A agency that I once worked for was asked to create a presentation of the future of trading for a well known trading platform. It was the typical fluff around voice interfaces, augmented reality, VR and all that drivel. But we all knew what the future really meant - taking people out of the equation - but we also knew that the people paying for the presentation didn't want to hear that.
When people talk about the risk of automation stealing jobs, they tend to think it's the low skilled jobs that are most at risk. But of your job is to look at data and make descisions based on that data, you're gonna be the first to go. And the more money pinned to your work, the quicker you will be replaced.
The terrifying thing is that you don't even need to reach human-level performance to cause structural unemployment. As a hypothetical example, if automation can make top-performers in your field 10x more efficient, that makes 90% of people in that role redundant.
This feels a lot scarier to me since as a ML engineer, I rarely see applications where we can reach human level performance easily but the idea of making existing top performers 2-5x more effective feels much more viable.
The idea of 80% of people in relatively high-skill jobs who are used to a relatively high income level becoming redundant is a pretty scary one.
I guess they can all become Javascript developers and fulfill the big tech companies' dreams of having hordes of cheap engineers on the labor market. That would help save some of the money poured into various initiatives like 'Dolphins who code', etc.
This idea has been around for over 10 years but I still don't see that many signs of it happening. I mean what interesting projects are around that actually make it possible to for example, let a non-technial person point and click develop a python or Java application? It happened with HTML/CSS and Dreamweaver/WordPress etc years ago but it's much easier with web stuff and I don't really see it elsewhere.
I think we're in the early stage of automating web development but sites like Wix have made impressive strides in making UI design accessible. I'm working on something in this field myself and I've done enough research and prototyping that I think it's definitely achievable.
Lots of high-earning people tied up in managing money is pretty inefficient for society, though. Maybe those people can switch to factory jobs and help reduce the cost of common things for the common person.
It's kind of a joke. People getting rich off moving money around is kind of silly, and it would certainly be better if those jobs could be eliminated by machines.
The vision of public blockchains (such as Ethereum) is to make the core of financial transactions automated and non-profit. While there may still be profit to be made on the margin (credit scores), the vision is that currency and equity trades will happen on decentralized markets that are automated and non-profit.
...Even if this vision occurs, it's not obvious how soon it would occur. For example, will we need stable-value cryptocurrencies before any of this can occur? Will anything significant occur in the next two years, or will it be five? OR more?
This is nothing new. I work marketing side running customer acquisition for 12 countries across 3 brands, with budget significant enough to bring in ~3% of a countries population annually in our higher coverage markets. And this year we will add additional countries and brands.
We are a team of 3. There is no way this would have worked 10+ years ago. Maybe in another 10 we will be 1...
>I rarely see applications where we can reach human level performance easily but the idea of making existing top performers 2-5x more effective feels much more viable.
This actually reminds me of Palantir's focus, which is to develop tools to aid analysts.
>>But of your job is to look at data and make descisions based on that data, you're gonna be the first to go.
I posted the other day about machine learning and radiology. It's the type of job that squarely fits your definition, although things move much more solely in medicine.
My dad is a neuroradiologist so I've seen a decent amount of the reading work (which is one component of the job - though a big one). I also thought it'd be well suited to ML, but I suspect the future is more suggestions and flagging things in scans rather than entirely replacing the doctor (I'd be surprised if this isn't happening already in some capacity).
It seems risky to lose the human understanding and interpretation of the images. Human augmentation is probably better?
Already people make assumptions because of not understanding constraints in how the underlying tech works (like how FMRI is monitoring changes in blood flow not neural activity directly).
The thing is, when you have 4 radiologists reading 100 scans each, if flagging and highlighting and generally improving performance of these personnel leads to two of them being able to process 200 scans each, the other two are out of a job.
They have been, de facto, replaced by computers, even if the job role still exists.
It's easier to see this if you break down the work not into images but into human-image-minutes. If there are 100 minutes of work, but you can assign the computer 50 of them which it can complete in seconds, the same number of human-image-minutes can be completed as before, but the computer has now taken them away from a human.
Unless the productivity gain is spent on doing more imaging. I think this aspect is underapreciated in many debates about automation, especially in health care. There is an insatiable demand for diagnostics for early detection of diseases or monitoring progression.
Most of the automation work I do in financial services is a 90% automation. This leaves room for more analytical tasks where people can free up time to drive more business decisions and insights.
It's also likely that human-in-the-loop will stay around, possibly indefinitely in medicine. A human-in-the-loop is a huge safety thing for medicine, but it defeats the whole point of HFT.
Isn't this type of trading sort of unique in how easy it is to consume the data? Even in other investing areas, people who just "look at data and make decisions" cannot be easily replaced until computers can read and understand natural language and understand the state of the world. I can't immediately think of anything else this type of automation could apply to.
Another obvious example is actuaries and any number of other analysts in the insurance industry. According to Google[1], actuaries earn an average of just over $97k/year. While this may not strictly be an "investing area," it's a clear target for automation.
Part of the reason actuaries make so much is that they are certified and legally required to sign off on analysis, similar to the way civil engineers put their signature (and reputation) on their work. But you would be surprised how many decisions come down to "actuarial judgement", having good business sense, intimate knowledge of insurance rules and regulations...and not doing anything special with the data. Hell, some actuarial methods are the same as they were in the 70s. Data science is slowly eating their lunch in predictive modeling areas, but I wouldn't be surprised if they're still around after data science itself has been automated.
I work on a team of actuaries in a more data science-y role. Believe me, I had the same impression initially: "Can't we automate some of this stuff?". But I've since come to see their value.
I don't know about actuaries, but I do remember there was an article lately about an insurance company in Japan that replaced hundreds of workers with IBM's Watson.
That's really interesting. In my circle of friends, most of us can't point to a single success of Watson, despite it's very publicized capabilities. This may only be a small company, but almost certainly larger insurance agencies & medical foundations must be on to this.
Actuaries have quite a high regulatory wall protecting them from other human competitors. But probably not from computers.
(I don't know about the legalities, but the laws requiring companies to have a actuary sign off on stuff probably can be satisfied by having on part time actuary shared between multiple companies sign off on computer generated analysis.)
I know in banking, regulators are satisfied with ML models making decisions about bank capitalisation, so long as the models are annually audited by an external auditor (eg. KPMG, EY)
Absolutely, but isn't this type of software generally used to augment existing jobs, rather than replace them? The program can make basic inferences like "10 negative GE articles -> Sell GE stock", but you still need a human to make more general decisions about an industry as a whole, related companies, how long-term an issue is, etc. Basically, nobody's job is as simple as reading the news and deciding whether articles are positive or negative. In the case of this Goldman Sachs story, some people's jobs really were as simple as looking at the performance of equities and making decisions based on that alone.
These algorithms are only good at front running the reaction of the market, which is fairly predictable in the short term when a relevant piece of news is published. They are nowhere for making long term investment decisions.
AI everywhere is as naive as software everywhere. It costs a lot of money to develop and maintain software professionally. It's uneconomical to staff an IT team to replace every routine job there is with software.
It will be the same with AI. If you apply enough bright people's mind for long enough, we can possibly automate certain niche jobs, but that's going to be completely uneconomical. You need to pay these AI specialists, and retain them so that you can adapt your algos as the problem moves, and there needs to be more than one in case one goes or is ill. You can easily spend more that the salaries you are going to spare.
Other problems are I believe too vast and open ended for AI to come up with anything useful. What company will be successful in 5 years? What is the right level of renumeration for the management of that company? Where is innovation going next? You really think that AI is anywhere near answering that kind of questions?
AI will shine in similar areas where software shines. Problems that are simple enough to be understood by computer scientists (and that narrows down this vast world quite a bit), and that are broad enough to justify the economics of making high investments to build data, or where there is already ample of existing and relevant data. Driving a car. Cleaning or building a house. Replicating certain hand movements. Things like that. I doubt an expert on a narrow and specialised domain has much to fear from AI.
> It will be the same with AI. If you apply enough bright people's mind for long enough, we can possibly automate certain niche jobs, but that's going to be completely uneconomical.
The thing is, it doesn't have to be an all-or-nothing proposition. If you can write software to make a team of ten traders 10% more productive, it may be possible to do the same job with only 9 traders.
Over time, those productivity gains compound and you have literally cut in half the number of people on your trading floor.
AI is a very flexible term, all software that makes choices technically qualifies as AI even if the benchmark is moved over time. But, remove the buzzards and rejecting a loan using a single if statement based on a Fico score really is machine decision making at work.
It should be noted that demand is not a static quantity. If a technology allowed a person to create more per-unit cost will be cheaper, raising demand (people on the bottom will still lose jobs, but it's not linear).
But this is about responding faster, not responding more often. It helps you react to a story before everyone else, but there's only so many stories in a day.
Yes. Not to mention that spreads are so tight that the only real challenge for a cash trading desk is how to read the flow, and manage inventory, into events. Computers don't do a great job of interpreting doublespeak and insinuation, which is a substantial part of earnings calls and other releases of information.
Derivatives trading has grown as cash trading has shrunk. And the banks haven't figured out how to automate that business yet.
Computers don't need to understand natural language, they just need a little guidance towards what the data means. You can train a neural network, or you can just write some solution-specific code. A lot of people are not that hard to replace.
Not really. The world is data. Anything you need to know about the world is in a table somewhere, and the web has done a great job of making the planets data accessible to machines.
Other areas may be more complex, but complexity isn't as big a barrier as it may seem.
Also, those areas are probably not as complex as people think they are. I'm reminded of the AI that started making breakthroughs in oncology by looking at the cells around a tumour, despite the scientific consensus that there was nothing to be learned from the cells around a tumor.
> Anything you need to know about the world is in a table somewhere
No it isn't. If I read an article about the Volkswagen emissions scandal, what table do I look in to figure out how much consumers will care, and how aggressive various governments will be with their punishment? When Disney buys the rights to Star Wars, where should I look to see if it's worth what they paid for it? Should I rely on data from 30 year old movies, or look at merchandise sales or something? If I start seeing a lot of articles about climate change and the dangers of fossil fuels, should I sell my stock in Exxon?
Not that humans are particularly good at this stuff, but I don't think computers will be as good or better for a long time.
I think a lot of people get hung up on how humans make decisions, with the expectation that you need to be able to understand the same ideas to perform the task.
Even if a computer can't figure out the stats you mention, maybe it doesn't matter? Maybe just reacting fast enough to what people in the market are doing is good enough? Maybe simple sentiment analysis on articles on Disney around the web is a good enough proxy for share price increase.
Autor and Acemoglu wrote about this in their paper 'Skills, Tasks and Technologies':
'Following, ALM, we refer to these procedural, rule-based activities to which computers are currently well-suited as "routine" tasks. By routine, we do not mean mundane (e.g., washing dishes) but rather sufficiently well understood that the task can be fully specified as a series of instructions to be executed by a machine (e.g., adding a column of numbers). Routine tasks are characteristic of many middle-skilled cognitive and manual jobs, such as bookkeeping, clerical work, repetitive production, and monitoring jobs. Because the core job tasks of these occupations follow precise, well-understood procedures, they can be (and increasingly are) codified in computer software and performed by machines.'
No doubt. The last people to be replaced are going to be software devs (because when you can write a computer program with an AI you can make the AI write the AI and you have general AI), cleaning ladies and probably police investigators.
because when you can write a computer program with an AI you can make the AI write the AI and you have general AI
There doesn't need to be a single AI to create the entire app. An AI to model data structure, an AI to build an interface, an AI to consume external APIs, etc. We already mostly bolt libraries together to make software; it doesn't take a huge leap for those libraries to be generated by machine learning.
Building anything but the most complex apps will be trivial in 10 or 20 years time.
I think that's more a sign that we want more from web apps than any increased complexity in the apps themselves. Building a web app now isn't any more technically difficult than building a Linux app was 20 years ago. The only difference is that the tools aren't as mature.
But isn't there still the step of translating the business logic into a machine readable format, and isn't that what the majority of software engineers do at this point anyways?
I always consider automation to first target jobs with "trivial decision making." I think trading fits into that, as you can pretty much distill it down to an algorithm. At least as far as I understand it.
Physicians are another great example. A lot of their job today seems to be about translating signs and symptoms and following a decision tree.
I'm excited about the opportunities to displace current duties with today's nice to haves, such as physicians who have time to dig deeper into a person's well being than their symptoms that day.
> But of your job is to look at data and make descisions based on that data, you're gonna be the first to go
Manufacturing and agricultural jobs have already been automated away, while data analysis jobs are only just now starting to feel the pinch - and then only in companies with very deep pockets working on a niche problem. 'First to go' has missed the mark by many years.
The difference is in marginal cost. Industrialization was a relatively gradual change. AI can make whole professions almost redundant basically overnight.
> If your job is to look at data and make decisions based on that data, you're gonna be the first to go
Depends on the type of data, how it's collected, how it's aggregated, etc.
I'm a market researcher so I look at data all day. Thing is - I look at both qualitative and quantitative data from a ton of different sources (financial filings, surveys, macreoconomic organizations, vendor briefings, engineer interviews, etc.).
I size the markets for embedded technologies that are automating away data-driven jobs. Most notably in the industrial sector, where years of near-zero industrial productivity growth have left a lot of manufacturing/oil&gas/utilities companies hungry for any way to bring costs down.
The newest technology right now is the "IoT Cloud Platform" which aggregates data from a bunch of industrial machines (directly from devices, or through IP enabled gateways), routes the data, and sends it to a cloud where it can be analyzed and monitored automatically to predict failure and prevent unplanned downtime. From a component standpoint its made up of a (1) piece of client software that sits on the machine or gateway, or a configured agentless client, (2) infrastructure VMs for load balancing/server provisioning, (3) host VMs to run an OS, middleware, and a runtime framework, and (4) applications that run on top of the host VM.
One of the more interesting demos of this tech is a Microsoft/GE joint project that used drones to take pictures of power lines, sent that data to the Azure IoT Suite, which then performed visual analysis to determine which power lines were damaged or deteriorating, saving the cost of sending humans out to climb up these structures. All the big companies - Amazon, Microsoft, IBM, SAP, Oracle - are trying to add more intelligent applications on top of these platforms, with machine learning, neural nets, and blockchain-based applications being some of the most advanced.
If your job follows a simple binary data check or logical chain - is this machine functioning within specs (if not, order replacement), did we hit the target price for this asset (if so execute x number of orders), does this piece of equipment look functional (if not, report to manager) - sure you should be worried about your job.
If you go one step up from the basic logical workflow, and enter the realm of data synthesis, or of handling data that requires skepticism/critical thinking, I think you have no reason to worry about an algorithm doing your job anytime soon.
The financial sector in general is getting some of its excess fat trimmed due to a period of increased regulation and low interest rates/returns. This is a good spin for these finance companies, rather than "we're laying off 2/3rds of our cash equities trading desk".
Having formerly been in market research, and automating-out many parts of that process myself, gotta go with op. There will be a need for people who can give data/study designs a sanity check/interpretation for at leeast for like a decade or two. Not that I don't think some exec somewhere will try or already are trying to automate it out, just that it's a terrible terrible idea.
There are a handful of markets that are large and liquid enough to fully move to electronic trading, like spot FX, vanilla interest rate swaps, perhaps treasuries trading.
But most other markets are very much relationship driven. For instance a trader will make a market on illiquid bonds based on what he thinks is the appetite from the short list of potential buyers, and that's based on sales people and brokers discussing with these investors and feeding him with feedback.
Very similar for mergers and acquisition which is mentioned in the article. It's probably something like 1/3 relationship and advisory to the client, 1/3 going through legal and regulatory issues, 1/3 working out financials, which are often based on unique and complex accounting standards, market specific idiosyncrasies, etc. Good luck training an AI algorithm to do that. And with what data?
There is massive room for improvement in the financial industry. But most likely in the more boring areas of operations and settlement (cash settlements for instance are massively labor intensive as there are always incidents, system issues, accounts details changing, etc).
My personal experience of corporate IT in a large international bank is that there is a handful of good people but an enormous army of average to well below average people who do not know the difference between cash and capital other than it starts with a c, i.e. are completely uninterested by the domain they work on, and aren't good technically either. And these will ensure that outside of a few players (including probably goldman), we are guaranteed that automation is far far away no matter how much money we throw at it...
Your comment is spot on. The work Goldman Sachs does is relationship driven. As a corporate client of GS, I don't need data. I need advice.
Don't confuse the work that investment banks do with the work that NASDAQ does or with high-frequency trading.
In addition, the reason why GS has less traders and more developers is more to do with government regulations preventing GS from doing certain types of trading. Not because developers or AI is better. Dumb article.
>As a corporate client of GS, I don't need data. I need advice.
Such as: "Buy these sub-prime assets that we've bundled into CDOs. By the way, we're actively betting against you and stand to make a ton of money when they tank."
Banks are huge, with different divisions. People who spout these types of lines don't actually understand how it works. Plus, there is the whole due diligence aspect. You can't blame anyone else. Also anytime anyone sells you something, they are telling you they think it's worth less than you do. Yet everyone is fine with that in every other industry.
Source: hedge fund trader who fights banks all the time, but doesn't agree with their scapegoating.
And to be fair to GS (not that it is the widow and the orphan), the "shitty deal" wasn't a reference to the collateral of the CDO, but to the fact that they didn't manage to sell a position on that transaction.
And you will get better advice when that advice is data-driven. And you need the data because it's GS's proprietary data. If you're paying for a relationship, all you're really paying for is a smiling human face to read the numbers on the screen to you.
But for many markets, data isn't readily available. Liquid equity markets have tons of data, but if you need to price bonds for highly illiquid bonds of private companies, valuation cannot easily be done by AI. Especially when factors such as the behavior and reliability of management play a role.
As Big Data encroaches more and more into reality these decisions are going to be less relationship based and more AI based. The reason before it had to be relationship based was because computers didn't have visibility into the real world to make these decisions. However, with the advent of big data, their visibility is increasing.
How many acquisitions of an irish insurance company by a german bank are you going to use to train your algorithm to understand the legal and regulatory issues? How are you going to teach your algorithm to anticipate how IFRS9 or the upcoming Banking regulations being discussed with the regulator going to affect that? How are you going to train your algorithm to draft the disclosures in the offering circular, or to challenge the management's view during the due diligence?
Are you going to mine gmail traffic? Are you going to mine websites? There are like one or two companies in the world that would face this particular combination of problems. You won't find a nice article on wikipedia to tell you how to handle it.
Human: "Siri, I need to sell $2B of bonds to finance my renewable energy project. When your other renewable energy clients met with PIMCO recently, what were PIMCO's main concerns?"
....where the time frame involved is 17 years from 2000 until now.
It's also weird that they don't mention the about to be dismantled Dodd-Frank Act (Volker Rule) that forced most sell side firms out of prop trading.
I guess the headline, "computers replace humans for the most mundane and rules based tasks" doesn't grab headlines as much:)
One other interesting tidbit
> Some 9,000 people, about one-third of Goldman’s staff, are computer engineers.
Goldman has always had a larger focus on tech than most other sell side firms, and that says alot given how much they all put into their technology, but a full 1/3 of all employee's sends a very strong signal of how much they value their tech.
Interestingly, it might have been those traders that slowed the progress of the firm in the early 2000's. You can argue, and I've talked to former GS people who made this argument to me, that having the existing traders really slowed down GS in the march towards high speed trading. Michael Lewis's also makes this point in his Flash boys book.
And not to put too fine of a point on it, but replacing a trader making $1,000,000 a year because he has a direct PnL with a 1/3 of an engineer making 1/2 of that, because the algo has the Pnl not the trader, is a big win for the bonus pool available to management:)
"And not to put too fine of a point on it, but replacing a trader making $1,000,000 a year because he has a direct PnL with a 1/3 of an engineer making 1/2 of that, because the algo has the Pnl not the trader, is a big win for the bonus pool available to management:)"
Even better if you can outsource that engineer to India after a while :-)
Having 1/3 of headcount in tech is completely normal for most banks and other financials (except retail banking). If you count all contractors and outsourced areas it'll probably be closer to 40-45%.
But most of that will be maintenance, rule checking, data entry, implementing regulatory changes, etc. Only a tiny amount of those will develop algorithms or work close to trading.
>>Some 9,000 people, about one-third of Goldman’s staff, are computer engineers.
Is that right, or is that just a confused journalist? Computer engineers? As in, hardware design so they can shave off nanoseconds here and there? Or do they just mean programmers of any kind here?
Probably right, just poorly worded. It's not just engineers, it's all staff in tech. Most of them will have a CS degree (or similar). Nearly none of them will develop trading algorithms. It's mostly about maintaining systems, fixing problems, updating regulatory changes, integrating third party data, etc.
Having a third of headcount in tech is not unusual for a bank nowadays.
I'm super skeptical of these type of automated trading outfits. There's no edge in them. All you've done is take a shoddy system done by hand into code. At the end of the day, they still crap out when the market conditions it was designed for shifts or naive view of the markets as something as a math formula.
Exceptions are HFT, arbitrages where you don't need to speculate and take the corresponding risks.
From the article
> employed 600 traders, buying and selling stock on the orders of the investment bank’s large clients.
The traders sound fancy but werent much more than call center staff, receiving orders by telephone and writing paper tickets. They weren't prop traders.
I believe the article is mostly referring to trading and not investing. Trading as in the operational functions of trades(organizing direct registered or street name broker buyers and sellers), not the investment strategy associated with holdings.
I realize the irony of this comment: But Marty Chavez is one of the few openly gay, latino Wall St. execs. Great, diversity, progress... This wasn't mentioned in the article and to be honest, that is rare and it was cool to read on article focused on Marty's skills as a professional banker (a good one) without feeling the need to bring up his personal life.
Traders who just forwarded phone orders didn't make that much. The bank only gets the commission, there's not much the trader can do to increase profits. The huge bonuses are/were in prop trading, not in executing client trades.
All true, but I've never met a securities trader who wasn't ambitious, and ones that aren't don't tend to get hired. I'm still betting on the suspender brigade over the pocket protectors.
What about other instruments? I used to work as an IT in a small investment bank, and the traders there were not doing any equity trading. They were dealing with bonds, swaps and stuff, and they were mostly doing arbitrage, IIRC. Also there was one guy on forex, but I vaguely remember that he was not supposed to speculate, rather he was supposed to trade whatever was necessary for the other guys to work.
Is the equity market really that significant that we seem to only talk about it?
> Is the equity market really that significant that we seem to only talk about it?
No, but most people only really understand equity markets. Few people know what it means to trade a swap or even bonds. But as most people have some experience with equities, it's easier to write about that. You won't find much automation in other areas, OTC trading can't be easily replaced by machines.
As I said above, for Cash fixed income (bonds) it's happening today, it's trending up for sure. Technology exists for liquid swaps also, that will be next.
The price is high, but bitcoin is still not a reliable currency that could present an alternative to established currencies. It seems to be mainly used for speculation and illegal activities.
There are probably not more people using it to pay for real-world goods than 2-3 years ago. The bullish view was that bitcoin would be popular with the public at some point. That hasn't happened yet.
From someone who has worked in OTC technology trading for 10 years:-
- A lot of cash fixed income trading is open to automation, in fact it amazes me when I watch some execution traders in action. Their process is highly predictable: Get order into their execution blotter, send out RFQ to brokers, get back prices, check best price against a known value e.g. limit price, trade/no-trade.
- This holds true for perhaps 80% of flow for a wealth/retail fund which trade mostly liquid bonds under 1MM. For real-money and hedge funds that ratio is perhaps <50% but still significant.
- AI is not the biggest driver of automation here but rather the openness of systems. By this I mean order management systems can interface to execution management systems that can interface to venues. FIX/FpML have helped this happen.
- I see this as augmentation for the trader rather than replacing him/her. Its a brand new colleague that can do the 'boring' work so they can go hunting for liquidity!
No one seems to grok the real question here: "Why does Goldman Sachs automated trading still need 200 engineers?". What are those engineers doing? And why will they still be needed in 5, 10, or 20 years? Where are goals created and trading decisions made? How are automated trading systems going to evolve? Once working, will automated trading systems need regular replacement and upgrades to protect them from bit rot?
Most of the automation infrastructure (allocations, counter-party APIs, etc) for this change started in the early 2000's. For example, the initial allocation network was in place by 2003. Other firms who prefer to remain unknown (don't need engineering recruiting PR) were nearly a decade ahead of GS. RGM, Vertu, others. Let me know if you want to work on this type of thing. Happy to help.
My question is are the jobs disrupted really stolen..
according to Mr Klarman(see the NYTime article) this automation trends lead to more inefficiency in markets not less and thus give opportunities to some traders to bet long term in the other direction..
Or are those disrupted jobs just getting re-deployed as new trading businesses betting on some long term stuff..
Klarman said Trump's protectionist stance against automation and globalization will lead to market inefficiencies.
He also said the increase in passive investment relying on the efficient markets hypothesis (EMH) will eventually lead to mispricings and inefficiencies that can be taken advantage of by active investors such as himself.
Those two things have nothing to do with the automation of execution trading and market-making. So he is not claiming that automating the execution trading and market-making done by banks in highly liquid markets (equities in OP) will lead to more inefficiency.
Since when are traders "the Masters of the Universe"?
Apparently a 1987 reference to a Tom Wolfe novel? Is this phrase commonly used in finance? or just a sensationalized title from the author. I feel like if any profession is going to be given that title, it should be engineers and scientists. Though I am definitely biased.
I am curious how the compensation of the engineers compares to that of the traders, whose job function their systems have replaced. Not only base salary, but bonuses, commissions, and all those extras. Do the engineers get comparable extras, or are they a "flat cost"?
The article talks about the number of traders they replaced with engineers. Assuming that other investment banks are on the same trend, where are these traders going to get new jobs? Are they just retiring because they made enough money while trading?
That was done over a decade or so. Many people will have retired, some continued working in another function, some just went somewhere else. There are still many traders out there, the market is not that much smaller than 10 years ago.
They are going to take their amazing wealth-creating skills out into the wider world and we are going to see a second industrial-revolution. Exciting times are ahead...
fun fact: Advanced AI techniques work in very high dimensional spaces in such a way that it's impossible to clearly understand why a program reaches a conclusion that it does. Market trading is about allocating capital that decides how labor itself is allocated. So, increasingly, we have computers that are telling people what to work on and we don't know why they are making those decisions. We could remove the computers, but then our economy would likely fall into a deflationary spiral / depression due to lack of growth.
Those engineers are probably not working on AI. The traders were receiving phone orders from clients and executed them. Now there's a platform where clients put in trades which get executed. The engineers develop the platform and enable the remaining traders to trade orders efficiently.
It's probably at least 30% front-end web developers, many testers, some back-end developers. Execution only trading engines aren't that hard to develop.
Edit: Due to the swift downvotes, let me elaborate. I am not saying this will be the case, I just think that philosophically, blind reliance on algorithms can be catastrophic.
Very different they are not talking about HF, it's more about automatize all the processes of entire desk especially around equities.
they are not mentioning at all about HF in the article.
When people talk about the risk of automation stealing jobs, they tend to think it's the low skilled jobs that are most at risk. But of your job is to look at data and make descisions based on that data, you're gonna be the first to go. And the more money pinned to your work, the quicker you will be replaced.