| ▲ | prisenco 2 days ago |
| For junior devs wondering if they picked the right path, remember that the world still needs software, ai still breaks down at even a small bit of complexity, and the first ones to abandon this career will be those who only did it for money anyways and they’ll do the same once the trades have a rough year (as they always do). In the meantime keep learning and practicing cs fundamentals, ignore hype and build something interesting. |
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| ▲ | kragen 2 days ago | parent | next [-] |
| Nobody has any idea what AI is going to look like five years from now. Five years ago we had GPT-2; AI couldn't code at all. Five years from now AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos. Anyone who tells you they know what the future looks like five years from now is lying. |
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| ▲ | noosphr 2 days ago | parent | next [-] | | Unless we have another breakthrough like attention we do know that AI will keep struggling with context and costs will grow quadratically with context. On a codebase of 10,000 lines any action will cost 100,000,000 AI units. One with 1,000,000 it will cost 1,000,000,000,000 AI units. I work on these things for a living and no one else seems to ever think two steps ahead on what the mathematical limitations of the transformer architecture mean for transformer based applications. | | |
| ▲ | kragen 2 days ago | parent [-] | | It's only been 8 years since the attention breakthrough. Since then we've had "sparsely-gated MoE", RLHF, BERT, "Scaling Laws", Dall-E, LoRA, CoT, AlphaFold 2, "Parameter-Efficient Fine-Tuning", and DeepSeek's training cost breakthrough. AI researchers rather than physicists or chemists won the Nobel Prizes in physics and (for AlphaFold) chemistry last year. Agentic software development, MCP, and video generation are more or less new this year. Humans also keep struggling with context, so while large contexts may limit AI performance, they won't necessarily prevent them from being strongly superhuman. | | |
| ▲ | BobbyTables2 2 days ago | parent | next [-] | | I think it’s currently too easy to get drunk on easy success cases for AI. It’s like asking a college student 4th grade math questions and then being impressed they knew the answer. I’ve use copilot a lot. Faster then google, gives great results. Today I asked it for the name of a French restaurant that closed in my area a few years ago. The first answer was a Chinese fusion place… all the others were off too. Sure, keep questions confined to something it was heavily trained on, answers will be great. But yeah, AI going to get rid of a lot of low skilled labor. | | |
| ▲ | kragen 2 days ago | parent | next [-] | | Sure, we might have hit a wall in some important sense, where further progress on some kinds of abilities is blocked until we try something totally different. But we might not. Nobody has any clue. | |
| ▲ | BoiledCabbage 2 days ago | parent | prev | next [-] | | > Today I asked it for the name of a French restaurant that closed in my area a few years ago. The first answer was a Chinese fusion place… all the others were off too. What's the point of this anecdote? That it's not omniscient? Nobody is should be thinking that it is. I can ask it how many coins I have in my pocket and I bet you it won't know that either. | |
| ▲ | CamperBob2 2 days ago | parent | prev [-] | | It’s like asking a college student 4th grade math questions and then being impressed they knew the answer. No, it's more like asking a 4th-grader college math questions, and then desperately looking for ways to not be impressed when they get it right. Today I asked it for the name of a French restaurant that closed in my area a few years ago. The first answer was a Chinese fusion place… all the others were off too. What would have been impressive is if the model had replied, "WTF, do I look like Google? Look it up there, dumbass." |
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| ▲ | lossolo 2 days ago | parent | prev [-] | | > Since then we've had "sparsely-gated MoE", RLHF, BERT, "Scaling Laws", Dall-E, LoRA, CoT, AlphaFold 2, "Parameter-Efficient Fine-Tuning", and DeepSeek's training cost breakthrough. OK, I will bite. So "Sparsely-gated MoE" isn’t some new intelligence, it's a sharding trick. You trade parameter count for FLOPs/latency with a router. And MoE predates transformers anyway. RLHF is packaging. Supervised finetune on instructions, learn a reward model, then nudge the policy. That’s a training objective swap plus preference data. It's useful, but not breakthrough. CoT is a prompting hack to force the same model to externalize intermediate tokens. The capability was there, you’re just sampling a longer trajectory. It’s UX for sampling. Scaling laws are an empirical fit telling you "buy more compute and data" That’s a budgeting guideline, not new math or architecture. https://www.reddit.com/r/ProgrammerHumor/comments/8c1i45/sta... LoRA is linear algebra 101, low rank adapters to cut training cost and avoid touching the full weights. The base capability still comes from the giant pretrained transformer. AlphaFold 2’s magic is mostly attention + A LOT of domain data/priors (MSAs, structures, evolutionary signal). Again attention core + data engineering. "DeepSeek’s cost breakthrough" is systems engineering. Agentic software dev/MCP is orchestration, that’s middleware and protocols, it helps use the model, it doesn’t make the model smarter. Video generation? Diffusion with temporal conditioning and better consistency losses. It’s DALL-E style tech stretched across time with tons of data curation and filtering. Most headline "wins" are compiler and kernel wins: FlashAttention, paged KV-cache, speculative decoding, distillation, quantization (8/4 bit), ZeRO/FSDP/TP/PP... These only move the cost curve, not the intelligence. The biggest single driver the last few years has been the data so de dup, document quality scores, aggressive filtration, mixture balancing (web/code/math), synthetic bootstrapping, eval driven rewrites etc etc. You can swap half a dozen training "tricks" and get similar results if your data mix and scale are right. For me a real post attention "breakthrough", would be something like: training that learns abstractions with sample efficiency far beyond scaling laws, reliable formal reasoning, causal/world-model learning that transfers out of distribution. None of the things you listed do that. Almost everything since attention is optimization, ops, and data curation. I mean give me exact pretrain mix, filtering heuristics, and finetuning datasets for Claude/GPT-5 and without peeking at the secret sauce architecture I can get close just by matching tokens, quality filters and training schedule. The "breakthroughs" are mostly better ways to spend compute and clean data, not new ways to think. | | |
| ▲ | kianN 2 days ago | parent | next [-] | | This is a great summary of why despite so much progress/tricks being discovered, so little progress to the core limitations to LLMs are made. | |
| ▲ | kragen 2 days ago | parent | prev | next [-] | | I don't disagree with any of this, though it sounds like you know more about it than I do. | |
| ▲ | BobbyTables2 2 days ago | parent | prev [-] | | Indeed. I’m shocked that we train “AI” pretty much as one would build a fancy auto-complete. Not necessarily a bad approach but feels like something is missing for it to be “intelligent”. Should really be called “artificial knowledge” instead. | | |
| ▲ | jofla_net 2 days ago | parent | next [-] | | This and parent are both approaching toward what I see as the main obstacle, that we as a species don't know how in its entirety a human mind thinks (and it varies among people), so trying to "model" it and reproduce it is reduced to a game of black-boxing.
We black box the mind in terms of what situations its been seen to be in and how it has performed, the millions of correlative inputs/outputs are the training data. Yet, since we don't know the fullness of the interior we can only see its outputs it becomes somewhat of a Plato's cave situation. We believe it 'thinks' this way but again we cannot empirically say it performed a task a certain way, so unlike most other engineering problems, we are grasping at straws while trying to reconstruct it.
This doesn't not mean that a human mind's inner-workings can't ever be %100 reproduced, but not until we know it further. | | |
| ▲ | tempodox 2 days ago | parent [-] | | And there is another important difference: Our environments have oodles of details that inform us, while LLM training data is just “everything humans have ever written”. Those are completely different things. And LLMs have no concept of facts, only statements about facts in their training data that may or may not be true. |
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| ▲ | kragen 2 days ago | parent | prev [-] | | "What do you mean, they talk?" "They talk by flapping their meat at each other!" |
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| ▲ | tmn 2 days ago | parent | prev [-] | | There’s a significant difference between predicting what it will specifically look like, and predicting sets of possibilities it won’t look like | | |
| ▲ | kragen 2 days ago | parent [-] | | No, there isn't. When speaking of logically consistent possibilities, the two problems are precisely isomorphic under Boolean negation. | | |
| ▲ | bryanrasmussen 2 days ago | parent [-] | | good point, someone recently said > Five years from now AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos. do all these seem logically consistent possibilities to you? | | |
| ▲ | kragen 2 days ago | parent [-] | | Yes, obviously. You presumably don't know what "consistent" means in logic, and your untutored intuition is misleading you into guessing that possibilities like those could conceivably be inconsistent. https://en.m.wikipedia.org/wiki/Consistency | | |
| ▲ | bryanrasmussen 2 days ago | parent [-] | | or I just wanted to make sure that you were adamant that the list of those three possibilities were equally probable, to reiterate > AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos. that each of these things, being logically consistent, have equal chances of being the case 5 years from now? | | |
| ▲ | kragen 2 days ago | parent [-] | | No. Fuck off. There's no uniform probability distribution over the reals, so stop trying to put bullshit in my mouth. | | |
| ▲ | bryanrasmussen 2 days ago | parent [-] | | OK well you obviously seem to be having some bad time about something in your life right now so I won't continue, other than to note the comment that started this said >There’s a significant difference between predicting what it will specifically look like, and predicting sets of possibilities it won’t look like which I took to mean there are probability distributions around what things will happen, and it seemed to be your assertion that there wasn't, that a number of things only one of which seemed especially probable, were equally probable. I'm glad to learn you don't think this as it seems totally crazy, especially for someone praising LLMs which after all spend their time making millions of little choices based on probability. |
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| ▲ | tombert 2 days ago | parent | prev | next [-] |
| I think the concern isn't so much about the current state of AI replacing software engineers, but more "what if it keeps getting better at this same rate?" I don't really agree with the reasoning [1], and I don't think we can expect this same rate of progress indefinitely, but I do understand the concern. [1] https://en.wikipedia.org/wiki/Jevons_paradox |
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| ▲ | mitthrowaway2 2 days ago | parent | next [-] | | Jevons paradox doesn't always apply (it depends on the shape of supply-demand curves) and it is entirely possible for technology to eliminate careers. For example, a professional translator can work far faster now than twenty years ago, but the result is that positions for professional translators are rapidly disappearing rather than growing. There's a finite demand for paid translation work and it's fairly saturated. There are also far fewer personal secretaries now than there were in the '70s. That used to be a very common and reasonably well-paying career. It may happen that increasing the efficiency of software development results in even more and even-better-paid software developers, but this isn't a guaranteed outcome. | |
| ▲ | prisenco 2 days ago | parent | prev | next [-] | | | "what if it keeps getting better at this same rate?" All relevant and recent evidence points to logarithmic improvement, not the exponential we were told (promised) in the beginning. We're likely waiting at this point for another breakthrough on the level of the attention paper. That could be next year, it could be 5-10 years from now, it could be 50 years from now. There's no point in prediction. | | |
| ▲ | tombert 2 days ago | parent | next [-] | | Yeah, that's how I feel about it. People like to assume that progress is this steady upward line, but I think it's more like a staircase. Someone comes up with something cool, there's a lot of amazing progress in the short-to-mid term, and then things kind of level out. I mean, hell, this isn't even the first time that this has happened with AI [1]. The newer AI models are pretty cool but I think we're getting into the "leveling out" phase of it. [1] https://en.wikipedia.org/wiki/AI_winter | | |
| ▲ | themafia 2 days ago | parent [-] | | The main problem with the current technology, to my eye, is you need these huge multi dimensional models with extremely lossy encoding in order to implement the system on a modern CPU which is effectively a 2.5D piece of hardware that ultimately accesses a 1D array of memory. Your exponential problems have exponential problems. Scaling this system is factorially hard. |
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| ▲ | themafia 2 days ago | parent | prev | next [-] | | > logarithmic improvement Relative to time. Not relative to capital investment. There it's nearly perfectly linear. | | |
| ▲ | shikon7 2 days ago | parent | next [-] | | Shouldn't it be the other way round, linear to time, and logarithmic relative to (the exponentially growing) capital investment? | |
| ▲ | prisenco 2 days ago | parent | prev | next [-] | | I don't follow, can you explain more? | |
| ▲ | 2 days ago | parent | prev [-] | | [deleted] |
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| ▲ | BoiledCabbage 2 days ago | parent | prev [-] | | > All relevant and recent evidence points to logarithmic improvement, Any citations for this pretty strong assertion? And please don't reply with "oh you can just tell by feel". |
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| ▲ | echelon 2 days ago | parent | prev | next [-] | | If software developers wind up replaced by AI, I think it's safe to say every industry's labor will be replaced. Trade jobs won't be far behind, because robotics will be nipping at their heels. If software falls, everything falls. But as we've seen, these models can't do the job themselves. They're best thought of as an exoskeleton that requires a pilot. They make mistakes, and those mistakes multiply into a mess if a human isn't around. They don't get the big picture, and it's not clear they ever will with the current models and techniques. The only field that has truly been disrupted is graphics design and art. The image and video models are sublime and truly deliver 10,000x speed, cost, and talent reductions. This is probably for three reasons: 1. There's so much straightforward training data 2. The laws of optics and structure seem correspondingly easier than the rules governing intelligence. Simple animals evolved vision hundreds of millions of years ago, and we have all the math and algorithmic implementations already. Not so, for intelligence. 3. Mistakes don't multiply. You can brush up the canvas easily and deliver the job as a smaller work than, say, a 100k LOC program with failure modes. | | |
| ▲ | bc569a80a344f9c 2 days ago | parent | next [-] | | > If software developers wind up replaced by AI, I think it's safe to say every industry's labor will be replaced. Trade jobs won't be far behind, because robotics will be nipping at their heels.
If software falls, everything falls. I don’t think that follows at all. Robotics is notably much, much, much harder than AI/ML. You can replace programmers without robotics. You can’t replace trades without them. | | |
| ▲ | echelon 2 days ago | parent | next [-] | | > Robotics is notably much, much, much harder than AI/ML. Are you so sure? Almost every animal has solved locomotion, some even with incredibly primitive brains. Evolution knocked this out of the park hundreds of millions of years ago. Drosophila can do it, and we've mapped their brains. Only a few animals have solved reasoning. I'm sure the robotics videos I've seen lately have been cherry picked, but the results are nothing short of astounding. And there are now hundreds of billions of dollars being poured into solving it. I'd wager humans stumble across something evolution had a cake walk with before they stumble across the thing that's only happened once in the known universe. | | |
| ▲ | bc569a80a344f9c 2 days ago | parent | next [-] | | Yes, robotics is harder. Here’s some links. Wiki as an intro, and a reasonably entertaining write up that explains the concept in some depth, specifically comparing the issue to LLM progress as of 2024 https://en.m.wikipedia.org/wiki/Moravec%27s_paradox https://harimus.github.io/2024/05/31/motortask.html Edit: just to specifically address your argument, doing something evolution has optimized for hundreds of millions of years is much harder than something evolution “came up with” very recently (abstract thought). | | |
| ▲ | echelon 2 days ago | parent [-] | | > Edit: just to specifically address your argument, doing something evolution has optimized for hundreds of millions of years is much harder than something evolution “came up with” very recently (abstract thought). You've got this backwards. If evolution stumbled upon locomotion early -- and several times independently through convergent evolution --, that means it's an easy problem, relatively speaking. We've come up with math and heuristics for robotics (just like vision and optics). We're turning up completely empty for intelligence. |
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| ▲ | Avshalom 2 days ago | parent | prev | next [-] | | Well a large chunk of HN thinks the existing generation of AI is capable of doing 80% of their job, this has not translated at all to robotic stevedores and even less to robotic plumbers so yeah all current evidence supports "Robotics is notably much, much, much harder than AI/ML" | |
| ▲ | bryanrasmussen 2 days ago | parent | prev [-] | | >Almost every animal has solved locomotion, some even with incredibly primitive brains. Evolution knocked this out of the park hundreds of millions of years ago. >Only a few animals have solved reasoning. the assumption here seems to be that reasoning will be able to do what evolution did hundreds of millions of years ago (with billions of years of work being put into that doing) much easier than evolution did for.. some reason that is not exactly expressed? logically also I should note that given the premises laid out by the first quoted paragraph the second quoted paragraph should not be "only a few animals have solved reasoning" it should be "evolution has only solved reasoning a few times" |
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| ▲ | ares623 2 days ago | parent | prev [-] | | There will be millions of meat based robots lining up to flood the market when every knowledge based worker is displaced. | | |
| ▲ | esseph 2 days ago | parent [-] | | Driving down the value of their labor, but still not competitive enough globally because it's just so much cheaper in other countries for that labor. | | |
| ▲ | grumple a day ago | parent [-] | | A laborer in Asia can't install plumbing in America, install electrical systems in America, etc... We also should end the exploitative nature of globalization. Outsourced work should be held to the same standards as laborers in modern countries (preferably EU, rather than American, standards). |
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| ▲ | tombert 2 days ago | parent | prev | next [-] | | I think this is making an assumption that the number of potential jobs is fixed. I don't agree with that assumption. I think as people learn how to use these tools then more industries pop up to use those tools. ETA: You updated your post and I think I agree with most of what you said after you updated. | |
| ▲ | BobbyTables2 2 days ago | parent | prev [-] | | If AI robots can replace labor, then they’ll figure out humanity only gets in their way. |
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| ▲ | teaearlgraycold 2 days ago | parent | prev [-] | | You imply the models have been improving in some capacity. | | |
| ▲ | Bolwin 2 days ago | parent [-] | | You really think gpt 3 could do half the things current models do? | | |
| ▲ | teaearlgraycold 20 hours ago | parent [-] | | Gotta go all the way back to GPT3 to make that point? Everything has plateaued since GPT4. And yes, they’ve added all sorts of nice features like structured outputs and big context windows. |
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| ▲ | echelon 2 days ago | parent | prev | next [-] |
| AI coding isn't eating the industry, offshoring is. Inflation, end of ZIRP, and IRS section 174 kicked this off back in 2022 before AI coding was even a thing. Junior devs won't lose jobs to AI. They'll lose jobs to the global market. American software developers have lost the stranglehold on the job market. |
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| ▲ | linotype 2 days ago | parent [-] | | Section 174 has been restored, thankfully. We’ll see if the damage is done. | | |
| ▲ | echelon 2 days ago | parent [-] | | Fingers crossed. The interest rate is still a tremendously bad problem. If it had been ZIRP and low interest, companies would have just borrowed to cover the amortization that 174 introduced. But unfortunately money doesn't grow on trees anymore. |
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| ▲ | RobRivera 2 days ago | parent | prev | next [-] |
| >ignore hyp and build something interesting AND don't be afraid to start small! Making a discord or twitch chat bot, quake mod, a silly sound board of all ypur favorite Groot quotes. |
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| ▲ | cyanydeez 2 days ago | parent | prev [-] |
| Also, morals and ethics are optional! |
