The Intersection of AI and User Onboarding: Contrarian Theses for Early Stage Investors

The Intersection of AI and User Onboarding: Contrarian Theses for Early Stage Investors

Introduction:
In the realm of technological advancements, two seemingly unrelated topics - Artificial Intelligence (AI) and user onboarding - hold significant importance. While AI revolutionizes industries and reshapes competitive landscapes, user onboarding plays a crucial role in retaining and engaging customers. This article explores the intersection of these two domains, presenting five contrarian theses for early-stage investors.

Thesis 1: Horizontal LLMs will lose, and verticalization will prevail.
The first thesis challenges the notion that Large Language Models (LLMs) will lead us to Artificial General Intelligence (AGI). Forward-thinking AI experts argue that LLMs alone are insufficient for AGI and that breakthroughs will emerge from novel technologies rather than just accumulating more data. Additionally, the thesis proposes that vertical LLMs, tailored to specific industries or applications, will outperform horizontal ones. Instead of a one-size-fits-all approach, leveraging multiple vertical LLMs will yield better results in most scenarios.

Thesis 2: AI's impact on markets is specific to individual companies, not entire industries.
Contrary to previous technology waves, AI's influence will not reshape entire markets. Instead, AI's application will be targeted at specific companies. Rather than analyzing the impact on industries, a more effective approach is to examine how AI enhances different steps in the corporate value chain. Companies with value chains conducive to leveraging AI will be at an advantage. Therefore, identifying shared steps in the value chain becomes crucial, potentially outweighing product or customer similarities.

Thesis 3: AI erodes traditional competitive advantages.
The belief that competitive advantages in the age of AI are eroded is the crux of the third thesis. Exemplified by Google's "we have no moat" memo, this perspective highlights that AI levels the playing field, rendering traditional barriers to entry less effective. Companies that embrace AI and incorporate it into their workflows stand to gain a competitive edge. To thrive in the AI era, businesses must adapt their strategies to leverage intelligence and learning, challenging the notion of established competitive advantages.

Thesis 4: AI bifurcates the economy into real and AI worlds.
The fourth thesis predicts that AI will divide the economy into two realms: the real world and the AI world. As AI agents become more prevalent, customer acquisition channels will converge into automated agents, transforming the way users interact with products and services. This shift may result in fewer interactions with traditional software application interfaces. Companies relying on product-led growth or community-driven go-to-market strategies may need to reassess their approaches as AI continues to reshape user experiences.

Thesis 5: Enhancing user onboarding through personal experience.
Moving beyond AI, this thesis focuses on optimizing user onboarding experiences. Recognizing that the moment a user engages with a product is of utmost importance, it encourages product creators to leverage this attention effectively. Counterintuitively, longer onboarding flows tend to outperform shorter ones. By providing users with a comprehensive onboarding experience, even if fewer users complete the flow, the engagement and understanding of the product significantly improve. Regularly evaluating and questioning the onboarding process, considering user confusion and anxieties, allows for continuous improvement.

Conclusion:
In conclusion, the intersection of AI and user onboarding reveals intriguing insights for early-stage investors. By challenging conventional wisdom, these contrarian theses shed light on the future landscape of AI and the importance of optimizing user onboarding. To capitalize on these trends, here are three actionable pieces of advice:

1. Embrace verticalization: Instead of relying solely on horizontal LLMs, explore vertical models tailored to specific industries or applications.

2. Reassess competitive advantages: With AI's leveling effect, focus on incorporating intelligence and learning into workflows to stay ahead of the competition.

3. Prioritize comprehensive onboarding experiences: Lengthier onboarding flows, accompanied by regular evaluation and user feedback, lead to better user engagement and understanding.

By staying attuned to the evolving AI landscape and continuously improving user onboarding, early-stage investors can position themselves for success in the AI-driven future.