It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I’m envisioning a world where multiple AI engines create and check each others’ work… the first thing they need to make work to support that scenario is probably fusion power.
It really depends on the context. Sometimes there are domains which require solving problems in NP, but where it turns out that most of these problems are actually not hard to solve by hand with a bit of tinkering. SAT solvers might completely fail, but humans can do it. Often it turns out that this means there’s a better algorithm that can exploit commanalities in the data. But a brute force approach might just be to give it to an LLM and then verify its answer. Verifying NP problems is easy.
Maybe it is because I started out in QA, but I have to strongly disagree. You should assume the code doesn’t work until proven otherwise, AI or not. Then when it doesn’t work I find it is easier to debug you own code than someone else’s and that includes AI.
I’ve been R&D forever, so at my level the question isn’t “does the code work?” we pretty much assume that will take care of itself, eventually. Our critical question is: “is the code trying to do something valuable, or not?” We make all kinds of stuff do what the requirements call for it to do, but so often those requirements are asking for worthless or even counterproductive things…
Literally the opposite experience when I helped material scientists with their R&D. Breaking in production would mean people who get paid 2x more than me are suddenly unable to do their job. But then again, our requirements made sense because we would literally look at a manual process to automate with the engineers. What you describe sounds like hell to me. There are greener pastures.
Yeah, sometimes the requirements write themselves and in those cases successful execution is “on the critical path.”
Unfortunately, our requirements are filtered from our paying customers through an ever rotating cast of Marketing and Sales characters who, nominally, are our direct customers so we make product for them - but they rarely have any clear or consistent vision of what they want, but they know they want new stuff - that’s for sure.
That hasn’t been my experience, but it sounds like good advice anyway. My experience has been that the more profitable the parent company, the better the job security and the better the pay too. Once “in,” tune in to the culture and align with the people at your level and above who seem like they’ll be sticking around long term. If the company isn’t financially secure, all bets are off and you should be seeking, and taking, a better offer when you can find one.
I knocked around startups for 10/22 years (depending on how you characterize that one 12 year gig that ended with everybody laid off…) The pay was good enough, but job security just wasn’t on the menu. Finally, one got bought by a big fish and I’ve been in the belly of the beast for 11 years now.
It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I’m envisioning a world where multiple AI engines create and check each others’ work… the first thing they need to make work to support that scenario is probably fusion power.
I usually write 3x the code to test the code itself. Verification is often harder than implementation.
It really depends on the context. Sometimes there are domains which require solving problems in NP, but where it turns out that most of these problems are actually not hard to solve by hand with a bit of tinkering. SAT solvers might completely fail, but humans can do it. Often it turns out that this means there’s a better algorithm that can exploit commanalities in the data. But a brute force approach might just be to give it to an LLM and then verify its answer. Verifying NP problems is easy.
(This is speculation.)
Yes, but the test code “writes itself” - the path is clear, you just have to fill in the blanks.
Writing the proper product code in the first place, that’s the valuable challenge.
Maybe it is because I started out in QA, but I have to strongly disagree. You should assume the code doesn’t work until proven otherwise, AI or not. Then when it doesn’t work I find it is easier to debug you own code than someone else’s and that includes AI.
I’ve been R&D forever, so at my level the question isn’t “does the code work?” we pretty much assume that will take care of itself, eventually. Our critical question is: “is the code trying to do something valuable, or not?” We make all kinds of stuff do what the requirements call for it to do, but so often those requirements are asking for worthless or even counterproductive things…
Literally the opposite experience when I helped material scientists with their R&D. Breaking in production would mean people who get paid 2x more than me are suddenly unable to do their job. But then again, our requirements made sense because we would literally look at a manual process to automate with the engineers. What you describe sounds like hell to me. There are greener pastures.
Yeah, sometimes the requirements write themselves and in those cases successful execution is “on the critical path.”
Unfortunately, our requirements are filtered from our paying customers through an ever rotating cast of Marketing and Sales characters who, nominally, are our direct customers so we make product for them - but they rarely have any clear or consistent vision of what they want, but they know they want new stuff - that’s for sure.
When requirements are “Whatever” then by all means use the “Whatever” machine: https://eev.ee/blog/2025/07/03/the-rise-of-whatever/
And then look for a better gig because such an environment is going to be toxic to your skill set. The more exacting the shop, the better they pay.
That hasn’t been my experience, but it sounds like good advice anyway. My experience has been that the more profitable the parent company, the better the job security and the better the pay too. Once “in,” tune in to the culture and align with the people at your level and above who seem like they’ll be sticking around long term. If the company isn’t financially secure, all bets are off and you should be seeking, and taking, a better offer when you can find one.
I knocked around startups for 10/22 years (depending on how you characterize that one 12 year gig that ended with everybody laid off…) The pay was good enough, but job security just wasn’t on the menu. Finally, one got bought by a big fish and I’ve been in the belly of the beast for 11 years now.