AI in Mining 2025: How Canstar Resources is Redefining Critical Mineral Exploration

Industry Adoption: How Multimodal AI is Accelerating the Search for Critical Minerals

The application of artificial intelligence in heavy industry is undergoing a significant inflection point, shifting from broad, horizontal platforms to specialized, vertical solutions that solve tangible commercial problems. Between 2021 and 2024, the AI landscape was characterized by the development of powerful but generalized foundational models. Google’s launch of Gemini in December 2023, which set new performance benchmarks, exemplified this era of demonstrating raw capability. During this period, commercial applications were often in digitally-native sectors or involved broad joint ventures, such as PwC Germany’s creance.ai with Aleph Alpha, aimed at exploring AI’s potential in professional services. The primary focus was on model building and proving technological viability, with private investment in generative AI surging from $3 billion in 2022 to $25 billion in 2023, signaling immense speculative interest.

Beginning in 2025, the market pivoted decisively toward targeted, industrial-scale deployment. This change is powerfully illustrated by Canstar Resources’ strategic partnership with TerraAI in June 2025. Backed by an $11.5 million initiative, this collaboration aims to leverage AI to accelerate the exploration of critical minerals—a direct application of advanced technology to a core challenge in the energy transition supply chain. This move mirrors a broader trend of embedding AI into specific industrial workflows, seen in Google Cloud’s partnership with Qualcomm for automotive AI agents and SOPHiA GENETICS’ collaboration with AstraZeneca for oncology research. The sheer variety of these applications, from mining to medicine, indicates that AI is no longer a future concept but a present-day tool for creating competitive advantage. For the energy and resources sector, the Canstar-TerraAI deal signals a critical new opportunity: using AI to de-risk exploration and shorten discovery timelines, posing a direct threat to competitors still relying on traditional methods.

Table: Global AI Investment Landscape

Table: Strategic AI and Multimodal Partnerships

Geography of Multimodal AI Systems

Between 2021 and 2024, the development of multimodal AI was heavily concentrated in the United States. Major technology corporations like Google, Microsoft, and NVIDIA, along with a thriving venture capital ecosystem, established the U.S. as the undisputed epicenter of foundational model research and investment. This was reinforced by initiatives like the U.S. Department of State’s Partnership for Global Inclusivity on AI, which featured a roster of exclusively American tech giants. While other hubs existed, such as Germany’s Aleph Alpha and Canada’s Mila research institute, the vast majority of capital and strategic corporate leadership originated from the U.S.

From 2025 onwards, while the U.S. remains the core of AI innovation—evidenced by the record-setting $2 billion seed round for California-based Thinking Machines—a distinct pattern of regional and sovereign AI deployment has emerged. The Government of Canada’s partnership with Cohere to build sovereign AI capabilities is a prime example of this strategic localization. Canstar Resources, a Canadian company, partnering with U.S.-backed TerraAI for exploration within Canada, further highlights this trend of applying globally developed technology to national priorities. Similarly, the IBM-BharatGen collaboration in India to create Indic language models and the Gcore-Ezditek joint venture to build an “AI factory” in the UAE signal a global decentralization of AI application. For critical minerals, this geographical shift means that while the core AI technology may originate in the U.S., its commercial and strategic impact will be defined by its deployment in resource-rich nations like Canada, which are actively fostering ecosystems to support it.

Technology Maturity of Multimodal AI Systems

The period from 2021 to 2024 was defined by the maturation of AI technology in a lab-like environment, focused on achieving and surpassing performance benchmarks. The release of Google’s Gemini in late 2023, with its state-of-the-art results on academic tests like the MMMU benchmark, was characteristic of this phase. Technology was largely in the R&D and early pilot stage, demonstrating potential rather than proven commercial value. Partnerships formed during this time, like that between PwC and Aleph Alpha, were largely exploratory, aiming to discover how this powerful new technology could be applied to business.

In contrast, 2025 marks the transition of multimodal AI from the lab to the field. The technology is now moving firmly into the commercial and scaling phases. The Canstar Resources and TerraAI partnership is not a pilot but a commercial agreement to accelerate exploration, using AI as a tool for a defined business outcome. This is mirrored by product launches like Peloton IQ and Thinking Machines’ “Tinker,” which package complex AI into tangible consumer and developer products. Furthermore, the ecosystem is now focused on enabling scale, as seen in the crucial hardware partnership between OpenAI and AMD for chip supply and BlackRock’s AI Infrastructure Partnership. For the mining industry, the AI technology leveraged by TerraAI is past the proof-of-concept stage; it is a commercially available capability validated by backing from a top-tier venture firm like Khosla Ventures. This signals that the market timing for adopting AI in mineral exploration is now, shifting the focus from technological risk to execution and integration risk.

Table: SWOT Analysis of AI in Critical Minerals Exploration

Forward-Looking Insights and Summary

The data from 2025 clearly signals that the era of speculative AI is giving way to an era of industrial application, and Canstar Resources’ partnership with TerraAI is a leading indicator for the natural resources sector. This move establishes a new competitive benchmark where AI-driven exploration is no longer a novelty but a strategic necessity. Looking ahead, we should expect a wave of similar partnerships between resource holders and specialized AI firms, as the pressure mounts to secure supply chains for the energy transition.

The most important signal to watch will be the tangible outcomes of the Canstar/TerraAI initiative: any announcements of accelerated discovery, reduced exploration costs, or higher-probability drill targets will serve as powerful validation for the entire industry. Concurrently, expect governments like Canada to amplify their support for such ventures to build sovereign advantages in critical mineral supply. The next technological evolution will likely involve integrating even more diverse data—from satellite imagery and hyperspectral data to real-time market signals—into autonomous “AI agents” that can recommend and even manage exploration campaigns. For energy executives, investors, and strategists, the key takeaway is clear: tracking which companies are effectively integrating AI into their upstream operations is now fundamental to competitive intelligence. The time to analyze this trend is now, and a dedicated research platform can provide the depth needed to stay ahead.

Frequently Asked Questions

What is the main shift in the AI industry that the article highlights for 2025?
The article highlights a significant shift from developing broad, general-purpose foundational AI models (2021-2024) to deploying targeted, specialized AI solutions for specific industrial problems. This transition is moving AI from the lab to the field, where it is used to create tangible commercial value in sectors like mining, medicine, and automotive.

Why is the Canstar Resources and TerraAI partnership considered significant?
The partnership is significant because it exemplifies the new era of industrial AI application. With an $11.5 million initiative, it’s a direct, commercial-scale deployment of AI to solve a critical challenge in the energy transition supply chain—accelerating the discovery of critical minerals. It signals that AI is no longer a future concept but a present-day tool for creating a competitive advantage and sets a new benchmark for the mining industry.

According to the SWOT analysis, how has the primary threat related to AI for companies changed between 2023 and 2025?
The primary threat has shifted from a development challenge to an adoption risk. In 2021-2023, the threat was the high barrier to entry for building AI from scratch. By 2024-2025, with commercially viable AI tools available, the threat is facing a significant competitive disadvantage by failing to integrate these capabilities, while competitors use them to de-risk exploration and shorten discovery timelines.

What is multimodal AI and how is it being applied in the context of this article?
Multimodal AI refers to systems capable of processing and analyzing various types of data, such as text, images, and complex geological information. In the context of the article, Canstar Resources is leveraging TerraAI’s multimodal platform to analyze vast and diverse datasets related to mineral exploration, helping to identify patterns and pinpoint high-probability drill targets more effectively than traditional methods.

What does the article predict will be the next technological evolution for AI in mineral exploration?
The article predicts the next evolution will involve the use of autonomous ‘AI agents.’ These advanced systems would integrate an even wider range of data—including satellite imagery, hyperspectral data, and real-time market signals—to not only recommend but also potentially manage entire exploration campaigns, further increasing efficiency and the speed of discovery.