The Future of AI: Consolidation, Competition, and Invisible Power

The Future of AI: Consolidation, Competition, and Invisible Power

Introduction:
Artificial intelligence (AI) is revolutionizing the way we create and consume content. Just as the internet reduced distribution costs to zero, AI is now pushing creation costs towards zero. However, the economic value generated by AI will not be evenly distributed along the value chain. Instead, it will undergo rapid consolidation and power law outcomes among infrastructure players and end-point applications. In this article, we will explore six new theories about AI and its impact on various industries.

1. Fine-tuned models win battles, foundational models win wars:
   In the world of AI, foundational models and fine-tuned models play different roles. Fine-tuned models are tailored to specific use cases, making them more cost-effective for narrow applications. On the other hand, foundational models are designed to perform broad tasks well. While fine-tuned models may excel in the short term, foundational models have the potential for gradual improvement over time.

2. Long-term model differentiation comes from data-generating use cases:
   To build feedback mechanisms and retrain AI models, providers must have access to data loops. This requires owning both the model and the endpoint solution. Startups that can capture this model-to-output-to-retrain loop will have a competitive advantage. The size and specialization of the model also play a role in this process.

3. Open source makes AI startups into consulting shops, not SaaS companies:
   Open-source AI models create challenges for AI startups trying to sell access to their models via API. When competing with free options, these startups often face downward pricing pressure. However, some companies, like OpenAI, have found innovative ways to navigate this challenge by taking equity stakes in promising startups. Open source also puts pressure on these companies to provide white glove services for continuous model tuning.

4. Most endpoints compete on GTM, not AI:
   In the AI services market, many endpoints compete based on their go-to-market (GTM) strategies rather than purely on AI capabilities. Fine-tuned models can provide a competitive advantage, but SaaS startups must also leverage inherent distribution or product capabilities to succeed. It is expected that major software providers will integrate generative AI into their products in the near future.

5. AI will not disrupt the creator economy, but amplify existing power dynamics:
   The creator economy thrives on content creation and distribution. AI tools can help creators produce better content more efficiently, leading to the accumulation of fans and revenue. However, AI will only amplify existing power law dynamics, where a small percentage of creators earn the majority of revenue. Content creation is inherently social, and AI's role in this space will further intensify winner-takes-all dynamics.

6. Invisible AI will be the most valuable deployment of AI:
   Invisible AI refers to companies that are powered by AI but do not explicitly mention it. AI products succeed when they enable entirely new modalities of digital interactions. This shift breaks traditional mental models of computing. Companies that seamlessly integrate search capabilities with generative AI can create unique and valuable experiences for users.

Actionable Advice:

1. Embrace fine-tuned models: Consider the benefits of fine-tuning AI models for specific use cases to lower costs and improve performance.
2. Leverage data loops: If you own both the model and the endpoint solution, build feedback mechanisms to create data loops that enhance model performance over time.
3. Differentiate through GTM strategies: Focus on go-to-market strategies that leverage distribution or product capabilities to stand out in a crowded AI services market.

Conclusion:
The future of AI holds immense potential for consolidation, competition, and innovation. Understanding the dynamics between foundational and fine-tuned models, the importance of data-generating use cases, and the impact of open source on AI startups is critical. Additionally, recognizing the significance of GTM strategies and the amplification of power law dynamics in the creator economy will help businesses navigate the AI landscape successfully. Finally, companies that can seamlessly integrate AI into their products and create invisible AI experiences will gain a competitive edge. As AI continues to evolve, it is essential to stay informed and adapt to the changing landscape.