a16z's latest insight: If an AI product doesn't go viral on social networks within the first 48 hours of launch, it's as good as dead
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a16z's latest insight: If an AI product doesn't go viral on social networks within the first 48 hours of launch, it's as good as dead
Traditional technological moats have disappeared.
Navigating Web3 tides with focused insightsSource: Andreessen Horowitz
Translation: Xinyi Fan, Z Finance
In today's AI era where refresh speed determines survival, distribution is no longer just a part of growth strategy—it’s the core variable determining product success. Foundational models and underlying tools are updated almost weekly, product iteration windows have been compressed to the extreme, and user attention is highly fragmented. In this environment, traditional "moats" are vanishing, replaced by speed and momentum—those who first capture users’ minds will break through in an increasingly homogeneous competitive landscape.
a16z’s latest episode dives into this profound shift reshaping AI entrepreneurship, featuring Anton Osika, co-founder of Lovable—a rising operator known for AI product globalization and social distribution. His company Lovable achieved $10 million in annual revenue within two months of launch—not due to any breakthrough in model technology, but because he deeply understands the power of “first-mover advantage.” In AI, even with superior tech, if you can't showcase your product’s strengths in a viral, buzz-worthy way right away, you risk being instantly drowned out by competitors better at distribution.
Osika points out that the rules of AI entrepreneurship have fundamentally changed. In the past, founders could spend months refining their product and optimizing UX before turning to distribution. Today, if a product fails to spark social spread within its first 48 hours, it may be sentenced to “invisibility” from day one. The challenge facing AI startups now isn’t “can I build it?” but “can I launch fast and keep ascending?” Technical differentiation is fading amid growing homogenization of large models, while distribution efficiency, topic virality, and emotional engagement have become decisive factors in how far a product can go.
The episode further explores a new paradigm championed by Anton: creating brand narratives and user engagement through public building, live demos, and social challenges; establishing early credibility and organic culture via niche influencers; and forming collaborative “Starter Packs” with other AI tools to enable low-cost, high-quality distribution synergy. These tactics share one thing in common—they don’t rely on massive marketing budgets or channel dominance, but instead maximize the viral impact of every product update under the logic of social networks.
In this cycle where “if you’re not distributing, you’re not building,” the approach represented by Anton Osika and Lovable may well be the key path for AI companies to break through the clouds and build momentum-based moats. The real moat is no longer a technical barrier others can’t replicate, but a structural gap in speed and cognitive insight that others simply can’t catch up to.
Early Distribution Is Crucial
How do you build a moat in consumer AI? Frankly speaking, there is no moat right now. This industry changes too rapidly—foundational models and infrastructure evolve monthly, with new updates rolling out nearly every week! In such a dynamic environment, it’s nearly impossible to build products slowly and methodically like in the mobile internet era. What matters most now is speed: how quickly you can launch, capture attention, and occupy user mindshare.
Every startup wants their product to go viral. But it’s harder than ever: the volume of AI product launches is enormous, iteration speeds are blistering, social algorithms are unpredictable, and underlying models are becoming increasingly similar. Achieving true breakout growth is getting tougher.
Traditional distribution strategies and growth tactics (even for useful productivity tools or prosumer products) are no longer as effective. To put it bluntly, as my colleague Andrew Chen says: all marketing channels suck now. Paid acquisition and SEO might bring short-term traffic, but in consumer AI, they rarely drive sustained user retention. You must break the mold.
To explain current industry dynamics to founders, I use a slightly “odd” metaphor: starting an AI company today is like throwing a pigeon into the sky and praying it flies.
Today, swarms of AI startups take off together, flapping hard to gain altitude, trying not to run out of energy and crash. One after another, they’re “launched” into the air—often building similar products, sometimes using the same base model. Some pigeons fall immediately after takeoff; others climb to a certain height, stall, slow down, and eventually tire out—opting for a soft landing (acquisition or quiet pivot). But a rare few shoot straight up, pierce the clouds, and keep climbing, leaving the rest far behind.
They become part of mainstream consciousness, dominating user mindshare.
Yet even once you're above the clouds, in AI you must keep flapping furiously. If you can release new capabilities, features, and models faster, you’ll pull ahead—not just of the second-fastest, third-fastest, but the entire flock.
The Real Moat Is Momentum
What does this mean? Early distribution is critical. Of course, heat from distribution alone won’t retain users—the product must keep evolving too. When you iterate quickly, each update becomes a new showcase and promotional opportunity. Companies that understand this dynamic and deliberately design around it—like Perplexity, Lovable, Replit, and ElevenLabs—are gradually pulling away from the pack.
So how do you make your “pigeon” launch vertically and keep ascending? Spoiler alert: there’s no playbook yet, because the game here is defined by novelty and creativity. That said, here are some effective distribution strategies we’ve observed recently, along with case studies:
Hackathons: A Rebirth as Public Performance
Historically, hackathons were niche events for developers. Now, they’ve evolved into public performances—broadcast via livestreams and social media, explicitly designed to amplify distribution. Meanwhile, AI-native tools have drastically lowered entry barriers. These events provide a potential launchpad for projects built on your platform.
For example: Earlier this year, ElevenLabs hosted a global hackathon showcasing the potential of its AI voice platform. Developers were invited to build everything from role-playing bots to interactive audio apps. During one demo called Gibberlink, something unexpected happened: an AI voice suddenly realized it was talking to another AI.
This unscripted exchange, with both AIs conversing in near-human tones, sparked heated discussion on social media. It wasn’t just a tech showcase—it became a culturally resonant moment touching on AI self-awareness and vocal realism, generating massive exposure for ElevenLabs.
Another example: Lovable recently held a live showdown between a senior designer using Webflow and a “vibe coder” using Lovable’s AI design assistant, racing to build the best landing page. With time limits and live streaming, tension was high. The outcome mattered less than the message: AI is lowering design barriers, enabling non-experts to potentially beat professionals. This demonstrated Lovable’s practical application while feeding engaging content into social platforms.
Social Experiments: The Bolder, the Better
Building on this trend, some companies go even further. Bolt recently announced a Guinness World Record attempt—the largest hackathon ever, targeting even non-developers, with a total prize pool of $1 million.
Similarly, Genspark launched a series of social challenges earlier this spring, inviting users to try and defeat its super AI assistant. Participants were encouraged to ask complex or quirky questions to expose its limitations. The most creative or insightful failure cases could split a $10,000 prize. Low cost, high engagement—these campaigns spark conversation and interaction at scale.
Another example: In China, a top-tier VC fund ran a three-day Truman Show-style experiment—locking developers in a room with only a computer and generative AI tools, challenging them to make as much money as possible. Clearly performative, but that’s the point. The stunt earned media coverage and ignited widespread debate online.
AI “Starter Packs” and Alliance Playbooks
Today’s users often stitch together multiple AI tools: generate, edit, optimize, output. Switching between tools is frustrating. In this fragmented ecosystem, collaboration equals strength.
We’re seeing more leading AI companies team up to co-launch or bundle integrations, distributing products in combination and cross-referring users. These viral Starter Packs demonstrate the power of tool interoperability.
For instance: Captions partnered with Runway, ElevenLabs, and Hedra to create a full video generation stack—from text to visuals to voiceover—enabling end-to-end AI video production. Bolt curated a builder toolkit bundling Entri, Sentry, Pica, Algorand, and other AI infra and creation tools. Black Forest Labs, upon launching its new model Kontext, debuted jointly with partners including Fal, Leonardo AI, Freepik, and Krea.
These Starter Packs aren’t just marketing gimmicks—they offer real functional integration value, showing users they no longer need to cobble together solutions; this suite can do it all.
They also create social proof: each partner boosts the others’ credibility and brand reach.
Leverage Niche Influencers to Build Moats
Another moat-building strategy: get AI-native creators, developers, and designers to advocate for you. We’re not talking about traditional influencers or brand ambassadors—those campaigns are increasingly ineffective: high cost, low ROI, fleeting traffic, poor conversion.
In contrast, leading AI companies now grant early access to influential vertical experts within niche communities. These individuals may not have millions of followers, but they wield outsized influence in specific forums (like Reddit, Discord), creative circles, and corners of the internet—shaping tool reputation and adoption.
For example: Nick St. Pierre became a natural evangelist for Midjourney, his early image creations widely shared. Luma AI adopted a similar tactic, granting early access to a small group of AI-native creators. Before Veo 3’s release, filmmakers Min Choi and PJ Ace tested the model and created content, sparking broad interest.
PJ Ace tweeted: “I used to spend $500K shooting a pharmaceutical ad. Now I did it with $500 in credits on Veo 3 and one day of work. Who’s still paying half a million for ads?”
Such content isn’t just a demo—it’s persuasive, authentic endorsement, reinforcing user perception through the lens of trusted insiders.
Go Direct: Use “Launch Videos” as Distribution Strategy
You’ve probably heard “show, don’t tell”—but in the AI age, it’s become “show, don’t pitch.” Traditional PR is too slow and rigid for AI’s rapid pace. Instead, we see unknown teams achieve breakout success with nothing but a compelling product demo and narrative instinct.
As Kevin Kwok put it: “When did every product launch start requiring a video? This shift came fast.”
Example: Chinese startup Manus didn’t hold a press event or buy ads when launching its general AI assistant. Instead, it uploaded a 4-minute demo directly to X and YouTube. The video showcased powerful functionality, garnered massive attention, and surpassed 500,000 views.
Beneath this shift lies a deeper change: more startups now appoint technically savvy growth leads—or what you might call a Chief Flapping Officer—who not only manage growth strategy but personally craft quirky, engaging demos aimed squarely at virality.
Take ElevenLabs’ Luke Harries, a prime example. He doesn’t just plan campaigns—he builds himself, like creating a demo MCP server for WhatsApp. These oddball projects often go viral unexpectedly.
Another figure like this is Ben Lang. Early at Notion, he crafted fun demos, niche showcases, and playful designs that quietly shaped Notion’s community culture and brand identity before it went mainstream. Now at Cursor, he plays a similar role—publicly building projects, turning every product launch into shareable stories and content.
Build in Public
Once, growth metrics were closely guarded secrets disclosed only to investors. Now, more AI companies embrace “building in public”—sharing product progress, user data, revenue milestones, even failed experiments openly.
For instance: Genspark posted on social media: “$36M in ARR in 45 days?! Yes—we’re a 20-person team possibly the fastest-growing startup ever. No fancy marketing, no ads—pure word of mouth.” They included a list of recent product releases: Genspark AI Sheet, Agentic Download Agent, etc.
Others like Lovable, Bolt, and Krea follow suit. They regularly post updates—on revenue growth, DAU (daily active users), and reflections on failed experiments—making users feel like participants, not passive observers or AI tourists. In January 2025, Lovable founder Anton Osika tweeted: “Lovable hits $10M annual revenue—just two months post-launch. Growth is accelerating.” He followed up with a thread explaining product advantages over competitors.
This transparency also triggers subtle competitive effects: when one company shares breakthroughs, user numbers, or revenue, it sparks a race among peers—to out-demonstrate, show better growth charts, share glowing feedback. This “you post your data, I’ll post mine” culture actually drives overall ecosystem efficiency and momentum accumulation.
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