While some caution is warranted, we think the better question is not whether today's deals resemble the dot-com era, but whether the underlying fundamentals do.
In recent weeks, investors have seen a pickup in large partnership announcements across AI model developers, hyperscalers and chip companies. These deals are expected to deploy capital over a number of years, with contingencies tied to the continued execution and leadership of the firms. Yet they also signal urgency—a race to meet exploding demand for compute. AI leaders are coordinating across the value chain in an attempt to ensure that supply keeps up with the speed of innovation.
Still, the scale and circular nature of these commitments, where suppliers, customers and investors overlap, has prompted comparisons to the late-1990’s tech bubble. While some caution is warranted, we think the better question is not whether today's deals resemble the dot-com era, but whether the underlying fundamentals do.
We identify three key distinctions:
- Robust balance sheets
In the 1990s, much of the buildout was financed by companies with limited profitability and heavy reliance on external capital. By contrast, today's wave is largely funded from the hyperscalers’ own free cash flow and robust margins. Considering that many past bubbles burst amidst tightening credit conditions, this buildout appears more resilient to that kind of stress.
Many have also drawn comparisons to the vendor-financing loops of the ‘90s, where telecom infrastructure companies financed each other to inflate growth1. However, today’s deals look different. Capital is arguably chasing AI, not the other way around—AI has captured 1 in every 2 VC dollars this year2— and spending is anchored in physical infrastructure like chips, electrical equipment and data centers. - AI revenue momentum
Whereas early internet firms built first and monetized later, AI is monetizing as it builds. Hyperscalers are already seeing returns through increased cloud demand and productivity gains in coding, advertising and enterprise tools. The model developers have more nascent business models, but with a 99% U.S. market share in global LLMs3, revenues are scaling4. Meanwhile, enterprise adoption is gaining traction. KPMG’s latest AI Survey shows average enterprise AI investment rising 14% from Q1 to $130M, supported by visible productivity and profitability gains from AI use cases5. - Demand outpacing supply
Any wave of heavy capital investment runs the risk of overbuilding. At the peak of the dot-com era, only about 7% of the fiber-optic network was being utilized, leaving vast excess capacity that took years to absorb. But today, data center vacancy rates are at record lows6 and utilization levels hover around 80%. Demand for compute continues to far outpace supply—more data has been created in the last 3 years than in all history7, and AI workloads are growing by the magnitude8.
Lessons from history
Still, we don’t think caution around AI is unwarranted. The scale of spending is enormous, the pace unprecedented and some assumptions around ROI, like the useful lives of assets, remain open questions. History reminds us that enthusiasm can run ahead of reality. Yet so far, today’s players are far better capitalized than those of the dot-com era, AI monetization is underway and the risk of overbuilding seems limited in the near term. As this story unfolds, investors should focus on selectivity, leaning into active management to separate transformative winners from bubbly valuations.