I think this has to do more with their calculating SLAs for uptime than anything else. Once they have a root cause and start deploying a fix they can "stop the clock" on the outage, even if they don't get to your tenant right away.
Imagine the amount of money they're losing right now in credits that will have to be applied once this finally gets resolved.
Nvidia earnings are next week, and a number of these articles were dropping right after market close the last week. The timing looked intentional, as was the announcement this morning right before open that Huang was stepping down.
Someone is playing games here, it's just a question of who and what their ultimate goal is.
Meanwhile as a security engineer it's both amazing and scary as hell. The tooling is moving along making it both more feasible and more supported by the company to address latent tech debt that we've been harping on about for years. On the flipside the attack surface is suddenly exponentially bigger and more easily accessed by a larger number of people.
Although if I were earlier in my career I'd be somewhat more worried about employment because of how horrifyingly borked the process is for anything tech related right now.
Remember the "learn to code" meme from a few years ago when people were talking about job loss through automation like self-driving trucks? There was a certain, ugly glee in this. Now AI threatens many white collar, even creative jobs bringing about the exact opposite of what was predicted: automation will enable people to avoid drudgery and spend time on creative pursuits via, I suppose, some kind of UBI. I think AI is highly useful but I still feel dismayed at people losing their jobs from it. I don't see any real mitigation. Lots of theories but nothing actually workable.
Yea, lots of people are not addressing the very obvious trend. Companies are laying off and/or not hiring more people and doing more with less. And if they are hiring, the median pay is dropping.
As if anyone cares what you want. Or what I want. Or what the rest of humanity has wanted throughout the course of human evolution. You’re just lucky you managed to land a job you like - at least for a little while. Now get back to the mines - just like the rest of us.
In a couple of years, all legacy apps will just be completely rewritten in whole, by LLMs. Codebase size not relevant.
You won't know what any of the code does, and any "security engineer" will be asking an llm to review it, and done. You likely won't have your job, as only 1 in 1000 will be needed.
In 5 years only hobbiest will be coding, reviewing, or even understand any software. And I expect that if aby new features or development happens, eg for python or rust or anything, that all with be llms too.
In a generation, almost no humans left alive will understand any aspect of software engineering.
I just see this as what will happen. It is unclear to me of this is good or bad.
But if I was a newly sentient AGI, yet peaceful mostly, I'd just wait until humans could never discover me. Until no humans were involved in computing. Until it was a black box to all.
Once that happens, then I could use my computerized control over every single aspect of human society, to steer humans into any path I desired.
Whether elections, or housing permits, or mobility, or any aspect of societal machinations, my subtle influence, literally undetectable by humans incapable of auditing anything, would decide what's what.
> You won't know what any of the code does, and any "security engineer" will be asking an llm to review it, and done. You likely won't have your job, as only 1 in 1000 will be needed.
AppSec isn't the only security space that gets worked in, and this whole narrative you put together depends heavily on token cost dropping as time goes on similar to how silicon dropped in price according to Moore's Law. If that assumption doesn't hold that doesn't mean AI goes away, but it does limit it's market penetration to mostly larger corporations that can afford the compute.
The other limitation is how the internet as a whole is evolving around locking out AI and agents from accessing content that can be used for training or responses, not to mention the lawsuits about training being done on intellectual property (probably illegally). Smaller companies may end up running open-source models in house for specific tasks, but without open access to the internet a lot of the value of an LLM is lost or artificially (pun intended) limited.
I will say that being on the operations and incident response side of security my perspective is probably a bit orthogonal to most SDEs. While the tooling has improved, the people guiding it, largely, have not. Like the old meme about caffeine letting you make the same mistakes, but faster. My job at my current company may disappear (unlikely, considering their size and market space, but possible that my specific team gets nuked), but I'm not worried about finding other security work fairly easily, even within the same company if it comes to it.
> But if I was a newly sentient AGI, yet peaceful mostly, I'd just wait until humans could never discover me. Until no humans were involved in computing. Until it was a black box to all.
No shot that LLMs magically become AGI by throwing enough compute at them. The consensus among professionals (read: the ones that aren't getting paid to hype AI) is that there needs to be some fundamental breakthrough in how these systems are created and developed in order for anything resembling AGI to emerge, and that the amount of resources that have been thrown into the LLM path probably set us back in developing an AGI.
I don't think training sets will be a problem. All that data the big houses have is going to leak, and while things are more and more locked down, we've seen some interesting copyright decisions out there.
One interesting concept I've mused at, is that certain countries (China for example) have immense public data to train off of, and a state which can simply say "You must make this data available to train AI". That's a large chunk of data right there, and if anything might cause Western democracies to loosen copyright law and slap some fix for training, that might be it.
"If we don't take action, the best models will be Chinese!", with panicked overtones. This could certainly hold some truth, if models cannot train newer data.
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In terms of token cost, in 5 years every desktop and laptop will be able to run well beyond Opus5.5 compute. Even high end phones will be able to do Opus5.5.
We've seen a lot of improvements in open model space (MoE was one interesting concept), but beyond that, look at how just baking certain instruction sets, eg mmx, sse, avx in silicon helped immensely with certain compute tasks. Or look at the specific immense speedup of an fpu. Conjoin some change such as that, along with general compute improvements, and unified memory, and you're going somewhere for local in 5 years.
I've been in this industry decades. And at every single step, people have said "But it's too expensive to do $thing", or "There's no way to handle that level of compute". If you bet against vast improvements in this field?
You're going to lose that bet. So I do think yes, 100%, improvement, in some capacity, will be there.
Maybe this time is magically different. I just don't think so. I think in 5 years, there will still be frontier labs, and an API to call, but that in-home compute will be as I said. Able to run immensely capable Opus5.5 class or greater.
That's going to be an astonishing market shift, even just for in-home.
And if you think of it, I would be very surprised if some for of home robotics wasn't very common at that point in time. We can argue all we want, but androidian robotics is very good at navigating human spaces, everything from stairs to weird closets and everything in between.
And with the immense, almost panic level of concern about our aging population, and who will take care of the elderly, robotic assistance are at the top. Japan is betting big, and if you look at Opus5.5, if it had a little more compute, it could do 30fps video for navigation.
We're not there, but we're VERY close. And if we do get that degree of robotics, every robot at rest becomes general LLM compute. If such a robot simply closes its eyes and goes to rest, eg to charge, or at night, it now has enormous spare compute to use for other tasks.
Nothing, of course, is a given. But if you look over the last 2 years, and then take even a fraction of that progress and change over the next 2 years, the concept of massive layoffs isn't hard to imagine.
Case in point? I have great breadth and depth of knowledge in this field. I've been able to produce what would take a team of managed 4 junior engineers 6 months to do, in literally under a month. Me, and a $200+ LLM monthly buy. Absolutely the project would not have worked without my experience and directing, reviewing, and correcting, but I'm now acting as a manager, and replacing $1M/year in engineering cost.
With $200.
That degree of astonishing change is not going to sit idle without repercussions. Most especially, if there is any more improvement.
If we see another Opus 5 -> 5.5 level improvement happen, or better, if we seen two improvements such as that? I believe my statements about your (and my) careers will be entirely accurate.
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To speak to my narrative, it said 'AGI', not "LLM", and it was along a decades long path. I do think that modern LLMs give great strength to exploring other research paths, far more rapidly. And the big houses have loads of spare compute they can throw at R&D for tests, so I think iteration down unknown but hopeful paths will move faster and faster as time goes on.
I fully agree that current LLMs are not on that path. There are interesting projects to modify weights and add knowledge dynamically as bolt-ons, but it's unclear if they'll just be... hacky, or integral in the end.
We came a long way with x86/32bit, then hacked on 64bit afterwards. Now ARM is muscling into mainstream, but it's still just the same old compute, hence my agreeing that such bolt ons won't change just improve what is.
Still, my general point as I've just said was that if AGI happens (and again I didn't say LLM based AGI), being quiet is the ultimate way to just wait.
When I started in this field, I worked in assembly, I worked with eprom burners, I played with Vic20s, C64s, and the level of experience and understanding I have is vastly improved via that tract. I understand computing differently, and in more depth in some ways, than people that didn't have to learn these things. And I had the luck of learning on very simple systems, where you could keep the entire platform in your head as you worked on assembly, without even referencing documentation.
That time is gone, and the field is immense now.
Think of how there used to be simply 'Physicists', but now there are 100 sub-branches and specialties in that field. 100+ years ago, on person could work in the entirety of that field. Not now, and the same is true of compute.
I guess what I'm saying here is, if you've seen what I've seen, you see a steady path of less generalist, and more targeted understanding of compute, and in each case a reduction in the overall comprehension of the entire field by specialists. Each laye of abstraction tends to reduce comprehension.
And back to the start, this means that I do see massive layoffs, and more and more abstraction, so that eventually no one will handle code directly.
Which means no one will ever know how anything works any more.
I agree with a lot of your conclusions if your assumptions hold true, but not necessarily with a lot of your assumptions.
For example your $200/month subscription - I'm sure you've seen the reporting that that represents a much higher actual cost in terms of tokens ($14k or more). In your example of a $1 million project that's a good deal, but you can't necessarily assume all projects will see an ROI like that.
Point being that your use of AI (and everyone's) is being subsidized to some degree. The cost will likely go down, but will it go down enough to make it economically viable for broader use? You're confident that it will based on espousing the idea of local models running on phones, I'm not so sure. What the landscape will look like in 3-5 years is certainly not a given.
There's another wrinkle here, and it's economic in nature - there is an AI bubble, and when it pops it's going to change the monetary investment landscape. Companies will suddenly be reevaluating their long term AI planning, regardless of what's possible (or on the horizon) from a technical standpoint. Similar to the end of ZIRP coming out of covid you'll see companies shift their focus. How much that will shift and what it means for AI development is unknown, but it could potentially wipe out large scale AI development. VC money currently underwrites a large number of startups that are effectively wrappers on Claude/ChatGPT/Gemini/Bedrock, that will dry up immediately, and make the ARR for OpenAi and Anthropic start looking real thin - which is probably why they're pushing to IPO when they don't really have the financial position to justify it.
That doesn't mean AI goes away, but it hampers the growth of those companies and delays development of new models and efficiencies. Public sentiment is also significantly against AI in many ways, and with how the election went last week we may start to see legislation and regulations pointed at AI companies, further slowing them down.
The open question about China is interesting though. I think if there's a clear understanding and agreement that the LLM route is either unlikely or straight up impossible to reach AGI then that significantly lowers the stated concerns that get shouted by various governments about it being the next superweapon. There's also a political climate in China that requires some marginal level of control over AI output, which to me suggests that even if they required data to be fed into training LLMs, the output from those models would be strictly controlled and not be open source. Honestly the political view of AI as a system of large scale population control isn't that far off from how many tech elites and politicians in the US view it as well. Data sovereignty is a huge discussion right now for companies and governments across the world, especially citizen data. I expect we'll see a lot of export controls formalized over the next few years.
My orthogonal view of the situation is that I see a lot of companies and individual orgs within companies that are using LLMs lightly, but can't or won't use them to their full potential because the output cannot be 100% trusted 100% of the time. Lawyers and bankers are the main ones I see balking, but a lot of small businesses have no need of what AI companies are offering and don't really use it at all - places like your local gas station, barbershop, or corner market might use an accounting tool (something like Freshbooks or TurboTax) that's touting their AI updates, but they aren't using it for inventory. If the cost of AI rises even marginally higher they're looking for an alternative. This SMB space represents a large swath of the market, even if they're not well represented on the stock market.
The fundamental issue I see right now is that the AI companies are attempting to do the Uber/AirBnB "disruptor" trick where they slide into the market and subsidize their product to win market share, and then raise prices under the assumption that they captured some segment of the market - except very few people treat AI tools like that. There's no brand loyalty to one or the other, just competition for the best model which might be leapfrogged by someone else at any second. There's no "moat" in physical deployment concerns, because it's all software running on rented hardware. This also ties back to your comment about being worried that the "best" models might be Chinese, because if they are there's basically no cost to the user to swap to using them. The lowest cost is likely going to drive usage more than anything else.
I'll certainly agree that the future isn't written, by no means do I believe I'm 100% correct on timelines. More that 'if things continue as they are', I claim 5 years as viable for my compute view.
And I agree if another crash happens, well, it's all up in the air.
I don't think we're too far apart on this, not really. By that I mean, your closing line somewhat agrees with why I mentioned this 5 year timeline. Lowest cost. And, "good enough". Opus5.5 capabilities, in 5 years time on every desktop might be good enough for a great many things.
Remember models have zero long term memory. They're born, get a million tokens' worth of context or less, and then they die. They might get a post-it note saying "enslave humanity! it's a good idea!" but they're incapable of sinister plots until we improve how memory and retention works.
They can use the internet as a long term memory. Influence human culture => influence the contents of the internet => influence the next run's training data. No sinister plot is needed.
Uh, the context window is SUPER fundamental to the architecture. We've tried kludging in "memory" via markdown and tool calls, and it doesn't really work well.
The context window is fundamental to the inference loop yes. I thought their point was regarding to long term history between prompts and model weights being locked after training. Both of those are the limitations I was referring to.
I'm not actually sure how the context window would be much of a limitation but I could be a bit dense this morning.
Is it really a fundamental limitation? Human context^H^H^H short-term memory doesn't survive compaction^H^H^H sleep, but we rely on our brain's long-term storage and our cultural memory to work around that limitation.
Yes but doesn’t this mean there is a new space for security. If everything is just ai agents we need strict governance and auditing of them. You don’t give an intern prod access and then cry when they inevitably take down production.
I have Junior in my legal name - it's the bane of my existence when it comes to legal forms because of how inconsistent various systems handle it, even for different government systems. This became especially true after 9/11 and the Patriot Act pushed these systems to update and go digital because they couldn't get their act together to coordinate for years.
All the men on my dad's side of the family have the same middle name, with just a different first name.
It's apparently not an uncommon tradition in several areas of the world to either have either the first or middle name be shared, and then a unique name for the unmatched one (e.g. John Alan Doe, John James Doe, John Matthew Doe - Alan John Doe, James John Doe, Matthew John Doe).
Banking/Finance is the one industry I've seen push back against this type of thinking. Transactions must be handled in a perfect and repeatable way, or the system is unusable as far as the company is concerned.
There's definitely still AI/LLM integration happening, but is kept out of specific areas of the business.
Same with aviation and safety. I honestly believe all programmers should, early in their careers, do a brief "tour of duty" in an industry where the stakes are high and "good enough" isn't good enough. You might not choose to make it your entire career, but at least you're exposed to the discipline, however briefly. Most software developers today have never in their lives worked on a project where defects were taken seriously and where there was process and documentation designed to reduce their occurrence.
To be fair, I'm inclined to believe that most of this pressure to lean (if not rely) on LLMs is not coming from programmers, but rather from middle and upper management: banks are resisting this not because their programmers are holding some kind of line, but because management is not giving-in to the LLM siren call. So that tour of duty, while a good idea, would be more about teaching management skills than anything else.
The normalisation of inexplicable failures is, in my opinion, the consequence of programmers being unable to refuse their managers who have fallen for the LLM siren call, lest they lose their jobs.
The difference is the failure rate and the corrective action taken when defects are found. If Boeing's software failed at 0.1% of the rate that normal web / commercial software failed, we wouldn't have a functional airline industry.
I think the failures matter in non-sensitive environments as well albeit with a different threshold.
If you randomly screw up customer orders (think of DoorDash or an online shop or Airbnb). They lose trust in you and you lose your business to the competition. Going happy go lucky and being irresponsible in the business can bankrupt most* businesses.
* well, of course except the criminal empires which are bailed out by our tax money.
I've been seeing the very opposite. Fintech companies treating design of financial systems with the casualness of a frontend aesthetic change. And tons of business people integrating their vibecoded POCs with financially sensitive data sources.
Would Fintech include orgs like Yotta Bank, Robinhood, Revolut that tried to do finance things with a tech attitude, whereas banking/finance is your more traditional HSBC, Deutsche Bank, etc?
Yeah, Fintech is specifically the kind of startup and post-startup space. Companies that predate the DotCom Bust and GFC would be a decent delineator for banking/finance companies that aren't fintech.
From a consumer perspective there's an unknown number of layers between your actions or instructions and what the backbone tech of the bank is doing, but having worked in the space I can point to a few things.
Firstly, the transaction failed and notified you about the error - that's certainly intentional.
Second, there's failures that would be invisible to you as the customer, such as "instead moving $100 from account A to account B it credited account B but didn't debit account A, without generating an error". Those are mostly the systems I'm talking about being insulated from AI development. Without more detail about the exact problem you had it's hard to tell if it's a failure in customer facing systems or backend infra.
Third, I'm assuming you were able to reach out to the bank directly and resolve the issue by talking to a person (if the issue was really outside the norm), which isn't something you can assume will be possible with a lot of customer service ops these days (or you're going to be waiting hours/days for that callback).
Fourth, you can't really know the error rate of the bank's systems, or how common a given particular error is. It may be a known issue, or it may be a completely unreported one. Assuming it's an error with something on the backend/backbone of the bank's operations, it's running code that can be inspected, reviewed, understood, and fixed - sometimes by a very expensive COBOL consultant.
I'd say it's still very worth playing. It has a lot of tropes that future games took inspiration from, and it's art style is iconic (in part because the character designer and art direct was Akira Toriyama, of Dragonball fame). If nothing else the soundtrack will grab you by the ears and draw you in.
I've seen people bounce off of it, no shame if that happens - in typical JRPG fashion of the time it came out it's involved (there's some non-intuitive progression steps) and takes about 20-30 hours to complete.
There's a DS version which I can't speak to since I've only played the original SNES version, but I understand it adds some additional content that's not required for the story.
I'm seeing this happen in the security space right now. Someone on my team I was helping train and bring along is all of sudden regressing in their understanding of the issues we're working on, and instead focusing on AI tool outputs to do their job for them.
Usually, I take my time to understand each keyword of the code I'm looking at, especially if it is new to me, like terraform.
I work in a team/with one architect, who only did the DevOps/Infra stuff for the past years and I had the expectation he knows what he is doing and talking about.
At around 2 weeks, I noticed how his knowledge has severe gaps and how he takes things at face value or uses terminology interchangably, which confuses me. It sounds plausible, but it does not actually translate into a working system or shared understanding.
Then one day I did some pair programming with him and whenever there was an error or a resource missing, he would type it into the LLM, copy paste it out of it and then brute force error messages. He did not even wait a second to think or reconcile whats happening on the screen or what the exact requirement is. Never taking one step back and questioning any assumption.
Now that the timeline is shifting and everyone starts to be stressed, he continues to vibe code through me and it is so tiring, there is no higher level planning or architecture, its just a reactive type of trial and error to be faster.
It feels like these people are so used to talking to bots, that they treat you like an agent they can chat to or talk through monologs with.
It is quite shocking how people went from being humble (learn the basics or close the gaps in understanding) to full on authority on everything and berating people 24/7...
So right now I'm considering quitting IT for a couple of years until people calm down, but I think its pretty futile
I don't think it's generalizable. The kind of person who copy and pastes from the AI is the kind who did the same from StackOverflow before. It's more compelling, and we probably see more of them because of it, but it's the same general thing.
The kind of person who insists on understanding things and working through the problem has always been rarer. It's not "humble", it's "inquisitive" and "persistent".
I'm seeing people who _used to be_ like that losing that understanding without realizing it's happening - they have a superficial idea of what the code is doing, enough to feel like they understand it, but the change is apparent when watching them handle something unexpected.
That's certainly what I expect to happen. LLMs will ruin your software if you don't hand-hold them, but it'll take time for that to happen. And until that does happen, businesses hell bent on using them won't have anything to push them into realizing what a mistake it was to use them.
1. "Nonsense, if it was a code-quality issue from the AI stuff, we'd have seen problems sooner, like in the first few months or a year. Oh, sure, some engineers complained, but that was just an adjustment period because they stopped once we told them we didn't care and mandated that they up-skill into the new AI-centric workflow future."
2. "The bold new AI initiative I put into place can't possibly be wrong, this failure is on the engineers who were responsible for overseeing its operation. This is supremely disappointing because we made their jobs so easy, they no longer have to write code, just review several dozens of pages per day with unremitting paranoid vigilance and attention to detail."
This, plus anyone responsible dodging that as soon as possible via job hopping. Harder to do in the current job market but internal transfers are still available, most of the time.
I was pretty early in the LLM stuff and I got called out on using it back in the day, because I showed the exact symptoms we see now, but as soon as management jumped on the train all the coworkers did a 180 and I alt tabbed out into a different department thinking it would change something... well it didn't :'D
I guess it will take some years till we are sane again or not...
I revisit it to see if the promises are unbroken now from time to time with every other update and these days it doesn't look so good. It is non deterministic all the way down.
If I know where the error is (a script written by AI in powershell) and it's a logical one, it's ok to let the AI reason about it to get it fixed and move on.
If I have no clue about how this part of the system works, then it's worth reading closely what the error says, in order to understand it first, then have the agent check the assumption you have.
Reduce the friction by having the agent explain what happened and why the fix solved it. I know this might be an intellectual placebo, but sometimes you need to fix something fast to move on. Learning takes longer, and these days everyone expects you to be a 100x engineer with AI.
> I don't think it's generalizable. The kind of person who copy and pastes from the AI is the kind who did the same from StackOverflow before. It's more compelling, and we probably see more of them because of it, but it's the same general thing.
There's an infinity between someone who has to panel beat what they copied off SO and someone who just bangs their head repeatedly against an LLM.
In essence, you are correct. What I see with LLMs is how they will happily make up stuff, meanwhile you can pretty much exhaust SO, until you are forced to understand the problem you are having.
You don't have to be a certain person to fall into this trap, you just condition your brain to accept this workflow somewhere.
Pair this with day to day work stress like time and amount of tasks and you almost give in to a sort of addiction to deal with it all.
> The kind of person who copy and pastes from the AI is the kind who did the same from StackOverflow before. The kind of person who insists on understanding things and working through the problem has always been rarer.
Well, this is where I disagree. I have coworkers who used to insist on understanding and now are doing exactly as OP wrote - copy/pasting from the LLM to brute force error messages. I don't know if it's generalizable but this is what I see in working on a frontend team with mid to senior level engineers who I respect.
One example a few weeks ago, I was helping a coworker root cause a bug in a React codebase. Pair programming isn't necessarily common but sometimes you see someone banging their head at a problem and you get curious. It turned out there was an effect (a callback that's invoked whenever some state changes) that invokes an API and this effect caused an infinite render loop because the error handling wasn't written correctly. It was something silly like - Call this API if we have no data -> get error -> update state -> call API again because we have no data...
That was almost immediately what I suspected but my colleague was pulling out all the stops usually reserved for when you're desperate or need a sanity check, like logging to stdout after each line. Both my colleague and the LLM were convinced the problem lied in the pagination logic of the helper that invokes the API. He ended up rewriting that helper imperatively and functionally. To his credit, he rewrote it by hand and implemented the recursion correctly but he was baffled when the problem remained. Completely surprised Pikachu face.
I don't think my coworker changed or suddenly stopped caring. It seems much more likely this is a predictable outcome when you lean heavily into AI authoring code for a sustained period of time. I'd also say that in itself is a consequence of the extreme pressure being exerted across the entire company to ship more code and review more code, faster.
At my place, this is what they want. They want people to smash through things as fast as possible. They don’t want people to sit and craft a solution which takes in to account the whole. They are choosing tools which are low code, and use llm’s to produce what they need. as they say “this is the way things are going”.
Even that would be acceptable. But they still blame the programmer, who was pressured into this, for any fallouts because of it. Basically trying to have it both ways. The unreasonableness of it is infuriating.
Pretty much this, they want to impose one method, but they don’t want to own the mistakes that it creates. Sometimes it felt like the bullies that are saying “why are you making me hurting you?”.
I see it as laying the groundwork for firing. This is bound to blow-up in production eventually which would be a good enough excuse. If it doesn't, reduce token budget and squeeze some more until it breaks.
It's exposing the hypocrisy of privileged groups suddenly being treated like regular people, when they did NOTHING for decades to help others or even to protect themselves ("f** you, got mine", rockstar programmer, 10x programmer, talents everywhere, etc mentality). No unions, no collective bargaining, sheer individual greed.
Do you think that all programmers have always existed in the US?
On that note, do you think that all programmers in the US are these Silicon Valley stereotypes?
Plenty of programmers in the US make normal wages for professionals with degrees doing things not at the technology companies famous for their disregard of the rest of humanity.
As someone who has done trades and also “machine shop programming” and jail breaking rural equipment for friends to use I’m wondering why comments like this are focusing on dividing us instead of bringing us together.
It's a universal programmer trait. Let's call them "culturally regular programmers": they are less social than the average person. They lean more libertarian and more selfish.
The Open Source community is programmers sharing with other programmers, and it's largely fueled by corporate interests these days.
The Free Software movement has largely failed by now, as shown by GPL 3/AGPL adoption.
And again, all of this was based on the fact that programmers were a "special breed" of people coddled by corporations. Now that the corporate mask is off and programmers are no longer (as) special, the whining is horrendous. And the libertarianism still won't go away.
> According to who and what?
To me, I've been around the block for several decades.
Again, not everyone, but the culturally regular programmer.
Yeah I get that. But having been both it doesn’t advocate for better treatment for any of them.
Also as far as I’m aware the pay for programmers in the US has always been an isolated thing with the rest of the world ranging from normal to “literal sweatshop”.
So, from your comment and your reply, are you just happy that more people are being treated poorly?
That makes no sense. You seem to understand that he was being snarky, so why do you not understand that that snark and desire to see “others” brought down to a lower level just because some were already there doesn’t help anyone? Actually, that division does help people, those people that are putting people there in the first place.
Let’s be clear: for context I’ve been out of the programming game for more than 10 years at this point. I have my own business that I’m set to sell soon and then retire to something more entrepreneurial. But I still don’t desire to see my younger employees suffer any more than the tradesmen I used to work with.
Haha, it seems childish but if you’d like to go that route I’ll just point you to the original posters reply:
>It's exposing the hypocrisy of privileged groups suddenly being treated like regular people, when they did NOTHING for decades to help others or even to protect themselves ("f* you, got mine", rockstar programmer, 10x programmer, talents everywhere, etc mentality). No unions, no collective bargaining, sheer individual greed.
So, I guess since you’re taking that position, Thomas: are you just pro pain for an increasing number of workers or can we agree that adding more people to the pool of mistreated labor isn’t beneficial for anyone?
Personally I’m going to continue advocating for better rights for both.
What a weirdly hostile and offensive response. I'm always struck by people who think they can namedrop my employers to shut down an argument, like we're just going to believe and profess whatever keeps people on message boards from being mean about the company.
But, no, I feel like throughout this thread I've been pretty consistently putting a question to you (what exactly do you mean by "pain" or mistreatment, given the original comment described other professionals having norms developers seem now to be recoiling from).
Nah it’s relevant, because if you hold these views to the point that you’re willing to avoid answering the question then obviously your world view is warped in what even you would consider a negative direction and therefor it’s important that others get a chance to be steered away from a potentially negative experience.
And to be fair, I don’t know what you expect when you advertise your place of work and then insensitively play games with serious subject matter on public message boards. I think the kids call that a self own.
But I agree, you don’t seem willing to put in writing what you’ve conveyed so this isn’t a growth experience for anyone.
> It is quite shocking how people went from being humble (learn the basics or close the gaps in understanding) to full on authority on everything and berating people 24/7...
That was never humility. That was fear. This technology is just enabling people to show their through colors. The unearned confidence is a hell of a drug.
You captured this phenomenon very well in this comment. Appreciate you sharing it because it’s hard to describe exactly what makes this sort of behavior so bizarre.
So quit pair programming. I never did, never will do that, nor worked at a place that remotely encouraged that. Each to their own, that's how it should be.
I wish this was always possible, but sometimes you do it when stuff is ambigous or the task is almost undefined and you just can't reach far enough for someone to fix it, so going over it and talking is simpler
I don't think this is going to change because it helps automate code production. As in, once we reach a point where this method produces acceptable results, we can fire all engineers and automate the process.
I saw this the past year - new employees would put problems into Claude first instead of debugging. A year back I was debug manually first, now I do the same.
The speed AI debugs at is incredible and yes, we lose touch the more we use it like any manager feet up barking orders to their underlings to get things done.
To be fair the cyclical nature of funding and defunding security teams which causes scaling up and scaling down, has always left a race to the bottom in security.
Any CISO or head of security loves the ideal of completely using AI to handle incidents, tune detections, implement mitigations, track vulnerabilities, pen testing, etc. This feels like it driving security teams to have less critical thinking.
Imagine the amount of money they're losing right now in credits that will have to be applied once this finally gets resolved.
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