Is it really difficult to commercialize AI?

Although products such as ChatGPT have shown great commercial potential, many startups are still struggling between technological innovation and profit models. This article will explore the various paths of AI commercialization, including subscription models, API payment models, advertising-driven monetization, and customized solutions, and analyze how to move from AI concepts to stable cash flow.

In 2023, just one year after the release of OpenAI's ChatGPT product, its valuation exceeded $80 billion, with annual revenue approaching $2 billion. This data shocked the global market, and the commercialization of AI seemed to have entered the fast lane.

However, the reality is that not all AI companies can easily monetize, and a large number of start-ups have failed in financing rounds. There is a huge gap between technological innovation and profit models.

How to move from AI concepts to stable cash flow? This is a difficult problem that must be faced in order to break through in the AI ​​era.

1. Business model selection for AI products: How to make the first pot of gold?

(1) Subscription model: stable and predictable income

The most common commercialization path for AI capabilities is the subscription model. AI tools such as Midjourney and Runway all adopt a subscription charging model. OpenAI also charges $20 per month under the ChatGPT Plus membership model to provide users with more powerful model access rights.

For AI products, the key to the subscription model is to provide differentiated value, such as better model results and smarter interactive experience, to improve user retention and conversion.

(2) API payment model: a shortcut to technology monetization

If companies do not want to face C-end users directly, they can choose the API charging model. For example, Stability AI provides developers with model API calls and charges by the number of times or the amount of calculation. The advantage of this model is that it can be scaled quickly, but the premise is that your AI model is accurate enough and has unique data advantages.

Stability AI's Developer Platform provides a variety of functions, including image generation, image editing, language models, and 3D models. Its models such as Stable Image, Stable Diffusion 3.5, Stable Video 1.1, and Stable Fast 3D have high performance and flexibility in the field of generative artificial intelligence. Stability AI's API allows developers to easily deploy and apply these models, providing a seamless, scalable, and secure deployment method.

(3) Advertising + AI-driven content monetization

AI can be used to improve content production efficiency and monetize through advertising or content. For example, TikTok uses AI to optimize content recommendations and increase advertising conversion rates, while news media use AI to generate content and improve traffic monetization capabilities.

(4) Customized AI solutions: a high-profit growth point in the B2B market

Some companies have strong AI capabilities but cannot scale up. They can choose a customized To B model. For example, Cohere focuses on enterprise-level large language models and makes profits by providing private deployment and customized AI services to enterprises. Although this model has a high average order value, it grows slowly.

2. Implementation strategy of AI commercialization

(1) Start with a small-scale trial and then expand on a large scale

Airbnb first tested market demand on a small scale in New York before gradually expanding to the world. AI products should also first verify demand on a small scale, such as through the MVP (minimum viable product) model to test market feedback.

(2) Building an AI+X industry ecosystem

AI is essentially an enabling tool, not an independent industry. Enterprises should look for vertical industry scenarios, such as AI+medical care, AI+finance, etc. For example, Hugging Face was originally an AI chat application, and later transformed into an AI model open source platform, forming a huge developer ecosystem.

(3) Build a data moat and raise the threshold for competition

The barrier of AI products lies in data accumulation. For example, Tesla uses autonomous driving data to train its AI algorithm to form a competitive advantage. For start-ups, they can consider entering the market from a niche, accumulating unique data, and thus enhancing model capabilities.

3. Three major challenges in AI commercialization

(1) High computing power costs

AI model training and reasoning require huge computing resources. For example, the training cost of OpenAI's GPT-4 alone is as high as hundreds of millions of dollars. Therefore, when choosing a business model, companies need to balance technology investment and returns to avoid losses due to high computing costs.

(2) Data barriers and privacy compliance

One of the core competitiveness of AI is data, but many countries are increasingly strict on data privacy protection, such as the EU's GDPR regulations. Therefore, companies need to establish a compliance system for data collection, storage and use, otherwise they may face legal risks.

(3) Market education and user acceptance

AI products often involve changes in user habits and require a lot of market education. For example, after a company introduces AI customer service, users may not have high trust in it, so it will take some time for them to adapt.

Conclusion: The final answer to AI commercialization

There is no "one-size-fits-all" answer to the commercialization of AI. Different industries and companies need to combine their own advantages and choose the most suitable model.

But what is certain is that AI is profoundly changing the business world. Product managers and corporate executives need to have AI thinking and find their own commercialization path in order to be invincible in this transformation.

In the future, the success of AI commercialization will depend on the organic combination of technology, products and market strategies. Are you ready?