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Money Machines – An Interview with an Anonymous Algorithmic Trader (logicmag.io)
223 points by jonbaer 10 days ago | hide | past | web | favorite | 82 comments





I'm an ex algo trader (currencies and metals) myself, and found this article interesting and mostly accurate.

A key takeaway is:

"One of the fallacies that people have is the assumption that because the people who are working at certain firms are smart, they must be successful."

The number of times a new hotshot hedge fund moved in to office building where I rent, only to be gone without a trace in a month is staggering. Usually, they'll raise money from an unsuspecting family office or two, employ a techy and an admin person to do the heavy lifting, whilst the smooth talking CEO alt-tabs between Bloomberg and reddit all day. Most have terrible risk management strategies, and the majority are simply trend following (sorry, machine learning/AI). Some of these firms are also my clients, and I have often needed to write off money they owe because they've literally gone bankrupt overnight.


Gone in a month? That sounds crazy fast. I'm an algo trading guy myself, and I can't imagine being up and running within a month of starting.

You have to code:

- Feed handlers

- Back office

- Research platform

- Strategy execution

- GUI

You need to have:

- PB agreements

- Network access (telco the world over suck as doing this in a reasonable time)

- Exchange memberships / ISDAs (depends what you're up to)

I guess you could have spun out from a larger group with some of this stuff, but then you'd probably not crash and burn that fast.

I do agree with the sentiment that fast talkers get more money than they can handle.


There are a lot of firms that can get you up and running in a few weeks. They sell generic hedge fund formation packages, which give you all the tech/PB access. It's very rudimentary, but a lot of people do seem to opt for them, especially those that don't know what they're doing.

Why are you ex algo trader? It still seems like a growing market, so it's interesting that you stopped doing it.

I'm right now working on optimizing my long term investment strategy, but the hardest thing is that there's nobody to talk to it about it (unlike when I have other coding problems), and I have no idea when I'm doing something totally stupid in my code base. Getting the right metrics to optimize is really hard, as mostly I'm just looking at numbers and graphs.


It's a crowded market, but not necessarily a lucrative one.

I worked for a large investment bank, was paid well, but was also rather unhappy. I'm not a fan of office politics and also hated the fact that what we were doing was actually quite unsophisticated, yet we dressed it up like the most complicated thing ever to clients.

We were using wavelets for analysing market micro-structure and using any generated signals to influence our trades. Wavelets were the topic of my thesis, so I was a good fit for the team. I know it sounds complicated, but if you looked at the signals we generated, they looked awfully similar to a rather basic trend-following style regression model. I believe we found a very complicated way to compute signals that a simple trend follower would compute in far fewer CPU cycles.

Anyway, I got a fairly decent bonus one year (because markets were trending nicely), and decided I want to set up my own tech company. I was always a nerdy guy, and I thought I could contribute more in the tech space. With hindsight it was a good move, and financially I believe I am now better off than most of my trading colleagues.

My advice to new guys joining our desk (maybe it helps you a bit): focus on your exit, more than your entry, and if you can help it, be really, really, really lucky :P


> what we were doing was actually quite unsophisticated, yet we dressed it up like the most complicated thing ever to clients.

Doesn't this describe nearly all of the financial industry? Do you happen to know of any exceptions?


Yep, and nope.

how long did you work in that space and how much income you got over that span?

Perhaps the most important paragraph in the article: "Everyone is competing against everyone else. If one firm succeeds in making the market more efficient through quantitative techniques, then there’s less money left over for other people to make exceptional investment returns. There will be one or two firms that are good at innovation and recognizing things that other people haven’t recognized. But everyone else will be fighting over scraps."

The market is, in theory, not a zero sum game. Algorithmic systems, in practice, are. In theory the only way you can genuinely beat the market is when you find an inefficiency in it. And inefficiencies are generally based on a lack of knowledge. If everybody knows what you know, that inefficiency will no longer exist. And now pair this with the reality that most (and it's probably safe to say all) algorithmic systems are not really especially complex. That doesn't mean they're easy to make though. The 'secret sauce' is in exactly what data is used, how the pieces you have interoperate, the way inputs and outputs are massaged, and so on. But this drives a huge need for secrecy. Nobody who is successful is going to want to talk about how their system is designed largely because most would not be especially difficult to replicate if they provided more than very abstract details.


Is this always true? One thing I wonder is, given that the amount of money needed to move the needle in a market could be actually quite large, how much money woyuld you need to make off a given inefficiency before it becomes a zero-sum game? Is it 10s of thousands? Hundreds of thusands? Millions?

And given some metric like trading volume how much does that affect that number?

I've heard of some hedge funds raising way less than they could have, maybe because their strategies would be killed much faster with 500mm than with 50mm ?


It's not a zero-sum game because debt is limitless. Imagine an extreme example where you rigged the game and literally won all the money in the world and then kept winning, what would happen next? People would find another form of payment via services and the money is worthless if you are the only one who has it. Therefore you might lend them money in exchange for a service so that they will keep playing your rigged game now accruing a credit of services in addition to money.

I'm trying to parse your comment. It sounds like you're basically trying to do part time what dozens of large companies spend large amounts of time and money trying to do, and then expecting them to discuss with you what they've done?

You know those books that show you how to get rich doing X? Those writers make far more money from the book than they do from X.

No doubt you can find corners of the investing internet that will discuss these things, but you'll probably find an awful lot of chaff in your wheat, because the successful ones won't be chatting about it on forums.

Now on a more constructive note, optimising your long term investment strategy is probably best achieved by getting a pension with some cheap trackers, and a savings account. If you're already doing that then great because that's 95% of your optimisations there.

I wont recommend any web sites, because the details will depend on the country where you live.


I bet he/she.they either 1. made fuck-you-money and retired 2. burnt-out from the stress and decided to make slightly less large piles of money in return for less stress.

Somewhere between 1 and 2, but very good guesses :)

Why did you quit ? (Assuming you did cos you said you are an ex alto trader).

How long did you do it? Is it possible to make a living as an algo trader?


See response above.

I was officially trading (i.e. coding the strategy) for just under 3 years, then got offered a senior non-trading role (but my pay was heavily linked to the desk's performance).

Can you make a living? Yes, if you're doing it as a salaried employee at a reputable institution. If you're going to try and algo trade with your 50k of savings, I would say "unlikely", and you might cause yourself a lot of anxiety.


Thx for replying.

When you say yes to making a living but as an employee is that because of at an institution you are trading with much larger sum of money and hence there is enough return to pay for your salary bonus and then some or something else like getting a regular paycheck in a other wise very volatile return world? Essentially is the amount capital the key thing or something else.

Also how do traders typically come up with a strategy? Original insight or mostly copying the herd with their own variations?


More capital is obviously better. But more importantly, what happens when you're not making money? How do you control your anxieties when your broker ac balance halves due to a trend reversing? Are you prepared to monitor your strategy 24/7? At a firm, there's probably someone there to do it for you - at home, it's just you. Trading is a casino and the emotions it imparts are similar - euphoria when you win, depression when you lose. If you have a job, you at least have a base salary that will bolster your living standard - without it, you're going to struggle in the bad times (unless, of course, you're already rich).

Regarding strategy generation, it's a mix. Quants like me almost always take the systematic, research like approach - analyse data, see if you can ascertain any predictable patterns and then paper trade them in the live market. If it makes money on paper, it might make money in the real market. Then just iterate and refine. Some guys do trade with their gut, but are rarely successful.


How did you analyze data and results. Did you use a commercial backtesting service or it’s common business practice to use in house data that these firms are sitting on - data that they have acquired over the years?

The OP you asked worked at an Investment Bank. This is not exactly the pinnacle of algo trading (at least in US/Europe) as they are hampered by regulations about prop trading etc.

I work at a big quant/algo prop trading firm. The reason you can't compete is that most successful algorithmic trading relies on economy of scale.

Yes capital to trade with is one aspect, but more importantly is the amount of knowledge needed to carry this off. Building a safe, robust strategy is hard even if you have a working idea.

It requires probably top 1% knowledge in distributed systems, Linux admin, networking, database administration, security and more. The chance that any individual (or even team of less than 5 people) has those skills covered in addition to the quantitative know how to actually come up with a strategy is quite small.

There is a reason why algo trading is dominated by large firms. Working at somebody like Citadel or Optiver, if you say "oh damn, there's this weird effect on our server. When x happens I get a period of noticeable slowdown" they will turn around and say "Ok, well John here is a core dev on the Linux kernel, he'll come to your desk and help you figure it out".

Imagine that but with every problem pretty much. Running trading strategies you will hit problems. The level of work to overcome all of them satisfactorily whilst not degrading your edge is so much work for an individual I suspect you would burn out pretty sharply.


OP here. While my division was in an investment bank, we were a prop desk that was eventually spun out of the bank, so we did have a "hedge-fund" vibe. I think what you're saying is spot on. The level of manual effort required to keep an automated system competitive and functional is often grossly underestimated by amateur traders.

As an algo trading guy myself I see the recent ML -> trading stuff as a bit of a bubble. There's a lot of job ads out at the moment looking for ML people in trading. Quant firms have not done great recently, and they all want to find new things. The question is whether there's anything out there to be found with ML techniques.

Also a lot of the well known quant firms that hire loads of phds don't actually do anything particularly interesting or profitable. They are simply benefitting from institutional momentum: there are still pension funds and family offices with a mandate to stick money in trend followers, so the big trend following names hold on to their assets.


I've been playing with the trading platform Alpaca[1] and wrote some code that essentially pulls trade information (each tick) for a handful of tech companies I am interested in. It's a lot of data, but once I have the data stored, I'm now thinking... Ok now what? I have volume, price, time, and a lot of other metrics.

Not sure where to go from here honestly. Should the code evaluate the data in real-time for each trade, or wait to the end/start of the trading day and do some evaluations then make a decision?

Obviously, I am not going to be able to compete with the big boys using really sophisticated code (written in a low level language like C), executing in colocation right next to the exchanges, running Apache Hadoop and Spark. Still, I am interested in just tinkering with simple algorithms with the data.

[1] https://alpaca.markets/


I'd say play around and see if you can predict the price - treat it like any machine learning/optimization problem - if you want to learn and have fun. If your goal is to make money, unfortunately it'll take a lot more. I've worked at a high frequency trading company, and now work at an algorithmic hedge fund, and you really need to scale horizontally to make money overall.

There's a lot I can talk about but here are some points:

- Most of the time you don't trade instruments in isolation. They are all correlated (even if inversely) one way or another , so movements in one will affect others. Having a myopic view of just one instrument will have too much unexplainable randomness.

- The other way to scale is having more data inform your pricing - i.e. don't just look at the prices of one or more instruments. Look at twitter, look at weather reports, news etc. It kind of suggested that in the article. The meaningful movements in the market are most of the time due to truths outside of the price itself.

- You have to decide whether you want to trade directionally and long term (like a hedge fund) and take on positions over time, or trade in and out of positions quickly (like high frequency trading firms) to minimize risk. You can be anywhere along that spectrum. The faster you are, the more myopic you can be and just react to current trading activity of other participants. The more long-term you are, the more you have to look at the big picture, more data, more instruments, making sure you have the right balance in your portfolio (whatever that means to you).

I'd be happy to expand more :)


Thanks for the great reply. Indeed I am interested in you expanding more. I've been a long-term "valueish" investor, following the preachings of Warren Buffett for over 15 years. My long-term portfolio is pretty well balanced, Apple, to banks, to Berkshire Hathaway, Amazon, Ford, AMD, and SPY.

Recently, when the market got way overdone and oversold, I'd even call some of it panic selling with the bottoming happening Christmas Eve. I am curious how I can be more analytical, and data driven to make decisions during these events, rather than just gut feeling of panicking, negative news, and oversold conditions. Well I did add to my Apple position in this late December, I should have pulled the trigger more. I was somewhat conservative, missing the huge runup last month in January of most all stocks.


I can give some advice, but you should take it with a grain of salt :) I have been more of an infrastructure guy, and building the platforms with quants/traders, and not really trading myself. I am also more familiar with HFT trades overall.

I think you're on the right track regarding value investment, if you're thinking about it long-term, and it's for personal investments. Passive index funds like SPY are the best bet for most people. Finding companies or following trends you in particular have some insight on can also help. Things like "having worked in industry X, and having read their whitepapers, this new company is clearly overbought and is all marketing hype".

If you want to get more analytical, look at portfolio theory, different hedging strategies, and try and find "alpha" - roughly meaning the extra factors explaining the price of an instrument that gives you an edge over others. Check out Quantopian - they have some nice articles too. The key is coming up with a model that accurately captures the risk of your assets which would allow you to properly allocate your money amongst them.


Very interesting. No obligation but if you're bored feel free to expand!

Sure! I'll just ramble a bit about HFTs.

I'd say starting a "pure" HFT firm is incredibly hard nowadays. There's a big upfront investment and huge operating costs, and there's a lot of consolidation happening in the industry.

In general, most hardware and software is tailored to high-throughput, and a huge part of the effort of HFT is fighting all that to prioritize latency. If you want to compete with the very best firms, that means renting rackspace in a colocation with the exchanges, overclocked CPUs, expensive network cards, FPGAs, paying out the nose for the most accurate market-data feeds, and many order-entry connections. Then you have to tweak the BIOS, kernel settings, isolate your processes on cores, write your own userspace networking stack etc. etc. etc. On top of that if you want to trade between different colocations, you might need a network of microwave towers because fiber is too slow (light travels about 2/3 slower in fiber because it effectively "bounces" along the fiber). At the end of the day though, if your algorithm or strategy doesn't work, you won't make any money - speed is only a part of it. At the previous firm, we had a market making desk that was very profitable, but was quite "slow" by HFT standards (triple digit microseconds vs sub-microsecond response times). You still need to be smart about it.

Having said all that, I think HFT gets a bad rap, about being a waste of resources/talent, and somehow "stealing" from the common man. The truth is, they are only "stealing" from each other - they compete to remove the tiniest inefficiencies in pricing. They ensure the cost of trading is actually as low as it can possibly be. By themselves they don't have enough capital to move the market in any meaningful way. In fact, most flash crashes occur when HFT firms step out of the way! They move out of the way of huge market movements because they can't cushion the blow without taking an unnecessary risk as a business. It's like asking your plumber to fix your sink while your house is on fire. Market orders, stop orders and panic are most of the problem when it comes to flash crashes. PSA: Please use limit orders :)

I might ruffle some feathers with the comments, but I think overall HFTs aren't the bogeyman most people think them to be. Sure there are some bad apples, but that's true of any industry, and it comes down to the people - not the system itself.


> At the previous firm, we had a market making desk that was very profitable

Are you saying, it was guaranteed to make money? I.E. the algorithm never failed, or never made losing trades? That seems insane, to always be able to make money even if it's fractions of a percent.


This particular desk wasn't perfect - it had its ups and downs (there were definitely days where money was lost). Overall though it was profitable.

How does "very profitable" equate to "never made losing trades?" If you're only right 60% of the time you can still be profitable overall with proper risk management.

Thank you!

I've spent a huge amount of time playing with similar stuff. The goal is to predict anything that you can make money on -- this can be obviously be price, but you can also try volatility, or predicting a probability distribution. Try relaxing constraints to find your signal: price ranges instead of exact prices, date ranges instead of exact dates. If you can output a probability, distribution, or confidence interval as well, that information can be used for position sizing (see Kelly criterion.)

If you start expanding beyond a small set of specific companies, don't forget to find data for delisted companies to avoid survivorship bias.


I looked at Kelly criterion, and what's interesting is that I was just thinking yesterday about whether I should optimize

E(log(wealth)) - c * std(log(wealth))

or

E(wealth) - c * std(wealth)

The first one is better for building up wealth over a long time, but when I looked at Modern Portfolio Theory, it is suggesting to use the second formula.

I'm also planning to spend about 5% of my wealth every year, and the second number gives me lower risk in the short time frame, so I think something in the middle would be a better criterion.


Kelly sizing is optimal long term, but is highly volatile. A common practice is to size by a fraction of Kelly, to reduce variance and since risk of ruin increases fast after 1.0 * Kelly. It also allows room for error in the probability predictions, to avoid going over 1.0 * Kelly by accident.

I try not to worry about stdev and focus on the soundness of my process, but in practice I have money split into higher-risk where I try to be clever, and low-risk failsafe investments to raise the floor of the worst case scenario.


This is kind of a backwards philosophy because there are so many things you can test in the market. What you want to try to do is find an edge and then capitalize on it with the tools at your disposal, just my opinion.

Try this strategy and see if you can automate it. It's called the wheel strategy. What you want to do is setup a $200K account, and sell puts with certain deltas, and then if assigned, sell calls with certain deltas, until you get called. This will get you a relative return of up to 20% per year if done well. You can use your skills to figure out the best delta position to trade, you can look for high volatility to take advantage of. Once you get this completed, you can look for other trends. I always recommend starting with this because it's the easiest way to make some money in the market. It's a lot of work to do it manually, but automated, it would let you earn income while you are not doing much.

You will also want to read and understand what calls and puts are, and what selling/buy calls/puts entails. Probably want to do this with stocks like Disney, GE, and so forth, something with large market caps, small movements, and something you are willing to hold long term and which will not go bankrupt/drop significantly in the near future, disclaimer: I do not like GE at all, however, you also want to try this with 1 contract to start, because 1 contract is 100 shares. You can probably work with a smaller starting capital. If you get this implemented and working, it can generate passive income for you in a sense, or you can blow an account.

Edit: You want to settle/close out around earnings because the volatility is insane at that time.


If you can reliably generate 20%/year, a skill which is worth billions of dollars, why are you giving away the secret for free?

Generally speaking, if someone offers a simple strategy for getting well-above-market returns, they're either lying or failing to mention that this strategy occasionally bankrupts you. I think this is the latter.


I can do way better than 20%. I do 100%. That's how I make my money and pay my bills. I charge for that skill because it has value. This is scalping which I don't do, it's just a good strategy to learn from, IMO.

I don't utilize it because my strategy is much better.

The risk with the wheel is getting assigned stock which you hold forever, but you sell calls against it and over time it ends up being profitable.


Option selling is “picking pennies in front of a train”. Small wins, big losses.

I only buy calls or puts, however, you have to look at the wheel in a different view. You are making pennies in front of a steam roller, but you are getting paid to buy the stock you want. Most people say, if AAPL goes to $140, I will buy. Well this lets you sell puts until AAPL hits $140, then you get assigned, so you get paid every week or 3 weeks it goes up. Then when you have the stock, you sell a call against it which helps reduce your overall cost basis.

This is a fairly easy strategy to automate and you can use a small account to do so.


From the article:

> You’ve already got robo-advisors, which use algorithms to manage assets for retail investors. We’re also probably only a few years away from you being able to log into a brokerage account and run a sophisticated institutional-grade algorithm yourself.

> People tend to assume that the diffusion of these technologies is a good thing. I’m more ambivalent. I think it could be a big mistake to have the population at large play around with algorithms. Some people who are very good at it might benefit from having access to this broadened toolset. But most people will just end up paying too much or make bad decisions because they’re being given access to a technology that they aren’t equipped to do anything useful with. They can lose money with it, however.

Hobby away, friend, but when you start betting money you can't afford to lose, probably worth just turning that hobby into a resume sent to those folks running hadoop with the shortest fiber lines into the exchanges.


> Hobby away, friend, but when you start betting money you can't afford to lose, probably worth just turning that hobby into a resume sent to those folks running hadoop with the shortest fiber lines into the exchanges.

Those folks with fiber lines into the exchanges are definitely not running hadoop.


The python library Backtrader is popular and has good intro docs.

https://www.backtrader.com/docu/quickstart/quickstart.html


From my limited experience it seems the game is less about "predicting" price - which seems to be impossible, and more about placing strategic bets that will give you a positive return over a long period of time.

I don't know about Alpaca, but Quantopian lets you do the equivalent and then also backtesting on historic data.

Shameless plug: We built a hosted Heroku-style platform for algo trading - https://kloudtrader.com/narwhal

Created it after not being able to do livetrading on Quantopian.

Here's the associated python library and documentation: https://docs.kloudtrader.com/ https://github.com/KloudTrader/libkloudtrader


Exploiting microtime moments in information flows is (in my view) parasitical on the real element of information-flow in markets. I don't think profiting in 10us of speed advantage seeing a thing, and acting on a thing, is itself market-informing and so it has to be (kind of definitionally) market-distorting.

algorithmic trading is market distorting. its not actually helping "us" with real value for our futures, funds, company value. Its not informed trade on real conditions, its trade on the mechanistic moments in trading message flows.

its a really bad feedback of second-order differential.


Things you can do with that 10us advantage like frontrunning are parasitical, but why is a speed advantage itself a bad thing?

Surely waiting a day to act on the information isn't helpful. How about a minute? A few hundred ms, about the speed of a conscious awareness? Getting down to 10us seems like the logical conclusion of all this.

I think the more nuanced view was talked 3/4 of the way down: with algo trading, prices are based on the models not the fundamentals. And then you add in models of what the other guys are doing. And pretty soon you don't care where the price should be, you just care about computing in 10us where everyone else thinks it's going to be in 15us.

> Another fallacy in the lead-up to the financial crisis was the assumption that financial markets were so efficient that participants didn’t need to do the underlying work to figure out what the securities were actually worth. Because you could rely on the market to efficiently incorporate all available information about the bond. All you need to think about is the price that someone else is willing to buy it from you at or sell it to you at.... if they assume that the price of an instrument already reflects all of the information and analysis that you could possibly do—then they are vulnerable to that assumption being false.

That's now flash-crash territory.


I used to believe the only socialised goal of a market I appreciated was setting price. I now realize a significantly higher percentage of trade is not aimed at that outcome but solely at extraction of profit in the movement of price. At the point nobody cares what the price is but only cares about it's Delta and velocity, I'm out. Why are we doing this?

it's simply realizing/acting on market information at a faster pace than humans could mechanically, I don't see how that distorts markets. If anything it makes markets more efficient because it adjusts prices to information faster. Yes it makes human traders less able to make money but I don't see why that is actually bad for the market.

If it were inefficient, unless the algo traders were putting arbitrarily large sums of money behind their positions, they would get eaten alive. Since the successful ones don't, it seems they are just pricing market reactions faster


You’re confusing algorithmic trading with high-frequency trading. The former is a superset.

Algo trading isn't exclusively hft.

In Ray Dalio's book Principles, he attributes his success to modeling the markets with computers, but never relying solely on them.

All trades would be looked at by an actual human, making sure that everything made sense and lined up.

I have my own doubts about a purely algorithmic approach (for now at least). Computers are great for many tasks, but for something as inherently irrational as the markets I think that should be at least partially moderated by a person with a deep understanding of the markets.


What trades are you referring to when you say they "would be looked at" by a person? The interview states quite the opposite:

> The level of human oversight varies. Among sophisticated quantitative investors, the process is fairly automatic. The models are being researched and refined almost constantly, but you would rarely intervene in the trading decisions of a live model. A number of hedge funds, mutual funds, and exchange-traded funds (ETFs) run on auto-pilot.


Good topic, would also recommend reading this guys stuff - Robert Carver, ex AHL:

https://qoppac.blogspot.com/ https://twitter.com/investingidiocy https://www.systematicmoney.org/

Also has a few videos scattered around that are worth a look too.


Is there an article somewhere that shows what a basic or advanced trading algorithm looks like and explains how they work? I'd be curious to see one walked through (even if it wasn't effective at making money and was just a proof of concept/example.)

The biggest problem with algo trading is that it all works fine and dandy, until it doesn't. You can make money several years in a row and then lose it all. Nassim Taleb makes this point exceedingly clear in his works.

This was a really good read. Thank you.

Agreed. Very thoughtful and interesting.

But isn’t there a strong financial incentive to try to understand why you’re doing what you’re doing, whether it’s an algorithm or a human executing the trades? Otherwise it seems very easy to lose a lot of money. I can't find the article, however a guy once drop a million in bitcoin using a trading bot on a short sale.

There's typically multiple layers (at different points between tick to an order hitting an exchange) of risk-management/circuit-breakers that prevent these types of things from happening at most shops that know what they're doing. No one wants a repeat of Knight Capital's 2012 meltdown.

I would imagine it may be easy for an unsophisticated/hobby "algo-trader" to make this type of mistake but with over a million in capital, you should probably be a bit more prudent with risk management.


The key bit here is where he says that a lot of people will lose their jobs soon. There are tons of jobs that exist only because of momentum. The financial incentive to pay all these people will dry up and it’s going to hurt. And we won’t opt for UBI until many people have suffered a great deal. People do not seem to appreciate the gravity of what is coming.

Basically every time I bring up jobs, ai and society someone comes out of the woodwork to insult my character or sarcastically dismiss me. Never any substantive counter-arguments. I’m really looking forward to what you guys will come up with this time. Always such a pleasure.


OK, here you go: what large group of jobs has been eliminated by "AI" so far? No time limit answering, unless you die first, in which case "time's up!" My assertion, made in public and under my real name is that barring some giant breakthrough, nothing like this is going to happen any time soon. As far as I can tell, as an active worker in the field (erstwhile finance FWIIW), "AI" is a force multiplier for statisticians, and basically that's it.

Jobs have been eliminated in America because of poor industrial and trade policy. Nothing to do with "AI." I get extremely irate when people make the assertion that "oh, well, the jobs are going away soon anyway" -using this complete and utter falsehood as an excuse to continue the looting of the country.

The guy interviewed here is a real mixed bag. Some of what he says is nonsense, some of it is accurate. FIRE has basically been a vampire squid on the economy without adding much value. Most of it could be "automated" away by using a magic 8-ball; it doesn't actually provide any value. Except the politicians rice bowls depend on the status quo.


Wrong question. You should be asking, what groups have been granted large productivity gains due to information systems and now run substantially leaner.

E.g. secretaries, call center employees, accountants, etc.


Statisticians. That's it! And it's one of the hottest job fields right now with the fancy name "data scientist." All the recent improvements in machine learning have done is make certain kinds of statistical thinking possible, and that's it. It's not eliminating anyone's job; it's actually growing the market for statisticians.

Anyway parent poster said "a lot of people will lose their jobs soon" because of "AI." I deny this has or will happen, and people's misunderstanding of it is downright criminal.


Not just statistics jobs. Also bad jobs. More bad jobs in fact.

Information systems have created the ease of the gig economy. It allows people to provide more granular, on demand jobs.

The net effect is more jobs. This may be a bad thinf


I work for my company as a lone coder in a coworking space, an ocean away from the rest of the company.

What gives, you my ask. I sit in one corner of a 6 desks arrangement and the rest of these desks plus another arrangement of 6 desks are occupied by a company in the RPA bussiness.

The CEO of the company sits right next to me (When I started working in this space almost a year ago all these desks were empty, so obviously I took the best position) so we have a good couple of chats the days he shows up (They have a "formal" office in the city).

They have a couple of whiteboards in the wall next to my right, where they use actual, physical pseudo kanban boards with postits and shit that get reorganized probably weekly, because they can't cope with the amount of new work they are getting.

They have doubled the number of programmers since May, in a country (Argentina) where things are not looking that bright for nobody.

From my desk, I can _feel_ the pain of all the jobs they are killing.

I can't tell you _exactly_ what "large group" of jobs they have killed because the chats are not that intimate, but I see the list of companies they are working with, and all the big ones from the country are there. all of them.

They _are_ killing jobs, a plenty, without discrimination. And this is barely starting.


And... which jobs exactly have they have killed? All you have documented here is some company doubling the number of programmers since May, which sure as heck sounds like creating jobs to me.

Some VC funded startup allegedly working with companies does not equal killing jobs with "AI." If this were really happening it would be real easy to document! The X industry is gone now. Like buggy whips. But it's not; it's science fiction. I know, I know, people _really_believe_ -that doesn't make it real. Numbers, people: name names, no anecdotes about the stories from people next to you in the WeWork or whatever the journalistic dipshits are releasing from a PR agency.


> And... which jobs exactly have they have killed? Unfortunately, I can't answer that question, because that's not that the people from the other company would discuss openly.

I had a couple of chats with the CEO about this, and he mentioned briefly that this affected jobs with a high level of repetitive tasks (E.g., invoice processing and stuff).

There was one process we kind of discussed at length enough as to answer this: In my country, it's normal for supermarkets to have 3rd party employees to take care of certain aisles that have only products from the 3rd party.

The thing is, putting products in order there is a time consuming process, and obviously prone to error.

The change would be, the employee would take a picture of the aisle once finished, send the picture to a service that would process it automatically and alert the employee before she left the supermarket if there was a problem or anything (Like, a product needing replacement or similar).

> All you have documented here is some company doubling the number of programmers since May

Yep, that's right. the downside of this, is that there are a lot of companies looking to automate processes that are time consuming, and that affects jobs directly.

hat the company decides to do in the end regarding the people in those jobs is up to the complany obviously. Sorry about the late reply, thought.


if RPA means robotic process automation then presumably these are lost manufacturing jobs?

You and your grandparent essentially say “it hasn’t happened yet.” Nobody said it’s happened. It’s going to happen.

Yeah, and "nanotech" is going to be a trillion dollar industry by 2015. Sorry "citations needed." Many people can assert things.

So you can’t reason about something that hasn’t happened yet. That’s your stance. You could throw any argument out the window this way.

It's a really effective and accurate argument with good Brier score when it's deployed against marketing woo, which is all that "AI" is.

I'm happy to place a long bet at whatever odds you like for whatever specific prediction you care to make if you want to put your money where your mouth is. Auger, "long now" whatever -I'm good for it.


Would you please stop posting trollish comments to HN so we don't have to keep banning you?

So I’m banned? For what? How is anything I’ve said trollish? This is a joke right? Which part is trolling? AI is a threat to jobs? People respond to AI “alarmists” with character attacks and sarcasm? I genuinely do not get it.

If banning me is such a horrible burden for you, you are free to email me so that we can discuss a solution.


We haven't banned this account, but we've banned previous accounts that I believe you've used. If you keep breaking the site guidelines, we're going to have to ban you again. I'd much rather you'd take the spirit of this site to heart. We're trying for something, as you know, that's a little bit better than internet default. That means posting civilly and substantively, with respect for others no matter how wrong they may be.

When a comment is both low-information and personally negative, it becomes trollish almost regardless of topic. I think I read "So you can’t reason", etc., as personal, though looking at it now I see that you meant "you" in the sense of "one". Sorry! Nonetheless, you've posted such comments in the past, including recently (e.g. https://news.ycombinator.com/item?id=19099603, https://news.ycombinator.com/item?id=19110921), so the main request stands.


"AI" doesn't actually mean anything. How about automation of white collar work, though? For that the easy answer is lawyers [1].

[1] https://www.bostonglobe.com/opinion/2014/05/09/the-lawyer-bu...


Bureau of Labor Statistics says we can expect 8% growth in lawyer jobs in next 10 years:

https://www.bls.gov/ooh/legal/lawyers.htm


Ok... and the US population is expected to grow more than that over the same time period. Not sure I see your point here.

Sorry, I don't buy the hype right now with AI. Sure it can help Google identify your pictures and make them prettier, but killing off jobs, I doubt it anytime soon.

To me, AI and driverless cars are hyped too much. Computer companies like to overpromise and then under deliver. When I first started in my career, CASE tools were going to kill off the programmer, fast forward 20+ years, and developers are still hanging around coding stuff.

Are jobs going to change? Sure.


Yes, momentum. That's what drives CEOs to invest in new tech by investing billions. How does this help CEOs and their companies? Imagine X Inc invested $5B in AI, Cloud, etc, only to write off that $5B a decade later. But this $5B is a good investment since it helps keep their stock price up during the bull market where every other company is following the same trend.



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