构建成本归零后的产品护城河：行为机制提取与判断力溢价

I keep thinking about Meta and how ruthless they have been for years about taking mechanics that work somewhere else and making them their own. Stories worked, so they put Stories everywhere. Short form video worked, so they went hard on Reels. Public text worked, so they built Threads. There are plenty more examples. People usually talk about this like Meta has no shame about copying, which is fair enough, but I think there is a much more useful lesson buried in what they have been doing. Meta is a consumer company. Their business depends on understanding what gets people to create, share, connect, consume, come back, and generally spend more time with their products. When someone else spends years and a bunch of money discovering a new behavior that people clearly like, why wouldn’t Meta study the hell out of it? Someone else already did a ton of the expensive discovery work for them. In a weird way, Meta has treated the rest of the consumer internet like a giant external product lab. I think every software company is going to need to get much better at doing some version of this. Software is becoming way too easy to build for product development to keep working the way it has. A lot of the scarcity used to come from implementation. You could have ten ideas that sounded good and maybe have the engineering capacity to seriously pursue two of them. The cost of building forced a bunch of prioritization on you whether you liked it or not. That is changing fast. Agents can already build prototypes, instrument things, test implementations, review code, fix issues, and do more and more of the production work around software. I expect that to keep moving quickly. Eventually “can we build this?” becomes a pretty boring question for a lot of software. Then we are left with the question that has always been harder anyway. Should we build it? This is where I think things get interesting. There are thousands and thousands of software companies running product experiments for us every day. They are spending their own time and money figuring out better ways to onboard people, create habits, collaborate, review work, build trust, share things, discover things, verify things, delegate work, build reputation, create marketplaces, personalize experiences, give feedback, make something multiplayer, and pretty much every other behavior you can imagine inside software. We get to watch all of it. I don’t mean make a spreadsheet of competitor features and start cloning whatever looks cool. That is probably one of the fastest ways to turn a good product into a pile of shit. The interesting part is figuring out the mechanic underneath the feature. Take a pull request. On the surface it is a developer tool feature. Underneath, someone proposes a piece of work, the work becomes visible to other people, feedback gets attached directly to it, people can inspect it asynchronously, approval changes its state, and the whole history sticks around afterward. That mechanic can travel pretty far outside software development. You could apply versions of it to AI generated work, marketing, finance, design, legal, operations, research, and plenty of other things. Stories are another obvious example. The interesting part was never just that a photo or video disappeared after 24 hours. The mechanic lowered the pressure to create, which made people more willing to share, then made consuming those updates incredibly easy, showed the creator who watched, gave people an easy way to reply, and created another reason to share again later. There is a whole behavioral system underneath the visible feature. Once you start looking at software this way, categories get a lot less useful. A game can teach an enterprise product something. GitHub can teach an AI company something. TikTok can teach a research tool something. Marketplaces can teach agent products about trust. Consumer social products probably have a lot to teach B2B software about getting people to contribute more without making it feel like work. You are basically looking for machinery that has already proven it can change human behavior. Then you figure out whether that machinery can help with a behavior that matters inside your own product. That part is important because every company has a different thing it should actually care about. Meta has spent a lot of time caring about attention because attention is tied pretty directly to their business. An AI product might care about whether people trust an agent enough to delegate increasingly important work to it. A marketplace might care about every successful transaction making the next transaction easier to trust. An analytics product might care about turning evidence into a decision and then getting that decision turned into an improvement. A company building software for teams might care about work leaving enough useful context behind that someone else can pick it up later without needing another meeting. Whatever it is, figure out the behavior first. Then go hunting for mechanics that might help create more of it. I can see companies eventually having entire libraries of these things. This mechanic increases contribution without requiring a giant reward. This one reduces the anxiety of sharing unfinished work. Another one makes verification more likely. Some mechanics build reputation as a byproduct of normal use. Others make a workflow multiplayer. A few only work once you have enough network density. Some work incredibly well in games and completely fall apart when you move them into productivity software. Over time it probably becomes more like a graph than a library, especially because AI can help us watch so much more of the software world than a product team ever could on its own. An agent could watch product launches, changelogs, screenshots, demos, reviews, user complaints, open source projects, App Store updates, and whatever else we can throw at it. It can start finding patterns, breaking mechanics apart, connecting them to behaviors we care about, and eventually suggesting how they might translate into our own products. Then another agent can build the prototype. That sounds a little sci-fi when you write it all out, but we are already close enough to pieces of this that I don’t think the direction is particularly hard to see. The funny part is that all of this makes the human side more important to me. Judgment gets more valuable when building gets cheaper. Taste matters more when you can add almost anything. Knowing which behavior actually matters becomes more important when you can optimize a hundred different ones. Understanding why something worked somewhere else matters a lot more than noticing that it worked. And knowing when to leave the damn product alone might become one of the most valuable product skills of all. I also think this means ruthless prioritization gets more important, which sounds backwards at first. For a long time engineering scarcity saved us from ourselves. A bunch of mediocre ideas never got built because there simply wasn’t time. We are losing that protection. We are about to become extremely good at building bad ideas quickly. That is probably going to create a whole new class of bloated products made by teams that confuse the ability to build something with a reason for it to exist. The companies I would bet on are the ones that get really good at the learning loop. See something interesting somewhere in the world. Understand what is actually happening underneath it. Form a hypothesis about why it might matter for your users. Build the smallest thing that lets you test it. Put it in front of people. Measure what changed. Keep the learning whether the idea worked or not. Then do it again. Meta figured out a version of this because their business forced them to. Their old Little Red Book is full of ruthless prioritization, keep shipping, the quick inherit the earth, code wins arguments, everything is up for debate, and a bunch of other ideas that make a lot more sense to me when I look at them through this lens. They built an organization that could watch human behavior change, move quickly when they saw something important, and keep learning. AI is going to make some version of that useful for pretty much every software company. The entire software industry is becoming an external R&D lab that you get to learn from. Everyone else can help discover what works. The code to try it yourself is getting cheaper every day. Which means knowing what deserves to exist is going to matter a hell of a lot more.