The Maasai Warrior and the AI Executive: Who Is Better Prepared for the Future?
I was standing in the Kenyan grasslands with two Maasai warriors named James and Jackson when a question came to me that I have not been able to shake. As someone who builds, studies, and speaks about artificial intelligence, I spend much of my life helping leaders prepare for the future of AI. Yet here I was, standing between two men whose intelligence had nothing to do with prompts, platforms, dashboards, or devices. Their preparation for life was not theoretical. It was embodied, practiced, inherited, and sharpened by necessity.
James stood to my left and Jackson to my right, each holding a spear, both calm in a way that felt almost foreign to someone from a culture of constant interruption. They were not checking phones. They were not scanning notifications. They were not waiting for an algorithm to summarize the moment. They were fully present in a landscape that demanded awareness, patience, memory, courage, and respect.
That moment stayed with me because it raised a question few executives are asking honestly enough. In the next evolution of AI, who is actually better prepared: the people using AI every day, or the people who have never become dependent on it? The obvious answer would be the AI users. The more complicated answer is that it depends on whether those users are becoming augmented or diminished.
There is a difference between using AI and becoming dependent on AI. One expands human capability. The other quietly erodes it. The distinction may become one of the most important leadership questions of the next decade. As AI becomes more powerful, human judgment becomes more valuable.
This is not an argument against artificial intelligence. I am an AI entrepreneur, and I believe deeply in the power of AI to transform healthcare, education, business, and human productivity. I have seen what happens when AI is applied thoughtfully in complex environments, and I have also seen what happens when organizations mistake tool adoption for transformation. The issue is not whether leaders should use AI. The issue is whether they can still think clearly when AI is unavailable, incomplete, biased, overconfident, or wrong.
The Maasai warriors I met were not “behind” because they were not dependent on technology. In many ways, they represented a form of intelligence the modern world is rapidly outsourcing. They read terrain, weather, animals, body language, distance, silence, and risk. Their knowledge is not stored in a cloud. It is stored in memory, repetition, tradition, and the body.
Modern executives possess a different kind of advantage. They can use AI to synthesize research, model scenarios, analyze markets, draft strategies, personalize communication, automate workflows, and compress hours of cognitive labour into minutes. That is not a small thing. Used well, AI can give leaders speed, scale, and access to information that would have seemed impossible a generation ago. Used poorly, it can give leaders the illusion of competence without the substance of understanding.
This is the paradox of the AI age. The person with AI may be able to move faster, but the person without dependency may be able to see more clearly. The person with AI may produce more, but the person without dependency may notice what the system missed. The person with AI may have access to every answer, but the person without dependency may still know how to ask the right question. The future will not belong to the person who simply uses AI the most.
It will belong to the person who can use AI powerfully without surrendering human judgment. That distinction is becoming urgent as generative AI moves from novelty into infrastructure. The Stanford AI Index has warned that AI capabilities are advancing faster than the systems needed to govern, evaluate, and manage them. That gap is not only technical. It is human.
Organizations are racing to deploy AI across departments, yet many are still unclear about where human accountability begins and ends. Leaders are asking employees to “use AI” without teaching them how to verify outputs, protect sensitive data, question assumptions, or recognize when a system sounds confident but is wrong. This is how dependency begins. It does not arrive dramatically. It begins when convenience becomes the default.
At first, AI saves time. Then it saves effort. Then it starts saving us from thinking through the uncomfortable parts of a decision. We ask it to summarize what we should have read, draft what we should have wrestled with, and decide what we should have debated. Eventually, the danger is not that AI becomes too intelligent. The danger is that we become too passive.
This matters because the next phase of AI will not be limited to chatbots and productivity tools. Agentic AI systems are already being designed to take action, coordinate tasks, interact with software, and make decisions across workflows. In that environment, dependency becomes more consequential. When AI moves from answering questions to executing decisions, the quality of human oversight becomes the load-bearing structure.
A leader who cannot verify AI output is not AI-enabled. That leader is AI-exposed. A team that cannot explain why an AI recommendation was accepted is not innovative. It is vulnerable. A company that automates judgment before strengthening judgment may move quickly, but it may also move quickly in the wrong direction.
This is where James and Jackson changed the way I was thinking about AI leadership. Their intelligence was not performative. It was not optimized for a slide deck, a quarterly report, or a social media post. It was optimized for survival, stewardship, and community. It was intelligence measured by consequence.
In the modern workplace, we often confuse productivity with preparedness. We celebrate faster outputs, shorter drafts, cleaner decks, and more automated workflows. Those things matter, but they are not the same as wisdom. A person can produce more and understand less. A company can automate more and become less resilient.
The next competitive advantage may be what I call AI non-dependence. AI non-dependence is not the rejection of AI. It is the discipline of using AI while preserving the human capabilities that make its use safe, ethical, and valuable. It means retaining the ability to observe, reason, remember, verify, decide, and take responsibility.
This is especially important for leaders in regulated industries, healthcare, finance, law, education, and government. In these environments, AI errors are not merely inconvenient. They can harm people, violate privacy, create legal exposure, reinforce bias, and damage trust. Responsible AI adoption requires more than access to powerful tools. It requires leaders who understand that human judgment is not a bottleneck to be removed.
Human judgment is the safeguard. It is the force that asks whether the data is appropriate, whether the answer is plausible, whether the recommendation is ethical, and whether the outcome serves people rather than merely optimizing a metric. AI can identify patterns, but people must interpret meaning. AI can produce options, but people must weigh consequences. AI can accelerate decisions, but people must remain accountable for them.
The real question for leaders is not “How do we get everyone using AI?” That question is too shallow. A better question is “How do we build an AI-literate organization that becomes more capable, not more dependent?” The difference between those two questions will separate serious AI leadership from corporate theatre.
This is where many organizations are getting stuck. They roll out tools before they build discernment. They measure usage before they measure quality. They praise speed before they examine whether better decisions are being made. They teach prompting before they teach verification.
The future of AI leadership will require a new kind of training. Employees will need to know how to use AI, but they will also need to know when not to use it. They will need to understand how models can hallucinate, how bias enters systems, how confidential information can be exposed, and how automation can create overconfidence. They will also need opportunities to practice deep work without AI support.
That last point may sound countercultural, but it may become essential. Leaders should create spaces where teams think before they prompt. They should ask people to draft their own point of view before asking AI to refine it. They should encourage employees to compare AI outputs against primary sources, lived experience, and expert judgment. They should treat verification as a leadership skill, not an administrative task.
This does not slow innovation. It strengthens it. AI adoption without human judgment is speed without direction. AI adoption with human judgment is leverage. The organizations that understand this will not simply adopt AI faster; they will adopt it better.
The Maasai warriors I met did not make me less convinced of AI’s importance. They made me more convinced that the human side of AI leadership is being dangerously underdeveloped. In a world obsessed with artificial intelligence, we should be equally obsessed with natural intelligence. Not as nostalgia. Not as romanticism. As strategy.
Natural intelligence includes the ability to notice what is changing before it becomes obvious. It includes patience, pattern recognition, physical presence, moral courage, memory, humility, and relationship to place. These are not soft skills. In an AI-saturated world, they may become hard advantages.
Imagine the future executive who can move fluently between both worlds. She can use AI to analyze complex data, but she can also sit in silence long enough to notice what the data does not show. She can deploy intelligent systems, but she can also ask whether those systems honour human dignity. She can automate tasks, but she does not automate responsibility. She is not anti-AI. She is anti-dependency.
That is the leader the next era requires. Not a leader who worships technology. Not a leader who fears it. A leader who understands that AI is a tool, not a compass. A leader who knows that the more powerful the system becomes, the more grounded the human must be.
The future may not belong to the people who use AI the most. It may belong to the people who can use AI powerfully and still think, see, decide, and lead without it. That is the real test of AI readiness. Not whether we can prompt the machine. Whether we can remain fully human while using it.
Standing with James and Jackson in Kenya, I was reminded that intelligence is not only digital. It is not only computational. It is not only speed, output, or scale. Intelligence is also awareness, courage, discernment, and restraint.
The AI revolution will change work, leadership, education, healthcare, and the global economy. It will reward those who learn, adapt, and build with courage. But it will also expose those who confuse automation with wisdom. In the next evolution of AI, the most prepared people may not be those who outsource the most thinking. They may be those who remember how to think at all.
