The AI Distribution Cheat Sheet. Eight plays that beat building a better product.

Roy Lee built Cluely into a company that raised 15 million dollars in ten weeks, and he did it on attention, not features. It began as Interview Coder, a tool to cheat on coding interviews, and the controversy was the distribution. Lee's read on the algorithm is blunt: it rewards authenticity above almost everything, and provocative, genuine content travels in a way polished marketing never will. His one hard rule is the direction of the punch.

As he puts it, never punch down, like never ever even remotely close to punch down. Aim up, at incumbents, at norms, at yourself, never at someone with less power. Every time a stranger attacks the idea, a few more people discover it, so the noise compounds instead of costing him. The product followed the distribution, because viral reach gathered the user data that told him what to build next.

Grant Lee co-founded Gamma during the 2020 pandemic, pitched investors in the middle of the night from a small London flat, and grew it to 100 million users. The growth engine was not paid acquisition. It was word of mouth, earned by obsessing over the first session. Gamma threw out the rigid 16-by-9 slide and built a content block that is malleable, mobile-responsive, and genuinely faster than the tools people already knew.

Lee compares this moment in AI to the one-button-mouse era, where the winners reduce the interface until a first-timer succeeds without a manual. Get that first session right and each new user recruits the next one, which is how Gamma moved from a prosumer toy to a business tool without a sales-led motion. The distribution was baked into the product experience, not bolted on after.

A non-technical 23-year-old built Riz GPT, an app that writes replies for dating-app conversations, using ChatGPT to generate the responses. The distribution was almost insultingly cheap. He paid roughly 50 dollars per promo to small creators inside the Rizz niche, the exact corner of the internet where his buyers already lived. Those videos drove nearly 200,000 downloads in a single week and pushed the app to 80,000 dollars in monthly revenue.

The lesson is not simply to use influencers. It is to find the micro-creators whose audience is your customer and pay many of them a tiny amount, instead of paying one big name a fortune. He ran the same play again on UMAX, which crossed 500,000 dollars a month, which proved the channel, not the app, was the real asset.

NGL reached 250 million downloads with no marketing budget at all. It is an anonymous question app: you post a link to your Instagram story, friends send you anonymous messages, and you reply publicly back on Instagram. That last step is the entire growth engine. Every public reply is an ad shown to the sender's own friend graph, which seeds the next wave of installs.

The founder, who had shipped eight apps before this one, designed for virality on purpose, with one clear action per screen, frictionless onboarding, and social proof built into the flow. The unlock that took it global was shipping on Android, because the friend graph only compounds when nobody is left out by platform. Localized weekly pricing by country turned all that reach into revenue without slowing the loop.

Oliver Henry built a home-decorating app, then hit the wall every solo founder hits: making enough short-form content to feed the algorithm is a full-time job. So he built one. Using OpenClaw he created an agent named Larry whose job is to research and produce high-converting TikTok slideshows, post after post, learning from the analytics. The early AI-generated images flopped, so the workflow stayed human where it had to and automated everything else.

The sharpest lesson was that views are not installs. Larry's real gains came from refining the call to action and reading user behavior, not from chasing raw reach. And in the end the data beat the founder's taste, because a post Henry personally disliked became the best performer, so he let the agent run the content calendar. The content engine became a system he owns, not a grind he survives.

Alex runs a portfolio of AI coaching apps, the biggest of which, Plug AI, reportedly makes 190,000 dollars a month. He is clear that the edge is not the technology, which is mostly a wrapper on a model anyone can call. The edge is branding: catchy, search-friendly names like Riz and Plug that tell you what the app does and ride a trend the moment it appears. He found the Riz idea in a Tinder subreddit and aimed each app at one tight vertical, a texting assistant, a life-advice coach, a color-analysis tool, so the name and the niche reinforce each other.

In a flooded market where confusingly similar apps fight for the same downloads, the name is the first and cheapest piece of distribution. Get it right and app-store search does some of the marketing for you.

Zach Yadegari was a teenager when Cal AI, an app that tracks calories from a photo of your food, crossed 20 million dollars in annual revenue inside ten months. He had done this before, growing a gaming site to five million users and selling it for 100,000 dollars at sixteen, which funded the next swing. Cal's growth was influencer marketing run like a finance desk, not a vibe. The team had a repeatable outreach pitch, but the discipline was in the modeling, because before paying a creator they predicted the campaign's return and only ran the ones that penciled out.

That turns influencer marketing from a lottery into a channel you can scale, since every deal is a forecast you check against actuals. Pick the channel, build the model, and let the spreadsheet kill the campaigns that will not pay back.

Alejandro and Mario built PushScroll, an app that makes you do push-ups before it unlocks social media, to 30,000 dollars a month from a single piece of content. The order is the whole trick. Before writing the app, they posted a viral TikTok showing a fake demo, a compelling hook and the benefit made visible, not a real product walkthrough. The video generated the demand, and only then did they build a fast MVP to catch it.

Their content-first playbook is explicit: warm up the social accounts first, design one visually obvious idea anyone can grasp in seconds, post consistently to find the hook that lands, and let the response tell you whether to build at all. Distribution is not the launch step here. It is the validation step, run before a line of code exists.