Excerpt:
“Even within the coding, it’s not working well,” said Smiley. “I’ll give you an example. Code can look right and pass the unit tests and still be wrong. The way you measure that is typically in benchmark tests. So a lot of these companies haven’t engaged in a proper feedback loop to see what the impact of AI coding is on the outcomes they care about. Lines of code, number of [pull requests], these are liabilities. These are not measures of engineering excellence.”
Measures of engineering excellence, said Smiley, include metrics like deployment frequency, lead time to production, change failure rate, mean time to restore, and incident severity. And we need a new set of metrics, he insists, to measure how AI affects engineering performance.
“We don’t know what those are yet,” he said.
One metric that might be helpful, he said, is measuring tokens burned to get to an approved pull request – a formally accepted change in software. That’s the kind of thing that needs to be assessed to determine whether AI helps an organization’s engineering practice.
To underscore the consequences of not having that kind of data, Smiley pointed to a recent attempt to rewrite SQLite in Rust using AI.
“It passed all the unit tests, the shape of the code looks right,” he said. It’s 3.7x more lines of code that performs 2,000 times worse than the actual SQLite. Two thousand times worse for a database is a non-viable product. It’s a dumpster fire. Throw it away. All that money you spent on it is worthless."
All the optimism about using AI for coding, Smiley argues, comes from measuring the wrong things.
“Coding works if you measure lines of code and pull requests,” he said. “Coding does not work if you measure quality and team performance. There’s no evidence to suggest that that’s moving in a positive direction.”
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.
It IS working well for what it is - a word processor that’s super expensive to run. It’s because there idiots thought the world was gonna end and that we were gonna have flying cars going around.
I find this hard to believe, unless it’s talking about 100% vibecoding
Yeah it is, it brings up a lot of good points that often don’t get talked about by the anti-AI folks (the sky is falling/AI is horrible) and extreme pro-AI folks (“we’re going to replace all the workers with AI”)
You absolutely have to know what the AI is doing at least somewhat to be able to call it out when it’s clearly wrong/heading down a completely incorrect path.
recent attempt to rewrite SQLite in Rust using AI
I think it is talking 100% vibe code. And yea it’s pretty useful if you don’t abuse it
Yeah, it’s really good at short bursts of complicated things. Give me a curl statement to post this file as a snippet into slack. Give me a connector bot from Ollama to and from Meshtastic, it’ll give you serviceable, but not perfect code.
When you get to bigger, more complicated things, it needs a lot of instruction, guard rails and architecture. You’re not going to just “Give me SQLite but in Rust, GO” and have a good time.
I’ve seen some people architect some crazy shit. You do this big long drawn out project, tell it to use a small control orchestrator, set up many agents and have each agent do part of the work, have it create full unit tests, be demanding about best practice, post security checks, oroborus it and let it go.
But it’s expensive, and we’re still getting venture capital tokens for less than cost, and you’ll still have hard-to-find edge cases. Someone may eventually work out a fairly generic way to set it up to do medium scale projects cleanly, but it’s not now and there are definite limits to what it can handle. And as always, you’ll never be able to trust that it’s making a safe app.
Yea I find that I need to instruct it comparably to a junior to do any good work…And our junior standard - trust me - is very very low.
I usually spam the planning mode and check every nook of the plan to make sure it’s right before the AI even touches the code.
I still can’t tell if it’s faster or not compared to just doing things myself…And as long as we aren’t allocated time to compare end to end with 2 separate devs of similar skill there’s no point even trying to guess imho. Though I’m not optimistic. I may just be wasting time.
And yea, the true costs per token are probably double than what they are today, if not more…
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.
Yeah, it’s a bad idea to let AI write both the code and the tests. If nothing else, at least review the tests more carefully than everything else and also do some manual testing. I won’t normally approve a PR unless it has a description of how it was tested with preferably some screenshots or log snippets.
That’s weird. I’ve made it write a few tests once, and it pretty much made them in the style of other tests in the repo. And they did have assertions.
Trust with verification. I’ve had it do everything right, I’ve had it do thing so incredibly stupid that even a cursory glance at the could would me more than enough to /clear and start back over.
claude code is capable of producing code and unit tests, but it doesn’t always get it right. It’s smart enough that it will keep trying until it gets the result, but if you start running low on context it’ll start getting worse at it.
I wouldn’t have it contribute a lot of code AND unit tests in the same session. new session, read this code and make unit tests. new session read these unit tests, give me advice on any problems or edge cases that might be missed.
To be fair, if you’re not reading what it’s doing and guiding it, you’re fucking up.
I think it’s better as a second set of eyes than a software architect.
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…
Hahaha 🤣
if you reject her pull requests, does she fix it? is there a way for management to see when an employee is pushing bad commits more frequently than usual?
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.
“Codestrap founders”
Let me guess they will spearhead the correct way to use AI?
If they help bring reality into the conversation that’s fine by me.
I once saw someone sending ChatGPT and Gemini Pro in a constant loop by asking “Is seahorse emoji real?”. That responses were in a constant loop. I have heard that the theory of “Mandela Effect” in this case is not true. They say that the emoji existed on Microsoft’s MSN messenger and early stages of Skype. Don’t know how much of it is true. But it was fun seeing artificial intelligence being bamboozled by real intelligence. The guy was proving that AI is just a tool, not a permanent replacement of actual resources.
Ask it which is heavier: 20 pounds of gold or 20 feathers.
They could be dinosaur feathers, weighting a pound each 🤷♂️
That was working the same in gpt one month ago. Do it, it is incredibly fun to see yourself.
it sure works well for slop marketers taking A/B testing to the new level of pointlessness.
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.”
these types of articles aren’t analyzing the usefulness of the tool in good faith. they’re not meant to do a lot of the things that are often implied. the coding tools are best used by coders who can understand code and make decisions about what to do with the code that comes out of the tool. you don’t need ai to help you be a shitty programmer
they are analyzing the way the tools are being used based on marketing. yes they’re useful for senior programmers who need to automate boilerplate, but they’re sold as complete solutions.
Exactly. This reads like people are prompting for something then just using that code.
The way we use it is as a scaffolding tool. Write a prompt. Then use that boiler plate to actually solve the problem you’re trying to solve.
You could say the same for people using Stackoverflow, you don’t just blindly copy and paste.
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.
Why is it always the dumbest people who become managers?
The others are busy working, they don’t have time to waste drinking coffee with execs
Yes it does not work right! also there are no new discoveries made by AI, we only see chat bots, self driving cars, automation in workplace, yet no discoveries. At some point I thought AI will help us solve cancer or way to travel in space, yet billionaires think of money.
Tell me that negative, tell that an idiot, but the only thing I see people profiting now that they can, and letter on nothing will happen.Yes it does not work right!
I agree.
also there are no new discoveries made by AI, we only see chat bots, self driving cars, automation in workplace, yet no discoveries. At some point I thought AI will help us solve cancer or way to travel in space, yet billionaires think of money.
We aren’t there yet.
AI and research around it started, or rather really took off, around 2018 (at least relating to what we mean by AI today; ruled based approaches existed much longer). It is very much a new field, considering most other fields existed for over 30 years at this point.Transformers, the current architecture of most models and what we consider when we speak of “AI”, started with a paper in 2017. It is very much new ground, considering the fundamentals behind it are much older. And well, to be pedantic, large language models aren’t really AI because there is no intelligence. They are just generating output that is the most probable continuation of the input and context provided. So yeah, “AI” cannot really research or make new discoveries yet. There may very well be a time, where AI helps us solve cancer. It definitely isn’t today nor tomorrow.I also don’t think that billionaries make money with AI. I mean, if you look at OpenAI: they are actually burning money, at a fast rate measured in billions. They are believed to turn a profit in 2030. Without others investing in it, they would be long gone already. The people with money believe that OpenAI and other companies related to AI will someday make the world changing discovery. That could very well lead to AI making discoveries on its own AND to lots of money. Until then, they are obviously willing to burn a tremendous amount of money and that is keeping OpenAI in particular alive at this moment. Only time will tell what happens next. I keep my popcorn ready, once the bubble bursts :D
Edit: Connected AI making discoveries to lots of money gained or rather saved. That is the sole reason for investments from people with big money.
Edit 2: Clarified what I meant exactly by AI. Thanks everyone for pointing it out.Neural networks existed since the 1970s.
Yes, I meant the current state-of-the-art architecture by the term “AI” and partly the boom thereafter. The field “AI” is obviously much older. Sorry for that and thanks for pointing it out.
I took a class in what is ultimately the current approach in AI and Machine learning in 2002 using textbooks that had their first editions in the 90s. The field is in reality 30 years old.
I wrote that part from memory and meant the current state-of-the-art architecture, which most of the models are based on now, instead of the whole field. It is actually a bit older than that. AI as academic discipline was established in 1956, so it is about 70 years old. Though you would not consider much of it useful relating to independently making discoveries. I should have read up on it beforehand. Sorry for that and thanks for pointing out.
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.
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.
I’ve mentioned the long-term effects I see at work in several places, but all I can say is be very careful how you use it. The parts of our codebase that are almost entirely AI written are unreadable garbage and a complete clusterfuck of coding paradigms. It’s bad enough that I’ve said straight to my manager’s face that I’d be embarassed to ship this to production (and yes I await my pink slip).
As a tool, it can help explain code, it can help find places where things are being done, and it can even suggest ways to clean up code. However, those are all things you’ll also learn over time as you gather more and more experience, and it acts more as a crutch here because you spend less time learning the code you’re working with as a result.
I recommend maintaining exceptional skepticism with all code it generates. Claude is very good at producing pretty code. That code is often deceptive, and I’ve seen even Opus hallucinate fields, generate useless tests, and misuse language/library features to solve a task.
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 feels like an exercise in Goodhart’s Law: Any measure that becomes a target ceases to be a useful measure.
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.
The problem is that code is hard to write. AI just doesn’t solve it. This is opposite of crypto, where the product is sort of good at what it does, (not bitcoin, though), but we don’t actually need to do that.
Exactly. I’ve heard the phrase “falling behind” from many in upper management.
When I entered the workforce in the late '90s, people were still saying this about putting PCs on every employee’s desk. This was at a really profitable company. The argument was they already had telephones, pen and paper. If someone needed to write something down, they had secretaries for that who had typewriters. They had dictating machines. And Xerox machines.
And the truth was, most of the higher level employees were surely still more profitable on the phone with a client than they were sitting there pecking away at a keyboard.
Then, just a handful of years later, not only would the company have been toast had it not pushed ahead, but was also deploying BlackBerry devices with email, deploying laptops with remote access capabilities to most staff, and handheld PDAs (Palm pilots) to many others.
Looking at the history of all of this, sometimes we don’t know what exactly will happen with newish tech, or exactly how it will be used. But it’s true that the companies that don’t keep up often fall hopelessly behind.
“But the fact that some geniuses were laughed at does not imply that all who are laughed at are geniuses. They laughed at Columbus, they laughed at Fulton, they laughed at the Wright brothers. But they also laughed at Bozo the Clown.”
— Carl Sagan
I think that’s called a cargo cult. Just because something is a tech gadget doesn’t mean it’s going to change the world.
Basically, the question is this: If you were to adopt it late and it became a hit, could you emulate the technology with what you have in the brief window between when your business partners and customers start expecting it and when you have adapted your workflow to include it?
For computers, the answer was no. You had to get ahead of it so companies with computers could communicate with your computer faster than with any comptetitors.
But e-mail is just a cheaper fax machine. And for office work, mobile phones are just digital secretaries+desk phones. Mobile phones were critical on the move, though.
Even if LLMs were profitable, it’s not going to be better at talking to LLMs than humans are. Put two LLMs together and they tend to enter hallucinatory death spirals, lose their sense of identity, and other failure modes. Computers could rely on a communicable standards, but LLMs fundamentally don’t have standards. There is no API, no consistent internal data structure.
If you put in the labor to make a LLM play nice with another LLM, you just end up with a standard API. And yes, it’s possible that this ends up being cheaper than humans, but it does mean you lose out on nothing by adapting late when all the kinks have been worked out and protocols have been established. Just hire some LLM experts to do the transfer right the first time.
If AI is so good at what it does, then it shouldn’t matter if you fall behind in adopting it… it should be able to pick up from where you need it. And if it’s not mature, there’s an equally valid argument to be made for not even STARTING adoption until it IS - early adopters always pay the most.
There’s practically no situation where rushing now makes sense, even if the tech eventually DOES deliver on the promise.
Yes but counterpoint: give me your money.
… or else something bad might happen to you? Sadly this seems the intellectual level that the discussion is at right now, and corporate structure being authoritarian, leans towards listening to those highest up in the hierarchy, such as Donald J. Trump.
“Logic” has little to do with any of this. The elites have spoken, so get to marching, NOW.
