Exploring the Value of AI: Startups vs. Incumbents and Building a Digital Legacy

Exploring the Value of AI: Startups vs. Incumbents and Building a Digital Legacy

Introduction:

AI has been a transformative force in various industries, and its impact on startups and incumbents has been a subject of interest. In previous waves of technological advancements, incumbents tended to capture a significant portion of the value, leaving startups with a smaller share. However, the landscape seems to be shifting in the current wave of AI innovation. This article delves into the dynamics between startups and incumbents in the context of AI, as well as the concept of building a digital legacy.

The Shift in Value Distribution:

In the early days of the internet, startups like Google, Amazon, and Facebook emerged as the primary beneficiaries, capturing the majority of the value. Incumbents such as Microsoft and Apple also secured a portion of the value by extending their existing franchises onto the internet. The split between startups and incumbents in this wave was roughly 60:40 or 70:30.

In the mobile era, incumbents like Apple and Google dominated, leveraging their existing market presence and creating mobile versions of their applications. However, startups like WhatsApp, Uber, and Instagram also managed to capture a significant share of the value. The split in this wave skewed more towards incumbents, with a ratio of approximately 20:80.

Crypto, on the other hand, witnessed almost exclusively startup capture of value. Bitcoin, Ethereum, and other cryptocurrency startups emerged as the key players, with minimal participation from existing financial services or infrastructure companies.

Overcoming Incumbent Advantages:

To compete with incumbents successfully, startups generally need to offer a product that is dramatically superior, overcoming the distribution, capital, and pre-existing product moats of incumbents. They may also focus on untapped customer segments or distribution moats that incumbents cannot serve. In essence, a 10X better product is often required to disrupt incumbents effectively.

However, the advantage that incumbents have enjoyed could be diminishing. Startups are now leveraging the broader internet as an initial training set, and models that can work robustly with smaller datasets are becoming more prevalent. This leveling of the playing field may contribute to startups having a larger share of AI-generated value.

The Promise of Current AI Innovations:

Unlike previous waves, the current wave of AI innovation holds promise for startups. The speed of innovation across various areas is remarkable, allowing for the creation of products that are significantly superior to those of incumbents. While GPT-3, for example, has not yet led to the emergence of many startups building big businesses, it is possible that a 5-10X better model could unleash a new ecosystem of startups while augmenting incumbent products.

Infrastructure-centric companies focused on AI are also emerging, with broad adoption and rapidly growing usage. OpenAI, Stability.AI, Hugging Face, and Weights and Biases are among the key players in this space. Their presence provides startups with valuable access to AI technologies and creates further opportunities within the ecosystem.

Building a Digital Legacy:

In parallel to the discussion on AI value distribution, the concept of building a digital legacy comes into focus. Kazuki Nakayashiki, co-founder of Glasp, emphasizes the importance of leaving behind knowledge, wisdom, and history for future generations. The mission of Glasp is to democratize access to others' learning and experiences, empowering individuals to leave, share, and weave their legacies.

While leaving a successful business or substantial wealth is admirable, Nakayashiki believes that the greatest legacy lies in living a courageous life and betting on the possibilities and hopes of humanity. Glasp aims to facilitate the preservation and sharing of knowledge and experiences, making them useful to others in the future.

Glasp's approach combines curation, knowledge management, and community-building. By making the learning process social, Glasp enhances the experience of learning partners. The platform emphasizes insight, ideas, emotions, and connections within content, promoting knowledge building rather than mere saving or liking of links.

Looking Ahead:

As AI continues to shape the startup landscape, it is crucial to identify actual end-user needs and untapped markets that can benefit from the technology. Startups must focus on serving these needs and leveraging the exciting advancements in AI to create products that offer significant value.

Before concluding, here are three actionable pieces of advice:

• 1. Focus on the actual end users' needs: Prioritize understanding and meeting the needs of your target audience, ensuring that your product provides a solution that is superior to what incumbents offer.

• 2. Embrace a long-term mission and vision: Establish a clear mission and vision for your startup, aligning all decisions and actions with it. This creates a strong foundation for your team and nurtures a distinct company culture.

• 3. Leverage the power of community and collaboration: Build a network of learning partners and foster a sense of community among your users. Encourage feedback, collaboration, and inspiration to enhance the learning experience and expand your knowledge ecosystem.

In conclusion, the value distribution between startups and incumbents in the AI landscape is evolving, with startups poised to capture a larger share in the current wave. By leveraging the strengths of AI and focusing on addressing unmet needs, startups can create products that surpass incumbent offerings. Simultaneously, building a digital legacy through platforms like Glasp enables individuals to leave a lasting impact by sharing their knowledge and experiences with future generations. Exciting times lie ahead for startups in the realm of AI innovation.