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Markets and Economy Client Conversations: How artificial intelligence could profoundly affect markets
Key takeaways
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Enabling AI infrastructure starts with hardware. Scaling and deploying requires utilities and computing resources.
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AI platforms are toolsets for producing and rolling out AI software. Once integrated and adopted, it may add value across the economy.
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You can use our conversation guide with clients to help you address their questions about this evolving topic.
From commodities to utilities to health care, the artificial intelligence (AI) value chain may present many investment opportunities. But the road to enablement, scaling, and deployment is not a simple one.
As a financial professional, you can keep your clients informed with easy talking points from our Client Conversations series. This conversation guide covers the following points you can use to help answer your clients’ questions about this evolving topic:
Enabling, scaling, and deploying AI infrastructure
Semiconductors are necessary for advanced computing, navigation systems, solar power, and more. AI has significantly increased global demand for high-end semiconductors and the critical materials that produce them. Many key semiconductor commodities are produced predominantly in China and may see sustained demand as AI advances.
Semiconductors deployed in data centers need lots of power to operate. The International Energy Agency predicts global data center electricity consumption will grow 15% annually to reach 3% of all electricity use by 2030. In developed markets, data centers are expected to account for 20% of electricity demand growth over this same period.
Key value-add components of AI development platforms
AI development involves a stack of technologies that includes computing and data resources, development environments, and cloud services for model training and deployment. Each of these tools helps speed up model development and deployment.
Companies that provide these services are usually well resourced, with significant cloud computing resource access and expertise in hardware acceleration and data management.
Major AI tools on the market make use of development platforms of some form, representing a critical link in the AI value chain.
Data management
AI platforms provide assistive tools for managing large datasets, including cloud-based sources
- Data labeling services
- Data library management
- Data centers streamlined for AI development
Training, evaluation, and fine-tuning
From managing hardware resources to tweaking model specifications, AI platforms provide a range of tools for producing quality AI systems.
- Hardware acceleration tools
- Hyperparameter tuning
- Performance assessment
- Distributed training
Deployment and monitoring solutions
Toolsets for operationalizing AI models include cloud uploads and management, streamlined access to system functions and outputs, and monitoring tools for ongoing performance updates.
- Monitoring suite for resource use
- Cloud-ready model activation
How AI may add value across the economy
AI’s ultimate value-add may come from its economic use cases. New product offerings and insights may improve revenue generation. Greater customer focus may lead to more tailored and refined experiences. Enhanced operational productivity by equipping workers with tools may help streamline or optimize workflows.
AI adoption is still in its early stages with just 9.4% of US firms using AI in their operations. AI may be a multi-year story with twists and turns in the short term.
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Important information
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All investing involves risk, including the risk of loss.
Past performance does not guarantee future results.
This does not constitute a recommendation of any investment strategy or product for a particular investor. Investors should consult a financial professional before making any investment decisions.
Investments in companies located or operating in Greater China are subject to the following risks: nationalization, expropriation, or confiscation of property, difficulty in obtaining and/or enforcing judgments, alteration or discontinuation of economic reforms, military conflicts, and China’s dependency on the economies of other Asian countries, many of which are developing countries.
Commodities may subject an investor to greater volatility than traditional securities such as stocks and bonds and can fluctuate significantly based on weather, political, tax, and other regulatory and market developments.
AI technology companies are sensitive to specific risks such as small markets, business cycle changes, economic growth, technological progress, obsolescence, and regulation. These companies may have limited products, markets, resources, or personnel, making their securities more volatile, especially for smaller start-ups. Rapid technological changes can adversely affect their results. AI companies often rely on patents, copyrights, trademarks, and trade secrets to protect their technology, but there is no guarantee these protections will be sufficient. Significant R&D spending does not ensure product or service success.
Investments focused in a particular sector, such as utilities, are subject to greater risk, and are more greatly impacted by market volatility, than more diversified investments.
Cryptocurrency is a digital currency that uses cryptography for security and is not controlled by a central authority, such as a central bank.
The health care industry is subject to risks relating to government regulation, obsolescence caused by scientific advances, and technological innovations.
The International Energy Agency (IEA) focuses on areas including data and statistics, training, innovation, and international cooperation.
The opinions referenced above are those of the author as of Feb. 12, 2026. These comments should not be construed as recommendations, but as an illustration of broader themes. Forward-looking statements are not guarantees of future results. They involve risks, uncertainties and assumptions; there can be no assurance that actual results will not differ materially from expectations.
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