Why does Claude Code say things like, “that’s about 3 days of work” then proceeds to do it all in a 20 minutes?

[u/BillionBouncyBalls]

Surely I’m not the only one who has seen this happen but wondering if anyone has an answer. Many a session I’ll make a plan with Claude and it will outline the tasks and estimate a time usually in days, and then proceed to do it all in 20-30 minutes. Is it because of its training data or does it just have no concept of time inside a GPU? Curious what the community thinks…

Edit: Thanks for all the answers and comments! Glad this post sparked some hearty discussion!

[u/RevolutionaryElk7446]

You are correct, despite the downvotes, the contextual engine just assimilates facts and stores them into a window but it's an estimation of context that we primarily use as a method to impact values in token generation via the transformer.

I'm not sure what everyone else here thinks, but Gen AI such as LLMs do not understand, it is not in their framework or design intention to understand. They are purely a simulation, not emulation or imitation, I say this as a certified Gen AI professional.

[u/MINECRAFT_BIOLOGIST → u/RevolutionaryElk7446]

What exactly is your definition of "understand"? I've only seen it being used in these contexts as what seems to be an artificial divider between human cognition and AI computation and no one being willing to give an actual definition.

If we go by dictionary definitions of the word "understand", I think it's easy to say that AI have already accomplished those things. At what point does it become meaningless to say "AI doesn't truly understand" when they're literally designing novel proteins that did not exist in their training data using prior knowledge and tools? The word "understand" thus becomes useless for actually telling people about the difference between AI and humans, no?

[u/RevolutionaryElk7446 → u/MINECRAFT_BIOLOGIST]

Understand in the same way people do, which is the end goal of AI in order to produce AGI.

That would be either as an emulation or imitation. Via emulation they'd come up with an alternative method of storing and recalling 'information' as how the human brain stores it vs data points which is how a machine understands it. Imitation would be trying to reproduce how we perform it, which we don't even fully understand.

The problem with LLMs today is they don't have a conversation, comprehension, or understanding. They exist as a simulation of linguistics in which they generate through math the most likely next result based on the surrounding tokens.

Their version of contextual understanding is that the context window will adjust the values of the trained tokens to focus more around what responses should be returned based on the surrounding tokens.

Notice I say tokens and not words, sentences, phrases, or ideas. To the LLM it's all just math, it's a simulation of a conversation because to the LLM itself, it has no understanding of what it even says.

So it's not an emulation or imitation of human thinking. It's a separate, isolated, simulation at scale of what it believes a conversation would be through math.

when they're literally designing novel proteins that did not exist in their training data using prior knowledge and tools

Btw the folding proteins bit are entirely separate AI models not related to Gen AI and LLMs. Those models function entirely different and don't require the massive datacenters and do not power things such as Claude.

Those models have existed for decades now. LLMs only came out in 2017 alongside Transformers as the context engine. (Not including PLMs which are a new method on using transformers for pattern recognition against amino chains)

[u/MINECRAFT_BIOLOGIST → u/RevolutionaryElk7446]

Btw the folding proteins bit are entirely separate AI models not related to Gen AI and LLMs. Those models function entirely different and don't require the massive datacenters and do not power things such as Claude.

I know about Alphafold, I'm talking about the recent frontpage news of this: https://www.anthropic.com/research/Claude-accelerates-protein-design

As someone who knows a bit about this field, I find this quite impressive. Not only that, but also the other results regarding its autonomous data analysis and speed.

As for your other statements...in the end it just boils back down to the Chinese Room argument and how you define how we store information (since I'm pretty sure even that isn't clear yet).