Posted by alanwreath 8 hours ago
I'm not sure this story is illustrative of that, when you have a VP of engineering saying “Over prior years, we didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles.”
He's saving face while almost certainly trying to figure out how to make the new systems work so that next time he won't need to rehire engineers.
Yup. They jumped the gun. Now they need to hire them back so they can loot their expertise and never hire another senior. I'm not saying this will work, but it's pretty obviously the plan.
Now, that training[*] will be for both AI models and lower-salaried hires.
Perhaps a second mistake by those who thought they didn't need their most experienced people: Now they think they just need to train the AI better, and then new-grad "AI native" hires will be the most cost-effective way to operate/oversee the AI and do whatever it can't.
[*] edit: originally typed "replacement" when I meant to type "training"
And to gloss over how that improvement would actually happen. (Not knowing what they've currently done and want to do, but for example, guessing: probably in partnership with vendors, consultants, etc., iterative and process and tools improvements, and involving a variety of approaches and refinements.)
And for people focusing too much on AI, Xiaomi kicked their first vehicle into production with a fully automated factory three years ago [0]. That's where the industry is going and has tried to go for decades now.
They might want to also reduced head out on the designing side, but it's also an ongoing trend that started before the AI boom.
That's not an industry that will keep hiring as much as they did in the past, however it turns out.
Clearly a lot of careful thought went into their strategy of using AI and firing engineers.
C-suites completely disconnected from reality and assuming we've already achieved ASI/AGI, and marketing teams & business journals are only furthering that narrative.
It's so weird. I don't know what it is about AI that causes people to throw all thought and caution to the wind and charge forward blind. Its like they've been chomping at the bit for decades to get rid of those pesky humans and are so hyped up over it they can't see clearly anymore.
These guys have squeezed out every cost and slack from their system. They've found the exact revenue-maximizing prices and segmentation for their products. They've cut quality to the point where customers will just barely not reject their product. They have used every legal and accounting trick at their disposal to keep that line going up. But, next quarter, line must still go up!
The final massive cost to cut are all those damn human bodies that they they still have to keep around. They've driven down salaries and benefits to the minimum they can get away with, and they've extracted the maximum value from employees they can. But they haven't figured out how to get rid of them entirely. They are staring down the barrel of the gun and just can't see a way to cut this cost further. Now, magic AI comes along, and everyone is saying that the black box can replace those bodies. The C-suites believe it. They have to believe it. Line must go up! This is how they'll do it for a few more quarters. This is why the messaging is so unified across the industry, across every C-suite out there. They all need to believe.
The real danger for the economy is when the runway finally runs out. And I believe we are at a perfect-storm scenario... AI is obviously a giant wash-trading bubble that alone would be sufficient to trigger a repeat of the 2007ff crisis. But on top of that, we got the issue you mentioned, i.e. everyone running out of kool-aid and noticing it too late, with no easy way of turning around, and we got the war risk and supply chain shocks thanks to Iran and Russia, and and and.
It's just a hype cycle. In my 15 years in data, I've seen around 3-4. Every time leadership get way too invested in the possibilities, and they waste tons of money on doomed efforts. A good example of the prior one was "Big Data" which was even more pointless than the current AI boom.
Don't get me wrong, there is valuable tech there (at the very least, being able to reliably generate structured data from unstructured input is incredibly valuable in data), but the current hype is way off the charts.
For those that lack initiative, strategy, a real understanding of their business, engineering, etc., the spewing words is the whole thing. It overshadows their entire understanding.
What does hype even mean concretely? I think this is just a coping mechanism if you ask me.
https://www.gartner.com/en/research/methodologies/gartner-hy...
The idea is there’s a rush of irrational exuberance when an “innovation trigger” makes a new toy looks promising, and everybody rushes to use it for everything, regardless of whether its suitability-for-purpose is proven. Inevitably many of those pioneers find that it’s not good for their particular problems after all; usage reaches a “peak of inflated expectations,” and crashes into a “trough of disillusionment.”
Then the tech enters a quieter and more gradual “slope of enlightenment” as people work out use cases where the tech actually adds value; then adoption reaches a “plateau of productivity.”
Worth a glance at the way they map this to prior waves of technological exuberance.
From your video, it looks like your definition of hype involves a situation where eventual adoption increases above what is in the hype today.
Here's what the parent comment thinks:
> It's just a hype cycle. In my 15 years in data, I've seen around 3-4. Every time leadership get way too invested in the possibilities, and they waste tons of money on doomed efforts. A good example of the prior one was "Big Data" which was even more pointless than the current AI boom.
Obviously the parent doesn't think of hype the way you think of it because they claim that big data was pointless -- they don't see the eventual "slope of enlightenment". They think of hype cycle in the colloquial way and I was responding to that.
I see this all the time in the website and frankly the patronising "but actually hype means something else" is pointless and pedantic. I urge you to respond to words within the context and not bringing in academic definitions.
Er, what? Intricacies of a transformer pipeline might be boring and nerdy, but the results are not. BTW, I've yet to find any strong argument on why the current ML approaches are bounded below the level you find appropriate to be bored.
My favorite theory about this is that we're all used to "speech == intelligence" and now that we have something that can produce coherent speech, it seems like it must be intelligent to people who don't know how it works. Even people who know how it works still anthropomorphize it to a weird degree. So a business person sees this thing that's both intelligent (to them) and superhumanly fast and it seems like the ultimate silver bullet.
1. Zero personal risk because cargo culting is a valid excuse in Executive World. If investors are on board, its good, no matter how stupid or destructive it actually is.
2. Top leadership's friendship with the country's leadership equals access to cheap debt financing since money is all fake and generated out of thin air
3. Too big to fail
> Its like they've been chomping at the bit for decades to get rid of those pesky humans and are so hyped up over it they can't see clearly anymore.
This is precisely it. Here's my analysis:
AGI is a savior figure for the capitalist class. A tech version of the Second Coming, delivering them from the pesky demands of workers, like a living wage or (gasp!) sick leave.
That's why they're all so obsessed with it, it has religious-ideological component to them. When you hear them talk about AGI, there's always this weird eschatological vibe with it.
Unfortunately, they're blinded by their beliefs and can't think things through even one step further. Even if their cyberjesus comes down to them through the machine and replaces all workers, who's gonna buy all their stuff then?
All they're doing in their capitalist zealotry is ringing in the end of capitalism.
Knowledge or skilled workers can be used by the AI for swarm training data generation; what value do the execs have to AI?
I think the most beautiful part of capitalism is selling elites rope to hang themselves.
In that order, apparently.
Step 1: 30 minute conversation with AI on how to use AI. Step 2: fire everyone.
My point being, Ford's had shit for brains for decades. Its a fucking wonder any of their vehicles make it out of the parking lot.
Pretty much everything Ford brings to the US that was designed in Europe is loathed by anyone who has to own it out of warranty.
Turns out that when you have a building full of engineers in Germany or England their domestic engineering culture results in work output not all that different from the sort of stuff people chastise BMW and Land Rover for.
That said, the Escort, and to a lesser extent the Focus, are generally considered very good vehicles.
I'm not saying it was a perfect car. The interior was cheap, the sheet metal seemed to be recycled tin cans, and it definitely showed its age by the time I got rid of it. But that engine and drivetrain seemed to be bulletproof.
That made reading their subsequent layoff blog posts pretty depressing
The editorialized headline is also misleading: "Ford rehires 350 engineers after AI fails to preserve expertise or train juniors" - there is nothing in the original story that suggests Ford were expecting AI to "train juniors".
And since the Bloomberg headline is behind a paywall the editorialized headline is most of what we have to go on.
This Verge story would be a better link: "Ford had to hire back former engineers to fix mistakes made by its automated systems" https://www.theverge.com/transportation/956316/ford-quality-...
And the crucial detail: nothing indicates Ford laid off the 350 people who were re-hired. It looks to me like it could be bringing back people who retired.
The headline gives the impression that Ford fired 350 engineers and tried to get AI to train the replacements and then re-hired them when that didn't work.
That impression is false, which means we're wasting time having conversations about it.
(The top comment thread on here right now - https://news.ycombinator.com/item?id=48674446#48675092 - starts with the assumption that Ford execs made the mistake of laying off 350 people and then discusses if they got good severance packages etc. - here's the best comment I've seen calling that out so far: https://news.ycombinator.com/item?id=48674446#48675486)