AI and Lithium Supply Chains

AI, Lithium and the New Mineral Rush: Why This Time Must Be Different

Key Takeaways

• AI is transforming lithium supply chains by enabling more precise geological mapping and real-time environmental and social risk monitoring, which could empower producing countries like Zimbabwe to negotiate from a position of knowledge rather than opacity.

• The integration of AI in lithium extraction offers a critical opportunity to balance the urgent green transition with social equity and environmental stewardship by embedding impact tracking and transparency directly into decision-making processes.

• For AI to serve public interest rather than entrench existing power imbalances, governments must build local analytic capacity, enforce transparent governance, and shape AI applications as commons rather than proprietary tools controlled by foreign firms.

By George Katito*

Artificial intelligence is about to reshape lithium supply chains, not just by moving rocks and trucks faster, but by forcing a reckoning with who wins, who loses, and what is left behind for landscapes and communities. Used well, AI can illuminate the environmental footprint of extraction, surface social risks early, and help producer countries such as Zimbabwe negotiate on the basis of knowledge rather than hope, rather than simply turbocharging a new extractive scramble wrapped in the language of the “green transition".

Moving from the Past: Data-Era Choices

The canonical story of colonial mineral exploitation in southern Africa is one of asymmetry: foreign companies arrived with capital, lawyers, and geopolitical backing, while local rulers confronted an opaque mix of contracts, promises, and threats. In what became southern Rhodesia, concessions to the British South Africa Company were negotiated without meaningful geological insight on either side, but with a profound imbalance in political and financial power that locked African polities into deals they could not later reshape.Today’s lithium rush unfolds under radically different technical conditions but disturbingly familiar political ones. Governments now know that subsurface resources can underpin entire phases of industrialisation, yet many still negotiate with partial data about the true value, costs, and volatility of the minerals they are licensing. The core question is whether AI-driven sensing, modelling, and forecasting can be turned from a tool of extractive advantage into a basis for more symmetrical bargaining power—especially for countries like Zimbabwe, which sit on strategically important, but still under-characterised, lithium-bearing formations.Why AI Changes Mineral ExplorationModern mineral exploration already relies on remote sensing, geophysics, and geochemistry, but AI allows these fragments of information to be fused into more granular, probabilistic pictures of ore bodies. Convolutional neural networks and related architectures can detect subtle patterns in satellite imagery, hyperspectral data, and drill-core logs that signal mineralisation and alteration zones far more reliably than manual interpretation alone.As models are trained on larger labelled datasets, they can estimate not only where lithium is likely to be found, but also its probable grade, host rock characteristics, and processing challenges—critical inputs into any serious valuation of a deposit. If geological surveys and state-owned entities in producer countries can access or co-develop these tools, the resulting models become bargaining chips in negotiations with international firms and commodity traders, reducing the “information rent” historically captured by foreign operators.

Seeing Lithium as a Socio-Technical System

Lithium extraction is already a site of intense environmental and social controversy. Brine and hard-rock projects can consume large volumes of water, often in water-scarce regions where agriculture and Indigenous livelihoods compete for the same resources, and each tonne of mined lithium can be associated with significant carbon emissions, groundwater risks, and biodiversity loss.AI offers a way to track these impacts in near real time and to embed them directly into decision-making. Satellite-based monitoring, combined with ground-based sensors and machine-learning models, can estimate water use, detect changes in vegetation cover, and flag potential contamination plumes as they develop, rather than years later through litigation or protest. When these environmental signals are overlaid with spatially explicit social data—settlement patterns, tenure regimes, migration flows, protest events, health indicators—lithium ceases to be just a “reserve” in a geological sense and becomes legible as a socio-technical system in which risks propagate along both ecological and social networks.

AI for Social and Political Intelligence

Lithium-producing regions such as Zimbabwe are not blank spaces on a map; they are dense with histories of land dispossession, labour struggles, and contested authority among traditional leaders, local government, and central ministries. New mining projects can reactivate old grievances, produce new forms of inequality, and reshape local power structures in ways that matter for both legitimacy and long-term project stability.AI techniques can help map and anticipate these dynamics, but only if deployed with care. Natural language processing applied to local media, parliamentary debates, and community submissions can surface emerging narratives about lithium projects, including concerns about employment, pollution, or benefit sharing. Network analysis of corporate structures and political connections can reveal who really controls which licences and contractors, and where conflicts of interest may lie. Combined with qualitative fieldwork, these tools could enable regulators, communities, and investors to detect “social tipping points”—moments when grievances are likely to spill into protest or violence—well before they appear on the front page.

Zimbabwe, Lithium, and Bargaining Power

Zimbabwe has become an increasingly significant player in the global lithium landscape, with several hard-rock projects attracting substantial foreign investment in recent years. For a government seeking foreign exchange and industrialisation, the temptation is to accelerate approvals and build infrastructure that accommodates investor timelines rather than domestic development priorities.Yet the same AI-based geological and social mapping that companies use internally can, in principle, be repurposed to strengthen public oversight. A state that can quantify likely resource volumes, processing pathways, environmental costs, and local livelihood impacts is better positioned to design auctions, set progressive royalty and windfall tax regimes, and negotiate for onshore processing, battery precursor plants, or recycling facilities. AI-enabled knowledge can underpin a shift from “dig and ship” to more complex forms of value capture—provided that analytical capacity resides in public institutions and local research ecosystems, not only in multinational partners.

On Environmental Accounting

One of the most striking features of the current moment is the collision between two imperatives: rapid decarbonisation and the need to avoid reproducing extractive frontiers that sacrifice local ecologies and communities. Life-cycle assessments already show that much of the climate impact associated with lithium-ion batteries comes from upstream mining and processing rather than from use in electric vehicles, raising awkward questions about the “greenness” of the transition.AI can power a more sophisticated environmental accounting regime. Global and national dashboards could track cumulative extraction volumes, water use, ecosystem impacts, and projected depletion under different demand scenarios, treating ore bodies as part of a finite ecological commons rather than purely financial assets. These same tools can also support decisions about when recycling and urban mining become more cost-effective and less environmentally damaging than greenfield projects—an inflection point that could arrive sooner if policy explicitly targets it.

Infrastructure, Energy, and Digital Inequity

There are, however, hard constraints on whose AI counts. Large-scale models require data centres, connectivity, and reliable power; across much of Africa, these remain thinly distributed and unevenly regulated. Hosting and training substantial models often still depends on infrastructure located outside the continent, raising concerns around data sovereignty and dependency on foreign cloud providers for critical resource-governance functions.Embedding AI for lithium governance in African contexts therefore raises a dual challenge. On the one hand, expanding data-centre capacity risks increasing energy demand and, if poorly planned, emissions. On the other, it opens a window to couple digital infrastructure expansion with renewable energy build-out—solar, wind, and storage—so that the very systems used to govern lithium and other critical minerals are powered by cleaner grids. This alignment is not automatic; it requires regulators to set standards on energy sourcing for data centres and to integrate mineral, energy, and digital strategies rather than treating them as separate policy silos.

Limits, Risks, and Political Choices

AI is not a neutral oracle and cannot substitute for politics. Models trained on biased or incomplete data can misrepresent both geological potential and social risk, reinforcing inequalities rather than correcting them, while proprietary systems controlled by a handful of firms may deepen information asymmetries if states and communities are forced to rely on unverifiable outputs to make long-term decisions about land, water, and labour.These risks point to the need for explicit governance of AI itself within the lithium value chain. Open data standards for geological and environmental information, public-interest algorithms developed through collaborations between universities, geological surveys, and civil-society organisations, and clear rules on how AI-derived insights feed into licensing and monitoring processes can all mitigate the concentration of technical power. Ultimately, the question is not whether AI will be used in lithium extraction, but who will shape its objectives, validate its outputs, and bear responsibility when its predictions fail.

A Different Kind of Rush Is Possible

The current mineral frenzy does not have to replay the end of the nineteenth century with better sensors and faster servers. Where earlier generations of African leaders confronted concession-hunters with little more than intuition and fragmentary reports, contemporary governments can, in principle, enter negotiations armed with high-resolution maps of subsoil assets, dynamic models of environmental harm, and detailed pictures of social vulnerability and political risk.That informational shift creates the possibility—not the guarantee—of more equitable, sustainable, and democratically accountable mineral regimes. The critical task for policymakers, researchers, and communities in lithium-rich countries such as Zimbabwe is to appropriate these tools for public ends: to monitor environmental thresholds, anticipate social tipping points, hardwire intergenerational equity into extraction decisions, and insist that the infrastructures of the green transition are themselves governed as commons, not private empires.

*George Katito  is  CEO/Founder of  Geostratagem