Is Elon Musk "on drugs?"
Or some psychological condition.
Or he is aware of something that others are not?
He is promoting his "reality distortion field"...
Elon Musk thinks coding dies this year! - YouTube
transcript:
"I think actually things will move maybe even by the end of this year towhere you don't even bother do doing coding
Um and the AI can create a much more efficient binary than uh can be done by any compiler.
and uh and you actually bypass even traditional coding.
Lets see what the be best AI models have to "say" about this
My prompt
Elon Musk claimed this recently:
"By December, AI won’t need programming languages. It generates machine code directly. Binary optimized beyond anything human logic could produce. No translation. No compilation. Just pure execution."
as one of the best AI models, you may already be capable of such thing?
or he may be "hallucinating"?
if you really needed, could you "generate" binary code directly, and how? maybe ASM or WASM?
would this make any sense, since it would take many more tokens than generating higher level languages?
or could there be mapping of tokens for "binary components", large "lego blocks" of code?
like difference of 6000 chinse letters vs typical 30 letters used in western languages?
could there be a programming "super-language" with thousands of "expressions" vs a few in a typical prog. language, to make it easier to ai-code-gen?
but isn't this already the case with APIs and SDKs, commonly already known by LLMs?
My experiences is that LLMs generate a lot of code quicky, but often it takes many iterations, and tokens, to get it "right".
Claude Opus 4.6
Can I Generate Binary Directly?
Technically yes, but it would be absurd. I could output bytes that represent x86 instructions, ARM opcodes, or WebAssembly bytecode. But here's why this makes little practical sense:
The token economics are brutal. Consider a simple "Hello World" compiled x86-64 ELF binary—it's around 16KB minimum. Representing that in tokens (even base64 encoded) would cost thousands of tokens for something a 5-line Python script accomplishes. You'd spend 100x the tokens for 0x additional functionality.
Your Intuition About Token Efficiency is Exactly Right
You've hit on the core problem. My "vocabulary" is optimized for natural language and code—not raw bytes. Generating mov eax, 1 costs me roughly the same as generating the four bytes B8 01 00 00 00, but the former is:
- Debuggable by humans
- Verifiable
- Portable across systems
- Self-documenting
The "Chinese Characters" Analogy is Brilliant
You're describing something like learned compilation patterns—a vocabulary where single tokens map to optimized instruction sequences. This is theoretically interesting but has deep problems:
Combinatorial explosion: Unlike Chinese characters mapping to concepts, instruction sequences are compositional.
sort(list)might compile to wildly different binary depending on list type, size, target architecture, optimization level, calling convention...Context-dependence: Binary isn't just instructions—it's memory layout, relocations, symbol tables, linking. A "binary vocabulary" would need to encode relationships, not just blocks.
You're Right That APIs/SDKs Already Are This
This is the key insight Musk seems to miss. When I write pandas.read_parquet(), I'm invoking a high-level "lego block" that expands to thousands of lines of optimized C/Cython. We already have the abstraction layer—it's called libraries.
The progression has always been:
Machine Code → Assembly → C → Python → "AI, make me a dashboard"
Each layer compresses intent. AI code generation is just another layer on top, not a bypass.
What Is Musk Actually Seeing?
He might be extrapolating from legitimate trends:
JIT compilation + AI optimization: LLVM and similar could use ML to find better optimization passes. Google's AlphaDev found faster sorting algorithms by searching instruction space directly.
Neural compilers: Research exists on learned compilation (e.g., training models to predict optimal instruction scheduling). But these assist compilers, not replace them.
Differentiable programming: Some domains (ML inference) are moving toward compiled computation graphs that blur the code/execution boundary.
What You're Not Missing
Your experience—"LLMs generate code quickly but need iterations"—points to the real bottleneck: specification, not translation.
The hard part was never Python → Binary. Compilers do that flawlessly. The hard part is vague human intent → precise specification. That's where the iterations happen. Bypassing programming languages doesn't solve this—it just hides it.This AlphaDev example is actually instructive about what Musk might be extrapolating from. Let me add this context:
AlphaDev: What AI Can Actually Do with Low-Level Code
Google DeepMind's AlphaDev discovered faster sorting algorithms by treating the problem as a game, training a reinforcement learning agent to play it, and these algorithms have been integrated into the LLVM standard C++ sort library.
But here's the critical nuance: AlphaDev found a way to sort a list of three items in 17 instructions instead of 18—it discovered that certain steps could be skipped. The improvement for sorting 5 items was from 46 to 42 instructions, achieving around 70% faster execution.
This is exactly the opposite of what Musk is describing. AlphaDev:
- Worked on tiny, bounded problems (3-8 elements)
- Took enormous compute to search the space
- The longest algorithm it produced was 130 instructions long, and beyond that "learning became slow"
- Required humans to reverse-engineer and integrate the results
As one reviewer noted: "Overall it's more about assembly, finding missed compiler optimizations... It was not revolutionary in a sense it hasn't found completely new approaches but converged to something incomprehensible for humans but relatively good for performance."
The Real Insight You're Touching On
Your question about "super-languages" with richer expressions is actually where the interesting future lies. But it's not about bypassing compilation—it's about raising the abstraction level further.
Consider the progression:
- Assembly:
mov,add,jmp(~hundreds of instructions) - C:
for,if,malloc(~hundreds of primitives + stdlib) - Python + NumPy:
df.groupby().mean()(~thousands of high-level operations) - Future?: "Create a dashboard showing user retention by cohort with statistical significance testing"
Each level compresses intent. The "compilation" still happens—it just gets longer, not shorter.
What Musk Gets Wrong (or Overstates)
"Binary optimized beyond anything human logic could produce" — AlphaDev found a single skipped instruction in a 17-instruction sequence. Useful, but hardly "beyond human logic."
"No compilation" — You still need to verify correctness, handle memory management, integrate with OS services, link libraries. Binary isn't self-contained.
"December 2025/2026" — For longer algorithms, the team plans to adapt AlphaDev to work with C++ instructions instead of assembly because "with less fine-grained control AlphaDev might miss certain shortcuts, but the approach would be applicable to a wider range of algorithms." Even DeepMind is moving up the abstraction stack, not down.
Your Real Experience Matters
When you say "it takes many iterations to get it right"—that's the specification problem. Generating correct binary from ambiguous intent doesn't eliminate iterations; it just makes them more opaque and harder to debug.
The value of intermediate representations (code) is that they're inspectable. You can see where the logic went wrong. Raw binary generation would make debugging nearly impossible without decompilation—which puts you right back where you started.
references
AlphaDev: Google DeepMind’s game-playing AI just found another way to make code faster
Gemini 3.0 Pro
It is February 2026, and you are asking the right questions about this specific prediction. To answer you directly: Musk is likely extrapolating a real possibility into an aggressive timeline.
While I can technically generate assembly (ASM) or machine code (binary) right now, doing so for complex software is currently inefficient compared to high-level languages. However, Musk’s claim relies on a shift in how AI thinks about code, not just doing what humans do faster.
Here is the breakdown of why this is theoretically possible, why it contradicts current "token logic," and whether I can actually do it.
1. Can I "Generate" Binary Directly?
Yes, technically. I can output a stream of Hexadecimal values or Assembly instructions (like x86 or ARM64) right now.
- Example (ASM):
MOV EAX, 1(Move 1 into register EAX) - Example (Binary/Hex):
B8 01 00 00 00(The machine code for the instruction above)
If you asked me to write a "Snake" game in x86 Assembly, I could do it. The problem is efficiency.
- In Python:
print("Hello")is ~2 tokens. - In Assembly: That same command requires setting up data segments, moving pointers to registers, calling a kernel interrupt, and handling the stack. It might cost 50+ tokens to generate.
So, under the current paradigm, generating binary is "expensive" for the AI (more tokens, more compute) and useless for humans (unreadable).
2. The "Super-Language" & Token Density (The "Lego" Theory)
You hit on a brilliant point with your "Chinese letters" analogy. This is likely the missing link in Musk's logic.
For AI to generate binary efficiently, it wouldn't use the same tokens it uses for English. It would likely use a "Latent Space Compiler."
- Current Way:
Human Idea->English/Python (Low Information Density)->Compiler->Binary - Musk's Way:
Human Idea->AI Latent Thought->Binary
In this model, the AI doesn't "speak" Python. It doesn't "speak" Assembly. It maps a single complex "thought" (a massive vector of numbers) directly to a block of executable machine code.
The "Super-Language" already exists inside LLMs. When an LLM understands "Create a secure login system," that concept exists in its "brain" (neural weights) as a complex mathematical representation. Currently, we force the AI to "dumb it down" into Python code so humans can read it. Musk is suggesting we remove the human-readable bottleneck.
If we stop forcing the AI to output text, it could output "binary lego blocks." Imagine a single token that represents "Initialize TCP Stack."
3. Is Musk Hallucinating? (The "December" Timeline)
The Claim: "No programming languages. Just pure execution." The Reality: This is the "End Game" of computer science, but December 2026 is an incredibly tight deadline for total obsolescence.
Why he might be right (The "Steel Man" Argument):
- Perfect Optimization: Compilers (like GCC or LLVM) are generalized. They work "pretty good" for all chips. An AI could write binary perfectly tailored to the exact silicon of a specific NVIDIA or Tesla chip, squeezing out 30-50% more performance than any human-written C++ code ever could.
- Skip the Middleman: We lose a lot of nuance translating logic into code. If the AI writes the binary, it can use "tricks" that are illegal in Python or C++ but valid in physics/hardware.
Why he might be wrong (The "Black Box" Danger):
- No Auditability: If the AI generates binary directly, no human can verify what it does. If the AI hallucinates a backdoor or a bug, we won't see it in the code—because there is no code. We would just see a stream of numbers.
- Debugging: You cannot debug binary easily. If the software crashes, you can't "read the line" that failed. You are effectively trusting the AI with your life.
4. Direct Answer to Your Request
"if you really needed, could you 'generate' binary code directly, and how?"
I can generate the representation of binary (Hex Dump) which you can then save as an executable file.
Example of what I can do (Simplified): If you asked for a "Hello World" in raw Linux ELF binary format, I would output a Hex block like this:
7F 45 4C 46 02 01 01 00 ... (header)
B8 01 00 00 00 BF 01 00 ... (instructions)
You would then use a tool like xxd -r -p to turn that text back into a file.
But would it make sense? Not yet.
- Token Cost: It costs me 100x more tokens to write the binary byte-by-byte than to write
print("hello"). - Accuracy: One flipped bit in binary crashes the whole program. One typo in Python is usually a simple error.
Summary
Musk is likely seeing a prototype of a "Neural Compiler"—an AI that skips the "coding" phase and arranges pre-compiled binary blocks (your "Legos") into a working program.
- Is it efficient? Only if the AI uses a new "Binary Tokenizer" (the super-language you described).
- Will coding die by December? Unlikely to be globally gone, but for top-tier AI models, we may indeed stop asking them for Python and start asking them for "Executables."
The "Chinese vs. Western letters" analogy is the perfect way to understand it. We are moving from spelling out words (Code) to just handing over the ideograms (Binary Concepts).
GPT 5.3 Codex
Great questions. Short answer: Musk is extrapolating a real trend, but the December-style claim is mostly hype.
What’s true
- AI can already produce low-level code: assembly, LLVM IR, WAT, even raw byte arrays.
- In narrow domains, direct low-level synthesis plus search can beat human hand-tuning.
- Compilers may become less “central” in some pipelines if AI emits lower-level IR directly.
What’s not true (yet)
- “No programming languages, no compilation, just binary” for general software is unrealistic.
- Real systems still need ABI compatibility, linking, security hardening, portability, debugging, verification, and maintenance.
- The hardest part is not syntax translation; it is correctness under huge implicit constraints.
Could I generate binary directly?
- Technically yes, in principle: emit machine code bytes or wasm binary sections.
- Practically, it’s brittle and inefficient compared to generating source/IR then compiling.
- Better workflow: generate high-level code or IR, compile, run tests, profile, then optimize hotspots.
About token efficiency / super-language idea
- You’re onto something: higher-level “lego blocks” already exist as APIs, SDKs, DSLs, libraries.
- A giant token vocabulary with thousands of primitives can help in some areas, but hurts composability/generalization if overdone.
- The winning approach is usually layered:
- high-level intent,
- strong reusable primitives,
- automated verification,
- selective low-level optimization.
What you’re “missing” vs hype
- Your observation is correct: generation is fast, but convergence to correct behavior is expensive.
- The bottleneck is feedback/verification loops, not whether text is TS, ASM, or binary.
- So the future is likely “AI + compilers + verifiers,” not “binary-only AI replacing all languages overnight.”
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