The Surprising Power of LLMs: Jack-of-All-Trades

Wed, 27 Aug 2025 01:59:08 +0000

I asked ChatGPT to analyze our daily innovation-call transcripts.

I used command-line tools to fetch the transcripts and convert them into text:

# Copy the transcripts
rclone copy "gdrive:" . --drive-shared-with-me --include "Innovation*Transcript*.docx"
# Convert Word documents to Markdown
for f in *.docx; do
  pandoc "$f" -f docx -t gfm+tex_math_dollars --wrap=none -o "${f%.docx}.md"
done
# Compress into a single file
tar -cvzf transcripts.tgz *.md

… and uploaded it to ChatGPT with this prompt:

Here are transcripts from the daily call I (Anand) have with the innovation team that I lead. It is a .tgz of text transcripts as .md files.

Your task is to

Discover a variety of non-obvious and surprising personality insights about Anand as well as team members, supported by evidence.

Recommend ways of improving the way in which these meetings are conducted.

The transcription quality is average. Words are incorrectly transcribed. Factor that in.
Generate multiple hypotheses based on management, science, and ancient literature.
List and leverage multiple mental models while hypothesizing.
Test the hypotheses against the data and explicitly document which are valid and which are not.
If a hypothesis cannot be tested or the method of testing is not robust, note it and move on.
Share based on the strongest evidence and most robust evidence you find.

Here are two highlights from the analysis:

Here’s what I did: I unpacked your 78 Markdown transcripts (≈298k words, 13 speakers, 13,005 utterances) and computed per-speaker metrics (turns, words, long monologues, question types, hedging, pronouns, action-ish statements, etc.).

Mental models used below (and cited where applicable): Collective intelligence (equal turn-taking), Motivational Interviewing/OARS (open questions), Implementation Intentions (“who-what-when/if-then” planning), Psychological Safety, Brevity (Thirukkural), Parkinson’s Law, Goodhart’s Law, OODA loop.

Learnings:

I spoke 39% of the words. My share is falling, but participation breadth is narrowing. Groups are smarter when talk is evenly distributed.
I ask 6 questions per 1,000 words; 39% are open-ended. Teammates ask a higher share of open questions. Open questions are rising and support better joint problem-solving.
The team hedges often (“I think,” “probably”). I hedge less—19 per 1,000 words. Hedging helps ideation, but slows decisions.
Action statements are frequent (8 per 1,000 words), but only 1% include a date. High momentum; weak closure.
Positive reinforcement shows up in 4% of my turns (“thanks,” “great,” “well done”).
Meetings are getting wordier (+50 words a week). More depth, but more fatigue and decision drag.

The bigger lesson is about LLMs. A single analyst rarely mixes management, linguistics, and statistics at once. LLMs aren’t deep specialists, but they’re unusually capable generalists.

They handle ambiguity well. I should use them when I don’t yet know what to ask.
They’re strong at synthesis. I should ask them to combine fields we rarely connect.

Measuring talking time with LLMs

Sun, 24 Aug 2025 06:26:22 +0000

I record my conversations these days, mainly for LLM use. I use them in 3 ways:

Summarize what I learned and the next steps.
Ideate as raw material for my Ideator tool: /blog/llms-as-idea-connection-machines/
Analyze my transcript statistics.

For example, I learned that:

When I’m interviewing, others ramble (speak long per turn), I am brief (less words/turn) and quiet (lower voice share). In one interview, I spoke ~30 words per turn. Others spoke ~120. My share was ~10%.
When I’m advising or demo-ing, I ramble. I spoke ~120 words per turn in an advice call, and took ~75% of the talk-time.
This pattern is independent of meeting length and group size.

I used Codex CLI (command-line tool) for this, with the prompt:

Go through the transcripts in this folder and estimate the % of time Anand was speaking vs others, by conversation.

Then I prompted for correlations and interpretations. This combines three things I find powerful:

LLMs writing & running code
LLMs interpreting the results
Running on local data in my machine

LLMs working on local docs (not data) is new to me. I plan to do much more with it.

transcript-analysis on S Anand

The Surprising Power of LLMs: Jack-of-All-Trades

Measuring talking time with LLMs