Ranked: The Top 10 Most Wanted Skills for AI Jobs

Three Critical Minerals Behind the AI Supply Chain

AI infrastructure depends on far more than chips and electricity. It also relies on a range of critical materials, including gallium, rare earths, and tantalum, that are essential to semiconductors, magnets, and power-management systems.

These three materials are highlighted in Figure 2.3 of the World Bank’s World Development Report 2026 because their production is concentrated in a relatively small number of countries.

The figures here are 2025 estimates from the U.S. Geological Survey’s Mineral Commodity Summaries 2026, using processed production for gallium and mine production for rare earths and tantalum.

Gallium: China’s 99% Share

Here is the World Bank’s breakdown of estimated processed gallium production:

China accounts for 99% of the total. However, that does not mean China holds 99% of the world’s gallium underground. Gallium is generally recovered as a byproduct of processing bauxite for aluminum, and China’s enormous aluminum industry has helped it develop gallium extraction at scale. Earlier oversupply and low prices also contributed to producers elsewhere exiting the market.

Gallium matters because compounds such as gallium nitride (GaN) can operate efficiently at high voltages and temperatures. These properties make gallium useful in high-performance semiconductors and power electronics, an increasingly important consideration as AI data centers require more computing power and electricity.

Rare Earths: More Than One Element

China produces 69.2% of the total, followed by the United States at 13.1% and Australia at 7.4%.

Despite the name, “rare earths” are actually a group of 17 elements. Different members serve different roles across computing hardware. Neodymium and praseodymium, for example, are used in powerful permanent magnets found in hard drives and other equipment, while other rare earths have applications in semiconductors, displays, optics, and data storage.

Tantalum: Power Management for AI Servers

Tantalum production has a very different geographic footprint:

The Democratic Republic of the Congo supplies 52% of estimated mine production, while Rwanda and Nigeria contribute 16% and 15.6%, respectively. Together, those three countries account for 83.6%.

Tantalum’s AI connection is largely about capacitors. Polymer tantalum capacitors can provide high capacitance in a compact package along with stable electrical performance, making them useful in demanding power systems. Industry reporting in 2026 points to AI servers as a growing source of tantalum capacitor demand.

When Mineral Supply Becomes a Strategic Bottleneck

Production concentration does not automatically translate into an AI advantage. Advanced chips, fabrication capacity, energy infrastructure, and capital all matter. However, when one or a handful of countries dominate production of a critical material, export restrictions or other supply disruptions can ripple through global technology supply chains.

That risk is already visible. China has introduced export controls on gallium-related items and selected medium and heavy rare-earth items in recent years. Those measures have encouraged governments and companies elsewhere to pursue alternative suppliers and new production capacity. In Central Africa, concentrated tantalum production presents a different set of challenges, including potential supply disruptions and responsible sourcing concerns.

As AI infrastructure expands, securing the minerals behind chips, servers, and power systems could become increasingly important alongside securing the chips themselves.

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To see where the U.S. is most dependent on foreign mineral supplies, see Ranked: U.S. Import Reliance for 37 Critical Minerals on the Voronoi app.

Where AI Exposure Is Highest Across the U.S.

Artificial intelligence is becoming capable of performing tasks across a growing range of white-collar and technical occupations.

This visualization maps all 50 states and Washington, D.C., by the share of workers employed in occupations considered highly exposed to AI-related disruption. It also highlights the states with the largest and smallest numbers of workers in these roles.

The data for this visualization comes from SmartAsset, using U.S. Bureau of Labor Statistics data and research from the Virginia Economic Information and Analytics Division. The analysis covers 26 occupations identified as having particularly high exposure to potential AI-related disruption.

AI exposure does not necessarily mean these jobs will disappear. Instead, it reflects how susceptible their tasks may be to changes such as automation, weaker hiring demand, wage pressure, or restructuring as AI tools become more capable.

Why Washington Tops the Map

Washington ranks first, with 5.7% of its workforce employed in highly AI-exposed occupations.

The state’s large technology sector helps explain its position, with companies such as Microsoft and Amazon supporting a significant concentration of software developers, programmers, database specialists, and other digital roles.

Virginia follows at 4.6%, reflecting its mix of technology firms, federal contractors, and knowledge-based employment. Washington, D.C., ranks third at 4.5%, while California and Utah round out the top five at 4.0% each.

At the other end of the ranking, Mississippi has the lowest share in the study, at 1.9%.

California Has the Most AI-Exposed Jobs

California has the largest absolute number of workers in highly exposed occupations, with approximately 724,000 positions.

Texas follows with about 500,000, while New York and Florida each have roughly 301,000.

At the other end of the scale, Wyoming has just 6,000 AI-exposed positions, followed by Alaska and Vermont at about 8,000 each.

What Types of Jobs Are Most Exposed to AI?

The 26 occupations span technology, communications, administration, finance, and other knowledge-based fields.

Mathematicians rank as the most exposed occupation, followed by proofreaders, correspondence clerks, court reporters, and media and communication workers. Computer programmers, database administrators, web developers, software developers, writers, translators, payroll clerks, and bookkeeping workers also appear on the list.

Many of these roles involve processing information, generating or reviewing text, working with structured data, or performing routine digital tasks, all areas where generative AI and other automation tools have advanced quickly.

Still, exposure should be interpreted as the potential for jobs to change rather than a direct estimate of how many positions will ultimately be eliminated.

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To learn more about this topic, check out this graphic on the smartest AI models in 2026.