Reports of our death are an exaggeration: Difference between revisions

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Deutsche Bank’s CEO John Cryan thinks his employees’ days are numbered. Machines will do for them, in due course. Not just back office grunts: ''everyone''. Cryan’s high-rolling bankers are vulnerable. Even, we suppose, Cryan himself. No bad thing, some might say — who will miss a few liquidated bankers?
Deutsche Bank’s CEO John Cryan thinks his employees’ days are numbered. Machines will do for them, in due course. Not just back office grunts: ''everyone''. High-rolling bankers are vulnerable. Even, presumably, Cryan himself.  


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


One can infer the view, widely held, that technology is about to reach a tipping point: no longer will machines be faster, cheaper and less aggravating than we sacks of meat at executing routine tasks, but they will equal they will even ''better'' — the sacks of meat who do the ''hard'' stuff.
No bad thing, you might say who will miss a few bankers?


But technology is not new. As long as we’ve had the lever, wheel and plough, humans have used machines to get things done: boring things; repetitive things; things requiring brute strength. The constraint has always been available technology.
One can detect in this the fashionable view that technology is at a tipping point, at which ''we'' will be tipped out. No longer will machines be better than we sacks of meat at routine tasks, but they will outdo us at the ''hard'' stuff, too.


So: machines follow unambiguous instructions better than humans do. By ''definition'': that’s what means is to be a machine. They’re quicker, stronger, nimbler, cheaper, less error-prone.  
A fashionable view. But a big call, all the same.  


But machines can only operate in constrained environments. They can react, flawlessly, to pre-conceptualised decisions with pre-configured responses. But take a machine out of its designed environment and it is useless. (Good luck getting a [[Jacquard loom]] to plough a field).  
Technology is not new. As long as there has been the lever, wheel or plough, humans have used machines to get things done: boring things; repetitive things; things requiring 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. They’re quicker, stronger, nimbler, cheaper and less error-prone.  


By contrast, humans are better than machines at handling ambiguity, conflict, novel situations. They’re not flawless at it — God knows we’re not perfect — but we can produce an answer. We don't hang, or freeze, or suffer a [[Blue Screen of Death]]. Syntax errors are par for the course: they don't cause a crash. Humans are good at configuring machines: we can form theories of operational theories, test them, adjust them - diagnose what is wrong with them. We have insight. Machines have no insight.  
But they can only operate in constrained environments. They make flawless decisions, as long as the question and the 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.  


Throughout history technology has created short-term dislocations — some big ones — and we are going through one now. But, to date, their long-term prognosis has been uniformly benign: labour-saving devices have freed the human race to do things it previously had no time to do, or hadn’t realized it was possible to do, before the technology came along. Technology opens up design-space. It stretches the intellectual ecosystem: It takes us places we couldn't go before.  
We sacks of meat are better at handling ambiguity, conflict, novel situations. We’re not flawless at it, but whatever the conundrum we can at least produce an answer. We don't hang or freeze waiting for a dialogue box to be clicked: syntax errors are par for the course: we don’t crash.


Technology domesticates the ground you know, and opens up frontiers that you don’t.
It’s a partnership. A division of resources. Technology is an extended phenotype. It has caused the odd short-term dislocation 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 has grown and rates of poverty and indolence have fallen. Whatever technology is doing, it ''isn’t'' putting us out of work.  
Frontiers. The Wild West. Here be dragons. Places to boldly go, to cavalierly split infinitives that no-one has split before. A frontier is, by definition, new: novel: unseen, untested. Good luck pointing a [[Jacquard loom]] at the Wild West.  


In any case, look at the stats: as technology has developed, the world's population has grown. The rates of change have tracked each other. There are more people on the world than ever before, but fewer in poverty or indolence. Whatever Technological change is doing, it isn’t making us redundant. We are working harder than ever.  
For technology opens up design-space. It expands the intellectual ecosystem: It domesticates the ground we know, and opens up frontiers we don’t: places where we need smart people to figure out new tools and new ways of operating.  


So, if you want to argue that this trend has changed - that henceforth, suddenly, faster, cheaper, more flexible “recipe followers”  will, net, put people out of work, you'll need to explain how. What has changed? Why is this time different? Remember, we have had sage pronouncements of shifting paradigms before: the dotcom boom, so we were told, changed the valuation of businesses forever. That didn’t work out so well.
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.


Mr Cryan's assertion: This time is different. Robots are going to put us out of work. 
So, are the robots coming for us?


Mine: That’s a big shout.  
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.  


For one thing, remember that Cryan is talking his own book. Banking is a harder business than it used to be. Opportunities to develop new businesses (read: ''opening new frontiers'') are diminished; managing to margin is ''de rigeur''. That being so, Mr. Cryan should fire as many people as he can. If 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 swans 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. This is no place for an algorithm. These are the situations of real risk: the “unknown unknowns”.


On that model, investment banking is far less judgment-based and evaluative than it used to be. 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. But the edge cases are the situations of real risk: the “unknown unknowns”.
So, 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.


Automation as a strategy for coping with “known knowns” is only good business. Humans are bad at following rules. They are expensive. They occupy real estate. They require human resources departments. They misunderstand. The cheaper they are, the more they misunderstand. They screw up. They leave. They don't write things down. Automation is a no-brainer.
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 do not need no fields, raw materials or warehouses: they only require skill. A skill that can be automated, can be replicated. The value of a “skill”, once automated, tends to nil. The margin it generates will tend to zero, too: everyone with a decent PC will be at it.  


But ''the race to automate “known knowns” is a race to the bottom''. The value in a product is the resounces and skill required in producing it. Banking products require no fields, they require no raw materials: they are only skill. A process that can be automated, can be replicated. The value of the “skill” required to produce it drops to nil.  The margins it will generate tend to zero: 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.  


If Mr. Cryan thinks that is the future of his business, he needs his head read. Your future, sir, is in your people. Your robots may accelerate that future, but they can't conceive of it, and they can't deliver it.
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.
 
High frequency algo trading is the obvious place where the machines already run the show, handling all the trading, routing and interrogation if venues for liquidity. It's a complex business, done at immense volume at lightning speed, and humans have no hope of competing. But there are plenty of humans still employed in programme trading. They code the algorithms. They monitor the algorithms’ performance. They intervene when the algorithms go haywire, as sometimes they do. The algo can only follows its rules instructions: it doesn't know it is going haywire - it can't introspect - let alone what to do if it does. The humans modify the algorithms to stop them going haywire again.  They sell. But in any case, the fundamental division of responsibility is the same: machines follow the rules, humans figure them out.
 
Algo-trading is the poster for artificial intelligence: elsewhere, the barriers to implemention are human, not  technological. Every bank of onboarding is a disaster : legacy systems piled on legacy systems creaking to deal with hopelessly convoluted approach documentation, credit risk management and regulatory compliance  that date from the nineteen-nineties when derivatives trading was a new and exciting idea. Instead of shaking this mess down, slimming down, commoditising, firms have outsourced large parts of it, making the whole mess exponentially worse and less soluble.

Revision as of 17:33, 8 September 2017

Deutsche Bank’s CEO John Cryan thinks his employees’ days are numbered. Machines will do for them, in due course. Not just back office grunts: everyone. High-rolling bankers are vulnerable. Even, presumably, Cryan himself.

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

No bad thing, you might say — who will miss a few bankers?

One can detect in this the fashionable view that technology is at a tipping point, at which we will be tipped out. No longer will machines be better than we sacks of meat at routine tasks, but they will outdo us at 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 get things done: boring things; repetitive things; things requiring 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. They’re quicker, stronger, nimbler, cheaper and less error-prone.

But they can only operate in constrained environments. They make flawless decisions, as long as the question and the 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, novel situations. We’re not flawless at it, but whatever the conundrum we can at least produce an answer. We don't hang or freeze waiting for a dialogue box to be clicked: syntax errors are par for the course: we don’t crash.

It’s a partnership. A division of resources. Technology is an extended phenotype. It has caused the odd short-term dislocation 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 has grown and rates of poverty and indolence have fallen. Whatever technology is doing, it isn’t putting us out of work.

For technology opens up design-space. It expands the intellectual ecosystem: It domesticates the ground we know, and opens up frontiers we don’t: places where we need smart people to figure out new tools and new ways of operating.

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[1]. Only the edge cases — where pioneers stand on the frontier gazing into the horizon — require judgment. This is no place for an algorithm. These are the situations of real risk: the “unknown unknowns”.

So, 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 do not need no fields, raw materials or warehouses: they only require skill. A skill that can be automated, can be replicated. The value of a “skill”, once automated, tends to nil. The margin it generates 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.

  1. There remain emergent risks, black swans and regulatory complexity, of course, but a lot of stuff could be automated which hasn’t been.