> The simplest, most reliable way to ensure the works you invest in are fully and entirely eligible for copyright is to just use humans for the whole project. Declare a "no-AI policy," and hire human artists to produce the works you hope to sell. Monitor their activity to ensure they're not using AI. Keep good records.
That seems true.
But also: generate all you like and then have humans edit, and keep a record of it, seems plausibly defensible too.
The current state of a lot of AI generated content material, not all / not necessarily program code, seems to really benefit from human editing.
It's almost never like that. Usually some team collaborates with some other team and sets something up with approval from departments A, B, and C with director X signing off. There's rarely one person you can point to. Also, wait a year and all those As, Bs, Cs, and Xs have switched to different roles or don't exist.
Commits per engineer 5.1x
Queries per data scientist 3.1×
Contracts per legal employee 3.1x
Deals per salesperson 1.7x
Hires per recruiter 1.4x
Invoices per AP processor 1.4x
These are the stats that might be hiding the possibly small potential for AI to generate "real" economic activity.
The first 3 stats, commits+queries+contracts, are secondary/meta statistics that could possibly go up 1000x and have very little affect on Anthropic's business, whereas that's much less likely to be the case with the last 3.
AI can make it very easy to do more and more busywork that doesn't move the needle.
If the above is true, AI econ must rely on a second order effect here in that busywork/BS is recognized more quickly and discontinued.
For example, if an organization paid an employee to compile a monthly report and it took them a very long time. But with AI, they can make that report every day. Now the organization realizes that they don't even need it.
I’ve actually seen this be the biggest advantage of AI in the corpo world: it is helping people realize their ideas are stupid or their busywork doesn’t matter way faster.
I’ve seen so many “dream” projects finally realized recently that organizations had been chasing for years only for them to realize it provides no value. Easily could have wasted years of time and tortured dozens of people but instead only took a relatively small amount of time due to AI.
I know some people will say this kills jobs but I really don’t think these are the jobs humans should be doing. It shouldn’t take someone’s entire life to realize people don’t actually want to “gamify their credit card application process” or whatever dumb thing product comes up with. Also when you spend so much time on a dumb idea a lot of people will try to justify it which is why I think we see so many obviously bad ideas go to market.
The amount of requests I get to help “make the next Facebook” or “make an application to predict the stock market” have gone down substantially and I think this is a good thing.
> it is helping people realize their ideas are stupid or their busywork doesn’t matter way faster.
While this is a possible explanation, it seems far more likely that people are finding more private/solo ways to pursue whatever their equivalent of “make the next Facebook” or “predict the stock market”.
I would be very surprised if the was a reduction in total duration of pursuit of these type of ideas. I think there’s just a reduction in them asking you as a dev to help.
Annual growth over that period has been ~2.2%, sometimes a little faster, sometimes a little slower. The doubling from 50 in 1982 to 100 in 2017 (35 years) averages to 2% per year.
Even if the overall productivity boost due to AI "only" turns out to be 40%, the lowest among your numbers, it's equivalent to 17 years of growth at 2% per year.
> Even if the overall productivity boost due to AI "only" turns out to be 40%, the lowest among your numbers, it's equivalent to 17 years of growth at 2% per year.
Two reasons to question the 40% number:
- Anthropic probably represents the extreme case of AI adoption, if they're not the absolute leader. I would see 40% as the absolute max for that category without knowing anything else.
- Anthropic is a tech company, willing to change their workflows rapidly, having maximal enthusiasm for the technology, and maximum knowledge of it. The vast majority of the world's companies don't come close to it for these aspects and therefore adoption and speed should lag significantly compared to Anthropic.
The numerator on that is “Real Value-Added Output for All Workers” [1], which is based on revenue (subtracting some costs), and the denominator is “Hours Worked for All Workers” [2].
Anthropic’s revenue has seen enormous growth [3]. They have few employees. Obviously, as these things are measured, they are highly productive! It might be hard to reproduce that sort of productivity outside the tech industry, though. We can’t all be Anthropic.
Brilliant. Now compare it to real wage gain, purchasing power, or other markers of societal well-being. Or income inequality!
I don’t see how we’d get to 40% unless a) humans are out of the loop, or b) we hire lots more humans which would make AI cost-ineffective. What am I missing?
A report doesn’t need to be discontinued if it’s automated. It’s just another graph on a dashboard. The cron job might put a little more load on the system, or the code might be a bit of a software maintenance burden, but cleaning it up can be put off for years. It’s there if you want it.
> For those hard theorems where we really want LLMs to help us, these Lean proofs are extremely long, like hundreds of thousands of lines of code long. A seemingly emergent trend is that the longer the Lean version of a proof, the messier and less faithful its machine-generated “natural language” translation seems to be.
This seems to be a common and continuing objection to the OpenAI math repo release. Specifically that at least some, perhaps many of
* the natural language proofs are low quality or otherwise extremely difficult to understand (although possibly correct?)
* the lean proofs are too long, convoluted, or otherwise extremely difficult to understand
I wonder if LLMs will be able to generate "high quality" natural language and lean proofs soon.
And specifically: iterate on the OpenAI repo to produce "better" proofs that prove the same things.
Easy to find email addresses of famous people are unlikely to respond to you to say the least.
reply