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{{a|Technology|{{image|Robolawyer|jpg|John Cryan’s legal department yesterday.}} }}''A shame you couldn’t say the same for John Cryan. Originally published in 2017, this article, and the grit in the steampunk wheel who wrote it — are still grimly hanging on, whereas the then CEO of Deutsche Bank is now the ex-CEO of Deutsche Bank.''
{{freeessay|technology|rumours of our demise|{{image|Robolawyer|jpg|John Cryan’s legal department yesterday.}}}}
=== Rumours of our demise are greatly exaggerated ===
In 2017, then-CEO of Deutsche Bank John Cryan thought his employees’ days are numbered. Machines would do for them. Not just back office grunts: ''everyone''. Even, presumably, Cryan himself.<ref>The horror! The horror! The irony! The irony!</ref>
 
“Today,” he warned, “we have people doing work like robots. Tomorrow, we will have ''robots behaving like people''”.
 
In any case, No bad thing, you might say — who will miss the bankers?
 
You can see where Cryan’s idea comes from: what with high-frequency trading [[algorithm]]s, [[AI]] medical diagnosis, self-driving cars: the machines are coming for us. Some see technology at a [[tipping point]], at which ''we'' will be tipped out. The machines have taken over our routine tasks; soon they will take the ''hard'' stuff, too.
 
A fashionable view. But a big call, all the same.
 
As long as there has been the lever, wheel or plough, humans have used machines to do tasks which are [[tedious]], repetitive or require brute strength beyond our frail earthly shells. They do this because machines follow instructions better than we do, ''by definition'': that’s what means is to be a machine. ''In the stuff they are good at'', they’re quicker, stronger, nimbler, cheaper and less error-prone.
 
But it’s an important caveat: as {{author|George Gilder}} recently put it:
{{Quote|“The claim of superhuman performance seems rather overwrought to me. Outperforming unaided human beings is what machines are supposed to do. That's why we build them.”<ref>{{br|Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy}} (2018)</ref>}}
===The division of labour===
Machines can only operate in constrained environments. They make flawless decisions, as long as both question ''and'' answer are pre-configured. But take a machine out of its designed environment and it is useless: Good luck getting a [[Jacquard loom]] to plough a field.
 
We [[sacks of meat]] are better at handling ambiguity, conflict and novel situations. We’re not perfect, but whatever the conundrum is we can at least produce an answer. We don't hang, or freeze waiting for a dialogue box to be clicked: even though syntax errors are par for the course: humans don’t (easily) crash. That’s the boon and the bane of the [[meatware]]: you can’t tell when one makes a syntax error.
 
But put human and machine together and you have a powerful proposition: the machine handles the rule-following; the human figures out what to do when you run out of road. It’s a partnership. A division of resources. Technology is an extended phenotype. But this is nothing new: this is always how we’ve used technology: the human figures out which field to plough and when; the horse ploughs it.
 
Now technology has caused the odd short-term dislocation — the industrial revolution put a bunch of hand-weavers out of work — but the long-term prognosis has been benign: “labour-saving devices” have freed us to do things we previously had no time to do, or hadn’t realised you could do, before the technology came along. As technology has developed, so has the world’s population grown, while poverty and indolence have fallen. ''People have got busier''. Whatever technology is doing, with due regard to the risk of confusing [[correlation]] and [[causation]], it ''isn’t'' putting us out of work.
 
Technology opens up design-space. It expands the intellectual ecosystem, domesticating the ground we know, and opening up [[frontier]]s we don’t. [[Frontier]]s are places where we need smart people to figure out new tools and new ways of operating. Machines can’t do it.
 
But it also creates space and capacity to care about detail. [[Parkinson’s law]] states: it frees us up to care about things we never used to care about. The microcomputer made generate duplicate and distribute documents far, far easier.
 
So, if you want to say this has all changed — that now the machines will put us out of work — you have to explain ''how''. What has changed? Why is this time different? We’ve heard this record before: twenty years ago, the wizards told us the internet had changed start-up valuations forever. Didn’t work out so well.
 
So, are the robots coming for us?
 
Firstly, remember Cryan is talking his own book. Banking is a hard business to make money in these days. Opportunities to develop new businesses (read: ''opening new frontiers'') are diminished; managing to margin is ''de rigeur''. Mr. Cryan needs to fire as many people as he can. What he doesn’t automate, his competitors will, and they’ll take his lunch. “We’re ditching the meat sacks”: that is what DB’s investors want to hear.
 
And banking requires less novel judgment than it used to. The West has been — well, won: ploughed over and converted into shopping malls. Much of it can be boiled down to formulating rules and following them by rote.<ref>There remain emergent risks, [[black swan]]s and regulatory complexity, of course, but a lot of stuff could be automated which hasn’t been.</ref> Only the edge cases — where pioneers stand on the frontier gazing into the horizon — require judgment. That is no place for an algorithm. These are the situations of real risk: the “unknown unknowns”.
 
As a strategy for coping with “known knowns” automation is good business. Humans are bad at following rules. They are expensive. They occupy real estate. They require human resources departments. They misunderstand. They screw up. They leave. They don't write things down. Machines are much better on all of these measures.
 
But, still ''the race to automate “known knowns” is a race to the bottom''. The value in a product is the resources and skill required in producing it. Banking products need no fields, raw materials or warehouses: only ''skills''.
 
Two types of skills, categorically: ''computational, analytical'' skills and ''interpersonal, imaginative'' skills.
 
Computational skills — those suited the symbolic manipulation — can and should be automatic algorithms, high-frequency trading strategies, data analytics, neural networks and machine learning. But the value of computational “skills”, once automated, tends to zero. ''Even those requiring artificial intelligence''. The margins they generate will tend to zero, too: everyone with a decent PC will be at it.
 
If Mr. Cryan thinks ''that'' is the future of his business, he needs his head read.
 
Your future, sir, is in your people: those ones who stand at the frontier, staring resolutely into the horizon. They may have robots at their disposal, but only your human pioneers can set them to work.
 
===Perez’ Folly===
Not long before departing the ship erstwhile head of UBS Evidence Labs Juan Luis Perez — not by background a banker — remarked that the incipient competition for banks was not “challenger” banks, but Apple, Amazon, or Google.
 
His argument was this: banking comes mostly down to three components: technology, reputation, and regulation.
 
Two of these — technology and reputation — are hard, substantial problems, while the other — regulation — is comparatively formalistic, especially if you have a decent technology stack. How do the banks stack up against the FANGS?
{| class="wikitable"
|+Banks v FAANGs: showdown.
!
!Technology
!Reputation
!Regulation
{{aligntop}}
|'''Banks'''
|Generally legacy, dated, patched together, under-powered, under-funded, conflicting, liable to fall over, susceptible to hacking.
|Everyone hates the Financial Services industry.
|All over it. Capitalised, have access to reserve banks, connected, exchange memberships, etc.
{{aligntop}}
|'''FAANGS'''
|Awesome: state of the art, natively functional, at cutting edge, well-funded, well-understood, robust, resilient. Ok could be hacked
|Who doesn’t love Amazon? Who wouldn’t love to have an account at the iBank? Imagine if banking worked like Google Maps!
|OK there is a bit of investment required here — and regulatory capital is a thing — but nothing is insurmountable with the Amazon Flywheel no?
{{aligntop}}
|'''Winner'''
|Cmon: are you kidding me? '''FAANGS''' all the way!
|'''FAANGS'''. Are banks even on the paddock?
|'''Banks''' have the edge right now. But look out white-shoe types: The techbros are coming for you.
|}
 
Apple, Amazon, and Google ''wipe the floor with any bank on technology'' is presented ''[[res ipsa loquitur]]'' — we can go with that without any first hand evidence, just based on how lousy bank tech its — and, sure, the FAANGs have better standing with the public. Who doesn’t love Amazon? Who ''does'' love Wells Fargo?<ref>Though we think Mr. Perez rather confuses the product for its manufacturer. We might feel different about Amazon if, rather than making neat space-aged knick-knacks it made a business of coldly foreclosing mortgages, and charging usurious rates on credit card balances. You don’t think it would? Have you seen the cut it takes from the play store?</ref>
 
Therefore, Mr. Perez’ argument goes, the only place where banking presently has an edge is in regulatory licences and approvals, capital, and regulatory compliance. It’s wildly complex, fiendishly detailed, the rules differ between jurisdictions, and the perimeter between one jurisdiction and the next is not always obvious. To paraphrase {{author|Douglas Adams}}: “You might think GDPR is complicated, but that’s just peanuts compared to MiFID.”
 
But, but, but — any number of artificially intelligent startups can manage that regulatory risk, right?<ref> The JC’s [[legaltech roll of honour]] refers.</ref>
 
But really. Let’s park a few uncomfortable facts and give Me Perez the benefit of the doubt:
 
====So where are they?====
Firstly — if bankd ''is'' such a sitting duck for predator FAANGS, ''where the hell are they''? It is 2023, for crying out loud. Wells Fargo is still with us. None of Apple, Amazon, or Google as so much as cast a wanton glance in its direction of let alone the [[Vampire Squid]]’s. ''Something'' is keeping them away.
====Techbros aren’t naturals at banking====
And it’s not just fear of regulation, capital and compliance: if it were, you would expect tech firms to be awesome at ''unregulated'' financial services.
 
But — secondly — ''they’re not''. 
 
We’ve been treated to a ten-year, live-fire experiment with how good tech firms will be in [[Cryptobabble|unregulated financial services]], during which the banks — “[[trad fi]]” — and, notably, the FAANGS have mainly stayed away, ''and it hasn’t gone well''.
 
[[Chauncey Gardiner|Credulous cryptobros]] have found, and promptly fallen down, pretty much every open manhole known to money management — and discovered some whole new ones of their own to fall down too. Helpfully, [https://web3isgoinggreat.com/ Molly White] has keeping a running score. Crypto, despite its awesome tech and fabulous branding, has been a disaster.
 
====Tech brand-love-ins won’t survive first contact with banking====
Thirdly a cool gadget maker that pivots to banking ''and does it well'' has as much chance of maintaining millennial brand loyalty as does a toy factory that moves into dentistry.
 
Those Occupy Wall Street gang? Apple fanboys. ''At the moment''. But it isn’t the way trad fi banks go about banking that tarnishes your brand. It’s <nowiki>''banking''</nowiki>. IT’s a dull, painful, risky business. Part of the game is doing shitty things to customers who lose your money. That ''isn’t'' part of the game of selling MP3 players.
 
''The business of banking will trash the brand.''
 
====Bank regulation ''is'' hard====
Fourthly regulatory compliance is hardly formalistic, let alone “the easy bit of banking”. If you could solve it with tech, the banks would have long since some it. They gave certainly tried. Regulations change, contradict, don't make sense, overlap, are fiddly, illogical, often counterproductive and they are subject to to interpretation by regulators, who are themselves fiddly, illogical and not known for their constructive approach to rule enforcement.
 
Getting regulations wrong can have ''bad'' consequences. Even apparently formalistic things like [[KYC]] and [[CASS 6|client asset protection]]. Banks ''already'' throw armies of bodies and [[legaltech]]<ref>Our [[legaltech roll of honour]] refers.</ref> at this and still they are routinely breaching minimum standards and being fined millions of dollars.
 
===The gorillas in the room===
{{Quote|A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.
:—Robert Heinlein}}
 
But in any case park all the above, for it is beside the point. For Mr. Perez overlooked the same core banking competence that Mr. Cryan did: quality ''people'', and quality ''leadership''.
 
We have fallen into some kind of modernist swoon, in which we hold up ourselves up against machines, as if ''techne'' is a platonic ideal to which we should aspire.
 
So we set our children modernist criteria, too from the moment they set foot in the classroom. The [[education]] system selects for individuals by reference to how well they obey rules, how reliably, and quickly, they can identify, analyse and resolve ''known'', pre-categorised, “problems”. But these are historical problems with known answers. This is a [[finite game]]. ''This is exactly what machines are best at''.
 
If we tell ourselves that “machine-like qualities” are the highest human aspiration, we will naturally find ourselves ''wanting''. We make it easy for the robots to take our jobs. We set ourselves up to fail.
 
But human qualities are different — humans can improvise, imagine, narratise, analogise — they can conceptualise Platonic ideals — in a way that algorithms cannot.
 
And there is the impish inconstancy, unreliability and unpredictability of the human condition — these make humans ''different'', not ''inferior'', to algorithms. They make us difficult to control and manage by algorithm.
 
And that is the point. ''We are not meant to be making it easy for machines to manage and control us.'' By suppressing our human qualities, we make ourselves more legible, machine readable, triageable, categorisable by algorithm. The economies of scale and process efficiencies this yields accrue to the machines and their owners, not us.
 
Why surrender before kick-off like that?
 
===On being a machine===
We are in a machine age. It is a machine age because machines have proven consistently good at doing things we could not, because we were too weak, too slow, too inconstant or too easily bored.
 
Machines are good. We don’t want to run down machines. But we have indoctrinated ourselves that machine qualities — strength, speed, consistency, [[fungibility]] and ''mundanity'' — are the highest goods. We are taught these. We aspire to them. 
 
But executing a task with strength, speed, consistency, fungibility and patience are the highest human good ''only if you haven’t got a suitable machine''. If you have got a machine, use it. Let your people do something more useful.
 
We are used to using the machine as a metaphor for mind: what about body as a metaphor for machine.
 
Automate temperature regulation, the pulmonary system, balance, digestion, aspiration. Humans have no business there. But leave interpersonal relationships, communication, perception, fight-or-flight, decision-making in times of uncertainty, imagination and creation to the conscious mind.
 
The challenge is not to automate indiscriminately, but to ''optimise'', so your people are not diverted from valuable work by box-ticking, form-filling and formalism.
===A real challenger bank===
Optimised [[automation]] has its place. [[All other things being equal]], an organisation that has optimised its machines will do better than one which hasn’t.
 
An organisation where machines are optimised is one whose ''people'' are also optimised: maximally free to work their irreducible, ineffable, magic hunting out new lands, identifying new threats, forging new alliances — ''playing the [[Finite and Infinite Games|infinite game]]'' — while uncomplaining drones till the fields, tend the flock work the pits, carry the rubble away from the coalface and police known pitfalls. To minimise the chance of [[human error]]. The machines must be historical. They look backward, by reference to available data, which is [[Big data|from the past]]. They cannot anticipate the future — because you can’t extrapolate the past from the future — any better than humans can. But  humans can improvise in the face of the unexpected in a way that machines can’t.
 
There is an ineffable, valuable role optimising those machines, adjusting them, reconfiguring them to be optimal in the environment as it evolves. 
 
Now, the dilemma. If, over thirty years, you have systematically recruited for those who best display machine-like qualities — if that is what your education system targets, your qualification system credentialises and your recruitment and promotion system rewards —  ''your people won't be very good at it weaving magic''.
 
Most likely, ''nor will you''. Your organisation will instead be ''fearful'' of human magic. Will see in it the seeds of [[Enron]], or [[Financial disasters roll of honour|Kerviel]], or [[Madoff]], or [[Archegos]].
 
But these disasters came about ''notwithstanding the modernist method''. They are ''products'' of it. These mendacious people found and exploited [[Zero-day vulnerability|zero-day flaws]] in the [[modernist]] system, ''which is [[Air crashes v financial crashes|what we should expect clever but dishonest people to do]]''. They will seek out the vunerabilities and they will exploit them. They will do this exactly  where the risk engines are not looking. Apparently harmless, sleepy backwaters.
 
For a fully [[Risk taxonomy|taxonomised]] system, that runs entirely in [[algorithm]], however smart, derived from the [[Policy|scar tissue]] of the past, is ''literally blind'' to [[Zero-day vulnerability|zero-day vulnerabilities]]. Unless mediated by people thinking and viewing the world unconventionally, ''it will repeatedly fail''. And this has been the [[financial disasters roll of honour|tale of the financial markets]] since Hammurabi published his code.
 
The age of the machines — our complacent faith in them — has made the situation worse. Machines will conspire to ignore “human magic”, when offered, which says “this is not right”. That magic was woven by [[Enron Corporation|Bethany MacLean]]. [[Collateralised debt obligation|Michael Burry]]. [[Harry Markopolos]]. [[Wirecard|Dan McCrum]]. The formalist system systematically ignored them, fired them, tried to put them in prison.
 
 
 
The difference between excellent banks and hopeless ones: the transparent ''informal'' networks by which a good institution mysteriously ''avoids'' landmines, pitfalls and ambushes, while a poor one walks into every one. 
 
Sundar Pichai can’t code that. The same human expertise the banks need to hold their creaking systems together, to work around their bureaucratic absurdities and still sniff out new business opportunities and take a pragmatic and prudent view of the risk — this is not a bug in the system, but a feature.
 
This is the view from the executive suite. They measure individuals by floorspace occupied, salary, benefits, pension contributions, revenue generated. Employees who don’t generate legible revenue show up on the map ''only as a liability''. The calculus is obvious: why pay someone to do badly what a machine could do cheaper, quicker, and more reliably for free? 
 
Thus, Cryan says and Perez imnplies: ''prepare for the coming of the machines''. Automate ''every'' process. Reduce the cost line. Remove people, because when they come for us, ''Amazon won’t be burdened by people''.
 
===Yes, bank tech is rubbish===
To be sure, the tech stacks of most banks ''are'' dismal. Perez is right about that. Most are sedimented, interdependent concatenations of old mainframes, Unix servers, IBM 386s, and somewhere in the middle of the thicket will be a wang box from 1976 with a CUI interface that can’t be switched off without crashing the entire network. These patchwork systems are a legacy of dozens of mergers and acquisitions and millions of lazy, short-term decisions to fix broken systems with sellotape and glue rather than maintaining and overhauling them properly. They are over-populated with low quality staff. Citigroup claims to employ 70,000 technology experts worldwide, and, well, [[Citigroup v Brigade Capital Management|Revlon]].
 
To be fair, it ''is'' hard to imagine Amazon accidentally wiring half a billion dollars  to customers because of crappy software. Banks do have a first-mover disadvantage here, as most didn’t start thinking of themselves as tech companies until the last twenty years, by which stage their tech infrastructure was intractably convoluted.
 
We ''presume'' Apple, Google and Amazon, who always have thought of themselves as tech companies, are naturally better at it and more disciplined about their tech infrastructure.<ref>See the [[Bezos memo]].</ref> But you never know.
 
In any case, a decent technology platform is a necessary, but not sufficient condition to success in banking. You still need gifted humans to steer it, and human relationships to give it somewhere to steer to. The software won’t see itself. Bank technology is not, of itself, a competitive threat  it is not. It is just the ticket to play.
 
=== Yes, bank staff are rubbish ===
Now, to lionise the human spirit ''in the abstract'', as we do, is not to say we sanctify bank employees as a class ''in the particular''.  The JC has a quarter century among them. They are unusually paid, but not gifted or intelligent.
 
It is an ongoing marvel how commercial banking organisations can be so reliably profitable given the calibre of the hoardes they employ to steer them. We have argued [[mediocrity drift| elsewhere]] that informal systems tend to be configured to ''ensure'' staff mediocrity over time. Others have too.<ref>See {{br|The Peter Principle}}; {{br|Parkinson’s Law}}: for classic studies.</ref>
 
But the current orthodoxy contributes to this system. Use machines , networks and connectivity to [[downskill]]. Why pay for expert staff to do drudgery in London when you can have [[School-leavers in Bucharest]] do it for a quarter the cost?
 
''Why are you suffering drudgery at all''? Why aren’t you using the great experience and expertise of your people to ''eliminate'' drudgery?
 
There is a negative feedback loop here: the experts in London are ''able'' and ''incentivised'' to eliminate drudgery. ''Able'' because they understand the product and the market, and know well what matters and what doesn’t. ''Incentivised'' because ''this stuff is boring''.
 
Outsourced school-leavers in Romania are not: by design they don’t understand the process — they are only on the park because of a [[playbook]] — and they’re not incentivised to remove drudgery because doing so would puts them out of a job. Recall the [[agency paradox]].
 
So we construct the incentives inside the organisation to cultivate a will to bureaucracy. Complicatedness is somewhere between a necessary evil and a virtue.
 
We continue to get away with it because of the scale these businesses run on, and because most are infected with exactly the same philosophy.
===Sunlit uplands===
If you were setting up a challenger bank today, what would you do?
Imagine setting them free: automating the truly quotidian stuff, re-emphasising away from bureaucracy as the greatest good, and towards relationships management and expertise?
 
Imagine a bank strategy that said the in
 
{{draft}}
{{ref}}
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