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127Market structure

The inference cost curve is moving 4x faster than the training one — and that breaks most AI business plans

Frontier-model training got the headline numbers in 2024 and 2025. Inference, the part that actually meets revenue, was the line item nobod…

26 May 202614 min
126Market structure

The Nvidia margin paradox: why FY27 datacentre profit may peak before revenue does

Nvidia just posted 85% datacentre growth, but a quieter line in the same release flagged the structural reason gross margin is set to compr…

25 May 202614 min
125Notes

ETFs, Mutual Funds and Index Funds Compared

22 May 20269 min
124Notes

Chart Patterns and Technical Analysis: What the Charts Say

22 May 202610 min
123Notes

Cloud Spending Trends in 2026 Every Developer Should Track

21 May 20269 min
122Notes

AI Layoffs and the Reskilling Imperative: A Practical Guide

21 May 202610 min
121Emergent systems

The warehouse-robot economics nobody is modelling: why swarm coordination — not arm dexterity — decides the next decade

Symbotic, AutoStore, Geek+, and the new wave of Amazon Sequoia deployments are routinely framed as a robotics-hardware story. The hardware…

19 May 202613 min
120Market structure

The hyperscaler capex cycle has crossed $600bn — here is the under-discussed line item that decides whether it pays back

Microsoft, Alphabet, Amazon, and Meta will jointly commit roughly $612bn of AI-related capex in calendar 2026 — more than the entire US fed…

12 May 202610 min
119Notes

Value Investing in an AI-Dominated Market

5 May 20268 min
118Notes

Semiconductors: The Picks-and-Shovels Play in the AI Era

5 May 202612 min
117Emergent systems

Why multi-agent LLM systems fail at exactly the scale they are sold to solve

The 2025 pitch deck for every multi-agent LLM company — Cognition's Devin team-mode, Sierra's agent fleets, Adept's Auto-Workflow — runs on…

5 May 202610 min
116Notes

Geopolitics and Your Investment Portfolio: Lessons from the Hormuz Crisis

5 May 202610 min
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Real Assets and Smarter Funds: A Diversification Primer

The inference cost curve is moving 4x faster than the training one — and that breaks most AI business plans

Frontier-model training got the headline numbers in 2024 and 2025. Inference, the part that actually meets revenue, was the line item nobody wanted to chart. That has flipped. Between April 2024 and April 2026, the per-million-token cost of running GPT-4-class inference fell roughly 92%, on a curve compounding twice as fast as the training-cost curve fell over the same period. This column is about what that re-prices, who it benefits, and why the API margin assumption inside most 2025-vintage AI business plans is about to fail.

The Nvidia margin paradox: why FY27 datacentre profit may peak before revenue does

Nvidia just posted 85% datacentre growth, but a quieter line in the same release flagged the structural reason gross margin is set to compress through FY27 — and it isn't competition. It's the Hopper-to-Blackwell transition, hyperscaler ASIC orders, and CoWoS-L allocation. A look at the three numbers that will decide whether Nvidia's profit pool widens or narrows from here.

ETFs, Mutual Funds and Index Funds Compared

Chart Patterns and Technical Analysis: What the Charts Say

Cloud Spending Trends in 2026 Every Developer Should Track

AI Layoffs and the Reskilling Imperative: A Practical Guide

The warehouse-robot economics nobody is modelling: why swarm coordination — not arm dexterity — decides the next decade

Symbotic, AutoStore, Geek+, and the new wave of Amazon Sequoia deployments are routinely framed as a robotics-hardware story. The hardware is the cheap part. The expensive, defensible, and woefully under-modelled part is the coordination layer — the multi-agent scheduler that turns 4,000 bots into one productive system rather than 4,000 expensive collisions. A look at the gross-margin gap between hardware-led and coordination-led deployments, and why the latter is set to dominate.

The hyperscaler capex cycle has crossed $600bn — here is the under-discussed line item that decides whether it pays back

Microsoft, Alphabet, Amazon, and Meta will jointly commit roughly $612bn of AI-related capex in calendar 2026 — more than the entire US federal R&D budget. The bullish framing is that this is a generational productivity bet. The bearish framing is that it is a bubble. Both miss the question that actually decides the IRR: utilisation. A look at the four-quarter trend in inference-utilisation rates, why the curve flattened in Q1, and what it means for the 2027 capex guides.

Value Investing in an AI-Dominated Market