Concern about whether artificial intelligence is now, or is becoming, an “investment bubble” is widespread. Some students of technology history might argue such overinvestment is virtually inevitable.
But a recent analysis of past financial bubbles and manias suggests by Coatue also suggests that long-term investment cycles can happen without being financial bubbles, and it is possible AI is such an example.
Perhaps it also is the case that even exuberant investment in AI models and high-performance computing assets proves to be justified by rapid advances in the viability and profitability of all the apps that depend on those investments.
In other words, the danger of excess investment in high-performance computing assets (infrastructure) proves not to be the case if apps requiring those facilities actually create monetizable value, at scale.
source: Coatue
The key is the distinction between infrastructure investments and financial bubbles. The analysis suggests at least some important infrastructure investment cycles did not become bubbles.
source: Coatue
Relating to the concern many have about AI overinvestment, much hinges on the productivity gains the infra can produce, compared to the valuation, leverage, speculation and actual impact on revenues and profits.
That noted, economists have observed that each major technological revolution, including railroads, electricity, automobiles, the internet, and perhaps AI, has followed a pattern of initial optimism, speculative excess, and subsequent consolidation, after which the infrastructure built during the boom enables sustainable long-term growth.
Though the disappointments and financial losses will be real, we might call the process “learning by investing.” The actual productivity potential is not known at the beginning, so there is no way to ascertain the “right” level of investment.
Technological Era | Approx. Timeline | Peak Investment Behavior | Bust Phase | Long-Term Outcome |
Canal & Railway Mania | 1830s–1850s | Rapid railway and canal construction driven by speculative capital from Britain and U.S. investors | Oversupply and bankruptcies in mid‑19th century Optimal learning and new technology bubbles - ScienceDirect | Created national transport infrastructure enabling commerce and industrial expansion |
Electrification & Utilities | 1880s–1910s | Massive capital outlays into competing electric utilities, equipment, and distribution grids | Consolidations and failures during utility overcapacity crises Federal Reserve | Universal household and industrial electrification |
Automobile & Radio Era | 1910s–1930s | Dozens of automakers and radio startups seek to dominate markets; speculative IPOs | Great Depression collapse wiped out smaller firms The Bubble That Knows It's a Bubble | Durable automotive and broadcast industries emerge |
Dot‑Com Internet Wave | 1995–2001 | Extreme venture capital inflows and equity valuations for unprofitable internet firms | 2000–2002 crash destroyed trillions in market cap Optimal learning and new technology bubbles - ScienceDirect | Fiber optics, e‑commerce, and data centers laid the base for Web 2.0 |
Clean Tech & Solar Boom | 2006–2011 | Subsidy‑driven boom in renewable startups and manufacturing overcapacity | Collapse of many firms after subsidy reductions Optimal learning and new technology bubbles - ScienceDirect | Cost per watt of solar fell dramatically, enabling today’s viability |
AI & Generative Models | 2020s–present | Trillions in compute build‑out, IPOs, speculative valuations exceeding dot‑com scale | Potential retrenchment ahead if returns lag costs The Bubble That Knows It's a Bubble | Neural infrastructure and models likely become core of enterprise computing |
So, yes, overinvestment, speculation and consolidation are perhaps inevitable. But, as the Coatue analysis suggests, the possible bubble occurs only if the actual observed benefits do not develop rather clearly.
But most observers might also suggest, in any case, that sustainable growth also is virtually inevitable.
Industry | Primary AI Use Cases | 2025 Adoption / Spending Trends | Key Outcomes |
IT & Telecommunications | Network optimization, predictive maintenance, customer support chatbots, AI-driven service provisioning | 38% adoption rate; projected to add $4.7 trillion in value by 2035 AI Adoption Statistics in 2025 | Reduced downtime, improved customer satisfaction, adaptive network efficiency |
Finance (Banking, Insurance, Investment) | Fraud detection, credit scoring, algorithmic trading, customer risk profiling, robo-advisors | Over$20 billion global AI spending;68% of hedge funds use AI in trading AI Adoption Statistics in 2025 | Faster fraud detection, increased trading precision, cost reduction in customer service |
Healthcare & Life Sciences | Diagnostics, patient triage, drug discovery, personalized treatment, text summarization for records | 36.8% CAGR in adoption;42% of hospitals using AI chatbots AI Adoption Statistics in 2025 | Improved diagnostic accuracy, faster patient response, reduced care costs |
Manufacturing & Industrial | Predictive maintenance, process automation, supply chain optimization, quality control | 77% of producers using AI, up 7% YoY AI Adoption Statistics in 2025 | 23% downtime reduction, improved supply chain resilience |
Retail & Consumer Goods | Demand forecasting, personalization, inventory optimization, chatbot-driven sales | 20% of tech budget allocated to AI, +5% YoY NetGuru | 15% conversion lift, 18% overstock reduction |
Software & Cloud Services (Big Tech) | Generative AI systems, agentic AI, infrastructure automation | $320 billion capex in 2025 by major cloud companies (Microsoft, Amazon, Google, Meta) Ropes Gray | Faster LLM integration, platform differentiation, scalability gains |
Robotics & Automation | Embodied AI for general-purpose robots, autonomous agents, fleet control software | 342% YoY increase in deal value Ropes Gray | Accelerated labor automation, robotics ecosystem expansion |
Energy & Utilities | Grid optimization, predictive maintenance for equipment, carbon monitoring | Fast-growing adoption Forbes (2025) | Efficiency gains, lower operational emissions, reduced outages |
