Gen Z and Millennials, two generations known for their heavy use of social media, have shown a significant shift in their trust when it comes to product recommendations. While social media usage for researching brands and products has increased by nearly 40 percent between 2015 and 2019, these younger generations still trust recommendations from friends and family more than they do from influencers. In fact, when asked what inspired them to make a purchase in the last month, 48 percent cited a discount on a product, followed by recommendations from friends and family at 39 percent. Interestingly, consumers actually trust smaller influencers more, with 56 percent of US and UK respondents considering influencers with up to 50,000 followers to be the most credible.

Gen Z and Millennials, two generations known for their heavy use of social media, have shown a significant shift in their trust when it comes to product recommendations. While social media usage for researching brands and products has increased by nearly 40 percent between 2015 and 2019, these younger generations still trust recommendations from friends and family more than they do from influencers. In fact, when asked what inspired them to make a purchase in the last month, 48 percent cited a discount on a product, followed by recommendations from friends and family at 39 percent. Interestingly, consumers actually trust smaller influencers more, with 56 percent of US and UK respondents considering influencers with up to 50,000 followers to be the most credible.

Moving on to the topic of AI, it is predicted that AI will push creation costs towards zero, much like the internet pushed distribution costs to zero. The economic value derived from AI will not be evenly distributed along the value chain, but rather concentrated among infrastructure players and end-point applications. Two key theories about AI emerge from this discussion. First, fine-tuned models are more likely to win battles, while foundational models win wars. Fine-tuning allows for cheaper requests in narrow use cases, making it more cost-effective in the long run. On the other hand, foundational models take step-changes in performance but lack the flexibility of gradual improvement. Second, long-term model differentiation comes from data-generating use cases. AI providers that can build feedback mechanisms into their products and use that feedback to retrain the model will have a competitive advantage. This suggests that startups capturing the model to output to retrain model loop will have a greater chance of success.

Open source also plays a significant role in the AI landscape. It has the potential to turn AI startups into consulting shops rather than SaaS companies. Open-source AI projects can erode market power and put downward pressure on pricing for model providers. However, OpenAI has found a way around this by taking equity stakes in promising startups with its $100M venture fund. This allows them to maintain control over the pricing of their models and avoid the downward pressure caused by competition with free alternatives.

When it comes to AI endpoints, many companies compete on their go-to-market (GTM) strategies rather than the AI itself. Jasper.AI, an AI writing and content tool, saw rapid growth in ARR within months of launching their AI product. This success led to many competitors entering the market, relying on the same open-source or AI models. To compete effectively, endpoints selling AI services will need to fully own fine-tuned models or rely on other attributes typically associated with SaaS startups. Additionally, major software providers are expected to integrate generative AI into their products in the next six months, further increasing competition in the market.

AI is not expected to disrupt the creator economy; instead, it will amplify existing power law dynamics. Creators who effectively utilize AI tools to create better content at a faster pace will be able to build a larger fan base. However, the revenue distribution in the digital media world already heavily favors the top 0.01%, and AI will only exacerbate this dynamic. Content creation is ultimately a social experience, and AI can enhance the search capabilities and generative abilities of companies in this space.

Finally, the most valuable deployment of AI is often invisible. Companies that are powered by AI but never explicitly mention it can benefit from the breakthroughs that AI brings to digital interactions. AI products that enable entirely new modalities of digital interactions have the potential to revolutionize industries and create new opportunities for growth.

In conclusion, the shift in trust towards friend and family recommendations over influencers among younger generations highlights the importance of genuine connections in consumer decision-making. The theories about AI discussed in this article shed light on the potential for fine-tuned models, data-generating use cases, and invisible AI deployment to drive success in the AI industry. To thrive in this evolving landscape, businesses should consider fully owning their models, focus on GTM strategies, and leverage the power of AI to enhance content creation and distribution.