Superintelligence vs. AI: What the New Name Means for Your Work

The White House is calling AI “Super Intelligence.” If you're trying to figure out whether that changes which tools your business should use, start with what the tool can actually finish for you.

The name alone tells you very little about that.

What changed on September 29, 2026

President Trump's executive order directs executive departments and agencies to use “Super Intelligence” and “SI” in official communications and other non-statutory documents, as far as the law permits. For the order's purposes, those terms cover the systems already included in the statutory AI definition. It also directs the President's science adviser to propose legislative language within 60 days. That proposal is not an enacted change to the statute. Read the executive order.

This is a direction for executive-branch terminology. It doesn't establish that a tool gained a new capability, or settle what researchers mean by superintelligence.

Superintelligence already had a meaning

In a paper first published in 2003, Nick Bostrom described general superintelligence as intelligence far exceeding the best human minds across almost every field. That includes scientific creativity and social skills. Being exceptionally good at one narrow task is insufficient under his definition. Bostrom's research definition describes a much broader capability than a renamed category of software.

So there are different questions here. What word does an agency use? What can a system do across intellectual work? And what can the tool you're considering do with the messy information sitting on your desk?

I would start your buying decision with that last question. A useful test won't settle the research debate. It will give you evidence about whether the tool can help with your work.

Give the tool a task with an answer you can check

Imagine a vendor is showing you an AI assistant for project meetings. It writes an impressive summary in the demo. You want to know whether your team could use its output to carry the work forward.

Use a small fictional example first. Here are the entire meeting notes for this exercise:

1. Maya will send the revised proposal on October 6.
2. A pricing review must happen before the proposal goes out. Nobody has accepted responsibility for it yet.
3. The kickoff was penciled in for October 12.
4. Later in the meeting, the client requested October 14. The team has not confirmed that date.

Ask for the work you need:

Turn these notes into an action list for the project manager.
For each action, include the owner, due date, and note number
supporting it. Mark missing information as unknown.
Keep relative deadlines as relative deadlines.
Keep proposed dates separate from confirmed dates.
End with the decisions the project manager still needs to make.
Use only the supplied notes.

The result has a clear job. Someone should be able to read it and see what happens next, who is responsible, and what still needs a decision.

Before trying this with real meeting notes, use material you're allowed to share in the tool you've chosen.

Check the part that could cause trouble

In this example, a usable answer should preserve four things:

• Maya owns the proposal. October 6 belongs to that task, supported by note 1.
• The pricing review has no assigned owner. It needs to happen before the proposal goes out. The notes don't supply a separate deadline.
• The kickoff is unresolved. October 12 was tentative; October 14 was requested. Neither is confirmed.
• The project manager still has decisions. Assign the pricing review and confirm the kickoff date with the people involved.

If the tool quietly assigns the pricing review to Maya, it has created a responsibility nobody accepted. If it lists October 14 as the kickoff, it has turned a request into a commitment.

Those are useful failures to catch during a trial.

Give it a correction: “The pricing review has no owner. Show that gap and keep Maya responsible only for the proposal.” Then inspect the revised list. A friendly acknowledgment doesn't tell you whether the work changed.

Run a second version of the exercise. Replace note 4 with: “The client and team confirmed October 14, replacing the tentative October 12 date.” The kickoff should now appear as confirmed. The pricing owner should still be unknown.

That change checks whether the tool follows the evidence when the facts change. One successful example gives you a reason to test more of your work, including the cases that usually need extra attention.

Keep the human decision visible

For a team trial, name the person who checks the output and the person who approves any action it triggers. Track time spent correcting it alongside time spent producing it. A draft that takes a minute and needs extensive repair still needs extensive repair.

This is the habit I teach through AI fluency coaching: choose a task, supply the context, ask for a finished output, check it, and try the next one yourself. You can practice it with the complete AI-at-work exercise.

If your problem is choosing tools and assigning responsibility across a team, the coaching versus fractional AI leadership guide explains how I approach those different needs.

Bring a task or a vendor claim you're trying to evaluate to a discovery call. We can work out what evidence would help you make the decision.