Human vs Artificial Intelligence

We've been wrestling with an agreed definition of "intelligence" since Galton, and arguably well before him. Teaching the concept to undergrad and postgrad students for several years now has forced me to address my own preconceptions about what it is, and I'm pretty much decided that it's not possible to box it in. Human intelligence is not a finished artefact — it grows. So how can we define it? It seems I know more of what it is not than what it is. But ask a tech CEO what intelligence is, and they'll tell you something different. The term has been hijacked.

On 29th March 2023, I created a ChatGPT account and typed my first question: "write me a synopsis for a book about psychological resilience." The response was general, contained no sources, but was reasonably accurate based on what I knew about the topic. I was impressed. That started my exploration of what this thing was that I was dealing with. And it became apparent that the LLM wasn't thinking any more than my calculator thinks when it multiplies. It was doing something remarkable, but was it intelligence?

"The original question, 'Can machines think?' I believe to be too meaningless to deserve discussion."

— Alan Turing

The truth is that the tech industry has pulled off an extraordinary sleight of hand. Originally, in the 1950s and 60s, intelligence for computer scientists seemed to mean reasoning and problem-solving. When machines couldn't do that reliably, it became pattern recognition. Despite its obvious errors, they now say it's "generating plausible text." The goalposts keep moving. As Korteling et al. (2021) put it: "No matter how intelligent and autonomous AI agents become in certain respects, at least for the foreseeable future, they probably will remain unconscious machines or special-purpose devices."

What AI Actually Is

I have a professional background in electrical and electronic systems, so I get excited about connecting hardware together, playing with software and making things work. I recognise that Generative AI can do amazing things. But they are hyper-fast predictive statistical machines — probability engines that predict likely next tokens based on training data. They don't use words, they use numbers. They don't understand language because they deal only in number representations of language. What they are good at is performing sophisticated pattern matching at scale. They process information but don't understand it, because there is nothing with which to understand.

This distinction matters because if we believe LLMs are akin to human brains in a jar, we are prone to being fooled by their apparent intelligence. AI as a therapist, for example. The harm is already documented. Teenagers have died by suicide after extensive chatbot interactions. Research from Brown University shows these systems endorse harmful proposals a third of the time when tested. They cannot read distress, cannot notice what's not being said. That's not a bug to be patched — it's the absence of human understanding.

When I read your email, I understand what you're asking beyond the words themselves. I interpret your tone, infer what you're not saying, consider our history. An AI system processes the statistical patterns of your words and produces statistically likely responses — but the response may be entirely inappropriate. There is nobody home.

What Makes Humans Intelligent

The things that seem "easy" to humans — reading a room, understanding sarcasm, catching a ball, knowing when your partner is upset even when they say they're fine — turn out to be computationally near-impossible. This is Moravec's Paradox: the skills that developed longest during our evolutionary history are the hardest to replicate artificially.

Human intelligence isn't merely cognitive computation. It's embodied, shaped by having a body that interacts with the world. It's relational, developed through and expressed in relationships with others. It's contextual, sensitive to situations, histories, and unspoken rules. And it's meaning-laden — we don't just process information, we have a phenomenological experience within it.

Consider what you actually do when you walk into a meeting at work. You anticipate before you even arrive. You gauge the temperature, read the faces, notice who's sitting where and what that might mean. You pick up on the political tension between colleagues who aren't making eye contact. You adjust your approach based on all this information, much of which was never explicitly communicated to you. None of this can be reduced to pattern matching, no matter how sophisticated.

This is social intelligence — akin to Gardner's (1983) interpersonal and intrapersonal intelligences. It's fundamental to human work and entirely beyond what AI systems do — not as a temporary limitation to be solved, but as a fundamental consequence of what statistical pattern-matching is.

The Real Intelligence Agenda

Why the relentless push by techno-capitalists to call these systems "intelligent"? Two agendas seem to be driving the narrative.

The first is the replacement of human labour. The explicit goal of the techno-capitalist, I believe, is to replace wiggly and unpredictable humans. People get sick, answer back, have babies, form unions, get upset when they're bullied. The SHRM and Burning Glass Institute (2023) recently surveyed how organisations are deploying AI. The report doesn't talk about "augmentation" or "human-AI collaboration." It talks about "the potential for labour replacement."

Second, surveillance and control. AI systems enable monitoring of worker behaviour at unprecedented scale — keystrokes, screenshots, every pause, every pattern of communication. Nothing new there in principle, but more granular and sophisticated today.

The gains flow upward, to those who control the technology, not those displaced by it. Workers with GenAI skills earn 21% more; companies with high GenAI exposure outperform by 22%. The trend seems clear: AI is coming for your job.

Why This Matters For Your Career

If intelligence is merely processing information and generating outputs, you become an expensive, unreliable version of something machines do better. Your years of experience reading clients, managing difficult stakeholders, and knowing when a deal is about to go sideways don't show up on a productivity dashboard. Your need for rest, relationships, and meaningful work becomes inefficiency.

But if intelligence includes the embodied, relational, contextual, meaning-laden capacities that humans possess — that which AI systems fundamentally lack — then the conversation shifts. You're not competing with machines on their terms. You're doing something different, something that can't be automated no matter how impressive the pattern matching becomes.

The question isn't whether AI systems are useful. They are incredible tools, and you should learn to use them to your advantage. The question is whether you'll let the tech industry redefine what human intelligence means so they can sell the spoof that they've replicated it. Because once organisations accept that framing, the conversation about your value becomes much harder to win.