AI: Startup Vs Incumbent Value in the Tech Industry

AI: Startup Vs Incumbent Value in the Tech Industry

The tech industry has witnessed the rise of artificial intelligence (AI) and its impact on both startups and incumbent companies. Surprisingly, the prior wave of value from AI mostly went to incumbents, despite the significant startup activity in the field. This is in contrast to previous technological waves, such as the internet and mobile, where startups captured a significant portion of the value.

During the first internet wave, startups like Google, Amazon, Paypal, and Facebook emerged as major players, capturing a substantial share of the value created. However, incumbents like Microsoft, Apple, and IBM also extended their franchises onto the internet and secured a portion of the value. The split between startups and incumbents in this wave was roughly 60:40 or 70:30.

In the mobile wave, most of the value went to incumbents like Apple and Google, as they introduced their mobile versions and apps. However, startups like Whatsapp, Uber, and Instagram also managed to capture a significant portion of the value. The split between startups and incumbents in this wave was approximately 20:80.

In the realm of cryptocurrency, startups have dominated the value creation, with companies like Bitcoin, Ethereum, Coinbase, and Binance leading the way. Existing financial services and infrastructure companies have had minimal participation in this wave of value creation.

To beat an incumbent as a startup in the AI field, you typically need to build something that is significantly better than what the incumbent offers. This means overcoming the distribution, capital, and pre-existing product moats of the incumbent. Alternatively, startups can focus on a brand new customer segment or distribution moat that the incumbent cannot serve. In general, a 10X better product is required to succeed.

The success of incumbents in the prior wave of AI startups may be attributed to their data advantage. However, 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 advantage may diminish.

Many AI startups in the past either directly competed with incumbents or operated in hard markets like education and healthcare. These hard markets often present challenges due to market structure, regulation, and a lack of responsiveness to end-user needs. However, the current wave of AI feels different due to technological advancements and the speed of innovation.

One significant change in this wave is the emergence of infrastructure-centric companies with broad adoption and rapidly growing usage. Companies like OpenAI, Stability.AI, Hugging Face, and Weights and Biases are paving the way for startups to access AI technologies and build innovative solutions.

Additionally, there are specific areas where AI can create value for startups. Highly repetitive and highly paid tasks like coding, marketing copy, and website image creation can be automated using AI, providing a core and useful feature within broader workflow tools. The ability to summarize or generate text and images in a high-fidelity manner opens up new opportunities for product applications.

However, it is crucial for startups to focus on identifying actual end-user needs and untapped markets that can benefit from AI technology. By understanding the needs of their target customers, startups can develop products that truly address their pain points and stand out in the market.

Before concluding, let's highlight three actionable pieces of advice for startups looking to leverage AI:

1. Focus on building a product that is 10X better than what incumbents offer. This will help overcome the distribution, capital, and pre-existing product moats of established companies.

2. Identify new customer segments or distribution moats that incumbents cannot serve. By targeting untapped markets, startups can carve out their own niche and avoid direct competition with incumbents.

3. Prioritize the actual needs of end-users. By understanding their pain points and developing solutions that address those needs, startups can create products with a higher chance of success.

In conclusion, the AI landscape in the tech industry has seen incumbents capture a significant portion of the value in the prior wave of startups. However, with advancements in technology and the emergence of infrastructure-centric companies, startups are poised to capture a larger share of the value created by AI. By focusing on building 10X better products, identifying new customer segments, and addressing actual end-user needs, startups can thrive in this exciting era of AI innovation.