AI Investing 2025: Inference, Agents, and the Next Trillion in Tech

Inside AI's fast expansion: What every investor should know

September 18, 2025

AI has moved from pilots to production across hardware, cloud, advertising, gaming, and enterprise services. The payoff is clear: productivity gains and faster answers at scale. Bloomberg Intelligence projects generative AI could produce $1.8 trillion in annual revenue by 2032, up to 16% of tech spending.

The shift: from training to inference

A new wave of reasoning models is changing how AI gets used, enabling more complex decisions across text, images, audio, and video. As these systems hit real workloads, demand for inference is set to overtake training sooner than expected. AI agents are also going mainstream-handling contract review, customer service, personalized shopping, and software development. By 2032, deployments tied to agents could support a market worth over $214 billion.

Where earnings stack up

This is a full-stack change, not a software-only story. On the hardware side, AI already exceeds 20% of global server revenue and could reach ~40% within a few years as specialized chips and infrastructure scale. In software, copilots, coding assistants, and creative tools are reworking workflows, creating new subscription tiers, and reinforcing data advantages. In advertising, AI-driven personalization and content generation could add more than $200 billion in incremental digital ad spending by 2032.

Investor checklist: signals that matter

• Training vs. inference mix: watch latency, cost per query, and energy per query; efficiency wins tend to compound at scale.
• Capex and utilization: track hyperscaler and enterprise spend, time-to-deploy, and bottlenecks in networking and memory.
• Supply chain depth: GPUs/accelerators, advanced packaging, optical networking, and HBM; look for pricing power and backlog quality.
• Data advantage: proprietary datasets, usage rights, and privacy-by-design; durable edges come from unique data and defensible access.
• Unit economics: measure gross-margin lift from AI features against inference costs, attach rates, and churn.
• Agent adoption: security controls (audit logs, sandboxing), compliance readiness, and pilot-to-paid conversion rates.
• Policy and compliance: monitor emerging rules and standards such as the EU AI framework; compliance costs and audit needs are rising.

How to get exposure

Bloomberg's Artificial Intelligence Index (BAIT) offers broad exposure to global companies advancing AI across hardware, software, and services. The Bloomberg AI Value Chain Index (BAIVT) focuses on the backbone of the ecosystem-semiconductor designers, cloud providers, model developers, applications, and enterprise adopters. Together they provide a transparent, rules-based way to follow the AI economy and spread exposure across core beneficiaries. A five-year performance view is available as of August 25, 2025.

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What's next in this series

• Part 2: Mapping the AI value chain - chips, cloud infrastructure, models, and applications, plus how index design captures value across the chain.
• Part 3: Spotlight on top AI companies - leaders and challengers best positioned to capture growth.

Bottom line

AI is a structural shift. Hardware spending, inference at scale, agent deployments, and new software models point to durable earnings drivers across sectors. Investors who track the training-to-inference transition, the data advantage, and real product ROI will be better positioned to allocate with conviction.

Disclaimer

This material is for illustrative purposes only and is not investment advice or a recommendation to buy or sell any security or strategy. Index-related information is impersonal and not intended for any specific investor. BLOOMBERG, BLOOMBERG INDICES, and related marks are trademarks or service marks of Bloomberg entities and their licensors. Accuracy is not guaranteed; no liability is assumed for errors or omissions to the maximum extent permitted by law.