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People delude themselves if they think LLMs are not useful for coding. People also delude themselves that all code will be AI written in the next 2 years. The reality is that it's incredibly useful tool but with reasonable limits.
Insurers, he said, are already lobbying state-level insurance regulators to win a carve-out in business insurance liability policies so they are not obligated to cover AI-related workflows. "That kills the whole system," Deeks said.
If insurers are going through extreme lengths to remove AI output from the list of things they will insure, this says everything about its future.
Because nothing says “effective risk management achieved” like an insurer signing off on, or forbidding the insurance of, an entire class of materials.
It’s a canary in a coal mine, like how insurers are now removing any ability for Floridians to insure against hurricanes or sea level rise, despite flat earthers screaming their heads off that climate change is a conspiracy and isn’t real.
(Note: I have seen the term “flat earther” starting to be used as a catch-all term for anyone who vehemently denies reality in spite of copious evidence that shows they are wholly and completely wrong)
Yeah these newer systems are crazy. The agent spawns a dozen subagents that all do some figuring out on the code base and the user request. Then those results get collated, then passed along to a new set of subagents that make the actual changes. Then there are agents that check stuff and tell the subagents to redo stuff or make changes. And then it gets a final check like unit tests, compilation etc. And then it's marked as done for the user. The amount of tokens this burns is crazy, but it gets them better results in the benchmarks, so it gets marketed as an improvement. In reality it's still fucking up all the damned time.
Coding with AI is like coding with a junior dev, who didn't pay attention in school, is high right now, doesn't learn and only listens half of the time. It fools people into thinking it's better, because it shits out code super fast. But the cognitive load is actually higher, because checking the code is much harder than coming up with it yourself. It's slower by far. If you are actually going faster, the quality is lacking.
It's like guiding a coked up junior who can write 5000 wpm, has read every piece of documentation ever without understanding any of it.
I code with AI a good bit for a side project since I need to use my work AI and get my stats up to show management that I’m using it. The “impressive” thing is learning new softwares and how to use them quickly in your environment. When setting up my homelab with automatic git pull, it quickly gave me some commands and showed me what to add in my docker container.
Correcting issues is exactly like coding with a high junior dev though. The code bloat is real and I’m going to attempt to use agentic AI to consolidate it in the future. I don’t believe you can really “vibe code” unless you already know how to code though. Stating the exact structures and organization and whatnot is vital for agentic AI programming semi-complex systems.
This is very different from my experience, but I've purposely lagged behind in adoption and I often do things the slow way because I like programming and I don't want to get too lazy and dependent.
I just recently started using Claude Code CLI. With how I use it: asking it specific questions and often telling it exactly what files and lines to analyze, it feels more like taking to an extremely knowledgeable programmer who has very narrow context and often makes short-sighted decisions.
I find it super helpful in troubleshooting. But it also feels like a trap, because I can feel it gaining my trust and I know better than to trust it.
checking the code is much harder than coming up with it yourself
That's always been true. But, at least in the past when you were checking the code written by a junior dev, the kinds of mistakes they'd make were easy to spot and easy to predict.
LLMs are created in such a way that they produce code that genuinely looks perfect at first. It's stuff that's designed to blend in and look plausible. In the past you could look at something and say "oh, this is just reversing a linked list". Now, you have to go through line by line trying to see if the thing that looks 100% plausible actually contains a tiny twist that breaks everything.
AI is a solution in search of a problem. Why else would there be consultants to "help shepherd organizations towards an AI strategy"? Companies are looking to use AI out of fear of missing out, not because they need it.
Exactly. I’ve heard the phrase “falling behind” from many in upper management.
AI is a solution in search of a problem.
The problem being CEOs asking themselves, “how do we acquire labour without having to pay for said labour, in order to maximize our own profit margins?”
AI was always meant to allow wealth to access labour without allowing labour to access wealth.
I, for one, am designing an entire production line of guillotines for when our capitalist system finally collapses. And for those in bunkers: a way of discovering air exchangers and all emergency exits so they can be filled with cement to turn bunkers into tombs. We need an effective method of culling sociopaths from our civilization, after all.
Hahaha. Im guessing this guy works in developer tools. These types of metrics are great but you rarely get there. You will get a few of them but the reality is the same people who want to use AI to produce faster are the same people that won't give you time to properly instrument your system for metrics like these. Good luck with your expectation that someone measures the impact of AI in a meaningful way.
I keep trying to use the various LLMs that people recommend for coding for various tasks and it doesn't just get things wrong. I have been doing quite a bit of embedded work recently and some of the designs it comes up with would cause electrical fires, its that bad. Where the earlier versions would be like "oh yes that is wrong let me correct it..." then often get it wrong again the new ones will confidently tell you that you are wrong. When you tell them it set on fire they just don't change.
I don't get it I feel like all these people claiming success with them are just not very discerning about the quality of the code it produces or worse just don't know any better.
It is possible to get good results, the problem is that you yourself need to have an very good understanding of the problem and how to solve it, and then accurately convey that to the AI.
Granted, I don't work on embedded and I'd imagine there's less code available for AI to train on than other fields.
Yes, I definitely want to train a new hire who is superlatively confident that they are correct, while also having to do my job correctly as well, while said new hire keeps putting shit in my work.
Lowkey I think anyone saying LLMs are useful for work is telling everyone around them their job is producing mostly low quality work and could reasonably be cut.
Recently had to call out a coworker for vibecoding all her unit tests. How did I know they were vibe coded? None of the tests had an assertion, so they literally couldn't fail.
Vibe coding guy wrote unit tests for our embedded project. Of course, the hardware peripherals aren’t available for unit tests on the dev machine/build server, so you sometimes have to write mock versions (like an “adc” function that just returns predetermined values in the format of the real analog-digital converter).
Claude wrote the tests and mock hardware so well that it forgot to include any actual code from the project. The test cases were just testing the mock hardware.
Not realizing that should be an instant firing. The dev didn't even glance a look at the unit tests...
Businesses were failing even before AI. If I cannot eventually speak to a human on a telephone then the whole human layer is gone and I no longer want to do business with that entity.
We never figured out good software productivity metrics, and now we're supposed to come up with AI effectiveness metrics? Good luck with that.
Sure we did.
"Lines Of Code" is a good one, more code = more work so it must be good.
I recently had a run in with another good one : PR's/Dev/Month.
Not only it that one good for overall productivity, it's a way to weed out those unproductive devs who check in less often.
This one was so good, management decided to add it to the company wide catchup slides in a section espousing how the new AI driven systems brought this number up enough to be above other companies.
That means other companies are using it as well, so it must be good.
Generative models, which many people call "AI", have a much higher catastrophic failure rate than we have been lead to believe. It cannot actually be used to replace humans, just as an inanimate object can't replace a parent.
Jobs aren't threatened by generative models. Jobs are threatened by a credit crunch due to high interest rates and a lack of lenders being able to adapt.
"AI" is a ruse, a useful excuse that helps make people want to invest, investors & economists OK with record job loss, and the general public more susceptible to data harvesting and surveillance.
Guy selling ai coding platform says other AI coding platforms suck.
This just reads like a sales pitch rather than journalism. Not citing any studies just some anecdotes about what he hears "in the industry".
Half of it is:
You're measuring the wrong metrics for productivity, you should be using these new metrics that my AI coding platform does better on.
I know the AI hate is strong here but just because a company isn't pushing AI in the typical way doesn't mean they aren't trying to hype whatever they're selling up beyond reason. Nearly any tech CEO cannot be trusted, including this guy, because they're always trying to act like they can predict and make the future when they probably can't.
My take exactly. Especially the bits about unit tests. If you cannot rely on your unit tests as a first assessment of your code quality, your unit tests are trash.
And not every company runs GitHub. The metrics he's talking about are DevOps metrics and not development metrics. For example In my work, nobody gives a fuck about mean time to production. We have a planning schedule and we need the ok from our customers before we can update our product.
I love this bit especially
Insurers, he said, are already lobbying state-level insurance regulators to win a carve-out in business insurance liability policies so they are not obligated to cover AI-related workflows. "That kills the whole system," Deeks said. Smiley added: "The question here is if it's all so great, why are the insurance underwriters going to great lengths to prohibit coverage for these things? They're generally pretty good at risk profiling."
Lmfao
Deeks said "One of our friends is an SVP of one of the largest insurers in the country and he told us point blank that this is a very real problem and he does not know why people are not talking about it more."
Maybe because way too many people are making way too much money and it underpins something like 30% of the economy at this point and everyone just keeps smiling and nodding, and they’re going to keep doing that until we drive straight off the fucking cliff 🤪
This is all fine and dandy but the whole article is based on an interview with "Dorian Smiley, co-founder and CTO of AI advisory service Codestrap". Codestrap is a Palantir service provider, and as you'd expect Smiley is a Palantir shill.
The article hits different considering it's more or less a world devourer zealot taking a jab at competing world devourers. The reporter is an unsuspecting proxy at best.
I wonder if it isn't that AI is good, its that all other software is ass.
I use a patching software, antivirus, and backup software at work and they're all now broken, after being patched. One is a 10.4B dollar company with a critical bug.
So is this just early adaptation problems? Or are we starting to find the ceiling for Ai?
The "ceiling" is the fact that no matter how fast AI can write code, it still needs to be reviewed by humans. Even if it passes the tests.
As much as everyone thinks they can take the human review step out of the process with testing, AI still fucks up enough that it's a bad idea. We'll be in this state until actually intelligent AI comes along. Some evolution of machine learning beyond LLMs.
We just need another billion parameters bro. Surely if we just gave the LLMs another billion parameters it would solve the problem…
One smoldering Earth later….
I realized the fundamental limitation of the current generation of AI: it's not afraid of fucking up. The fear of losing your job is a powerful source of motivation to actually get things right the first time.
And this isn't meant to glorify toxic working environments or anything like that; even in the most open and collaborative team that never tries to place blame on anyone, in general, no one likes fucking up.
So you double check your work, you try to be reasonably confident in your answers, and you make sure your code actually does what it's supposed to do. You take responsibility for your work, maybe even take pride in it.
Even now we're still having to lean on that, but we're putting all the responsibility and blame on the shoulders of the gatekeeper, not the creator. We're shooting a gun at a bulletproof vest and going "look, it's completely safe!"
fear of losing your job is a powerful source of motivation
I just feel good when things I make are good so I try to make them good. Fear is a terrible motivator for quality
Its early adoption problems in the same way as putting radium in toothpaste was. There are legitimate, already growing uses for various AI systems but as the technology is still new there's a bunch of people just trying to put it in everything, which is innevitably a lot of places where it will never be good (At least not until it gets much better in a way that LLMs fundementally never can be due to the underlying method by which they work)
This feels like an exercise in Goodhart's Law: Any measure that becomes a target ceases to be a useful measure.
