AI’s Race to Zero: How Open Models, Cheap Training, and Data Wars are Redefining Value.

Artificial intelligence (AI) has evolved from an emerging technology to a core driver of economic transformation across various sectors, including finance, healthcare, manufacturing, and entertainment. However, as AI matures, it undergoes an inevitable process of commoditization, where code and models become widely accessible, reducing their role as sources of competitive advantage. Consequently, firms no longer compete solely on algorithmic sophistication but increasingly seek differentiation through strategic business models and exclusive access to high-quality data.

This essay explores three fundamental dynamics shaping AI’s evolution and eventual commoditization: (i) the necessity of effective public-private coordination in AI investment, (ii) the strategic dilemma of being a first mover versus adopting a second-mover strategy in AI development, and (iii) the critical role of data as the key differentiator in an industry where AI models are becoming ubiquitous.

From my perspective, I argue that AI’s transition into a commodity does not diminish its value but rather shifts the competitive landscape. The advantage will no longer be in the models themselves but in organizations' ability to access and process unique data while integrating regulatory and ethical frameworks to ensure trust and sustainability in AI applications. Winning in AI won’t be about smarter code, but smarter data—that’s the real bet.

1. Public-Private Coordination in AI Investment

Developing cutting-edge AI requires substantial investment in infrastructure, research, and human capital. While private-sector firms have led AI innovation, public investment remains crucial in funding fundamental research, establishing ethical AI regulations, and promoting open standards (Mazzucato, 2018; Agrawal, Gans, & Goldfarb, 2019).

Economically, AI exhibits characteristics of a public good, justifying state intervention to prevent innovation from being monopolized by a handful of corporations (Brynjolfsson & McAfee, 2017). The role of governments in financing AI research, as seen in the early funding of OpenAI by the U.S. government or the European Union’s push for responsible AI initiatives, underscores the importance of public-private collaboration.

Nevertheless, the private sector remains a pivotal driver of AI advancement. Companies like Google DeepMind, Microsoft, and Meta have developed large-scale AI infrastructures and advanced models while also promoting open standards—such as Meta’s LLaMA and Mistral AI’s recent open-source releases (Varian, 2018). The challenge lies in balancing market incentives with state intervention to mitigate market failures that could hinder innovation or lead to adverse externalities.

2. The Second-Mover Strategy in AI Development

The strategic choice between being a first mover (first mover advantage) and waiting to optimize a technology (second mover advantage) has been extensively studied in economics and game theory (Rodrik, 2004; Farrell & Klemperer, 2007). While first movers may capture early market share and establish strong brand recognition, they often face high experimentation costs and uncertainty. In contrast, second movers can learn from early adopters' mistakes and refine implementations for greater efficiency (Gans, 2016).

AI is filled with examples where second movers have outperformed pioneers. OpenAI was among the first to develop large-scale language models with GPT, but companies such as Anthropic and Cohere have since optimized and tailored these models for specific applications, leveraging lessons from earlier iterations (Farrell & Klemperer, 2007). Similarly, Apple introduced Siri before Google launched Google Assistant, yet Google’s second-mover approach allowed it to refine its model with superior data strategies (Varian, 2018).

In a landscape where AI models are rapidly commoditizing, I argue that the optimal strategy is not necessarily being the first to release a disruptive model but rather leveraging the second-mover advantage to optimize AI applications for commercial scalability and regulatory compliance.

3. The Commoditization of AI Code and Data Differentiation

A defining shift in the AI industry is the widespread accessibility of state-of-the-art models due to open-source initiatives. Open models such as Meta’s LLaMA, UAE’s Falcon, and Mistral AI’s releases illustrate how sophisticated AI architectures are no longer exclusive to tech giants (Hernández-Orallo, 2017).

However, merely accessing AI models does not generate sustainable competitive advantage. What differentiates firms in the commoditized AI era is their ability to acquire and leverage unique, high-quality data. Companies like Bloomberg, which has trained AI models specifically on proprietary financial data, or banks with vast transactional datasets, hold significant advantages in model personalization and accuracy (Domingos, 2015; Varian, 2018).

This raises a fundamental question: If AI becomes a commodity, how can firms continue to capture value? The answer lies in building proprietary data ecosystems and integrating models into infrastructures that create barriers to entry for competitors. Additionally, regulatory oversight and ethical AI frameworks will be crucial in ensuring that differentiation in AI is not merely about data access but also about trust and transparency (Brynjolfsson & McAfee, 2017).

So...

AI’s evolution toward a commodity does not imply a reduction in its value but rather a shift in competitive dynamics. The differential advantage will no longer stem solely from developing advanced AI models but from the ability to access and process unique data.

From my experience in AI research for financial services and algorithmic ethics, I argue that AI’s future competitiveness will be determined by three key factors: (i) effective public-private coordination to ensure equitable and regulated AI development, (ii) business strategies that leverage second-mover advantages to optimize AI for commercial and regulatory applications, and (iii) the creation of proprietary data ecosystems that enable firms to extract value in a market where AI models are increasingly accessible.

Governments and corporations that understand these dynamics and act accordingly will shape the future of AI, ensuring not only its profitability but also its broader social impact.

** The graph illustrates the evolving dynamics in the artificial intelligence (AI) industry, emphasizing the commoditization of large language models (LLMs) and the shifting sources of competitive advantage.

1. The Declining Value of LLM Code: The value of proprietary AI models is decreasing over time as more open-source alternatives emerge (McKinsey, 2023; WEF, 2024). This trend follows a classic commoditization curve, where once-exclusive technology becomes widely available, reducing its differentiation power.
2. The Rise of Open-Source AI: Adoption of open-source models (e.g., Meta’s LLaMA, Mistral AI, and Falcon) is rapidly increasing, making advanced AI capabilities more accessible to businesses and researchers (World Economic Forum, 2024; Stanford AI Index, 2023).
3. Reduction in AI Training Costs: The cost of training AI models is decreasing exponentially due to improvements in computational efficiency and model optimization techniques like distillation (MIT Technology Review, 2024; Goldman Sachs, 2023).
4. The Growing Value of Proprietary Data: While models are becoming commodities, exclusive datasets are becoming the key differentiator. Companies with unique and high-quality data will hold a competitive edge in AI applications, particularly in finance, healthcare, and cybersecurity (Deloitte AI Report, 2023; PwC, 2024).

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References

• Agrawal, A., Gans, J., & Goldfarb, A. (2019). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
• Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W.W. Norton & Company.
• Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books.
• Farrell, J., & Klemperer, P. (2007). "Coordination and Lock-In: Competition with Switching Costs and Network Effects." Handbook of Industrial Organization, Vol. 3.
• Gans, J. (2016). The Disruption Dilemma. MIT Press.
• Hernández-Orallo, J. (2017). The Measure of All Minds: Evaluating Natural and Artificial Intelligence. Cambridge University Press.
• Mazzucato, M. (2018). The Entrepreneurial State: Debunking Public vs. Private Sector Myths. Penguin.
• Rodrik, D. (2004). "Industrial Policy for the Twenty-First Century." Harvard University Working Paper.
• Varian, H. (2018). "Artificial Intelligence, Economics, and Industrial Organization." NBER Working Paper No. 24839.

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