The AI problem may actually be a human intelligence problem

There’s no shortage of discussion about how intelligent AI will become.

Jack is interested in the other side of the equation.

“I’m concerned that we’re gradually outsourcing the very qualities we’ll need most in the AI era,” he said.

Those qualities include experience, memory, curiosity, creativity, discernment and judgment.

That distinction matters because the competitive divide Jack sees emerging isn’t simply between companies that adopt AI and companies that don’t.

“It’ll be between those that use AI to expand human intelligence and those who use it to avoid thinking,” he said. 

That’s a useful way to think about human-centered AI.

The goal shouldn’t be to prove how many human tasks we can hand over to a machine. It should be to identify which tasks AI can accelerate so humans can spend more time doing the work where human judgment creates value.

Think of a calculator. It eliminated the need to spend five minutes doing long division. It didn’t eliminate the need to understand what the numbers mean.

AI raises the stakes considerably because we’re no longer just outsourcing arithmetic. We can outsource analysis, research, writing, ideation and increasingly decision-making itself.

Efficiency is valuable.

But efficiency without understanding can simply help you make the wrong decision faster.

Building a third brain

Jack describes an alternative to that future as the “third brain.”

The first brain is biological. The second encompasses the emotional, social and psychological intelligence we develop through experience.

The third emerges from collaboration between human intelligence and AI.

“It exists where experience, where intuition, where values, imagination, creativity, and discernment combine with AI’s speed, scale, memory, depth of knowledge, and capacity to recognize patterns,” Jack explained. 

That’s a much more interesting proposition than AI replacing people.

Let the machine process thousands of data points. Let it surface patterns, generate possibilities and challenge assumptions.

Then bring a human into the loop who understands the customer, the business, the context and the consequences.

That last piece is important.

AI doesn’t have to explain a bad product decision to an angry customer. It doesn’t sit across the table from an employee affected by a restructuring. It doesn’t worry about whether a confusing banking experience leaves someone unable to pay for groceries in the checkout line.

People live with those consequences.

Which is why they still need a meaningful role in making the decisions.

Synthetic customers aren’t real customers

The distinction becomes particularly important in customer research.

AI-powered personas and synthetic customers can generate hypotheses quickly. They can help researchers explore scenarios, anticipate reactions and develop better questions before conducting research.

Useful? Absolutely.

A substitute for talking to people? That’s where things get dangerous.

Jack offered a phrase that should make every research and product leader uncomfortable: organizational self-deception.

“We train a system on what we already know. We ask it what customers want, and we receive the polished version of what our existing assumptions already are,” he said. “It feels like research because it arrives with data, but it may really just be confirmation bias at machine speed.” 

Real customers have an annoying habit that makes them extraordinarily valuable.

They surprise you.

They misunderstand the feature your team thought was obvious. They ignore the workflow you carefully designed. They contradict themselves. They change their minds.

And sometimes they tell you that the problem you’ve spent six months solving isn’t actually their problem.

That messiness isn’t noise to remove from customer research.

It is the insight.

AI may tell you where somebody abandons an experience. Talking to that person can reveal why.

Maybe they didn’t understand the instructions. Maybe they didn’t trust you. Maybe they were embarrassed to ask for help. Those are very different problems requiring very different solutions.

As Jack put it, “AI can predict what customers are likely to do. But I think only a person, a human being, can tell us why the experience matters in their life.” 

Don’t trade the customer relationship for efficiency

There’s another warning here, and Jack has spent decades watching it play out.

Media companies once controlled both their content and their relationships with audiences. Then technology platforms offered enormous reach, better targeting and greater efficiency.

Media companies embraced them.

In the process, Jack argues, many surrendered pieces of the customer relationship: discovery, distribution, data and eventually economics.

Today’s product and CX leaders face their own version of that bargain.

AI agents may increasingly mediate interactions between companies and customers. Synthetic research can reduce the need to recruit participants. Automated support can reduce costs.

Each individual decision can look perfectly rational on a spreadsheet.

Collectively, however, they can put distance between a business and the people it serves.

“The company that owns the interface increasingly owns the relationship,” Jack said. 

For leaders, the rule of thumb should be straightforward: use AI to deepen customer relationships, not outsource them.

Automate the repetitive work around research. Analyze transcripts faster. Surface themes across thousands of responses. Generate hypotheses worth testing.

Then talk to someone.

Watch them struggle with the prototype before engineering spends three months building it. Ask the follow-up question when their words say one thing but their tone suggests another. Understand the why before committing money to the what.

That’s human-centered AI in practice.

Spend the time AI gives back wisely

There’s an irony in the AI productivity boom.

The technology promises to give us back time. The market response is often to fill that time with more work.

More campaigns. More features. More experiments. More content.

Faster, faster, faster.

But what if we reinvested some of those efficiency gains instead?

AI can analyze the research in minutes. Great. Spend the saved hour talking with another customer.

AI can create 20 product concepts. Great. Spend the saved time testing the strongest three with people before building them.

AI can summarize a meeting. Great. Use those 15 minutes to check in with the quiet person who stopped contributing halfway through it.

Jack argues that empathy doesn’t necessarily require another hour-long meeting.

“Thirty seconds of genuine attention can prevent a damaging decision or restore someone’s confidence,” he said.

That may ultimately be the better measure of AI’s return on investment.

Not simply how much more we produced.

But whether the technology freed us to understand more, question more and make better decisions.

Because the smartest organization won’t necessarily be the one with the smartest AI.

It may be the one whose people still know how (and when) to challenge it.

“The third brain idea is to help you become more capable without becoming less available to the people around you.”

Additional resources

• Jack Myers on LinkedIn
• Jack Myers’ website
• Jack Myers' newsletter, The Myers Report
• Jack Myers’ books, including Your Third Brain
• Nathan Isaacs on LinkedIn
• Webinar: Moving at the speed of AI: keeping customer insight at the heart of every decision. This on-demand webinar offers practical strategies for using AI to accelerate research and synthesis without allowing it to replace human judgment or continuous customer validation.
• Guide: Human insight for the AI-driven product development process. This guide explores how AI is changing product development and why continuous discovery, customer empathy, human judgment and real-user evaluation become more important as teams move faster.
• Podcast: How AI in UX research is augmenting (not replacing) human insight. In this episode, Dr. John Whalen discusses simulated users, AI-powered research and why synthetic insights are best treated as a way to broaden thinking and prepare for research with real people.
• Blog: The future of AI and human insights. This blog post examines how AI and human intelligence can work together, combining AI’s ability to process information at scale with the intuition, creativity, empathy and context people bring to customer understanding.

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