Key AI Investment Themes to Watch in the Year Ahead
November 24, 20255 min read
Key AI Investment Themes to Watch in the Year Ahead
Top AI trends shaping near‑term investment flows.
Tendrill
AI Themes Investors Should Be Watching Over the Next Year
Artificial intelligence remains the most powerful secular theme in global markets, driving capex cycles, reshaping industries, and creating new competitive moats. Over the next year, several emerging trends are likely to define where investment dollars flow and which companies capture the next leg of AI-driven growth.
Accelerating AI Infrastructure Buildout
AI demand continues to outpace even aggressive expectations, and hyperscalers are responding with record capex. According to earnings reports from Microsoft, Google, and Amazon, AI-related data center spending is on track for another year of double‑digit growth.
Key areas of infrastructure likely to see increased investor attention include:
Leading-edge semiconductors (GPUs, AI ASICs, custom accelerators)
High-bandwidth memory (HBM) supply growth from SK Hynix, Samsung, and Micron
Advanced packaging capacity expansions at TSMC and Intel
Power infrastructure buildout, including transformers, switchgear, and backup systems
Ongoing reports from TrendForce and Omdia continue to highlight tight supply conditions for HBM and advanced packaging, suggesting that upstream bottlenecks could become a recurring storyline throughout the year.
The Rise of Proprietary AI Models
While the last two years were dominated by foundation model announcements, the emerging trend is the rise of proprietary, domain‑specific models built by enterprises. Companies are increasingly choosing to fine‑tune internal models rather than rely on general-purpose systems.
Chipmakers producing more efficient inference accelerators
Software vendors enabling enterprise data integration, governance, and security
As noted by a recent McKinsey analysis, organizations that tailor AI to proprietary datasets see significantly higher ROI than those using off-the-shelf models (McKinsey report: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai).
AI’s Impact on Productivity and Corporate Margins
The next year will also test a critical investment thesis: whether AI can materially improve productivity and margins across industries. Early signals are emerging in sectors like:
Financial services (automated underwriting and fraud detection)
Healthcare (medical coding, documentation, and radiology assistance)
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Software development (AI coding copilots reducing engineering cycles)
Goldman Sachs has estimated that generative AI could eventually lift global productivity by 1.5% per year, though the timing remains uncertain (Goldman Sachs report: https://www.goldmansachs.com/insights/pages/gs-research/generative-ai-could-raise-global-gdp/index.html).
Investors will be watching for earnings commentary confirming whether these gains are starting to materialize.
Regulatory Shifts and AI Governance
Governments are accelerating AI oversight, and regulatory outcomes could influence market winners and losers. The EU AI Act, U.S. executive orders, and increasing scrutiny around data privacy and model transparency will shape:
Compliance spending
Barriers to entry for smaller AI developers
Competitive dynamics between open‑source and proprietary models
Cybersecurity firms are also poised to benefit as AI‑powered attacks grow more sophisticated and enterprises seek AI-native defense tools.
Energy Demand and the AI Power Crunch
One of the most underappreciated investment themes is the energy impact of AI. Data center power demand in the U.S. is expected to grow at a compound rate of nearly 12% per year through 2030, according to projections cited by Bloomberg.
This creates tailwinds for:
Utility companies expanding grid capacity
Renewable developers positioned near major data center hubs
Firms building next-gen cooling systems and power-dense infrastructure
Nuclear power as a long-term, carbon-neutral solution for baseline AI compute demand
Power availability is increasingly becoming a gating factor for data center expansion, and investors are beginning to price in multi-year demand cycles across energy and industrial names.
The Consolidation Wave in AI Software
AI software remains highly fragmented, but consolidation is accelerating. Large enterprise software providers are actively acquiring niche AI startups to integrate specialized capabilities—particularly in:
Customer service automation
Sales and marketing intelligence
Workflow orchestration
Data labeling and synthetic data generation
This trend is likely to continue as the cost of building proprietary models rises and enterprises seek end‑to‑end AI platforms rather than point solutions.
Conclusion
The next year will be pivotal for AI markets as the narrative shifts from promise to execution. Infrastructure buildouts, proprietary enterprise adoption, regulatory shifts, and the energy implications of AI will define the next major investment cycles. For investors, the most important opportunities may lie not only in model developers, but across the broader ecosystem enabling the rapid scale‑up of AI across the global economy.
Sources:
McKinsey, “The State of AI”: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
Goldman Sachs, “Generative AI Could Raise Global GDP”: https://www.goldmansachs.com/insights/pages/gs-research/generative-ai-could-raise-global-gdp/index.html
Bloomberg Data Center Energy Forecast (BloombergNEF): https://about.bnef.com/