01
Technological × Financial
#
AI capital spending has quadrupled since GPT-4 (in Q2 2023).
33.1% vs 32%
AI-era capital intensity has crossed the dot-com peak on filed actuals (aggregate capex 33.14% of revenue in Q1-2026, against ~32% at the 2000 peak)
$117bn → $448bn
2023 → 2025; quarterly aggregate capex reached $148.4bn in Q1-2026 alone
$239.1bn → $411.5bn
calendar-year aggregate on the cash-PP&E measure, 2024 → 2025
The reading — and its limits
The defensible claim is the growth, which the companies attribute to AI — not a clean "AI capex" figure (no hyperscaler reports AI capex as a separate line; totals include warehouses, offices, other bets). The dot-com-peak crossing was previously stated as a 2026 guidance expectation; it is now an actual (Q1-2026, 33.14%). The five report on three different fiscal calendars, so every observation is mapped to the calendar quarter it predominantly covers.
Method Capex computed by us as a quarterly series from each firm's own SEC EDGAR XBRL filings (Alphabet, Amazon, Meta, Microsoft, Oracle), discrete quarters derived from year-to-date facts and reconciled to each issuer's annual figure; the growth rate and the dot-com-peak anchor (Morgan Stanley) corroborated.
02
Technological × Financial
#
The price of machine intelligence has fallen about 10x a year, while enterprises' AI costs are growing exponentially.
$60 → $6 → $0.06
cost per million tokens, for the same performance (2021 → 2023 → 2024): ~1,000x cheaper in three years
$1.7bn → $37bn
enterprise AI spend, 2023 → 2025 (~22x)
The reading — and its limits
The curve is per-token for equivalent performance — the falling price of a fixed capability, not the cost of any one task. Unit price collapses; total spend explodes anyway as usage surges. (Spend estimates vary by scope across analysts; Menlo's ~$37bn generative-AI figure is not the same as broader all-AI estimates and shouldn't be added to them.)
Method Cost curve from a16z "LLMflation" + Epoch AI (reproducible, published code); enterprise spend from Menlo Ventures' 2025 report (named).
Klarna — a case of AI deployment success and business-outcome failure.
5,527 → 3,422 FTEs
Dec-2022 → Dec-2024 — ~40%, attributed to AI plus a hiring freeze
"the work of 700 agents"
the AI assistant handled ~2.3m conversations (~2/3 of all chats) in its first month
then reversed
by mid-2025 the CEO said quality had fallen and began rehiring humans
The reading — and its limits
The deployment worked — the AI did the volume. The business outcome didn't: in the CEO's own words, cost had become "too predominant" a factor and service quality suffered, so the company began rehiring. The savings were modelled; the cost of unwinding was not.
Method Headcount from Klarna's IPO prospectus (Mar-2025); the reversal from the CEO on record (CNBC, Bloomberg, May-2025).
04
Technological × Financial
#
AI fear is repricing the Indian IT industry.
−49.6%
foreign holdings in Indian IT have nearly halved (₹712,646 Cr → ₹358,900 Cr, Oct-2024 → Sep-2026), while total foreign equity holdings fell −12.5%; metals, services, capital goods and telecom grew (same window) and financials ended near flat (−4.5%)
−17.7% vs −8.1%
trailing year: Nifty IT vs Nifty 50 (gap ~9.6 points; September alone −11.2% vs −6.1%)
The reading — and its limits
The −49.6% is the fall in foreign IT holdings through capital exiting and the rest being repriced down (74.3% of the fall is price, 25.7% net selling, as-of 30-Sep-2026). Crucially, the capital that left IT largely stayed in India — total foreign equity fell far less (−12.5%), and metals, services, capital goods and telecom grew while financials ended near flat — so this is rotation within India, a verdict on the IT sector, not flight from the country. The driver is read from what the companies and the market say, not from price alone: Indian IT's own filings name an "AI productivity impact," and the correction is described as a structural re-rating of the labour-arbitrage, headcount-to-revenue model. In addition to AI, cyclical concerns are at work — weak US discretionary spend, tariff drag, BFSI (~30% of the sector) under pressure — so the honest reading is AI-structural fear layered on a real cyclical slowdown, with AI being what makes this a re-rating rather than a dip.
Method Foreign-holding rotation computed by us from NSDL's fortnightly sector-wise FPI reports (equity AUC). Index levels from NSE Indices' own historical file. The rotation is window-free; the index comparison is the trailing year (NSE's public cap).
05
Technological × Financial
#
Foreign money is leaving India for the AI trade it can't buy at home — ~$58bn out since 2024, while Taiwan and Korea swell.
~$58.44bn
net foreign equity outflow from India since the Sept-2024 peak (~₹5.27 lakh cr), as-of 30-Sep-2026, computed from NSDL
| MSCI EM weight |
Sept-2024 |
Sep-30-2026 |
| Taiwan |
18.77% |
28.94% |
| South Korea |
11.67% |
21.4% |
| India |
19.9% |
10.65% |
| China |
24.42% |
19.8% |
The reading — and its limits
India's weight roughly halved while Taiwan and Korea — the AI-hardware markets — swelled, because index weight follows market value and ~$750bn of passive money mechanically follows the index. India isn't in the AI-hardware trade (its largest names are banks, energy, consumer), so as the world rotated to AI there was nothing in the Indian index for that money to land on. The $58.44bn (as-of 30-Sep-2026) is the cumulative outflow since the peak; the calendar-year figures (2025 ≈ −$19bn, 2026 Jan–Sep ≈ −$28bn) measure a different window and are not additive with it. The summer's brief run of inflows reversed in September — both September fortnights were net outflows — and India's weight fell to a new low on the series.
Method Outflow computed by us from NSDL depository data (net FPI equity flows, cumulative since the Sept-2024 peak). Index weights from MSCI Emerging Markets Index factsheets read directly — Gold where a factsheet (including archive-recovered), Silver where computed from the iShares EEM fund proxy (3 gap-month fills, 5 overlap checks), tier marked per row.
06
Financial × Technological × Risk
#
When you buy an index, you're not buying diversification — you're increasingly buying concentration into one trade (AI/semiconductor).
| Index |
Const |
Eff-N |
Top-10 |
AI-core |
Top-10% |
Largest |
| KOSPI (Korea) |
828 |
7.0 |
64.5% |
52.6% |
90.4% |
Samsung Electronics 28.8% |
| DJIA (US, price-wtd) |
30 |
21.2 |
55.1% |
18.2% |
n/a |
Goldman Sachs 10.5% |
| Nifty 50 (India) |
50 |
24.3 |
52.9% |
0% |
36.4% |
HDFC Bank 9.9% |
| NASDAQ-100 |
101 |
31.7 |
46.8% |
57.1% |
46.8% |
NVIDIA 8.4% |
| EURO STOXX 50 |
50 |
30.1 |
43.4% |
16.7% |
27.4% |
ASML 10.8% |
| S&P 500 |
504 |
43.5 |
39.1% |
39.0% |
64.4% |
NVIDIA 8.4% |
| MSCI EM |
1,153 |
27.4 |
39.0% |
37.4% |
66.7% |
TSMC 15.6% |
| MSCI World |
1,239 |
83.7 |
27.8% |
29.7% |
61.2% |
NVIDIA 5.8% |
| TOPIX (Japan) |
1,636 |
105 |
23.7% |
6.1% |
79.0% |
MUFG 3.9% |
The reading — and its limits
Three exceptions prove the point — Japan (TOPIX) is genuinely diversified (behaves like 105 stocks, AI-core just 6.1%); India (Nifty) is concentrated but in the old economy (AI-core 0% — its "tech" is IT services, the AI-disrupted category, not AI hardware); and the Dow (DJIA) sits near the top by top-10 (55.1%, second only to Korea) but this is a price-weighting artefact — its weights track nominal share price, not company size, so two high-priced blue-chips (Goldman, Caterpillar) dominate and the AI names (Nvidia, Microsoft) are muted. The Dow's low AI-core (18.2%) does NOT mean it escapes the AI trade — it holds the same names, down-weighted by share-price accident. Everywhere else, buying the index increasingly means buying the same handful of AI names — and at September's close NVIDIA was back as the largest single holding in the S&P 500, NASDAQ-100 and MSCI World.
Method Effective-N and concentration measures computed by us from each index's own full constituent file (index-owner or full issuer-holdings files). AI-core on a named-basket definition (semiconductors + hyperscalers) that crosses GICS sectors.
07
Geopolitical × Technological
#
India sits on one of the world's largest rare-earth endowments and turns under 1% of it into output.
| Rank |
Country |
Rare-earth oxide |
| 1 |
China |
44.0 Mt |
| 2 |
Brazil |
21.0 Mt |
| 3 |
India |
~8.52 Mt |
| 4 |
Australia |
6.3 Mt |
| 5 |
Russia |
3.8 Mt |
The reading — and its limits
The constraint was never geology. Comparator countries are on USGS reserves; India's figure is its own AMD in-situ resource (adjacent measures, so the rank is order-of-magnitude) — and we use India's own primary because the originator outranks the aggregator (USGS itself currently lists India as "NA"). India's decade of rare-earth trade: nil imports, 18 tonnes exported.
Method Endowment from India's AMD/DAE, read from the PIB Parliament record; output and world-share from USGS Mineral Commodity Summaries 2026, read directly.
The 60/40 portfolio is no longer valid — it was never universal, and the regime that supported it has turned.
| Market |
pre-2022 |
2022-on |
What it means |
| US |
−0.34 |
+0.11 |
inverted hard — bonds now fall with equities |
| UK |
−0.17 |
+0.14 |
inverted hard |
| Germany |
−0.27 |
~0.00 |
hedge neutralised |
| Japan |
−0.09 |
−0.02 |
no hedge to lose (rates pinned near zero) |
| India |
+0.02 |
+0.15 |
never a 60/40-style hedge |
The reading — and its limits
The hedge inverted hard where it was deepest (US, UK) when the 2022 hiking cycle hit; it was neutralised in Germany and never existed in Japan or India. A correlation that "always held" turns out to have been a feature of one rate regime in a few markets — not a universal law the world could safely build a portfolio on. (Bond legs are long-government-bond fund proxies, not cash bonds.)
Method 63-day rolling correlation of daily equity-index vs long-government-bond returns, per market, pre-2022 vs 2022-on, computed by us from primary price series (same method as the US base reading). US refreshed to 30-Sep-2026.
The price of money has doubled — and it isn't coming back down.
~2.6x
the US 10-year yield (5.29%, 30-Sep-2026) against its 2012-2021 average (2.04%)
~10x
the US 10-year's rise from its 0.52% COVID-era low to 5.29%
3 years above 4% — now above 5%
the US 10-year has averaged above 4% in each of the last three years and in September 2026 closed a month above 5% for the first time since the easing cycle began in Sep-2024 — through 175bp of Fed cuts, and as the Fed turned to its first hike (Sep-2026)
The reading — and its limits
"Doubled" is measured against the 2012-2021 decade average — on the September level it is ~2.6x, more than doubled; against the longer 2010s it is ~2.2x, so the window is stated. The deeper point is not the level but the persistence: the long end rose through an entire Fed easing cycle, and in the month the Fed finally reversed with its first hike, the 10-year closed at the high of the cycle (5.29% at 30-Sep-2026, the top of the Sep-2024-onward series) — on our reading, the market moved first and the Fed came round to it. The Fed's own convergence-to-2% forecasts have missed six years running. Two independent witnesses — the bond market and the forecaster's own record — say the same thing: a re-based regime, not a cycle.
Method Historical averages computed by us from the US Treasury's own daily par-yield series (1990-2026); the current level from the month-end 10-year series (30-Sep-2026). The Fed path: −175bp over Sep-2024 → Jun-2026, then the first hike (upper 3.75% → 4.00%, effective 17-Sep-2026).
10
Geopolitical × Financial
#
The dollar isn't being dethroned, but select countries are hedging with gold.
| Gold as % of reserves |
2018 |
2025 |
| India |
5.5% |
16.2% |
| China |
2.4% |
8.5% |
| Poland |
4.5% |
28.2% |
| Turkey |
21.6% |
61.1% |
The reading — and its limits
A hedge, not a replacement — the trigger was the 2022 freezing of ~$300bn of Russian reserves (dollar reserves held abroad can be seized; domestic gold cannot). The clearest evidence is the freeze itself: in the two quarters after it, the dollar's reserve share rose, the euro was flat, and the renminbi — already past its peak — kept falling. Nothing replaced the dollar; sovereigns bought gold instead. The share rise is partly buying and partly gold's price rise; for the active buyers the tonnage rise confirms real accumulation. China's reported holding is a likely-understated floor. And the buyers are not a bloc: Poland, a NATO and EU member, is the most deliberate accumulator of all.
Method Computed by us from IMF primary (SDMX API) — COFER for the dollar share, IRFCL gold-value/total-reserves per country, with tonnage from reported gold volume. India cross-validated against the RBI half-yearly report (880t). Held as nineteen series, quarterly for the currency shares and annual for the per-country gold.
India spends a third of its revenue just servicing its debt — the most among major economies.
| Country |
Interest-to-revenue |
Debt-to-GDP |
| India |
~37% |
~84% |
| United States |
~19% |
~124% |
| United Kingdom |
~13% |
~102% |
| Japan |
~13.5% |
~207% |
| Germany |
~3-4% |
~63% |
The reading — and its limits
The two columns don't line up — and that's the point. Japan owes far more relative to its economy (~207%) yet pays a smaller share of revenue in interest than India does on ~84% debt. The reason is the rate at which the debt was raised: India borrows at much higher interest rates, so a given amount of debt costs far more to service each year. The burden is set by the rate, not just the size of the debt. Over 25 years the point proves itself: Japan's debt ratio rose from 118% to 214% while its interest burden fell by more than half.
Method Interest-to-revenue from each country's own treasury/budget primary (India = Union Budget interest payments / revenue receipts). Debt-to-GDP from the IMF (general government gross debt, 2025). Tracked over time as ten series on the IMF's consistent general-government basis, 2000 onward.
12
Geopolitical × Financial
#
Supply chains didn't shorten — they re-routed.
| Share of US goods imports |
2017 |
2024 |
Change |
| China |
21.9% |
13.8% |
−8.1pp |
| Mexico |
13.1% |
15.2% |
+2.1pp |
| Vietnam |
2.0% |
4.2% |
+2.2pp |
| India |
2.1% |
2.7% |
+0.6pp |
The reading — and its limits
China's true share fell by less than the US figures show — tariff-avoidance routes Chinese goods through Vietnam and Mexico, so part of their "rise" is Chinese value re-routed, not displaced. The data shows where goods ship from, not their ultimate origin. This is re-routing, not retreat.
Method Computed by us from UN Comtrade primary bilateral data (US reporter, full continuous 2017-2024), cross-validated against US-Census-based reads.
The risk reset is being priced — insurance premiums are rising far faster than their own history, across lines and across continents.
| Line |
Where |
Recent pace vs its own history |
| Motor |
US |
+8.8% vs +2.6% = 3.4x |
| Motor |
EU |
+6.0% vs +1.4% = 4.1x |
| Health |
EU |
+4.1% vs +2.3% = 1.8x |
| Health |
India |
+16%/yr (~3.5x inflation); the rise is price, not volume |
| Home |
EU |
+4.2% vs +2.4% = 1.7x |
The reading — and its limits
The same regime break shows up across two statistical authorities and two continents — motor sharpest (3.4-4.1x), health and home both clearly accelerating. The US homeowners line is absent because US CPI folds it into shelter (which is why State Farm's home repricing doesn't show in US inflation — the EU dwelling series fills that gap). The US health-insurance CPI is deliberately not used — its methodology produces wild swings (−27% in one year) and isn't reliable. India's public-vs-private payout gap is a persistent level, not a decline (tested and confirmed across six years, COVID spike included); India FY25-26 figures are provisional pending the next IRDAI report. The US and EU are now diverging: US motor inflation fell to 2.8% in 2025, back below its own long-run average, while EU motor at 7.9% remains its second-highest reading on record. The repricing is real everywhere; whether it persists is not yet settled.
Method US from BLS, EU from Eurostat (motor / health / home), India from IRDAI Annual Reports + MoSPI — all read directly. "Pace" = recent (2022-25) annual rate vs the 2006-19 average. India premium-vs-inflation over the matching FY2024-25 window; the payout figure is the standalone-health-insurer incurred-claims ratio. Tracked as annual series; the US motor line is held on both the annual-average and December-over-December bases.
The safe-haven metals turned volatile — silver's most volatile year since 1983, by the size of its daily price swings.
30 days
silver moved >5% in a day in 2026 (through 31-Jul, LBMA fix) — the most since 2011, and by realized volatility (~71%) the most volatile silver year since 1983
| Large daily moves |
Silver >5% |
Gold >3% |
| 1980 (Hunt-brothers / inflation era) |
~77 |
62 |
| 1974 |
~48 |
39 |
| 2008 (GFC) |
27 |
35 |
| 2011 |
31 |
9 |
| 2020 (COVID) |
14 |
8 |
| 2026 (through 31-Jul) |
30 |
11 |
The reading — and its limits
2026 silver (30 days >5%, ~71% realized volatility) is the most volatile since 2011 by count and the most volatile since 1983 by the size of its daily price swings — but NOT an all-time record: 1980 (the Hunt-brothers squeeze) and 1974 were far wilder. Gold (11 days >3%, ~31% volatility) is elevated to roughly 2008 levels but middling on the long record (~6th). This is about the frequency of large moves returning to the haven assets after the calm 2010s, not an all-time extreme — and the pace cooled after the first half.
Method Count of days with a silver >5% / gold >3% fix-to-fix daily move, and annualised realized volatility, on the LBMA/ICE benchmark (USD), full record 1968-2026, computed by us. 2026 through 31-Jul.
When risk outruns the models, insurers stop underwriting it — and the rules have to be rewritten to bring them back.
~1 million policies
State Farm's planned California withdrawal (3.1m → ~2m by 2028); stopped writing new home policies in 2023, non-renewed ~72,000 in 2024
Forward-looking pricing, now permitted
the regulator changed the rules to allow forward-looking catastrophe models (and reinsurance costs) in pricing, to stop insurers exiting the market
+17% / $400m
the regulator approved an emergency 17% premium increase on existing policies to keep State Farm's California unit solvent, conditioned on a $400m capital infusion from the parent
The reading — and its limits
The chain is unpriceable risk → financial stress → exit. A non-stationary peril (wildfire shifting faster than the historical record) met a pricing framework that required rates be set on past data and barred forward-looking models — so the distribution moved but the rules forbade pricing it, and the insurer's most honest response was to exit. The proof comes from both directions: the insurer that walked away, and the regulator that rewrote the rules — conceding the old framework could no longer price the risk.
Method State Farm withdrawal figures and stated reasons from its own filings and California Department of Insurance filings. The pricing-framework constraint and its 2024-25 reform from the California Code of Regulations directly (10 CCR 2644.25.1 reinsurance; 2644.4.5 / 2644.5 catastrophe models) — not secondary characterisation.
India's volatility ran hot through the first half — then fell quiet.
46.9%
of 2026 trading days had India VIX above 15 (67 of 143, 1-Jan to 31-Jul) — but every elevated day fell by 11-Jun; none since
| India VIX above 15 (share of days) |
period |
| 55-100% / yr |
2008-2016 (the norm) |
| 10% / 30% / 25% |
2023 / 2024 / 2025 (the calm) |
| 61% |
2026 first half (to 15-Jun) |
| 47% |
2026 (to 31-Jul, after the summer calm) |
The reading — and its limits
The elevation was a first-half phenomenon — every one of the 67 elevated days fell by 11-Jun, then the summer calmed (VIX back to ~11-12). Whether that calm is the regime settling or a lull before the next spike is not yet resolved by the data — an open question, not a conclusion. The reading is about character (persistent-and-moderate in the first half), not the absence of a trigger (the US-Iran flare-up of late February is nameable).
Method Share of India VIX daily closes above 15 across 2026 trading days through 31-Jul (NSE India VIX historical, read directly; validated against Yahoo INDIAVIX — 61.1% vs 60.9% to 15-Jun, identical peak). Historical by-year series from Yahoo INDIAVIX.
Geopolitical step-function shocks, made visible: crude moved more than 5% in a day on 23 days in 2026.
23 days
WTI crude moved >5% in a single day in 2026 YTD (through 31-Jul) — up from 18 through mid-June; the count kept building
The reading — and its limits
Crude's large daily moves are geopolitically-driven step-functions — the extreme becoming ordinary in a commodity the real economy runs on. Shared evidence for the Risk reset (the frequency of discontinuous moves) and the Geopolitical reset (the driver of the shocks).
Method Count of trading days with a >5% WTI close-to-close daily move, 2026 YTD through 31-Jul, computed by us.