The traditional technological moat has disappeared.

Source: Andreessen Horowitz

Translated by: Xinyi Fan, Z Finance

In today's AI era, where refresh rates determine life and death, distribution is no longer just a part of growth strategy but the core variable for product success or failure. The update frequency of foundational models and underlying tools is almost weekly, product iteration windows have been 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 seize the user's mental high ground first can break through in a homogenized competition.

The latest episode of a16z focuses on this profound change that is reshaping the AI startup landscape, featuring Anton Osika, co-founder of Lovable—a rapidly rising figure in the field of AI product overseas expansion and social distribution. Under his leadership, Lovable achieved an annual revenue of ten million dollars within two months of launch, not because of any miraculous breakthrough in the model itself, but because he deeply understands the power of "first-mover advantage." In the AI field, even if you have strong technology, if you cannot present your product's advantages in a way that captures user attention and sparks discussion, you may be instantly overshadowed by competitors who are better at distribution.

Osika points out that the rules of the AI startup game have fundamentally changed. In the past, entrepreneurs could spend months refining products and optimizing user experiences before seeking distribution strategies; now, if a product does not achieve social diffusion within the first 48 hours, it may be sentenced to an "invisible death" from the start. Today's AI startups face the challenge not of "Can I create it?" but of "Can I quickly make a splash and sustain growth?" Technical differences have become increasingly weak under the trend of homogenization in large models, while distribution efficiency, topic explosion, and user emotional engagement are the key factors determining how far a product can go.

The program will further explore a new paradigm that Anton practices: rapidly creating brand narratives and user engagement through open building, live demos, and social challenges; establishing product reputation and native culture through early involvement of influencers; and forming collaborative "Starter Packs" with other AI tools to achieve low-cost, high-quality distribution synergy. The commonality of these practices is that they do not rely on large market budgets or excessive dependence on channel resources, but maximize the communication effect of each product iteration under the rules of social networks.

In this AI cycle where "if you don't distribute, you might as well not have done it," the approach represented by Anton Osika and Lovable may be the key path for AI companies to navigate through the clouds and build momentum-based moats. The real moat is no longer a technological barrier that others cannot imitate, but the speed and structural cognitive differences that others cannot keep up with.

Early Distribution is Crucial

In the consumer AI field, how to build a moat? Unfortunately, there is currently no moat at all. The changes in this industry are simply too rapid—the foundational models and underlying infrastructure are changing almost every month, with new updates released almost weekly! In this dynamic environment, it has become nearly impossible to build products slowly and methodically as in the mobile internet era. At this moment, the most critical factor is speed: how quickly you can launch products, how quickly you can gain user attention, and how quickly you can occupy users' minds.

Every startup hopes their product can go viral. But today, this is harder than ever: the number of AI product launches is enormous, the speed of updates and iterations is extremely fast, social algorithms are unpredictable, and with the underlying models tending toward homogenization, achieving true explosive growth is becoming 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 AI, they struggle to deliver sustained user retention. You must break the mold.

To explain the current industry dynamics to founders, I used a somewhat "strange" metaphor: starting an AI company now is like throwing a pigeon into the sky and praying it can fly.

Today, a flock of AI startups is like a group of pigeons flapping their wings together, striving to accelerate and rise higher to avoid running out of momentum and falling from the sky. These companies are launched into the air one after another, often building similar products and sometimes using the same underlying models. Some pigeons fall shortly after taking off; some can reach a certain height but then stagnate, slowing down and eventually exhausting themselves, 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 continue to rise, leaving the other pigeons far behind.

They become part of mainstream consciousness, occupying the mental high ground of users.

However, even if you have already flown up into the clouds, in the AI industry, you must continue to work hard and flap your wings desperately. If you can launch new capabilities, features, and models faster, you can widen the gap between yourself and 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 the heat generated by distribution will not retain users; your product must also keep up. When you can quickly iterate on your product, each update is a new opportunity for display and promotion. Companies that understand this dynamic and 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 continue to rise? Spoiler alert: there is currently no ready-made success manual, because the rules of the game at this stage are: rely on novelty and creativity. However, here are some effective distribution strategies we have recently observed, along with case analyses behind them:

Hackathons: Rebirth in the Form of Public Performances

In the past, hackathons were small circle events aimed at developers. But now, they resemble a public performance show: widely disseminated through live streaming and social media, aimed at expanding distribution impact. At the same time, AI-native tools have significantly lowered the participation threshold. Such events provide a stage for new projects supporting your product that have the potential to go viral.

For example, ElevenLabs held a global hackathon earlier this year to showcase 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 conversed 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 significant exposure to ElevenLabs.

Another example: Lovable recently hosted a live showdown where a seasoned 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 enhancing the tension. The focus of the show was not on who won in the end, but on letting the audience see: AI is lowering the design barrier, potentially allowing non-professionals to outperform professional designers. This not only showcased 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 above trends, some companies are taking it a step further. Bolt recently announced that they would challenge the Guinness World Record by hosting the largest hackathon in history, targeting even non-developers, with a total prize pool of up to $1 million.

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 generate a lot of discussion and user interaction.

Another example: in China, a top venture capital fund held a three-day Truman Show-style experiment: locking developers in a room with a computer, only allowing them to use generative AI tools, with the goal of making as much money as possible. This reality show-style gimmick is clearly performative, but that is precisely 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: generating, editing, optimizing, and outputting. The multiple tool switching can be cumbersome. In such a fragmented ecosystem, collaboration is power.

We are seeing more and more leading AI companies teaming up to launch joint releases or feature integration packages, spreading products in a combinatorial manner and driving traffic to each other. These viral Starter Packs showcase the potential for collaborative tool usage.

For example: Captions partnered with Runway, ElevenLabs, and Hedra to create a complete video generation stack, from text generation to voiceover, forming a one-stop AI video production process; Bolt launched a carefully curated builder's toolkit, packaging AI infrastructure and creative tools like Entri, Sentry, Pica, and Algorand; Black Forest Labs collaborated with partners like Fal, Leonardo AI, Freepik, and Krea to jointly showcase when releasing its new model, Kontext.

These Starter Packs are not just marketing gimmicks; they also possess real functional integration value, demonstrating to users that from creativity to output, there is no longer a need to piece things together—this combination can get the job done.

Additionally, they create a social endorsement effect: each partner adds credibility and brand influence to one another.

Collaborate with Influencers in the Field to Build a Moat

Another strategy for building a moat is to have AI-native creators, developers, and designers speak on your behalf. This does not refer to traditional influencers or brand ambassadors. Traditional influencer marketing is becoming less effective: high investment, low output, fast traffic influx and outflow, 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 sway in specific communities, forums (like Reddit, Discord), and creative communities on the internet, genuinely influencing the reputation and adoption rate of tools.

For example, Nick St. Pierre is the "natural evangelist" for Midjourney, and his early works using generative images became widely circulated; Luma AI has recently adopted a similar strategy by granting early access to a small group of AI-native creators; before the release of Veo 3, filmmaker Min Choi and PJ Ace tested the model in advance and created content, attracting widespread attention.

PJ Ace once tweeted: "I used to spend $500,000 to shoot a pharmaceutical commercial, and now I’ve done it with a $500 credit on Veo 3 in just one day." "Who can still afford a $500,000 ad now?"

Such content is not only a product demonstration but also a persuasive real recommendation, reinforcing user perception through the perspective of "insiders."

Direct Attack: Using "Release Videos" as a Distribution Strategy

You may have heard the saying: "show, don’t tell," but in the AI era, it has transformed into "show, don't pitch." Traditional public relations are too slow and rigid for the current fast pace of AI; instead, we see many little-known small teams achieving breakout success solely through an impressive product demonstration and an intuitive narrative.

As Kevin Kwok said, "When did it become a requirement for all new product launches to have a video? This trend has shifted so quickly."

For example, when the Chinese startup Manus launched its general AI assistant, it did not hold a press conference or run ads but directly uploaded a 4-minute demonstration video on X and YouTube. The video showcased the product's powerful features, attracting widespread attention with over 500,000 views.

Behind this change is an underlying transformation: more and more startups are appointing a growth leader who understands technology, or even a Chief Flapping Officer: responsible not only for operational growth strategies but also for personally creating interesting or even quirky interactive demos, pursuing viral dissemination effects.

For instance, Luke Harries from ElevenLabs is a typical representative. He not only plans marketing activities but also gets hands-on with projects, such as building an MCP server demo for WhatsApp; these quirky construction projects often go viral unexpectedly.

Another similar role is Ben Lang. He was responsible for creating interesting demos, niche showcases, and design play 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 release into a shareable story and content.

Build in Public

In the past, growth data was a closely guarded secret disclosed only to investors. Today, more and more AI companies choose to build in public: sharing product progress, user data, revenue milestones, and even failed experiments.

For example, Genspark tweeted on social media: "Achieving an annual recurring revenue (ARR) of $36 million in 45 days?! Yes, our small team of just 20 people might be the fastest-growing startup in history. No fancy marketing, no ads, all thanks to word of mouth." They also included a recent product list: Genspark AI Sheet, Agentic Download Agent, and more.

Others like Lovable, Bolt, and Krea have adopted similar practices. They regularly update social media with everything from revenue growth and DAU (daily active users) to reflections on failed experiments, making users feel like they are part of the building process, not mere spectators or AI tourists. Lovable founder Anton Osika tweeted in January 2025: "Lovable has achieved a $10 million annual revenue goal—just two months after launch. Growth continues to accelerate." He accompanied this with an analysis of the product's advantages compared to competitors (unfolded in thread format).

This transparency also brings about a latent competitive effect: when a company's product breakthroughs, user numbers, or revenue are shared, it stimulates other startups in the same field to ramp up, showcasing demos, growth charts, and user feedback. This atmosphere of "you share data, and I follow suit" actually enhances the overall ecosystem's dissemination efficiency and momentum accumulation.

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