AI-Native Company
A company built from the ground up with AI at the core of its products and operations, as opposed to traditional companies that add AI to existing processes.
AI-native companies like OpenAI, Anthropic, Midjourney, and Perplexity have demonstrated dramatically different unit economics from traditional software companies. Midjourney generates over $200 million in revenue with fewer than 40 employees. The number of AI-native startups has exploded — over 2,500 received funding in 2024 alone, with 68 achieving unicorn status. AI-native companies often achieve higher revenue per employee but face higher compute costs. The distinction between AI-native and AI-augmented companies is becoming a key factor in competitive dynamics across industries.
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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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