How Anis Belkacem Built MEE6 and Where AI Takes Online Communities Next

I started programming and made my first website when I was 12. What attracted me was the leverage of internet products: if what you build fits the market, you can reach millions of people fast. At 12, reading that Facebook started in a dorm and Google in a garage makes the whole thing feel possible. Later in college, I realized I would rather build products than sit in classes I was not excited about. I wrote down ideas and decided to launch one product every two weeks. Six months later, MEE6 was born. That period shaped how I still think about innovation: build, learn from real users, and iterate fast.

The biggest challenge was scaling the infrastructure and the product at the same time without using external capital as a shortcut. Because we were bootstrapped from the early days, we had to be very smart about server costs. At our scale, a small technical inefficiency becomes very expensive very fast, so we spent a lot of time optimizing our systems, capacity, and architecture instead of just throwing more servers at every problem. Reliability was also not optional. A small issue could affect millions of people. On the product side, Discord communities have very different needs. We managed the resulting complexity by organizing MEE6 around plugins, where each set of features serves one clear purpose.

I launched more than 20 websites and internet products in my teenage years. Most did not work, but each one taught me something. Product-market fit is not what you think about your idea; it is what users repeatedly do. Are people adopting the product? Are they coming back? Are they willing to pay? Launching one product every two weeks helped me shorten that learning loop. MEE6 worked because it solved a real and frequent problem for Discord community owners. They needed better tools to manage and engage their communities, and the need was already there.

Adoption starts with solving a problem people care about and making the first experience easy. Engagement comes from delivering that value repeatedly. Retention comes from trust: the product has to remain reliable, useful, and easy to understand as it grows. It also depends on the problem. Is it important or optional? Does it happen every day or once a year? Is it shared by millions of people or a small niche? You do not need to be strong on every dimension, but you need to be very strong on at least one. Initial excitement is easy. Recurring value is much harder.

We build the smallest experiment that can tell us whether the idea has value. We look at real behavior more than opinions: adoption, repeated usage, retention, and whether users make the feature part of how they work. Positive feedback can still hide weak behavior. You also have to give an idea enough time to find its audience without becoming emotionally attached to it. You never know for sure what will still matter in one or two years, so intellectual honesty is important. If the value does not strengthen after iteration, we stop and put the team’s energy somewhere more useful.

AI will remove a lot of repetitive work: filtering spam, answering common questions, onboarding members, and surfacing conversations that need human attention. But I think the biggest opportunity is engagement, not moderation. Communities will offer more personalized experiences, introduce members with shared interests, and stay active when moderators are offline. Human judgment will still be essential. Every community has its own culture, rules, and limits. AI can provide enormous leverage, but people still need to direct it, set the tone, and course-correct it when context matters.

MEE6 already combines automation for moderation, onboarding, leveling, social alerts, and many community workflows. With AI, we can go further. One example is AI Characters. Community builders create a character, give it a name, profile picture, backstory, personality, and tone of voice, and let it participate in conversations. It makes communities more fun and active. The important thing is not to add AI just because it is impressive. It has to give community builders more leverage while staying configurable, reliable, and simple. At the end of the day, AI should serve the community owner’s intent.

Bootstrapping forced us to focus on providing value to our users, not to our shareholders. We kept control of the product, built a solid subscription model, and made decisions based on what would create a durable business. The trade-off is that you cannot hire ahead of every problem or run ten expensive bets at once. A smaller team has to stay focused, and some opportunities move more slowly. But there is an advantage in that constraint: there is no large funding round to hide weak fundamentals. The product has to work, users have to care, and the economics have to make sense.

It takes strong technical foundations, but it is also an organizational and product problem. You need reliability, clear ownership, good monitoring, gradual rollouts, and fast feedback when something breaks. You also need abstractions that keep complexity away from users. Our plugin model is a good example: MEE6 supports many use cases without showing every feature to everyone at once. Scale is quite humbling, to be frank. You can think you have the best idea in the world, then release it and get real-world feedback instantly. The sustainable approach is to stay close to users and keep improving the fundamentals.

What excites me most is that AI lets software offer services that were previously too expensive or slow to include. A good example is custom branding for a Discord server. Before, a community owner who wanted a logo or other branding images had to hire a designer, spend hundreds of dollars, and wait a few days. Today, we can let users generate many different images and variations in a few seconds, at a cost of only a few cents. AI will raise the bar for what users expect from software. It turns expensive services into affordable features. As product builders, we’ll have to deliver much more value to stay relevant. My advice is to stay curious, keep building, and test the smallest version with real users. The technology moves fast, but the fundamentals remain the same: solve a real problem, build the best user experience, and stay intellectually honest about what works.