Generative Tech Market Map and Post-PMF Strategy: Building Defensibility in the AI Industry


Hatched by Glasp

Aug 22, 2023

3 min read


Generative Tech Market Map and Post-PMF Strategy: Building Defensibility in the AI Industry

The field of artificial intelligence (AI) has seen rapid advancements in recent years, with the emergence of various AI models and technologies. In this article, we will explore the generative tech market map and the different layers of the tech stack. We will also discuss the importance of building defensibility in the AI industry, both through the use of proprietary technology and other strategies.

At the base of the generative tech market map are the general AI models, such as GPT-3 for text, DALL-E-2 for images, Whisper for voice, and Stable Diffusion. These models have the capability to generate outputs in broad categories like text, images, videos, speech, and games. They serve as the core technology breakthrough in the AI industry, enabling a wide range of applications.

Moving up the market map, we encounter specific AI models that capture even more nuance for specific jobs. These models are trained on more narrow and specialized datasets, allowing them to perform tasks like writing tweets, ad copy, song lyrics, or generating e-commerce photos and 3D interior design images. They provide a more tailored and precise output compared to general AI models.

At the next layer, we have hyperlocal AI models. These models are specialists in their respective domains and can generate outputs that align with specific preferences and styles. For example, a hyperlocal AI model can write a scientific article in the style preferred by Nature or create interior design models suited to a specific person's aesthetic. This layer benefits from proprietary and trusted data, which provides a powerful defensibility.

However, it is important to note that data network effects, which refer to the increasing value of data as more users contribute to it, may not always provide a strong defensibility. Competitors can find similar datasets and develop models that are comparable to yours. The slight differences in performance may not be noticeable to customers, allowing competitors to claim to have what you have. Therefore, relying solely on data network effects may not be a sustainable strategy in the long run.

To truly build defensibility, companies need to consider other factors such as speed to market, fundraising speed, and sales speed. Aggressive sales efforts can help embed your product in the market and create network effects that enhance your defensibility. By being the first to market and continuously iterating based on customer feedback, you can establish a strong position that is difficult for competitors to replicate.

Additionally, finding an investor who shares your vision and is willing to sprint with you can provide the necessary resources and support to accelerate your growth. This investor should understand the fast-paced nature of the AI industry and be willing to invest in your long-term success.

Furthermore, building a strong brand can also contribute to your defensibility. A brand represents a promise to customers and can differentiate your product from competitors. If your proprietary technology alone does not stand out, consider focusing on a 10x improvement in user experience. By embedding your technology deeply into users' lives or business operations, you can create a strong brand presence that is difficult for competitors to replicate.

In conclusion, the generative tech market map and the post-PMF strategy provide valuable insights into building defensibility in the AI industry. While the core technology breakthroughs and hyperlocal AI models play crucial roles, they should be complemented by other strategies such as product speed, fundraising speed, sales speed, network effects, embedding, and building a strong brand. By considering these factors and taking proactive steps, companies can establish a strong position in the AI industry and thrive in the face of competition.

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