Today: Loading...
REQUEST A QUOTE

ExpoTech AI-generated data centers rely on specialized contracts and licenses that govern physical infrastructure, power sourcing, and intellectual property. At the core are Power Purchase Agreements (PPAs) to secure vast net-new electricity, Infrastructure-as-a-Service (IaaS) and Colocation Licenses for server housing, and Data & Model Licenses to handle training data and generated outputs.

1. Energy & Infrastructure Agreements

Power and hardware are the most critical, highly negotiated bottlenecks for AI-ready data centers.

  • Power Purchase Agreements (PPAs) Long-term (10-25 year) contracts designed to guarantee the data center developer a steady supply of base-load power (often net-new renewable energy). These protect buyers against spot-market volatility and help meet strict ESG targets.
  • Colocation Leases & Managed Services Because deploying custom hardware is capital-intensive, many AI firms opt for colocation leases. These agreements dictate shared power, cooling, and network redundancies, often bundling computing capacity under managed services where a third party handles server maintenance.
  • Infrastructure-as-a-Service (IaaS) / GPU Cloud Contracts Instead of building physical space, AI companies license specialized GPU Estates (compute power) from hyper-scale clouds. These outline terms like cluster availability, compute latency, and leasing models.

2. Intellectual Property (IP) & Data Licensing

AI models require continuous training and produce proprietary outputs. Contracts governing this data are highly scrutinized.

  • Data Licensing Agreements These govern how input data (both proprietary and third-party) can be used to train, refine, or fine-tune models. They strictly define limits on data ownership, retention, and whether the provider can generate "synthetic data" from a client's inputs.
  • End-User License Agreements (EULAs) In the AI context, EULAs outlines exactly what users can do with the AI-generated outputs, restricting uses for illegal activities, ethical boundaries, or reverse-engineering.

3. Operational and Compliance Contracts

Running an AI data center involves strict legal, security, and administrative oversight.

  • Master Service Agreements (MSAs) The overarching corporate contract that establishes the baseline legal and commercial conditions between the service provider and the client.
  • Service Level Agreements (SLAs) Embedded within the MSA, these specify uptime guarantees, mandatory issue-resolution response times, and penalties (like invoice credits) if a provider fails to meet agreed-upon technical performance standards.
  • Compliance & Data Sovereignty Rules Because massive datasets are processed across borders, data center contracts include specific administrative and technical safeguards (e.g., encryption standards) to align with regional privacy regulations.

4. Types of Licensing Agreements

  • Exclusive License The licensee is the only party granted the right to use the IP in a specific territory or market.
  • Non-Exclusive License The licensor can grant the same rights to multiple different licensees simultaneously.
  • Commercial & Retail Licensing Businesses use this to allow third parties to use logos, characters, or brands on physical merchandise (e.g., apparel, toys).
  • Software Licensing (SaaS) Grants users the right to use software without owning the underlying code (often guided by EULAs—End User License Agreements).
  • Technology & Patent Licensing Allows manufacturers to use patented technology or technical knowledge developed by another inventor or corporation.

5. Why these Contracts are Evolving

Because AI infrastructure is dynamic, standard IT agreements (like traditional SaaS contracts) are insufficient. Legal teams are increasingly negotiating audit and monitoring rights to ensure compliance with data privacy, as well as clear transition provisions to avoid vendor lock-in if the partnership terminates.

For further reading on how energy procurement and infrastructure are intersecting in modern AI, review the Inside the Deals Unlocking Net-New Power analysis or check out detailed guides on AI Technology Agreements and Licensing from legal experts.

Ownership of AI-Generated Improvements 

AI models evolve through use, often creating fine-tuned versions informed by proprietary data. Without clear ownership provisions, vendors may attempt to reuse improvements or claim rights over refinements. In an area of unsettled or developing law, adding certainty to your agreements and relationships can help protect your IP rights and lower costs to defend unauthorized use.  

Real-World Example: Amazon Web Services (AWS) allows customers to train models on its infrastructure but typically reserves broad rights to “service improvements”—potentially enabling AWS to indirectly benefit from customer fine-tuning. This underscores the importance of clearly defining who owns model refinements.  

Key Strategies:   

  • Define “improvements” comprehensively to cover fine-tuning, prompt engineering, derivative datasets, and model outputs, while carving out standard vendor-side service updates.  
  • Draw a distinction between general service enhancements and customer-specific refinements, ensuring that refinements trained on your proprietary data remain your property.  
  • Negotiate usage restrictions so that your refinements cannot be repurposed in vendor offerings, even in anonymized or aggregated form.  
  • Require disclosure obligations from vendors regarding how your data has influenced improvements.  
  • Secure rights to audit vendor systems to verify compliance with ownership and usage restrictions.  

 

Sample Clause: “Improvements include modifications, enhancements, fine-tuned models, and derivative works derived from the Licensed Technology. All Improvements trained on Licensee Data shall be owned exclusively by Licensee and shall not be used for other customers.”  

Joint Development & Model Training 

Collaborations involving data sharing or joint engineering teams can create uncertainty over ownership and usage rights. If not properly structured, joint development can inadvertently give competitors leverage.  

Real-World Example: The Google DeepMind–National Health Service partnership faced controversy due to unclear data sharing and ethical boundaries in developing an AI diagnostic tool.  

Key Strategies:  

  • Clarify ownership up front by spelling out whether resulting models are jointly owned, separately owned, or subject to a licensing arrangement.  
  • Use field-of-use restrictions so that even if joint models are shared, competitors cannot apply them in sensitive markets.  
  • Specify governance mechanisms (e.g., joint steering committees, regular reporting) to monitor contributions and outputs.  
  • Include data-use firewalls to ensure that shared data cannot be transferred to unrelated projects.  
  • Require survivability of rights (to use or access the jointly developed model) upon termination.  

AI Chips and Software

At this time, AI-ready data centers generally require thousands of sophisticated “AI chips.” As reported by the New York Times, Nvidia currently has more than 80% of the market share for these specialized processors. AI chips execute large scale-matrix operations and perform parallel processing faster than standard CPUs. These AI chips can perform tasks with less power consumption per operation than standard CPUs, and newer generations of AI chips are even more power-efficient. However, the demand for faster processing of large data sets often outweighs the benefits of power efficiency, as faster chips inevitably consume more power because they are used to perform ever more operations in the same time frame.

Recent AI software developments may potentially alleviate some demand for AI chips and place more focus on improvements in AI software efficiency, notably with respect to training and use of AI models. In the United States, OpenAI, Meta, Google, and Microsoft are well-known for their AI software and models that use large numbers of AI chips. DeepSeek, a Chinese AI startup, recently released its DeepSeek-R1 AI model, which it touts as being 20 to 50 times cheaper to use than OpenAI’s o1 model, depending on the specific task. Data center operators will need to keep close attention to developments in both AI hardware and software. Time will tell whether AI software improvements can quell the current demand for AI chips.

At present, AI chips remain a critical part of data center infrastructure. In the last few years, lead times on ordering high-end AI chips have ranged from a few months to the better part of a year. When new AI chips are released, lead times can be long. If you are looking to obtain AI chips for a data center located outside of the United States, restrictions may apply. For example, recent export control restrictions can be a significant obstacle to obtaining the latest and next-generation AI chips, depending on the country where the data center will be located. Interim export control restrictions limit the number of AI processors that can be exported from the United States to certain countries, and in some cases these restrictions prohibit sales entirely. To keep up with market demand, data centers will need to plan ahead for the continuous upgrade of AI chips.

Development Agreements and IP

Software development may be necessary to integrate and maintain equipment and to meet customer needs for connectivity, such as through application programming interfaces (“APIs”) and mobile and browser-based connections. Developers can design and implement custom hardware, such as dedicated processors, network switches, and environmental support systems. When contracting for these development services, it is important either to obtain ownership of the developed software or to receive a perpetual, irrevocable license to the developed software and access to any source code. Without these rights, projects may have to start from square one with a new developer in the event of a dispute.