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.”
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.
Nah, it is more that LLMs are a neat technology that allows computers to generate stuff on their own. Which has all sort of uses. It has solved the problem of typing big texts on your own. (read: it did not solve the problem of reviewing big texts)
But it has also gaslit managers into thinking it can do much more than its capabilities, so they demand it to be put into everything. With disastrous results.
Exactly. I’ve heard the phrase “falling behind” from many in upper management.
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.
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.
— Carl Sagan
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.
It makes sense for the tech companies to be rushing AI development because they want to be the only one people use. They want to be the Amazon of AI.
A ton of tech companies operate like that. They pump massive investments into projects because they see a future where they have the monopoly and will get their investments out a hundred fold.
The users should be a lot more wary though.
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.
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.
LLMs don’t need to be better. They just need to be more profitable. And wages are very expensive. Doesn’t matter if they lose a couple of customers when they can reduce cost.
It is all part of the enshittification of the company and for the enrichment of the shareholders.
Except LLMs aren’t profitable. They’re propped up by venture capital on the one hand and desperately integrated into systems with no case study on the effects on profit on the other. Video game CEOs are surprised and appalled when gamers turn against AI, implying they did literally no market research before investing billions.
When venture capital dries up and companies have to bear the full cost of LLMs themselves - or worse: if LLM companies go bankrupt and their API goes dead - any company that adopted LLMs into their workflow is going to suffer tremendously. Imagine if they fired half their employees because the LLM does that work and then the LLM stops working. So even if you could lose some money this quarter to invest in it and maybe gain some back by the end of this year, several years from now the company could be under existential threat.
And again, it can be acceptable to take this sort of risk if the technology is one you might at some point not be able to serve customers and business partners without. But LLMs and genAI are not that sort of technology. Maybe business partners will hate you if you don’t go along with the buzzword mania, but then you should fake it and allow it to cause as little damage as it can.
A company that adopts LLMs is not enshittifying, it is setting itself up to be a victim of LLM enshittification.
Shareholders would be richer in the short term if they didn’t waste money investing in LLM adoption, and much richer in the long term if they were one of the few companies that doesn’t go bankrupt when the LLM bubble pops.
The purpose of LLM adoption is to weaken the social-political position of workers, to create an extra rival to break their collective bargaining power even if it costs capital unfathomable amounts of money. Like when capitalists oppose universal basic income despite it massively increasing their profit margins if it were adopted because workers wouldn’t get sick as often, capitalists are fully capable of acting in solidarity with each other for purposes of class warfare, even if it comes at a personal loss.