Reports of our death are an exaggeration

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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.

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.

“Today,” he warned, “we have people doing work like robots. Tomorrow, we will have robots behaving like people”.

You can see. They have displaced us in our routine functions. Soon they will take the good stuff, too. 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 algorithms, AI medical diagnosis, accident-free 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.

Technology is not new. 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. 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 George Gilder recently put it: “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.”

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 frontiers we don’t. Frontiers 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. 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 himself by background a Banker — was heard to remark that UBS competition was not from start-up challenger banks, but from Apple, Amazon, or Google.

His argument was this: a bank’s business success is mostly down to three components: its technology stack, its reputation and goodwill, and its regulatory status. Two of these — tech and reputation — are hard, substantial problems, he proposed, while the other — capital and regulatory compliance — is comparatively formalistic, especially if you have a decent technology stack.

Apple, Amazon, and Google wipe the floor with any bank on technology — I can go with that — and materially better standing with the public. Who doesn't love Amazon? Who does love the Chase Manhattan bank?

So, should the tech giants come for banking, ’’look out.

Let’s park a few uncomfortable facts and give Me Perez the benefit of the doubt:

Firstly, none of Apple, Amazon, or Google as so much as cast a wanton glance in the direction of banking, despite the revenue opportunities dwarfing those in tech. There must be some reason for that.

Secondly a company that makes cool gadgets has as much change of keeping its branding following a pivot to banking as does it is a toy factory could that moves into dentistry.

Thirdly it is naive in the extreme to prime that regulatory compliance is formalistic, let alone “the easy bit of banking”.

Park all that. For what Mr Perez overlooks as a core competence in a successful bank is the same thing Mr Cryan did. Its people'". They are the irreducible, ineffable, magic difference. The tech stack can give them wings, but they have to decide where to fly. They narratise.the bond, form networks, make connections, persuade. These things a machine cannot do.