In today's AI era, where refresh rates determine life and death, distribution is no longer just part of a growth strategy; it is the core variable determining product success or failure. The update frequency of foundational models and underlying tools is almost weekly, the product iteration window is compressed to the extreme, and user attention is highly fragmented. In such an environment, the traditional notion of a 'moat' is disappearing, replaced by speed and momentum—whoever can quickly occupy the mental high ground of users can break through in homogeneous competition.
The latest episode from a16z focuses on this profound change that is reshaping the entrepreneurial landscape in AI, featuring Lovable co-founder Anton Osika—a strategist who rapidly gained fame in AI product distribution and social distribution industries. Under his leadership, Lovable achieved tens of millions in annual revenue within two months of launch, not because of any miraculous breakthrough in the model itself, but because he deeply understood the power of 'first-mover advantage'. In the AI industry, even if you possess strong technology, if you can't present your product advantages in a compelling, attention-grabbing way for users to see and understand immediately, you may be instantly drowned out by competitors better at distribution.
Osika pointed out that the game rules of AI entrepreneurship have fundamentally changed. In the past, entrepreneurs could spend months refining products and optimizing user experience before seeking distribution strategies; now, if a product does not generate social diffusion within the first 48 hours, it may be sentenced to an 'invisible death' from the outset. Today's AI startups face the challenge not of 'Can I make it?' but of 'Can I quickly make a splash and keep soaring?'. Technical differences have become increasingly negligible under the trend of model homogenization, while distribution efficiency, topic explosion, and user emotional engagement are key factors determining how far a product will go.
The program will further explore a new paradigm practiced by Anton: rapidly creating brand narratives and user engagement through public building, live demos, and initiating social challenges; establishing product reputation and native culture through early involvement of KOLs; and forming collaborative 'Starter Packs' with other AI tools for low-cost, high-quality distribution synergy. The commonality among these practices is that they do not rely on large market budgets nor excessively depend on channel resources, but instead maximize the communication effect of every product iteration under the rules of social networks.
In this 'if you don’t distribute, it’s as if you haven’t done anything' AI cycle, the strategy represented by Anton Osika and Lovable might be the key path for AI companies to break through the clouds and build a momentum-based moat. The real moat is no longer a technology barrier that others cannot imitate, but rather a speed and structural cognitive gap that others cannot keep up with.
Early distribution is crucial
In the consumer-grade AI industry, how to build a moat? Sadly, there is currently no moat. The changes in this industry are just too rapid—the foundational models and underlying infrastructure change almost every month, with new updates released almost weekly! In such a dynamic environment, it has become nearly impossible to build products slowly and methodically like in the mobile internet era. At this moment, the key is speed: how fast you can launch a product, how quickly you can capture user attention, and how swiftly you can occupy users' minds.
Every startup hopes for their product to go viral. But today, this is harder than ever: the sheer number of AI product releases, the speed of updates and iterations, and the unpredictable changes in social algorithms, combined with the homogenization of underlying models, make achieving true exponential growth increasingly difficult.
Traditional distribution strategies and growth methods (even for productivity tools or useful products aimed at professional consumers) are no longer as effective. To put it bluntly, in the words of my colleague Andrew Chen: all marketing channels are ineffective now. Paid user acquisition and SEO may still bring temporary user growth, but in consumer-grade AI, they struggle to achieve sustained user retention. You have to break the mold.
To explain the current industry dynamics to founders, I used a somewhat 'quirky' metaphor: starting an AI company now is like releasing a pigeon into the sky and praying it can fly.
Today, flocks of AI startups are like pigeons soaring together; they strive to accelerate and ascend to avoid exhausting their momentum and falling from the sky. These companies are launched into the air one after another, often building similar products, sometimes even using the same underlying models. Some pigeons fall shortly after takeoff; some hover at a certain altitude, slowing down, eventually exhausted, possibly opting for a soft landing (like being acquired or quietly pivoting). But a very few will soar straight into the clouds, breaking through the layers and continuing to rise, leaving other pigeons far behind.
They have become part of mainstream recognition, occupying the mental high ground of users.
However, even if you have flown to the clouds, in the AI industry, you still must keep striving and flapping hard. If you can launch new capabilities, features, or models faster, you can pull away from the second-fastest, third-fastest, or even the entire flock.
The real moat is momentum.
So what does all this mean? Early distribution is crucial. Of course, relying solely on distribution to generate heat won't retain users unless your product can keep up. When you can quickly iterate on your product, each update becomes a new showcase and promotional opportunity. Companies that understand this dynamic and clearly build their products around it, such as Perplexity, Lovable, Replit, and ElevenLabs, are gradually pulling away from other competitors.
So, how can you make your 'pigeon' soar vertically and continuously rise? Spoiler alert: there is currently no ready-made success handbook because the rules of the game at this stage are: rely on novelty, rely on creativity. However, here are some effective distribution strategies we have recently observed, along with case analyses behind them:
Hackathon: Reborn in the form of a public performance
In the past, hackathons were small circle events aimed at developers. But now, they resemble a public performance: widely disseminated through live broadcasts and social media, with the goal of expanding distribution influence. At the same time, AI-native tools have significantly lowered the participation threshold. This type of event provides a potential platform for your product-supporting new projects to gain popularity.
For example: ElevenLabs held a global hackathon earlier this year, showcasing the potential of its AI voice platform. Developers were invited to build various projects based on it, ranging from role-playing robots to interactive audio applications. During a demo called Gibberlink, something unexpected happened: an AI voice suddenly realized it was conversing with another AI.
In that unscripted exchange, the two AIs spoke in a near-human tone, sparking heated discussions on social media. This not only showcased powerful technical capabilities but also became a culturally 'quirky' discussion point: about whether AI has self-awareness and the authenticity of voice simulation. This event brought massive exposure to ElevenLabs.
Another example: Lovable recently held a live showdown where a senior designer using Webflow competed against a 'vibe coder' using Lovable's AI design assistant to see who could create a better landing page. The competition was time-limited and live-streamed, significantly increasing the tension. The focus of this showcase was not on who won but on showing the audience that AI is lowering the design barrier and may even allow non-professionals to outperform professional designers. This not only demonstrated the practical application scenarios of Lovable's product but also injected interesting narrative material into social platforms.
Social experiments, the more 'extreme', the better.
Building on the aforementioned trends, some companies are taking it a step further. Bolt recently announced that they will challenge the Guinness World Record by hosting the largest hackathon ever, targeting even non-developers, with a total prize pool of up to $1,000,000.
Similarly, Genspark launched a series of social challenges this spring, encouraging users to try to outsmart its super AI assistant. Participants were invited to pose complex or quirky questions to the AI, attempting to reveal its limitations. The most creative or profound failure cases could share a $10,000 prize pool. Such activities are low-cost but can spark a lot of discussions and user interactions.
Another example: In China, a top venture capital fund hosted a three-day Truman Show-style experiment: confining developers in a room with a computer, only using generative AI tools, aiming to make as much money as possible. This reality-show-like gimmick is clearly performative, but that's the point. This experiment not only garnered media coverage but also sparked widespread discussion on social platforms.
AI 'Starter Packs' and Alliance Strategies
Today's users often need to piece together multiple AI tools themselves: generating, editing, optimizing, and outputting. The frequent switching between tools is frustrating. In such a fragmented ecosystem, collaboration is power.
We see more and more leading AI companies collaborating to launch joint releases or functional integration packages, propagating products in a modular format and driving traffic to one another. These viral Starter Packs demonstrate the potential for collaborative tool usage.
For example: Captions, Runway, ElevenLabs, and Hedra have teamed up to create a complete video generation stack, from text to image generation to voiceover, forming a one-stop AI video production process; Bolt has launched a carefully curated builder toolkit, packaging AI infrastructures and creative tools like Entri, Sentry, Pica, and Algorand; Black Forest Labs collaborated with partners like Fal, Leonardo AI, Freepik, and Krea for the launch of its new model, Kontext.
These Starter Packs are not just marketing gimmicks; they offer real functional integration value, showing users that from creativity to output, they no longer need to piece things together, as this set can get it done.
In addition, they also create a social endorsement effect: each partner adds credibility and brand influence to each other.
Join forces with KOLs to build a moat.
Another strategy for building a moat is to let AI-native creators, developers, and designers speak for you. This does not refer to traditional influencers or brand ambassadors. Traditional influencer marketing is becoming less effective: high investment, low output, quick traffic in and out, and low conversion rates.
In contrast, truly leading AI companies are beginning to grant early access to influential native users within their circles. These individuals may not have millions of followers, but they hold significant influence in specific communities, forums (like Reddit, Discord), and creative online communities, which can genuinely impact the reputation and adoption of tools.
For example: Nick St. Pierre is the 'natural evangelist' for Midjourney; his early generative image works became widely circulated. Luma AI has recently adopted a similar strategy, granting early access to a small group of AI native creators; before the release of Veo 3, filmmakers Min Choi and PJ Ace tested the model early and created content, garnering wide attention.
PJ Ace once tweeted: 'I used to spend $500,000 on a pharmaceutical commercial, but now I only used a $500 credit on Veo 3 and a whole day to get it done.'
'Who is still willing to spend $500,000 on an advertisement?'
These types of content are not only product demonstrations but also carry persuasive real recommendations, strengthening user recognition through the perspectives of 'insiders'.
Direct Strike: Using 'Video Release' as a Distribution Strategy
You may have heard the saying: 'show, don’t tell', but in the AI era, it has become 'show, don’t pitch'. Traditional PR is too slow and rigid for the current rapid pace of AI; instead, we see many unknown small teams achieving breakout success merely through a brilliant product demonstration and an intuitive approach to narrative.
As Kevin Kwok said: 'When did it start that all new product launches must have a video? This trend has shifted so quickly.'
For example: When the Chinese startup Manus launched its universal AI assistant, it did not hold a press conference or run ads, but instead uploaded a 4-minute demo video directly to X and YouTube. The video showcased the product's powerful features, generating widespread attention with over 500,000 views.
Behind this change is a fundamental shift: more and more startups are appointing a growth leader who understands technology, even referred to as a Chief Flapping Officer: not only responsible for operational growth strategies but also personally involved in creating interesting or even bizarre interactive demos, pursuing a viral communication effect.
For instance, Luke Harries of ElevenLabs is a typical representative. He not only plans marketing campaigns but also personally works on projects, such as building an MCP server demo for WhatsApp; these quirky building projects often unexpectedly go viral.
Another similar figure is Ben Lang. He was responsible for creating fun demos, niche showcases, and design plays at Notion early on, quietly shaping Notion's community culture and brand identity before the product broke out. Now he holds a similar role at Cursor, publicly building projects and turning every product launch into shareable stories and content.
Build in Public
In the past, growth data was a closely guarded secret revealed only to investors. Now, more and more AI companies choose to build in public: showcasing product progress, user data, revenue milestones, and even failed experiments.
For example, Genspark posted on social media: 'Achieving an annualized revenue (ARR) of $36,000,000 in 45 days?! That's right, our team of just 20 people might be the fastest-growing startup in history. No fancy marketing, no advertisements, all thanks to word of mouth.' They also attached a list of recently released products: Genspark AI Sheet, Agentic Download Agent, etc.
Others like Lovable, Bolt, and Krea have also adopted similar practices. They regularly update on social platforms, sharing everything from revenue growth to DAU (daily active users), to reflections on failed experiments, making users feel they are part of the construction process, not mere spectators or AI tourists. Lovable founder Anton Osika tweeted in January 2025: 'Lovable has achieved the $10,000,000 annual revenue goal today—only two months after launch. Growth is still accelerating.' along with interpretations of the advantages of the product compared to other competitors (expanded in thread form).
This kind of openness and transparency brings about a hidden competitive effect: when a company's product breakthroughs, user numbers, or revenue are publicly displayed, it stimulates other startups in the same industry to take action, rather than just showcasing demos or growth charts. This atmosphere of 'you show data, we follow suit' actually promotes the entire ecosystem's communication efficiency and momentum accumulation.
This article is reprinted with permission from: (Deep Tide TechFlow)
Original title: (In Consumer AI, Momentum Is the Moat)
Original author: Bryan Kim, a16z
Translation: Xinyi Fan, Z Finance
'The AI market is brutal! a16z: If a product doesn’t go viral in the community within two days of launch, it might as well wait to die.' This article was first published in 'Crypto City'.