There's lots of history for people to draw analogies from, but people have been doing so poorly.
Before I get going I need to lay some facts down. They're not fun facts, but they are facts. Lots of facts are objectively bad facts, but that doesn't change their fact-ness.
The nature of industrial software production has materially changed. This is a done thing. The coding LLMs have reached product market fit. It turns out that automating the language of automation was easier than you'd think. The cost of producing good enough coding LLMs is coming down sufficiently that open weight models are becoming viable (if less robust than the premium priced and rented frontier models) and pressure from Chinese model forges is further depressing token costs. Large scale software production is no longer viable using handcrafting techniques. Mid-scale software production is following suit. Industrial production of software is not the first industry to go through this transition, but it is the most recent and largest. This ship has sailed and our industry is in new waters whether we like it or not.
This factual statement says nothing about the ethics of the development of these tools. The ethics is where the large majority of the objection to these tools is founded, and there is some poor arguing going on about it.
But they don't even work!
No; but they are consistently bad in predictable ways, same as the transition from handcrafted machine parts to machined machine parts. The economics mean you get a lot more code for your dollar than handcrafting, and the rich guy who owns the code factory makes the call and not you. Same as it has been for the last 240 years of the industrial revolution. Software ain't special.
Now for the ethics.
The AI revolution isn't the sugar revolution
There are those who draw parallels to industrial sugar production starting in the late 1400s. This brought world wide change as calories were no longer scarce in Europe, the finance system of Europe stabilized, sugar stopped being an aristocrat-only food and started being available on street-corners, people started being able to have jobs that didn't involve agriculture thanks to the calorie surplus, which liberated enough mind and bodies from farmwork to lay the foundations for the scientific revolutions that led to alternate power sources, mechanization, automation, and ultimately computers. However, cheap sugar from 1470 to the early 20th century required chattel slavery.
A good book on this is Born in Blackness: Africa, Africans, and the Making of the Modern World, 1471 to the Second World War by Howard French (ISBN 978-1631495830, 2021).
The anti-AI argument drawing parallels to chattel slavery is doing so in a highly allegorical way. Here is this flagrant crime against human rights, see how it matches the crime of AI model training upon the property rights of IP owners. For one, this argument is patently offensive to peoples historically subjected to chattel slavery. For another, the property rights of IP owners is itself a modern invention post-dating the bust-up of chattel slavery in most of the world. This is an inappropriate argument, and should not be made.
Yes, these tools automate mining the commons for resale. The producers are even mining property without permission of their owners. Some got slapped for it (see the Anthropic settlement; my book was included in the haul but I wasn't in the settlement because my publisher didn't register the copyright) some are successfully wooing lawmakers to allow this little bypass of property rights for the good of everyone. And by everyone they mean everyone rich enough to own a hyperscale model forge. That should be challenged. Making parallels to chattel slavery is not how you do it.
The AI in everything boosters look at what sugar did to the world and see AI making that kind of change. Freedom from farming your own content! Expert analytical power at your fingertips! More time for fun pursuits, and less chasing obscure syntax errors! Boosters often overlook the cost of their futures. This is far from the first time humanity has seen a technological improvement, got all starry-eyed, kicked off a gold-rush, and created brand new toxic brownfields in the process to pollute generations (some late 1700s coal-ash heaps are still poisoning water tables).
The AI revolution isn't a new industrial revolution
Another common historical parallel is the industrial revolution built on the back of coal, and later on oil. The industrial revolution changed whole economies, providing work in cities for anyone tired of farmwork, increased the speed of travel which shrunk the apparent size of the world, increased interconnectednes of the whole world...
...and brought the current climate disaster we're living through. Training the big frontier models takes profound power and raw materials, exacerbating our current problems with decarbonizing our power-generation. Clearly, we don't need to make the same mistake twice?
This argument is based on our current US-centric technological model, which is being actively undermined from many quarters. The Chinese economy is somewhat more centrally planned, and is managing to both decarbonize and increase model forge capabilities in ways that simply aren't happening in the US. Europe is also making lots of noise about unwiring their technical dependence on US tech firms due to lack of trust in US privacy and security laws. The US tech companies are following US automakers in slowly weaning their global sales to focus on luxury sales domestically. This is a recipe for a major market realignment somewhere in here which will change the US economic calculus for trillion dollar hardware investments.
Second, we have known for two decades that Machine Learning models are much cheaper to train if you're training for a specific, targeted workload. We've had ML-based cancer detection for a while now. Building coding-AI is a targeted usage, and training open weight models are becoming viable as a result. Maybe not as deep-featured as the premier high-rent frontier models; but if you need that luxury experience, the US tech industry will happily take your money. Training for general purpose intelligence is far more expensive, but that market has not reached product market fit yet the way coding AI has.
Third, we have 60+ years experience watching industrial production reliant on cheap labor leave the US for cheaper shores. The US tech industry has been off-shoring industrial software production talent for decades whenever the company got big enough to economically operate in more than one country. Industrial production in other industries that has remained onshore tends to be one of: dependent on local distribution such as oil refining, fitting highly niche industries where talent isn't global yet, small-crafters who can't afford to offshore or are in it for the craft.
This is the same exact industrial revolution operating on a new industry, thatssit.
Eventually even the boosters will realize that limited model scope is the path to profitability, but it might take a stock market collapse to jog their memory.
The AI revolution is like any other industry transitioning away from hand-crafting
One historical parallel that has clear pedigree is the Luddite movement in the wake of increasing industrial automation changing the nature of work for whole industries of hand-crafters. This argument is closer to viable because it is founded in worker rights and not property rights. The rise of industrial automation in the 1780-1880 period also paralleled a rise in monopolies and crony capitalism where a few got fantastically rich while workers grew somewhat less skilled and less paid due to the reduced need for hand-crafting. It's also the era where Karl Marx did his writing. We're clearly in that spot again, and socialism is much less of a dirty word for US-based leftists. Reducing the need for highly trained hand-crafters in software will further reduce the aggregate quality of life for people involved with industrial software production, increasing the net-worth of the people owning the production pipeline with less of the RSU trickle-down we used to see with handcrafted software.
A parallel I haven't seen much is examining current model herding workloads. I bring you weather forecasting. In the age before computed forecasts, figuring out the weather was intuition, some basic math, and luck. In the modern era, official forecasters look at groups of different model outputs to build a consensus view, and then further tweak the consensus view due to intuition and experience. The last human review step provably improves forecast accuracy, which is why you want an expert there. Industrial software production is already experimenting with model-consensus views, but the cost of running multiple premier rented frontier-models in parallel is fast becoming too expensive for most.
This is its own damn thing
My last thought here: we still don't understand the talent economics of coding-AI. Right now we're riding the talent surplus of a bunch of expert hand-crafters retooling their skills for the model herding world, with a bunch more out of work right now acting as a latent pool to draw from. And yet, our educational pipelines are still tooled for producing hand-crafters. How do you effectively train consensus-modellers for code? Do we even know what a junior looks like yet? How do we train coding AI for new programming languages without significant existing codebases to train on? Does this mean new languages will take less guidance from human ergonomics and more on training ergonomics? So much is changing that when the latent talent supply dries up the entire industry will change once again to figure out how to hire for this. The labor market changes for industrial software production have only just started.
But do not draw parallels to chattel slavery, even indirectly, or by allegory.