Outsourcing comes and goes in waves. Good talent in India and the Philippines tend to work for FAANG companies, often at very comparable salaries to the west.
The remainder of the talent tends to struggle with some of the outsourced work, but with AI they can now give a semblance of competence.
In the UK a major retailer, Mark and Spencer got hacked after outsourcing work to India. They couldn't fulfill orders online for months, and they are now reducing the amount of work they outsource to India.
We will see something similar happen to other companies in a year or two, but until then we just have to tighten our belts and hope we don't get layed off before then.
They do though. My company hires in India and has had a hard time retaining talent there because anyone with mediocre talent can get western salaries by switching to larger companies than our own.
We've lost a decent number of engineers to Google, Facebook, and Amazon in India.
My company wants to pay roughly $50k USD in India per dev and that's just not enough. They've tried to compete by making nicer facilities and better in office benefits (like a cafeteria) but it's just not enough.
FAANG may offer those devs more than your company will in India but I'd be very surprised if they offer them similar compensation to FAANG devs in the West and specifically the US.
It's extremely level dependent. New grad salaries in India are way below Western levels, but senior talent can command Silicon Valley level packages. People switch companies at the drop of a hat to climb another rung on the ladder, which also makes retention very difficult.
Yea almost no one is making Bay Area level TCs in either location aside from those directly relocated from the Bay or working in HFT at Citadel, Jane Street, or DE Shaw, but you will be able to make Germany or UK level TC (US$50k-110k) with the right track record and experience. Ofc, an equally large cohort will be earning low salaries in the $5k-15k range, but those are largely employed at WITCH or EPAM type companies which employers are trying to cut out of the loop.
Then you're making the same mistake that Americans are increasingly making as well.
If you aren't top tier talent (Google, Citadel, Bloomberg, or sexy FinTech startup equivalent) we can get by offering £50k-£90k TC for 10-12 YoE in London. This is reflected in the annual TC distribution on levels.fyi [0] - London TC distributions are severely right skewed.
For top talent we are fine matching US salaries 1:1, but most of those roles are basically for people who worked in the US but faced visa issues.
The thing is, most American companies aren't interested in hiring "real talent" at scale in the UK because the salary ends up becoming the same as the US but the pool of candidates is shallower - especially when I can hop over to CEE and open an office in Warsaw, Cluj, or Prague and get significantly higher quality talent at the £50k-£80k range.
> Outsourcing comes and goes in waves. Good talent in India and the Philippines tend to work for FAANG companies, often at very comparable salaries to the west.
In those locations?
Based on sheer CS grad numbers why wouldn't companies just shift their r& operations there then?
> Based on sheer CS grad numbers why wouldn't companies just shift their r& operations there then?
There are lots of CS grads, yes. But most colleges out there are mostly degree mills, and this carries on to the workplace, where your average software engineer or engineering manager has very little understanding of what they’re actually doing (this[1] article was posted on HN, which will tell you the quality of engineering in India).
For anything slightly complicated, companies seem to be only interested in hiring from the best colleges and pay out of their nose in the process. A friend of a friend does some hardware work at a FAANG, and gets paid at almost that level.
Conversation about outsourcing aside, it isn’t fair to pick one example and generalize to say an entire country’s talent pool is poor.
The US has the best engineering talent pool in the world and you can find dozens of examples at major companies as bad (or worse) than the one you linked.
The FAANG I work for is trying to do just that. But while new grads are indeed a dime a dozen, you can't staff an R&D with only new grads, and finding and retaining skilled seniors is so tough that it has resorted to offering US-based Indians packages with US level comp to entice them to move back for a few years to bootstrap teams.
Yup! I've seen it at a big American cable company too - I was even a part of the initial team responsible for re-shoring, and now I'm seeing them offshoring everything back to (one of the worst) huge Indian outsourcing companies again.
One issue is that in many industries senior leadership just doesn't stick around for long enough. If your CEOs rotate out every 5-10 years then you're basically SOL; the next guy comes in, gets bamboozled by sweet talk of vastly reduced costs of offshoring and BOOM, round you go again!
> In the UK a major retailer, Mark and Spencer got hacked after outsourcing work to India.
Does sound more like correllation than causation. Was there evidence that the Indian devs made the mistakes that led to the hack or was it just the good old 'let's fall back on racism to avoid blame' by management? I still remember the articles where Boeing tried to peg the 737 MAX crashes on Indian engineers who worked for $10 an hour.
Its more about social engineering (this was the case for the m&s hack), if you outsource your work, you have less visibility over the people that work for you and it becomes more of a black box. This leads to worse employee awareness in general.
> Good talent in India and the Philippines tend to work for FAANG companies, often at very comparable salaries to the west.
As other commenters have pointed out, this is simply bullshit. Good talent in India and the Philippines earn nowhere near US dev salaries. Unless by "very comparable" you mean 1/3 - 1/2.
I think you might be reading a bit too much into this.
He’s been with Meta for 11 years and is likely in a very comfortable financial position, given the substantial stock options he’s received over that time.
He also mentioned the arrival of a new child, and it’s well known that Meta's work-life balance isn’t always ideal.
On top of that, Meta, like many major tech companies, has been shifting its focus toward LLM-based AI, moving away from more traditional PyTorch use cases.
Considering all of this, it seems like a natural time for him to move on and pursue new, more exciting opportunities.
> On top of that, Meta, like many major tech companies, has been shifting its focus toward LLM-based AI, moving away from more traditional PyTorch use cases.
This is very wrong. Meta is on the forefront of recommendation algorithms and that's all done with traditional ML models made using PyTorch.
Some recommendations are uncanny, except that I don't want any of them in my Facebook news feed and no matter how often I select "never show me this feed again," it keeps trying.
Everyone is fine-tuning constantly though. Training an entire model in excess of a few billion parameters. It’s pretty much on nobody’s personal radar, you have a handful of well fundedgroups using pytorch to do that. The masses are still using pytorch, just on small training jobs.
Fine-tuning is great for known, concrete use cases where you have the data in hand already, but how much of the industry does that actually cover? Managers have hated those use cases since the beginning of the deep learning era — huge upfront cost for data collection, high latency cycles for training and validation, slow reaction speed to new requirements and conditions.
That's wrong. Llama.cpp / Candle doesn't offer anything on the table that PyTorch cannot do (design wise). What they offer is smaller deployment footprint.
What's modern about LLM is the training infrastructure and single coordinator pattern, which PyTorch just started and inferior to many internal implementations: https://pytorch.org/blog/integration-idea-monarch/
Pytorch is still pretty dominant in cloud hosting. I’m not aware of anyone not using it (usually by way of vLLM or similar). It’s also completely dominant for training. I’m not aware of anyone using anything else.
It’s not dominant in terms of self-hosted where llama.cpp wins but there’s also not really that much self-hosting going on (at least compared with the amount of requests that hosted models are serving)
I will not be surprised if there is but that is not a problem that cannot be fixed with some effort. The point is if we can produce deploy-able, full-stack apps, which are manageable, this changes what it means for software and for startups.
I live in a remote village in Himalayas, WB, India that I am sure no one on HN has heard of. I got 5G based broadband that is flaky just a few weeks back. By the end of this year, I am sure I will be able to attempt 4-5 products and market them more than I have ever done in my 16 years of professional life.
Sounds like first decade or two of aviation, back when pilots were mostly looking at gauges and tweaking knobs to keep the engine running, and flying the plane was more of an afterthought.
Go to ChatGPT.com and summon a ghost. It's real. It's not a particularly smart ghost, but gets a lot of useful work done. Try it with simpler tasks, to reduce the chances of holding it wrong.
That list of "things LLM apologists say" upthread? That's applicable when you try to make the ghost do work that's closer to the limits of its current capabilities.
The capabilities of LLMs have been qualitatively the same since the first ChatGPT. This is _precisely_ a hype post claiming that a future where LLMs have superhuman capabilities is inevitable.
They've definitely improved in many areas. And not just the easily-gamed public metrics; I've got a few private tests of my own, asking them certain questions to see how they respond, and even on the questions where all versions make mistakes in their answers, they make fewer mistakes than they used to.
I can also see this live, as I'm on a free plan and currently using ChatGPT heavily, and I can watch the answers degrade as I burn through the free allowance of high-tier models and end up on the cheap models.
Now, don't get me wrong, I won't rank even the good models higher than a recent graduate, but that's in comparison to ChatGPT-3.5's responses feeling more like those of a first or second year university student.
And likewise with the economics of them, I think we're in a period where you have to multiply training costs to get incremental performance gains, so there's an investment bubble and it will burst. I don't think the current approach will get in-general-superhuman skills, because it will cost too much to get there. Specific superhuman skills AI in general already demonstrate, but the more general models are mostly only superhuman by being "fresh grad" at a very broad range of things, if any LLM is superhuman at even one skill then I've missed the news.
There remains a significant challenge with LLM-generated code. It can give the illusion of progress, but produce code that has many bugs, even if you craft your LLM prompt to test for such edge cases. I have had many instances where the LLM confidentially states that those edge cases and unit tests are passing, while they are failing.
Three years ago, would you have hired me as a developer if I had told you I was going to copy and paste code from Stack Overflow and a variety of developer blogs, and glue it together in a spaghetti-style manner? And that I would comment out failing unit tests, as Stack Overflow can't be wrong?
LLMs will change Software Engineering, but not in the way that we are envisaging it right now, and not in the way companies like OpenAI want us to believe.
Proper coding agents can easily be set up with hooks or other means of forcing linting and tests to be run and prevent the LLMs from bypassing them already. Adding extra checks in the work flow works very well to improve quality. Use the tools properly, and while you still need to take some care, these issues are rapidly diminishing separately from improvements to the models themselves.
> (from upthread) I was being sold a "self driving car" equivalent where you didn't even need a steering wheel for this thing, but I've slowly learned that I need to treat it like automatic cruise control with a little bit of lane switching.
This is, I think, the core of a lot of people's frustrations with the narrative around AI tooling. It gets hyped up as this magnificent wondrous miraculous _intelligence_ that works right-out-of-the-box; then when people use it and (correctly!) identify that that's not the case, they get told that it's their own fault for holding it wrong. So which is it - a miracle that "just works", or a tool that people need to learn to use correctly? You (impersonal "you", here, not you-`vidarh`) don't get to claim the former and then retreat to the latter. If this was just presented as a good useful tool to have in your toolbelt, without all the hype and marketing, I think a lot of folks (who've already been jaded by the scamminess of Web3 and NFTs and Crypto in recent memory) would be a lot less hostile.
1) Unbounded claims of miraculous intelligence don't come from people actually using it;
2) The LLMs really are a "miraculous intelligence that works right out-of-the-box" for simple cases of a very large class of problems that previously was not trivial (or possible) to solve with computers.
3) Once you move past simple cases, they require increasing amount of expertise and hand-holding to get good results from. Most of the "holding it wrong" responses happen around the limits of what current LLMs can reliably do.
4) But still, that they can do any of that at all is not far from a miraculous wonder in itself - and they keep getting better.
With the exception of 1) being "No True Scotsman"-ish, this is all very fair - and if the technology was presented with this kind of grounded and realistic evaluation, there'd be a lot less hostility (IMO)!
That would be true if I was making a argument of criticism of a certain person or class-of-people, but I'm not. I'm describing my observations about the frustrations that AI-skeptics feel when they are bombarded with contradictory messages from (what they perceive as) "the pro-AI crowd". The fact that there are internal divisions within that group (between those making absurd claims, and those pointing out how correct tool use is important) does mean that the tool-advisers are being consistent and non-hypocritical, but _it doesn't lessen the frustration by the people hearing it_.
That is - I'm not saying that "tool advisers" are behaving badly, I'm observing why their (good!) advice is met with frustration (due to circumstances outside their control).
EDIT: OK, on reading my previous comment, there is some assumption that the comments are being made by the same people (or group-of-people) - so your response makes sense as a defence against that. I think the observations of sources of frustration are still accurate, but I do agree that "tool advisers" shouldn't be accused of inconsistency or hypocrisy when they're not the ones make outlandish claims.
You don't fancy Cyanide Custard? Radioactive Slime Jello made with actual radioactive waste? Sweet Tooth Delight made with real human teeth? Or a few other desserts with NSFW names that would go down a treat for a family dinner. It is hard to please some people /s.
But yes, websites will now be filled with these low-quality recipes, and some might be outright dangerous. Cyanide custard should ring alarm bells, but using the wrong type of mushroom is equally dangerous and much more challenging to spot.
The problem isn't that Boeing is less safe; it is that the company's culture shifted to the extent that technical staff could no longer report perceived safety issues.
It is part of the regular economic cycle. Most companies try to do more with less, like in any downturn. This cycle is different, as senior management now has AI to justify hiring fewer knowledge workers. Things will turn around eventually when the AI bubble bursts. Companies will then scramble for graduates who have likely moved to work in different industries and complain about a talent shortage.
> senior management now has AI to justify hiring fewer knowledge workers
Justify to whom? Shareholders just care about metrics like earnings and revenue. If they don't need workers to optimize this metrics, they have right to hire fewer workers.
The number of employees you hire is seen as a proxy for growth and future earnings and revenue. With AI the argument is that you can grow with less staff.
The remainder of the talent tends to struggle with some of the outsourced work, but with AI they can now give a semblance of competence.
In the UK a major retailer, Mark and Spencer got hacked after outsourcing work to India. They couldn't fulfill orders online for months, and they are now reducing the amount of work they outsource to India.
We will see something similar happen to other companies in a year or two, but until then we just have to tighten our belts and hope we don't get layed off before then.
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