Who gains from AI, who pays for it, and what India should do about the difference.
This page grew out of two talks delivered at the Takshashila Institution in June 2026. Both talks are preserved below exactly as delivered; this guide is the version that will keep growing over time. Feedback is always welcome, please feel free to leave a comment below.
This is the map. The ten ideas above are the argument; these six are the doors into it. Each part is being written up from the talks; until it moves in, its links take you to exactly where it lives in the talk — and Part 1's exhibit is already embedded below.
A general purpose technology is actually the easy bit. Rearranging our civilization around it is the hard part. To better understand AI, you have to understand other general purpose technologies from the past: electricity and the internet are great examples. What those GPTs did to work, and the bargaining around work helps us understand what is likely to happen this time around... but with one crucial difference. This GPT's capability frontier moves very, very quickly.
Capex is expensive in AI. But the good news is that inference is becoming cheaper by the day. The real thing to keep track of when people talk about the AI stack is whether each stack's revenue generates enough for the whole thing to keep humming. And the thing that really decides this is... depreciation.
AI changes tasks before it changes jobs, and contracts before it changes statistics. The evidence so far: broad usage, concentrated stress at the entry rung, high-intensity adopters expanding headcount, and firms changing shape.
How will India be affected? Depends on which India you are talking about. The bad news is that India is exposed most right where it shines: IT and ITeS. The good news, on the other hand, is that India also has real options. But as always, there's an asterisk, and that's where the reading matters.
Measure the shock before it is obvious, make good experiments cheaper, keep frontier access open, and treat the safety net as a state-capacity problem. Easier said than done? You bet!
There's economics, which is confusing but exciting. Then there is accounting, which is boring, but clarifying. The bad news is that you have to keep both stories on track in your head.
Parts appear here as they are written; the talks below remain the complete argument in the meantime. The library and the open questions are already live.
The two seminars this guide grew out of, preserved exactly as presented — dual-mode pages that read as essays and present as slides.
How value is created, who captures it, and why depreciation suddenly matters. Three acts: the economics lens, the accounting lens, and how to read today's headlines through both.
Open the talk → 27 June 2026A socio-economic map of diffusion, disruption, and optionality. Five moves: GPT history, the task lens, the 2026 evidence, India, and the choices.
Open the talk →Here's a curated, reasonably-frequently-updated (no promises!) reading list, related to this topic. Have I read every word in each of these? Hell no. Should you? Also hell no. Exercise judgment, and happily have your LLM of choice read the longer ones. But curate your reading, and never ask the LLM to choose for you — that'd be my way of going about it.
GeometricInvestor · Substack
The frame the whole Money part runs on: AI as a chain of required returns, each layer having to earn enough to justify the one beneath it. Agrawal's two-year update is the same frame with numbers attached, so read them together. Both are written by investors in what they measure, and this one is pseudonymous, so position cannot be checked at all.
David Cahn · Sequoia Capital
Superseded in magnitude, not in logic. Cahn asked in 2024 where the revenue was that justified the capex, and sized the hole at $600bn. Now it is about $443bn of hyperscaler capex against roughly $435bn of annualised AI revenue — which stacks the same end-user dollar up to three times. Keep the frame, replace the arithmetic. Sequoia is an investor in the buildout it questions.
Daniel Nishball · SemiAnalysis
The library's sharpest live disagreement. Nishball says the profit pool is migrating from infrastructure to the labs. Agrawal (Altimeter) puts the app-and-lab layer at 7% of gross profit (assumed margins; revenue base double-counts — see Agrawal); Srivastava (Baseten) puts 90-95% of inference spend at frontier labs. Value moving to labs is not value moving to applications, which the 7% bucket hides.
Rohit Krishnan · X, July 2026
A live model-market equilibrium sketch: frontier labs keep a premium where capability really matters, but open-weight and Chinese models pressure routine enterprise work toward cheap-enough substitutes. The strategic response is full-stack ownership across energy, chips, cloud, models, applications, and the customer relationship.
“The future therefore splits between massive full stack labs and individual businesses which have to specialise in some layers.”
Du · arXiv 2603.28576
Three figures, three quantities. Du's ~600x is a market-wide index, 2020-2026, and the only one documenting its basket: 62 milestone observations, OpenRouter plus Epoch AI. Agrawal (Altimeter) has $30 to $5 per million tokens — retail GPT inference, ~3 years, from the Stanford Review interview, not the essay. Gerstner (Altimeter) has ~99% over ~2.5 years on an undefined unit. Quoting one hides two.
Cottier et al. · arXiv 2405.21015
Why frontier training remains capital intensive — the expensive-to-build half of the central tension.
International Energy Agency · IEA report
The best starting point on power, data centres, and grid timing.
Goldman Sachs Research · Goldman Sachs
The optimistic macro case; pair with Acemoglu's task-based caution and let the reader hold both.
Daron Acemoglu · NBER w32487
The cautious task-based macro case — the disciplined counterweight to the 7% headline.
Alex Tabarrok · Marginal Revolution
The counterweight that keeps the guide honest: a falling labour share is not a transfer from labour to capital, and real compensation is at an all-time high. The worry is the future Jevons world, not the present tense.
Bai et al. · arXiv 2604.22750
Why agentic workflows change token economics: unit prices fall, total bills rise.
Google Cloud · Google blog
A concrete catalogue of enterprise adoption — useful ballast against both hype and doom.
Pranay Kotasthane · Takshashila Institution
The missing India vantage on compute: a consumer-integrator with supply access but little capability ownership. The best person I know working on AI and semiconductors in India.
Bresnahan & Trajtenberg · Journal of Econometrics, 1995
The core frame: a GPT changes society by changing the production map, through complements and reorganization.
Acemoglu & Restrepo · JEP 33(2), 2019
The vocabulary the whole Work part runs on: displacement vs reinstatement vs productivity, and so-so technologies that displace without big gains.
Arnold Kling · Substack
Patterns of sustainable specialization and trade: adjustment is a search process, so optimism should come from search capacity, not inevitability.
Handa et al. · arXiv 2503.04761
Task-level evidence from millions of Claude conversations — the augmentation/automation split is the number to watch.
OpenAI economic research · OpenAI
Scale and activity mix of consumer AI use — the diffusion baseline.
OpenAI · openai.com
Economically valuable tasks as a benchmark target — capability measured in work products, not quiz answers.
METR · metr.org
The task-horizon lens: how long a task can you delegate? The single most useful capability curve for economics.
Brynjolfsson, Chandar & Chen · Stanford Digital Economy Lab
Early-career exposure evidence: a 16% relative employment decline for the 22–25 cohort in exposed occupations. The margin is jobs, not pay.
ILO · ilo.org
Youth employment, skills, informality, and structure — the most-cited India source in the work deck for a reason.
CSEP · csep.org
Current employment structure and labour-market constraints — the base map for the India part.
Autor, Dorn & Hanson · AER 103(6), 2013
How to connect a national shock to local labour markets — the method the India part borrows for AI-exposed cities.
Brian Albrecht · Economic Forces
Diamond–Mirrlees for the AI age: tax output, not intermediate inputs. I agree — and it does not reduce my worries about a post-AGI world. Hold both.
Acemoglu & Johnson · Annual Review of Economics 16, 2024
The canonical this-has-happened-before brick: British real wages roughly halved 1806–1820 and shared prosperity took decades plus political voice. The productivity bandwagon is a choice, not an automatic.
Luis Garicano · Silicon Continent
Travel-agent employment fell over 60% from its peak, yet surviving agents' relative pay rose from 87% to 99% of the private-sector average. The machine took the separable part of the bundle and left people the strong part.
“Labour markets price jobs, not tasks.”
Alex Imas · Ghosts of Electricity (Substack)
The demand-side complement to Garicano: Starbucks automated for years, concluded it was a mistake, and is re-hiring baristas for handwritten notes on cups.
“The relational sector grows precisely because it isn't automated.”
Brian Albrecht · Economic Forces
The optimist's structural-change rebuttal to humans-as-horses — my counter: the induction argument is just saying the sun will rise tomorrow because it always has. One day it won't. A ready-made dialectic.
Dan Shipper · Every
Managers make good workers in an AI-first world: agentic tools amplify returns to judgment and allocation rather than flattening them. Verify the linked PDF before quoting specifics.
Johnston, Holtz, Ong, Tambe, Richmond & Chatterji · OpenAI economic research · arXiv 2606.26959
The freshest as-we-speak evidence of the shift to delegated production: 5× weekly users in H1 2026, ~10× more 8h+ delegations since January. Caveat: OpenAI-internal usage is the frontier, not the average firm.
Philip Trammell · Substack (with Dwarkesh Patel), Dec 2025
The cleanest mechanism for who captures value: while labour is a bottleneck (Baumol) its share can rise; once it isn't (Jevons), the capital share climbs toward one. A post, not a working paper.
“Piketty was wrong about the past. He's probably right about the future.”
Korinek & Stiglitz · NBER w28453, 2021
The most uncomfortable, most India-relevant implication in the literature: when capital substitutes for labour, the engine of catch-up growth shuts off. Say it plainly.
Kim (INSEAD) & Koning (HBS) · HBS WP 26-090 · SSRN, June 2026
Microdata for how firms reorganise: AI-native startups are 25% smaller, 13% more engineers, ~15% fewer entry-level workers and managers — comparable valuations, more value per employee. Cited in both talks; one treatment in the guide.
Kharazian, Simon & Stevens · Ramp/Revelio Labs, June 2026
Firm-level spend linked to workforce records: high-intensity AI adopters grew headcount 10.2% over two years and entry-level headcount 12.0%; low-intensity adopters did not. The mechanism is not exposure alone, but reorganization intense enough to change the firm.
“AI adoption changes employment only when it changes the firm.”
Tedeschi, Rama & Cruickshank · Stripe Economics, June 2026
The n=1 limit of the AI-native firm and the optimistic flip side of the canary: ~4m Americans earning $100k+ solo. The India question — solo-scaling on DPI rails — should be stated as a question, not a finding.
Alter Magazine · alter (drawing on Bhattacharya, Deaton, Rajagopalan)
Before asking what AI does to Indian jobs, ask whether we can measure Indian jobs. Bonus vignette: when the US blocked computer sales, the ISI built India's first computer in 1953 from war-surplus parts.
“A country that could not buy computing technology built its own from the debris of wars.”
Bharti & Yang · World Inequality Lab, 2025 · SSRN
A century of human-capital divergence: China bottom-up, India top-down. Education inequality is ~25% of wage inequality in India vs under 12% in China — why India did services and China did factories.
Murphy, Shleifer & Vishny · QJE 106(2), 1991
The crisp backbone for engineers-vs-lawyers: talent that organises production innovates; talent that rent-seeks redistributes. Lands hard with an Indian professional audience.
Dan Wang · W.W. Norton, 2025 (book)
The engineering-state vs lawyerly-society thesis, heavily highlighted in my copy. Pair with Hessler's Other Rivers and Ang's How China Escaped the Poverty Trap.
Rodrik & Stiglitz · The New Global Economic Order (Routledge, 2025)
The missing macro spine for the India half: manufacturing became an enclave, services must absorb labour but hit a home-market ceiling — and AI now threatens the services escape hatch too.
Ishan Bakshi · The Indian Express, June 2026
Live baseline evidence for the India half: manufacturing exports are weak, high-skill IT services face AI pressure, finance will not absorb low- and mid-skill workers at scale, and gig platforms are becoming an urban employment sink. Use as a tracking source, not a clean causal AI claim.
“The gig economy is emerging as an urban employment sink.”
The Morning Context · themorningcontext.com
The single most on-thesis India-AI-labour case: ~$50k revenue per employee vs a US benchmark near $300k. India's services-arbitrage model is the thing AI most directly erodes. (Original is paywalled; the headcount economics are corroborated in Lightspeed's open write-up.)
Johanna Deeksha · Scroll.in
Ground-level texture for India climbing the manufacturing ladder: a dormitory-labour regime traced back to 1920s Shanghai cotton mills. The vivid counterweight to abstract labour-share macro.
Nandlal Mishra & Pramit Bhattacharya · Data For India
The rigour footnote for any manufacturing-employment claim: household surveys say 68.5M by 2022–23, enterprise surveys a static ~48M. Keep every claim defensible.
Praveen Chakravarty · The New Indian Express
Telangana's Composite Backwardness Index across 242 caste groups: the strongest predictor of backwardness is access to English-medium education, not land. The bridge from caste to who reaches the AI economy.
Mishra · arXiv 2606.13314
AI exposure mapped onto caste — the working paper behind the inequality-layers section.
Jesús Fernández-Villaverde · X thread (with John Burn-Murdoch)
TFR already 1.88, projected to US levels by ~2031, Kerala 1.3 vs Bihar 2.9. The dividend window is closing faster and unevenly. Not a crisis yet, but it is coming.
Cox & East · NBER w35129
First causal national estimate: no positive spillover to US-born workers from immigration enforcement. The India parallel is sons-of-the-soil politics meeting internal migration.
Korinek & Lockwood · NBER w34873, 2026
The careful framework behind the token-tax section: stock vs flow, and Diamond–Mirrlees as the spine — tax output, not the intermediate input. The steel line people quote is not in this paper; for that, see their Brookings piece.
Anton Korinek & Lee M. Lockwood · Brookings, 8 January 2026
The public-facing version of the NBER primer, and the document where the steel line is actually in print under their own names. The deck's token-tax slide quotes it directly rather than at second hand.
“Taxing the fundamental infrastructure of AI development would be like taxing steel during the industrial revolution—a self-defeating policy that could slow the productivity growth needed to fund public priorities.”
Anthropic Economic Futures · Anthropic
The policy-menu source: token taxes, sovereign wealth funds, automation adjustment assistance, the physical-vs-human-capital tax bias. My caveat: the scenario buckets are not well defined.
Economic Survey 2016-17, Ch 9 · Government of India
The UBI argument is really a state-capacity argument: a reliable cash floor plus portable benefits plus active placement, delivered by a state that can actually do those things.
Carlo Cordasco · Aeon, June 2026
The mechanism behind Amara's law: visible costs are measurable inside the old practice, while the most important benefits often require new practices and concepts before they can even be named. The policy test is recoverability: preserve redundancy where losses may be irreversible, but prefer managed experimentation where costs can be contained.
“Measurability and importance are not the same thing.”
Shane Greenstein · Digitopoly, June 2026
The Hayekian reading of Amara: the short-run shortfall is mundane friction; the long-run surprise comes from dispersed experiments no planner could design. Exactly the PSST point, made rigorous.
“Anybody who says they predicted this is selling something.”
Noah Smith · Noahpinion
The named optimist to argue with, not a strawman: finite compute means opportunity cost governs deployment. He concedes the three cracks — inequality, adjustment frictions, AI capturing its own means of production.
Noah Smith · Noahpinion
The honest caveat before prescribing India's next bargain: history only happens once, so there is no science of development. Humility on which lever is decisive.
Santosh Desai · Anil Dharker Memorial Lecture
A humanities-register frame for the close: India as a pattern of accommodating contradictions rather than resolving them. Use sparingly; it lands.
“India is about making sure we play the game forever, not about making sure we win it.”
WSJ · Wall Street Journal
Depreciation schedules are where the AI boom's profitability gets decided. The course pushes back: Lochmiller (Crusoe, which rents this hardware) showed H100 spot prices falling after launch, then rising above what the first buyers paid; Srivastava (Baseten) saw a B200 cluster requoted from $263 an hour to $510. Asked whether useful life runs past six years, Lochmiller said he does not know. Nobody does.
Azeem Azhar · Exponential View
Above water, not ashore: trailing revenue about $110bn, about 19% depreciation headroom on stated assumptions. Agrawal's two-year update reaches much the same conclusion by a different route — bottom-up revenue and assumed margins by layer. But Azhar's figures are unverified here and carry much of the Money deck's depreciation arithmetic. Check them before they go on a slide.
Ethan Mollick · X (on the BBC 'Industrial Revelations' series)
Mollick's one-line version of this guide's opening claim: steam changed nothing until thousands of skilled workers worked out how to apply it to their existing jobs. Swap steam for AI and that is the adaptation problem exactly — the technology is the easy part; the reorganization is the long grind. He points to the BBC 'Industrial Revelations' series as the illustration.
“Steam alone wasn't enough, it required thousands of skilled workers figuring out how to apply this new power to their old work.”
Urbanomics · Gulzar Natarajan, July 2026
Useful reinforcement, not a view change: the right test is whether downstream applications prove enough commercial value before upstream margins, financing, power, and depreciation squeeze the story. Read it with Azeem Azhar, GeometricInvestor, and the talk's own ledger frame in mind: not agreement, but 'we shall see.'
“The critical place in the market to keep a close eye on may be the downstream side of AI applications and product development.”
Sydney Ember · The New York Times, July 2026
Independent corroboration of the Stripe signal: 5.7 million business applications in 2025, with the rise skewed toward firms unlikely to hire. This is evidence that AI may be lowering the minimum viable firm, not proof that the Coasean singularity has arrived; applications are not operating businesses, and roughly 500,000 actual firms formed in 2023.
Ben Thompson · Stratechery, July 2026
The clearest economic account of why open weights do not make inference free: the useful unit is the cost of completing a task, and rents migrate into serving efficiency, learning data and sticky harnesses. Thompson stops short of the counter-evidence — on Vercel's gateway in June, open-weight models ran 29% of tokens on under 4% of spend. Open weights do not cap model-layer rents. They concentrate them.
“Let the frontier labs win by being better; don’t let them define safety or security, or pull up the ladder of humanity’s collective knowledge.”
Apoorv Agrawal · Substack (Altimeter), April 2026
The headline the Money part leans on: $435bn of AI revenue, $285bn of gross profit, semis taking 79% against infra's 14% and apps-and-labs' 7%. Caveats: the margins are assumed, not measured; the $435bn stacks the same end-user dollar up to three times; and eight weeks later Agrawal sized the stack at $300bn / $150bn / $100bn, roughly 65/24/10. Altimeter's disclosed US book is 41.6% semiconductors.
“For the underlying data behind this analysis, ping me. Happy to share the full datasheet.”
Apoorv Agrawal · Substack (Altimeter), 2024
The chain was already inverted in 2024: compute taking about 83% of revenue and 87% of gross profit while applications earned almost nothing. Agrawal (Altimeter) predicted the flip. Two years on it had barely moved, 87% to 79% (assumed margins; revenue base double-counts — see Agrawal), and the non-movement is the finding. It carries footnotes naming Coatue, Meritech and Intel/AMD; the update does not.
Apoorv Agrawal (Altimeter) · Stanford MS&E 435 course lecture 1, Spring 2026
Agrawal (Altimeter) teaches the course and wrote the essay behind its headline number; his fund's disclosed US book is 41.6% semiconductors — he is describing a stack he is positioned in. The gift is the ARPU ladder: Alphabet 4bn users at ~$100 a year, Meta 3.5bn at $70, ChatGPT 1bn at $10, 95% paying nothing. Note the hole: he promises slide 16, the profit-by-layer chart, and runs out of time.
Chase Lochmiller (Crusoe) · Stanford MS&E 435 course lecture 3, Spring 2026
The course's most useful cost breakdown. Lochmiller (Crusoe, which rents this hardware) puts a site at ~$60M per MW all-in — $20M shell and power, $40M IT — against $15M per MW a year renting bare compute, a four-year payback. Then he says on tape that he may have double-counted, that engineering labour is missing, and that he assembled it that afternoon. For India: gas turbines went $1M to $3M per MW.
Tuhin Srivastava (Baseten) · Stanford MS&E 435 course lecture 7, Spring 2026
The sharpest number in the course: about 90-95% of inference spend goes to frontier models, about 5% to custom or open ones. Srivastava (Baseten) sells the custom alternative and says so in the same breath — read the 5% as a pitch and the 90-95% as evidence. His B200 cluster was requoted from $263 an hour to $510. The East India Company exchange with a public-company CEO is positioning, not a finding.
Sachin Katti (OpenAI) · Stanford MS&E 435 course lecture 5, Spring 2026
The dissent inside the course, from the buyer. Katti runs infrastructure at OpenAI, the largest customer of the layer he says will not keep the profits: value migrates up the stack, as in mobile. A gigawatt is roughly half a million GPUs and about $70bn — which sits beside Gerstner's (Altimeter) $50bn and the ~$60bn implied by Lochmiller's (Crusoe) $60M per MW, three numbers for the same unit, and nothing published reconciles them.
Ali Ghodsi (Databricks) · Stanford MS&E 435 course lecture 4, Spring 2026
Reorganisation beats capability, from the buyer himself. Databricks went from nine months per connector, one engineer each, to seven connectors in a quarter shipped by seven engineers working together. The fix was process redesign, not a better model. Ghodsi sells the layer above the models and expects them to commoditise. The pull-quote is machine-caption text; verify against the video before a slide.
“The next GPT 7 or Opus 6 would not have helped us do this better.”
Brad Gerstner (Altimeter) & Sunny Madra (Groq, then NVIDIA) · Stanford MS&E 435 course lecture 2, Spring 2026
Two men long the answer, stating the paradox best. Gerstner (Altimeter) runs a book whose largest disclosed US position is NVIDIA at 28.6%; Madra came from Groq to NVIDIA. Their number: unit inference cost down ~90% in a year and ~99% over two and a half — while H100 prices went up. Cheaper intelligence and dearer hardware at once, from people long both halves. The $50bn per gigawatt figure is from here.
Vercel · Vercel blog, July 2026
The best public read on where inference money actually goes, and it cuts against the commoditisation intuition. In June, open-weight models ran 29% of gateway tokens on under 4% of spend, while Anthropic took 61% of spend on 32% of tokens. Cheap models take the cheap work, which leaves the paid work more concentrated. The limit: one gateway's traffic, Vercel's own developer customers, not the market.
“Open-weight models ran 29% of gateway tokens, up from 11% in April, on under 4% of spend.”
Altimeter Capital Management, LP (CIK 0001541617) · SEC EDGAR, filed 15 May 2026
The disclosure the course does not make. Altimeter runs the seminar and writes the essay behind the 79% number; at 31 March 2026 it held $5.70bn across 13 US-listed longs — NVIDIA 28.6%, Meta 19.6%, semiconductors 41.6% of the book. Read that claim next to this book (assumed margins; revenue base double-counts — see Agrawal). 13F excludes shorts and the private book, so it understates the overlap.
Apoorv Agrawal, interviewed · The Stanford Review, May 2026
Useful mainly as a check on the headline. Eight weeks after publishing $300bn semis, $75bn infra and $60bn apps, Agrawal (Altimeter) sized the same stack here at $300bn / $150bn / $100bn, roughly 65/24/10 rather than 79/14/7. Nothing was retracted; the numbers moved in conversation, as assumed-margin estimates do. This interview, not the essay, is where the $30-to-$5 per million tokens line lives.
MS&E 435 (Stanford), on Epoch AI estimates · Stanford MS&E 435 course site, Spring 2026 (Wayback snapshot, 20 July 2026)
A toy that makes one point stick: run the defaults and energy binds first, at 15% a year against chips at 45%, memory at 28% and interconnect at 35%, with demand at 310%. Effective supply is the minimum of the layers, so the slowest sets the ceiling: power. For India: grid and energised shell bind, not chips. The defaults are Epoch AI's, with the energy figure from Epoch and the IEA, under CC BY 4.0.
“This is a pedagogical toy, not a forecasting model.”
Nothing matches — clear the search or pick another part.
A field guide that only asserts is not worth rereading. These are the questions the talks ended on — verbatim — and the ones this guide is still working on. The question is not "Will AI help India?" The question is: which Indians, which cities, which firms, and which ladders?
If AI is better at many tasks, what should humans become relatively best at?
from The Economics of AIIf AI can do 40% of your tasks, which 60% becomes more valuable, and which 20% should never have been in your job?
from AI, Work, and IndiaIf a hospital, bank, or startup can be unplugged from a frontier model overnight, what is that model worth on its balance sheet?
from The Economics of AIIf only approved institutions can touch frontier capability, who gets to decide what AI is for in Indian conditions?
from AI, Work, and IndiaDesign education around projects where students must use AI, explain what they trusted, show what they rejected, and defend the final judgment. What would that look like in an Indian classroom?
from AI, Work, and IndiaWhen you hear an AI headline, which ledger changed — and what evidence would prove it?
the question to leave withFor ten years I wrote blogposts about economics. This is what I now believe replaces most of them: one growing, navigable document instead of a scattered archive — with the talks that seeded it preserved exactly as delivered, and every claim carrying its source.
The guide grows the way my reading works: I clip an article, argue with it properly, and then it earns a place here — as a library entry, as evidence inside a section, or occasionally as a new section. Updates follow my reading rhythm, not a publishing calendar; the changelog above is the honest record of both.
The register throughout: steel-man the optimists, keep the sharpest dissent, and hold two true things at once. Where the evidence is genuinely unsettled, the guide says so instead of picking a team.
Written by Ashish Kulkarni, built with Claude. Sources marked ✓ were checked against primary documents during talk preparation (June 2026). Single self-contained page; the only external call is to Google Fonts.
A living document should show its work. Every meaningful addition lands here first, and the best of them become posts on the blog.
Feedback, corrections, and pointers to what I've missed all make this guide better. A threaded comment section is on the way; until it lands, the fastest line is email — ashish@econforeverybody.com.