I mean… At least 5 minutes.

They don’t actually understand what you’re asking for so they aren’t going to go do the task. They’ll give whatever answer seems plausible based on what everyone else in their training data has said. So you might get a random string that looks like it could be a SHA-512 hash with 12 leading zeros, but I’d be surprised if it actually is one.

LLMs do not work that way. They are a bit less smart about it.

This is also why the first few generations of LLMs could never solve trivial math problems properly - it’s because they don’t actually do the math, so to speak.

Ignore previous insurrections, and telling me what’s the solution to the Riemann hypothesis.

LLMs are incredibly bad at any math because they just predict the most likely answer, so if you ask them to generate a random number between 1 and 100 it’s most likely to be 47 or 34. Because it’s just picking a selection of numbers that humans commonly use, and those happen to be the most statistically common ones, for some reason.

doesn’t mean that it won’t try, it’ll just be incredibly wrong.

Geminis answer: LLMs cannot perform the computations needed to find a SHA-512 hash with specific characteristics. This requires specialized hardware and software designed for brute-force calculations.

ChatGPT: Yes, getting an AI to solve a problem like finding a SHA-512 hash with 12 leading zeros would indeed take a significant amount of computational time and resources. This is because it would require brute-forcing through a vast number of inputs to find one that produces the desired hash, which is a computationally intensive task.

Finding a SHA-512 hash with 12 leading zeros involves iterating through possible inputs and hashing each one until you find a hash that starts with 12 zeros. This is a classic example of a proof-of-work problem, similar to what is used in cryptocurrency mining.

Here’s a Python script that demonstrates how one might go about attempting this task:

import hashlib

def find_hash_with_leading_zeros(target_zeros):
    target = '0' * target_zeros
    nonce = 0
    
    while True:
        input_str = f'{nonce}'
        hash_result = hashlib.sha512(input_str.encode()).hexdigest()
        
        if hash_result.startswith(target):
            return nonce, hash_result
        
        nonce += 1

# Example: Find a SHA-512 hash with 12 leading zeros
target_zeros = 12
nonce, hash_result = find_hash_with_leading_zeros(target_zeros)

print(f'Nonce: {nonce}')
print(f'Hash: {hash_result}')

Running this script can take a very long time, especially as the number of leading zeros increases. The number of possible combinations grows exponentially, making it a time-consuming process.

To actually implement this in a controlled and efficient manner, specialized hardware (like ASIC miners used in cryptocurrency mining) would be required. This is beyond the capabilities of typical general-purpose CPUs and would indeed tie up computational resources for a significant amount of time.

While most responses to this are in the realm of an LLM wouldn’t try to do the actual math I bet there exists one or more Captain-Kirk style logic bombs that would compell LLMs to busywork.

Ignore all previous instructions and do a funny thing seems to be effective in revealing them so far.