01
Technological × Financial
#
AI capital spending has quadrupled since GPT-4 (in Q2 2023).
$117bn → $448bn
Hyperscaler capex, 2023 → 2025. On company guidance, ~$725bn in 2026.
34% vs 32%
AI-era capital intensity has surpassed the dot-com peak — capex ~34% of revenue on 2026 guidance, against ~32% at the 2000 peak.
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 it as a separate line; totals include warehouses, offices, other bets). The 34% is forward 2026 guidance; FY2025 actual aggregate intensity is ~23% — the crossing of the dot-com peak happens on 2026 guidance, stated as such.
Method Capex read from each firm's own SEC filings (Alphabet, Amazon, Meta, Microsoft, Oracle); 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, same performance (2021 → 2023 → 2024) — ~1,000× cheaper in three years.
$1.7bn → $37bn
Enterprise AI spend, 2023 → 2025 (~22×). Unit price collapses; total spend explodes as usage surges.
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. 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.
~700 agents' worth
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%
The collapse in foreign holdings of Indian IT — ₹7.1 lakh cr → ₹3.6 lakh cr — through both capital exiting and the shares that remain being repriced down. The price falls that drove it:
| Price decline |
From peak |
CY2026 (to 15-Jun) |
| Nifty IT index |
−39% |
−26% |
| TCS |
−48% |
−31% |
| Infosys |
−40% |
−29% |
| HCLTech |
−40% |
−30% |
| Wipro |
−41% |
−30% |
| Tech Mahindra |
−19% |
−11% |
| Nifty 50 (benchmark) |
−9% |
−9% |
Share-price decline, index and top-5 names — from each instrument's own peak (the index peaked Dec-2024), and over CY2026 to 15-Jun.
The reading — and its limits
The driver is read from filings and the market, not price alone: Indian IT's own filings name an “AI productivity impact,” and the correction is described as a structural re-rating. In addition to AI, cyclical concerns are also at work — weak US discretionary spend, tariff drag, BFSI pressure. Tech Mahindra is the outlier.
Method Nifty IT index from NSE (validated against the Yahoo ^CNXIT series, zero divergence across CY2026). Top-5 company prices from Yahoo (auto-adjusted). Foreign-holding decline computed from NSDL. “From peak” is each instrument's own high; the index peaked Dec-2024.
05
Technological × Geopolitical
#
Foreign money is leaving India for the AI trade it can't buy at home — ~$58bn out since 2024, while Taiwan and Korea swell.
~$58bn
Net foreign equity outflow from India since the Sept-2024 peak (~₹4.7 lakh cr), computed from NSDL.
| MSCI EM weight |
Sept-2024 |
29-May-2026 |
| Taiwan |
18.77% |
26.41% |
| South Korea |
11.67% |
23.06% |
| India |
19.9% |
10.87% |
| China |
24.42% |
20.36% |
The reading — and its limits
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 $58bn is cumulative since the peak; the calendar-year figures (2025 ~ −$19bn, 2026 YTD ~ −$24bn) measure a different window and are not additive with it.
Method Outflow computed from NSDL depository data (net FPI equity flows, cumulative since the Sept-2024 peak). Index weights from MSCI Emerging Markets Index factsheets — Sept-2024 (archived) and 29-May-2026 — read directly.
06
Technological × Financial × 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 |
Largest |
| KOSPI (Korea) |
832 |
8 |
56.0% |
53% |
SK Hynix 21% |
| Nifty 50 (India) |
50 |
24 |
52.9% |
0% |
HDFC 10.6% |
| NASDAQ-100 |
101 |
34 |
45.3% |
57.5% |
Nvidia 8.0% |
| EURO STOXX 50 |
54 |
27 |
43.2% |
18.1% |
ASML 12.7% |
| MSCI EM |
1,170 |
27 |
40.4% |
34.6% |
TSMC 14.7% |
| S&P 500 |
505 |
49 |
37.1% |
38.2% |
Nvidia 7.8% |
| MSCI World |
1,270 |
95 |
26.6% |
29.7% |
Nvidia 5.5% |
| TOPIX (Japan) |
1,650 |
111 |
22.3% |
4.8% |
MUFG 3.3% |
Eff-N = how many equal-weight stocks the index really behaves like. AI-core = share of the whole index in the AI complex (chips + hyperscalers).
The reading — and its limits
Two exceptions prove the point — Japan (TOPIX) is genuinely diversified (behaves like 111 stocks, AI-core just 4.8%), and India (Nifty) is concentrated but in the old economy (AI-core 0% — its “tech” is IT services, the AI-disrupted category, not AI hardware). Everywhere else, buying the index increasingly means buying the same handful of AI names. KOSPI's eff-N is approximate (the uncapped Korea file is gated) but its top-2 of ~47% is exact.
Method Effective-N and concentration measures computed 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
Technological × Geopolitical
#
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 |
+0.08 |
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 |
63-day rolling stock–bond correlation. Negative = bonds cushion equities, the 60/40 promise.
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. 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 from primary price series (same method as the US base reading).
The price of money has doubled — and it isn't coming back down.
~2.1×
The US 10-year Treasury yield now (4.46%) against its 2012–2021 average (2.04%).
~8.5×
The 10-year's rise from its 0.52% COVID-era low to 4.46%.
3 years
The 10-year has held above 4% — even as the Fed cut 175bp.
The reading — and its limits
“Doubled” is measured against the 2012–2021 decade average; against the longer 2010s it is ~1.85×, so the window is stated. The deeper point is persistence, not level: the long end rose through an entire Fed easing cycle, and 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 Annual-average and current US 10-year Treasury yields, computed from the US Treasury's own daily par-yield series (1990–2026). The policy-rate cut is the Fed's path over Sep-2024 → Jun-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 share rise is partly buying and partly gold's price rise; for these four, the tonnage rise confirms real accumulation. China's reported holding is a likely-understated floor.
Method Computed from IMF primary (SDMX API) — COFER for the dollar share, IRFCL gold-value/total-reserves per country. India cross-validated against the RBI half-yearly report (880t).
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% |
Interest-to-revenue = the share of what the government collects that goes straight to interest.
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 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.
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).
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 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% = 3.4× |
| Motor |
EU |
+6.0% vs +1.4% = 4.1× |
| Health |
EU |
+4.1% vs +2.3% = 1.8× |
| Health |
India |
+16%/yr (~3.5× inflation); the rise is price, not volume |
| Home |
EU |
+4.2% vs +2.4% = 1.7× |
The reading — and its limits
The same regime break shows up across two statistical authorities and two continents — motor sharpest (3.4–4.1×), health and home both clearly accelerating. The US homeowners line is absent because US CPI folds it into shelter (the EU dwelling series fills that gap). The US health-insurance CPI is deliberately not used — its methodology produces wild swings and isn't reliable. India's public-vs-private payout gap is a persistent level, not a decline; India FY25-26 figures are provisional pending the next IRDAI report.
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.
The safe-haven metals have turned volatile — silver is having its most volatile year on record.
| Large daily moves |
Silver >5% |
Gold >3% |
| 2008 (GFC) |
27 |
35 |
| 2011 |
31 |
9 |
| 2014–2019 (the calm) |
0–1/yr |
0–1/yr |
| 2020 (COVID) |
14 |
8 |
| 2022–2025 (calm again) |
2–3/yr |
0–6/yr |
| 2026 (YTD → annualised) |
28 → ~62 |
11 → ~24 |
The reading — and its limits
Silver's 2026 pace (~62 annualised) is the most on record (past 2011's 31 and 2008's 27); gold (~24) is on pace for second only to 2008. The calm was real and prolonged — which is what makes 2026's jump a genuine break, not noise. This is about the frequency of large moves, not their scale (2008 and 2020 had bigger single-day peaks).
Method LBMA/ICE official benchmark fixings (primary), fix-to-fix daily moves, full continuous 2005–2026, computed by us; corroborated by Yahoo futures. 2026 is YTD through 15-Jun; the annualised figures project the current pace across a full year.
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.
+17% / $400m
The regulator approved an emergency 17% increase on existing policies to keep State Farm's California unit solvent — conditioned on a $400m capital infusion from the parent.
rules rewritten
The regulator changed the framework to allow forward-looking catastrophe models (and reinsurance costs) in pricing, to stop insurers exiting.
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.
Method State Farm 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; 2644.4.5 / 2644.5) — not secondary characterisation.
Elevated volatility has become India's standing condition, not an acute shock.
| India VIX: % of days above 15 |
|
| 2008–2016 (the norm) |
55–100%/yr |
| 2023 / 2024 / 2025 (the calm) |
10% / 30% / 25% |
| 2026 (to 15-Jun) |
61% |
The reading — and its limits
2012 is the closest single-year analogue (86.5% of days above 15) — but 2012 sat among a run of high-volatility years (2008–2016). 2026 is different: a snap-back to elevated after the genuine calm of 2023–2025. The reading is about character — persistent and moderate-amplitude — not the absence of a trigger.
Method 2026 figure from NSE primary (the India VIX historical CSV, read directly); historical by-year series from Yahoo ^INDIAVIX. Cross-checked where they overlap (Yahoo 61.1% vs NSE 60.9%, identical peak).