Who Will Make Money with AI? Exploring the Generative AI Value Chain

Who Will Make Money with AI? Exploring the Generative AI Value Chain

The rapid evolution of Artificial Intelligence (AI) has sparked widespread speculation about who will profit from this transformative technology. To understand the potential financial landscape, it’s essential to dissect the AI value chain, which, according to McKinsey, consists of six critical components: hardware, cloud, foundation models, model hubs, application, and services. Each segment presents unique challenges and opportunities in terms of capital investment, return on investment, and competitiveness.

1. Hardware: The High-Stakes Game

The AI hardware market is currently dominated by giants like NVIDIA, which has recently faced scrutiny from the US government due to its dominant position. This situation could potentially open doors for new players in the hardware segment. However, the barrier to entry remains high due to the substantial capital required. Only well-funded companies can realistically aspire to compete in this arena, making it a high-stakes game with potentially lucrative rewards for those who can innovate and scale efficiently.

2. Cloud: A Mature Market

The existing cloud providers are pivotal in the AI ecosystem, offering the infrastructure needed to train and deploy AI models. While the emergence of generative AI has not necessarily opened up new opportunities for emerging cloud providers, shifts in market share could occur based on strategic decisions by existing players. The cloud segment is less about entering as a new competitor and more about how existing giants like Amazon Web Services, Google Cloud, AliCloud, and Microsoft Azure can adapt and capitalize on generative AI trends.

3. Foundation Models: A Costly Race

The foundation models race is an expensive and competitive endeavor. These models require significant investment in data acquisition, computing power, and talent. The ongoing race to develop the best and most efficient AI model is fierce, with costs ranging from tens of thousands to hundreds of millions of dollars. The rapid pace at which models are replaced by superior versions complicates the ability to achieve a return on investment, making this a high-risk area of the AI value chain.

4. Model Hubs: The Power of Community

The power of platforms like Hugging Face have carved out a unique niche by fostering a robust open-source community that contributes to and benefits from shared AI models. Valued at billions of dollars , Hugging Face plays a crucial role in democratizing access to cutting-edge models, thereby influencing the broader AI landscape. Their success underscores the importance of community and collaboration in the AI ecosystem.

5. Application: Overcrowded Yet Opportunistic

The AI application market is currently overflowing with tens of thousands of applications, each vying for attention. The low barrier to entry for creating AI-driven applications means that differentiation is challenging. Success in this crowded market requires looking beyond simple use cases and addressing larger, more complex problems that offer sustainable value irrespective of underlying model advancements.

6. Services: The Unsung Hero

Contrary to some analyses that downplay the potential of services in AI, this segment, especially in B2B contexts, holds significant promise. This is why System in Motion is specializing in AI services - such as consulting, training, integration, and outsourcing. This positions us to guide other businesses through the complexities of AI implementation. By focusing on education, prototyping, and continuous improvement, service providers can help clients innovate and develop new business models centered around AI, thus capturing substantial value.

Market Performance and Future Outlook

Despite the hype surrounding AI, AI-focused ETFs have been underperforming compared to broader market indices like the S&P 500. This discrepancy highlights the possibility that AI, as a technology, might become commoditized before most players can realize substantial profits. Stakeholders in the AI value chain must therefore strategize to capture short-term returns while preparing for a future where AI could be more of a utility than a rare commodity.

The AI value chain presents a complex landscape with varying degrees of investment requirements and potential returns. For non-tech companies the critical steps are:

• understand the potential of the technology in your industry,
• generate quick returns on investment, at least faster than the rate of change of AI,
• and find the best way to generate new business model in their industry.

This is what the AI Accelerator is about. To know more watch our webinar replay or contact us .

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