The AI boom refers to the surge in investment, public attention, and commercial adoption of generative artificial intelligence that began around the November 2022 launch of ChatGPT and continued through the mid-2020s. It followed roughly a decade of steady but less visible progress in deep learning and marked the point at which large-scale AI tools reached hundreds of millions of consumers and became a dominant theme in corporate strategy, financial markets, and public policy.
Unlike earlier AI research cycles, the boom was driven by a small number of consumer-facing products, most notably ChatGPT, that made the capabilities of large language models immediately tangible to non-technical users, compounding word-of-mouth growth with rapid enterprise adoption of similar tools.
Drivers
OpenAI's ChatGPT reached an estimated 100 million users within two months of launch, a growth rate widely described at the time as the fastest of any consumer application in history. Rival releases followed in quick succession, including GPT-4 in 2023, Google's Gemini, Anthropic's Claude, and open-weight competitors such as Meta's Llama and DeepSeek-R1, intensifying what commentators called a "code red" scramble among incumbent technology companies. Microsoft, under CEO Satya Nadella, invested billions of dollars in OpenAI and embedded Copilot branding across its product line, while Nvidia became a primary beneficiary as demand for training and inference GPUs sent its market capitalization past one trillion dollars and later past three trillion.
Scale of investment
Hyperscale cloud providers committed hundreds of billions of dollars to AI data center capital expenditure across 2023 through 2026, alongside a wave of venture funding into AI startups that made several, including OpenAI and Anthropic, among the most highly valued private companies in history. Consumer categories beyond chat expanded rapidly, including image generation, AI coding assistants such as GitHub Copilot and Cursor, and increasingly capable AI agents.
Bubble debate
By the mid-2020s, economists and technology commentators were split on whether AI investment reflected sustainable productivity gains or a speculative bubble reminiscent of the dot-com era, pointing to gaps between capital expenditure and measured revenue at many AI labs, concentrated dependence on a handful of large customers, and circular financing arrangements between chipmakers, cloud providers, and AI labs. The January 2025 DeepSeek shock, which briefly wiped roughly 600 billion dollars off Nvidia's market value on claims of dramatically cheaper model training, was widely read as an early stress test of these bubble concerns, though spending by major labs continued to accelerate afterward rather than contract.