future-of-work on S Anand

Things I Learned - 19 Jul 2026

Sun, 19 Jul 2026 00:00:00 +0000

This week, I learned:

Writing is slightly, but only slightly, better than typing (for adult learning.) One factor is that typing is faster, so many people take notes verbatim, summarizing and thinking less. ChatGPT + Claude
Graphology for personality is pseudoscience. ChatGPT + Claude
When I decide to spend time, or someone says “Let’s do X”, it’s worth checking: is this something AI can easily try, and is it clear to verify? If so, reinforcement learning loops could make AI good at it, making it a depreciating asset.
Studying how to live in an AI world is exhausting. (Not as bad as my MBA days, but not as easy as my data scientist days, either.) It requires me to make a larger mental shift, i.e. change my perspective, than I have since 2000, and that feels like work.
Both nl FILE and cat -n FILE add line numbers to files, but nl skips blank lines by default, cat doesn’t.
After using rtk for 2 months, I’m slightly downgrading it. It saves tokens but agents mess up shell commands when using it. It’s still probably a net saving, so I’ve changed my AGENTS.md from “Always prefix with rtk” to “Prefix supported, high-output commands with rtk… skip for bash builtins, pipes, loops, etc.”
I find 🔴🟡🟢 convenient status indicators in my notes. Similar ones are: 🟥🟨🟩, ❤️💛💚, 📕📙📗. I’m not fully convinced by: 😄😐😞, █ ▒ ░, ↑ → ↓, ▁▂▃▄▅▆▇, ■ ⬔ □, ● ◐ ○, ⚫ ⚪ 🔘, 🌕 🌗 🌑, etc. though they might have their uses.
Model updates means a SKILL.md and a plugin review / update, e.g. with GPT 5.6 Sol. So, like with any open source repo, use from people who update it regularly and benchmark it and version control it by model.
I asked Gemini 3.5 Flash thinking: “Which of our employees have worked on Microsoft PowerApps? Search @Google Drive and @Gmail”. It found one employee and a referral in under a minute. I asked ChatGPT with GPT 5.6 Sol with gws access. It found 3 more, plus 5 possibilities, in 12 minutes. Truly a rottweiler.
Parallel Search Turbo seems like a pretty good search API, especially for agents. Low price, high speed, and maybe good quality. #ForNow ChatGPT
Group chats in ChatGPT will probably get deprecated #ForNow.
What I learned from benchmarking my Ideation Protocol skill extensively:
1. Once you know the rubric, models can easily create a good prompt to optimize for a known rubric #ForNow. So rubric design matters more.
2. ⭐ Rubric design is really knowing what you want/need. To do this, iterating on output matters.
3. Position bias is real #ForNow. Always check if an (P, Q) comparison matches a (Q, P) comparison.
4. Models are still biased towards longer content, and potentially towards their own output #ForNow.
5. How to optimize a prompt or skill: Research and figure out what you really want, first. Then, ask a smart model for a prompt that optimizes for it. Benchmark only if you’ll use it a lot - it’s still a lot of work, and meta-prompting does a good job #ForNow. gbrain skillopt might be premature optimization.
You can use GPT 5.6 Sol in Claude Code #ForNow. (But what’s the point? Harnesses seem to be working better with their own models #ForNow.)
Our clients keep saying “We need to build a data lake” or “We need an enterprise data strategy.” I keep telling them, “No, agents can do it for you.” What I missed is: technology is the smaller part of the problem. Finding who has what data, getting access to it, and sorting out permissions (“governance”) is the bigger part.
Giving agents expert task-specific, testable procedures seems better than expert roles or mental models #ForNow. But benchmark in any case. ChatGPT
Python 3.3 introduced str.casefold(). It performs more comprehensive Unicode caseless matching than lower(); 'Straẞe'.casefold() becomes 'strasse'. (🟢 Unicode case-folding is standardized.) contextlib.closing(x) calls x.close() when its context exits. (⚪) In a dataclass, use x: list = dataclasses.field(default_factory=list), not a mutable literal default. (⚪) I learnt these while reviewing Codex-generated Python—illustrating, rather than proving, that reviewing AI-generated code can teach and catch errors. (🟡 Review remains useful across tooling. Review 2029.) “Do not discriminate against intelligence—artificial or otherwise” is a rhetorical value judgment, not an empirical conclusion. (⚫ Rhetorical value judgment, not testable. Review now.)
Here’s a nice idea from ChatGPT. “When itching to correct or clarify, FIRST restate their position to their satisfaction. ‘Did I get you right, fully?’” This emerged from the prompt suffix: Based on your research, and my past conversations, what are the top areas where and how (specifically) I can apply this principle on myself and others to maximize impact?
Automated evals can catch stuff humans miss. And vice versa. And given how many evals we create, we need automated evals to be written in an easy-to-review way. Do Automated Evals Work?
The BINEVAL paper reiterates that a bunch of Yes/No binary questions beats scales or ratings for many benchmarks. You know exactly how to grade and WHY you got a certain score. This is more reproducible and easier to learn from / act on.
When asked “How long will this software take?” models typically provide estimates assuming human speed #ForNow. Maybe they haven’t been trained enough on agentic timelines. So, when my colleague got a 2-4 week estimate which he was able to solve in hours, it was a surprise. (But, of course, it’s best to verify before promising speed.)
SKILL.md dramatically lowers the cost of learning a skill (since you don’t learn it - the agent does). That means that the value of creating skills is much higher - hundreds can use what you create (giving you recognition, if not money). I think I’ve underestimated the number of skills people will have available (I thought dozens - but it may be thousands #ForNow) and the number of skills people will create (I thought tens of thousands - but it may be millions #ForNow.) A Wikipedia (community curated, verified, high quality catalog) of skills might emerge #ForNow, if it hasn’t already.
Tacit knowledge is often just un-measured knowledge. Once I put a sensor on the bellboy’s hands at The Curzon Court, AI can figure out how he opens the door with the key and why I can’t do the same. The subset of tacit knowledge that’s AI-resistant is where attempts are expensive (“How to negotiate a merger” rather than “How to open a door”) and feedback is slow/vague (“Does the client trust me” rather than “Did the door open”).
The fact that Composio has ~20,000 tools is a market signal that connectors are commoditizing, and are a depreciating asset #ForNow.
A weak model needs a forgiving harness - which ends up slowing down model learning. Stricter, accurate verification environments are better for fastest model learning.
ChatGPT Work lets you run for longer, faster, install plugins and skills, host a website, etc #ForNow. It’s somewhere between Chat and Codex. It consumes Codex limits - something to watch for (since chat limits are quite generous).
Codex temporarily removed the 5-hour usage limit. Tibo. So, since I have 3 banked rate-limit resets #ForNow, I can, in theory, use 4 full weeks of Codex usage at one go. Reality: I don’t have problems large enough for a SINGLE week’s consumption!
From what I see of the State of AI Design and State of Prototyping, Figma is way ahead of competition #ForNow, e.g. Adobe, with Figma Make and Weave. I was also surprised how popular Cursor is (#2 behind Claude Code #ForNow). It’s also interesting that designers are coding directly #ForNow, using Figma just for edits / steering. But many research tools (note takers, survey analysis/research, etc.) will likely get eaten up by AI coding agents #ForNow, given how much designers are building their own tools.

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 Future of Work with AI

Wed, 11 Mar 2026 13:56:58 +0800

I often research how the world will change with AI by asking AI. Today’s session was informative. I asked Claude, roughly

Economics changes human behavior. As intelligence cost falls to zero, here are some changes in my behavior [I listed these].

Others will have experienced behavioral changes too. Search online and synthesize behavioral changes.

It said this.

🟡 People spend time on problem framing & evaluation. AI can execute the middle. (I’m OK at this. Need to do more framing + evaluation.)
🟢 People don’t plan, they just build. (I’m prototyping a lot.)
🟢 People build personal data & context. (I’m mining my digital exhaust.)
🔴 People queue work for agents, delegating into the future. (I’m not. I need to do far more of this.)
🟢 People shift from searching to asking for answers. (I do this a lot, e.g. this post.)
🟡 People are AI-delegating junior jobs and developing senior level taste early. (Need to do more.)
🟡 People treat unresolved emotions as prompts. (Need to do more.)

Rough legend: 🟢 = Stuff I know. 🟡 = I kind-of know. 🔴 = New learning.

My next question was:

Using economics, psychology, sociology, etc. predict new behavior changes and implications.

It said: the bottleneck shift to trust, taste, and attention.

🟢 Taste is a skill. Few people will deepen craft engagement (reading, cooking, music, …) but most won’t, creating a gap.
- Fuzzier areas (e.g. art, philosophy, unnamed ones) have more value than verifiable ones (e.g. code, science) since they’re harded to automate.
🟢 Context is an asset. People & companies will record decisions, reasoning, relationship histories, .. to create (marketplace-tradeable) assets.
🟡 Trust is an asset. People who stand behind AI output will create value in law, medicine, education, etc. Consulting will restructure around this. This will require investments # in:
- Observability
- Human augmentation - gracefully degrading without harm when humans inevitably falter
- Synthetic practice
- Cryptographic provenance
- liability regimes that internalize tail risk
🟢 AI-less is exercise. Few people will deliberately train without AI, and will be valuable in niches (sports, emergencies, status-symbols). Exams / filters will partially screen for this.
🟡 AI-less feels broken. AI-speed will be expected in services and interactions. Relationships are assets that become valuable and premium.
🔴 Orgs re-form. Org sizes fall. But size has advantages (trust, brand, connects) so new orgs will form with teams evaluated on outcome (portfolio, ROI) and trust (network, ratings).
🔴 Experience becomes luxury. Non-reproducible experiences become expensive. Provable authenticity commands a premium.
🔴 Two-speed world. Some places (countries, companies, colleges, communities) become more AI friendly. Capital, talent and productivity will concentrate here.

What implication will these have on the nature of work?

Historically, apprenticeship (execution) preceded mastery (judgement). Now, execution is free. That messes things.

🟡 How do we develop judgement? Simulators, red-teaming, AI-free tests, …?
🔴 What will managers do? Less coordination & oversight. More judgement (validate output not process), motivation, accountability. Like a film director, not supervisor.
🟡 How will we hire/pay? More outcome-based hire/pay. More freelancing, portfolio or reputation based hiring. Long-term retainers reserved for trust.
🔴 How will we describe our work? Less about tasks (which AI does) and more about who you are, where you fit, what you contribute.

So work will organize around:

Trust: context, judgement, accountability
Presence: caring, building with hands, performing, connecting
Direction: framing, evaluating, curating

… and less around translation (execution, coordination, oversight).

Fri, 12 Sep 2025 00:00:00 +0000

Slides for my DataHack Summit talk (controversially) titled RIP Data Scientists are at https://sanand0.github.io/talks/2025-08-21-rip-data-scientists/

Summary: as data scientists we explore, clean, model, explain, deploy, and anonymize datasets. I live-vibe-coded each step with DGCA data in 35 minutes using ChatGPT.

Of course, it’s the tasks that are dying, not the role. Data scientists will leverage AI, differentiate on other skills, and move on.

But the highlight was an audience comment: “I’m no data scientist. I’m a domain person. I’ll tell you all this: If you don’t follow these practices, you won’t have a job with me!”

They’re catching on! 🙂

Tue, 05 Aug 2025 00:00:00 +0000

At the DoMS, IIT Madras 2025 commencement, I shared how MBA students might adapt to an AI world.

Takeaways

The MBA syllabus will age fast; adaptability is a core competency https://youtu.be/sSyBUSuLduQ?t=02m03s
LLMs have a “jagged-edge”. Management tools—redundancy, cross-checks, human escalation—still work https://youtu.be/sSyBUSuLduQ?t=07m02s
LLMs already deliver hours of analyst work in minutes. Entry-level roles WILL vanish https://youtu.be/sSyBUSuLduQ?t=14m21s
High-value knowledge jobs—strategy, finance, contract risk, market research—are being refactored by AI https://youtu.be/sSyBUSuLduQ?t=23m01s
Learn less of grunt-work. LLMs can handle that https://youtu.be/sSyBUSuLduQ?t=45m22s
Study with LLMs as Socratic sparring. Run “draft-critique-rewrite” sprints https://youtu.be/sSyBUSuLduQ?t=49m17s

Funny bits

“It’s awkward when you don’t know whether to clap… I’ll give you a signal right at the end.” https://youtu.be/sSyBUSuLduQ?t=01m08s
Grok-4: “Apparently it basically repeats whatever Elon Musk says.” https://youtu.be/sSyBUSuLduQ?t=12m22s
To the lone student without a phone: “I love you… the rest of you, scan the QR code.” https://youtu.be/sSyBUSuLduQ?t=20m02s
“At least for the next few years, use AI. Don’t talk about AI.” https://youtu.be/sSyBUSuLduQ?t=39m09s
“Please sleep. Tell the LLM to do the assignments… This is going on record, but I’m not faculty, so I can say what I want.” https://youtu.be/sSyBUSuLduQ?t=49m52s