Every day there’s more big job cuts at tech and games companies. I’ve not seen anything explaining why they all seam to be at once like this. Is it coincidence or is there something driving all the job cuts?
I actually think it’s just bandwagoning by a bunch of cowards.
We saw this same phenomenon early last year too: Facebook laid off a bunch of employees, then Apple announced the same, then Microsoft, then Google, then Salesforce, then the infamous Twitter layoffs.
I think big tech is so sensitive to negative press that they all just wait and lay off folks at the same time so no single company takes all the bad press.
It doesn’t even have to be Illuminati-level coordination, either. All it takes is for some exec at Tech Company B to see that Tech Company A is firing people. Then Tech Company B decides to clean house too. The cascade is just a bunch of morons deciding to hop on the “let’s fuck over our employees to help our balance sheet” train.
Definitely agree it’s not an Illuminati cabal meeting in hoods and masks.
But it’s not not that either - there’s lots of overlap on boards of directors and VCs invested in these companies. They’re in the same circles and probably play golf together. Or, they hang out on the tarmac before their Davos keynotes on saving the world.
I live in Silicon Valley and I’ve heard that there’s a WhatsApp group with a bunch of “big tech” CEOs and CTOs in it and they chat and share memes with each other 👀
It’s more devious than that. If company A lays off 1000 people due to “legitimate” reasons (e.g. twitter generally doing poorly), that’s 1000 people looking for new jobs. Company B, C, and D can then take that as an opportunity to lay off 1000 people each that aren’t immediately vital to the success of the company. Company A might not have the funds or desire to rehire right away, but the other three will slowly start building back up. You end up with 4000 people competing for 100 open positions. Many may not be willing to accept a pay cut, but some percentage will, and gradually the rest will be slowly starved down to accepting less pay.
Software engineering is notoriously a high paid career path, and executives at these companies hate that, so any opportunity they get to suppress wages, they’ll jump on. Especially if you know every other big company is doing it to, so they won’t be able to turn that into an advantage against you
During the pandemic, tech profits soared which led to massive hiring sprees. For all the press about layoffs at the big guys, I think most still have more workers than they did pre-pandemic.
Interests rates soared. Before the pandemic interest rates were ludicrously low, in other words it cost almost nothing to borrow money. This made it easier to spend on long term or unclear projects where the hope seemed to be “get enough users, then you can monetize.” Once interest rates rose, those became incredibly expensive projects, so funding is now much more scarce. Companies are pulling back on bigger projects or, like reddit, trying to monetize them faster. Startups are also finding it harder, so fewer jobs.
And of course, AI. No one is quite sure how much that’ll change the game but some folks think most programmers will be replaceable, or at least 1 programmer will be able to do the work of several. So, rather than hire and go through everything severance etc might entail, I think a lot of companies are taking a wait and see approach and thus not hiring.
Let’s not throw the baby out with the bath water. AI had the potential to alleviate a lot of pressures of society, to free up much of our time spent doing tedious mindless tasks. We just need to make sure to use it for the benefit of the many rather than the profit of the few. I don’t want a union that wants to keep labor busy and well compensated, I want a union that keeps people safe, happy, and compensated properly
I fully believe we’ll get a standardized 60 hour work week before we get a 20 hour one. Hell, I’m pretty sure we’d relegalize slavery before we get a 20 hour work week. Your average American will bend over backwards for a chance to please “the boss” and actively rat on their colleagues for avoiding work because our cultural understanding of loyalty is functionally equivalent to boot licking.
A lot of technology problems and utilizing AI for the betterment of humanity could all be dealt with easily if we just removed a large chunk of the bloated administrative, management and ownership hogs at the top that contribute nothing, stall everything and constantly sabotage development with their politics, infighting and warring with competitors. If you remove the profit factor, corporate greed and economic shortsightedness in these situations, a lot of problems can be dealt with fairly easily and fairly quickly.
Unfortunately, we are greedy monkeys who want to rule the world and once you give power to one monkey or a small group of monkeys, they immediately try to overpower all the other monkeys and rule the jungle.
Yeah, except there’s no way the owners would give up any of the profits for the betterment of society. Every technological improvement since the industrial revolution made productivity skyrocket, and yet the capitalists made sure working people were still hovering just above destitution. The only reason some of us have it better is because unions fought them, and that includes Luddites that would destroy the means of production.
I completely agree, although I think AI is more likely to have impact marketing, communications, PR, creative and PM type roles (and there are a lot of those in tech companies). I suspect we will see a noticeable reduction in tech workers over the next decade.
1 programmer will be able to do the work of several
This is true right now. If you know how to use AI tools, it's not that hard to work 5-10x faster as a programmer than it used to be. You still have to know what you're doing, but a lot of the grunt work and typing that used to comprise the job is now basically gone.
I have no idea, but I can't possibly imagine that that's having no impact on resource allocation and hiring / firing decisions.
So maybe I don't know what I'm talking about. I will only share what I have experienced from using them. In particular I haven't messed with Copilot very much after the upgrade to GPT-4, so maybe it's a lot more capable now.
In my experience, Copilot does a pretty poor job at anything except writing short blocks of new code where the purpose is pretty obvious from context. That's, honestly, not that helpful in a lot of scenarios, and it makes the flow of generating code needlessly awkward. And at least when I was messing with it there didn't seem to be a way to explicitly hint to it "I need you to look at this interface and these other headers in order to write this code in the right way." And, most crucially, it's awkward to use it to modify or refactor existing blocks of code. It can do small easy stuff for you a little faster, but it doesn't help with the big stuff or modifying existing code, where those are most of your work day.
To me, the most effective way to work with AI tools was to copy and paste back and forth from GPT-4 -- give it exactly the headers it needs to look at, give it existing blocks of code and tell it to modify them, or have it generate blocks of boilerplate to certain specifications ("make tests for this code, make sure to test A/B/C types of situations"). Then it can do like 20-30 minutes' worth of work in a couple of minutes. And critically you get to hold onto your mental stamina; you don't have to dive into deep focus in order to go through a big block of code looking for things that use old-semantics and convert them to new-semantics. You can save your juice for big design decisions or task prioritization and let it do the grunt-work. It's like power tools.
Again, this is simply my experience -- I'll admit that maybe there are better workflows that I'm just not familiar with. But to me it seemed like after the GPT-4 transition was when it actually became capable of absorbing relatively huge amounts of code and making new code to match with them, or making modifications of a pretty high level of complexity in a fraction of the time that a human needs to spend to do it.
I wonder if it might be the specific type of work that you do that allows for this. I don’t pay for ChatGPT, so I wouldn’t know the quality of the code it outputs with GPT-4, but I personally wouldn’t blindly trust any code that comes out of it regardless, meaning I’d have to read through and understand all the generated code (do you save time by skipping this part maybe?), and reading code always takes longer and is overall more difficult than writing it. On top of that, the actual coding part only accounts for a small fraction of the work I do. So much of it is spend deciding what to code in order to reach a certain end goal, and a good chunk of the coding (in my case at least) is for things that are much easier to describe with code than words. So I’m still finding it hard to imagine how you could possibly get anything more than a 1.5x output improvement.
The main time savings I’ve found with generative AI is in writing boilerplate code, documentation, or writing code for a domain that I’m intimately familiar with since those are very easy to skim over and immediately know if the output is good or not.
I actually got curious about it specifically because of this thread, and earlier today did a little experimentation with Copilot's Cmd-I feature as compared with copying and pasting to GPT. I'm actually pretty convinced now that the issue is that Copilot using a cheaper model for reasons of computational cost. Giving Copilot the exact same task I was giving to GPT, it struggled to create code that could even compile, even after multiple rounds of me trying to help it, where GPT-4 was able to just give output and its output worked.
I think the assumption that it's being set up under is that people will be doing a ton of queries throughout the work day, more so than the average GPT-4 user will type into the chat interface, and so they can't realistically do all that computation on people's behalf for $20/month.
(Edit: And this page makes some statements about "priority access" to GPT-4, indicating that they're throttling access to the more capable models depending on demand.)
In practice, the majority of the time I'm carefully looking over diffs anyway before committing anything, since as you mentioned the vast majority of work time is spent modifying existing code. So the times it messes up aren't a real serious issue. But again I think (after some pretty minimal experimentation today) that the real issue you're seeing is just that GPT-4 is way more capable at this stuff than is GPT-3.5 / Copilot.
But this is guessing based on some pretty minimal experimentation with it. I sounded real confident in my initial statement but now that I'm looking at it maybe that's not warranted.
All I can really say is, I'd agree with the statement that a single 3-hour task isn't real representative of the actual overall speedup, and my experience has been that it can be a lot more than that. It can't replace the human who needs to understand the code and what needs to happen and what's going wrong when it's not working, but depending on what you're doing it can be a huge augmentation.
There’s also the pure reality that, yeah, it’s easier today to get a project off the ground than ever before, and AI is good at that, but you know what AI is absolute shit at? Modifying ludicrously cumbersome, undocumented, brutally hacked together legacy code and addressing technical debt - the two most common tasks of most actual software engineers.
Working with GPT actually helped me write better code, because it's more familiar with good patterns in unfamiliar languages and frameworks and can write idiomatically. It got me out of one-language-centric habits I hadn't known I had.
Yes, it's 100% true that the person driving the AI needs to have good design sense of what they want the final system to look like, and still work well with their team. You can fuck it up faster if you can code faster, absolutely that's true.
What you said, I know that. Do the people that run these companies know that?
What you said, I know that. Do the people that run these companies know that?
Yeah. Exactly. They did the same to various degrees when web frameworks first hit the scene, and numerous other advancements before and after.
But as you said, the new tech genuinely does make us both faster and better.
It just doesn’t fix the crap parts of the job that the CEOs always hope it will. (As someone else pointed out, it specifically doesn’t magic wand away decades of technical debt, haha.)
I would say a 3-hour-task isn’t representative in the other way around. When you tackle a 1000-hour task, you’ll probably spend more than 1000 hours working out what the requirements even are. A significant portion of my workday are meetings, not coding.
And with a long-form task, you’ll go back reading existing code much more often than writing new code, too.
I love the speed-up. And I’m sure it factors into CEO and CIO decisions. But they’re on their way to learning, once again, that “code faster” never had anything to do with success or failure in efforts that require programmers.
Source: I sought great power, and I became one of the fastest coders, but it didn’t make my problems or my boss’s problems go away.
Do you work in a technical role? I’ve dabbled in using AI to help out when working on projects, but I would say it’s hit or miss on actually helping, as in sometimes it helps me move a bit faster and sometimes it slows me down.
However, that’s just for the raw “let’s write some code part of the work”. Anything beyond that in my roles and responsibilities doesn’t really intersect with what AI can currently do, so I’m not sure where I would get a 5-10x speed-up from.
Honestly I’m not sure if I’m taking a wrong approach or if everyone else is blowing things out of proportion.
I want to offer my perspective on the AI thing from the point of view of a senior individual contributor at a larger company. Management loves the idea, but there will be a lot of developers fixing auto-generated code full of bad practices and mysterious bugs at any company that tries to lean on it instead of good devs. A large language model has no concept of good or bad, and it has no logic. It'll happily generate string-templated SQL queries that are ripe for SQL injection. I've had to fix this myself. Things get even worse when you have to deal with a shit language like Bash that is absolutely full of God awful footguns. Sometimes you have to use that wretched piece of trash language, and the scripts generated are horrific. Remember that time when Steam on Linux was effectively running rm -rf /* on people's systems? I've had to fix that same type of issue multiple times at my workplace.
I think LLMs will genuinely transform parts of the software industry, but I absolutely do not think they're going to stand in for competent developers in the near future. Maybe they can help junior developers who don't have a good grasp on syntax and patterns and such. I've personally felt no need to use them, since I spend about 95% of my time on architecture, testing, and documentation.
Now, do the higher-ups think the way that I do? Absolutely not. I've had senior management ask me about how I'm using AI tooling, and they always seem so disappointed when I explain why I personally don't feel the need for it and what I feel its weaknesses are. Bossman sees it as a way to magically multiply IC efficiency for nothing, so I absolutely agree that it's likely playing a part in at least some of these layoffs.
So basically, once again, management has no concept of the work and processes involved in creating/improving [thing], but still want to throw in the latest and greatest [buzzword/tech-of-the-day], and then are flabbergasted why their devs/engineers/people who actually do the work tell them it’s a bad idea.
A large language model has no concept of good or bad, and it has no logic.
Tragically, this seems to be the minority viewpoint - at least among CS students. A lot of my peers seem to have convinced themselves that the hallucination machines are intelligent… even when it vomits unsound garbage into their lap.
This is made worse by the fact that most of our work is simple and/or derivative enough for $MODEL to usually give the right answer, which reinforces the majority “thinking machine” viewpoint - while in reality, generating an implementation of & using only ~ and | is hardly an Earth-shattering accomplishment.
And yes, it screws them academically. It doesn’t take a genius to connect the dots when the professor who encourages Copilot use has a sub-50% test average.
To add to this, at my company, we’ve received a mandate to avoid putting any code into an AI prompt because of privacy concerns. So effectively no one is using it here.
Yep as far as most companies should be concerned, using something like CoPilot means giving free license to Microsoft to all your trade secrets and code that you input.
We had the same. And you would have thought for a heavily regulated industry we’d keep it that way.
But no, some executive wonk from Microsoft flew over, gave our c-suite a “it’s safe, promise” chat over champagne and lobster, and now we’re happily using copilot.
I’m pretty excited about LLMs being force multipliers in our industry. GitHub’s Copilot has been pretty useful (at times). If I’m writing a little utility function and basically just write out the function signature, it’ll fill out the meat. Often makes little mistakes, but I just need to follow up with little tweaks and tests (that it’ll also often write).
It also seems to take context of my overall work at the time somehow and infers what I’ll do next occasionally, to my astonishment.
It’s absolutely not replacing me any time soon, but it sure can be helpful in saving me time and hassle.
Those little mistakes drove me nuts. By the end of my second day with copilot, I felt exhausted from looking at bad suggestions and then second guessing whether I was the idiot or copilot was. I just can’t. I’ll use ChatGPT for working through broad issues, catching arcane errors, explaining uncommented code, etc. but the only LLM whose code output doesn’t generally create a time cost for me is Cody.
There’s your mistake, treating bash like a language and not like a scripting tool. Its strength is that it’s a common standard available on almost every machine because its older than most of us, its weakness is that it’s full of horribly outdated syntax because its older than most of us. If used to script other processes it’s great, but when you start using it as a language then the number of ways you can do something horrible that sort of works makes JavaScript look slick!
Interests rates soared. Before the pandemic interest rates were ludicrously low, in other words it cost almost nothing to borrow money. This made it easier to spend on long term or unclear projects where the hope seemed to be “get enough users, then you can monetize.” Once interest rates rose, those became incredibly expensive projects, so funding is now much more scarce. Companies are pulling back on bigger projects or, like reddit, trying to monetize them faster. Startups are also finding it harder, so fewer jobs.
Note that this also impacted other projects that take a lot of capital up front, then provide a return over a very long term. There was a nuclear power plant project with NuScale in Utah that got shelved over this; with interest rates suddenly going from way low to way high, the economics get upended.
I’d bet that in general, infrastructure spending dropped across the board.
It’s Q1. Companies always push hard for Q1 profits above all else. There’s usually hiring freezes and cuts to maximize profits, come Q2, they’ll hire a bunch of people and the cycle will continue.
It’s an election year in the midst of an onsetting recession, so shareholders want to consolidate. I think on top of that models are being sold as something that can replace certain task forces - normally there would be rehires, and there still will be but I think it will be after its seen that they aren’t ideal replacements.
Tech is hard, leaders aren’t always technical. AI is great at bullshitting, and it’s swooned many CEOs into thinking it will 10x (make them 10x more efficient than they previously were) existing employees / replace the need for programmers. Lots of leaders just look to what other leaders at companies are doing - some see what elon does at twitter as proof that downsizing drastically won’t kill your company.
Programming is like editing a book with many chapters. New developers need time to learn the story line of the book before they can begin editing anything. If the book has been around and edited continuously for over a decade, it’s going to take some time for those developers to understand the book well enough to start making meaningful contributions. Lots of these tech companies have multiple books each with many chapters, and one thing leadership either doesn’t realize or doesn’t seem to factor into the equation is that maintaining these books and all their story arcs and character development gets harder and harder over time. Truly in the tech industry, it’s more expensive to train a new hire than it is to promote an existing hire.
But again, leaders are listening to folks like elon musk…
Sometimes I like to think of the economy as a small village where people directly goods with each other. The invention of money means you can make a living off of selling to just one person and still have something to offer the farmer, but for this thought experiment this I want to focus on the actual, real, goods and services of the economy.
So imagine a small village. You have the farmer who grows food. You have the blacksmith who builds car parts, and the mechanic that builds cars and tractors. And you also have the village fool who makes people laugh in exchange for tips. The mechanic gives tractors to the farmers in exchange for food, and gives some of that food to the mechanic in exchange for parts. When any of them need a laugh they’ll give something to the fool to hear a joke. And you have your other industries, etc. One day a new person comes to town, who will represent the new tech industry. They realize that they can build a machine that tells the farmer the best days to plant and harvest which will help the farmer grow more food. The farmer happily accepts, paying the tech person some food in exchange. Similarly they’re able to help optimize the other industries, and with the value they’re providing and them being in short demand they’re able to get great wages.
With their prosperity, other tech people start coming to the village and helping the other industries get more efficient. Most of the concrete efficiencies are optimized, so they start working on more abstract ones. Someone builds an app to help the villagefolk find someone to trade with (“I have 2 gears but I need 3 loaves” gets matched with “I have 2 wheat bushels and need 2 gears” which gets matched with “I have 3 loaves and need 2 wheat bushels”), in exchange getting a small cut of those resources, and a larger cut if someone pays for preferential matching (advertising). Other tech people find work helping the other tech people at their jobs (IDEs, libraries, issue trackers, etc.) And other tech people build animatronic village fools to entertain the village themselves (video games).
More tech people come as they’ve heard of how much they can earn at this village. Eventually they start having some trouble finding work to do, everything seems optimized. Some of the wealthy members of the town (let’s say the farmer of the biggest field) says to many of these tech people that they’ll pay them food in exchange that the farmer gets a portion of whatever the tech person ends up earning with what they build (low interest rates). With all the good ideas used up, the projects these tech people are working on aren’t working well (crypto) or are duplicates of already existing tools (how many social media apps do we need, etc.). Still though, the farmer is giving them a lot of food so yet more tech people come to the village, and many of the children of the village (like the farmer’s son) are becoming tech workers too.
Eventually, after a bad crop season (maybe because the farmer’s son didn’t help harvest), the farmer is short on food and stops lending out food to these tech workers. They try to go around to the other villagefolk but most have already been optimized. The tools that optimized life are already built and the required tech people for maintenance is a lot less than those needed to build it, and the number of truly new opportunities to help new industries isn’t enough to provide work to all the tech people.
TL;DR
Tech people earned their crazy salaries when they were helping migrate the non-digital world to the digital world. There were so many obvious opportunities for efficiencies and not enough tech people to go around. ‘Spreadsheet’ calculations literally used to be a day-long affair with a team of people - of course a business would pay anything to a tech person to automate that. Now that times the whole economy.
These obvious efficiencies are finite but we treated them as infinite and kept training new tech workers. Low interest rates helped keep us employed for longer than we should have as we were paid to work on bad products in the hope that maybe there’d be a diamond in the rough and yet we STILL kept training new workers. Meanwhile other careers that provide more concrete value, like mechanics & HVAC professionals, have had a labour shortage as Tech attracted so many young people to itself. This eventually led to persistent inflation which then ended low interest rates. With higher interest rates a lot of speculative tech can’t get funding; Tech is only getting paid for the actual new value it can provide today, which is way less than it used to be.
Lots of tech companies saw huge growth during covid thanks to everyone having extra money to spend (see crypto and NFTs if you want clear examples that we just had too much laying around).
Many of these companies then saw their revenue and userbase increase month-after-month and thought the growth was going to continue forever (or, more cynically, they knew it was going to crash but acted like it was going to continue). This led to a bunch of hires to “drive growth.”
But obviously, pandemic spending habits have mostly stopped, and the money faucet is being turned off. Companies can’t afford all the workers they hired, so they’re “let go due to market downturns.”
TL;DR Companies either thought they were going to have unrealistic growth and made dumb hiring decisions, or knew the growth was going to end and thus made cruel hiring decisions.
Add something about the federal funds rate exceeding 2.5% for the first time since 2008 and you’re on the right track. I think interest rates affect startups more than Google so bigger tech firms were hoarding talent to prevent new competitors from having those workers.
I think interest rates affect startups more than Google so bigger tech firms
Definitely. Google has lots of cash already, whereas startups are often in need of more money.
The other thing that’s happened recently is that businesses used to be able to write off (deduct in their tax return) all their R&D expenses in the year they were incurred, whereas now they need to be amortized over five years. This has a huge impact to startups because a lot of their initial work is R&D, and now they have much larger tax bills than they used to have. www.axios.com/…/taxes-irs-startups-section174
The correction I would make is that they can afford the workers. But the leadership needs to continue the growth. All they have left is to cut expenses, and the easiest way to do that is layoffs.
The latter, probably, given that they look identical. At first I thought: “AI”, but the fingers are pretty good. I think they’re just generic prop suits.
Tailoring makes or breaks a suit. They don’t tailor suits for stock photos.
Yes, it is a concerted effort to create a glut. This is like the wga strike, they want to starve you a little so you’ll come back begging for a job before you lose your home.
They know the next 20 year will be a shortage of labour and stagflation. They’re just trying to start this lean period with the upper hand.
I don’t like calling it overhiring as if it was accidental or something. They didn’t hire thousands of people over covid thinking covid would never end, they just knew they could pick up people to fill the role for now and kick them to the curb as soon as they weren’t needed.
Yeah, here in Germany, workers have stronger protections, laying them off isn’t as easy, and I feel like the layoff waves have largely not occurred here, because companies didn’t hire so much during the pandemic.
Add comment