Comment by ori_b
21 hours ago
My prediction: If we can successfully get rid of most software engineers, we can get rid of most knowledge work. Given the state of robotics, manual labor is likely to outlive intellectual labor.
21 hours ago
My prediction: If we can successfully get rid of most software engineers, we can get rid of most knowledge work. Given the state of robotics, manual labor is likely to outlive intellectual labor.
I would have agreed with this a few months ago, but something Ive learned is that the ability to verify an LLMs output is paramount to its value. In software, you can review its output, add tests, on top of other adversarial techniques to verify the output immediately after generation.
With most other knowledge work, I don't think that is the case. Maybe actuarial or accounting work, but most knowledge work exists at a cross section of function and taste, and the latter isn't an automatically verifiable output.
I also believe this - I think it will probably just disrupt software engineering and any other digital medium with mass internet publication (i.e. things RLVR can use). For the short term future it seems to need a lot of data to train on, and no other profession has posted the same amount of verifiable material. The open source altruism has disrupted the profession in the end; just not in the way people first predicted. I don't think it will disrupt most knowledge work for a number of reasons. Most knowledge professions have "credentials' (i.e. gatekeeping) and they can see what is happening to SWE's and are acting accordingly. I'm hearing it firsthand at least locally in things like law, even accounting, etc. Society will ironically respect these professions more for doing so.
Any data, verifiability, rules of thumb, tests, etc are being kept secret. You pay for the result, but don't know the means.
I mean law and accounting usually have a “right” answer that you can verify against. I can see a test data set being built for most professions. I’m sure open source helps with programming data but I doubt that’s even the majority of their training. If you have a company like Google you could collect data on decades of software work in all its dimensions from your workforce
2 replies →
"Given the state of robotics" reminds me a lot of what was said about llms and image/video models over the past 3 years. Considering how much llms improved, how long can robotics be in this state?
I have to think 3 years from now we will be having the same conversation about robots doing real physical labor.
"This is the worst they will ever be" feels more apt.
but robotics had the means to do majority of the physical labour already - it's just not worth the money to replace humans, as human labour is cheap (and flexible - more than robots).
With knowledge work being less high-paying, physical labour supply should increase as well, which drops their price. This means it's actually less likely that the advent of LLM will make physical labour more automated.
Robotics is coming FAST. Faster than LLM progress in my opinion.
Curious if you have any links about the rapid progression of robotics (as someone who is not educated on the topic).
It was my feeling with robotics that the more challenging aspect will be making them economically viable rather than simply the challenge of the task itself.
1 reply →
The question is how rapid the adoption is. The price of failure in the real world is much higher ($$$, environmental, physical risks) vs just "rebuild/regenerate" in the digital realm.
2 replies →
That’s the deep irony of technology IMHO, that innovation follows Conway's law on a meta layer: White collar workers inevitably shaped high technology after themselves, and instead of finally ridding humanity of hard physical labour—as was the promise of the Industrial Revolution—we imitate artists, scientists, and knowledge workers.
We can now use natural language to instruct computers generate stock photos and illustrations that would take a professional artist a few years ago, discover new molecule shapes, beat the best Go players, build the code for entire applications, or write documents of various shapes and lengths—but painting a wall? An unsurmountable task that requires a human to execute reliably, not even talking about economics.
> If we can successfully get rid of most software engineers, we can get rid of most knowledge work
Software, by its nature, is practically comprehensively digitized, both in its code history as well as requirements.