happy_dog1

I think we have to be careful when assuming that model capabilities will continue to grow at the same rate they have grown in recent years. It is very well-documented their growth in recent years has been accompanied by an exponential increase in the cost of building these models, see for example (of many examples) [1]. These costs include not just the cost of GPUs but also the cost for reinforcement learning from human feedback (RLHF), which is not cheap either -- there is a reason that SurgeAI has over $1 billion in annual revenue (and ScaleAI was doing quite well before they were purchased by Meta) [2].

Maybe model capabilities WILL continue to improve rapidly for years to come, in which case, yes, at some point it will be possible to replace most or all white collar workers. In that case you are probably correct.

The other possibility is that capabilities will plateau at or not far above current levels because squeezing out further performance improvements simply becomes too expensive. In that case Cory Doctorow's argument seems sound. Currently all of these tools need human oversight to work well, and if a human is being paid to review everything generated by the AI, as Doctorow points out, they are effectively functioning as an accountability sink (we blame you when the AI screws up, have fun.)

I think it's worth bearing in mind that Geoffrey Hinton (infamously) predicted ten years ago that radiologists would all be out of a job in five years, when in fact demand for radiology has increased. He probably based this on some simple extrapolation from the rapid progress in image classification in the early 2010s. If image classification capabilities had continued to improve at that rate, he would probably have been correct.

[1] https://arxiv.org/html/2405.21015v1 [2] https://en.wikipedia.org/wiki/Surge_AI

I wonder whether for a lot of the search & literature review-type use-cases where people are trying to use GPT-5 and similar we'd honestly be much better off with a really powerful semantic search engine? Any time you ask a chatbot to summarize the literature for you or answer your question, there's a risk it will hallucinate and give you an unreliable answer. Using LLM-generated embeddings for documents to retrieve the nearest match, by contrast, doesn't run any risk of hallucination and might be a powerful way to retrieve things that Google / Bing etc. wouldn't be able to find using their current algorithms.

I don't know if something like this already exists and I'm just not aware of it to be fair.

Good question, I don't personally know. The linked article would suggest there are plenty of people working on human feedback for chatbots, but that still doesn't give us any hard numbers or any sense of how the number of people involved is changing over time. Perhaps the best datapoint I have is that revenue for SurgeAI (one of many companies that provides data labeling services to Google and OpenAI among others) has grown significantly in recent years, partly due to ScaleAI's acquisition by Meta, and is now at $1.2 billion without having raised any outside VC funding:

https://finance.yahoo.com/news/surge-ai-quietly-hit-1b-15005...

Their continued revenue growth is at least one datapoint to suggest that the number of people working in this field (or at least the amount of money spent on this field) is not decreasing.

Also see the really helpful comment above from cjbarber, there's quite a lot of companies providing these services to foundation model companies. Another datapoint to suggest the number of people working providing labeling / feedback is definitely not decreasing and is more likely increasing. Hard numbers / increased transparency would be nice but I suspect will be hard to find.

I've shared this once on HN before, but it's very relevant to this question and just a really great article so I'll reshare it here:

https://www.theverge.com/features/23764584/ai-artificial-int...

it explores the world of outsourced labeling work. Unfortunately hard numbers on the number of people involved are hard to come by because as the article notes:

"This tangled supply chain is deliberately hard to map. According to people in the industry, the companies buying the data demand strict confidentiality. (This is the reason Scale cited to explain why Remotasks has a different name.) Annotation reveals too much about the systems being developed, and the huge number of workers required makes leaks difficult to prevent. Annotators are warned repeatedly not to tell anyone about their jobs, not even their friends and co-workers, but corporate aliases, project code names, and, crucially, the extreme division of labor ensure they don’t have enough information about them to talk even if they wanted to. (Most workers requested pseudonyms for fear of being booted from the platforms.) Consequently, there are no granular estimates of the number of people who work in annotation, but it is a lot, and it is growing. A recent Google Research paper gave an order-of-magnitude figure of “millions” with the potential to become “billions.” "

I too would love to know more about how much human effort is going into labeling and feedback for each of these models, it would be interesting to know.

I love nanobind, use it all the time and highly recommend it. It makes it very easy to pass numpy, PyTorch, cupy or tensorflow arrays to your C++ extension, to specify what array shape and flags are expected, and to wrap either C++ or Cuda code. When paired with scikit-build, it makes building Python packages with C++ extensions a breeze. I would give it more than one star on github if I could.

I added a reply to the parent of your comment with a link to an article I found fascinating about the strange world of labeling and RLHF -- this really interesting article from The Verge 2 years ago:

https://www.theverge.com/features/23764584/ai-artificial-int...

There's a really fascinating article about this from a couple years ago that interviewed numerous people working on data labeling / RLHF, including a few who had likely worked on ChatGPT (they don't know for sure because they seldom if ever know which company will use the task they are assigned or for what). Hard numbers are hard to come by because of secrecy in the industry, but it's estimated that the number of people involved is already in the millions and will grow.

https://www.theverge.com/features/23764584/ai-artificial-int...

Interestingly, despite the boring and rote nature of this work, it can also become quite complicated as well. The author signed up to do data labeling and was given 43 pages (!) of instructions for an image labeling task with a long list of dos and don'ts. Specialist annotation, e.g. chatbot training by a subject matter expert, is a growing field that apparently pays as much as $50 an hour.

"Put another way, ChatGPT seems so human because it was trained by an AI that was mimicking humans who were rating an AI that was mimicking humans who were pretending to be a better version of an AI that was trained on human writing..."

This April 3rd Pew research study is some fairly interesting reading:

https://www.pewresearch.org/internet/2025/04/03/how-the-us-p...

They find that the general public is overall much more skeptical that AI will benefit anyone, much more likely to view it as harmful and much less excited about its potential than "AI experts". A majority of Americans are more concerned than excited. There is interestingly a large gender gap between men and women -- women are much less likely to view AI favorably, to use it frequently or to be excited about its potential than men.

There is some research to suggest that consumers are less likely to buy a product and less likely to trust it (less "emotional trust") when AI is used prominently to market it:

https://www.tandfonline.com/doi/full/10.1080/19368623.2024.2...

So I think the data suggests that while there is excitement around AI, overall consumers are much less excited about AI than people in the industry think and that it may actually impact their buying decisions negatively. Will this gap go away over time? I don't know. For any of you working in tech at the time, was there a similar gap in perceptions around the Internet back in the days of the dot com bubble?

The other problem as pointed out is that MANY things are labeled as AI, ranging from logistic regression to chatbots, and probably there is more enthusiasm around some of these things than others.