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Open-weight AI models can widen access, enable local adaptation and make independent evaluation easier, but releasing weights can also reduce a developer’s control over safeguards and downstream use. Geothermal power offers steady, low-emissions electricity and heat that could suit data centres, including AI workloads, yet projects remain expensive, site-dependent and slow to permit. Both technologies therefore represent conditional opportunities rather than guaranteed outcomes.
What “open” means for an AI model
“Open-weight” usually means that trained model parameters can be downloaded and run by others. It does not necessarily mean that the training code, data, documentation or permissions are available. The OECD’s 2025 primer on AI openness warns that software’s “open source” label does not automatically map neatly onto AI systems.
The components that may or may not be released
- Weights: the numerical parameters needed to run or fine-tune a trained model.
- Code: the architecture, training pipeline and inference software.
- Data: training datasets, data documentation and filtering methods.
- Documentation: evaluation results, known limitations and instructions for use.
- Permissions: the license terms governing commercial use, modification and redistribution.
A release can expose one component while keeping others proprietary. Public weights therefore do not establish that the model is fully transparent or that its training data can be inspected.
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The useful question is not whether one category is universally safer. It is what a particular release lets people do, and which controls remain with the original developer.
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| Decision axis | Open-weight release | Closed-weight release |
|---|---|---|
| Access | Users may download or otherwise obtain the weights, subject to the license. | Users generally access the model through a hosted service or controlled interface. |
| Inspection and evaluation | Independent researchers can run tests on the released artifact and examine behavior in their own environments. | Testing is limited by the provider’s interface, rate limits and disclosure choices. |
| Adaptation | Organizations can fine-tune, quantize or integrate the model locally when the license permits it. | Adaptation is usually limited to provider-supported settings or APIs. |
| Hosting and privacy | Local or private deployment can reduce the need to send sensitive prompts to a third party, but the operator assumes security and maintenance duties. | The provider operates the infrastructure and updates, while customers must assess data handling and service dependence. |
| Safety mitigations | Safeguards can be modified, removed or bypassed after release; downstream operators may add their own controls. | The provider can apply centralized filters, monitoring and rapid policy changes, but users have less visibility into how they work. |
| Modification and redistribution | Who may alter or share the model depends on the license; “open-weight” alone does not settle those rights. | Modification and redistribution are normally controlled by the provider’s terms. |
Openness can lower barriers to experimentation and help smaller organizations evaluate or adapt systems they could not train from scratch. The same access can make it harder for a single developer to prevent harmful fine-tuning, remove a problematic copy or enforce a new restriction after release. The International AI Safety Report 2026 frames the policy challenge as capturing open-weight benefits while managing these distinctive risks; neither openness nor closure guarantees safety.
Capability comparisons need a date and a task
The 2026 safety report says major releases since its previous edition narrowed the gap with leading closed-weight models. That is a time-bounded observation about prominent releases, not a claim that every open model matches every closed model on every benchmark. A fair comparison should specify the model versions, evaluation set, context length, tools, latency and cost.
Questions to ask before choosing a release
- Does the model perform adequately on the intended language, domain and workload?
- Are commercial use, fine-tuning and redistribution allowed under the exact license?
- Can the organization operate the required hardware and secure model files?
- What independent evaluations cover misuse, privacy, robustness and bias?
- Who can issue safety updates, and what happens to copies that are already deployed?
Geothermal power today and in the IEA’s scenario
Geothermal remains a small part of the energy system. The International Energy Agency (IEA) says it supplies less than 1% of global energy demand. Its larger numbers are scenario estimates that depend on technology improvements, supportive policy and substantial cost reductions.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Measure | Figure and qualification |
|---|---|
| Current share | Less than 1% of global energy demand, according to the IEA’s 2024 analysis. |
| Geothermal utilization | Over 75% globally in 2023; the IEA compares this with below 30% for wind and below 15% for solar photovoltaic generation. |
| Potential contribution | Up to 15% of global electricity-demand growth to 2050 in the IEA’s scenario. |
| Modeled capacity | As much as 800 GW in that scenario. |
| Modeled annual generation | About 6,000 TWh, roughly the current electricity demand of the United States and India combined; this is modeled output, not a present-day forecast. |
These figures describe a possible expansion, not an achieved trajectory. IEA Executive Director Fatih Birol said in a 13 December 2024 IEA release: “New technologies are opening new horizons for geothermal energy across the globe, offering the possibility of meeting a significant portion of the world’s rapidly growing demand for electricity securely and cleanly.”
Why geothermal is attractive for grids and data centres
Geothermal plants can produce power continuously, unlike resources whose output varies with sunlight or wind conditions. The U.S. Department of Energy describes geothermal’s capacity factor as generally around 90% and highlights its fit for constant data-centre operations, including some AI computing. The IEA’s measured global utilization figure of over 75% in 2023 uses a different scope and metric; the two numbers should not be treated as interchangeable.
Steady generation can complement variable renewables by reducing the amount of storage or backup needed for a given reliability target. Geothermal reservoirs can also provide direct heat, creating uses beyond electricity generation.
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What makes geothermal projects difficult
Finding a suitable resource is the first hurdle. Developers face uncertainty about underground temperature, permeability and fluid flow, especially for newer approaches that seek resources outside conventional high-quality fields. Drilling and exploration can consume significant capital before a project proves productive.
- Cost: equipment, drilling and financing remain expensive; lower future costs depend on innovation and deployment at scale.
- Permitting: environmental reviews, land rights and grid interconnection can extend schedules.
- Environmental and social concerns: water use, induced seismicity, surface impacts and local acceptance must be addressed site by site.
- Lead time: the IEA says commissioning can take up to a decade.
Those constraints mean a data-centre operator cannot assume that a promising geothermal resource is nearby or that a power-purchase agreement will deliver new capacity quickly.
How the AI connection should be understood
AI model deployment can increase demand for data-centre electricity, but releasing model weights does not itself cause geothermal development. The connection is an infrastructure question: a facility with a large, steady computing load may value a firm power source if local geology, transmission and regulation make a geothermal project feasible.
The DOE’s description establishes a potential fit between geothermal’s steady output and constant data-centre loads, not a claim that geothermal can supply all AI electricity demand or that every data centre can use it. The IEA also notes growing interest from technology companies while keeping its large deployment figures conditional.
A practical way to evaluate both opportunities
- Define the AI requirement: specify the task, data sensitivity, latency and whether local hosting is necessary.
- Check the release terms: verify which components are available and what the license allows before fine-tuning or redistribution.
- Test the exact model version: use task-relevant evaluations and document safety, privacy and reliability results.
- Map the energy load: estimate continuous power demand, cooling needs, backup requirements and expected growth.
- Screen geothermal feasibility: examine resource quality, drilling risk, permitting, transmission and community impacts rather than relying on a headline capacity factor.
- Compare alternatives: assess geothermal alongside efficiency, storage, grid purchases and other firm low-emissions options on cost, timing and reliability.
For geothermal data, modeling and planning tools, the National Renewable Energy Laboratory’s geothermal data-and-tools portal lists resources such as GETEM and SAM for industry, policy and research users. These are professional analysis tools, not proof that a particular site or project will be economical.
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