AI Bubble
The debate over whether current AI investment levels and valuations represent a sustainable technology shift or a speculative bubble that will eventually deflate, similar to the dot-com era.
With over $650 billion in planned Big Tech AI capex for 2026 and AI startup valuations exceeding $157 billion, concerns about an AI bubble have intensified. Skeptics point to the gap between AI spending and revenue — most AI companies are not yet profitable. The DeepSeek shock in January 2025 triggered a $1 trillion sell-off in AI stocks. However, bulls note that AI adoption rates (78% of organizations) and productivity gains (35% average improvement) far exceed dot-com era metrics. History suggests the technology may be real even if some investments prove excessive.
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Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Enterprise AI Adoption
The rate at which businesses integrate AI technologies into their operations, measured across functions like customer service, software development, marketing, and supply chain management.
Generative AI
AI systems that can create new content — text, images, code, audio, video — rather than simply analyzing or classifying existing data. Large language models and diffusion models are the primary architectures.
GPU (Graphics Processing Unit)
A specialized processor originally designed for rendering graphics, now the primary hardware used for training and running AI models due to its parallel processing capabilities.
Large Language Model (LLM)
An AI model trained on vast amounts of text data that can understand, generate, and manipulate human language. LLMs power chatbots, coding assistants, and content generation tools.
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