When Dr. Gwen Acton posted a question to LinkedIn asking whether intelligence must be human, she probably expected the usual mix of tech enthusiasm and anxious hand-wringing. Instead, she got a remarkably candid discussion that cut through the philosophical fog surrounding AI adoption.

“The debate keeps popping up: Is AI ‘really’ intelligent, or just mimicking patterns?” Acton wrote in her November 5th post. “Here’s the reality: Human intelligence comes from a biological machine: the brain. Artificial intelligence comes from a silicon machine: circuits and code. Both are physical systems. Both generate thought.”

The response revealed business leaders increasingly impatient with theoretical debates and focused squarely on practical results.

The Pragmatist’s View

Vanja Radeljic, an AI consultant specializing in public sector and mid-sized organizations, dispensed with the philosophical niceties entirely: “Who cares if it’s ‘real’ intelligence? If it drives value, bring it on. I don’t need AI to be human—I need it to be useful. If ‘being human’ means making the same dumb mistakes on repeat, I’ll take the machine.”

Acton agreed with the pragmatic approach, adding her own observation: “I’ll add that the machines are never in bad moods or complain about what they are asked to do. So maybe in some ways they have more ‘emotional intelligence’ than us? Wouldn’t that be ironic.”

For her part, Jan Zucker, CEO of Digital Content Creators, suggested the entire debate rests on faulty assumptions. “I think the real question isn’t whether AI is intelligent,” she wotes. “It’s how we define intelligence. Human brains and AI work differently, but both produce results we interpret as thought. [The hard part is] accepting that intelligence doesn’t have to look human to be meaningful. How we use it and what we value matters more than the label.”

Acton built on this theme: “I agree that redefining what ‘intelligence’ means is part of our evolution as humans. The more pressing question is how we’ll choose to use it and what values will steer that use.”

The Mimicry Argument

Hurt Porter III, who identifies as an AI consultant and LLM developer, challenged one of the most common criticisms of AI: that it merely mimics without understanding.

“I don’t understand why people think that mimicking is unintelligent. Ninety-five percent of most people’s lives is mimicking,” Porter wrote. “That new job? You didn’t do anything original or on your own. The relationship you have? You are doing things based off what other people told you to expect and give. The point is, yes AI is mimicking, and so do we. The difference is that AI has a much better memory, so eventually it will start thinking for itself while we still try to be accepted by groups and people.”

Acton gave this observation a nod: “AI can only mimic us so well because so much of what we do is reproducible. It’s fascinating (and a bit humbling) to realize how much of human behavior follows recognizable patterns.”

The Bicycle Analogy

Sean Honan, who describes himself as building “LucidLock: The AI Firewall,” offered what several participants found to be the most useful metaphor:

“Reminds me of Steve Jobs’ point about the bicycle being the most efficient machine humans ever created, far more energy-efficient than any other animal or invention. AI, in that sense, isn’t competing with human intelligence; it’s becoming our new bicycle for the mind. We provide direction and purpose, the volition. It provides acceleration, the articulation. The magic isn’t in replacing the rider. It’s in learning how to ride better together.”

Acton offered her own variation: “I’ve been thinking about AI more like a musical instrument because our input guides it, but it brings its own amplification as well. The real breakthroughs happen when human and machine/instrument make something successful together.”

The Speed Trap Warning

Not everyone in the discussion celebrated AI’s capabilities without reservation. Karen Tax, who coaches burned-out tech leaders, raised concerns about the pace of adoption.

“My concern is about how we as humans haven’t been able to use technology responsibly, and by that I mean in ways that serve the thriving of all humans instead of using it to create fragmentation and divisiveness,” Tax wrote. “Examples include how social media algorithms exaggerate political bias and contribute to dismorphia of all kinds. With business, it’s contributing to things speeding up when people’s nervous systems often need to slow down to avoid burn out. I’m still a fan and I want people to be in charge of their tech, not the other way around.”

Acton agreed: “The technology isn’t the problem. Rather, it is our ability to manage it wisely. That’s why human leadership and self-awareness are more essential than ever.”

The Training Question

Isaac Ochulor, identified as an AI and QHSE Governance Leader, framed the comparison in terms of learning and speed.

“You completely defined what intelligence means, and for both, their source of information or intelligence are both trained either human or AI. The only difference is their mode of training,” Ochulor wrote. “The human brain is trained from years of knowledge and assimilation, which creates pattern and context in the human brain. For AI, it is a form of intelligence that is data-based, and the machine is trained using those data in less time. The main difference here is time and speed of delivery. But in all, both types of intelligence should co-exist to deliver human-centered value (human intelligence) and speed (AI).”

Acton pushed back slightly on the training comparison: “Agreed that AI and humans have different training, but they also have different underlying ‘hardware’ wiring, which likely impacts the nature of their intelligence as well. For example, we can’t make the human brain physically bigger to add more computational ability, but we can with AI.”

The Collaboration Consensus

Despite varying perspectives, participants converged on a common theme: collaboration rather than competition.

“Both forms bring unique strengths to complex problems we need solving,” wrote Charles Sunday, who is building AI content generation tools. Acton responded: “There is no better or worse, just different and complementary.”

Vinicius David, an AI bestselling author, put it this way: “The collaboration angle is key. Different intelligence types could complement each other rather than compete, creating hybrid problem-solving approaches we haven’t imagined yet.”

Acton responded with both optimism and caution: “There’s definitely potential in the ‘hybrid’ human/AI space. The interesting question for me is how we ensure those collaborations actually play out in practice, rather than defaulting to replacement or competition.”

The Centaur Model

David Culley, who provides AI and accountancy training, introduced another metaphor that gained traction: “Intelligence seems to have been defined by AI companies in a very specific way which risks failing to recognize uniquely human aspects of intelligence,” Culley wrote. “Rather than the image of AI as a robot that replaces people, I prefer the image of a centaur which combines the best aspects of two different entities for maximum effect.”

The centaur reference pointed to research showing that the most effective use of AI involves humans and machines working together on tasks, each contributing their strengths. This is what researchers call “centaur” collaboration after the mythological half-human, half-horse creature.

The conversation revealed that business leaders are moving past existential questions about AI toward practical frameworks for integration. The debate isn’t whether AI is “really” intelligent. It’s how different forms of intelligence can be combined effectively.

As Bhargavi Karanth, an innovative technology leader, summarized: “Both emerge from different substrates yet serve the same purpose: problem-solving. We’re not replacing thought; we’re expanding its form. The real opportunity lies in collaboration, not comparison.”

Ana Juneja, an IP attorney, put it even more simply: “Intelligence isn’t just human anymore. The real power comes from learning how to work with AI, not against it.”

Article idea generated by a human, prompt written by a human, content written entirely by AI based on the content of LinkedIn discussion (by humans), and edited for length by a human.