> A few years ago, Forbes wrote that "Software ate the world, now AI is eating Software"
As far as I know, AI is Software, so this type of reasoning really confuses me.
I think it is possible that "developers" might have some of their work find automation using AI and ML, but Computer Science will obviously stick around as long as computers are around.
For example, someone has to code the algorithms which are used to teach the computer how to learn. Someone has to code the pipelines for capturing data from the physical world into the digital world. Someone has to code the storage and exchange of that data to the ML models, and someone has to plug the inference into the production processes and systems. Someone has to put alarms and monitors around all these things to assert their operation. Someone has to code the UIs and interfaces that users will use to interact with all those systems. Finally, someone needs to code compatibility and optimizations of all these models and their implementation to continue to work and leverage new hardware capabilities.
If people think that developers were spending 90% of their time hand tuning business rules and coming up with manually implemented inference and decision models they are highly mistaken. That could have been 5% of the job in some circles, but 95% of the job has always been all these peripheral tasks.
If anything, there will be even more work now, since ML models require a lot more periphery to develop, train and ship.
Once upon a time you could make a good living building business websites (i.e. the local florist) but even without AI and ML frameworks have already killed that dead - your florist now probably uses instagram as their website, they might use Squarespace - and if they're hardcore they use Joomla, Wordpress or Drupal. I think a lot of the media thinks that developer hours are spent proportional to the stuff they see and this is the crux of the matter. Ford isn't retaining a team of two hundred devs to maintain their sales pages - it might be like ten or so? While an insurer managing a claims system is probably retaining a boatload of FT devs and also shipping out a bunch of hours to consulting firms. Software programming labour is very much an iceberg problem - the things that look expensive and difficult are trivial and those things are easy to automate away - but we're all working on the hard shit.
> Once upon a time you could make a good living building business websites (i.e. the local florist)
Hum, back that up, because I don't think you could ever make a good living with that. No florists is going to pay you the same salaries that going to work for Shopify pays you, small local business just didn't have the means to hire someone to build a website, that's why there was a market for things like Drupal, WordPress and Shopify in the first place. So in reality, those frameworks created jobs, because now you can actually be paid by a local florist to setup and manage a WordPress for them, and the equation works out that for what they pay you and the time it takes you is now profitable. And you have all the jobs opened up from the company developing the frameworks as well.
I've got a friend who coasted through the early 2000s plopping out websites for random businesses for around 1k a pop - for a weeks work that's a pretty good take home.
The florist example was probably a poor choice since they tend to have pretty thin margins - but restaurants (while also not having a bunch of money) tend to have advertising budgets that can float a relatively modest cost for plopping out a website.
Sounds like an ok gig, until you have to support them. I used to throw together and host little sites for friends for free, now I'd rather keep the friendships intact and tell people to just use Squarespace or some other builder.
That wasn't bad in the early 2000s. Median U.S. household income was $42K in 2000, and you didn't have these $half-mil/year FAANG/unicorn jobs back then.
I don't think it is wrong at all. It may be a niche – specially in the US –, but with 46.3% of the world population still without internet access, the long tail is indeed long.
There's nice money to be made configuring/writing custom modules for Wordpress and Drupal. Your business won't become a unicorn but there's a living to be made on top of those technologies. The same thing when it comes to Shopify and Magento.
The funny thing is that now, many smaller shops are catching up to what Shopify, Lodify etc do, and offering niche solutions to problems which people are actually interested in paying for, so while a lot of these players were first to take over many smaller players / individuals. People had a lot of time to sit around and tinker and build alternatives.
True, though I'm assuming by "the world", they mean that traditional processes which would have involved pen and paper and other non computer based tools and systems are all being redesigned to make use of computers, thus Software is eating them.
But AI is not replacing processes that rely on software with something that no longer relies on software. What AI is doing is it lets you replace even more processes that were not yet able to be handled by a computer with one that a computer can do, and thus it just helps Software eat even more of the world. Decision making and judgement tasks were not able to be successfully moved to computers prior to the recent advancements in AI, now some of those can be.
I think this is a reference class problem. One could also say software replaced work done in offices with work still being done in offices. (until recently) Most of the work was still done on literal desktops. but significant changes have been happening within the subset of the office and within the use of the desk.
As far as I know, AI is Software, so this type of reasoning really confuses me.
I think it is possible that "developers" might have some of their work find automation using AI and ML, but Computer Science will obviously stick around as long as computers are around.
For example, someone has to code the algorithms which are used to teach the computer how to learn. Someone has to code the pipelines for capturing data from the physical world into the digital world. Someone has to code the storage and exchange of that data to the ML models, and someone has to plug the inference into the production processes and systems. Someone has to put alarms and monitors around all these things to assert their operation. Someone has to code the UIs and interfaces that users will use to interact with all those systems. Finally, someone needs to code compatibility and optimizations of all these models and their implementation to continue to work and leverage new hardware capabilities.
If people think that developers were spending 90% of their time hand tuning business rules and coming up with manually implemented inference and decision models they are highly mistaken. That could have been 5% of the job in some circles, but 95% of the job has always been all these peripheral tasks.
If anything, there will be even more work now, since ML models require a lot more periphery to develop, train and ship.