Do AI models have babies

A new AI model arrives. It has a bigger number, an impressive demonstration and a launch announcement that makes your perfectly functional assistant sound like an antique.

Was the previous model its parent? Did somebody copy it, improve it or build something different? And when people talk about an AI “swarm,” how concerned should we be about the population growth?

The answer gets much clearer once we stop using the word AI for every layer of the system. No stork required.

Start with the model

A model is a mathematical system with learned numerical settings, often called parameters or weights. Training changes those settings so the system becomes better at its training objectives. A foundation model is trained broadly enough to support many different applications. [1]

Consider GPT-4 as a historical example. OpenAI’s technical report describes a model pretrained to predict the next token in a document, followed by additional work to shape its behavior. A token is a unit used to represent text, which may be a word or part of one. This is a description of computation and training, not a family biography. [2]

Developers can also adapt an existing model through further training, including fine-tuning for particular tasks. Giving a model instructions or relevant information is different: prompting can change the response without changing the model’s learned parameters. [1]

A new number does not explain the engineering

When a company announces a new model, read what it says changed. A release may involve different training, further refinement, new capabilities or a different balance of speed and cost. The name alone does not tell you the development history.

Likewise, a product update is not necessarily a new model. An assistant can become more useful because the application gains a search tool, better access to relevant information or a clearer interface. The model and the product around it are related, but they are not interchangeable. [1, 3]

For readers comparing tools, the useful question is specific: what can this version do better, under what conditions, and how was that tested? A larger number on the box is not an answer.

An agent adds a way to act

People use “agent” in different ways. One common meaning is a system in which a model helps choose steps and use tools to complete a task. Anthropic distinguishes these flexible systems from workflows that follow predefined paths. Both can be useful. [3]

Imagine a research assignment about electric bicycles. One part of a system could compare specifications while another checks service availability. Their findings could then be combined. Those are separate assignments, not necessarily separate newly trained models. The same underlying model can be used in different roles.

An application can also connect models from different providers. When that happens, software passes information between them. It is an engineered connection, not evidence that competing chatbots have formed an unsupervised group chat. [4]

A swarm still needs resources

A multi-agent system runs several agents that work together. Anthropic’s published research example uses a coordinating agent and others that investigate parts of a question in parallel. Its report also describes the tradeoffs: more resource use and additional coordination and reliability challenges. [5]

Adding another agent creates more work for computers to perform. It does not conjure unlimited computing capacity. How many agents can run, which tools they can access and when they stop depend on the system’s design and controls.

So “more agents” and “superintelligence” are not synonyms. To judge a system, examine its demonstrated abilities and failure modes. Counting participants is no substitute for checking the result. A hundred assistants repeating one mistake would be a very well-attended mistake.

AI can help build AI

There is a real development worth watching underneath the dramatic language. AI tools can contribute code and help improve parts of the technology used to build other AI systems. Google DeepMind’s AlphaEvolve is a concrete example: its published work describes code and algorithm improvements involving data-center operations, hardware design and model training. [6]

That is substantial. It still does not make every instance of AI-assisted programming equivalent to a model independently creating its own successor. Ask which part was automated, what people supplied, how results were evaluated and what infrastructure was required.

The vocabulary gives us a more useful picture: models are trained, applications are built, agents carry out tasks, and people design systems that connect them. Sometimes one helps improve another. None of those statements needs a birth certificate.

Sources

[1] IBM. What are foundation models

[2] OpenAI. GPT 4 Technical Report

[3] Anthropic. Building effective agents

[4] Microsoft AutoGen documentation. Model clients

[5] Anthropic. How we built our multi agent research system

[6] Google DeepMind. AlphaEvolve and algorithm discovery

Sources checked November 6 2026