No we should write one of the many modern programming languages that handle certain projects way better, including kotlin, go, or Java. The only things python is best in class at are scripting and as a harness for high performance c++ or fortran.
I mentioned those explicitly. People are still using it as a backend language for projects like huge SaaS deployments though, with the hypothesis being that dev time is expensive. With modern languages like kotlin and go, I think that gap is much too narrow to justify using a slow and badly designed language with good syntax.
Which part of that sentence was confusing? I found it perfectly clear. Their internal AI use is exploding, which is a signal that they need to structure for that, and so they’re laying people off as one of the first steps towards actioning that signal.
Nowhere did they indicate there is less work to do, in fact quite the opposite.
The sentence is not confusing, the sentence doesn't mean anything. There's nothing confusing about it, but there's no information either. "We're making great strides in AI" and "We need to cut 20% of people" are simply two statements without any connection aside from the fact that they are next to each other in the sentence.
> "We're making great strides in AI" and "We need to cut 20% of people" are simply two statements without any connection aside from the fact that they are next to each other in the sentence.
Huh? How is it not connected? More productivity means fewer people are required. I'm not sure how you are not able to connect these obviously connected statements.
There’s an optimal number of employees required at any productivity point.
Why don’t Google hire 3 times the number of developers? They have the money right? What’s your logic for not hiring more?
Hiring and firing people aren't symmetric actions.
They're asymmetric because hiring more people costs more than just the salary. For example, some folks' entire jobs are to recruit and hire people. Once they are hired, you have to onboard them, etc. So the more you hire, the more you have to pay the folks with supporting roles (either directly or by way of them not having infinite time/capacity).
Firing people isn't free, either. It comes at the cost of bad PR and severance, but the latter is voluntary and calculated by the company, and the former is quickly forgotten by anybody that matters to a publicly traded company (investors).
That means not hiring those two people in the first place is usually cheaper than firing them later.
To the original point: Cloudflare isn't hiring fewer people; they are firing people. If they are trying to grow (like every single investor is counting on them to do), then why would they fire people (the cheaper action) now when they would likely need to hire people (the more-expensive action) later in order to meet that increased growth?
The charitable answer would be that the people they are firing were deemed unable to adapt to using AI for all of this supposed increased productivity. But Cloudflare aren't saying that. In fact, they're saying the opposite by stating it's not about individual performance.
your's is a caveat against my larger more correct point: there's an optimal number of employees needed at any given productivity point.
its true that hiring and firing are asymmetrical, and CF has shown that they are willing to bear the brunt of the asymmetry and fire people despite the downsides.
that asymmetry lies doesn't disprove the original point: cloudflare simply doesn't require the _same_ number of people to work for them with AI.
if you disagree with this then you believe that companies should only have monotonically increasing number of employees which is quite ridiculous a claim
Enlighten me then as to the secret meaning behind the words used to communicate in the language we call English. Saying that AI is really transforming the company is fine. Saying that 20% of staff need to be laid off is fine. Those are understood terms. How do they relate? There's no explanation. Did cost need to be reduced? Did those people no longer add value? Was there certain projects that weren't profitable? Nothing is explained because meaning is avoided.
> Their internal AI use is exploding, which is a signal that they need to structure for that, and so they’re laying people off as one of the first steps towards actioning that signal.
I don't see anywhere where the jump from "structuring for AI" directly leads to "laying people off", unless "structuring for AI" means there is less work for people to do, do you?
Noone knows what the correct structure for this new world looks like. We’ll see what they end up hiring for. But it’s fairly standard to lay off a bunch of people and hire new, rather than retrain, when you need to restructure
Not really. This is all so new, noone is using it correctly, because noone knows how to yet. We’re all just kind of flailing our arms around with it, but it’s clearly a force multiplier and its increased use is an actionable signal
It is not possible to make a piece of software completely secure because software sits atop hardware and hardware introduces its own security vulnerabilities that leak into software without possible recourse.
The current bottleneck is silicon. Every chip that is manufactured gets housed and powered. (It makes sense: the cost of compute is dominated by capex, the power costs are irrelevant, so they're ok paying a premium for power).
The space data center hypothesis relies on compute supply growing faster than power supply. (Both are bottlenecked on parts of the supply chain that will take ages to scale.)
Even if you believe that's the case, the point at which orbital data centers start making sense is incredibly sensitive to the exact growth rates.
The current bottleneck is not silicon. There is plenty of silicon locked up in previous gen GPUs that are no longer efficient enough to run relative to newer models. The bottleneck is the economics of owning the older GPU models - which is why all the GPU neoclouds are gonna go bust unless they can get customers to continue renting old GPUs.
The economics are vastly different when opex is near zero for these things
H100 rental prices are still as high as when the cards were brand new. The prices vastly exceed the power costs.
In a world where power or DC permits are the current bottleneck those H100s would be getting retired in favor of Blackwells. But they aren't. They are instead being locked in for years long contracts.
Because you'd need to trash the old GPUs in order to make room for new GPUs. Right now new GPUs get online mostly in new DCs. TSMC fab capacity is much more limiting than DC building and it will likely keep being the case. It's much easier to build a DC than a fab.
If silicon were relatively abundant and power/DC space scarce, you'd get an order of magnitude more bang for the Watt by replacing the H100s with newer GPUs.
But nobody is doing that. Blackwells are being installed as additional capacity, not Hopper replacements.
So it is pretty clear that silicon is the primary bottleneck.
ASML are not the chokepoint for chips. Zeiss are. ASML can hire more engineers and build more machines. Zeiss cannot hire more mirror grinders. And noone wants to train as one.
Uncertain long-term career prospects that depend on a single employer. If you pay enough to make long-term prospects irrelevant, you may end up attracting the wrong kind of people. People who can't be trained do the job well enough, or people who will quit after earning enough. And you may end up losing your existing employees. They may quit if they don't get paid as much as the new hires, or they may FIRE if they do.
How come FAANG companies don't have this same problem of people quitting after earning enough? They get paid much more, even without taking on the risk of being tied to a single employer.
The answer is that some people do quit and retire early, but even more are attracted to that career like moths to a flame, and work until they can't.
I do think they should raise pay for their existing employees at the same time. In fact, they should tie the compensation to progression in skill and experience, so that people who just came for the money and aren't cut out for the work or aren't in it for the long haul aren't attracted to the job. That's basically the traditional model anyway.
And yeah paying employees well might cost a bit of money (but really, not that much in the scope of things). If talent is their production bottleneck, it will be well worth the expense.
It's a more diverse industry with many companies and many types of jobs. If you don't like one job, you can quit and try another. Which people tend to do multiple times in their careers.
Software industry has always been plagued by attrition. Some companies pay well and mostly employ younger people. Older employees eventually filter out, either because they have already earned enough and prefer better work-life balance, or due to ageism. And then there are occasional downturns, where many people lose their jobs, can't find new ones, and end up leaving the industry permanently.
People generally prefer careers with multiple viable employers. Not just in the world, but also in the same metro area. That way you are less likely to get stuck in a job you don't like. But if you are an employer with unique requirements. Of skills that take years to learn and that people are not likely to acquire on their own. Then you may need to pay ridiculous money (more like AI than FAANG) to substantially widen your talent pipeline. And if you pay ridiculous money, you risk ridiculous consequences.
It sounds like you're arguing that Zeiss needs to pay even more than FAANG to succeed in attracting people to work for them, which is a point that I agree with.
Surely you would increase the salary of the current employees if you're hiring new people with higher salaries.
Also, it sounds like the entire premise is "people don't want to work because they're not being paid enough" which is enough of a good reason by itself.
