5 ms·

The last fairly technical career to get surprisingly and fully automated in the way this post displays concern about - trading. I spent a lot of time with trad

The last fairly technical career to get surprisingly and fully automated in the way this post displays concern about - trading. I spent a lot of time with traders in early '00's and then '10's when the automation was going full tilt. Common feedback I heard from these highly paid, highly technical, highly professional traders in a niche indusry running the world in its way was: - How complex the job was - How high a quality bar there was to do it - How current algos never could do it and neither could future ones - How there'd always be edge for humans Today, the exchange floors are closed, SWEs run trading firms, traders if they are around steer algos, work in specific markets such as bonds, and now bonds are getting automated. LLMs can pass CFA III, the great non-MBA job moat. The trader job isn't gone, but it has capital-C Changed and it happened quickly. And lastly - LLMs don't have to be "great," they just have to be "good enough." See if you can match the above confidence from pre-automation traders with the comments displayed in this thread. You should plan for it aggressively, I certainly do. Edit - Advice: the job will change, the job might change in that you steer LLMs, so become the best at LLM steering. Trading still goes on, and the huge, crushing firms in the space all automated early and at various points in the settlement chain.

deleted 2y ago
[deleted]
throw09890989 2y ago
Actually the profession is already taking big hit. Expecting at least partial replacement by LLMs is _already_ one of the reasons for the reduction of new jobs. Actually _replacing_ developers is one of the killer apps investors see in AI. I'd be impressed if the profession survived unscathed. It will mutate to some form or another. And it will probably shrink. Wages will go down to the levels that OpenAI will sell their monthly pro. And maybe 3rd world devs will stay competitive enough. But that depends a lot on whether AI companies will get abundant and cheap energy. IIUC that's their choke point atm. If it happens it will be quite ironic and karmic TBH. Ironic as it is its our profession very own R&D that will kill it. Karmic because it's exactly what it did to numerous other fields and industries (remember Polaroid killed by instagram, etc). OTOH there's no riskier thing than predicting the future. Who knows. Maybe AI will fall on it's own face due to insane energy economics, internet rot (attributed to itself) and what not.
- dogman144 2y ago
  Good comment! I see all these arguments in this post, and then think of the most talented journeyman engineer I know who just walked away from Google because he knew both AI was coming for his and coworkers jobs and he wasn’t good enough to be past the line where that wouldn’t matter. Everyone might be the next 10x engineer and be ok… but a lot aren’t.
jvanderbot 2y ago
They most insightful thing here would have been to learn how those traders survived, adapted, or moved on. It's possible everyone just stops hiring new folks and lets the incumbents automate it. Or it's possible they all washed cars the rest of their careers.
- dogman144 2y ago
  To answer what I saw, some blend of this: - post-9/11 stress and ‘08 was a big jolt, and pushed a lot of folks out. - Managed their money fine (or not) for when the job slowed down and also when ‘08 hit - “traders” became “salespeople” or otherwise managing relationships - Saw the trend, leaned into it hard, you now have Citadel, Virtu, JS, and so on. - Saw the trend, specialized or were already in assets hard to automate. - Senior enough to either steer the algo farms + jr traders, or become an algo steerer themselves - Not senior enough, not rich enough, not flexible enough or not interested anymore and now drive Uber, mobile dog washers, joined law enforcement (3x example I know of).
- dweinus 2y ago
  I knew a bunch of them. Most of them moved on: retired or started over in new careers. It hit them hard. Maybe not so hard because trading was a lucrative career, but most of us don't have that kind of dough to fall back on.
  - dogman144 2y ago
    +1 this. Saw it first hand, it was ugly. Post-9/11 stress and ‘08 cleared out a lot of others. Interestingly, I’ve seen some of them surface in crypto.
oangemangut 2y ago
Having also worked on desks in the 00s and early 10s I think a big difference here is what trading meant really changed; much of what traders did went away with innovations in speed. Speed and algos became the way to trade neither of which humans can do. While SWE became significantly more important on trading desks, you still have researchers, quants, portfolio analysts, etc. that spend their working days developing new algos, new market opportunities, ways to minimize TCOS, etc. That being said, there's also a massive low hanging fruit in dev work that we'll automate away, and I feel like that's coming sooner rather than later, yes even though we've been saying that for decades. However, I bet that the incumbents (Senior SWE) have a bit longer of a runway and potentially their economic rent increases as they're able to be more efficient, and companies need not hire as many humans as they needed before. Will be an interesting go these next few decades.
- jvanderbot 2y ago
  This actually surfaces a much more likely scenario: That it's not our jobs that are automated, but a designed-from-scratch automated sw/eng job that just replaces our jobs because it's faster and better. It's quite possible all our thinking is just required because we can only write one prototype at a time. If you could generate 10 attempts / day, until you have a stakeholder say "good enough", you wouldn't need much in the way of requirements, testing, thinking, design, etc.
  - lifeisstillgood 2y ago
    This is interesting - yes of course true But like so much of this thread “we can do this already without AI, if we wanted” Want to try 5/10 different approaches a day? Fine - get your best stakeholders and your best devs and lock them in a room on the top floor and throw in pizza every so often. Projects take a long time because we allow them to. (NB this is not same as setting tight deadlines, this is having a preponderance of force on the side of our side
- datavirtue 2y ago
  We need that reduction in demand for workers, though. Backfilling is not going to be a thing for a population in decline.
epolanski 2y ago
I don't see it. Trading is about doing very specific math in a very specific scenario with known expectations. Software engineering is anything but like that.
- grugagag 2y ago
  Yes, software engineering is different in many areas but today a lot of it is CRUD and plumbing. While SW engineering will not die it will certainly transform a lot, quite possibly there will be fewer generalists than today and more specialized branches will pop out - or maybe being a generalist will require one to be familiar many new areas. Likely the code we write today will go the same way writing assembly code went and sure, it will not completely disappear but...
  - skydhash 2y ago
    > software engineering is different in many areas but today a lot of it is CRUD and plumbing Which you can do away in a few days with frameworks and code reuse. The rest of the time is mostly spent on understanding the domain, writing custom components, and fixing bugs.
ChuckMcM 2y ago
I like this comment, it is exceptionally insightful. Any interesting question is "How is programming like trading securities?" I believe an argument can be made that the bulk of what goes for "programming" today is simply hooking up existing pieces in ways that achieve a specific goal. When the goal can be adequately specified[1] the task of hooking up the pieces to achieve that goal is fairly mechanical. Just like the business of tracking trades in markets and extracting directional flow and then anticipating the flow by enough to make a profit is something trading algorithms can do. What trading software has a hard time doing is coming up with new securities. What LLMs absolutely cannot do (yet?) is come up with novel mechanisms. To illustrate that, consider the idea that an LLM has been trained on every kind of car there is. If you ask it to design a plane it will fail. Train it on all cars and plans and ask it to design a boat, same problem. Train it on cars, planes, and boats and ask it to design a rocket, same problem. The sad truth is that a lot of programming is 'done' , which is to say we have created lots of compilers, lots of editors, lots of tools, lots of word processors, lots of operating systems. Training an LLM on those things can put all of the mechanisms used in all of them into the model, and spitting out a variant is entirely within the capabilities of the LLM. Thus the role of humans will continue to be to do the things that have not been done yet. No LLM can design a quantum computer, nor can it design a compiler that runs on a quantum computer. Those things haven't been "done" and they are not in the model. The other role of humans will continue to be 'taste.' Taste, as defined as an aesthetic, something that you know when you see it. It is why for many, AI "art" stands out as having been created by AI, it has a synthetic aesthetic. And as one gets older it often becomes apparent that the tools are not what determines the quality of the output, it is the operator. I watched Dan Silva do some amazing doodles with Deluxe Paint on the Amiga and I thought, "That's what I want to do!" and ran out and bought a copy and started doodling. My doodles looked like crap :-). The understanding that I would have to use the tool, find its strengths and weaknesses, and then express through it was clearly a lot more time consuming than "get the tool and go." LLMs let people generate marginal code quickly. For so many jobs that is good enough. People who can generate really good code taking in constraints that the LLM can't model, is something that will remain the domain of humans until GAI is achieved[2]. So careers in things like real-time and embedded systems will probably still have a lot of humans involved, and systems where every single compute cycle needs to be extracted out of the engine is a priority, that will likely be dominated by humans too. [1] Very early on there were papers on 'genetic' programming. Its a good thing to read them because they arrive at a singularly important point, "How do you define 'Which is better'?" For a solid, qualitative and testable metric for 'goodness' genetic algorithms out perform nearly everything. When the ability to specify 'goodness' is not there, genetic algorithms cannot out perform humans. What is more they cannot escape 'quality moats' where the solutions on the far side the moat are better than the solutions being explored but they cannot algorithmically get far enough into the 'bad' solutions to start climbing up the hill on the other side to the 'better' solutions. [2] GAI being "Generalized Artificial Intelligence" which will have to have some way of modelling and integrating conceptual systems. Lots of things get better then (like self driving finally works), maybe even novel things. Until we get that though, LLMs won't play here.
irunmyownemail 2y ago
"See if you can match the above confidence from pre-automation traders with the comments displayed in this thread. You should plan for it aggressively, I certainly do." Sounds like it was written by someone trying to keep any grasp on the fading reality of AI.
manquer 2y ago
> LLMs can pass CFA III. Everyone cites these kind of examples as LLM beating some test or other as some kind of validation. It isn’t . To me that just tells that the tests are poor, not the LLMs are good. Designing and curating a good test is hard and expensive. Certifying and examination bodies often use knowledge as a proxy to understanding or reasoning or any critical thinking skills.they just need to filter enough people out, there is no competitive pressure to improve quality at all. Knowledge tests do that just as well and are cheaper. Standardization is also hard to do correctly, common core is a classic example of how that changes incentives for both teachers and students . Goodhart's law also applies. To me it is more often than not a function of poor test measurement practices rather than any great skill shown by the LLM. Passing the CFA or the bar exam while daunting for humans by design does not teach you anything practicing law or accounting. Managing the books of a real company is nothing like what the textbook and exams teaches you . —- The best accountants or lawyers etc are not making partner because of their knowledge of the law and tax. They make money same as everyone else - networking and building customer relationships. As long as the certification bodies don’t flood the market they will do well which is what the test does.
- crystal_revenge 2y ago
  > To me that just tells that the tests are poor, not the LLMs are good. I mean the same is true of leetcode but I know plenty of mediocre engineers still making ~$500k because they learned how to grind leetcode. You can argue that the world is unjust till you're blue in the face, but it won't make it a just world.
  - manquer 2y ago
    There are influencers making many multiples of that for doing far less. Monetary returns has rarely if ever reflected skill, social value, or talent in capitalist economies, this is always been the case, not sure how that is relevant I was merely commenting on why these tests exist and the dynamics in the measurement industry from an observer, we shouldn't conflate exclusivity or difficulty of a test to its quality or objective.
  - disconcision 2y ago
aprilthird2021 2y ago
Okay, but one thing you kinda miss is that trading (e.g. investing) is still one of the largest ways for people to make money. Even passively investing in ETFs is extremely lucrative. If LLMs become so good that everyone can just let an LLM go into the world and make them money, the way we do with our investments, won't that be good?
- parentheses 2y ago
  The financial markets are a 0 sum game mostly. This approach would not work.
- dogman144 2y ago
  you are missing that trading in what I’m talking about != “e.g investing.” And, certainly, prob a good thing for some, bad thing for the money conveyor belt of the last 20 yrs of tech careers.
  - aprilthird2021 2y ago
    I am not missing that. I understand the difference. I'm saying the economic engine behind trading is still good (investing). So while people don't do the trading as much (machines do it), the economic rewards are still there and we can still capture them. The same may be true in a potential future where software becomes automated.
9cb14c1ec0 2y ago
> LLMs don't have to be "great," they just have to be "good enough." NO NO NO NO NO NO NO!!!! It may be that some random script you run on your PC can be "good enough", but the software the my business sells can't be produced by "good enough" LLMs. I'm tired of my junior dev turning in garbage code that the latest and greatest "good enough" LLM created. I'm about to tell him he can't use AI tools anymore. I'm so thankful I actually learned how to code in pre-LLM days, because I know more than just how to copy and paste.
- dogman144 2y ago
  You might care about that but do you think your sales team does?
- wewtyflakes 2y ago
  You're fighting the tide with a broom.
  - 9cb14c1ec0 2y ago
    No, I'm fighting for the software I sell my customers to be actually reliable.
    - ethbr1 2y ago
      That ship seems to have sailed at the same time boxed software went extinct. How many companies still have dedicated QA orgs with skilled engineers? How many SaaS solutions have flat out broken features? Why is SRE now a critical function? How often do mobile apps ship updates? How many games ship with a day zero patch? The industries that still have reliable software are because there are regulatory or profit advantages to reliability -- and that's not true for the majority of software.
      - Panzer04 2y ago
        Not sure I agree. Where it matters people will prefer and buy products that "just work" compared to something unreliable. People tolerate game crashes because you (generally) can't get the same experience by switching. People wouldn't tolerate f.e broswers crashing if they can switch to an alternative. The same would apply to a lot of software, with varying limits to how much shit will be tolerated before a switch would be made.
spaceman_2020 2y ago
The key difference between trading and coding is that code often underpins uncritical operations - think of all the CRUD apps in small businesses - and there is no money involved, at least directly.

deleted 2y ago

[deleted]

throw09890989 2y ago

Actually the profession is already taking big hit. Expecting at least partial replacement by LLMs is _already_ one of the reasons for the reduction of new jobs. Actually _replacing_ developers is one of the killer apps investors see in AI. I'd be impressed if the profession survived unscathed. It will mutate to some form or another. And it will probably shrink. Wages will go down to the levels that OpenAI will sell their monthly pro. And maybe 3rd world devs will stay competitive enough. But that depends a lot on whether AI companies will get abundant and cheap energy. IIUC that's their choke point atm. If it happens it will be quite ironic and karmic TBH. Ironic as it is its our profession very own R&D that will kill it. Karmic because it's exactly what it did to numerous other fields and industries (remember Polaroid killed by instagram, etc). OTOH there's no riskier thing than predicting the future. Who knows. Maybe AI will fall on it's own face due to insane energy economics, internet rot (attributed to itself) and what not.

dogman144 2y ago
Good comment! I see all these arguments in this post, and then think of the most talented journeyman engineer I know who just walked away from Google because he knew both AI was coming for his and coworkers jobs and he wasn’t good enough to be past the line where that wouldn’t matter. Everyone might be the next 10x engineer and be ok… but a lot aren’t.

jvanderbot 2y ago

They most insightful thing here would have been to learn how those traders survived, adapted, or moved on. It's possible everyone just stops hiring new folks and lets the incumbents automate it. Or it's possible they all washed cars the rest of their careers.

dogman144 2y ago
To answer what I saw, some blend of this: - post-9/11 stress and ‘08 was a big jolt, and pushed a lot of folks out. - Managed their money fine (or not) for when the job slowed down and also when ‘08 hit - “traders” became “salespeople” or otherwise managing relationships - Saw the trend, leaned into it hard, you now have Citadel, Virtu, JS, and so on. - Saw the trend, specialized or were already in assets hard to automate. - Senior enough to either steer the algo farms + jr traders, or become an algo steerer themselves - Not senior enough, not rich enough, not flexible enough or not interested anymore and now drive Uber, mobile dog washers, joined law enforcement (3x example I know of).
dweinus 2y ago
I knew a bunch of them. Most of them moved on: retired or started over in new careers. It hit them hard. Maybe not so hard because trading was a lucrative career, but most of us don't have that kind of dough to fall back on.
- dogman144 2y ago
  +1 this. Saw it first hand, it was ugly. Post-9/11 stress and ‘08 cleared out a lot of others. Interestingly, I’ve seen some of them surface in crypto.

oangemangut 2y ago

Having also worked on desks in the 00s and early 10s I think a big difference here is what trading meant really changed; much of what traders did went away with innovations in speed. Speed and algos became the way to trade neither of which humans can do. While SWE became significantly more important on trading desks, you still have researchers, quants, portfolio analysts, etc. that spend their working days developing new algos, new market opportunities, ways to minimize TCOS, etc. That being said, there's also a massive low hanging fruit in dev work that we'll automate away, and I feel like that's coming sooner rather than later, yes even though we've been saying that for decades. However, I bet that the incumbents (Senior SWE) have a bit longer of a runway and potentially their economic rent increases as they're able to be more efficient, and companies need not hire as many humans as they needed before. Will be an interesting go these next few decades.

jvanderbot 2y ago
This actually surfaces a much more likely scenario: That it's not our jobs that are automated, but a designed-from-scratch automated sw/eng job that just replaces our jobs because it's faster and better. It's quite possible all our thinking is just required because we can only write one prototype at a time. If you could generate 10 attempts / day, until you have a stakeholder say "good enough", you wouldn't need much in the way of requirements, testing, thinking, design, etc.
- lifeisstillgood 2y ago
  This is interesting - yes of course true But like so much of this thread “we can do this already without AI, if we wanted” Want to try 5/10 different approaches a day? Fine - get your best stakeholders and your best devs and lock them in a room on the top floor and throw in pizza every so often. Projects take a long time because we allow them to. (NB this is not same as setting tight deadlines, this is having a preponderance of force on the side of our side
datavirtue 2y ago
We need that reduction in demand for workers, though. Backfilling is not going to be a thing for a population in decline.

epolanski 2y ago

I don't see it. Trading is about doing very specific math in a very specific scenario with known expectations. Software engineering is anything but like that.

grugagag 2y ago
Yes, software engineering is different in many areas but today a lot of it is CRUD and plumbing. While SW engineering will not die it will certainly transform a lot, quite possibly there will be fewer generalists than today and more specialized branches will pop out - or maybe being a generalist will require one to be familiar many new areas. Likely the code we write today will go the same way writing assembly code went and sure, it will not completely disappear but...
- skydhash 2y ago
  > software engineering is different in many areas but today a lot of it is CRUD and plumbing Which you can do away in a few days with frameworks and code reuse. The rest of the time is mostly spent on understanding the domain, writing custom components, and fixing bugs.

ChuckMcM 2y ago

I like this comment, it is exceptionally insightful. Any interesting question is "How is programming like trading securities?" I believe an argument can be made that the bulk of what goes for "programming" today is simply hooking up existing pieces in ways that achieve a specific goal. When the goal can be adequately specified[1] the task of hooking up the pieces to achieve that goal is fairly mechanical. Just like the business of tracking trades in markets and extracting directional flow and then anticipating the flow by enough to make a profit is something trading algorithms can do. What trading software has a hard time doing is coming up with new securities. What LLMs absolutely cannot do (yet?) is come up with novel mechanisms. To illustrate that, consider the idea that an LLM has been trained on every kind of car there is. If you ask it to design a plane it will fail. Train it on all cars and plans and ask it to design a boat, same problem. Train it on cars, planes, and boats and ask it to design a rocket, same problem. The sad truth is that a lot of programming is 'done' , which is to say we have created lots of compilers, lots of editors, lots of tools, lots of word processors, lots of operating systems. Training an LLM on those things can put all of the mechanisms used in all of them into the model, and spitting out a variant is entirely within the capabilities of the LLM. Thus the role of humans will continue to be to do the things that have not been done yet. No LLM can design a quantum computer, nor can it design a compiler that runs on a quantum computer. Those things haven't been "done" and they are not in the model. The other role of humans will continue to be 'taste.' Taste, as defined as an aesthetic, something that you know when you see it. It is why for many, AI "art" stands out as having been created by AI, it has a synthetic aesthetic. And as one gets older it often becomes apparent that the tools are not what determines the quality of the output, it is the operator. I watched Dan Silva do some amazing doodles with Deluxe Paint on the Amiga and I thought, "That's what I want to do!" and ran out and bought a copy and started doodling. My doodles looked like crap :-). The understanding that I would have to use the tool, find its strengths and weaknesses, and then express through it was clearly a lot more time consuming than "get the tool and go." LLMs let people generate marginal code quickly. For so many jobs that is good enough. People who can generate really good code taking in constraints that the LLM can't model, is something that will remain the domain of humans until GAI is achieved[2]. So careers in things like real-time and embedded systems will probably still have a lot of humans involved, and systems where every single compute cycle needs to be extracted out of the engine is a priority, that will likely be dominated by humans too. [1] Very early on there were papers on 'genetic' programming. Its a good thing to read them because they arrive at a singularly important point, "How do you define 'Which is better'?" For a solid, qualitative and testable metric for 'goodness' genetic algorithms out perform nearly everything. When the ability to specify 'goodness' is not there, genetic algorithms cannot out perform humans. What is more they cannot escape 'quality moats' where the solutions on the far side the moat are better than the solutions being explored but they cannot algorithmically get far enough into the 'bad' solutions to start climbing up the hill on the other side to the 'better' solutions. [2] GAI being "Generalized Artificial Intelligence" which will have to have some way of modelling and integrating conceptual systems. Lots of things get better then (like self driving finally works), maybe even novel things. Until we get that though, LLMs won't play here.

irunmyownemail 2y ago

"See if you can match the above confidence from pre-automation traders with the comments displayed in this thread. You should plan for it aggressively, I certainly do." Sounds like it was written by someone trying to keep any grasp on the fading reality of AI.

manquer 2y ago

> LLMs can pass CFA III. Everyone cites these kind of examples as LLM beating some test or other as some kind of validation. It isn’t . To me that just tells that the tests are poor, not the LLMs are good. Designing and curating a good test is hard and expensive. Certifying and examination bodies often use knowledge as a proxy to understanding or reasoning or any critical thinking skills.they just need to filter enough people out, there is no competitive pressure to improve quality at all. Knowledge tests do that just as well and are cheaper. Standardization is also hard to do correctly, common core is a classic example of how that changes incentives for both teachers and students . Goodhart's law also applies. To me it is more often than not a function of poor test measurement practices rather than any great skill shown by the LLM. Passing the CFA or the bar exam while daunting for humans by design does not teach you anything practicing law or accounting. Managing the books of a real company is nothing like what the textbook and exams teaches you . —- The best accountants or lawyers etc are not making partner because of their knowledge of the law and tax. They make money same as everyone else - networking and building customer relationships. As long as the certification bodies don’t flood the market they will do well which is what the test does.

crystal_revenge 2y ago
> To me that just tells that the tests are poor, not the LLMs are good. I mean the same is true of leetcode but I know plenty of mediocre engineers still making ~$500k because they learned how to grind leetcode. You can argue that the world is unjust till you're blue in the face, but it won't make it a just world.
- manquer 2y ago
  There are influencers making many multiples of that for doing far less. Monetary returns has rarely if ever reflected skill, social value, or talent in capitalist economies, this is always been the case, not sure how that is relevant I was merely commenting on why these tests exist and the dynamics in the measurement industry from an observer, we shouldn't conflate exclusivity or difficulty of a test to its quality or objective.
- disconcision 2y ago

aprilthird2021 2y ago

Okay, but one thing you kinda miss is that trading (e.g. investing) is still one of the largest ways for people to make money. Even passively investing in ETFs is extremely lucrative. If LLMs become so good that everyone can just let an LLM go into the world and make them money, the way we do with our investments, won't that be good?

parentheses 2y ago
The financial markets are a 0 sum game mostly. This approach would not work.
dogman144 2y ago
you are missing that trading in what I’m talking about != “e.g investing.” And, certainly, prob a good thing for some, bad thing for the money conveyor belt of the last 20 yrs of tech careers.
- aprilthird2021 2y ago
  I am not missing that. I understand the difference. I'm saying the economic engine behind trading is still good (investing). So while people don't do the trading as much (machines do it), the economic rewards are still there and we can still capture them. The same may be true in a potential future where software becomes automated.

9cb14c1ec0 2y ago

> LLMs don't have to be "great," they just have to be "good enough." NO NO NO NO NO NO NO!!!! It may be that some random script you run on your PC can be "good enough", but the software the my business sells can't be produced by "good enough" LLMs. I'm tired of my junior dev turning in garbage code that the latest and greatest "good enough" LLM created. I'm about to tell him he can't use AI tools anymore. I'm so thankful I actually learned how to code in pre-LLM days, because I know more than just how to copy and paste.

dogman144 2y ago
You might care about that but do you think your sales team does?
wewtyflakes 2y ago
You're fighting the tide with a broom.
- 9cb14c1ec0 2y ago
  No, I'm fighting for the software I sell my customers to be actually reliable.
  - ethbr1 2y ago
    That ship seems to have sailed at the same time boxed software went extinct. How many companies still have dedicated QA orgs with skilled engineers? How many SaaS solutions have flat out broken features? Why is SRE now a critical function? How often do mobile apps ship updates? How many games ship with a day zero patch? The industries that still have reliable software are because there are regulatory or profit advantages to reliability -- and that's not true for the majority of software.
    - Panzer04 2y ago
      Not sure I agree. Where it matters people will prefer and buy products that "just work" compared to something unreliable. People tolerate game crashes because you (generally) can't get the same experience by switching. People wouldn't tolerate f.e broswers crashing if they can switch to an alternative. The same would apply to a lot of software, with varying limits to how much shit will be tolerated before a switch would be made.

spaceman_2020 2y ago

The key difference between trading and coding is that code often underpins uncritical operations - think of all the CRUD apps in small businesses - and there is no money involved, at least directly.