2,850 karma · joined May 25, 2012
Its like making a jig in woodworking. The measure of the jig's success is not whether it gets re-used or widespread adoption, its whether it made it easier to achieve some actual objective. Because the jig is a means to some other end.
Lots of these "we wouldn't have done it otherwise" applications are means, not ends.
Here's an example: my office has a few cars for employees to use as rentals. The number is small enough that it would never be worth any serious software dev to build a tool to manage, but large enough that its a moderate amount of work for someone to manage the requests/getting supervisor approvals/schedule changes due to breakdowns.
AI one-shot that guy a tool. Its now dead simple, he's got a calendar, automatic emails going to people's supervisors with click-here-to-approve links, rescheduling options, fleet management. It doesn't even look bad.
Who cares if its using some un-backed-up sqlite database in the backend, has some placeholder tab for a feature he changed his mind about, or violates the DRY principles a bunch or uses some inferior authentication mechanism. Its an in house tool, isn't mission critical, and it makes his life significantly easier.
Basically everyone is now a few prompts away from their own bespoke tools, and only they will be able to judge the benefit thereof.
Edit: to tie this more directly to the article, I would argue that this is an example of "infinity-x" coding, because the user was in fact not capable of coding a solution on their own without AI.
https://mcfunley.com/whom-the-gods-would-destroy-they-first-...
But in reality, a lot of corporate software exists just because there are plenty of companies who are afraid of owning code. They don't want to maintain any in-house coding skills, and therefore are willing to buy literally any vaguely-relevant CRUD app that the manager heard about at the conference. I don't think replacing that class of software with vibe coded alternatives will be any worse, because the bar is starting on the floor.
There are entire software categories that consist entirely of code that is only one or two evolutionary steps away from some engineer's spreadsheet originally written in 1995. One fine example I work with has changed its backend database 3 times in the past 4 years. Their most recent decision to use mongodb came with the questionable decision to store json as a raw string literals complete with bizarre escaping inside a database literally designed to store json-shaped-objects.
I don't think Opus could store data that poorly, even if the end user prompting it didn't know what they were doing.
In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week.
The impacts are here, they're just not evenly distributed yet.
I think the history of children's literature may be shorter than you think.
https://www.12000.org/my_notes/CAS_ode_tests/index.htm
He does a few other side-by-side comparisons but doesn't include open source engines in them.
https://www.12000.org/my_notes/CAS_integration_tests/reports...
https://www.12000.org/my_notes/CAS_integration_tests/reports...
Note that alternative open source solvers like Fricas fail 10x the integrals in that corpus.
I consider these scenarios:
1) We stumble onto an algorithmic improvement in intelligence. This isn't just "what humans do but faster", its "better than what humans do". I've got no idea what that might mean (it could be fundamentally different heuristics, it could be that we've got some intellectual blind spot that they cast off). It doesn't matter, the instant this happens AI is smarter than us and we won't be able to keep up. We're intelligencing at O(n^2) and they're doing O(n log(n)).
2) AI gets good enough at physics and engineering that they can really quickly use up all "the room at the bottom" as Feyman put it. They design and build a factory that produces a mystery metal amalgam that computes at some small percentage of the minimum predicted by the Landauer principle, within a few percent of Bremermann's limit. It's not "smarter" its just suddenly tens-of-orders of magnitude faster. But those orders of magnitude matter: there's only 8 billion of us, and there's plenty more than a factor of 10 billion "at the bottom".
3) It turns out that this is a "sum is greater than the parts" situation. No human can be an expert in all subjects, but we eventually build a big enough AI that it is. Turns out, you don't need extreme speed or different algorithms, just knowing everything all at once is enough to catapult AI dramatically beyond our grasp. Always knowing the best statistical test to apply, the best mathematical techniques, and relevant physics means that AI never makes a mistake, and can learn with maximum efficiency.
A system which will just randomly decide to give the legal team reasons to not back you up is:
* A system whose output will get brought up in lawsuits and make legal's job harder.
* A system that will make the dev team perpetually chase its tail while it oscillates between the several different valid interpretations of the rules.
The lab tested for chromium in two ways: one test (ICP) measures all chromium of any kind, and the other measures hexavalent chromium specifically. The ICP test returned a concentration that was an order of magnitude smaller than the hexavalent test (0.0003 vs 0.0104 mg/L). That is to say, the tests contradict each other (because the whole is smaller than the part).
https://www.documentcloud.org/documents/28055380-j2673-1-uds...
The lab tested for chromium in two ways: one test (ICP) measures all chromium of any kind, and the other measures hexavalent chromium specifically. The ICP test returned a concentration that was an order of magnitude smaller than the hexavalent test. That is to say, the tests contradict each other (because the whole is smaller than the part), and are both at the bottom of range for the tests performed.
https://www.documentcloud.org/documents/28055380-j2673-1-uds...
I look at some truly impressive projects like CLASP which sprang into existence not because of someone noodling around, but because they had a bigger goal which required the team build it.
So my advice to any mathematician who feels lost, like they don't know what to work on, would be to go collaborate with someone who has an actual goal, to look for inspiration in the kinds of math they need.
Today, there are a lot of opportunities to jump forward that only get capitalized on through coincidence (e.g. two people bump into each other at a conference, or researcher happens to have a colleague working on a related problem through the lens of a different discipline). If AI does nothing but guarantee that everyone will have such a coincidence by serving as that expert from a different discipline, that will still be a massive driving force for progress.
The question of "whats a mathematician to do" is still clear: you need to find and curate and clearly express interesting and valuable problems.
Where do these "application errors" occur if neither on a server nor a client?
I think the reality is that management sees "5xx means server error, so our team's KPI is now server error rate, the lower the better!" Then the team just stops using 500 errors as much as possible. They probably justify it with things like "well, such and such problem isn't our fault so its not really a server error." This kind of thinking is perverting the intent of 5xx messages. They are supposed to indicate any failure to handle the request that happens on the server, NOT measure whether the dev team is making a good application.
I work with a team that does stuff like this, returning a 200 and a body containing "error: I didn't do what you said because _insert error here_"
The problem is that you returned OK instead of ERROR when things were not OK and there was an ERROR.
Its a design that smells of teams trying to hit some kind of internal metrics by slightly deceptive means.
Everyone was going around acting like this meant 50% of 2nd graders were stupid with terrible parents. (Or, conversely, that 50% of 2nd graders were geniuses for "knowing" it was potatoes at all)
But I think that was the wrong conclusion.
The right conclusion was that all the kids guessed and they had a 50% chance of getting it right.
And I think there is probably an element of this going on with the small models vs big models dichotomy.
You could in principle design systems with enough fouling mitigations that you'd be fine, but its likely that the cost of those mitigations is roughly the same as just purifying the water up-front.