AI: Startup Vs Incumbent Value - How the Landscape is Shifting

AI: Startup Vs Incumbent Value - How the Landscape is Shifting

The value generated from AI has historically been skewed towards incumbents rather than startups. In previous waves of technological advancements, such as the internet and mobile, startups managed to capture a significant portion of the value. However, when it comes to AI, incumbents have emerged as the primary beneficiaries. This trend is beginning to change, and startups are now poised to claim a larger share of the AI-generated value.

During the internet wave, companies like Google, Amazon, and Facebook emerged as successful startups, while incumbents like Microsoft and IBM extended their franchises onto the internet. The split was roughly 60:40 or 70:30 in favor of startups. Similarly, in the mobile era, incumbents like Apple and Google dominated, but startups like Whatsapp and Uber managed to capture a considerable portion of the value. The split here was around 20:80.

In contrast, the crypto industry has seen almost 100% startup capture of value creation, with very little participation from existing financial services or infrastructure companies. Bitcoin, Ethereum, and Coinbase are prime examples of startups that have made significant strides in the crypto space.

To overcome incumbents, startups in the AI space must either build a product that is dramatically better, overcoming distribution, capital, and pre-existing product moats, or focus on a brand new customer segment or distribution moat that the incumbents cannot serve. Generally, a 10X better product is required to disrupt the market.

One reason incumbents have traditionally held an advantage is their data advantage. However, this advantage is diminishing as companies now have access to the broader internet as an initial training set and are adopting models that work more robustly with smaller data sets. This leveling of the playing field opens up opportunities for startups to compete on a more equal footing.

The AI landscape is evolving, and this time it feels different. The speed of innovation across various areas is remarkable, making it easier for startups to create products that are 10X better than those of incumbents. While GPT-3, a cutting-edge AI model, has shown promise, there is still room for improvement. A 5-10X better model could lead to a whole new startup ecosystem while enhancing incumbent products.

Unlike previous AI waves, there are now infrastructure-centric companies with broad adoption and rapidly growing usage. OpenAI, Stability.AI, Hugging Face, and Weights and Biases are among the companies driving the infrastructure needed for AI innovation. This creates more opportunities for startups to leverage these technologies and build successful businesses.

There are specific use cases where AI can have a profound impact, such as highly repetitive, highly paid tasks like coding, marketing copy, and website imagery. In these cases, AI becomes an essential part of a broader workflow tool where existing solutions are weak or nonexistent. Additionally, the ability to summarize or generate text and images with high fidelity opens up new possibilities for product applications.

While the technology advancements in AI are exciting, it is crucial to avoid the "hammer-looking-for-a-nail" problem. Startups must identify actual end-user needs and unserved product markets that can benefit from this wave of technology. By focusing on the needs of users, startups can create products that truly add value and disrupt existing markets.

Before concluding, here are three actionable pieces of advice for startups venturing into the AI space:

1. Focus on actual end-user needs: Understand the pain points and challenges faced by users in specific industries or domains. Build solutions that address these needs directly, providing tangible value and differentiation.

2. Leverage infrastructure-centric companies: Partner with or utilize the services offered by infrastructure-centric companies like OpenAI, Stability.AI, Hugging Face, and Weights and Biases. These companies provide the necessary tools and platforms to accelerate AI development and deployment.

3. Continuously innovate and iterate: The AI landscape is constantly evolving, with new models, techniques, and technologies emerging regularly. Stay up to date with the latest advancements, experiment with new approaches, and iterate on your products to stay ahead of the competition.

In conclusion, the AI landscape is shifting, and startups are poised to claim a larger share of the value generated from AI. With advancements in technology and the emergence of infrastructure-centric companies, startups now have the tools and opportunities to build 10X better products and disrupt existing markets. By focusing on actual end-user needs and leveraging the right resources, startups can thrive in the AI space and create significant value. Exciting times lie ahead for AI startups!