Materials covers mining, metals and steel, chemicals, industrial gases, construction materials and packaging. It is capital-intensive and cyclical, and it supplies the copper, aluminum, steel and specialty gases that data centers and chip plants consume.
Representative public companies: Freeport-McMoRan (FCX), BHP (BHP), Rio Tinto (RIO), Linde (LIN), Air Products (APD), Nucor (NUE), Albemarle (ALB). Named for context only, not as recommendations.
Research current as of September 12, 2026 · Adoption stage: Scaling deployments
AI use in materials is uneven and is usually disclosed as process improvement rather than as a product. Freeport-McMoRan reported 47 million pounds of extra copper in Q2 2026 from leaching programs that use new applications, technologies and data analytics, and targets a 300 million pound annual run rate by the end of 2026. In July 2026 Chinese state media reported that Nanjing Steel uses a large language model developed with Huawei to predict converter temperatures, and that Shougang runs 45 AI agents. BHP's 2026 Xplor startup cohort includes firms applying AI to old exploration records. The clearer AI effect so far is on demand: in July 2026 Rio Tinto said up to 70% of the value of materials in a data centre comes from its commodities, and Linde and Air Products announced new gas supply builds for semiconductor plants.
| Company | What was deployed | Date |
|---|---|---|
| Freeport-McMoRan (FCX) | New applications, technologies and data analytics built into leaching processes across US and South American copper operations, plus large-scale testing of an internally developed additive at Morenci. 47 million pounds of incremental copper production in Q2 2026; target of a 300 million pound annual run rate by end of 2026. |
2026-07-23[1] |
| Nanjing Steel Group (China, not US-listed) | A specialized large language model developed with Huawei used to predict converter temperatures in steelmaking. Temperature prediction accuracy above 90%, first-grade hot metal rate up more than 10 percentage points, and energy use per unit of output down 12% (company claims reported by Chinese state media). |
2026-07-31[2] |
| Bao Gang United Steel (China, not US-listed) | A closed-loop AI smelting control system. Endpoint carbon-temperature hit rate up more than 15% and average smelting cycle shortened by 1.5 minutes (company claims reported by Chinese state media). |
2026-07-31[2] |
| BHP (BHP) | Selected 10 companies for its 2026 Xplor exploration accelerator, including RadiXplore, which uses AI to analyze historical exploration records, and Mineural, which applies machine learning with BHP's geological expertise, mainly targeting copper. | 2026-02-01[3] |
| Air Products (APD) | Agreement to build, own and operate four large air separation units, bulk gas systems and pipelines supplying nitrogen, oxygen, argon and helium to new semiconductor fabs and packaging plants in Taiwan. | 2026-07-21[4] |
Projections grounded in what is happening now, not certainties. Each one names the signal that would confirm it.
Two AI impacts converging: materials discovery (new alloys, polymers, battery chemistries) AND operational optimization. Predictive maintenance cutting mining downtime 50%, $2.3B market by 2033.
AI capturing retiring workers' institutional knowledge — solving the expertise loss problem that's plagued materials industries for decades. Dow engineering new thermal management materials specifically for AI infrastructure (immersion cooling fluids).
Physical capital moat remains — manufacturing at scale is where incumbents win.
A score of 1.00 = no change.
>1.00 means AI (net) boosts this industry's relative value.
<1.00 means AI (net) erodes it.
These are relative scores — how this industry's share of the economy shifts in relation to all 31 industries we track.
| Timeframe | Score | Range | In Plain English |
|---|---|---|---|
| 1 Year | 1.09 | 1.07 – 1.11 | Slight tailwind (+9%) |
| 2 Years | 1.20 | 1.16 – 1.24 | AI tailwind (+20%) |
| 3 Years | 1.55 | 1.45 – 1.64 | AI tailwind (+55%) |
| 5 Years | 2.18 | 1.99 – 2.36 | AI tailwind (+118%) |
| 10 Years | 2.45 | 2.23 – 2.66 | AI tailwind (+145%) |
The same engine run under three sets of assumptions. The spread between the rows shows the uncertainty. It is not a forecast of which one happens.
| Scenario | 1 yr | 2 yr | 3 yr | 5 yr | 10 yr | Rank |
|---|---|---|---|---|---|---|
| Boom Faster AI progress, energy breakthroughs | 1.10 | 1.24 | 1.58 | 2.34 | 2.92 | #18 |
| Base Base case AI progress, current trajectory energy | 1.09 | 1.20 | 1.55 | 2.18 | 2.45 | #18 |
| Doom Slower AI progress, constrained energy, market correction | 1.04 | 1.15 | 1.50 | 2.00 | 1.99 | #17 |
Each component is scored from research, weighted differently at each time horizon, and added together:
Then cascaded through 205 cross-industry relationships.
Built from earnings calls, patent filings, regulatory documents, deployment disclosures and peer-reviewed research. 205 mapped cross-industry relationships. Recalibrated continuously since February 2026, with every parameter change logged.
Refreshed with every major development. Real deployment numbers, quarterly earnings data, regulatory shifts (EU AI Act, export controls), and macro conditions like the energy bottleneck. Not a static model — a living one.
This report uses: Base case AI development pace and Current trajectory energy trajectory. Members can adjust both and see how scores change in real time.
When AI changes these industries, it ripples into Materials.
| Industry | Effect | Strength | Timing |
|---|---|---|---|
|
Consumer Durables & Apparel
AI growth in Consumer Durables & Apparel creates demand or opportunities that benefit Materials
|
Positive | 60% | Medium-term |
|
Capital Goods
AI growth in Capital Goods creates demand or opportunities that benefit Materials
|
Positive | 55% | Near-term |
|
Food, Beverage & Tobacco
AI growth in Food, Beverage & Tobacco creates demand or opportunities that benefit Materials
|
Positive | 50% | Medium-term |
|
Utilities
AI growth in Utilities creates demand or opportunities that benefit Materials
|
Positive | 50% | Medium-term |
|
Energy
Energy sector capex creates demand for specialized industrial materials
|
Positive | 45% | Medium-term |
When AI changes Materials, 8 other industries feel it:
See how all 31 industries compare in our Full AI Market Cascade Dashboard