Goldman Sachs automated trading replaces 600 traders with 200 engineers

"Those 600 traders, there is a lot of space where they used to sit."

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.

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.

"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 :-)

A lot of dope smoking in this article.

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...