Finding "Product-Human Need Fit" in the Post-Covid Era: 6 New Theories About AI

Finding "Product-Human Need Fit" in the Post-Covid Era: 6 New Theories About AI

In the post-Covid era, individuals have found their voices amplified more than ever before. They are demanding products and experiences that align with their unique identities and values. This shift in consumer behavior has brought about the need for a new approach to innovation – one that extends beyond the privileged few and considers the overlooked basic needs of underrepresented communities. It is no longer enough to simply achieve "product-market fit," but rather, companies must strive for "product-human need fit."

The impact of the pandemic was felt by all, but it disproportionately affected underrepresented communities. As the world continues to change and people adapt, new opportunities arise. However, it is crucial to dig deep into the human motivation behind the consumers these companies serve. While efficiency, performance, and achievement are admirable, they should not blind us to the fact that a significant portion of our community is still struggling to meet their basic human needs.

The post-Covid era has also seen the rise of various social and political movements, such as Black Lives Matter, Stop AAPI Violence, and Transgender Rights. These movements have shed light on the deprivation of basic human needs that many people face. It is essential for companies to acknowledge and address these needs in their products and services.

On the other hand, we cannot discuss the future without considering the role of artificial intelligence (AI). Just as the internet revolutionized distribution costs, AI is poised to revolutionize creation costs. The economic value derived from AI will not be evenly distributed along the value chain. Instead, it will lead to rapid consolidation and power law outcomes among infrastructure players and end-point applications.

AI models are becoming increasingly accessible, with many trained on similar data sets and the underlying math widely available. The only limiting factor for companies in this space is compute power. Anyone with sufficient skills and resources can build a copycat AI product. However, the real differentiator lies in the developer community and ease of use, including UI/UX. The network effect around the ecosystem is also crucial.

Open-source AI models also put downward pricing pressure on model providers that sell access via API. When competing with free, companies often have to compromise on pricing. Fine-tuned models may win battles, but foundational models win wars. Long-term differentiation in the AI space comes from data-generating use cases.

It is worth noting that open source has transformed AI startups into consulting shops rather than software-as-a-service (SaaS) companies. The success of these startups is followed by the rapid emergence of copycats. Ultimately, the purchasing decision for AI endpoints is driven by go-to-market (GTM) strategy rather than pure vendor comparison. Sales, marketing, and overall vibe matter more than model performance.

While AI is often marketed as a transformative force, the winners in this space will be determined by software questions rather than AI capabilities. Startups competing on the basis of SaaS face a challenge, as larger companies with inherent distribution or product capabilities can more easily integrate AI into their existing products. Distribution becomes a key factor in determining success.

In a world where content creation is virtually costless, distribution becomes the critical factor. Creators who effectively leverage AI tools to produce better content at a faster pace will be able to build a substantial following. The digital media landscape already exhibits a dynamic where only a small fraction of creators generate significant revenue. AI will only amplify this trend further.

Additionally, there is a concept of "invisible AI" where companies harness the power of AI without explicitly mentioning it. These companies use AI to create something previously unimaginable, leading to delightful experiences for users.

To navigate this changing landscape, here are three actionable pieces of advice:

• 1. Prioritize understanding the human needs behind your target audience. Conduct thorough research and listen to the voices of underrepresented communities to ensure your products and services meet their requirements.

• 2. Embrace AI as a tool for innovation and differentiation. However, focus on software questions and overall user experience rather than solely relying on AI capabilities. Remember, distribution is key.

• 3. Harness the power of AI in content creation to build a loyal following. Utilize AI tools to produce high-quality content at a faster pace, allowing you to stand out in a world where content is abundant.

In conclusion, the post-Covid era calls for a new approach to innovation that considers the unique identities and values of individuals. Achieving "product-human need fit" is essential for companies to thrive. Simultaneously, the rise of AI presents both opportunities and challenges. By understanding the impact of AI on distribution, differentiation, and content creation, companies can navigate this rapidly evolving landscape successfully.