The paradox is that we love reading our own AI generated writing and hate reading anyone else's AI generated writing.
On a recent weeklong trip to the Philippines, I generated over a 500 page novel's worth of content from AI around various aspects of Filipino history, culture, social dynamics etc. and actually went over it at least 3 times to fully absorb the material.
But if someone handed me even a 3000 word essay on the Philippines clearly written by AI, I would not be able to get to the end of it.
I’ve said it before, but the best analogy I've heard is that sharing your prompts is like telling your friend about that dream you had last night in terms of comparable level of interest.
The entire "blame" paradigm is unproductive. Does Lyft "blame" Uber for it's lowered market share?
The entire system (including nurses and technicians) are just agents making semi-rational decisions in their own self interest. Is it important to judge people within an existing system or is it important to look at locus points that, when pressure is applied, can make durable changes to the system?
> But, for scientists, I find that these tools address the problem of the exploding complexity barrier in the frontier. Every day, it grows harder and harder to contain a mental map of recent relevant progress by simple virtue of the amount being produced.
AI is going to both help and hinder this process though. At the end of the day, mathematics is mostly a social process at this point. The goal is not raw number of theorems proven, it’s how proving theorems affects the working operational models of mathematicians. Only a rare few new theorems in mathematics nowadays have direct real world applicability.
If AI produced legitimate theoretical breakthroughs at a pace mathematicians are unable to absorb, then the impact will be neutral to negative.
Weird question, do you think AIs might prove a lot of theorems that are mainly useful to other AIs (i.e, make nearly no impact on the human culture of working mathematicians), which then get used to prove results that humans do actually care about?
It seems like if AIs can prove and index a huge number of (largely uninteresting to humans) things there might be sort of "parallel cultures"? Big results are most valuable to humans and AIs both (most context efficient!), but a very large number of less general but still non-obvious results might be an effective approach to solving problems?
> Only a rare few new theorems in mathematics nowadays have direct real world applicability.
I am no mathematician and very naïve about this, but in a world that is rapidly becoming extremely calculation and network dependent that sounds hard to believe.
> If AI produced legitimate theoretical breakthroughs at a pace mathematicians are unable to absorb, then the impact will be neutral to negative.
I think the idea here is that all mathematicians will just be using AI for their future work so they don’t really have to absorb it as long as it’s in the training data.
> > Only a rare few new theorems in mathematics nowadays have direct real world applicability.
> I am no mathematician and very naïve about this, but in a world that is rapidly becoming extremely calculation and network dependent that sounds hard to believe.
I am a mathematician. It is true. The key is we're talking about new theorems, and direct, current real world applicability. Some theorems that have no applicability now may in the future, as theory often precedes applications by a long way and the usefulness is likely to come from other things built on top of the new maths, and a lot of pure maths will never have direct real world applications but contributes to our overall understanding.
The key word in that sentence is “new.” New math is typically explored without expectation of practical use. There are exceptions, but it is generally true.
On the other hand, there are many applied mathematicians and theorists from other fields that mine new maths for applications to their fields. But they are almost always not the ones that come up with the new math.
Historically, of course, mathematics was always driven by the need to explain things. Many of the mathematicians from the 17th and 18th centuries were physicists (or, less commonly, engineers). But for the last hundred years or so that really hasn’t been the case.
Out of interest, what would you estimate the proportion of new maths that is used by other fields to be? Do you think much of this new maths is potentially underutilised as it were?
> Only a rare few new theorems in mathematics nowadays have direct real world applicability.
Has this ever been different?
Math is abstract, rightfully so. It does not have to have direct applicability. Understanding builds over time and applications eventually follow. Number theory used to be a fringe "pure" theory field without applications for the longest time. If we'd only be interested in (and thus fund) what has direct applicability then society would be much worse off.
Side note: I recall my high school class mates rolling their eyes in every math class with "when will I ever need this in my life?" never asking the same question about PE or history or art classes. Now they struggle with their tax return and are routinely getting screwed over by loan sharks. But make no mistake, they can be proud of their A for hitting the goal 5 out of 5 times during soccer in PE class.
That’s not an analyst, that’s a pundit. An analyst can have a clear point of view that is different from yours and, very far off the consensus in any direction. But the value of an analyst is they have a consistent point of view that they apply to any situation and flag as their point of view evolves.
A pundit starts from a pre-declared conclusion and works backwards to generate the argument. An analyst lets the conclusion be dictated by the analysis.
Apple's problem might be they were right too early which is sometimes worse than being wrong. The original vision of Siri was substantively correct in how AI would supercharge our phones but huge parts of the vision got forgotten when Siri was acquired by Apple and the original founders left. The original technical choices around Siri constrained it from evolving into something useful.
A funny story that happened the other day: A friend knew he had to be at dinner at a place across town but he forgot why he had to be at that dinner. While we were waiting for his rideshare to come, he was flipping through every kind of app trying to reconstruct the original context for his appointment.
In theory, this is where AI should shine. He should have been able to say "Hey Siri, pull up all of the info that references tonight's dinner appointment" and AI should be the unified interface into a bunch of app-specific data pools.
But of course he's never in 1 million years would have thought about using Siri to do that because of how bad Siri is.
> I think when LLMs first came out people thought they could just say something like, "Make a Facebook clone". But now we're realizing we need to be more exact with our requirements and define things better. That has always been the bottle neck in software.
This was substantially predicted by Fred Brooks in 1986 in the classic No Silver Bullets [1] essay under the sections "Expert Systems" and "Automatic Programming".
In it, he lays out the core features of vibe coding and exactly the experience we are having now with it: Initial success in a few carefully chosen domains and then a reasonable but not ground breaking increase in productivity as it expands outside of those domains.
The LLMs turn out fully formed clones of stuff for which there exists copious amounts of code openly searchable on the web doing the exact same thing.
LLMs require developer-like specification, task/subtask breakdown and detail where such example code already exists.
As a professional prior to LLMs, how many problems that you work on have many existing free solutions but you neglected to use that code and decided to spend days doing it yourself?
Well put, and same challenge to a lot of these demos & LoC numbers: if you were a pro prior to LLMs, how many of these demos could you fully recreate if you ignored copyright?
I’ve often reimplemented things at work that exist elsewhere. If I could just copy & paste whole solutions from GitHub and change the branding/naming slightly, I could make curl in an afternoon.
> how many problems that you work on have many existing free solutions but you neglected to use that code and decided to spend days doing it yourself?
Only when the existing free solutions are licensed with something like GPL. Now I can just say, write me a C webserver library similar to mongoose and I get the functionality without the license burden.
You might as well have ignored or removed the GPL notice. Running it through the LLM laundering gets you a "fork" of unknown origin, questionable quality. You're still potentially open to supply chain issues but the chain is obfuscated.
And you now own full responsibility for maintenance.
I just vibe coded a socks proxy because existing ones were too thick. And let me tell you, you are absolutely right. Go libraries I’ve never heard of, new implementations that has not been tested.. I think the word for this is YOLO
This is all substantially correct and gives us hints as to where to focus for AI to make the processes go faster.
Eg: I had a product manager say to me that he envisions a future where any meeting with stakeholders that does not result in an interactive prototype by the end of the meeting would be considered a failure. This feels directionally correct to me.
The other thing I expect to see is Vibecoding being the "Excel 2.0" where it allows significant self-serve of building interactive apps that's engaged in a continual war with IT to turn them into something with better security guarantees, proper access control & logging, scalability, change management etc.
But the larger historical point here is that every revolutionary transition produces, in the early stages, "Steam Horses". The invention of the steam engine had people imagining that the future of transportation would involve horse shaped objects, powered by steam, pulling along conventional carts. It wasn't until later developments that we understood the function of transportation as divorced from the form.
I started talking about Steam Horses originally in the context of MOOCs, which was a classic Steam Horse idea.
> he envisions a future where any meeting with stakeholders that does not result in an interactive prototype by the end of the meeting would be considered a failure.
Just learn something like balsamiq. You don't need code to build out a prototype. Just like you don't need actors and a camera when a few sketches can capture a scene.
On a recent weeklong trip to the Philippines, I generated over a 500 page novel's worth of content from AI around various aspects of Filipino history, culture, social dynamics etc. and actually went over it at least 3 times to fully absorb the material.
But if someone handed me even a 3000 word essay on the Philippines clearly written by AI, I would not be able to get to the end of it.
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