AI: Startup Vs Incumbent Value

AI: Startup Vs Incumbent Value

In the world of technology and innovation, the distribution of value between startups and incumbents has always been a topic of interest. When it comes to AI, it seems that the prior wave of value has predominantly gone to incumbents, despite the significant activity from startups. This is in contrast to the internet wave, where startups like Google, Amazon, and Facebook captured a substantial amount of the value.

In the internet wave, the value split between startups and incumbents was roughly 60:40 or 70:30 in favor of startups. However, when it came to mobile, most of the value went to incumbents like Apple and Google, with startups like Whatsapp and Uber capturing a smaller share. The split in the mobile space was more skewed towards incumbents, with a ratio of around 20:80 in favor of incumbents.

Interestingly, the crypto space has been dominated by startups, with very little participation from existing financial services or infrastructure companies. Companies like Bitcoin and Ethereum have captured almost 100% of the value creation in this space. This stark difference in the distribution of value raises questions about why incumbents have been more successful in capturing value in some areas, while startups have prevailed in others.

To beat an incumbent as a startup, you typically need to build something that is dramatically better than what the incumbent offers. This could mean overcoming the incumbent's distribution, capital, and pre-existing product moats. Alternatively, startups can focus on serving a brand new customer segment or finding a distribution moat that the incumbent cannot penetrate. In general, a 10X better product is required to disrupt the market and overcome the advantages of incumbents.

One possible explanation for the success of incumbents in the past is their data advantage. However, as companies now have access to the broader internet as a training set and are utilizing models that work effectively with smaller data sets, this advantage may be diminishing. Startups now have the opportunity to leverage AI and create products that can compete with incumbents on a more level playing field.

In the previous wave of AI companies, many directly challenged incumbents or operated in hard markets like education and healthcare. These markets often have regulatory barriers or market structures that make it difficult for startups to succeed. However, this time around, the landscape seems different. There is a rapid pace of innovation across various areas, which makes it easier for startups to create 10X better products and overcome the advantages of incumbents.

One exciting development in this wave of AI startups is the emergence of infrastructure-centric companies. These companies, such as OpenAI, Stability.AI, Hugging Face, and Weights and Biases, provide essential infrastructure and tools for the AI ecosystem. This creates more opportunities for startups to access and leverage AI technologies.

Additionally, there are specific use cases where AI can play a significant role. Highly repetitive and highly paid tasks such as coding, marketing copy, and image generation can benefit from AI-powered workflow tools. The ability to summarize or generate text and images in a high-fidelity manner opens up new possibilities for product applications.

However, it is crucial for startups to avoid the trap of building solutions without a clear market need. It is essential to identify actual end-user needs and unserved product markets that can benefit from AI technology. By focusing on addressing these needs, startups can create valuable and impactful products.

In the world of venture capital, AI is also starting to play a role. VC firms, such as Correlation Ventures, are using machine learning tools to aid in investment decisions. These tools analyze various factors like team experience and board composition to predict future investor returns. This data-driven approach complements the traditional gut instincts of venture capitalists and provides a more comprehensive analysis of potential investments.

According to a forecast by Gartner Inc., AI will be involved in 75% of venture capital investment decisions by 2025, up from less than 5% currently. This indicates the growing importance of AI in the VC industry and its potential to enhance investment strategies.

In conclusion, the distribution of value between AI startups and incumbents has varied across different waves of technological advancements. While incumbents have historically captured a significant portion of the value, the current wave of AI presents new opportunities for startups. With advancements in technology and infrastructure, startups now have the potential to create 10X better products and compete with incumbents. By focusing on actual end-user needs and leveraging AI technologies, startups can finally start to realize the true value of AI.

Actionable Advice:

1. Build a product that is dramatically better than what incumbents offer. Focus on overcoming distribution, capital, and pre-existing product moats.
2. Identify new customer segments or distribution moats that incumbents cannot serve and target those markets.
3. Avoid the hammer-looking-for-a-nail problem. Prioritize actual end-user needs and find unserved product markets that can benefit from AI technology.