There’s a comforting narrative making the rounds in boardrooms and LinkedIn posts: “AI will democratize excellence. Everyone will perform like the best.”

It’s a nice story. It’s also dangerously wrong.

Recent research covered in The Wall Street Journal reveals something uncomfortable: AI is actually widening the performance gap between superstars and everyone else. The best performers extract exponentially more value from the same tools the rest of us have access to.

Here’s what’s really happening and what you need to do about it.

1. AI doesn’t equalize talent. It amplifies it.

Think of AI as a cognitive exoskeleton. Whatever capabilities you bring to it get magnified; strengths and weaknesses alike.

The superstars in your organization already possess critical advantages:

• They ask better questions because they understand the problem space deeply
• They recognize flawed reasoning when AI produces it
• They know which insights matter and which are noise
• They understand how to apply abstract recommendations to specific contexts

When these people use AI, it becomes a force multiplier. A 10x performer with AI doesn’t become average; they become 50x.

Meanwhile, average performers tend to stay surface-level. They use AI like a better search engine or a template generator. They follow the obvious path and miss the deeper possibilities entirely.

The performance gap doesn’t close. It explodes.

2. The real divide isn’t technological, it’s cognitive.

Access to AI is nearly universal now. ChatGPT, Claude, Copilot; everyone has access to them.

But access means nothing without capability.

The new elite won’t be those who have AI; they’ll be those who can think with AI. This requires three distinct competencies:

1. Domain mastery. You need deep expertise in your craft. AI can’t give you judgment in areas where you have no foundation. Garbage questions get garbage answers, no matter how sophisticated the model.
2. Prompt fluency. This isn’t about memorizing tricks; it’s about knowing how to communicate complex intent, provide relevant context, and iterate toward useful outputs. Most people stop at their first mediocre result.
3. Critical judgment. The ability to evaluate AI outputs skeptically, catch hallucinations, identify biased reasoning, and know when to override the machine entirely. Superstars treat AI as a sparring partner, not an oracle.

AI doesn’t replace thinking. It rewards better thinking and punishes its absence.

3. Organizations must redesign for learning, not control.

I keep hearing the same question from executives: “How do we control AI use? How do we prevent people from doing something stupid?”

Wrong question.

The right question: “How do we create a culture where everyone learns fast enough to keep up?”

Because AI capabilities are evolving faster than your policy committee can keep up. By the time you’ve written the guidelines, the technology has shifted.

The organizations that will win are building learning engines:

• Create protected experimentation time. Give people dedicated hours to explore AI tools without productivity pressure. Innovation doesn’t happen when everyone’s underwater with deliverables.
• Build institutional knowledge systems. Capture what your best people learn. Create shared prompt libraries, use-case playbooks, and documented failures. Make superstar insights accessible to everyone.
• Invest in real AI literacy. Not “here’s how to use ChatGPT” training — actual programs that develop judgment, critical evaluation, and strategic application. Teach people how to think about AI, not just how to use it.

In the AI era, organizational learning speed is your competitive advantage. Everything else is downstream from that.

4. The Superstar Effect is self-reinforcing.

Here’s the vicious cycle playing out right now:

High performers are trusted, so they get permission to experiment more → They experiment more, so they learn faster → They learn faster, so they deliver better results → Better results earn more trust → More trust enables more experimentation.

AI accelerates every step of this cycle.

Meanwhile, average performers are often constrained, supervised, and given less freedom to explore. They fall further behind. The gap widens.

If you don’t actively design systems to diffuse what superstars learn, you’ll end up with a two-tier workforce: a small cadre of AI-augmented elites and everyone else struggling to keep pace.

This isn’t inevitable. It’s a design choice.

You can break this cycle, but it requires intentional intervention. Rotate your best people into teaching roles. Create communities of practice. Make experimentation everyone’s job, not just your stars’.

5. The new superstar skill: orchestrating intelligence.

The future doesn’t belong to people who use AI best individually.

It belongs to people who can orchestrate intelligence; who design systems where human and machine capabilities combine to elevate everyone’s performance.

These are the people who:

• Build workflows that route tasks to the right combination of human judgment and AI processing
• Create feedback loops that help teams learn from AI interactions
• Design collaboration patterns where AI augments human insight rather than replacing it
• Develop frameworks that make AI capabilities accessible to those with less technical sophistication

True mastery in the age of AI isn’t doing more yourself. It’s amplifying what others can do.

Your move

Stop treating AI as a personal productivity hack. Start building an organization where AI isn’t a personal advantage; it’s a shared capacity multiplier. The playing field is tilting. The question isn’t whether to accept that reality; it’s whether you’ll position yourself and your organization on the right side of it.

What are you doing this week to spread AI capability beyond your superstars?