The fastest path to achieving product-market fit is not blind iteration; it is about choosing the right track and making a firm investment before others follow suit.

Written by: Jason Rosenthal, a16z crypto partner

Translated by: Chopper, Foresight News

Product-market fit (PMF) is one of the key factors determining the life and death of a startup. When you find it, there is a chance to survive and grow; once deviated, no amount of resources can save you.

Simply burning cash for financing will only extend the timeline towards failure. Growth strategies detached from real strategies and uncontrolled airdrops may seem bustling, but are actually just covering up the reality of not yet finding product-market fit. The two most advantageous features of the cryptocurrency industry—token economics and network effects—can sometimes mislead teams, causing them to deviate from the true path to product-market fit.

The good news is that as benchmark applications like stablecoins mature and traditional finance and general users enter the space, high-quality teams in the crypto industry can now find product-market fit more quickly.

Here are three effective models currently working in the industry, especially suitable for teams that have not yet found PMF or are in the process of business transformation.

Model One: Partner with Top Clients and Co-build Products as Needed

Find the most professional potential customers in your track, deeply co-create and customize products based on their real needs. The needs of the clients become your product development specifications.

This model is much slower than launching a generic product first and then iterating publicly. However, if your initial clients carry transactions in the trillions, their recognition is worth far more than media exposure, locked-up scale, and retail enthusiasm. A product recognized by a wide range of clients is the best definition of product-market fit; and top benchmark clients are the strongest indicators of industry popularity.

Nowadays, many crypto startups are announcing collaborations and jointly launching products with traditional financial institutions. The industry's product roadmap is actually being written under the demands of institutions. Blockchain is gradually taking on the role of global financial infrastructure.

Model Two: Lock onto Index Growth Tracks and Position Early

Some product-market fit comes from doing existing market services better; while others stem from predicting future trends before the market has fully awakened, positioning early, and seizing key ecological positions.

The clearest growth curve at the moment is the emergence of AI entities as independent economic subjects: AI entities can autonomously call interfaces, allocate funds, and execute transactions at machine speed. The inherent mode of "human decision-making in processes" is collapsing far faster than most people expected.

Using the AI entity commercial ecosystem as an example: The Merit Systems team early on discerned the trend and built AgentCash based on x402. This product allows AI entities to use crypto asset payment interfaces to pay service fees, freeing AI entities from human billing management and achieving programmatic transactions, building essential underlying infrastructure.

Payment capability is key for AI entities to evolve from "tool assistants" to "independent economic participants." Whoever first builds this underlying payment track will hold the voice of infrastructure in the age of AI economics.

Model Three: Become the First Core User of Your Own Product

The most vibrant infrastructure companies never wait for external developers to validate their technology. They will proactively build applications based on their underlying track, personally running the business loop, proving technical capability through practice, and then attracting other developers to co-build.

Amazon is the pioneer of this approach, and the success of AWS is a textbook case. Amazon did not initially market cloud services to startups. Instead, it built the underlying infrastructure needed for its e-commerce business, refined it until it operated stably at scale, and then gradually opened it up for commercialization.

The Matter Labs team is also replicating this classic approach.

The team did not simply package Prividium as an abstract enterprise-level product; instead, it targeted a specific application scenario: tokenization of deposits, creating the Cari Network. Several regional banks in the U.S., including Huntington Bancshares, First Horizon, M&T Bank, KeyCorp, and Old National Bancorp, can achieve instant inter-institutional transfer of customer deposits through blockchain, while ensuring that funds remain within the regulatory banking system. ZKsync is not just building underlying connections but has also found benchmarking applications for its infrastructure.

Conclusion

The three models mentioned above share the same underlying logic: the fastest path to achieving product-market fit is not about blind trial and error or closed-door iteration. Instead, it is about choosing the right track, making a firm layout and seizing opportunities before others follow suit.

Co-create products with core clients that have long-term endorsement value; position early in growth tracks before the industry reaches a consensus; and make yourself the first benchmark user of your own product.

Choose the model that fits your product, and then act decisively.