The Mine as an AI Laboratory

Latin America has historically been seen as an extractive region: Chile and Peru through copper; Brazil through iron; Argentina through lithium; Colombia through coal, gold and other resources. Now, Latin American mining also produces an immense amount of industrial data that can train, validate and refine global artificial intelligence systems.

For decades, regional mining law revolved around a familiar set of issues: ownership of concessions, environmental permits, easements, operating contracts, foreign investment, royalties and the distribution of rents. These will remain central, but digitalization adds a distinct layer: the informational one. In a modern operation, value also lies in the data that describes how minerals are explored for, extracted, processed, transported and optimized.

Every sensor installed on a drill rig, every satellite image used to delimit a deposit, every piece of telemetry from an autonomous truck and every parameter calibrated in a mill generates information that can feed predictive models. That information is not an operational byproduct: it is a strategic asset. And the underlying legal question, which many contracts still fail to answer with sufficient precision, is who may learn from it, and within what limits.

Mining AI looks little like consumer AI. It is not primarily a conversational assistant or a text generator. It is a technological infrastructure that connects sensors, images, geological information, predictive models, digital twins, automation, maintenance, operational safety and process decisions.

In this context, software becomes a complete chain: sensor, data, model, prediction, operational decision and system learning. Each link has a different legal regime, and it is the sum of them all that generates value.

The most relevant applications are no longer marginal: optimization of crushing, flotation and milling; predictive maintenance of critical assets; remote operation and fleet automation; digital twins to simulate and adjust parameters in real time; computer vision and drones for safety, inspection and environmental monitoring; real-time ore characterization; and satellite geophysics and artificial intelligence for critical mineral exploration.

Chile offers especially visible examples. BHP has reported using generative artificial intelligence and digital twins at operations such as Escondida to support blasting strategies, ore blending and the implementation of predictive control in SAG mills. Codelco, for its part, signed an agreement with Microsoft to evaluate initiatives in artificial intelligence, advanced analytics, automation, autonomous systems and digital security. These cases show that the region is not merely a consumer of technology: its operations are places where that technology is tested under real conditions of high geological, technical, operational and regulatory complexity.

The phenomenon is also being driven by startups and specialized providers that are transforming business models precisely around converting mining data into decisions. MineSense combines hardware, software and proprietary algorithms to characterize ore in real time. Plotlogic uses sensors and artificial intelligence to deliver geological and material information within operational timeframes. Fleet Space applies satellite technologies, geophysics and AI to exploration. KoBold Metals has built an exploration thesis based on large volumes of geological information and predictive models for critical minerals. In all these cases, competitive advantage is not explained by an isolated algorithm, but by the integration of sensors, data, models, know-how and deployment on site.

The Big Question: Who Captures the Learning?

The legal consequence is clear: when a mining company contracts software, SaaS platforms, sensors, cloud services, digital twins or artificial intelligence solutions, it is not just buying a tool. In practice, it may be enabling a third party to access critical operational data. The relevant contractual question is what that provider can do with that information.

In our view, what matters is distinguishing levels of use. A first level is using the data to provide the contracted service. A second level is using that data to improve the provider's own model, including retraining or tuning with data from the operation. A third level is aggregating data from different operations to generate benchmarks, generic models or sector-wide knowledge. A fourth, even more sensitive level is using data, or learnings derived from one operation, to develop or improve solutions that will later be offered to third parties, including competitors.

In practice, many industrial contracts still do not clearly separate these scenarios. Nor do they always resolve who owns the model improvements generated during operation, what happens to the learnings when the contract ends, whether the provider may anonymize or aggregate information to reuse it freely, or what audit rights the mining company has over automated decisions that affect its production, safety or environmental compliance.

The most significant disputes in the coming years will probably not arise from literal copying of source code, but from the appropriation of industrial data, models calibrated on site, improvements generated during operation and technical know-how derived from use.

Patents, Trade Secrets and Contracts

A patent makes sense when there is a concrete technical solution: a sensor installed on a shovel or conveyor belt, a method for processing geophysical signals, a real-time ore classification system, an autonomous navigation architecture or a predictive process control mechanism. What is protectable is not the abstract algorithm, but its technical application to a specific problem that is verifiable in the physical world.

In that regard, the source code implementing the AI is not protectable by patent; however, AI can be incorporated depending on how the invention is presented. In a patent, AI can be contemplated as part of an industrial device, system or process in which the AI is used to operate a component of the device or system or to execute a function or action of the process; as part of an industrial process in which the AI is a form of implementing it; and/or as a computer-implemented invention when a direct technical contribution of the AI that solves a technical problem can be demonstrated. In any of these cases, it is necessary to explain the AI's contribution and its specific implementation, avoiding closed processes or “black boxes”, and to describe how the results are achieved, the models/functions used, the training processes or learning models, any adaptations made and/or the architecture and physical means required for its implementation.

Copyright protects another layer: source code, technical documentation, interfaces, dashboards and other expressive elements of software. Its advantage is that it arises with creation and does not require constitutive registration. Its limit, besides evidentiary capacity, is that it does not protect ideas, methods, functionalities or underlying algorithms.

Trade secrets, by contrast, will probably be the most strategic instrument for advanced mining AI. Training datasets, model parameters, calibrated weights, operating rules, decision thresholds, implementation methodologies and knowledge accumulated on site are assets that often should not be disclosed in a patent. But protecting them requires reasonable and effective confidentiality measures: technical, contractual and organizational.

The contract is the piece that ties all of the above together. It must define ownership of raw and derived data, training and retraining rights, ownership of outputs, restrictions on secondary use, aggregation and benchmarking, rights over improvements, the distinction between prior intellectual property and new developments, traceability of automated decisions, cybersecurity obligations, data portability, deletion or retention at contract end, and liability for failures.

In the new “smart mining”, intellectual property does not sit in a single registry. It is distributed across the architecture of the system.

AI Regulation for Mining Economies

The discussion on AI regulation in the region tends to focus on applications visible to the general public: deepfakes, facial recognition, platform bias, personal data protection, content generation or copyright. All of these matter. However, for economies whose backbone is the extractive industry, there is an equally urgent and less-addressed dimension: high-impact industrial AI, whose errors can have physical, labor, environmental and economic consequences.

For that reason, the regulatory frameworks of Chile, Peru, Brazil, Colombia and Argentina should pay particular attention to AI systems applied to critical productive infrastructure. Not to hold back their adoption, but to make it legally sustainable, by requiring traceability, human oversight, risk management, technical documentation, cybersecurity and a clear allocation of responsibilities.

In this context, data mining should also be looked at carefully: text and data mining exceptions may favor innovation, but if imprecisely designed they could facilitate the external capture of industrial knowledge or protected assets under the guise of algorithmic training. In a mining economy, this discussion connects directly with the question of who can extract value from the data generated in the territory in managing the country's key asset.

Sovereignty over Technological Information

For the legal, innovation, technology and procurement teams of mining companies, the practical agenda is concrete: audit existing contracts with SaaS, cloud, sensor, AI platform and automation system providers; review whether there are specific clauses on training, model improvement, secondary use of data, aggregation, benchmarking and restrictions on use with competitors; and organize the intellectual property strategy as an integrated package.

It will be essential to differentiate the system's levels of autonomy contractually. A tool that recommends is not the same as one that assists a human decision, and neither is the same as one that acts directly on the operation. That difference must be reflected in standards of oversight, traceability, auditing, liability and incident response. The new mining wealth lies not only in copper, lithium, iron, gold or coal; it also lies in the industrial data that make it possible to train models, calibrate algorithms and improve decisions at a global scale. Latin America has a concrete strategic opportunity: its operations are unique laboratories where industrial AI is tested under real conditions of maximum complexity.

The question is who will capture the learning it generates. Mining technological sovereignty is not played out solely in state ownership, concessions or royalties. It is also played out in who will learn from what our mines teach.