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In Consumer AI, Momentum Is the Moat

Bryan Kim

Table of Contents
Table of Contents

    How do you build a moat in consumer AI? Right now, sorry to say, there is none. The landscape is simply evolving too quickly. When foundational models and infrastructure are shifting every month — introducing new updates weekly! — it’s become nearly impossible to build as slowly or methodically as we did in the mobile era. In this dynamic environment, what matters is velocity: how fast you can launch, gain traction, and seize mindshare.

    Early distribution is everything

    Every startup wants to go viral. That’s becoming harder to do, thanks to the sheer volume of AI product launches, the pace of iteration, the fickle nature of social algorithms, and the ubiquity of the underlying models. Real breakout moments have become more difficult to engineer.

    Default distribution or growth marketing strategies that were once considered textbook are no longer cutting it, even for productivity tools and prosumer products with real utility. To put it bluntly, in the words of my colleague Andrew Chen, every marketing channel sucks right now. Paid acquisition and SEO can still give you a short-term boost in users, but in consumer AI, those strategies rarely create lasting retention anymore. You need to break the mold.

    The AI “flock” in flight

    When trying to explain this new dynamic to founders, I’ve adopted a wonky metaphor: Launching an AI company today is like ejecting a pigeon into the sky and willing it to take flight. (Stick with me.) 

    There’s a whole flock of AI startups flapping around together, trying to gain enough speed and altitude so they don’t burn out and plummet. They’re being blasted into the sky in quick succession, often building similar products, sometimes even using the same underlying models. Some of these symbolic pigeons barely leave the ground. Others climb to a certain altitude and level off; their pace of growth slows, eventually they tire, maybe they find a soft landing (say, an acquisition or a quiet pivot). But there are a rarefied few that blast straight up, pierce through the cloud cover, and continue to climb, leaving their counterparts frantically flapping to catch up.

    They make it into mainstream consciousness. They reach mindshare air.

    Now, in AI, even once you’ve made it above the clouds, you have to keep flapping your wings harder than your competitors. The faster you ship new capabilities, new features, new models, the more distance you put between yourself and the second fastest pigeon, and the third, and the rest of the flock. 

    The takeaway: In AI today, it’s often the case that whoever builds first, iterates fastest, and distributes best wins. 

    Momentum as moat

    What does this all mean? Early distribution is key. Of course, traction from distribution only sticks if your product keeps up. When you’re shipping fast, each product iteration gives you something new to show and share. The players who understand this dynamic and explicitly build for it — companies like Perplexity, Lovable, Replit, and ElevenLabs — are pulling away from the pack. 

    So, what are the tricks to shoot your “pigeon” vertically and keep it climbing? Spoiler: I don’t have a playbook for this moment, when novelty and ingenuity are the name of the game. However, here are some recent distribution tactics we’ve seen work, and some case studies behind them. 

    1. The hackathon, reborn as a public performance

    Hackathons used to be niche, developer-targeted sprints. Now they’re public performance arenas: livestreamed, shared across social, and designed to generate distribution. At the same time, AI-native tools have lowered the barrier to entry.

    These events provide an environment for a new project (built using your product) to go viral. 

    For example: ElevenLabs hosted a global hackathon earlier this year to demonstrate the potential of its AI voice platform. Developers and builders were invited to build anything, from roleplay bots to interactive audio. Then, during a demo of Gibberlink, an unexpected thing happened: an AI voice suddenly realized it was talking to another AI.

    That unscripted exchange, where two bots began conversing in human-like tones, took off on social. It wasn’t just impressive tech; it was a moment of cultural weirdness that kicked off debate about AI self-awareness and voice realism. That event gave ElevenLabs massive exposure.

    Another example: Lovable recently staged a live showdown where a seasoned designer armed with Webflow challenged a vibecoder using Lovable’s AI design copilot to build the best landing page. The event was timed and livestreamed, raising the stakes. This was less about the end-product and more about the spectacle of seeing AI level the playing field— along with the voyeuristic allure of a “vibe coder” potentially out-designing a pro. It showed Lovable’s product in action and seeded a social narrative.

    These events are part theater, part stress-test, and part viral engine. 

    2. Social experiments that go hard

    Taking this idea to the next level, Bolt recently announced they’re aiming to break the Guinness World Record for the largest hackathon, specifically targeting non-developers, with a $1 million prize.

    In a similar vein, earlier this spring Genspark launched a series of social “challenges,” inviting users to try to break its Super Agent. Participants were encouraged to test the AI assistant with complicated or unconventional tasks to expose its limitations. The most creative or insightful failures earned a share of the $10,000 prize pool. These lightweight, low-cost campaigns don’t cost much, comparatively, but can spin up buzz and user engagement. 

    Another example: In China, a top venture fund ran a three-day, Truman Show-style experiment where they locked developers in a room with nothing but a computer and access to generative AI tools. The participants were challenged to make as much money as possible, using only AI. These stunts are clearly performative, but that’s the point. The experiment was covered in the media and debated on social platforms.

    3.     AI “starter packs” and alliances

    Today, consumers often need to stitch together multiple AI tools to make what they want, toggling between apps for generation, editing, refinement, and output. In that fragmented landscape, partnerships are power. 

    Increasingly, we’re seeing top players in AI join forces. There has been a spate of coalition-style launches, where AI startups are bundling their capabilities and cross-pollinating audiences. These viral “starter packs” demonstrate what’s possible when tools are used together. 

    Take Captions teaming up with Runway, ElevenLabs, and Hedra to create a fully generative video stack (text to visuals to voice). Or Bolt, which launched a curated “builder pack” of AI agents alongside infra and creative tools like Entri, Sentry, Pica, Algorand, and more. Similarly, Black Forest Labs launched its new model Kontext alongside a cohort of partners like Fal, Leonardo AI, Freepik, and Krea. These starter packs aren’t just clever marketing, they’re functional stacks that show users how to get from idea to output without having to force-fit half a dozen different tools together.  

    They’re also social proof, with each partner lending legitimacy to the others. 

    4. Co-opt insider influencers

    Another advantage when building a moat: evangelizing AI-native builders and designers on  your behalf. These are not influencers or brand ambassadors in the traditional sense. In fact, we’ve been seeing growing skepticism around classic influencer marketing (too much “babysitting” for too little ROI). Their posts may spur short-lived traffic spikes, but those users rarely convert. 

    In the AI era, it’s not about hiring big name creators, or even about stacking your cap table with A-list angels for the optics. Instead, I’m seeing more leading AI companies give access to credible early adopters, people who are respected within their domains and plugged into the right subreddits, Discord servers, and weird, creative corners of the internet. Think developers, artists, technologists, and builders who may not have millions of followers, but whose opinions have real sway over how tools are perceived. 

    These are people like Nick St. Pierre, who became a de facto evangelist for Midjourney early on. Luma recently followed a similar playbook, giving early access to a small group of AI-native creators.

    Filmmakers like Min Choi and PJ Ace produced impressive videos ahead of Google’s Veo 3 launch that helped demonstrate the product’s capabilities.

    These posts aren’t just demos, they’re endorsements of the product by people with credibility.

    By establishing a “cult of diehards,” leading AI companies are dredging moats rooted in community and hands-on experimentation.

    5. Go direct: Launch videos as distribution strategy

    You’ve heard the expression “show, don’t tell”? In AI, it’s show, don’t pitch. Traditional PR can be too slow and sanitized for the new pace of AI. On the other hand, we’ve seen tiny teams with no name recognition create breakthrough moments based on the power of their product and their instinct for a good story. 

    Take Manus, the AI general assistant, as an example. The Chinese startup posted a 4-minute demo reel straight to X and YouTube. The video piqued interest over the product’s capabilities and generated over half a million views. 

    One foundational shift I’m seeing is startups hiring a builder as their head of growth. Your growth officer should be creating weird demos that have a chance of going viral; think of him or her as a chief “flapping” officer. One example that comes to mind is Luke Harries, the growth leader at ElevenLabs. He’s not just running campaigns, he’s building interactive, offbeat demos, like building an MCP server for WhatsApp. 

    Luke Harries of ElevenLabs, via 20VC  

    Another example of the archetype is Ben Lang, who did this successfully at Notion as one of the company’s earliest employees. Long before the product went mainstream, Ben was spinning up fun experiments, showcasing design capabilities, and making niche demos that helped establish community and shape Notion’s identity. Now he’s doing the same thing at Cursor, where he’s building in public and turning product drops into sharable content.    

    6. Build in public

    Growth metrics used to be closely guarded, quietly disclosed to select investors. Lately, we’re seeing more AI companies building in public, broadcasting their traction and product milestones. 

    Lovable, Bolt, Krea and others have embraced this approach, regularly posting updates on everything from revenue benchmarks to daily active users to unsuccessful experiments. That kind of transparency makes people feel like they’re part of the build, rather than bystanders or AI tourists. 

    These updates can also provoke tacit competition. When one company shares a big milestone or new feature, it can goad startups in the same space into openly comparing demos, growth charts, or user testimonials. I’ve found higher stakes can ultimately mean more momentum for everyone involved.

    What did I miss? Reach out at bryan.kim@a16z.com.
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