_The Death of Coding Is Cancelled: Why Your AI Assistant Is Quickly Becoming an Imbecile_
"At the start of the project, it seems like a Demiurge. But that ends quickly and forever. Here’s a mathematical proof of why AI will never replace programmers."
FTA: Why a Programmer’s Brain Is Not a “Statistical Calculator”
This is the boundary that AI evangelists refuse to acknowledge. There is a fundamental difference between a human and an LLM system.
A programmer doesn’t keep 10,000 lines of text in their head. They keep meaning. They understand the architecture, the intent, and the reasoning behind every decision. For AI, your project is just a flat sequence of symbols.
The human brain excels at filtering out the irrelevant. But AI must process every single token it’s been “paid” with context to handle.
A human learns on the job, whereas an LLM is static. It doesn’t get “smarter” from spending three hours helping you with refactoring. It just burns more energy.
The “Horse and Car” Trap
The LLM true believers will tell you: “The models will get better! Just wait for GPT-6 or Gemini XYZ”
This is a classic fallacy. You can breed a faster, hardier horse indefinitely. But you will never breed a car out of a horse.
Scaling LLMs is the path of quantitative improvement of a statistical predictor. But intelligence (AGI) requires:
Causal reasoning.
Autonomous goal-setting.
Long-term memory that doesn’t burn down a power plant on every query.
The Uncomfortable Truth
At first, AI makes you faster. But the moment the system’s complexity ramps up, a human’s work speed starts to outpace the AI that has ground itself to a halt. You spend more time crafting prompts and waiting for inference than actually writing code.
I hope you now understand the source of my concern. We are building an economy on top of tools that physically cannot scale with the complexity of our problems. Today’s AI assistants are powerful statistical mirrors reflecting our own intelligence back at us. But don’t mistake the reflection for the thing itself.
Don’t believe Sam Altman when he says that by 2028 we’ll have an early version of AGI.
Although… if you really want to — go ahead and believe it.
But it won’t be by 2028, and any AGI that does emerge will be built on a completely different architecture. Which one exactly? I think you’ll find out soon enough.
https://medium.com/predict/the-death-of-coding-is-cancelled-why-your-ai-assistant-is-quickly-becoming-an-imbecile-e4d0236c6f07
GrumpyRabbit
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"content": "_The Death of Coding Is Cancelled: Why Your AI Assistant Is Quickly Becoming an Imbecile_\n\n\"At the start of the project, it seems like a Demiurge. But that ends quickly and forever. Here’s a mathematical proof of why AI will never replace programmers.\"\n\nFTA: Why a Programmer’s Brain Is Not a “Statistical Calculator”\nThis is the boundary that AI evangelists refuse to acknowledge. There is a fundamental difference between a human and an LLM system.\n\nA programmer doesn’t keep 10,000 lines of text in their head. They keep meaning. They understand the architecture, the intent, and the reasoning behind every decision. For AI, your project is just a flat sequence of symbols.\nThe human brain excels at filtering out the irrelevant. But AI must process every single token it’s been “paid” with context to handle.\nA human learns on the job, whereas an LLM is static. It doesn’t get “smarter” from spending three hours helping you with refactoring. It just burns more energy.\nThe “Horse and Car” Trap\nThe LLM true believers will tell you: “The models will get better! Just wait for GPT-6 or Gemini XYZ”\n\nThis is a classic fallacy. You can breed a faster, hardier horse indefinitely. But you will never breed a car out of a horse.\n\nScaling LLMs is the path of quantitative improvement of a statistical predictor. But intelligence (AGI) requires:\n\nCausal reasoning.\nAutonomous goal-setting.\nLong-term memory that doesn’t burn down a power plant on every query.\nThe Uncomfortable Truth\nAt first, AI makes you faster. But the moment the system’s complexity ramps up, a human’s work speed starts to outpace the AI that has ground itself to a halt. You spend more time crafting prompts and waiting for inference than actually writing code.\n\nI hope you now understand the source of my concern. We are building an economy on top of tools that physically cannot scale with the complexity of our problems. Today’s AI assistants are powerful statistical mirrors reflecting our own intelligence back at us. But don’t mistake the reflection for the thing itself.\n\nDon’t believe Sam Altman when he says that by 2028 we’ll have an early version of AGI.\n\nAlthough… if you really want to — go ahead and believe it.\n\nBut it won’t be by 2028, and any AGI that does emerge will be built on a completely different architecture. Which one exactly? I think you’ll find out soon enough.\n\nhttps://medium.com/predict/the-death-of-coding-is-cancelled-why-your-ai-assistant-is-quickly-becoming-an-imbecile-e4d0236c6f07",
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