AI Platforms & Models
This is where the actual AI gets built and run. The companies building the models that everyone else uses, and the platforms hosting those models.
This is the layer most people think of when they hear the words AI company. Google, Meta, Microsoft, OpenAI, Anthropic, xAI, Mistral. The teams training the foundation models that everyone else builds on. It is also the most expensive layer to play in. A single training run can cost hundreds of millions of dollars. The bet is that being at the base of the application stack is where the lasting value accrues.
Alphabet (Google)
Built Gemini. Owns DeepMind. Has the best data in the world via Search. The incumbent with the most to lose and the most to gain.
Meta
Built Llama (open source). Owns Instagram and WhatsApp, which generate enormous training data. Playing a different game to everyone else.
Microsoft
Owns a large stake in OpenAI (ChatGPT). Azure is how most businesses access AI. The enterprise distribution channel.
Nebius Group
A European AI cloud company that emerged from the old Yandex international business. After Yandex sold its Russian operations in 2024, the Dutch parent kept the global assets, rebranded as Nebius, and refocused on AI infrastructure. Early stage but interesting positioning.
Snowflake, MongoDB, Oracle
Data platforms. AI is useless without clean, accessible data. These companies store, manage, and serve that data to the AI.
What is a foundation model?
A foundation model is a large, general purpose AI model trained on vast amounts of data that can then be adapted to specific tasks. GPT 4, Gemini, Llama, and Claude are all foundation models. They're called foundation models because everything else gets built on top of them, applications, tools, agents. The companies building foundation models are making a very expensive bet that being at the base of the stack is where the lasting value accrues.
What's the difference between training and inference?
Training is when an AI model learns, you feed it enormous amounts of data and it adjusts billions of internal parameters until it gets good at the task. It's extremely expensive and only happens once per model version. Inference is when the trained model actually answers your question, it's much cheaper and happens billions of times a day. Most of the chip and data centre demand you're hearing about is driven by inference at scale, not just training.
What does "open source" mean when Meta says Llama is open source?
It means Meta has released the underlying model weights and code publicly so anyone can download, run, and modify it. The alternative is closed source, like OpenAI's GPT 4, which you can only access via their API. Open source AI is controversial because it democratises access but also means anyone, including bad actors, can use it without guardrails.
What is agentic AI?
This is the next big shift. Most AI today is reactive, you ask it something, it answers. Agentic AI takes actions autonomously on your behalf. You give it a goal and it figures out the steps, uses tools, browses the web, writes code, sends emails, and loops until the job is done, without you prompting each step. Think less chatbot and more digital employee. Palantir, ServiceNow, and Microsoft are all building agentic products. It's where most of the enterprise money is heading right now.