SI vs AI: What the New Name Means for Developers

Imagine opening a vendor’s dashboard and finding that yesterday’s “AI assistant” is now called a “Super Intelligence assistant.”

Your first question would probably be: did the model change?

That is a useful starting point for understanding the recent discussion around SI and AI. A new name can change how people describe a system. Understanding what changed underneath takes more investigation.

On September 29, 2026, President Donald Trump signed an executive order introducing “Super Intelligence,” or SI, as the terminology for AI within the U.S. executive branch. The White House describes the change as a naming policy for government communications and documents. White House announcement

For developers, this creates a practical problem: the same phrase can now refer to an administrative label or a claim about a system’s intelligence.

Before drawing conclusions, we need to know which meaning someone intends.

The order directs executive agencies to use the new terminology in specified communications and non-statutory documents, where legally permitted. It does not require rewriting historical documents. For its purposes, SI initially covers the systems already encompassed by the existing statutory definition of AI. It also requests proposed legislative language within 60 days; that request does not itself enact a new definition. Executive order, sections 2–3

That context helps distinguish four terms that often get mixed together:

Research frameworks make the capability distinction more explicit. Google DeepMind’s Levels of AGI separates performance from generality and discusses autonomy as a deployment consideration. A system’s skill, the breadth of that skill, and the independence it receives are different dimensions. Levels of AGI

This distinction becomes concrete when you are choosing software.

Suppose a provider introduces an “SI coding agent.” Before changing your integration, ask what the release actually includes:

• A different model or model version?
• New tools, retrieval, or access to your repository?
• Better results on tasks resembling your workload?
• Different latency, pricing, or usage limits?
• More permissions to act without review?

Each answer affects an engineering decision. The product label alone cannot answer those questions.

For example, consider an assistant that generates API integrations. A convincing demonstration might produce a clean function from a short prompt. Your application still needs to handle expired credentials, pagination, rate limits, empty responses, and upstream failures.

A useful evaluation would give competing versions the same API specification and requirements, then run the generated code against controlled responses.

I would start with a small set of representative tasks and record:

Keep the prompts, available tools, and retry budget consistent. Record the model version and evaluation date. Repeat tasks where variability matters, and retain failed attempts alongside successful ones.

These results can support a specific claim, such as “this version handles our pagination cases more reliably.” They would not establish general superintelligence.

The terminology also deserves care in documentation.

If you are explaining the government policy, define SI with its context and date. If you are documenting an integration, identify the actual provider, model, capabilities, and limitations.

Treat changes to public identifiers as ordinary compatibility decisions. Renaming a UI label is straightforward; renaming an API field, configuration variable, or package can affect existing users. A terminology update should go through the same review you would give any other interface change.

The next time an SI announcement appears, look for the release notes and evaluation results. If the system has improved, those should help you explain where it improved and whether that improvement matters for your application.

What evidence would you want to see before adopting a product marketed as “Super Intelligence”?