AI: The Evolution of Startups and Incumbents in Value Creation

AI: The Evolution of Startups and Incumbents in Value Creation

Introduction:
The emergence of artificial intelligence (AI) has brought about significant changes in various industries, from finance to mobile applications. However, the dynamics of value creation and market capture by startups and incumbents differ across different sectors, such as crypto and mobile. In this article, we will explore the contrasting patterns of AI-driven value creation, the role of technology and data differentiation, the impact of hard markets, and the potential for future advancements. Additionally, we will draw insights from Derek Thompson's book, "Hit Makers: The Science of Popularity," to understand the importance of familiarity, distribution, and repetition in driving the success of AI-powered products and content.

1. Contrasting Patterns of Value Creation:
   In the crypto industry, startups have largely dominated the value creation landscape. From Bitcoin to Ethereum and prominent platforms like Coinbase, Binance, and FTX, the majority of innovation and market capture have been driven by startups. On the other hand, the mobile industry witnessed a different trend, with incumbents like Apple and Google holding a significant share of the value. While mobile versions of incumbent apps, such as Salesforce on the iPhone, gained traction, startups like Whatsapp, Uber, Doordash, Instagram, and Instacart also made their mark. This indicates a relatively balanced split of value creation between startups and incumbents, possibly around a 20:80 ratio.

2. Technology and Data Differentiation:
   One hypothesis suggests that the prior wave of AI resulted in the creation of better products, although not significantly superior to incumbents or existing market structures. This could explain why incumbents in certain industries, such as finance and infrastructure, did not actively participate in value creation. However, the importance of data differentiation, which previously favored incumbents, is diminishing. Startups now leverage the broader internet as an initial training set and adopt models that work robustly with smaller data sets. This shift levels the playing field, enabling startups to compete more effectively against incumbents.

3. The Impact of Hard Markets:
   In some industries, such as education and healthcare, market structure, regulation, and a lack of focus on end-user needs have hindered technological innovation. Incumbents in these hard markets often maintain their dominance despite being only partially as effective as new solutions. By bundling their offerings with core pre-existing products and leveraging their large customer base, incumbents can still emerge victorious. This phenomenon highlights the challenges faced by startups in industries where market structure and regulations hinder innovation and adoption.

The Future of AI-powered Startups:

1. Advancements in AI Technology:
   The next generation of AI language models, such as GPT-4, holds the potential to revolutionize various sectors, ranging from consumer interactions to white-collar work. Improved natural language processing capabilities can reshape dialogue-based interactions and serve as co-pilots for text-based tasks across industries. Additionally, advancements in image generation, speech-to-text, text-to-speech, music, and video open up new opportunities for startups to leverage AI technology for innovation and value creation.

2. Valuable Infrastructure for the Industry:
   While incumbents like Google have failed to fully capitalize on their AI advantages, startups are emerging as providers of valuable AI infrastructure to the rest of the industry. These startups offer specialized tools and services that enable businesses to harness the power of AI in their operations. By bridging the gap between AI research and practical applications, these startups contribute to the growth and adoption of AI across various sectors.

3. App Use Cases Without Strong Incumbents:
   Certain AI-powered use cases, such as marketing copy generation, image generation, and code generation, are witnessing significant adoption and growth. Startups in these areas, such as Copy.AI, Jasper, Midjourney, Stable Diffusion, and Github Copilot, are providing solutions that address specific needs in the market. Imperfect fidelity is acceptable as long as there is a human in the loop to review and refine the AI-generated content. The absence or weakness of workflow tools in these use cases creates opportunities for startups to integrate AI features into broader workflow solutions.

Conclusion:
The evolution of AI-driven startups and incumbents in value creation is influenced by various factors, including technology advancements, data differentiation, and market structure. While startups have largely dominated the crypto industry, incumbents hold a significant share in mobile applications. However, advancements in AI technology, the emergence of startups providing valuable infrastructure, and the presence of app use cases without strong incumbents offer promising opportunities for startups to participate in new market cap and impact the world. By leveraging AI technology effectively, startups can overcome challenges and create innovative solutions that drive value creation in their respective industries.

Actionable Advice:

1. Embrace the potential of advanced AI language models and other emerging AI technologies to drive innovation and disruption in your industry.
2. Identify gaps in AI infrastructure and develop specialized tools and services that cater to the needs of businesses seeking to leverage AI in their operations.
3. Explore use cases where there is a lack of strong incumbents and develop AI-powered solutions that address specific market needs, focusing on integration with existing workflows.