management on S Anand

Tacit is just un-instrumented

Mon, 13 Jul 2026 20:43:33 +0800

At The Curzon Hotel, my key card didn’t work. But every time I went to the reception, they’d send a bellboy who would use the same key card, jiggle it a bit, pull it in and out a few times, and the door would open.

Every night. For five nights. I just couldn’t get the knack of it.

I’ve been at the other end of this. People often reach out to me saying, “Anand, this software isn’t working.” Then I go do the same thing they did, and it works. (Sometimes, I just need to watch them do it and it works.)

It’s an intangible skill, I guess.

That gave me some food for thought. This is exactly the kind of skill an AI cannot pick up, right? I mean, jiggling keys, physical world, tacit knowledge, precisely the kind of things that would be AI proof.

So I asked Claude Fable for its opinion. “Can AI pick up the key knack?”

“It already has.” Claude said. Apparently, opening locks is one of the most studied problems in robotics.

The only reason opening the key feels hard to learn is because we didn’t / couldn’t put it in words. But a sensor on his hand would. Tacit is just un-instrumented. Once we measure it, it becomes training data.

As long as something is cheap to try and fast + clear to verify, it doesn’t matter how “physical” it is - we can build a model around it.

	Cheap to try	Expensive to try
Fast + clear to verify	Pottery	Surgery
Slow + vague to verify	Friendships	Mergers

This applies to organizations firms in different ways.

Organization	Automate (cheap to try, clear to verify)	Cut the cost of trying (expensive to try, clear to verify)	Cut the cost of verifying (cheap to try, vague to verify)	Keep human (expensive to try, vague to verify)
Insurance	Photo-based simple claims	Fraud investigations (proven or not) → AI triage of which cases to open	Underwriting rule tweaks (losses mature in years) → early-warning loss indicators	Risk appetite; reinsurance structure
Asset mgmt	Rebalancing, index tracking (tracking error verifies daily)	Large trade execution (implementation shortfall is measured) → execution simulators	Stock picks (skill or luck? takes years) → forecast scoring, attribution	Private-market deals; manager selection
Waste mgmt	Route optimization; robotic sorting	Fleet electrification pilots (cost per route is clear) → route and energy simulation	Recycling awareness campaigns → bin-level contamination sensors	Landfill siting; 30-year municipal contracts
Logistics	Routing, load planning, ETAs	Network redesign, e.g. a new hub (cost-to-serve verifies in months) → digital twin of the network	Driver incentive tweaks (retention causality is murky) → cohort telemetry	Building capacity ahead of demand
Healthcare equipment	Visual defect detection on the line	Clinical trials (clear endpoints, millions per try) → in-silico trials, device digital twins	Hospital sales messaging (committee sales, vague attribution) → pipeline instrumentation	Ten-year platform bets (R&D + regulation + adoption)
Card processor	Transaction fraud scoring (millions of labeled tries a day)	Core platform migration (latency and uptime verify instantly) → shadow and parallel runs	Fee and pricing tweaks (merchant churn is slow, confounded) → churn cohorts	Betting on new payment rails
Scientific publisher	Integrity checks, formatting, metadata	Replicating a paper’s results (re-run the code and data; verdict is clear) → automated re-execution	Desk rejections (did we reject a breakthrough) → track the fate of rejects	Open-access business model transition
Virtual school	Auto-grading; tutoring on known-answer problems	Full course production (completion and scores verify fast at scale) → AI-drafted courses	Engagement nudges (engagement isn’t learning) → better assessment	Accreditation; university partnerships
Physical school	Timetabling, worksheets, admin	Campus expansion (enrolment verifies) → demand modeling	Classroom pedagogy tweaks (education’s replication crisis) → proper assessment	School culture and head succession

Or at a role level.

Role	Automate (cheap to try, clear to verify)	Cut the cost of trying (expensive to try, clear to verify)	Cut the cost of verifying (cheap to try, vague to verify)	Keep human (expensive to try, vague to verify)
CMO	Ad copy variants (CTR verifies in hours)	National campaign launches → test with synthetic consumers, test markets	Brand and content posts (“half my advertising is wasted”) → brand-lift measurement	Repositioning the company
CFO	Reconciliations, close, variance commentary	Refinancing and hedging moves (P&L verifies) → backtests, scenario sims	Forecasts (cheap to issue, never scored) → track accuracy, Brier-style	M&A; capital allocation
CHRO	Policy Q&A, payroll queries	Comp restructuring (offer acceptance, attrition verify in months) → model before rollout	Training programs (nobody knows if they worked) → real skill assessments	Succession; senior hires; culture
CIO	Code with test suites	System migrations and cutovers → staging, canary, parallel runs	Developer productivity tooling (adopted cheaply, impact unclear) → DORA-style metrics	Build-vs-buy platform bets
CRO (Sales)	Lead scoring; outreach drafts (reply rates verify fast)	Enterprise pursuits (win/loss is clear, each pursuit costs months) → rehearse against simulated buyers	Relationship nurturing (coffee now, payoff unclear when) → pipeline telemetry per touch	Key-account and channel strategy

The common pattern here is:

	Cheap to try	Expensive to try
Fast + clear to verify	Automate high-volume	Build simulators
Slow + vague to verify	Capture data	Spend on leadership development

Keep in mind that this is at a task-level, not role-level. A single role may span the entire spectrum of tasks.

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.

Tue, 01 Jul 2025 06:46:10 +0000

LLMs are smarter than us in many areas. How do we control them?

It’s not a new problem.

VC partners evaluate deep-tech startups.
Science editors review Nobel laureates.
Managers manage specialist teams.
Judges evaluate expert testimony.
Coaches train Olympic athletes.

… and they manage and evaluate “smarter” outputs in many ways:

Verify. Check against an “answer sheet”.
Checklist. Evaluate against pre-defined criteria.
Sampling. Randomly review a subset.
Gating. Accept low-risk work. Evaluate critical ones.
Benchmark. Compare against others.
Red-team. Probe to expose hidden flaws.
Double-blind review. Mask identity to curb bias.
Reproduce. Re-running gives the same output?
Consensus. Ask many. Wisdom of crowds.
Outcome. Did it work in the real world?

For example, you can apply them to:

Vibe coding: Non-programmers might glance at lint checks (Checklist) and see if it works (Outcome).
LLM image designs: Developers might check if a few images look good (Sampling) and check a few marketers (Consensus).
LLM news articles: An journalist might run a Checklist, a Double-blind review with experts, and Verify critical facts (Gating).

You already know many of these. You learnt them in Auditing. Statistics. Law. System controls. Policy analysis. Quality engineering. Clinical epidemiology. Investigative journalism. Design critique.

Worth brushing up on these skills. They’re more important in the AI era.

ChatGPT: https://chatgpt.com/share/6863733f-4ebc-800c-ad3f-a2b472d9e9ca

The Personal MBA

Fri, 07 Apr 2006 12:00:00 +0000

The Personal MBA. John Kaufman says reading (and practicing) these 42 books should be as good as any MBA (and that an MBA is, perhaps excessively, expensive). Some of these books are worth a read in any case.

Master Yourself

Mastery by George Leonard
Now, Discover Your Strengths by Marcus Buckingham & Donald O. Clifton

Manage Your Life and Work

Getting Things Done by David Allen
The 7 Habits of Highly Effective People by Stephen Covey

Learn the Fundamentals

What the CEO Wants You to Know by Ram Charan
Profitable Growth Is Everyone’s Business by Ram Charan

Strategic Thinking

On Competition by Michael Porter
Blue Ocean Strategy by W. Chan Kim, Renee Mauborgne

The Only Thing Constant is Change

Seeing What’s Next by Clayton M. Christensen, Erik A. Roth, Scott D. Anthony
Re-imagine by Tom Peters

Masters of Management

The Essential Drucker by Peter Drucker
First, Break All the Rules by Marcus Buckingham & Curt Coffman

The Finest Minds in Business

The Essays of Warren Buffett by Warren Buffett & Lawrence Cunningham
Poor Charlie’s Almanack by Charlie Munger & Peter Kaufman

Dollars on the Books

The McGraw-Hill 36-Hour Course in Finance for Nonfinancial Managers by Robert A. Cooke
Essentials of Accounting, 8th edition by Robert Anthony, Leslie Pearlman

Numbers and Negotiations

How to Read a Financial Report by John A. Tracy
Getting To Yes by Roger Fisher, William Ury, and Bruce Patton

Operational Effectiveness

The Goal by Eliyahu Goldratt & Jeff Cox
Lean Thinking by James Womack & Daniel Jones

Form and Function

The Substance of Style by Virginia Postrel
The Design of Everyday Things by Donald A. Norman

Project Management and Marketing

The Art of Project Management by Scott Berkun
The Marketing Playbook by John Zagula & Richard Tong

Do Your Own Thing

The Art of the Start by Guy Kawasaki
The Bootstrapper’s Bible by Seth Godin

Speak Your Mind

On Writing Well by William Zinsser
Flawless Consulting by Peter Block

The Delicate Art of Human Relations

How To Win Friends and Influence People by Dale Carnegie
Crucial Conversations by Kerry Patterson, Joseph Grenny, Ron McMillan, and Al Switzler

Sell, Sell, Sell!

The Little Red Book of Selling by Jeffrey Gitomer
Influence: The Psychology of Persuasion by Robert B. Cialdini

Economics and Worldviews

Economics in One Lesson by Henry Hazlitt
The Economist

Businesses, Past and Present

American Business, 1920-2000 by Thomas McCraw, John Franklin, A. S. Eisenstadt
Brand New by Nancy F. Koehn

Rules and Morals

Law 101 by Jay M. Feinman
A Primer on Business Ethics by Tibor Machan & James Chesher

Analyze This

The 80/20 Principle by Richard Koch
Principles of Statistics by M.G. Bulmer

Voices of Experience

The Little Book of Business Wisdom by Peter Krass (Editor)
Swanson’s Unwritten Rules of Management by Bill Swanson

Pointy Haired Boss

Mon, 14 Feb 2005 12:00:00 +0000

Why Your Pointy Haired Boss Is A Mathematical Certainty.

The Occupational Employment and Wages report … [shows] how many people have what job and what they get paid. But what is that dot … that employs nearly 2 million people and pays nearly $90,000? Why it’s General and operations managers, of course. It’s an attractive, well-paying job, that doesn’t seem to be too discriminating about who gets hired.

Comments

Aditya Chaturvedi 14 Feb 2005 12:00 pm: Cool

Most successful executives are filled with self doubt and humility

Thu, 21 Jun 2001 12:00:00 +0000

Intruiging, but Collins (author of Built to Last) finds that the most successful executives are filled with self-doubt and humility.

Unhappy workers are better than happy ones

Fri, 15 Jun 2001 12:00:00 +0000

Here’s a new one. A study shows that unhappy workers are better than happy ones. Throw the Hawthorne effect out of the window.