In the past couple months, there have been whispers about artificial intelligence’s potential transition from “inflated expectations” to “trough of disillusionment”, in the words of Gartner. 2023 and 2024 set an unachievable standard of progress, comparable to the dot-com bubble in the early 2000’s. The concept of a hype cycle holds tremendous power on capital markets; once the narrative shifts towards a decline, uncertainty grows exponentially. Markets reflect the ever decreasing attention span of the global population; the current lack of rapid growth in AI casts a shadow of boredom on an incredibly successful, innovative industry. Although, skeptics aren’t entirely mistaken; high-tech is reporting facing issues with scalability due to capacity constraints and talent shortages. The concept of a talent shortage is confusing in this economic environment, where the Fed is cutting rates with “a weakened labor market” as a major citation. To understand this, one has to possess a more comprehensive understanding of the demanding nature of a role working with LLM’s (large language models) and advanced machine-learning. These roles require deep technical expertise and there are misconceptions of what that requirement is, even within the world of IT professionals. “81% of IT professionals believe they can use AI, but only 12% actually have the skills to do so” (information week). This is an industry so complex, that only the best of the best can even comprehend what is required for these roles in terms of technical ability; this becomes a major limiting factor in growth projections. Financial analysts can see a competitive job market for IT professionals, along with rapid growth in technology-centered higher education enrollment, and come to the conclusion that talent acquisition may not be a prominent limiting factor in scaling. This results in inflated expectations for earnings growth, overstating trading multiples and derived intrinsic valuations.