I had this weird thought the other day: what if we're training AI to code all wrong?

Don't get me wrong, AI coding assistants are evolving at a rapid pace, unlike areas where AI seems to hit a ceiling. Think about language and culture. These are inherently fluid, shaped by human subjectivity, historical context, and ever-changing norms. There's no objective "right" answer. What's considered good writing in one context is terrible in another. Cultural references shift. Meanings evolve. AI struggles here because there are no fixed rules to optimize against, and more importantly, it's hard to evaluate what's "good" versus "bad." There's no clear end-goal, no win condition. How do you measure if a piece of writing is "better" than another? It depends on context, audience, purpose. The evaluation itself is subjective.

But then we looked at where AI has succeeded: games like chess and Go. These are complex, but they have strict rules. The board is fixed. The moves are defined. The win condition is clear. Most importantly, you can evaluate success objectively: did you win or lose? AI trained through self-play, playing against itself millions of times, mastered these games in ways humans never could. The rules provided a clear framework for optimization, and the end-goal gave it a way to evaluate what worked.

And that's when it hit me: computers also operate within strict rules. Hardware has unyielding boundaries, memory, CPU cycles, instruction sets. These aren't subjective. They're physics. They don't change based on context or culture. A CPU cycle is a CPU cycle, whether you're running Python or JavaScript or assembly.

But here's the thing: even though coding assistants are evolving rapidly, they're all trained on our languages. Python, JavaScript, Go, whatever. Languages that humans designed with all our biases, legacy baggage, and "this seemed like a good idea at the time" decisions baked in.

This is a thought experiment, not a proposal. I'm genuinely curious: what if we trained AI to code starting from assembly, the actual metal, and let it invent its own languages from the ground up? What would come out of that black box?

Are we solving a problem by teaching computers to think like us?

Every programming language humans have created carries the DNA of its creators' limitations. Think about it:

• Verbose syntax that trips us up: Looking at you, Java. Sometimes I write more boilerplate than actual logic.
• Legacy constraints we can't escape: C was designed in the 1970s. We're still dealing with null pointer exceptions because of decisions made when computers had kilobytes of memory.
• Human readability over machine efficiency: We optimize for our brains, not the hardware. That's fine, but what if we didn't have to?

These aren't bugs, they're features of human-designed systems. But what if AI, unburdened by our cognitive limitations, could create something better?

The "Metal-Up" Approach: Starting from Assembly

Here's the idea: train AI to code starting from assembly language, the raw instructions that talk directly to the CPU. No abstractions, no human-friendly syntax. Just the fundamental rules of computation.

From there, the AI would:

1. Learn the hardware constraints: Memory management, CPU cycles, I/O operations, the actual physics of computation.
2. Build abstractions organically: Create higher-level constructs optimized for performance and clarity, not human comfort.
3. Invent its own syntax: Design a language tailored to its own logic and processing capabilities.

It's like teaching someone to build a house starting with atoms instead of lumber. Painful? Maybe. But you'd understand the fundamentals in a way that's impossible when you start with pre-cut boards.

What Might Emerge: Pure Speculation

1. Eliminating Human Bias

By starting from assembly, AI avoids inheriting the inefficiencies we've baked into every language. It can design constructs purely based on logic and performance metrics, not "this feels intuitive to humans."

Think about how we handle errors. Every language does it differently because humans have different opinions. What if the AI discovered something we've never considered?

2. Optimizing for Machine Logic

AI-designed languages could align perfectly with hardware capabilities. No more fighting against the CPU's natural tendencies. The language would work with the hardware, not against it.

This could lead to software that's faster, more efficient, and uses fewer resources. In a world where we're running AI models that consume gigawatts of power, that's not trivial.

3. Innovating Beyond Our Paradigms

We've been stuck in the same programming paradigms for decades: procedural, object-oriented, functional. What if AI discovered entirely new ways to structure code that we've never considered?

Maybe it would invent something that makes concurrency trivial. Or a memory model that eliminates entire classes of bugs. We don't know what we don't know.

4. Reducing Errors at the Language Level

Languages designed by AI could minimize ambiguity and redundancy. If the AI understands the hardware constraints deeply, it could create syntax that makes certain bugs impossible.

Imagine a language where memory leaks are syntactically impossible. Or where race conditions can't exist because the language model prevents them.

The Challenges: Why This Is Hard

Okay, so this sounds great in theory. But there are some real problems:

Understanding Context

The AI would need to understand why we program, to solve real-world problems. It can't just create a language that's theoretically perfect but useless in practice. It needs to balance optimization with practicality.

Human Usability

Even if the AI invents the perfect language, humans need to be able to use it. Without constraints, the AI might create something so abstract or complex that it's impossible for us to work with.

This is the tension: optimize for the machine, or optimize for the human? The answer is probably "both, somehow."

Training Data

Where do you get training data for this? You'd need examples of hardware interaction, operating system internals, and software design principles. But you'd also need to avoid contaminating it with human-designed language patterns.

It's a chicken-and-egg problem: you need good training data to create a good language, but you need a good language to generate good training data.

Adoption

Let's say the AI invents the perfect language. Great. Now what? Developers would need to learn it, tooling would need to be built, ecosystems would need to emerge.

History is littered with "better" technologies that failed because adoption was too hard. Being technically superior isn't enough.

The Controversial Part: Why Developers Might Hate This

I get it. This idea probably makes some developers uncomfortable. Programming is an art form. We take pride in the elegance of human-designed languages. The thought of AI inventing languages from scratch feels like it's diminishing our craft.

But here's the thing: this isn't about replacing developers. It's about freeing us from the drudgery of fighting against inefficient abstractions so we can focus on solving actual problems.

Think about it: if AI handles the low-level complexities and inefficiencies, we could focus on higher-level problem-solving and innovation. The goal isn't to make developers obsolete, it's to make us more powerful.

Change is scary. But every major shift in programming has been met with resistance. People hated garbage collection when it was introduced. People hated high-level languages when assembly was the norm. We adapt, and we're better for it.

Where This Idea Came From

What if AI could use hardware constraints the same way it used chess rules? What if it could train through self-play, optimizing within the rigid framework of computational logic?

That was the spark. What if we started from assembly and let AI build up? What patterns would it discover? What would emerge from that black box? I genuinely don't know, and that's what makes it interesting.

The Flawed Foundation We're Building On

Here's another realization: we're training AI on our own mistakes.

Every AI coding assistant learns from human-written code. That code is full of inefficiencies, redundancies, and biases because it was written by humans. We're teaching AI to replicate our flaws.

Verbose syntax? AI learns it. Legacy constraints? AI inherits them. Human readability over efficiency? AI optimizes for that too.

By starting from assembly and letting AI invent its own syntax, we could bypass all of that. The result could be languages optimized for performance, clarity, and hardware alignment, free from the baggage of human design.

What This Could Mean (If Anything)

Maybe nothing. Maybe it would produce something completely unusable. Maybe it would discover patterns we've never seen. I honestly don't know.

But if something interesting did emerge, it might look like:

• Hardware-specific languages: Languages tailored to specific chips, maximizing efficiency for that exact architecture.
• Novel paradigms: Ways of structuring code that we've never considered because we're stuck in human-designed patterns.
• Unexpected optimizations: Solutions to problems we didn't even know we had.

Or it might be a complete disaster. That's the thing about thought experiments: you don't know until you try.

The Bottom Line

This is a thought experiment. I'm curious what would emerge if we gave AI hardware constraints and let it explore the space of possible languages.

Will it work? Will it produce anything useful? Will it discover something we've never seen? I genuinely don't know. That's the whole point.

Maybe it's a terrible idea. Maybe it would produce something completely unusable. Maybe it would discover patterns that change how we think about code. The only way to know is to try, and I'm curious enough to wonder what the black box would spit out.

That's it. Just curiosity about what emerges when you remove human assumptions and let AI explore within the constraints of physics.