AI Has a New Name: Super Intelligence. Why Business Leaders Should Start Using SI Now.

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What changes when Artificial Intelligence (AI) becomes Super Intelligence (SI)? 

The executive order directing federal agencies to use “Super Intelligence” and “SI” in place of “Artificial Intelligence” and “AI” has big implications. A new Super Intelligence Force was also established to coordinate the federal response. 

This all makes SI more than a slogan. As I’ve written about before, language is a critical leadership lever that can shift mindsets and mobilize action. 

With SI now the official language of the U.S. executive branch, business leaders need to understand what changed and why, including the implications for leadership, innovation, and organizational transformation. 

The Definition of Super Intelligence 

SI is currently defined using the same basic definition as AI, while allowing 60 days to the task force to propose a formal federal definition. 

The concept of SI has been around for a while. In 1997, Nick Bostrom from Oxford University introduced the idea that superintelligence is intelligence that exceeds the best human minds in every field. Depending on your perspective, we might already have achieved this. 

For business leaders, SI works best as a strategic term. AI has become a catchall for chatbots, automation, and applications. SI raises the conversation to the level CEOs and boards should address: how a dramatic increase in intelligece could—and should—reshape the way a company makes decisions, operates, and competes. 

The SI Summit that created a word that will stick 

The White House summit assembled leaders from chipmakers, data centers, cloud platforms, and frontier model companies. David Sacks, Chair of the President’s Council of Advisors on Science and Technology, called it the first gathering of the critical players across the industry’s full stack. Many were competitors. Some had deep disagreements. They still produced an agreement. 

Six companies signed the White House Accord on Super Intelligence. It calls for internal controls, an internal team to verify them, outside audits, and an independent board committee.

Specifically: 

1. 1. Implement robust internal controls to monitor the capabilities and alignment of its models during training and deployment around areas like cybersecurity, biosecurity, and chemical threats, and to ensure that its models do not hack or access technical systems in unintended ways. 
   2. Empower an internal team to ensure all of the controls, monitoring, and detection are operating as intended, and that any issues are remediated. 
   3. Partner with an independent external auditor or evaluator to carry out independent assessments of whether the controls, monitoring, and detection are operating as intended. 
   4. Designate an independent committee of the board of directors to oversee and receive reports from the teams operating the controls and the internal and external auditors and evaluators, as well as to ensure any issues identified are remediated. 

The summit’s most useful lesson came from who was included. Anthropic CEO Dario Amodei attended despite sharp disagreements with many others in the room. All the key players who needed to sign up for voluntary controls on the future of AI (now SI) were present. That’s been a fundamental principle of change management. Engagement is key for buy-in and ultimately implementation.  

The new federal task force extends that pattern. Its leaders come from intelligence, the Federal Trade Commission, the Pentagon, and the Office of Personnel Management. It also plans outreach to consumers, public interest groups, religious organizations, infrastructure providers, and technology companies. 

Build Your Own Super Intelligence War Room 

Most businesses will never train a frontier model. But every business, small and large, will face choices about where these systems are used in strategy, innovation, operations, and more. And also what data they give access to the SI to use, and who carries the risk when things fail. 

The idea of “superintelligence” can be a forcing function to elevate AI from chatbot discussions to truly taking a look at the implications for business strategy. 

For example, a small company can bring together the owner, the salesperson closest to customers, the operations lead, and outside technology or legal advisers. A large company needs the leaders responsible for technology, business strategy, workforce decisions, customer experience, security, legal risk, and operations. Include at least one informed skeptic as well. 

Start with one important use case tied to the business strategy of the company. For example: how you develop new products, identify emerging competitors, monetize your business model, or anything else that’s mission critical. Describe how a new “super intelligent system” could provide you with a step change in innovation and value, whether saving money or generating new revenue streams. Also explore what a system like this would ideally do on its own and where a person must review its work to help minimize risks. 

This is basically a strategic brainstorm, but anchored in real value creation and a recognition that SI is here to stay – and play a critical role in business strategy. 

Names Matter 

Names matter. Use specific technical terms like chatbot, data center, open weight model, or application to “declutter” the concept of artificial intelligence (e.g., making it more descriptive while simplifying what it means. 

Use “Super Intelligence” in strategy conversations to signal that the company is making choices about a new source of capability with consequences across the business. 

The first test of any SI strategy is simple: what will you do or create with real super intelligence?