the guidance / guardrails, agent roles, etc are the critical thing now, it's kind of like meta programming AI to get the outcomes you want. Any problems need addressing at this level. You don't need to understand all the code, but you should understand how the system is building up and adjust as needed. This would be an architectural review process you go through with AI. Features to code that should plug into existing architectural structures are generally no problem. It's just boilerplate code AI is generating. But architecture is only one aspect, you should have lots of guidance for all kinds of thing. There's security, performance, correctness, QA, Ux, Deployment, Process, Monitoring etc and having feedback systems for everything. You keep refining and reviewing your guidance and the impact it is having. As well as this you are molding the product for the things you want via feature development. What I'm not sure of is if you are some cog somewhere in a company like Meta whether whatever slice you become responsible for becomes a bit meh if you are mostly just prompting a feature and not much more. As a developer in a smaller company (we are global with a smallish tech team) I get to do a lot of things from interacting with our customers through to the entire backend and get to make critical decisions about nearly everything. Nearly everything in the company is starting to be driven via guidance and feedback loops (including on the guidance itself).
I think a lot of these reactions by mathematicians are more akin to group theory than anything else. Reality is that things will naturally sort themselves out and there will be some role mathematicians play in all of it (most likely). To me it seems the timeline that some skill essentially gets retired such that if you want to keep doing it is more like "historical re-enactment" is getting quite short. Things like manually coding something is going to be a hobby. Some aspects of math are going to be a hobby, and professional math will be all about pushing up against the edge of what we can do mathematically with AI.
my guess would be virtually all games are going to made with AI assitance. People are going to have to get over their AI phobia and just judge the quality of the result, there's awful things and good things.
Interestingly, a number of my non coder gamer friends are having a lot of fun lately making their own games using AI.
i have a good answer. you can create a game from scratch with ai, but that will take much longer, cost more, and be buggier, because about half the code is usually the engine itself.
so not only is it a ton of code to write but it's a lot to debug and test. that said, it's a lot of fun writing your own engine from scratch if you are into that kind of fun. but if you want to make a game, why write the engine also?
not in my experience, might cost more in the initial setup possibly (well within my plans limits anyways), then after that modifying and building the game is really incremental the AI game engine seems way more adaptable a fit for purpose. Costly thing with games (in terms of tokens) is generally dealing with images.
It doesn't match my experience. We are producing more software, and being a lot more ambitious about the things we develop. The trick is mostly to do with getting good guidance in place and then you can spend a lot more time iterating the product itself. I spend a lot more time play testing the product now and trying different things / refining. However, there is a lot of previous work that's been done to create a huge amount of technical guidance on how to do things. Lots of things on Specification, Design/Architecture/Coding, Performance, Testing, QA testing, etc. I think getting all this guidance/tooling/process in place is critical to producing software.
I think it highly depends on what field you're in. Personal projects that I find useful in my everyday life? Churning them out like crazy. Enterprise software? It has slowed our productivity down to a halt--part of this is due to people not knowing how to use these tools, and the other part of it is the collective AI psychosis of our leadership (I work at a Fortune 500 software company).
Do you think its enterprise software? or the team? I work on a product ( https://www.mywildeye.com/ ) but we do a lot of back end enterprisey type stuff to manage our logistics / manufacturing as well. We are a small dev team with a lot of latitude to make decisions. For us, AI allows a dev to deal with a lot more things. We also churn out internal tools like crazy. Our Hardware, Firmware, Accounts, and Support people are all creating tools. Our CEO (former developer) is also getting back to making things, we recently released a free ios app https://epicfields.app/ so farmers can monitor water demand using free satellite data.
Very Large Enterprise is very hard to change, or rewrite, especially old systems. Our company mostly sells contracts to large corporations / government institutions (1500+ employees), with an insane amount of transactions, functionality, etc. Even with the latest models you cannot automate everything, the world is far too complex, and there is quite a bit of pushback when anything changes.
In order to keep some of these extremely large contracts (using Samsung as an example here) for example, we had to revert certain changes that were global but now we have a "Samsung version" because we want to maintain a large presence in Asia and Samsung leaving would be a huge blow.
At this scale, code isn't even the bottleneck, it's customer wants, politics, etc.
I started on the internet in 1991... used ftp, usenet, email, irc, telnet, gopher.
gopher is the one that seems really dead. ftp is still pretty common, usenet is still active (though maybe just for "Linux ISOs"), telnet lost its main use which was for remote shells and muds/moos but still use it from time to time for random devices that support it. IRC only had 400 or so active people on it at any given time. I haven't really used it in the last decade. The web destroyed gopher almost instantly.
I think its valuable for mathematicians to think through how AI changes things for them. I think it's a bit premature to know exactly the impact AI will have. Math is one field that I feel will just naturally sort itself out without trying to predict or prescribe how it should work, it's a bit like software development, you adopt it in and see where it takes you. The presentation then sort of tries to argue for human understanding because AI might optimize for the wrong thing. That may well be what we need for the immediate future, but it's hard to know how things will play out. It might be such that it will become more important to people that AI understands something. ie, Does AI, with access to all current medical knowledge, think this cancer drug will be effective and safe? or has only humans said it's ok? At the moment we are in the chaos of change and its going to take a while for things to settle.
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