Slide 1

Slide 1 text

Understanding What We Have Wrought Patrick McKenzie — Bank of England October 1st, 2025

Slide 2

Slide 2 text

No content

Slide 3

Slide 3 text

“Hidden” SPOFs

Slide 4

Slide 4 text

No content

Slide 5

Slide 5 text

No content

Slide 6

Slide 6 text

No content

Slide 7

Slide 7 text

No content

Slide 8

Slide 8 text

No content

Slide 9

Slide 9 text

No content

Slide 10

Slide 10 text

Five Whys • Why did several banks share a software monoculture? • Why was this configuration change not caught in testing? • Why was this configuration change so hard to roll back and/or mitigate? • Why did this configuration change have such a large “blast radius?” • Why did this configuration change substantially disrupt “important business services”?

Slide 11

Slide 11 text

A Recipe For Software Monoculture

Slide 12

Slide 12 text

No content

Slide 13

Slide 13 text

Defense in Depth: Automated Testing Crowdstrike Root Cause Analysis

Slide 14

Slide 14 text

Control Plane Issues Are Frequently Sev-0

Slide 15

Slide 15 text

4,500 Banks Couldn’t Have Correlated Failures, Right?

Slide 16

Slide 16 text

This was a Near Miss… Including For You • Banking system in U.S. largely recovered to normal by extended hours on Friday. • Most affected service, teller transactions, are societally critical but can be deferred a short while. Competent, immediate efforts to divert transactions to electronic channels. • We had not yet had Crowdstrike completely deployed within the banks – Saved by our sloth and incompetence! – Consider an alternate universe in which all US-based counterparties go “dark for a day” • You may experience a technical crisis when weather is not normal, either incidental to the fact of market stress or in a complex casual relationship with that stress.

Slide 17

Slide 17 text

Potential Policy Responses • Blameless postmortems – Ask fintechs if you can read these! – Particularly the near misses! • If an engineer can cause an outage on accident then what could their laptop do on purpose? – Red team exercises

Slide 18

Slide 18 text

Stablecoins

Slide 19

Slide 19 text

No content

Slide 20

Slide 20 text

No content

Slide 21

Slide 21 text

No content

Slide 22

Slide 22 text

No content

Slide 23

Slide 23 text

No content

Slide 24

Slide 24 text

No content

Slide 25

Slide 25 text

No content

Slide 26

Slide 26 text

No content

Slide 27

Slide 27 text

“I think the answer is that it’s messy, but the funds are real. We see legitimate inflows into Tether from many sources—large ones—that result in market makers selling, creating, and sending billions of dollars to Tether’s bank accounts. They do this to mint the tokens and maintain relationships with Tether and its banks. Everything checks out, albeit in a messy way.” — Sam Bankman-Fried to Bloomberg (Aug 8th 2021)

Slide 28

Slide 28 text

“They have the money they say they have … I’ve seen a whole lot and the firm has seen whole a lot and they have the money. And so there has always been a lot of talk ‘Do they have it or not?’ and I’m here with you guys and I’m telling you we’ve seen it and they have it.” — Howard Lutnick to Bloomberg TV, Jan 16th, 2024

Slide 29

Slide 29 text

“Cantor Fitzgerald is not conducting continuous diligence on Tether’s financial statements, but I believe my statements were accurate when made.” — Howard Lutnick to U.S. Senate, Jan 25th , 2025

Slide 30

Slide 30 text

No content

Slide 31

Slide 31 text

No content

Slide 32

Slide 32 text

Patrick McKenzie on blog, October 28th, 2019

Slide 33

Slide 33 text

AI and future of trading

Slide 34

Slide 34 text

Kaplan et al., 2020

Slide 35

Slide 35 text

No content

Slide 36

Slide 36 text

No content

Slide 37

Slide 37 text

No content

Slide 38

Slide 38 text

No content

Slide 39

Slide 39 text

I Spent August and September Falling in Love

Slide 40

Slide 40 text

AI Risks in Trading Space • ”Every hedge fund will train their own model” not the outcome to bet on. – Concentration risk among 3 large lab providers, of which one could be (in any given six month window) effectively sole source to the UK trading community. • Time to detection and resolution increasingly dependent on whether Claude Code / OpenAI Codex / etc is up or not. – Knight Capital: 20 minute critical window – CrowdStrike: 90 minute time to resolution • Recursive dependencies put larger chunks of economy on narrower shoulders

Slide 41

Slide 41 text

Thank You [email protected] Happy to chat if I can ever be useful, particularly informally. https://www.bitsaboutmoney.com Bits about Money is freely available and relevant to your interests.