We're building AI workflows at my company. Yes chatbots, but also more interesting/complex workflows that I won't get into. Let's just say we have the data, expertise, and industry structure to leverage AI in valuable and useful ways.
As an engineer, development still comes down to requirements gathering, solid engineering principles, and the tools we already have at our disposal - network calls, rendering the UI, orchestrating containers and job, etc.
All that is to say that I thought AI was going to be sexy, like Westworld, and not so boring...
> Sam Altman knew exactly which buttons to push. Congressional testimony about the need for regulation (from the company furthest ahead). Warnings about AI risk. OpenAI's playbook: Build in public, warn about dangers, present yourself as the responsible actor who needs resources to "do it safely."
And this is why Matt Levine calls Sam Altman the greatest business negger of all time
Whens the last time you saw management tell you which compiler or toolchain you need to use to build your code ? But now we have CEOs and management dictating how coding should be done.
In the article the author admits: "I started coding again last year. But I hadn't written production code since 2012" and then goes on to say: "While established developers debate whether AI will replace them, these kids are shipping.".
Then I ask myself, what are they selling ? and lo and behold, it is AI/ML consulting.
The author misses the deeper game: if you genuinely believe AGI is imminent, then current economic metrics become meaningless. Why optimize for revenue when the entire concept of scarcity-based economics dissolves?
The $560B for those who believe in AGI isn't about ROI using today's money-in/money-out formula; it's about power positioning for a post-capitalist transition.
Every major player knows that whoever controls the infrastructure once the threshold is crossed might control what comes after.
The "bubble" narrative assumes these actors are optimizing for quarterly returns rather than civilizational leverage.
The biggest issue with AI isn't AI itself, but the fact that it seemingly "saved" an overinflated economy. Economy needs a deep reset with high rates for longer and the AI narrative is just kicking the can down the road
Great article! I share the experience mentioned in the article, LLMs facilitate a head-on interaction with any topic. It is similar to instructional YouTube videos (that imo were already transformative) but with the ability to ask detailed questions. And this is what becomes better with each iteration. When creative communities finally settle down on generative AI there will be not just a plethora of AI slop, but so much highly creative never seen before content. It might lead to a new golden age of indie low budget movie productions.
>While established developers debate whether AI will replace them, these kids are shipping. Developers who learned their craft in the age of pull requests and sprint planning sneer at their security failures, not realizing that 'best practices' are about to flip again. The barbarians aren't at the gate. They're deploying to production.
Shipping where? What production? What kids? I've yet to see this. I see the tools everywhere, but not anything built with them. You'd think it would be getting yelled about from the mountaintops, but I'm still waiting.
I totaly agree with the author. Not even the smartphone or the iphone brought such a sudden change to so many people and in many cases, for free. I know we want to oppose this huge thing just because it doesn't make sense moraly but when you learn using this tool there is no way back.
Just imagine what is coming in the next 5-10 years. Even if the tools remain at the same level as today, people have learned to use it so well that ever sector every industry will speed up tremendously. We will see great new products and ideas emerging. Just can't wait for the revolution.
> Within weeks I built a serverless system processing 5 million social media posts daily, tracking topic clusters and emerging narratives in real-time. Then brand monitoring dashboards. Then a "robojournalist" that could deep-dive any trending story. Then hardware and firmware specs for a coffee machine. Then my first mobile app.
I think a key point from this article that I agree strongly with is the simple point that it is crucial that everyone recognise we are currently in an AI bubble.
I often find people contest this with the non-sequitur of "No, it's not a bubble, there is real value there. We are building things with it". The fact there is real value in the technology does not contradict in any way that we are in a bubble. It may even be supporting evidence for it. Compare with the dot com bubble : nobody would tell you there was no value in the internet. But it was still a bubble. A massive hyper inflated bubble. And when it popped, it left large swathes of the industry devastated even while a residual set of companies were left to carry on and build the "real" eventual internet based reworking of the entire economy which took 10 - 15 years.
People would be well advised to have a look at this point in time at who survived the dot com bubble and why.
The article resonates. AI coding assistants are cool and fun to use, but they just help solve a solved problem faster. The really exciting thing is exploring the new problems we can solve with this tool. It has really reignited my passion for building.
Have we made some significant advancements similar to what happened with the Internet back in the 90's with HTML? Yes. But, this is a bubble we are currently in just like the .com bubble with lots of irrational exuberance.
That said, that job market is not as crazy as it was during .com, in fact right now most technologists are finding it more difficult to find work at the moment. Most of this AI hype started when the employment market started to slow down. Usually these bubbles pop after the employment market goes crazy. The employment starts to go nuts when crazy money enters the picture. So if, for example the fed really starts to cut rates and/or investment starts to really pick up and we have another boom period, the tail end of that seems to historically be when the bubbles pop.
Put another way, there is a good chance that the bubble will continue to inflate for a few years before it pops.
Not a bad position to take, and very similar to my personal one (that gets immediately conflated as "LOL AI DOOMER" by the AI Booster Club): yes, this is a bubble, and yes, it will eventually pop, but the tools won't go away. What's been democratized isn't the entirety of human skills, but the narrow field of custom ML-based tooling, and that's going to change quite a lot in the decades ahead as people utilize them in unexpectedly novel ways.
It'll never be AGI or superintelligence, it won't create or cause the singularity, and it'll never be a substitute for learning, practicing, and honing skills into mastery. For the fields LLMs do displace in part or in whole, I still expect it'll largely displace the mediocre or the barely-passable, not the competent or experts. Those experts will, once the bubble pops and the hype train derails, find the novel and transformative uses for LLMs outside of building moats for big enterprises or vamping for investor capital.
I especially enjoy the on-prem/locally-run angle, as I think that is where much of the transformation will occur - in places like homes, small offices, or private datacenters where a GPU or two can accelerate novel tasks for the entity using it, without divulging data to corporate entities or outright competitors. Inference is cheap, and a modest gaming GPU or AI accelerator can easily support 99.9% of individual use cases offline, with the right supporting infrastructure (which is improving daily!).
This is a good article, but it has a flaw that I keep seeing in these. Articles like this say "I built this app, and that app, and another app, and another one". Ok, let's see them. Are they any good? Please post the github link, or link to the webpage.
I'm reminded of the motto of the Royal Society: Nullius in verba.
Somewhat related to the article, but mostly anecdotal. In SF, i have had chats with (~15) engineers who after some prodding admitted that they feel the whole AGI thing is passing them by (not that it is close). In a sense that they want to be doing something deeper, build/research something which is more than an API call (paraphrasing and not disparaging making API calls), and want to build where the action is (read: train models or be at the forefront). I understand you need a specific skillset to be in that position, just that it's slightly off putting that to do any meaningful work in this field you need a lot of compute. I understand they raised funding and what not, yet want something more than they are working on. I am not sure of the solution, but the cause sure seems to be the hype that is created currently.
I'm having trouble with the concept of claiming that one "wrote" something that was entirely, or almost-entirely, generated by AI. I use Tesla's FSD all of the time. When it's on, I'm "driving" insofar as I'm monitoring my surroundings and preparing to take over if the car does something crazy. However, I'll usually tell people that the car drove, not me.
I also don't understand the value of using AI to write stuff in loads of unfamiliar languages. I get why one might choose Rust vs. Golang vs. JavaScript depending on the mission, but I would think that those differences go away entirely when you're depending on an LLM to author something in those languages AND you aren't skilled enough in those languages to understand when something's suboptimal or not. This just feels like an express train to bankruptcy via technical debt.
I'm also having trouble with the notion of AI accelerating the creation of side projects. For me, actually writing the code (or figuring out how the language works) is part of the fun that I get from doing side projects. If I wanted to create something as quickly as possible, I'd just buy a SaaS subscription or physical version for what I want.
It's also insane to me that we're just not AT ALL considering how LLMs stunt the growth of our juniors. Spending hours banging my head against the wall on tiny bugs is how I got to where I am today. I'm going to guess that's the case for many of the people on HN as well. That learning process goes away entirely once an LLM goes into the mix. You can just ask it to fix whatever's broken, no understanding of the bug required. This is fine for seniors who know why things happen how they happen, but I can't imagine juniors making up this skill gap.
It's like learning a new language vs having your phone generate whatever in the target language. The end result is the same, but there's no way you can really learn that language with your phone doing the work, unless one assigns no value to learning that language in the first place.
Finally, I have trouble accepting the idea of giving up the keyboard once you become an "architect." I very much understand that us "architects" have less free time in the day to fire up the IDE (death by meetings, basically), but giving that up entirely feels somewhat career-limiting to me. Then again, this is a moot point if the market moves towards making software development an AI-only activity.
What's crazy to me is that most developers and architects sneered at low/no-code solutions because they created unmaintainable codebases that were too proprietary to make sense of, yet here we are lapping up code generated by "coder" LLMs and accepting that they "might" produce insecure code here and there. Insane.
Fair enough but he doesn't give much factual reasoning to support that. If you believe the brain is a biological computer and AI computing keeps advancing, at some point it will be able to do the same stuff or better, which is what most people think of as AGI.
I wrote about that for my uni entrance exam 43 years ago and it's always just seemed obvious common sense to me. I know Turing wrote about it before then but I never read that - it's just seems kind of obvious it'll happen.
I'm not sure what mathematical principle you could invoke that would make sense?
Roger Penrose made an argument like we can know Gödel's theorem is true without being able to prove it but AI can't, but I think you can figure both are guessing in a similar pattern recognising kind of way.
This is the most coherent and IMO accurate take on AI/LLM I have seen in 5 years.
As a specialist in one of the original industries Geoffrey Hinton predicted would be gone (Radiology) my job remains safe and even more in demand 9 years later.
Meanwhile, as a hobbyist programmer, I’m suddenly able to build multiple production tools solving real problems, simply because AI agents are doing the scut-work for me and optimising my time into code review and architectural design. For $200/month, it’s paying for itself many times over.
30 comments
[ 5.2 ms ] story [ 51.0 ms ] threadAs an engineer, development still comes down to requirements gathering, solid engineering principles, and the tools we already have at our disposal - network calls, rendering the UI, orchestrating containers and job, etc.
All that is to say that I thought AI was going to be sexy, like Westworld, and not so boring...
Westworld robots are still a long way off, but think about how far we’ve come so quickly.
It’s pretty incredible that natural language computing is now seen as boring when it barely even existed 5 years ago.
And this is why Matt Levine calls Sam Altman the greatest business negger of all time
>Look inside.
>Written by someone having a stake in LLM business.
Every time.
Whens the last time you saw management tell you which compiler or toolchain you need to use to build your code ? But now we have CEOs and management dictating how coding should be done.
In the article the author admits: "I started coding again last year. But I hadn't written production code since 2012" and then goes on to say: "While established developers debate whether AI will replace them, these kids are shipping.".
Then I ask myself, what are they selling ? and lo and behold, it is AI/ML consulting.
Asking for a friend.
The $560B for those who believe in AGI isn't about ROI using today's money-in/money-out formula; it's about power positioning for a post-capitalist transition.
Every major player knows that whoever controls the infrastructure once the threshold is crossed might control what comes after.
The "bubble" narrative assumes these actors are optimizing for quarterly returns rather than civilizational leverage.
Shipping where? What production? What kids? I've yet to see this. I see the tools everywhere, but not anything built with them. You'd think it would be getting yelled about from the mountaintops, but I'm still waiting.
I call bullshit. Let's see some repos.
I often find people contest this with the non-sequitur of "No, it's not a bubble, there is real value there. We are building things with it". The fact there is real value in the technology does not contradict in any way that we are in a bubble. It may even be supporting evidence for it. Compare with the dot com bubble : nobody would tell you there was no value in the internet. But it was still a bubble. A massive hyper inflated bubble. And when it popped, it left large swathes of the industry devastated even while a residual set of companies were left to carry on and build the "real" eventual internet based reworking of the entire economy which took 10 - 15 years.
People would be well advised to have a look at this point in time at who survived the dot com bubble and why.
That said, that job market is not as crazy as it was during .com, in fact right now most technologists are finding it more difficult to find work at the moment. Most of this AI hype started when the employment market started to slow down. Usually these bubbles pop after the employment market goes crazy. The employment starts to go nuts when crazy money enters the picture. So if, for example the fed really starts to cut rates and/or investment starts to really pick up and we have another boom period, the tail end of that seems to historically be when the bubbles pop.
Put another way, there is a good chance that the bubble will continue to inflate for a few years before it pops.
It'll never be AGI or superintelligence, it won't create or cause the singularity, and it'll never be a substitute for learning, practicing, and honing skills into mastery. For the fields LLMs do displace in part or in whole, I still expect it'll largely displace the mediocre or the barely-passable, not the competent or experts. Those experts will, once the bubble pops and the hype train derails, find the novel and transformative uses for LLMs outside of building moats for big enterprises or vamping for investor capital.
I especially enjoy the on-prem/locally-run angle, as I think that is where much of the transformation will occur - in places like homes, small offices, or private datacenters where a GPU or two can accelerate novel tasks for the entity using it, without divulging data to corporate entities or outright competitors. Inference is cheap, and a modest gaming GPU or AI accelerator can easily support 99.9% of individual use cases offline, with the right supporting infrastructure (which is improving daily!).
All in all, an excellent post.
I'm reminded of the motto of the Royal Society: Nullius in verba.
I also don't understand the value of using AI to write stuff in loads of unfamiliar languages. I get why one might choose Rust vs. Golang vs. JavaScript depending on the mission, but I would think that those differences go away entirely when you're depending on an LLM to author something in those languages AND you aren't skilled enough in those languages to understand when something's suboptimal or not. This just feels like an express train to bankruptcy via technical debt.
I'm also having trouble with the notion of AI accelerating the creation of side projects. For me, actually writing the code (or figuring out how the language works) is part of the fun that I get from doing side projects. If I wanted to create something as quickly as possible, I'd just buy a SaaS subscription or physical version for what I want.
It's also insane to me that we're just not AT ALL considering how LLMs stunt the growth of our juniors. Spending hours banging my head against the wall on tiny bugs is how I got to where I am today. I'm going to guess that's the case for many of the people on HN as well. That learning process goes away entirely once an LLM goes into the mix. You can just ask it to fix whatever's broken, no understanding of the bug required. This is fine for seniors who know why things happen how they happen, but I can't imagine juniors making up this skill gap.
It's like learning a new language vs having your phone generate whatever in the target language. The end result is the same, but there's no way you can really learn that language with your phone doing the work, unless one assigns no value to learning that language in the first place.
Finally, I have trouble accepting the idea of giving up the keyboard once you become an "architect." I very much understand that us "architects" have less free time in the day to fire up the IDE (death by meetings, basically), but giving that up entirely feels somewhat career-limiting to me. Then again, this is a moot point if the market moves towards making software development an AI-only activity.
What's crazy to me is that most developers and architects sneered at low/no-code solutions because they created unmaintainable codebases that were too proprietary to make sense of, yet here we are lapping up code generated by "coder" LLMs and accepting that they "might" produce insecure code here and there. Insane.
Fair enough but he doesn't give much factual reasoning to support that. If you believe the brain is a biological computer and AI computing keeps advancing, at some point it will be able to do the same stuff or better, which is what most people think of as AGI.
I wrote about that for my uni entrance exam 43 years ago and it's always just seemed obvious common sense to me. I know Turing wrote about it before then but I never read that - it's just seems kind of obvious it'll happen.
Roger Penrose made an argument like we can know Gödel's theorem is true without being able to prove it but AI can't, but I think you can figure both are guessing in a similar pattern recognising kind of way.
As a specialist in one of the original industries Geoffrey Hinton predicted would be gone (Radiology) my job remains safe and even more in demand 9 years later.
Meanwhile, as a hobbyist programmer, I’m suddenly able to build multiple production tools solving real problems, simply because AI agents are doing the scut-work for me and optimising my time into code review and architectural design. For $200/month, it’s paying for itself many times over.