7 ms·

I see many comments saying, "AI can't do X with 80-100% accuracy; therefore our professions are in good hands." While I don't want to sound overly pessimistic,

I see many comments saying, "AI can't do X with 80-100% accuracy; therefore our professions are in good hands." While I don't want to sound overly pessimistic, the models are improving at a rapid rate. If asked ~3 years ago where the state of the models are today, it would sound like sci-fi if answered, "the models are creating full MVP apps in ~30 minutes with one prompt". The hurdles the models are facing now, like reducing hallucination rates, ensuring compliance, and keeping a clean codebase, do not seem far away from being resolved IMO. Fetching specific information is already partially done with various MCP servers / RAG. I am, of course, a bit worried about the future of software engineers. If these quirks are resolved, where do their professions fit in the industry? Delegating tasks to the AI model? Unfortunately, this does not require years of expertise, which is a double-edged sword. Reviewing AI's output? Ask it to explain each line not understood. I think we will see more waves of larger layoffs, similar to how human computers were replaced by digital computers. To some, doing complex mathematical calculations mentally is a fun task / challenge, but it is ultimately significantly slower and more error-prone than calculating with a computer. In the same way, I think hand-crafting code will be seen as a fun "challenge" and AI will be seen as the "modern-day calculator".

rsalus 3mo ago
I don't know, even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope. I think we'll see short-term layoffs and a more stratified engineering field during the transition, but the fundamental need for deep technical expertise isn't going away.
- coldtea 3mo ago
  >I don't know, even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope. Not really. It will be a cuttthroat landscape, and the scope wont matter as much anymore. First because everyone else will equally be able to throw LLMs at the scope, but also because the scope has natural limits: your market fit, customer expectations, and (for software/hw products) physical world/manufacturing limitations. They'll want to reduce their margins.
  - rsalus 3mo ago
    AI usage will directly impact said margins. Moreover, for the scenario you describe, companies need to have the capability to precisely estimate the cost of a given deliverable - not something possible with current tooling + models. You're also underestimating the market trend towards vertical integration: companies are not going to be constrained by a sector or niche. They will expand to capture as much value as they can, because now their capacity to do so is partially decoupled from labor. It will certainly be a cutthroat landscape for engineers, but companies will be building _more_ capacity, not less. In other words, the demand won't disappear for skilled technical labor, it will just move higher up the value chain.
    - coldtea 3mo ago
      >You're also underestimating the market trend towards vertical integration: companies are not going to be constrained by a sector or niche. They will expand to capture as much value as they can, because now their capacity to do so is partially decoupled from labor. They will still totally be, because the capacity to do so was never coupled to labor, it was coupled to domain knowledge, client network, other players dominating the market, and so on...
- insane_dreamer 3mo ago
  > even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope. 1) they won't, they'll just cut costs or 2) they will, but unless it's a new scope or one that can absorb growth, they'll just be competing with other companies in the space and taking away business from them either way, labor loses
- pjmlp 3mo ago
  No, my life experience tells me those companies will fire the ones they no longer need instead.
p2detar 3mo ago
Why would it stop with just developer layoffs? When software companies rely on LLM providers to run their business, I’d argue we‘ll see a massive bust of these companies around the world - from on-prem products to SaaS. Customers may build the software they need entirely in-house or via prompt-engineer consultants, without the need to buy software tools like today. It could be a very very different world.
- davnicwil 3mo ago
  This won't happen in most cases because the valuable thing is largely the knowledge encoded in the software, which the buyers of the software don't have and don't want to have since they're focused on their own business. There's also, of course, the not insignificant value in the software itself actually working, being operated, being updated when necessary, all of that. Again just extra hassle no business will want to shoulder when they can just buy something that does it for them.
  - ai_fry_ur_brain 3mo ago
    Why would I build my own CRM instead of paying 50k a year or whatever? The engineer plus tokens for maintenance will cost you way more than 50k. These people are delusional and just repeating delusional vibe coder tweets.
    - bix6 3mo ago
      Not every business has $50k for a CRM
      - dakolli 3mo ago
        [flagged]
        horsawlarway 3mo ago
        I'm going to throw out two counter arguments. 1. There is value in a tool that solves precisely your needs, in the way you want it solved. I've repeatedly seen enterprise SaaS purchases where the company ends up wrapping/layering on top additional tooling, software, and infra to solve core needs that are absent (or misaligned) from the saas tooling, but required for their specific usage. I've directly experienced this with: analytics tooling, customer survey tooling, feature flag tooling, and interview tooling. If your going to dedicate a dev anyways - the numbers can change here. Is this every SaaS product for every business? Fuck no - but there are products that might be adjacant to your core business where you have both strong preferences & experience, are already spending for customization, and now it makes sense to pull the whole thing in-house. 2. The 50k/m crm is competing with that 500/m crm. which realistically appears to soon be competing with that 50/m. Even if we stick with your stated observation that end businesses don't benefit from building their own tooling (which is fair and often true, although I'd wager it's not as clear-cut as you imply) - you're dismissing competition that is absolutely willing to undercut the market because they can slip on quality (slop - as you say) but still serve a need to customers who place cost as the primary buying metric. If the customer is better served by the 500/m crm, why stop there? Why not go for the 100/m crm? The 50/m crm? Why not chug on down to the lowest possible cost competition, which likely will be 2-5 guys with an llm they ask to go copy "[insert crm of choice]", and then bill just slightly over infra costs. Or the other thing I'm seeing happen in a lot of spaces right now... the "do it all" SaaS companies, that are pumping out into adjacent verticals that previously would have been too expensive to develop. The bill stays the same, but now it's not just a crm, it's the original crm, plus a clone of all the adjacent market leaders... scheduling, billing & invoicing, marketing, SEO, site hosting and design, social engagement, etc... One SaaS elbowing into other verticals but keeping the bill the same, which I consider functionally equivalent to the competing on price, just wrapped in a different flavor (it won't be 2 dudes in a basement, it'll be 2 dudes on the "crm" squad in a bigger eng dept).
downrightmike 3mo ago
That's what the AI comapnies would like, but they can't pay back the 100's of billions they are blowing without 10,000x the price they charge. The investors won't allow it. we're not even in a revenue cycle yet and they are already trying to dump their deep losses on retail by trying to IPO
mrandish 3mo ago
> the models are improving at a rapid rate. If asked ~3 years ago where the state of the models are today, it would sound like sci-fi Absolutely true, many things will continue to improve in significant ways. However, if we look at the modern history of rapid disruptions driven by technology (a side interest of mine), persistent patterns emerge. Similar to avalanches or flash floods, such periods of very rapid disruption are often triggered by one or more significant breakthroughs in certain technologies. Early rates of change tend to be fast and furious but eventually begin to taper as recently unlocked low-hanging fruit is harvested and those racing through newly found terrain encounter all-new significant barriers and points of friction. Early in such periods, extrapolating the recent extraordinary rates of change forward has poor predictive power. Sudden extreme bursts tend to regress back toward the long-term trend line. Arguably, the current disruption in LLMs can be traced to post ~2010 research slowly building to the 2017 transformer paper and the adjacent work it quickly inspired. So today is, arguably, mid or late-ish in the LLM rapid burst phase. The rate of fundamental, broad-based breakthroughs lifting all LLM applications has clearly slowed with many of the most impactful recent discoveries being in scaling, optimization, tuning and productization toward specific domains. That doesn't mean there can't be another transformer breakthrough tomorrow but, historically, black swans rarely travel in flocks.
- jason_oster 3mo ago
  Progress happens in a series of S-curves. While your observation is correct that advances occur initially rapidly then taper off, the next step tends to arrive sooner than the previous, and with greater magnitude [1]. Tim Urban's article from 2015 has a great explanation of this phenomenon [2]. [1]: https://ourworldindata.org/technology-long-run https://ourworldindata.org/technology-long-run [2]: https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html https://waitbutwhy.com/2015/01/artificial-intelligence-revol...
- jvanderbot 3mo ago
  This is of course true in general. But the question is not "how with this evolve" but how will we deal with the rapid changes in the industry? I suspect a long term k-shape salary curve, even worse than today, with the lower 80-90pctile salaries bottoming out such that many have to exit the industry to make ends meet. You can laugh and blame them for not saving as much as they should, but that's still a fairly horrifying prospect for most of us. I think a _lot_ about stock trading a profession vs algorithmic trading. It was brutal - suicides, many pivoting out to doing car dealership-style work. Probably a 1/10 or 1/20 survivor rate every couple years, with almost all of it a very painful five year period.
techblueberry 3mo ago
What is your theory of when AI gets to 100%. PMs and business analysts build all the software? Or just like a 700 or so 1-founder companies in the world and everyone else is without work? The matrix?
- retinaros 3mo ago
  Why would you need a pm or a biz analyst
  - kibwen 3mo ago
    Better question, why would you need a CEO?
    - therealdrag0 3mo ago
      RACI
- rfgplk 3mo ago
  > Or just like a 700 or so 1-founder companies in the world and everyone else is without work? This. But instead of 700 it's more likely that everyone will be a founder (more or less). It's already scary how easy it is to launch an MVP or produce prototypes with the latest models.
  - nearbuy 3mo ago
    Until an LLM becomes better at coming up with products people want and running a company. Then we'll cut out the founders and have LLM run companies.
  - timr 3mo ago
    > It's already scary how easy it is to launch an MVP or produce prototypes with the latest models. No it isn’t. The things that were hard are now harder. The things that were comparatively easy are now easier. But if you build another piece of vibe-coded crap in a world awash in vibe-coded crap, you will not stand out. Nobody cares about your unpolished, one-shot prototype, so cranking them out faster is not really helpful. Differentiation is always a problem of effort and care, and this isn’t going to change.
dyauspitr 3mo ago
I think we’ll see a lot of layoffs and then the tech industry is going to become more vertically, integrated with product, business analysts and developers all combining into one role. It sucks because there goes one of the highest being roles in America right now that actually employed a lot of people. Ironically, I don’t think tech support is going to be fully replaced by these anytime soon. That’s one place you definitely need to have actual people talking to other people. Lawyers and doctors are gonna be legally protected too because you still need a human to sign off on all those actions though we will probably need far fewer.
- ai_fry_ur_brain 3mo ago
  [flagged]
pharos92 3mo ago
I would highly encourage you to watch this short clip https://youtu.be/5eqRuVp65eY?si=3fLT6S5q2OIUcu6r https://youtu.be/5eqRuVp65eY?si=3fLT6S5q2OIUcu6r
- onel 3mo ago
  That is a great video, but short is very subjective, the video is 24 minutes long :))
ai_fry_ur_brain 3mo ago
[flagged]
- azan_ 3mo ago
  Something tells me that you have some kind of anti-AI agenda and that you are not really looking at things objectively. Maybe it’s your nick “ai_fry_your_brain”, maybe it’s “ If you disagree with me, you're a slopper.”, who knows!
- monkepon 3mo ago
  Not all work is the same. Some of it is debugging, some is testing, rubber ducking, etc. LLMs reduce effort on such types of work, and the range of what they can handle keeps growing. If you don't see that yet, that's exactly why you should invest the time and mental effort to learn these AI tools deeply.
- dudusxnnx 3mo ago
  Naive bordering on the juvenile take on complex issues? Check. Polarizing and unable to process nuance? Check. Biased, arrogant and hypocritical? Check. My god, you’re the perfect developer. You’ll fit right in. Have a seat.
- jorisw 3mo ago
  Ad hominem arguments in every one of your comments.
IAmGraydon 3mo ago
>If asked ~3 years ago where the state of the models are today, it would sound like sci-fi if answered, "the models are creating full MVP apps in ~30 minutes with one prompt". The first one-shot app was created with ChatGPT in June 2023 - 3 years ago. In my experience, the current result of one-shotting apps is just as bad today as it was back then. What “full MVP app” are you talking about? I know of none that have been anywhere near production ready. With all due respect, I think you’re portraying fantasy as reality. I would love to be proven wrong.
- latentsea 3mo ago
  > The first one-shot app was created with ChatGPT in June 2023 - 3 years ago. In my experience, the current result of one-shotting apps is just as bad today as it was back then. Hard disagree. Take a good SaaS starter template and do a bunch of harness engineering. You can get an agent to shit out production grade stuff. You might argue that's cheating, but there's nothing stopping you from doing it, and it works. It's only getting better too.
  - jakeydus 3mo ago
    The original comment asked for someone to name one and you didn’t, though.
    - latentsea 3mo ago
      Cos I'm too busy doing it at work.
      - human305893 3mo ago
        Can you share one when you get home? I'm interested to see some examples.
      - ai_brain_rot 3mo ago
        Send your agent off on a task and give us an example come on now
camgunz 3mo ago
Nothing corroborated this. Performance on benchmarks has practically leveled off. The big gains have come from architecture (have a secondary LLM review output) or searching the internet. Also prices are going up. Everything points to the likelihood that we're at the top of the curve.
- snemvalts 3mo ago
  Most benchmarks can be trained for as well, so they are over-representative of model's engineering skills. The entire nature of a benchmark is collapsing some qualitative work (software engineering task, architecture choice, code quality) into a quantitative score which can be optimized for.
- andy12_ 3mo ago
  > Performance on benchmarks has practically leveled off Ehm, no? DeepSWE[1] for example shows that new models like gpt-5.5 continue to show big improvements compared to older models. > Also prices are going up. Prices for frontier intelligence have gone up, but prices for the same level of intelligence have gone way down (what you can get for pennies now was SOTA just a couple of years ago). The pareto frontier is still expanding. [1] https://deepswe.datacurve.ai/ https://deepswe.datacurve.ai/
- Gareth321 3mo ago
  > Nothing corroborated this. Performance on benchmarks has practically leveled off. [There is plenty of data to support the claim that AI continues to improve, even exponentially.](https://epoch.ai/trends https://epoch.ai/trends) As for benchmarks I feel compelled to remind you that as soon as a metric becomes a goal, it ceases to be a useful metric. The models optimise for solving the benchmark and we create new benchmarks to assess broader intelligence. As models converge on 100%, progress obviously slows. That doesn't mean intelligence isn't improving fast. It just means that that benchmark is being well served and we need other benchmarks to assess other forms of intelligence. I would like to take your bet that we're near the top of the curve. I take the side of Geoffrey Hinton, the Nobel Prize laureate scientist known for his work on artificial neural networks. He believes AI is getting better even faster than he predicted. He estimates that every seven months AI becomes able to handle tasks twice as long.
sensanaty 3mo ago
Are the models all that much better? To me it seems the tooling surrounding the models got better, but the models themselves are basically interchangeable unless you're following a bunch of flawed overfitted benchmarks. Also MVP apps are great and all, but I've seen 0 evidence of actually useful software from all this tooling, if anything all the software I'm using has just become more buggy and less reliable over time

rsalus 3mo ago

I don't know, even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope. I think we'll see short-term layoffs and a more stratified engineering field during the transition, but the fundamental need for deep technical expertise isn't going away.

coldtea 3mo ago
>I don't know, even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope. Not really. It will be a cuttthroat landscape, and the scope wont matter as much anymore. First because everyone else will equally be able to throw LLMs at the scope, but also because the scope has natural limits: your market fit, customer expectations, and (for software/hw products) physical world/manufacturing limitations. They'll want to reduce their margins.
- rsalus 3mo ago
  AI usage will directly impact said margins. Moreover, for the scenario you describe, companies need to have the capability to precisely estimate the cost of a given deliverable - not something possible with current tooling + models. You're also underestimating the market trend towards vertical integration: companies are not going to be constrained by a sector or niche. They will expand to capture as much value as they can, because now their capacity to do so is partially decoupled from labor. It will certainly be a cutthroat landscape for engineers, but companies will be building _more_ capacity, not less. In other words, the demand won't disappear for skilled technical labor, it will just move higher up the value chain.
  - coldtea 3mo ago
    >You're also underestimating the market trend towards vertical integration: companies are not going to be constrained by a sector or niche. They will expand to capture as much value as they can, because now their capacity to do so is partially decoupled from labor. They will still totally be, because the capacity to do so was never coupled to labor, it was coupled to domain knowledge, client network, other players dominating the market, and so on...
insane_dreamer 3mo ago
> even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope. 1) they won't, they'll just cut costs or 2) they will, but unless it's a new scope or one that can absorb growth, they'll just be competing with other companies in the space and taking away business from them either way, labor loses
pjmlp 3mo ago
No, my life experience tells me those companies will fire the ones they no longer need instead.

p2detar 3mo ago

Why would it stop with just developer layoffs? When software companies rely on LLM providers to run their business, I’d argue we‘ll see a massive bust of these companies around the world - from on-prem products to SaaS. Customers may build the software they need entirely in-house or via prompt-engineer consultants, without the need to buy software tools like today. It could be a very very different world.

davnicwil 3mo ago
This won't happen in most cases because the valuable thing is largely the knowledge encoded in the software, which the buyers of the software don't have and don't want to have since they're focused on their own business. There's also, of course, the not insignificant value in the software itself actually working, being operated, being updated when necessary, all of that. Again just extra hassle no business will want to shoulder when they can just buy something that does it for them.
- ai_fry_ur_brain 3mo ago
  Why would I build my own CRM instead of paying 50k a year or whatever? The engineer plus tokens for maintenance will cost you way more than 50k. These people are delusional and just repeating delusional vibe coder tweets.
  - bix6 3mo ago
    Not every business has $50k for a CRM
    - dakolli 3mo ago
      [flagged]
      - horsawlarway 3mo ago
        I'm going to throw out two counter arguments. 1. There is value in a tool that solves precisely your needs, in the way you want it solved. I've repeatedly seen enterprise SaaS purchases where the company ends up wrapping/layering on top additional tooling, software, and infra to solve core needs that are absent (or misaligned) from the saas tooling, but required for their specific usage. I've directly experienced this with: analytics tooling, customer survey tooling, feature flag tooling, and interview tooling. If your going to dedicate a dev anyways - the numbers can change here. Is this every SaaS product for every business? Fuck no - but there are products that might be adjacant to your core business where you have both strong preferences & experience, are already spending for customization, and now it makes sense to pull the whole thing in-house. 2. The 50k/m crm is competing with that 500/m crm. which realistically appears to soon be competing with that 50/m. Even if we stick with your stated observation that end businesses don't benefit from building their own tooling (which is fair and often true, although I'd wager it's not as clear-cut as you imply) - you're dismissing competition that is absolutely willing to undercut the market because they can slip on quality (slop - as you say) but still serve a need to customers who place cost as the primary buying metric. If the customer is better served by the 500/m crm, why stop there? Why not go for the 100/m crm? The 50/m crm? Why not chug on down to the lowest possible cost competition, which likely will be 2-5 guys with an llm they ask to go copy "[insert crm of choice]", and then bill just slightly over infra costs. Or the other thing I'm seeing happen in a lot of spaces right now... the "do it all" SaaS companies, that are pumping out into adjacent verticals that previously would have been too expensive to develop. The bill stays the same, but now it's not just a crm, it's the original crm, plus a clone of all the adjacent market leaders... scheduling, billing & invoicing, marketing, SEO, site hosting and design, social engagement, etc... One SaaS elbowing into other verticals but keeping the bill the same, which I consider functionally equivalent to the competing on price, just wrapped in a different flavor (it won't be 2 dudes in a basement, it'll be 2 dudes on the "crm" squad in a bigger eng dept).

downrightmike 3mo ago

That's what the AI comapnies would like, but they can't pay back the 100's of billions they are blowing without 10,000x the price they charge. The investors won't allow it. we're not even in a revenue cycle yet and they are already trying to dump their deep losses on retail by trying to IPO

mrandish 3mo ago

> the models are improving at a rapid rate. If asked ~3 years ago where the state of the models are today, it would sound like sci-fi Absolutely true, many things will continue to improve in significant ways. However, if we look at the modern history of rapid disruptions driven by technology (a side interest of mine), persistent patterns emerge. Similar to avalanches or flash floods, such periods of very rapid disruption are often triggered by one or more significant breakthroughs in certain technologies. Early rates of change tend to be fast and furious but eventually begin to taper as recently unlocked low-hanging fruit is harvested and those racing through newly found terrain encounter all-new significant barriers and points of friction. Early in such periods, extrapolating the recent extraordinary rates of change forward has poor predictive power. Sudden extreme bursts tend to regress back toward the long-term trend line. Arguably, the current disruption in LLMs can be traced to post ~2010 research slowly building to the 2017 transformer paper and the adjacent work it quickly inspired. So today is, arguably, mid or late-ish in the LLM rapid burst phase. The rate of fundamental, broad-based breakthroughs lifting all LLM applications has clearly slowed with many of the most impactful recent discoveries being in scaling, optimization, tuning and productization toward specific domains. That doesn't mean there can't be another transformer breakthrough tomorrow but, historically, black swans rarely travel in flocks.

jason_oster 3mo ago
Progress happens in a series of S-curves. While your observation is correct that advances occur initially rapidly then taper off, the next step tends to arrive sooner than the previous, and with greater magnitude [1]. Tim Urban's article from 2015 has a great explanation of this phenomenon [2]. [1]: https://ourworldindata.org/technology-long-run https://ourworldindata.org/technology-long-run [2]: https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html https://waitbutwhy.com/2015/01/artificial-intelligence-revol...
jvanderbot 3mo ago
This is of course true in general. But the question is not "how with this evolve" but how will we deal with the rapid changes in the industry? I suspect a long term k-shape salary curve, even worse than today, with the lower 80-90pctile salaries bottoming out such that many have to exit the industry to make ends meet. You can laugh and blame them for not saving as much as they should, but that's still a fairly horrifying prospect for most of us. I think a _lot_ about stock trading a profession vs algorithmic trading. It was brutal - suicides, many pivoting out to doing car dealership-style work. Probably a 1/10 or 1/20 survivor rate every couple years, with almost all of it a very painful five year period.

techblueberry 3mo ago

What is your theory of when AI gets to 100%. PMs and business analysts build all the software? Or just like a 700 or so 1-founder companies in the world and everyone else is without work? The matrix?

retinaros 3mo ago
Why would you need a pm or a biz analyst
- kibwen 3mo ago
  Better question, why would you need a CEO?
  - therealdrag0 3mo ago
    RACI
rfgplk 3mo ago
> Or just like a 700 or so 1-founder companies in the world and everyone else is without work? This. But instead of 700 it's more likely that everyone will be a founder (more or less). It's already scary how easy it is to launch an MVP or produce prototypes with the latest models.
- nearbuy 3mo ago
  Until an LLM becomes better at coming up with products people want and running a company. Then we'll cut out the founders and have LLM run companies.
- timr 3mo ago
  > It's already scary how easy it is to launch an MVP or produce prototypes with the latest models. No it isn’t. The things that were hard are now harder. The things that were comparatively easy are now easier. But if you build another piece of vibe-coded crap in a world awash in vibe-coded crap, you will not stand out. Nobody cares about your unpolished, one-shot prototype, so cranking them out faster is not really helpful. Differentiation is always a problem of effort and care, and this isn’t going to change.

dyauspitr 3mo ago

I think we’ll see a lot of layoffs and then the tech industry is going to become more vertically, integrated with product, business analysts and developers all combining into one role. It sucks because there goes one of the highest being roles in America right now that actually employed a lot of people. Ironically, I don’t think tech support is going to be fully replaced by these anytime soon. That’s one place you definitely need to have actual people talking to other people. Lawyers and doctors are gonna be legally protected too because you still need a human to sign off on all those actions though we will probably need far fewer.

ai_fry_ur_brain 3mo ago
[flagged]

pharos92 3mo ago

I would highly encourage you to watch this short clip https://youtu.be/5eqRuVp65eY?si=3fLT6S5q2OIUcu6r https://youtu.be/5eqRuVp65eY?si=3fLT6S5q2OIUcu6r

onel 3mo ago
That is a great video, but short is very subjective, the video is 24 minutes long :))

ai_fry_ur_brain 3mo ago

[flagged]

azan_ 3mo ago
Something tells me that you have some kind of anti-AI agenda and that you are not really looking at things objectively. Maybe it’s your nick “ai_fry_your_brain”, maybe it’s “ If you disagree with me, you're a slopper.”, who knows!
monkepon 3mo ago
Not all work is the same. Some of it is debugging, some is testing, rubber ducking, etc. LLMs reduce effort on such types of work, and the range of what they can handle keeps growing. If you don't see that yet, that's exactly why you should invest the time and mental effort to learn these AI tools deeply.
dudusxnnx 3mo ago
Naive bordering on the juvenile take on complex issues? Check. Polarizing and unable to process nuance? Check. Biased, arrogant and hypocritical? Check. My god, you’re the perfect developer. You’ll fit right in. Have a seat.
jorisw 3mo ago
Ad hominem arguments in every one of your comments.

IAmGraydon 3mo ago

>If asked ~3 years ago where the state of the models are today, it would sound like sci-fi if answered, "the models are creating full MVP apps in ~30 minutes with one prompt". The first one-shot app was created with ChatGPT in June 2023 - 3 years ago. In my experience, the current result of one-shotting apps is just as bad today as it was back then. What “full MVP app” are you talking about? I know of none that have been anywhere near production ready. With all due respect, I think you’re portraying fantasy as reality. I would love to be proven wrong.

latentsea 3mo ago
> The first one-shot app was created with ChatGPT in June 2023 - 3 years ago. In my experience, the current result of one-shotting apps is just as bad today as it was back then. Hard disagree. Take a good SaaS starter template and do a bunch of harness engineering. You can get an agent to shit out production grade stuff. You might argue that's cheating, but there's nothing stopping you from doing it, and it works. It's only getting better too.
- jakeydus 3mo ago
  The original comment asked for someone to name one and you didn’t, though.
  - latentsea 3mo ago
    Cos I'm too busy doing it at work.
    - human305893 3mo ago
      Can you share one when you get home? I'm interested to see some examples.
    - ai_brain_rot 3mo ago
      Send your agent off on a task and give us an example come on now

camgunz 3mo ago

Nothing corroborated this. Performance on benchmarks has practically leveled off. The big gains have come from architecture (have a secondary LLM review output) or searching the internet. Also prices are going up. Everything points to the likelihood that we're at the top of the curve.

snemvalts 3mo ago
Most benchmarks can be trained for as well, so they are over-representative of model's engineering skills. The entire nature of a benchmark is collapsing some qualitative work (software engineering task, architecture choice, code quality) into a quantitative score which can be optimized for.
andy12_ 3mo ago
> Performance on benchmarks has practically leveled off Ehm, no? DeepSWE[1] for example shows that new models like gpt-5.5 continue to show big improvements compared to older models. > Also prices are going up. Prices for frontier intelligence have gone up, but prices for the same level of intelligence have gone way down (what you can get for pennies now was SOTA just a couple of years ago). The pareto frontier is still expanding. [1] https://deepswe.datacurve.ai/ https://deepswe.datacurve.ai/
Gareth321 3mo ago
> Nothing corroborated this. Performance on benchmarks has practically leveled off. [There is plenty of data to support the claim that AI continues to improve, even exponentially.](https://epoch.ai/trends https://epoch.ai/trends) As for benchmarks I feel compelled to remind you that as soon as a metric becomes a goal, it ceases to be a useful metric. The models optimise for solving the benchmark and we create new benchmarks to assess broader intelligence. As models converge on 100%, progress obviously slows. That doesn't mean intelligence isn't improving fast. It just means that that benchmark is being well served and we need other benchmarks to assess other forms of intelligence. I would like to take your bet that we're near the top of the curve. I take the side of Geoffrey Hinton, the Nobel Prize laureate scientist known for his work on artificial neural networks. He believes AI is getting better even faster than he predicted. He estimates that every seven months AI becomes able to handle tasks twice as long.

sensanaty 3mo ago

Are the models all that much better? To me it seems the tooling surrounding the models got better, but the models themselves are basically interchangeable unless you're following a bunch of flawed overfitted benchmarks. Also MVP apps are great and all, but I've seen 0 evidence of actually useful software from all this tooling, if anything all the software I'm using has just become more buggy and less reliable over time