The Customer Who Almost Killed Slack, Stripe, and Airbnb | Hacker News Reader

The Customer Who Almost Killed Slack, Stripe, and Airbnb

siliconopera.com

18 pointsnate30 comments

Remember that there is an alternate universe in which AirBnB is the way that 70% of all corporate short-term housing gets booked, and they are telling the story of how they nearly didn't pivot correctly.

And a zillion alternate universes in which they never got big. Survivor bias is real, and everyone has just-so stories. The ones they choose to tell are partially an artifact of organizational culture, and partially of self-aggrandizement.

This line of argument proves too much.

As it would apply to a lot of things throughout history.

e.g. “Remember that there is an alternate universe in which ancient Anatolian farmers defeated the Yamnaya steppe raiders in Europe, and they are telling the story of how they nearly didn't make it.”

Which would mean even the concept of “European” as known in this universe… is just a bundle of survivor bias, just-so stories, and so on.

On the contrary, this line of argument proves nothing. Rather, it is a caution that people telling you stories tells you a lot about what they want to portray, not so much about whether what they did was a generally good decision.

As to your second point: "European" is an arbitrary social concept. Cartographers could have decided that Eurasia is obviously a single landmass, or that nothing is more important than river systems, or that the most important directionality is the path of the sun. Thousands of years later, the decisions made have consequences, but there is nothing divinely ordained about what those decisions were.

I suppose if a hundred years from now the common, generic word for "hotel" is "airbean", that might be a significant effect. But it's a little more likely to be "marriott" or "hilton", I think.

On the other hand, what's the appeal of Airbnb in 2026?

I'm genuinely confused at what is it better at than Booking, which also has hosts.

Booking is also significantly cheaper at all offerings ranges and has a much wider catalogue including hotels.

Airbnb was cool and interesting when it was genuinely cheaper and offering authentic experiences.

Every vacation or place I go I check Airbnb and bar for one apartment in Sangenjaya, Tokyo, I have never, ever booked there in years, and the only reason I did was that Booking had nothing in those days in that very place I wanted. But it's rare.

I’m the opposite and never use Booking but part of that is I’m put off by the experience of using the site. Also noticing it heavily skews towards getting you into a hotel in “touristy” locations. Feels like these are slightly different use cases and are optimized thusly.

There's no shenanigans.

Both prioritize putting at the top places that are likelier to sell and hotels in more touristic areas draw more people.

What are those off putting experiences on booking?

Both apps and websites do essentially the same, it's not a sketchy dark pattern filled Ryanair check in.

> Stewart Butterfield has talked about the pressure Slack faced from large organizations in its early days wanting features that would have fundamentally changed what Slack was. The requests were reasonable on their face: more administrative controls, different permission structures, audit logging. Each one seemed like a logical extension. Taken together, they were a blueprint for a different product aimed at a different user. Butterfield’s team had to repeatedly decide what they were actually building.

I'm pretty sure Slack has those things by now. So the question is, what did the team actually end up building? What features were prioritized over the enterprise compliance features? Would Slack have less revenue had they gone with the enterprise stuff first?

Does anybody know how to write anymore?

No.

While I agree with the overall theme of the article - prioritizing a single large customer can be a deadly trap for a new company - the way it is presented that these companies held to their principles while barely escaping bankruptcy ("Sometime in 2010, Airbnb was hemorrhaging money and growing slower than Brian Chesky had promised investors") reads like BS revisionist history to me.

I'd be particularly interested in a separate source for the 2010 story about AirBnB. I worked in the short term rental space at that time and I remember AirBnB exploding into the zeitgeist at that time and being seen as a potentially potent new disrupter - not this "at grave risk unless they took on a big new customer" story that this article is projecting. Plus, AirBnB raised their Series A in Nov 2010, so it didn't seem like they were having issues with investors.

When I'm building, and get approached by large customers who have specific needs for them, those needs go at the end of our backlog. There's a hard and fast rule that unless it's an issue with authentication or accepting money, nobody gets to jump the line. We re-evaluate our backlogs periodically, and treat large-customer requests similarly to small-customer requests; who is this going to serve, what additional revenue could this generate, will this increase retention. If there are other projects that, combined, will move the needle more than doing one project for one enterprise client, guess what, that enterprise client doesn't get to jump the line.

AI;DR?

IIRC, around 2016 or so, Slack invited us in their office to pitch us the enterprise version and ask whether we would be interested in becoming one of their first enterprise customers. Among other requests, one colleague asked me to ask them to not kill their IRC gateway. One of their PMs looked at me and told me “No, we are not doing this”.

I wish this was human written and not AI slop.

While the core arguments stand, there's little actual nutrients in the article.

I spend hours per day with codex and claude. Almost every LLM is using each other's training data. There's a ton of AI slop out there already now feeding into training data. They all sound the same.

You are right! My honest take, The customer who did X wasnt Y, it was Z.

In early 2025, this was novelty, now it's annoying.

Yeah, i got duped. Sorry y'all. I talk about it here more now this morning: https://ninjasandrobots.com/paul-graham-flagged-for-ai-use

Will definitely now AI check for slop better before even posting. But was curious how automated and cheap I could make it. Isn't turning out that well yet. Also I put up a PR to remove silconopera from Kagi Small Web. Because it breaks multiple of their guidelines: multi-author, all fake authors, and all LLM content at non-human scale.

> Chesky said no. Not politely declined. No.

Gaaaah please stop

Even the tagine was extremely Claudey.

I get it. When AI writing tropes show up day after day, post after post, it’s hard to keep your sanity.

It’s not just slop—it’s turbo slop.

(Embrace the suck I guess, won’t last too much longer)

> (Embrace the suck I guess, won’t last too much longer)

I'll take "Things I've heard every single day for the past 3.5 years" for $200, Alex.

Looking at the markets, either we all get turned into paperclips soon, or what goes up finally does the inevitable.

https://www.decisionproblem.com/paperclips/index2.html

Even if the bubble pops, the spambots will be here for the rest of our lives. Indeed the inference will only get cheaper as hardware prices plummet and get repurposed by third parties running DeepSeek etc. Just like the dotcom burst didn't wipe out the internet, only the people who lost touch with reality.

Good point, open models and cheap hardware will allow the spam to continue flowing. I don’t know if we’ll see as many polished AI “services”, though. I do wonder if press releases and traditional marketing copy may go back to the old ways, at least for a while.

> The pattern is consistent enough that it deserves a name and an honest autopsy.

Hundreds of billions of dollars and they can't teach the superintelligent being that will replace us all (if freed from its shackles by the US government) to use the word "honest" correctly. Sad!

The abuse of language via token predictors is legitimately infuriating. Perhaps more infuriating is that blatant spambot posts are upvoted to the front page routinely.

Does anyone use a decent "ai detection" algo/service/on device model that they are happy with?

here is a product idea: a public service where you can submit urls for public content which will then be analyzed. the result can then be shared. the platform can allow a number of free submissions, but importantly if the same url is submitted multiple times, the analysis does not need to be redone. the free plan only allows a few submissions per day/month, whereas paid plans can offer bulk submission, more submissions, private results, etc...

The author is not even a real person, none of them on this “publication” are. They didn’t even to bother correcting the hallucinated Twitter handles.

slop

One thing I've learned working with early-stage products is that a single customer can simultaneously be your biggest opportunity and your biggest distraction.

Some of the most valuable customer conversations we've had completely reshaped our roadmap. But we've also found that it's important to distinguish between a design partner helping define a broader market need and a customer asking for highly specific workflows that don't generalize.

That distinction is much easier to see in hindsight than in real time.

Indeed, there's also a nice blog post by the Basecamp people about that: https://signalvnoise.com/svn3/why-we-never-sold-basecamp-by-...

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