A16z in-depth summary: Key to breaking through the encryption and AI industries, three types of efficient PMF (Product Market Fit) landing models

In the cross track of encryption and AI, countless projects have fallen into the same trap: relying on financing for blood transfusion, relying on airdrops to build data, and using marketing to create popularity, but still unable to implement real business. Industry rules have long proven that funds, traffic, and popularity cannot save misaligned products. PMF (Product Market Fit) is the core criterion that determines the life and death of a project.

All growth that deviates from PMF is ineffective growth and will only slow down the pace of project decline. The unique token economy and network effects in the encryption industry can easily lead teams to fall into false prosperity, mistaking traffic and lock-in for product value, thereby deviating from the core direction of finding real market demand.

However, with the popularization of stablecoins, the continuous entry of traditional finance, and the deep integration of AI and encryption, the industry infrastructure has become increasingly mature, and the path for high-quality projects to achieve PMF has become clear and efficient. Based on a large number of industry implementation cases, a16z has summarized the three most effective product market fit models for the current encryption and AI tracks, providing clear breakthrough ideas for projects in the transition period that have not yet landed in real business.

1、 Head customer co creation model: Based on top demand as the standard, create benchmark products

Most startup projects are accustomed to building generic products behind closed doors, and then publicly iterating and passively adapting to market demands. This trial and error model has high costs, long cycles, and is highly susceptible to homogenization and internal competition. The most efficient implementation path is to bind top core customers in the industry, customize according to their needs, and jointly create.

The top institutions with the highest requirements and largest volume within the track represent the industry's most authentic and rigorous demand standards. The project takes the core demands of top customers as the sole criterion for product design. Although the development cycle is longer and the polishing accuracy is higher, the value gained is far from comparable to traffic, exposure, and lock in data. The recognition of top institutions is the most authoritative proof that the product meets market demand, and it is also the core endorsement for breaking through circles and establishing industry trust.

The current development trend of the encryption industry has already shifted from retail driven and traffic driven to institutional led, compliant implementation, and infrastructure driven. More and more encryption teams are choosing to collaborate deeply with traditional banks and financial institutions to implement customized financial infrastructure services. This also marks that blockchain is breaking out of niche circles and gradually carrying the underlying operational capabilities of traditional global finance. Relying on top customers to polish products is a necessary path for the infrastructure of the track.

2、 Forward looking track positioning mode: Seizing the first mover dividend of AI intelligent agent economy

There are two ways to achieve PMF: optimizing the existing market or laying out the incremental future. In the current era of intensifying competition within the industry, simply optimizing existing services is no longer enough to widen the gap. The real opportunity to break through the game comes from laying out future trend tracks in advance before the industry reaches a consensus.

The biggest incremental trend in the current cross track between encryption and AI is the transformation of AI agents from auxiliary tools to independent economic participants. The traditional manual control, manual trading, and manual settlement models are rapidly disintegrating. AI agents can use program logic to autonomously call interfaces, allocate funds, and complete high-frequency transactions, building a new automated economic system at machine speed.

The core bottleneck of the intelligent agent economy is not computing power or algorithms, but automated payment infrastructure. Only by achieving unmanned, programmatic, and on chain automation in the payment process can AI agents truly break free from manual control and become independent market participants.

The industry benchmark project AgentCash has already validated this logic. The team relies on the x402 protocol to build a dedicated infrastructure, connecting the encrypted asset payment chain of AI agents, supporting agents to automatically complete interface calls, fee settlements, and fund transfers, completely eliminating the manual billing management mode. In the new era of intelligent agent economy, whoever takes the lead in building the underlying payment infrastructure will be able to control the core discourse power of the track and lock in the long-term PMF advantage in advance.

3、 Self validation mode: Make yourself the first core user of the product

The biggest misconception for underlying infrastructure projects is that they only focus on technical research and development, waiting for external developers and partners to implement and verify, with only technical concepts but no actual business support. The underlying projects that truly have long-term vitality follow a core approach: self use first, self verification, and proving the technical value with their own landing results.

This model has long been verified by the Internet industry. The core logic of Amazon building AWS cloud services is to first rely on its own e-commerce business to polish the underlying infrastructure, complete large-scale and difficult scenario landing verification, and then open up its technical capabilities to the outside world, ultimately becoming a top global cloud service provider.

The encryption track ZKsync ecosystem project Privium also practices this idea. The team is not limited to abstract underlying technical concepts, but has accurately landed on the real financial scenario of tokenized deposits, incubated Cari Network, and provided on chain fund circulation services for multiple regional banks in the United States.

Multiple mainstream institutions, including Huntington Bank and First Horizon Bank, can rely on their blockchain links to achieve real-time cross institutional circulation of customer deposits, with full compliance and closed-loop funds remaining within the traditional banking system. This model of "technology+real landing scenarios+benchmark customers" thoroughly verifies the practicality of underlying technology and quickly achieves product market fit.

Core summary: PMF has no shortcuts, only precise paths

The three types of implementation modes summarized by a16z may seem to have different paths, but in reality, their core is unified: abandoning blind trial and error, traffic hype, and ineffective growth, and quickly implementing real needs with precise paths.

Whether it is co creating products with benchmark customers, exploring the incremental track of AI intelligent agents, or self verifying the value of technology, the essence is to break away from false prosperity and return to the core logic of matching products with the market. For encryption and AI startup projects that are currently in a state of confusion, transformation, and stagnation, choosing a mode that is suitable for their own track and quickly landing in real scenarios is the only way to break through bottlenecks and achieve long-term survival.