The Pivot from AI Training to Inference

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  Stocks and Investing on Mon, Jul 13th, 2026 by KELO

The Shift from Training to Inference

For the past several years, the market's attention was dominated by the "training phase." This period saw massive capital expenditures directed toward the hardware required to build foundation models—primarily high-end GPUs and massive data centers. However, current trends indicate a critical pivot toward "inference."

Inference is the process of actually running the AI models to generate a result. As AI integrates into the daily operations of global enterprises, the volume of inference requests is scaling exponentially compared to training. This shift redistributes value across the supply chain. While chip designers remain vital, the emphasis is moving toward efficiency, latency, and the cost per token. Investors are now looking at companies that can optimize the "runtime" of AI, including those specializing in edge computing and specialized inference hardware that consumes less power than the behemoths used for training.

The Energy Bottleneck and the Infrastructure Play

One of the most overlooked aspects of the AI revolution is the physical reality of power. The extrapolation of current AI growth suggests a looming energy crisis that could throttle the pace of innovation. Data centers are consuming electricity at rates that exceed the capacity of existing power grids in many regions.

Consequently, the "smartest" AI investments are increasingly found adjacent to the software itself. This includes the "picks and shovels" of the energy sector: providers of advanced cooling systems to prevent server meltdowns, companies specializing in Small Modular Reactors (SMRs) for carbon-neutral baseload power, and grid modernization firms. The realization that AI cannot exist without a massive overhaul of the electrical infrastructure has created a secondary layer of investment opportunities that are less volatile than the software market but equally essential.

From Chatbots to Agentic AI

On the software side, the industry has moved beyond the "chatbot" paradigm. The current frontier is Agentic AI—systems that do not merely provide information but can autonomously execute multi-step workflows. While a chatbot might tell a user how to book a flight, an AI agent can access a credit card, navigate a booking site, handle the payment, and add the event to a calendar.

This transition from generative to agentic AI shifts the value proposition toward integration and reliability. Companies that hold proprietary, high-quality data (vertical AI) are now better positioned than general-purpose model providers. For instance, AI agents tailored specifically for legal discovery or pharmaceutical research provide higher utility and higher pricing power than a general-purpose assistant. The investment focus has thus shifted toward "Vertical AI" players who possess the domain expertise to refine models for high-stakes professional environments.

Risk Mitigation and the Long-Horizon Strategy

Despite the optimism, the volatility of the AI sector remains high. The risk of a "plateau'—where LLMs stop showing significant intelligence gains regardless of the amount of data added—remains a theoretical possibility. Furthermore, regulatory frameworks regarding copyright and AI safety continue to evolve, posing a risk to companies relying on scraped data.

To navigate these risks, a diversified approach is recommended. Rather than betting on a single "winner" in the model wars, the more sustainable strategy involves a basket approach: balancing the high-growth potential of Agentic AI startups with the stability of energy infrastructure and the consistency of the cloud giants who provide the necessary compute layers. The goal is to capture the systemic growth of the technology rather than gambling on the survival of a single platform.