Despite the hype around large language models, they represent a minority of AI compute usage at a tech giant like Meta. The vast majority of AI capital expenditure is dedicated to other tasks like content recommendation and ad placement, highlighting the continued importance of diverse, non-LLM AI systems in large-scale operations.
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In 2022, investors punished Meta's stock for its Reality Labs CapEx. Today, the market applauds even larger AI-related spending (66% of MAG-5's operating cash flow). This signals a fundamental belief that AI investments translate directly to tangible near-term earnings, unlike speculative bets like the Metaverse.
Recognizing there is no single "best" LLM, AlphaSense built a system to test and deploy various models for different tasks. This allows them to optimize for performance and even stylistic preferences, using different models for their buy-side finance clients versus their corporate users.
Reports that OpenAI hasn't completed a new full-scale pre-training run since May 2024 suggest a strategic shift. The race for raw model scale may be less critical than enhancing existing models with better reasoning and product features that customers demand. The business goal is profit, not necessarily achieving the next level of model intelligence.
While the market seeks revenue from novel AI products, the first significant financial impact has come from using AI to enhance existing digital advertising engines. This has driven unexpected growth for companies like Meta and Google, proving AI's immediate value beyond generative applications.
Today's AI is largely text-based (LLMs). The next phase involves Visual Language Models (VLMs) that interpret and interact with the physical world for robotics and surgery. This transition requires an exponential, 50-1000x increase in compute power, underwriting the long-term AI infrastructure build-out.
Instead of relying solely on massive, expensive, general-purpose LLMs, the trend is toward creating smaller, focused models trained on specific business data. These "niche" models are more cost-effective to run, less likely to hallucinate, and far more effective at performing specific, defined tasks for the enterprise.
It's crucial to balance the hype around LLMs with data. While their usage is growing at an explosive 100% year-over-year rate, the total volume of LLM queries is still only about 1/15th the size of traditional Google Search. This highlights it as a rapidly emerging channel, but not yet a replacement for search.
AI companies operate under the assumption that LLM prices will trend towards zero. This strategic bet means they intentionally de-prioritize heavy investment in cost optimization today, focusing instead on capturing the market and building features, confident that future, cheaper models will solve their margin problems for them.
The true commercial impact of AI will likely come from small, specialized "micro models" solving boring, high-volume business tasks. While highly valuable, these models are cheap to run and cannot economically justify the current massive capital expenditure on AGI-focused data centers.
The narrative of a broad AI investment boom is misleading. 60% of the incremental CapEx dollars in the first half of 2025 came from just four firms: Amazon, Meta, Alphabet, and Microsoft. Owning or being underweight these four stocks is a highly specific bet on the capital cycle of AI.
