Three AI Factory Types – Evangelos Simoudis

As 2025 drew to a close, the narrative surrounding enterprise AI began to shift. We moved from 2025 being the “Year of Experimentation” to 2026 shaping as the “Year of Deployment.” Under such a mandate, enterprises must develop and deploy their AI applications scalably, efficiently, and economically. For the large enterprise, starting with the Fortune 500, achieving these goals will require the adoption of a factory-like approach, leading to the development of AI Factories.

Introduction

In a recent RBC survey, 60% of enterprises stated their intention to move AI pilots to production, up from just 39% a year ago. However, as companies strive to become AI-first, they must employ a process for developing, deploying, and managing multiple AI applications consistently. 

Satya Nadella, Microsoft’s CEO, recently stated that model capability keeps getting better, but organizations continue to struggle to achieve reliable outcomes as deployments stall. As Nadela stated, usefulness will decide value. Better models don’t automatically result in value. The enterprise needs better processes and adequately trained employees in addition to the proper orchestration layers and agentic applications. 

Few enterprises, including most of the Fortune 500, have established such a process. As Pitchbook recently reported, the European countries with the largest economies continue to lag in AI-focused venture investments. To address this problem, I introduced the concept of the Enterprise AI Factory. In this post, I compare the Enterprise AI Factory, a US market-driven concept, with the Sovereign AI Factories that the European Union and China are establishing.

The European Union’s AI Factories

Under the supervision of the European AI Office, the European Union (EU) is building an AI factory network. Each factory will:

Provide shared access to large-scale compute.
Co-locate data, models, talent, and experimentation environments.
Include accelerators that support startups, SMEs, large enterprises, academic and corporate researchers, and the public sector.
Create federated, cross-border AI capacity.

This is a two-tier network. The first tier consists of Sector Specialist AI Factories that offer compute, data, and AI expertise to key industries. For example, Stuttgart’s AI factory focuses on manufacturing industries, including automotive, whereas Bologna’s focuses on agriculture. A company like BMW can use a process developed by the Stuttgart AI factory to scale its AI application deployment efforts, but also to fine-tune a factory-developed model using its proprietary data.

The second tier consists of gigafactories that will be used exclusively to develop foundation models that will rival those developed by the US and China. For example, Vienna will be one of the AI gigafactory cities.

The EU model combines technology, incubation, and collaboration opportunities. In this way, the path to adopting and scaling AI becomes easier for enterprises. In addition, the model facilitates the collaboration between AI startups and enterprises, enabling them to access cutting-edge AI technologies developed by the startups. 

China’s AI Networks

China does not have a direct analogue to the EU’s AI Factories as a single, branded, national program. Instead, it embeds AI factory–like capabilities inside its industrial policy, city governance, and state–enterprise system. China does not need AI factories as distinct entities because it has already designated AI as critical infrastructure. Cities and state enterprises act as production environments. Technology and regulation are co-designed.

China created three interlocking mechanisms:

National Computing Power Hubs that include massive GPU clusters. These are aligned with enterprise AI, smart cities, defense, and surveillance. 
City-level AI platforms, e.g., Hangzhou City Brain, that provide compute, data access, and tools. These are production-oriented and prioritize policy-aligned use cases. They are deeply integrated with municipal data (traffic, mobility, energy, public safety), and are tightly coupled with local champions, e.g., Alibaba in Hangzhou, and Tencent in Shenzhen.
Hyperscalers, e.g., Alibaba Cloud and Baidu AI Cloud, that train foundation models, serve government and enterprise customers, and are aligned with national objectives through regulation and procurement.

The Chinese model provides a centralized, top-down approach to helping enterprises with their AI adoption and scaling. It emphasizes technology self-sufficiency and broadening the use of AI across the enterprise and government. While the Computing Hubs and hyperscalers continue to rely on US semiconductors, e.g., NVIDIA GPUs, China’s efforts are now focused on self-sufficiency even in that area.

Comparing the EU and Chinese Models

The table below compares the EU AI Factories to China’s AI Networks: 

AI Factory Comparison

Analysis

If we could summarize the three approaches, we would say that the US supports the technology provider and lets the enterprise decide how to experiment with and deploy AI, the EU tries to support both the technology provider and the enterprise, and China directs the technology provider and the enterprise.

To date, the US federal government has adopted the approach of supporting the AI technology providers, exemplified by the support to the so-called Magnificent 7, through specific policies, reduced regulation, and national security-oriented investments. As a result of these policies, hyperscalers and other technology companies are making and planning investments of unparalleled scale in various AI technologies, including data centers and foundation models. 

Recognizing the importance of embodied AI to many industries as a productivity enhancer and cost reducer, in addition to the previously published AI Action Plan, the US government is working to release a national robotics strategy. 

The US market expects and compels SMEs and large enterprises to find their own way to adopt and scale AI. In the process, they have to determine what technologies and tools to license, build, or acquire, what people to retrain and hire, and what processes to adopt. To large enterprises, we recommend that they consider developing the knowledge, agent, and orchestration layers of the AI Stack that is at the core of their AI Factory. These three layers encapsulate the enterprise’s proprietary knowledge and will likely provide it with the highest ROI. 

SMEs may find it easier to utilize tools provided by hyperscalers and foundation model providers, and augment them with third-party AI applications. 

EU’s AI Factories benefit companies that lack an internal AI industrialization layer. They help SMEs, most of which are missing AI capability, by providing them with the what and how. The help large enterprises by providing the AI scaling machinery that many enterprises are missing. The winners will be SMEs that use the AI Factories to quickly move from first deployment to repeatable adoption and large enterprises that use the AI Factories as training grounds for organizational change, and not just infrastructure. In other words, SMEs start by “borrowing” the capabilities of an AI Factory. Large enterprises start by “borrowing” one to learn how to build their own.

None of the EU’s AI factories focus on embodied AI. This is a major omission and will negatively impact countries with a high concentration of manufacturing, logistics, and biotech, e.g., Germany, France, and others, where embodied AI is expected to play a key role, e.g., dark factories. 

China treats AI and “Embodied Intelligence” as a national priority under central planning. The country’s 15th 5-Year Plan specifically mentions AI and embodied AI. As such, its approach to AI Factories differs fundamentally from that of both the US and the EU. China embeds AI factories directly into its industrial, urban, and enterprise operating models. The state, often at the city or sector level, co-designs and co-locates compute, data, model development, deployment, and regulation. 

Large enterprises and local governments are not expected to decide whether or how to adopt AI at scale. The central government instructs them to do so, with infrastructure, capital, and data access. As a result, China has largely bypassed the “pilot-to-production” bottleneck that constrains many Western enterprises. Successful companies treat AI deployment as an execution problem. They enforce organizational alignment instead of negotiating it.

China has a commanding lead in the physical application of AI. It controls the bulk of the supply chain for critical components like rare earth magnets, battery cells, and motors. Chinese vertical integration allows them to produce hardware at 1/10th to 1/20th the cost of global peers. The country benefits from massive, centralized datasets. For example, some Chinese firms claim to possess the world’s largest dexterous manipulation datasets for training. Consumers show a high willingness to adopt AI and embodied AI. 

Conclusion

Seen through this lens, the three approaches reveal distinct theories of economic transformation. The US optimizes for innovation velocity by empowering technology providers and accepting uneven enterprise outcomes. The EU optimizes for inclusion and industrial continuity. It does this by subsidizing AI capability and AI industrialization. It assumes that markets alone would not deliver them. China optimizes for capability accumulation and deployment speed by tightly coupling state priorities, enterprises, and infrastructure. The implications are clear. The US will continue to dominate foundational technologies. The EU will determine whether its enterprises can cross the threshold from AI-enabled to AI-first. China will continue to lead in the large-scale operationalization of AI. The long-term competitiveness of each region will depend on how effectively AI factories drive sustained enterprise productivity and national economic advantage.