AI Glossary

AI systems that execute autonomous, multistep, goal-oriented actions with minimal human intervention. Agentic AI moves beyond traditional AI models that respond to human prompts, acting as 'workers' that execute tasks and workflows. To learn more, read our whitepaper on Agentic AI and the Future of Enterprise Software.

The interface where AI can deliver business value, from productivity tools to vertical software and AI applications including SAP, Atlassian, Salesforce, ServiceNow and Vista Equity Partners’ portfolio companies.⁷

In our view, AI is a connected ecosystem built across layers of technology and waves of value creation. Layers represent the market segments that help make AI possible, including: hardware and semiconductors, digital infrastructure, energy, foundational models, cloud and delivery platforms, data and development platform, AI applications and software.

The platforms through which businesses access and deploy AI applications – Amazon, Microsoft and Google are generally viewed as leading providers in this layer.⁵

The general-purpose processor in a computer responsible for executing a broad range of tasks. CPUs manage coordination, data preparation and non-AI workloads, but are less suited than GPUs or other purpose-built AI chips for the intensive compute required to train and run AI models.¹⁰ Intel introduced the world’s first commercially available CPU in 1971.¹¹

The environments where AI is trained, refined and integrated, from companies like Snowflake and Databricks.⁶

Facilities that store data, host models and deliver the compute capacity required to train and deploy AI at scale. This includes companies like Digital Realty, Equinix and Ciena.²

Model training and inference require reliable, low-cost power through renewable generation, grid modernization, battery storage and more. Companies providing these solutions include Siemens Energy, NextEra Energy and Brookfield Renewable.³

The deployment of AI models, software and infrastructure within large organizations to automate processes, support decision-making and enhance operational efficiencies across business functions.

The large language models (LLMs) that give AI its intelligence, currently driven by OpenAI, Google Gemini, Anthropic and others.⁴

A general-purpose AI model trained on broad datasets that can be used for a wide range of tasks through prompting. Foundation models serve as the starting point for most modern AI products rather than being trained from scratch for each use case. This designation is relative and evolves as new models are released. Examples of foundation models today include Anthropic’s Sonnet.

The most advanced, highest-capability version of AI models available at a given point in time. Frontier models are appropriate for tasks requiring advanced reasoning but are not the primary choice for most enterprise workflows.⁸ This designation is relative and evolves as new models are released. Examples of frontier models today include Anthropic’s Opus or Fable.

A class of AI models that produce content such as text, images, audio, video or code.

A processor designed for rendering graphics. GPU has become a dominant hardware for AI training and inference. GPUs can perform large numbers of mathematical operations simultaneously, making them well suited for the intensive compute AI workloads may require. NVIDIA popularized the term “GPU” and later enabled developers to use GPUs for general-purpose computing tasks like AI and data processing. However, the underlying hardware and technology dates to the 1980s.¹²

The physical foundation of AI computing, including advanced chips and semiconductors from companies such as Nvidia, AMD, Intel and Broadcom.¹

A category of the largest computing infrastructure providers that operate data centers at massive scale, delivering compute, storage and networking services globally (e.g. AWS, Google Cloud, Microsoft Azure).

The variable cost incurred every time a model is used. Unlike model training, inference costs are paid by whoever is using the model, whether that be an individual or a business. Model providers charge for inference based on usage, which is measured in units called tokens. To learn more, read our whitepaper on inference and the economics of enterprise AI.

An AI model whose “weights” – the core parameters that define how the model works - are publicly released. This allows an individual or company to run the AI model on their own infrastructure (i.e. chips, data centers and cloud computing) rather than paying a commercial provider to deliver the model and support it. Meta’s Llama is an Open-Source Model. An example from the internet era is the Linux Operating System versus Microsoft or MacOS, where developers can access a free blueprint but have to build and maintain it themselves. Open-source models can perform at or near competing commercial models for many enterprise tasks, often at a significantly lower cost, though they typically require more internal technical resources to deploy safely.⁹

A purpose-built inference chip developed by SambaNova Systems that processes data in a fundamentally different way than traditional CPUs and GPUs.^{13 14} RDUs are optimized for the data movement patterns common in LLM inference, offering potential advantages in speed and energy efficiency for specific AI workloads.

The basic unit of text that AI language models read and generate – roughly equivalent to three-quarters of a word or 7 characters. When a model is used, it processes input tokens (such as a question and any context) and generates output tokens (the answer). The model provider charges for both based on how many tokens are being consumed.

Tokens-per-second measures inference speed.

Tokenomics refers to the cost and revenue economics of token generation, encompassing the price charged per token, the compute cost to produce it and the margin in between.