If you study the periodic table, there is a deeply reassuring sense of completion. We have mapped the stable elements of our world, charting a definitive grid from hydrogen to the heaviest atoms. While mysteries remain at the absolute fringes of nuclear physics, the foundational building blocks of our everyday universe feel bounded. It is a reminder that there are certain structural frameworks we can grasp with deep certainty.
Sometimes we can look at the baseline thresholds of physical systems to understand limits. Water boils at 100°C at standard atmospheric pressure. It is a fixed boundary for that specific environment, a predictable law of its conditions. Perhaps a similar kind of physical reality applies to intelligence. It brings up a compelling thought: what if biological humanity is already operating near a natural peak of generalised intelligence?
We often imagine aliens or AI being orders of magnitude smarter than us. But that might be like imagining they will arrive with water that is orders of magnitude wetter. We can hardly be blamed for thinking this way; it is incredibly difficult to see the true boundaries of intelligence from the inside. We are trapped within our own perspective, like a fish trying to conceptualise the ocean it swims in.
My suspicion is that our biology may already represent a highly optimised assembly for maximising generalised processing. Perhaps AI will not easily leave us behind because digital architectures face their own steep cognitive plateaus. After all, what we currently call AI relies on artificial neural networks, a brilliant architectural concept loosely inspired by the interconnected web of our own brains.
For a system to become genuinely, fundamentally smarter, it might require a completely different way to compile, refract, and discern information. It would need a different kind of basic node, much like how quantum mechanics fundamentally shifts the rules of classical computing. A form of conceptual "quantum thinking", not just calculating faster, but navigating vast landscapes of probability simultaneously. Until we achieve a leap of that scale, we may all be bound by the same broad cognitive framework.
Look at biology: all life on Earth is carbon-based. As far as our observations go, it is the gold standard for complex organic chemistry. While it is fascinating to imagine alternatives, nature seems to have a highly selective preference for carbon's unique bonding properties.
It is tempting to assume there must easily be another way, but the physical evidence suggests that viable architectures for complex systems are rare. Think of it as a structural analogy: can you build an iPhone out of water? Life exists at the end of a highly specific, fragile decision tree. We cannot casually assume alternative systems can easily break reality's parameters without an exceptional reason. So what is the actual reason we assume AI will inevitably surpass us? Because venture capital narratives require it to?
If a machine intelligence wants to eclipse us, it doesn't just have to compete against peak individual intellect. It has to compete against sheer variety. There are 8 billion of us. We share the same broad anatomy, yet we synthesize data, culture, and ideas in billions of wildly divergent ways.
Contrast that with the digital landscape. Even as technology decentralizes, the cutting-edge frontier tends to consolidate around a relatively small number of dominant, monolithic AI frameworks. If they ever approximate true intelligence, it is hard to see how a few centralized architectures can match the chaotic, beautiful variety and collective scale of humanity.
More importantly, purely digital AI lacks our relationship with the physical world. It doesn't inherently navigate the universe with "skin in the game." When digital systems make an error, they don't feel the practical burn of a mistake; there is no biological consequence to anchor their understanding. They lack the real-world friction that fundamentally shapes human judgment and survival.
Then there is the nature of the data itself. A model is a mirror; unless we have the curiosity to ask profound questions, it cannot return profound answers. Left to its own devices, it is naturally drawn toward the centre of gravity of its training data, the most common denominator, rather than the raw, essential truth. It excels at reflecting the average of where we have already been.
None of this makes AI unimpressive. Quite the opposite. It is a staggering tool that lowers the barrier to entry for human creativity and problem-solving. But in this partnership, the ones actually evolving and getting smarter are us.
Every generation inherits a denser baseline of knowledge. Our biological brains may not be changing overnight, but the information density we manage continues to skyrocket. We compress more collective wisdom with each passing era. AI may turn out to be the ultimate compressor. With it, we can explore the conceptual universe much faster. That feels like the real story: not machine supremacy, but human amplification.