Recently, there’s been an interesting development in the big model industry. Perplexity AI, a hot big model company in Silicon Valley, completed a financing round two months ago, valuing it at over $500 million. However, using Lepton AI’s middleware, Lepton’s co-founder Jiayang Qing managed to create an open-source version with just 500 lines of code over a weekend, sparking a heated discussion in the industry. The related demo on GitHub quickly garnered five thousand stars in just a few days. This incident reflects a broader trend, and I’ll analyze it based on my own year and a half of entrepreneurial experience.
Currently, big model companies can be categorized into three types:
- Base model companies, which primarily provide big model capabilities. This area requires significant capital and resources are highly concentrated among leading companies and giants.
- Middleware companies, which offer middleware between big models and applications. Jiayang Qing’s Lepton AI falls into this category.
- Application-layer companies, which directly provide consumer-facing applications. These can further be divided into platform-type application companies, like Perplexity AI, and vertical application companies focusing on niche markets, such as Harvey AI, which recently completed financing for its legal application.
The incident with Perplexity AI and Lepton AI highlights a pain point for application-layer companies — high competitive pressure with insufficient moats. For instance, Perplexity, which aims to solve general information search problems, faces challenges from four fronts: pressure from giants like Google, competition from vertical knowledge applications like Harvey, market encroachment from other knowledge service companies, and disruptions from middleware companies like Lepton AI. Vertical application companies face slightly less pressure, but they still confront these four forces and have a smaller market size, resulting in fewer resources.
So, what can be done? Many entrepreneurs believe that accumulating proprietary data can create a sufficient moat. This is very sensible, but it presupposes a systematic methodology for acquiring proprietary data. Here, I propose a methodology: using contrarian insights to gain a time advantage, leveraging the founder’s personal strengths for breakthroughs, and focusing on data-first products or operational capabilities.
First, contrarian insights, or insights not commonly understood, are essential. Entrepreneurs must find less trodden paths to build their core competitive advantage with minimal resources. What was once a contrarian insight can become common knowledge, such as the combination of big models with chat interfaces, which was novel before ChatGPT but is now common.
Second, the founder’s personal advantage is crucial. While contrarian insights offer temporary protection, they quickly become common knowledge once proven useful. Here, a deep understanding of user pain points in vertical applications can be a personal advantage. For example, one of Harvey’s co-founders was a lawyer. Even if a team doesn’t have a co-founder from a specific industry, previous experiences that can be leveraged as industry advantages are valuable.
Finally, building a data-first product or operational capability is key. The founder’s personal advantage must be systematized to sustain. There are two strategies:
- Product-driven: The founder uses their deep understanding of user needs to design a product that naturally accumulates high-quality data, enhancing the product experience and creating a flywheel effect.
- Operation-driven: The founder uses their resources and experience to build an operational system that continually acquires proprietary data, making operations or sales more efficient and faster.
The former suits products focused on Product-led Growth (PLG), while the latter suits those driven by Sales-led Growth (SLG). Both must prioritize data. If product-driven, each feature should contribute to data accumulation. If operation-driven, operations should focus on data, not just revenue or other metrics.
Returning to Perplexity’s case, Jiayang Qing could replicate Perplexity’s main functions over a weekend but not its data accumulation. As a middleware company, Lepton likely doesn’t intend this as a core strategy. However, many new application startups may use this to challenge Perplexity further. Whether Perplexity can withstand this depends on its ability to build a moat with proprietary data.
Jing Conan Wang 