dropped dramatically as driver density increased. The same $30/hour guarantee cost $15 per trip early on but dropped to $2.50 as trip frequency doubled. Slack’s premium features like voice calling and searchable history become more compelling as more of a company adopts the product, driving organic free-to-paid conversion.

Features can be copied. Networks can’t. Products with strong network effects face less price competition because switching costs are embedded in the network itself. When all your coworkers are on Dropbox, the cost of switching isn’t learning new software. It’s losing all your shared folders.

These three forces are mutually reinforcing. Higher engagement creates more sharing opportunities, boosting acquisition. More new users keep the existing community engaged. Stronger monetization incentivizes more participation on the hard side. Amplifying one lifts the others.

Stage 4: The Ceiling

Growth doesn’t last forever. Every network eventually hits a ceiling, and often multiple ceilings in sequence.

Saturation

At some point, you’ve reached most of the people who would use your product. eBay hit this around 2000 when month-over-month growth flatlined for the first time. The response was layering new products on top of the existing network: “Buy It Now” fixed pricing (now 62% of eBay’s $40B+ GMV), seller stores, improved checkout, and PayPal integration. What looks like exponential growth from the outside is usually many S-curves stacked on top of each other, each requiring its own cold start solution.

There’s also network saturation, which is subtler. Each new connection yields diminishing returns. Snapchat’s internal data shows a user’s top friend drives 25% of send volume while the 18th friend contributes less than 1%. Facebook’s “7 friends in 10 days” metric captured this too: 7 connections is powerful, 14 is better but not 2x better, and 10,000 friends might actually decrease engagement through noise.

Instagram fought saturation using the Adjacent User framework. At 400 million users, growth had gone linear. The growth team identified about eight distinct types of potential users who were aware of the product but couldn’t fully engage, from US women aged 35-45 to women in Jakarta on older 3G Android phones. They solved for each segment sequentially and reignited growth past a billion users.

The Law of Shitty Clickthroughs

Every marketing channel degrades over time. The first banner ads on Hotwired in 1994 had a 78% clickthrough rate. Today they get 0.3-1%. Email marketing dropped from 30% to 13%. This applies to viral channels too: a 50% decline in invite conversion causes roughly an 80% decline in total viral growth because of compounding across invitation generations.

The defensive response is constantly layering new acquisition channels rather than over-investing in any single one. The offensive response is leaning into network effects themselves as the primary growth engine. Buying billions of installs at $10+ each is simply not viable.

When the network revolts

The hard side’s importance grows as the network matures, and so does their leverage. Uber drivers protested outside headquarters demanding higher pay. Vine’s top 18 creators organized a collective ultimatum demanding $1.2 million each and feature changes. Vine refused. The platform eventually shut down.

The concentration is extreme. Uber’s top 15% of drivers handled 40% of trips. Slack’s top 1% of customers generated 40% of revenue. Zoom’s top 344 accounts, under 1%, generated 30% of revenue. The same feedback loops that make the hard side productive also give them the organizational power to revolt.

As platforms mature, the hard side naturally professionalizes. Part-time eBay sellers become full-time businesses. Casual YouTube creators build production teams. This is necessary because as markets saturate, acquiring new hard-side members gets prohibitively expensive. But professionalization concentrates power among fewer players, creating a structural tension that never fully resolves.

Uber’s XChange Leasing program is a cautionary tale: the company lost $525 million trying to professionalize drivers by financing vehicles, attracting the wrong incentive profiles and enabling fraud. Some of those cars ended up as far away as Iraq and Afghanistan.

Eternal September

In 1993, AOL mailed millions of internet access CDs across America, flooding Usenet, the internet’s first global discussion system, with new users who overwhelmed its decade-old culture of “netiquette.” The core community fragmented and migrated elsewhere. By 2010, major institutions had shut down their Usenet servers. Dave Fischer famously observed: “September 1993 will go down in net history as the September that never ended.”

The broader concept is context collapse: when a network grows so large that distinct social contexts (close friends, colleagues, bosses, parents) merge into one undifferentiated audience. Creators self-censor or leave, which causes consumers to leave too, triggering a vicious cycle. The defense is building networks within networks: messaging threads, Slack channels, Facebook Groups, Instagram “finstas,” Snap Stories. These let users maintain distinct contexts even as the overall network scales to millions.

Usenet’s decentralized, open-source nature, once a virtue, became fatal because no entity had the resources, authority, or speed to adapt when millions of new users arrived simultaneously. Centralized companies, for all their downsides, can iterate fast enough to fight context collapse.

Overcrowding

Too much content, too many participants, too many signals make it increasingly hard for anyone to find what they want or stand out. YouTube started as a dating site with zero videos and evolved through chronological lists, to hand-curated homepage picks, to popularity rankings, to algorithmic recommendations. Every networked product follows this same discovery evolution as it scales.

Overcrowding affects both sides differently. Consumers struggle to find relevant content. Creators struggle to stand out. The preferential attachment problem means early, established users accumulate disproportionate advantages in followers, ratings, and visibility, potentially driving new creators to competing platforms with more level playing fields. Eugene Wei calls this “Old Money”: early users on social networks accumulate outsized social capital, creating status inequality.

Algorithms help but create their own problems. Optimizing for pure engagement can reward controversial clickbait. Optimizing for revenue can surface high-priced, low-relevance items. The fight against overcrowding never truly ends.

Stage 5: The Moat

Network effects create defensibility because competitors must solve their own cold start problem against an incumbent with existing density. But simply having network effects isn’t a magic shield. Every player in a networked category has them. The real question is who leverages them best.

Cherry-picking

Large networks are networks of networks, and not every sub-network is well-served. Startups can strategically pick off the most valuable, underserved segments. Andrew Parker’s famous 2010 observation mapped this out: Indeed took jobs from Craigslist, StubHub took tickets, Etsy took crafts, Airbnb took rooms, Tinder took dating, Zillow took real estate.

Airbnb literally reverse-engineered Craigslist’s posting system to automatically cross-post listings with links driving users back to Airbnb. Facebook and LinkedIn grew by importing contacts from Hotmail and Yahoo Mail. Snapchat carved out photo messaging, one high-frequency use case, from broader social networks, generating 10-20 messages per active user per day.

The risk is platform dependence. If you remain just a feature of the incumbent’s ecosystem, you’re vulnerable to being copied or cut off.

Big Bang Failures

The “Big Bang Launch,” launching to a massive, broad audience simultaneously, is structurally wrong for networked products. Google+ is the definitive example. It signed up 90 million users by leveraging Google’s homepage and product ecosystem. Users averaged 3 minutes per month versus 6-7 hours on Facebook. The problem: broad distribution creates many weak, disconnected networks rather than dense, engaged ones. The aggregate numbers looked impressive. The underlying networks were hollow.

The paradox of small markets: the first atomic network almost always looks like a trivially small, unattractive market. eBay started in stamps and coins. Airbnb started with air mattresses on living room floors. TikTok started with lip-syncing videos. Fred Wilson at Union Square Ventures passed on Airbnb. Bessemer Ventures called eBay a “no-brainer pass.” These are among the most successful networked products ever built. You can’t skip the atomic network phase by throwing distribution at the problem.

Competing over the hard side

When two networks of similar size compete, the battle comes down to who captures the hard side. Uber built elaborate systems to identify “dual-apping” drivers using machine learning, behavioral signals like pausing and unpausing sessions, and even Android APIs detecting competing apps. They deployed targeted financial incentives, including tiered bonuses, guaranteed surge multipliers, and personalized weekly offers, spending over $50 million per week in a single region at peak. This successfully eliminated smaller competitors like Sidecar (which described markets “going to zero” within 6 weeks of stopping bonuses), but stalled against Lyft in markets where the split was roughly 50/50.

DoorDash offers a counter-lesson: rather than competing head-on with Uber Eats in cities, they started in underserved suburbs and college towns, built strong economics there, then expanded into urban markets. A flanking maneuver rather than a direct assault.

Bundling

Bundling, using an existing large network to distribute a new product, sounds like an unfair advantage. But Chen’s point is that it almost never works the way people expect.

Internet Explorer shipped with every copy of Windows. It still only captured 3-4% of the browser market at first, because the product was terrible. It took Microsoft years of genuine product investment to actually compete with Netscape. Bing has been bundled across Windows, Edge, and every Microsoft product for years. Nobody uses it. Google+ leveraged Google’s homepage, YouTube, Gmail, and Photos. It signed up 90 million users, and they averaged 3 minutes per month.

When bundling does work, it requires two things: a genuinely excellent product and the ability to leverage the parent network not just for user acquisition but for engagement and retention. Instagram used Facebook’s social graph to recommend real-life friends, which dramatically boosted long-term retention. That’s genuine network integration, not just a download prompt. Microsoft Office succeeded because Word and Excel were rewritten to be genuinely superior during the DOS-to-GUI transition, and then combined with Windows distribution.

Distribution gets people to the door. It doesn’t make them stay. This applies at every scale. It’s tempting to think that if you could just get your product in front of enough people, growth would follow. But the Wimdu story, the Google+ story, and the Bing story all say the same thing: if the product or the network quality isn’t there, more eyeballs just means more people bouncing off.

All supply is not created equal

The Airbnb vs. Wimdu story is the clearest example in the book of why the moat is about network quality, not just network size. Wimdu was a Berlin-based clone launched in 2011 by the Samwer brothers, who had previously cloned eBay and Groupon in Europe and sold both back to their American originals. Wimdu came out swinging: $90 million in funding, 400 employees, 50,000 listings in the first year. They scraped Airbnb’s listings, posed as guests to recruit hosts, and copied the design down to the taglines.

But Wimdu’s top 10% of inventory was equivalent to Airbnb’s bottom 10%. They had recruited property managers running low-end hostels, not the unique, authentic hosts that made Airbnb special. Guests showed up expecting something personal and got something forgettable. No word of mouth. No magic. No reason to come back. Wimdu went to zero within a few years.

Brian Chesky’s key insight was that the Samwers wanted a quick flip, not an 18-year commitment. By refusing to acquire Wimdu and forcing them to compete long-term, Airbnb exploited its competitor’s fundamental misalignment of incentives. Airbnb’s organic, years-old European supply, though smaller, had already formed authentic atomic networks with high guest satisfaction and genuine word of mouth.

My take

The book follows five stages and the framework is solid. But the front half hit much harder than the back half for me. The concepts around atomic networks, Flintstoning, solving for the hard side, come for the tool: these are my actual problems right now. I’m building AvoidXray, a film photography community. I’m seeding it with my own content. I’m thinking about how to turn a tool into a community. The early chapters gave me a vocabulary for things I was already experiencing but couldn’t name.

The later sections on hitting ceilings and building moats are interesting, but they’re about problems I haven’t had yet. When you’re still at stage one, reading about how Uber managed driver revolts or how YouTube evolved its recommendation algorithm feels abstract. There are plenty of stories but fewer clear “here’s what you should do” moments. I think this is partly a “where you are” problem. If I were already scaling, the back half would probably resonate more.

That said, some ideas from the later sections are useful even at stage one. The bundling lesson, that distribution without product quality is worthless, applies whether you have a million users or a hundred. The Wimdu lesson, that quantity of supply means nothing if the quality is hollow, matters from day one. And the Law of Shitty Clickthroughs is a reminder not to over-invest in any single growth channel, no matter how well it’s working right now.

The book is also repetitious in places. It keeps reminding you it’s called The Cold Start Problem, but the cold start section isn’t even the longest part. The five-stage framework covers the entire lifecycle of a networked product, and the later stages matter just as much for building a real business. The title undersells the scope.

But the stories are genuinely good. The Fresno credit card drop, Tinder’s campus parties, Reddit’s fake accounts, Wimdu’s hollow listings, Gmail’s accidental scarcity, Google+‘s 3 minutes per month. These aren’t abstract business concepts. They’re specific, memorable, and they change how you think about the thing you’re building.

Concepts I’ll carry forward

Atomic network: start with the smallest viable community, not the whole market
The hard side: figure out who creates the value and attract them first
Come for the tool, stay for the network: give people a reason to show up before the network exists
Flintstoning: manually seed activity, but know it doesn’t scale and have an exit plan
Dense communities first: campuses, cities, niche groups. Saturate one before expanding
Anti-network effects are the default: the vicious cycle of emptiness kills more than competition does
The Law of Shitty Clickthroughs: every channel degrades, so layer them constantly
All supply is not created equal: quality of the hard side matters more than quantity
Distribution is not enough: bundling, big launches, and big numbers mean nothing without product quality and genuine network density
The Cold Start Problem itself becomes the moat: once your network reaches critical mass, competitors have to solve the same bootstrapping challenge from a worse position

The Art of Caring

Zigao Wang — Sat, 28 Mar 2026 00:00:00 GMT

I’m at LET’S VISION 2026 in Shanghai today, an Apple ecosystem conference by SwiftGG. The talk that stuck with me most was by Klemens Strasser, an indie dev from Austria who makes apps like The Art of Fauna, PocketShelf, Letter Rooms, and Study Snacks. Apple Design Award winner in 2025 for accessibility, finalist in 2022 and 2023. His talk was called “The Art of Caring,” and that title pretty much captures everything he’s about.

I talked to him afterward and came away rethinking a few things I thought I already understood.

Klemens on stage talking about accessibility in indie apps.

Accessibility is design, not a feature

Klemens opened with a line that reframed how I think about this: accessibility isn’t about serving a small portion of users. It makes every user’s experience better, and it will make every user’s experience better.

Before this talk, I thought about accessibility the way most developers do. VoiceOver, dynamic type, contrast ratios. Check the boxes, move on. Klemens treats it as something much deeper.

In The Art of Fauna, he added phobia settings for snakes, insects, and other animals. That’s not a standard accessibility feature. But some people don’t even know they have a phobia until the image shows up on screen. By putting that filter on the very first screen, before onboarding even starts, he’s catching something most developers would never think about.

And that’s the bigger point: most apps show accessibility options after onboarding, or bury them in settings. But some users can’t even get through onboarding without them. Putting accessibility last means treating it as an afterthought. Putting it first means you actually designed for it.

Care shows up at friction points

Klemens puts a photo of himself in the “contact developer” section of his apps. Small thing, but it tells users there’s a real indie developer behind this, not a faceless company.

He does the same with subscriptions. When users are frustrated about paying, he doesn’t just throw up a paywall. He explains why the app costs money and why there are no ads. Because adding ads would go against the entire reason the app exists. The principle is simple: approach users at their moment of frustration and show that you care.

He applies the same thinking in reverse for ratings. The “Rate this app?” prompt shows up right after the user completes a hard puzzle, when they feel accomplished. Not on launch. Not randomly. At the moment they feel best about the experience.

Most developers treat friction points as things to hide or minimize. Klemens treats them as relationship moments. That’s a different way of thinking about product design entirely.

A line from his talk that sums up the whole philosophy: "If you care a bit more, you might not only find success but also be happy doing so."

The two question filter

Every idea Klemens has goes through two questions:

Is this fun?
Can I make it accessible?

The first answer is usually yes. The second is usually no. If he can’t make it accessible, he passes entirely.

What I find interesting about this is that it puts accessibility into the ideation phase, not the development phase. Most developers think about accessibility after they’ve already built something. By then, it’s hard to retrofit. Klemens filters ideas out before writing a single line of code.

You don’t need a complex validation process

I asked him how he knows an idea is worth building into a full app. His answer was simple: build a prototype, play with it yourself, and if it makes you smile, show it to friends. If they don’t get it, drop it. If they like it and give feedback, keep going.

No surveys. No landing page experiments. Just real reactions from himself and people he trusts.

This works because his apps are small and personal. When you’re building a cozy puzzle game or a book tracker, the people around you are your target audience. The feedback loop is fast and honest. It probably wouldn’t work for everything, but for indie apps, it’s hard to argue with results.

Not every app needs a purpose statement

This is the part that changed how I think about building things.

I asked Klemens what he sees as the core value of his apps. PocketShelf started from a personal problem: he wanted to track his reading. That fits the typical narrative of “scratch your own itch.” But The Art of Fauna didn’t start that way at all. It started as pure fun. Users later told him it helped them calm down, and that became part of what the app is about.

The purpose came after shipping, not before.

I’ve been reading The Cold Start Problem by Andrew Chen, and there’s this idea of “come for the tool, stay for the network,” how Instagram started as a photo editing tool before becoming a social network. I asked Klemens if he’d ever add social features, since most of his apps are deliberately local and self-contained. He said maybe for Art of Fauna eventually, but it’s not a priority.

That actually reinforces the framework. The tool has to be genuinely good on its own first. And maybe some tools are better off just being tools.

What I’m taking away

I keep getting stuck waiting for the perfect idea. Something that solves a clear problem, has a market, fits a strategy. Klemens basically showed me a different approach: care deeply about craft and about the people using what you make. Filter hard on accessibility. Build a prototype and see if it makes you smile.

And let the purpose show up on its own.

With Klemens after the talk.