AI: Startup Vs Incumbent Value

AI: Startup Vs Incumbent Value

In the ever-evolving landscape of technology, the distribution of value often shifts between startups and incumbents. Over the years, we have witnessed this shift in various waves, from the first internet wave to the rise of mobile and now the emergence of AI. Surprisingly, the prior wave of value from AI predominantly went to incumbents, despite the significant startup activity in the field.

When we look back at the first internet wave, it was the startups like Google, Amazon, Paypal, and Facebook that captured most of the value. However, incumbents such as Microsoft, Apple, IBM, and Oracle also managed to extend their franchises onto the internet and secure a portion of the value. The split between startups and incumbents in this wave was roughly 60:40 or 70:30 in favor of startups.

In the mobile wave, the dynamics shifted, and most of the value went to incumbents like Apple and Google. Startups like WhatsApp, Uber, and Instagram did manage to capture a significant portion of the value, but the split was more skewed towards incumbents, with a ratio of around 20:80 in favor of incumbents.

Crypto, on the other hand, witnessed a 100% capture of value by startups. Bitcoin, Ethereum, Coinbase, and other cryptocurrency companies emerged as the major players, while existing financial services and infrastructure companies had limited participation in value creation. This wave showcased the potential for startups to dominate an emerging technology space.

When it comes to AI, the trend seems to lean towards incumbents once again. While there were many "AI first" startups in the last decade, the truly transformative AI applications landed with companies like Google, Facebook, TikTok, Netflix, and Amazon. These incumbents leveraged their existing resources and customer base to create AI-powered products and services that dominated the market.

To beat an incumbent as a startup in the AI space, you usually need to build something dramatically better or focus on a brand new customer segment or distribution moat that the incumbent cannot serve. In other words, you need a 10X better product. Incumbents often have the advantage of distribution, capital, and pre-existing product moats, making it challenging for startups to compete.

However, the landscape may be changing. One possible reason why incumbents have been successful in the AI space is their data advantage. As companies now have access to the broader internet as an initial training set and are switching to models that work well with smaller data sets, the data advantage of incumbents may diminish.

Moreover, this time around, the technology itself seems dramatically stronger, making it easier for startups to create products that are 10X better than what incumbents offer. The emergence of infrastructure-centric companies like OpenAI, Stability.AI, Hugging Face, and Weights and Biases also provides startups with access to cutting-edge AI technologies and a supportive ecosystem.

In addition, there are specific use cases where AI can bring significant value. Highly repetitive, highly paid tasks like coding, marketing copy, and generating images for websites can benefit from AI-powered workflow tools. The ability to summarize or generate text and images with high fidelity opens up new possibilities for product applications.

However, startups must be cautious not to fall into the trap of the hammer-looking-for-a-nail problem. It is crucial to identify actual end-user needs and unserved markets that can truly benefit from the advancements in AI technology. By focusing on the needs of the users and building products that address those needs, startups can carve out their place in the AI landscape.

Looking ahead, it seems that startups will finally start to get real value from AI. The speed of innovation, the strength of the technology, and the availability of infrastructure-centric companies all contribute to this shift. Exciting times lie ahead as AI continues to reshape industries and unlock new opportunities.

Actionable Advice:

• 1. Focus on building a product that is 10X better than what incumbents offer. Identify the areas where incumbents have weaknesses and leverage those to your advantage.

• 2. Tap into specific use cases where AI can bring significant value. Look for highly repetitive tasks or areas where there is a lack of efficient workflow tools. Build AI-powered solutions that address these needs.

• 3. Pay attention to actual end-user needs and unserved markets. Avoid the trap of building technology for the sake of technology. Understand the pain points of users and develop products that solve their problems.

In conclusion, the dynamics of value distribution between startups and incumbents in the AI space have been shifting. While incumbents have dominated in the past, there are indications that startups will have a larger share of the value in this wave of AI. With advancements in technology, access to infrastructure-centric companies, and a focus on user needs, startups have the potential to create disruptive AI-powered products and services. Exciting times lie ahead as AI continues to revolutionize industries and shape the future.