We tried it. DOGE was a complete failure of tech startup idiots asking models questions as if they were oracles and blindly trusting them to make subjective decisions about which Congressionally-created programs we should kill. Some of the tech were smart enough to realize how much damage they did and got out quickly.
FOMO, US tech monoculture, complicit tech media hyping AI, actual religious AI believers, C-suites looking for short time gains, fear of investors‘ backlash, etc
At least in my company the CFO came back after talking with other CFOs and then used Lovable to build an app. He then mentioned to his immediate team who then picked it and started running with it. It is now one of the yearly goals. The fun part is when it came time to put money where their mouth is they say the company has no funding. So more FOMO.
I'm not convinced that they needed to be conned. That assumes that they're normally able to correctly make this type of decision without a dedicated effort to trick them. (Not saying there wasn't any dedicated effort, just that they're capable of making decisions with similarly poor judgment on their own)
my company spends millions a year on tokens and when asked about ROI the CTO just says "LoC is up! LoC isn't a good measure of productivity but it's a measure, right? right?"
They were conned because there’s been a massive top down propaganda campaign at the highest levels of corporate America that GAI is right around the corner.
For years many in management believed our value to the company was "just" in our ability to produce code. You could see it from how they would "resource" projects and write job descriptions and manage. The output of the job, to them, was code written / bugs fixed / features implemented. In organizations like this, software was a cost centre, and it was treated that way.
LLMs can write code. They're actually pretty good at it. So problem solved, right? Cost centre cost reduction. Bam!
In reality the more competent in the job were really good at understanding business problems and holding domain specific knowledge, working with the other people on the team to translate that into a problem a computer could solve, and with understanding and diagnosing what was happening in the broader system, not just in a "program."
Someone needs to write the prompts given to the LLMs and decide if what they came back with even makes any sense. Someone needs to respond to pages in the middle of the night. Someone needs to be able to look at the system and have a bigger picture understanding of how it fits with the business' needs, etc. etc. That's a software engineer.
I honestly think not enough in middle and upper management really understand what software development actually is.
> For years many in management believed our value to the company was "just" in our ability to produce code.
Yeah, this is nuts because at every company I've worked at it's assumed that engineers are thinking about things like product market fit, how a feature would be sold/ the "value" of the feature itself, how we would support the feature (not just the code, but how support would manage it), etc.
I don't think people realize how much of a hand engineers have in these conversations because we don't champion that, but we think a lot about the product as a whole. Obviously we don't spend as much time thinking about how the product will be sold as a sales person will, but we absolutely think about it, in my experience.
We think a lot about the business, like a massive amount about the system as a whole across these organizational boundaries.
This comes across strongly any time you hear management talking about "fungibility of engineers". Everyone is a full stack everything engineer, and AI makes that even easier for them to trick themselves into believing.
If anything, I feel like AI has made domain expertise more important, not less, as the "confidently wrong" error case for agents has no one able to sanity check it. At least before AI a human would dip their toe in the water and usually realize that having no idea what they were doing, and not even being able to understand what the comments mean, was a sign that they need to go find someone more experienced to help.
It's almost as if success in business has nothing to do with creating actual value in our society, but instead engaging in a death cult ideology of share value maximization, and that means that reasonable people are out competed in this social system by brain dead ideologues or something.
Maybe it’s not a binary? Maybe managers should both be able to delegate AND occasionally put in the effort to learn how things are working on the ground? Otherwise after about 3 layers of hierarchy all of the signal is gone in a massive game of telephone, leaving high level executives completely clueless.
Delegation does not have to be because of a lack of knowledge. In fact, it seems like if one delegates for this reason its probably a sign of trouble to come. We delegate because of lack of time.
I guess its impossible for an executive to know ALL the details of the work they delegate, but I'd be willing to wager that executives who understand the details function better in the long run.
It certainly isn't tautological that executives be imbeciles about the businesses they run.
I have mostly enjoyed AI programming and I do like using Codex. The truth is that it sometimes makes me more way more productive, but not usually. Many days are spent writing specs and babysitting prompts and it can suck. Even expensive Codex 5.4/5.5 with high thinking writes code that is just ... lazy. It takes a lot of work to get it to write excellent code. It's definitely a full time job all by itself.
I'm not talking about rocket scientist code either - I'm talking about things using raw for( instead of range-based for, or writing code that is absolutely fucking riddled with imperative logic, hacks, and kludges, when something should clearly be data-driven. Stuff that is so bad I have to tell it to start over. It routinely designs amazing architecture and absolute shit architecture, sometimes on the same day. It's just so weirdly inconsistent. If you ask it to fix a bug then you have to double check if it used a hack and sometimes it will admit to it. Sometimes it lies.
I just do not see how AI is going to replace large numbers of seasoned engineers. That would be a disaster for companies that try it. Could it replace large numbers of juniors? Yes. And maybe I am being fantastically naive. I'm 100% willing to concede that it's possible or even likely.
While being slow to pass judgment or disregard an approach is a valuable trait in a senior Eng, I think 3 years is plenty of time to wait for proof of concept to pan out. It’s not panning out, it doesn’t seem on the verge of panning out, and soon the real cost is going to be passed on and the subsidies will end. LLMs see ripe to be the new IDEs, but not the new Engineers
Problems reported in the first 90 days of ownership. Overwhelming minor niggling complaints like a piece of trim making a noise or even just misunderstandings about systems.
American automakers love crowing about that survey because it's easy to do well on. And then the car falls to shit six months later, but hey, it held together for the first 90 days so all good.