Growth: The self-fulfilling prophecy of technology ecosystems.

There are some common patterns that have been played out both on web/mobile and the blockchain space, regarding products, demand and business models. 

The first pattern has to do with product trajectory.  

With the introduction of a new technology, the first wave of entrepreneurs are rushing to build products that give the general public access to the technology. 

Following a bubble and a crash, developers and entrepreneurs realizing the need for infrastructure and the difficulty to attract and monetize end users in other ways, they create new markets within the growing ecosystem of the new technology, by building the infrastructure or the tools for other entrepreneurs to build products on top. 

The prime example: Web

Web infrastructure played a critical role in lowering the costs of starting a web company, which enabled more entrepreneurs to come in. The most valuable companies became those offering solutions to other startups and businesses, while the companies offering services directly to the users, monetized them indirectly, by borrowing business models from the financial sector.

For example,  even though Google's core products are email and search, Google makes money by selling advertising space in real time. Every user search is accompanied by an auction mechanism involving high-frequency assessments of expected profit. A model borrowed by high frequency trading firms and automated trading. 

The Web3 landscape

When Bitcoin and blockchain were introduced in 2008, blockchain entrepreneurs rushed to build products that give access to the general public. During 2010 and 2014,  exchanges and wallets were formed, offering on-ramp and/or off-ramp access to blockchain users. 

However, the biggest moment for the blockchain space, in terms of the number of users it brought in, was the launch of the ETH blockchain in 2015 and the introduction of smart contracts.

With smart contracts there was an explosive creation of products, known as dapps, native and non native stablecoins and the first DeFi services.

Demand for all these products grew but not in a huge and engaging way. Since the market was comparably still small and developers were rushing into the space, the next logical step was to create demand from within.   

After the crash of 2018, crypto entrepreneurs focused more on building infrastructure rather than on creating consumer products to attract new users. High blockchain transaction fees were disincentivizing users to use dapps and developers saw an opportunity to build new low fee blockchain networks and Layer 2 solutions to enhance the service of existing ones, At the same time, interoperability was another major pain point for the space, since applications on different blockchain networks could not talk to each other.

The biggest valuations in the space were attained by blockchain companies offering solutions to these problems and not consumer products, even though there were a couple of exceptions. 

Moreover, we also see new markets created in blockchain. For example, blockspace, just like real time advertising space on the Web, allows Validators to sell immediacy and transaction inclusion to the users offering the best fees. 

A similar trajectory for AI?

Considering the third technology and following a series of lucky breakthrough moments up until recently, AI came to mainstream.

Since AI needs humans more than any of the two previous technologies, the first entrepreneurs built interfaces to give access to the models behind them. LLMs automate the gathering and analysis of information and it is to the humans to create meaning out of it.

Until now, most companies monetize through APIs. Access to the interfaces is free for end users but you have to pay to train the models in your datasets. 

If it’s eventually very difficult to monetize end users, the next logical step for entrepreneurs is to attack the market from within again. Limitations such as interoperability between models, lower compute costs, storage solutions, etc might bring the next phase in AI investment space. 

Another trajectory would be to find demand from within the ecosystem. A new type of “consumer” might be created with AI and these are AI agents. AI agents are programs designed to achieve specific goals and make decisions. So demand for the new AI products and services might not come from humans but AI agents that consume the outputs of other models or create “communities” to achieve their goals.

To summarize

Growth for products and services for a new technology can be stimulated by creating new demand and needs within the ecosystem of the new technology. So a new technology is a fertile ground for the creation of new markets, with little competition for the first movers. These new markets might be easier to be found in places where the pain points of the new technology exist, usually in infrastructure, or by commoditizing access (Web browsers, transactions in blockchain, AI interfaces) and monetizing indirectly (advertising in Web, transaction immediacy in blockchain, AI agents in AI).