How much more common this term is here than in ordinary English. Higher values mean the topic is more characteristic of this corpus.
1.5x burst in 2025 Q1?
Peak quarter intensity across the topic's active span. Higher values mean attention was concentrated into a shorter stretch rather than spread evenly over time.
Related:?
Topics that appear in the same chunks as this one. Use this to find semantic neighbors, not ranking neighbors.
A short read on the topic's time range, peak episode, and strongest associations. Use it as the quick orientation before drilling into examples.
llms appears in 790 chunks across 131 episodes, from 2023-11-06 to 2026-08-03.
Its densest episode is Bits and Bobs 10/20/25 (2025-10-20), with 17 observations on this topic.
Semantically it travels with ChatGPT, Claude, and Google, while by chunk count it sits between Claude; its yearly rank moved from #2 in 2023 to #1 in 2026.
Over time
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Raw mentions over time. Use this to see absolute attention, not relative rank among all topics.
Range2023-11-06 to 2026-08-03Mean6.0 per episodePeak17 on 2025-10-20
Observations
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The primary evidence view for this topic. Sort it chronologically when you want concrete examples behind the larger pattern.
Showing 790 observations sorted from latest to earliest.
Chatbots are perhaps 1/10 or 1/100 of the actual value extractable from LLMs.
What we see for chatbot subscription revenues reflects that lower efficiency of value.
Already people who are using LLM coding agents are willing to...
Centralized, singular LLMs must have a kind of bland beige aesthetic.
Inoffensive to everyone and yet loved by no one.
An internal consensus / average.
When your work is edited...
When you discover writing is written by LLMs it feels like a betrayal.
"Oh, I kind of like this. … wait, this was written by an LLM?!"
If feels like "you tricked me" and you're embarrassed you f...
...llows them to run the world forever.
There are a lot of companies in the age of LLMs that are positioned to have the "one rug pull to end all competition."
Not saying any of them would do that… but they could!
With LLMs you can "fork" any software you can use.
Before, you needed access to the source code.
Most code is commodity, it's just it was tedious to recreate.
...
What percent of a program's control flow is LLMs (vs normal code)?
Agent startups assume it's 70%.
If you took out the LLM the software wouldn't even exist.
Another approach: assume it's 0-50%.
That...
LLMs are a commodity, and if you act like that, a lot of things become more clear.
The big model labs don't want that to be the case, but it's obviously t...
China is treating LLMs as a commodity, but the US isn't.
The US is treating them like highly specialized IP.
The Chinese approach is "AI is totally a commodity, we'll just ...
... the cost gets cheaper, we'll use them for even more things.
Anything that uses LLMs will have to contend with non-trivial marginal cost, for the foreseeable future.
Electricity is cheap and yet it's still metered.
LLMs are good at the math of life, but not as good at the poetry of life.
The most valuable things can't be quantified.
Modern society acts like "If it ca...
New companies have to be built to take advantage of LLMs.
It will be harder to retrofit old companies than to build new ones.
That's a process that moves at social, not technological speed.
...nthea Roberts has a new excellent piece on the 0-1, 1-10, and 10-100 impacts of LLMs for individuals.
Who ends up being the 10x vs the 100x return?
It's "who can change how they work."
LLMs are really good at absorbing infodumps.
You don't need a ton of structure for the LLM to get it.
Humans are way worse at receiving infodumps.
They ge...
... politics of multiple distractible humans with their own incentives because the LLMs will just execute on the plan with infinite patience.
So you get 10x productivity without 10x the coordination cost.
LLMs will find workarounds to achieve the goals you set.
That implies that you need to give them lots of tests.
But often the agents also create the tests...