Strategic Foresight Portal

Argentine Mining Strategic Foresight Center

Global markets define mineral demand. Strategy defines what Argentina should mine.

CeProMin is designed as the intelligence architecture that translates demand signals from global value chains into differentiated mineral policy, investment priorities, and coordinated federal execution.

Strategic Logic

Demand-led mining model

Market Demand to Mineral Priorities

Filter by demand cluster

Mining Demand Foresight — Copper, Lithium & Rare Earths

2030 / 2040 / 2050 demand outlook

Internal foresight work quantifying global demand for the three minerals central to Argentina's mining strategy, benchmarked against IEA, S&P Global, Wood Mackenzie, ICSG, USGS and Adamas Intelligence, with source, year and scenario tagged on every figure.

Mineral Base demand (2024-25) 2040 range Growth multiple Best-quantified driver

Cross-cutting signals

Argentina — Copper: artisanal-scale today, global-scale pipeline

    Argentina — Lithium: producing & pipeline

      Argentina — Rare Earths: exploration stage

        Sources: IEA Global Critical Minerals Outlook 2025/2026, S&P Global, Wood Mackenzie, ICSG, USGS Mineral Commodity Summaries 2026, Adamas Intelligence, Rio Tinto / Lithium Argentina / Zijin company filings, Segemar, Panorama Minero, Buenos Aires Herald (2025-2026).

        Strategic Gaps CeProMin Addresses

        Current constraints in Argentina

        Measure

          Anticipate

            Guide

              Expected Strategic Impact

                Institutional Execution Model

                  Fusion Energy Research

                  AI foundation models for plasma science

                  IBM Research, UKAEA, and STFC Hartree Centre built TokaMind, the first AI foundation model for fusion plasma analysis. Fusion's commercial path will reshape global demand for lithium, tungsten, beryllium, and rare earth minerals — a direct strategic signal for Argentina's mineral priorities.

                  Foundation Model

                  TokaMind

                  9M-parameter model trained on MAST tokamak data. Harmonises 40 sensor signals, outperforms task-specific models, and recovers corrupted readings via masked pre-training.

                   Benchmark Suite

                  TokaMark

                  14-task benchmark suite for plasma-analysis AI. TokaMind outperforms all task-specific baselines across the full suite.

                  Programme Director

                  Leadership profile

                  Upcoming Mining Events

                  LIVE

                  Live Mining News

                  LIVE
                  Loading latest mining news…