$200 per page of quality proof output is super cheap - typical Fermi calculation shows a  typical mathematician cost about $100 000 a year, and output about 100* pages of peer-reviewed papers per years at best, so about $1000 per page for pure maths (applied maths are maybe half as expensive ?). 

So 1st consequence: every single theoretical scientist get fired. Computer scientist are more expensive and get fired even earlier. Also journalists, lawyers, accountants and basically any job that requires high writing skills. 

*wages may be about  $200 000 and output as high as 200 pages depending of country/talent/field but the order of magnitude is the same.

** first post here ;)

I believe the central impact will be a powerful compression of knowledge and a flood of legibility, which will be available to institutions and leadership first. Examples include:

• Speechwriting
• Report summarization
• Report generation

Even the higher number, like $20,000 per page, is a good deal for something like Wikipedia, where the page is available to millions of readers, or for things like the Stanford Encyclopedia of Philosophy. This will have a big impact on:

• Online encyclopedias
• Online textbooks

While this could easily be used to generate high-quality propaganda, I feel like it still weighs much more heavily in favor of the truth. This is because bullshit's biggest advantage is that is is fast, cheap and easy to vary, whereas reality is inflexible and we comprehend it slowly. But under the proposed conditions, advanced bullshit and the truth cost the same amount, and have a similar speed. This leaves reality's inflexible pressure on every dimension of every problem a decisive advantage in favor of the truth. This has a big impact on things like:

• Any given prediction
• Project proposals

Especially if it is at the lower end of the price scale, it becomes trivial to feed it multiple prompts and get multiple interpretations of the same question. This will give us a lot of information both in terms of compression and also in terms of method, which will cause us to be able to redirect resources into the most successful methods, and also to drop inefficient ones. I further expect this to be very transparent very quickly, though mechanisms like:

• Finance
• Sports betting

It will see heavy use by the intelligence community. A huge problem we have in the United States is our general lack of language capability; for example if GPT-6 knows Mandarin as well as any Mandarin speaker, and translates to English as well as any translator, then suddenly we get through the bottleneck and gain access to good information about Chinese attitudes. I expect this same mechanism will make foreign investment much more attractive almost universally, since domestic and foreign firms will now be working on an almost level playing field in any country with widespread internet access. If this prediction holds, I expect a large boom in investment in otherwise underdeveloped countries, because the opportunities will finally be legible.

Another interesting detail is that if GPT-6 can provide the best summaries of the available knowledge, this means that most of the world's institutions will then be working from a much more uniform knowledge base than we do currently. My initial reaction was that this is clearly for the best because the biggest roadblock to coordination is getting on the same page with the other stakeholders, but it also occurs to me that it makes transparent to everyone the cases where certain stakeholders have an untenable position. I suspect this in turn makes it more likely that some parties get the screws put to them, and further when they a) understand their own position and b) understand that everyone else understands it, they are more likely to try something radical to shift the outcome. Consider North Korea, for example.

Someone retrains the model using reinforcement learning to be more of an agent. Maybe a chatbot that tries to convince people of things, or give good advice, or make good predictions, or some combination thereof. This unlocks its hidden intelligence, so to speak, since it no longer thinks it is predicting other people's text. It's now basically a human-level AGI; it's expensive, but if they make it bigger and train it for longer maybe they can make a new version which is superhuman, and then things will go off the rails, because a superhuman AGI is well worth $20,000 per page of output.

However, this would take at least a few more months, probably more than a year, to achieve. I suspect that a lot of important things would happen sooner than that.

I think a GPT-6 that reaches AGI levels of ability while having high costs, it would likely have a larger attention amount and use part of it to have a working memory. There's little to be gained by replacing the average human by a $200 per page AGI.

This means it can actually do long chains of reasoning by manipulating what's in his short term memory similar to how humans do long chains of reasoning by operating on what's in their working memory.

The ability to do reasoning the means that the quality isn't very dependent on what can be found on the internet. 

An AGI that's human level for the average problem likely has problems where it outperforms humans. 

But I'm pretty sure stuff would go crazy even before then. How?

We can end up with an intelligence explosion via automated ML research. One of the tasks that could be automated by the language model is "brainstorming novel ML ideas". So you'll be able to pay $200 and get a text, that could have been written by a brilliant ML researcher, containing novel ideas that allow you to create a more efficient/capable language model. (Though I expect that this specific approach won't be competitive with fully automated approaches that do stuff like NAS.)

'Predicting random text on the internet better than a human' already qualifies it as superhuman, as dirichlet-to-neumann pointed out. If you look at any given text, there's a given ratio of cognitive work needed to produce the text, per word-count. "Superhuman" only requires asking it to replicate the work of multiple people collaborating together, or processes which need a lot of human labour like putting together a business strategy or writing a paper. Assuming it's mediocre in some aspects, the clearest advantage GPT-6 would have would be an interdisciplinary one - pooling together domain knowledge from disparate areas to produce valuable new insights.

Some tasks require much less than a page of output: --Brainstorming ideas, e.g. for product/character names, plot twists, etc. --Providing advice, e.g. as a therapist or financial advisor or lawyer --Answering questions, e.g. as a financial advisor or lawyer Perhaps there would be an economic niche for automating away some of these tasks.

Put it in the hands of inventors and researchers writing up business plans (with relevant metadata and project/company outcomes), and in the hands of VC, CVC, and other investors (including internally at larger companies). This would eliminate an enormous amount of friction, and energy wasted on both easily foreseeable mistakes and charades all parties have to play into today. That alone is sufficient to increase the rate of long-term economic and technological growth. By increasing ROR on investment, it will also increase total investment economy-wide. I'd expect this to become very common even at >$20k/page, since the expected values for success can be 8-9 figures and the vast majority of companies and projects fail.

Based on "Why Tool AIs Want to Be Agent AI's" by Gwern, I would expect an AGI level GPT-6 to self improve and become a world gobbling AI.

The moment it gets a hint that it could answer better by getting (unknown bit of data from the Internet, extra memory, some other resource), the software's own utility function will push the machine in that direction.