How do you even compete and survive in a world where you’re up against neural nets?
An interesting answer showed up in a meeting today. Here’s you. Here are the neural nets. You seem like different things, right?
Remember how in math, to make sense of something, you first reduce everything to a common denominator? Then it all gets clear.
Let’s try that. You, my friend — you’re a neural net too. And the fact that you also happen to have a body, arms, legs — nobody cares about that, because right now you’re sitting in front of a screen, working with your head, not your hands and feet.
So it’s one neural net against another. We’ve got our common denominator. Let’s go.
Next question. How is your neural net better than, say, ChatGPT? Let’s look at it like engineers.
Right now there’s a competition between neural nets running on this planet. What makes one better than another? What makes DeepSeek better or worse than Grok? The logical answer: they’re trained differently. Which is why Grok can do something DeepSeek can’t. And DeepSeek can do something Grok can’t. Why? Because every neural net is basically a matrix of weights. A set of numbers. ChatGPT is a set of numbers, DeepSeek is a set of numbers, and you’re a set of numbers too (with a slightly more complicated architecture).
So what’s so unique about the numbers in your matrix? What have you got that Grok and DeepSeek don’t?
The numbers appear through training. How did you once learn to tell cats from dogs? Nobody explained to you: “four legs, one tail, one head — that’s a cat, and the same thing arranged differently is a dog.” They just showed you a lot of cats and dogs, and your neural net trained itself. Exactly the way every other neural net trains. Nobody explains the difference between a cat and a dog. You show it a million dogs and a million cats. The right numbers appear in the matrix.
And so your whole life you’ve been learning to tell good design from bad. Architecture from garbage. Cats from dogs. Ask me to put into words how a cat differs from a dog — I’ll fail. Ask me to describe how good architecture differs from bad — I’ll manage, but only in the vaguest terms. Because past that, it all depends on context. But show me a real problem in context — and knowing that context, I’ll tell you instantly: “This architecture is shit. And that’s a cat.”
Because your neural net has spent a lifetime learning to tell one from the other. And your entire experience (which is, essentially, a set of numbers) is a unique set of weights. It lets you take information in and put the right answer out — an answer you can’t always even explain.
This wired-in neural net of yours, with your whole experience encapsulated inside it, with your intuition running in there… that’s your competitive edge over Claude and ChatGPT.
And one more thing. (I’m not the first to make this point — I heard it put well recently, and I agree.)
Unlike you, a neural net risks nothing. AI feels no pain and no fear. You, though, carry the responsibility for every decision your neural net makes. Public blame or public praise. Criminal charges. The approval or disapproval of your friends, your colleagues, your family.
The neural nets don’t give a damn. You do. And that’s exactly what makes your decision worth something.
Good luck out there, friend.