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The better I am at solving a problem, the less I use AI assistants. I use them if I try a new language or framework. Busy code I need to generate is difficult

The better I am at solving a problem, the less I use AI assistants. I use them if I try a new language or framework. Busy code I need to generate is difficult to do with AI too. Because then you need to formalize the necessary context for an AI assistant, which is exhausting with an unsure result. So perhaps it is just simpler to write it yourself quickly. I understand comments being negative, because there is so much AI hype without having to many practical applications yet. Or at least good practical applications. Some of that hype is justified, some of it is not. I enjoyed the image/video/audio synthesis hype more tbh. Test cases are quite helpful and comments are decent too. But often prompting is more complex than programming something. And you can never be sure if any answer is usable.

brulard 1y ago
> But often prompting is more complex than programming something. It may be more complex, but it is in my opinion better long term. We need to get good at communicating with AIs to get results that we want. Forgive me assuming that you probably didn't use these assistants long enough to get good at using them. I'm web developer for 20 years already and AI tools are multiplying my output even in problems I'm very good at. And they are getting better very quickly.
- GoblinSlayer 1y ago
  Yep, it looks like LLMs are used as fast typists, and coincidentally in webdev typing speed is the most important bottleneck when you need to add cookie consent, spinners, dozens of ad providers, tracking pixels, twitter metadata, google metadata, manual rendering, buttons web components with material design and react, hover panels, fontawesome, recaptcha, and that's only 1% of modern web boilerplate, then it's easy to see how a fast typist can help you.
avemuri 1y ago
I agree with your points but I'm also reminded of one my bigger learnings as a manager - the stuff I'm best at is the hardest, but most important, to delegate. Sure it was easier to do it myself. But putting in the time to train, give context, develop guardrails, learn how to monitor etc ultimately taught me the skills needed to delegate effectively and multiply the teams output massively as we added people. It's early days but I'm getting the same feeling with LLMs. It's as exhausting as training an overconfident but talented intern, but if you can work through it and somehow get it to produce something as good as you would do yourself, it's a massive multiplier.
- radlad 1y ago
  I don't totally understand the parallel you're drawing here. As a manager, I assume you're training more junior (in terms of their career or the company) engineers up so they can perform more autonomously in the future. But you're not training LLMs as you use them really - do you mean that it's best to develop your own skill using LLMs in an area you already understand well? I'm finding it a bit hard to square your comment about it being exhausting to catherd the LLM with it being a force multiplier.
  - wpietri 1y ago
    Great point. Humans really like to anthropomorphize things. Loud rumbles in the clouds? There must be a dude on top of a mountain somewhere who's in charge of it. Impressed by that tree? It must have a spirit that's like our spirits. I think a lot of the reason LLMs are enjoying such a huge hype wave is that they invite that sort of anthropomorphization. It can be really hard to think about them in terms of what they actually are, because both our head-meat and our culture has so much support for casting things as other people.
  - avemuri 1y ago
    No I'm talking about my own skills. How I onboard, structure 1on1s, run meetings, create and reuse certain processes, manage documentation (a form of org memory), check in on status, devise metrics and other indicators of system health. All of these compound and provide leverage even if the person leaves and a new one enters.the 30th person I onboarded and managed was orders of magnitude easier (for both of us) than the first. With LLMs the better I get at the scaffolding and prompting, the less it feels like catherding (so far at least). Hence the comparison.
- conartist6 1y ago
  But... But... the multiplier isn't NEW! You just explained how your work was affected by a big multiplier. At the end of training an intern you get a trained intern -- potentially a huge multiplier. ChatGPT is like an intern you can never train and will never get much better. These are the same people who would no longer create or participate deeply in OSS (+100x multipler) bragging about the +2x multiplier they got in exchange.
Cthulhu_ 1y ago
> But often prompting is more complex than programming something. I'd challenge this one; is it more complex, or is all the thinking and decision making concentrated into a single sentence or paragraph? For me, programming something is taking a big high over problem and breaking it down into smaller and smaller sections until it's a line of code; the lines of code are relatively low effort / cost little brain power. But in my experience, the problem itself and its nuances are only defined once all code is written. If you have to prompt an AI to write it, you need to define the problem beforehand. It's more design and more thinking upfront, which is something the development community has moved away from in the past ~20 years with the rise of agile development and open source. Techniques like TDD have shifted more of the problem definition forwards as you have to think about your desired outcomes before writing code, but I'm pretty sure (I have no figures) it's only a minority of developers that have the self-discipline to practice test-driven development consistently. (disclaimer: I don't use AI much, and my employer isn't yet looking into or paying for agentic coding, so it's chat style or inline code suggestions)
- sksisoakanan 1y ago
  The issue with prompting is English (or any other human language) is nowhere near as rigid or strict a language as a programming language. Almost always an idea can be expressed much more succinctly in code than language. Combine that with when you’re reading the code it’s often much easier to develop a prototype solution as you go and you end up with prompting feeling like using 4 men to carry a wheelbarrow instead of having 1 push it.
  - michaelfeathers 1y ago
    I think we are going to end up with common design/code specification language that we use for prompting and testing. There's always going to be a need to convey the exact semantics of what we want. If not, for AI then for the humans who have to grapple with what is made.
    - rerdavies 1y ago
      Sounds like "Heavy process". "Specifying exact semantics" has been tried and ended up unimaginably badly.
echelon 1y ago
> The better I am at solving a problem, the less I use AI assistants. Yes, but you're expensive. And these models are getting better at solving a lot of business-relevant problems. Soon all business-relevant problems will be bent to the shape of the LLM because it's cost-effective.
- onemoresoop 1y ago
  You're forgetting how much money is being burned in keeping these LLMs cheap. Remember when Uber was a fraction of the cost of a cab? Yeah, those days didn't last.
  - a4isms 1y ago
    I have been in this industry since the mid 80s. I can't tell you how many people worry that I can't handle change because as a veteran, I must cling to what was. Meanwhile, of course, the reason I am still in the industry is because of my plasticity. Nothing is as it was for me, and I have changed just about everything about how I work multiple times. But what does stay the same all this time are people and businesses and how we/they behave. Which brings me to your comment. The comparison to Uber drivers is apt, and to use a fashionable word these days, the threat to people and startups alike is "enshittification." These tools are not sold, they are rented. Should a few behemoths gain effective control of the market, we know from history that we won't see these tools become commodities and nearly free, we'll see the users of these tools (again, both people and businesses) squeezed until their margins are paper-thin. Back when articles by Joel Spolsky regularly hit the top page of Hacker News, he wrote "Strategy Letter V:" https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/ https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/ The relevant takeaway was that companies try to commoditize their complements, and for LLM vendors, every startup is a complement. A brick-and-mortar metaphor is that of a retailer in a mall. If you as a retailer are paying more in rent than you're making, you are "working for the landlord," just as if you are making less than 30% of profit on everything you sell or rent through Apple's App Store, you're working for Apple. I once described that as "Sharecropping in Apple's Orchard," and if I'm hesitant about the direction we're going, it's not anything about clinging to punch cards and ferromagnetic RAM, it's more the worry that it's not just a question of programmers becoming enshittified by their tools, it's also the entire notion of a software business "Sharecropping the LLM vendor's fields." We spend way too much time talking about programming itself and not enough about whither the software business if its leverage is bound to tools that can only be rented on terms set by vendors. -------- I don't know for certain where things will go or how we'll get there. I actually like the idea that a solo founder could create a billion-dollar company with no employees in my lifetime. And I have always liked the idea of software being "Wheels for the Mind," and we could be on a path to that, rather than turning humans into "reverse centaurs" that labour for the software rather than the other way around. Once upon a time, VCs would always ask a startup, "What is your Plan B should you start getting traction and then Microsoft decides to compete with you/commoditize you by giving the same thing away?" That era passed, and Paul Graham celebrated it: https://paulgraham.com/microsoft.html https://paulgraham.com/microsoft.html Then when startups became cheap to launch—thank you increased tech leverage and cheap money and YCombinator industrializing early-stage venture capital—the question became, "What is your moat against three smart kids launching a competitor?" Now I wonder if the key question will bifurcate: 1. What is your moat against somebody launching competition even more cheaply than smart kids with YCombinator's backing, and; 2. How are you insulated against the cost of load-bearing tooling for everything in your business becoming arbitrarily more expensive?

brulard 1y ago

> But often prompting is more complex than programming something. It may be more complex, but it is in my opinion better long term. We need to get good at communicating with AIs to get results that we want. Forgive me assuming that you probably didn't use these assistants long enough to get good at using them. I'm web developer for 20 years already and AI tools are multiplying my output even in problems I'm very good at. And they are getting better very quickly.

GoblinSlayer 1y ago
Yep, it looks like LLMs are used as fast typists, and coincidentally in webdev typing speed is the most important bottleneck when you need to add cookie consent, spinners, dozens of ad providers, tracking pixels, twitter metadata, google metadata, manual rendering, buttons web components with material design and react, hover panels, fontawesome, recaptcha, and that's only 1% of modern web boilerplate, then it's easy to see how a fast typist can help you.

avemuri 1y ago

I agree with your points but I'm also reminded of one my bigger learnings as a manager - the stuff I'm best at is the hardest, but most important, to delegate. Sure it was easier to do it myself. But putting in the time to train, give context, develop guardrails, learn how to monitor etc ultimately taught me the skills needed to delegate effectively and multiply the teams output massively as we added people. It's early days but I'm getting the same feeling with LLMs. It's as exhausting as training an overconfident but talented intern, but if you can work through it and somehow get it to produce something as good as you would do yourself, it's a massive multiplier.

radlad 1y ago
I don't totally understand the parallel you're drawing here. As a manager, I assume you're training more junior (in terms of their career or the company) engineers up so they can perform more autonomously in the future. But you're not training LLMs as you use them really - do you mean that it's best to develop your own skill using LLMs in an area you already understand well? I'm finding it a bit hard to square your comment about it being exhausting to catherd the LLM with it being a force multiplier.
- wpietri 1y ago
  Great point. Humans really like to anthropomorphize things. Loud rumbles in the clouds? There must be a dude on top of a mountain somewhere who's in charge of it. Impressed by that tree? It must have a spirit that's like our spirits. I think a lot of the reason LLMs are enjoying such a huge hype wave is that they invite that sort of anthropomorphization. It can be really hard to think about them in terms of what they actually are, because both our head-meat and our culture has so much support for casting things as other people.
- avemuri 1y ago
  No I'm talking about my own skills. How I onboard, structure 1on1s, run meetings, create and reuse certain processes, manage documentation (a form of org memory), check in on status, devise metrics and other indicators of system health. All of these compound and provide leverage even if the person leaves and a new one enters.the 30th person I onboarded and managed was orders of magnitude easier (for both of us) than the first. With LLMs the better I get at the scaffolding and prompting, the less it feels like catherding (so far at least). Hence the comparison.
conartist6 1y ago
But... But... the multiplier isn't NEW! You just explained how your work was affected by a big multiplier. At the end of training an intern you get a trained intern -- potentially a huge multiplier. ChatGPT is like an intern you can never train and will never get much better. These are the same people who would no longer create or participate deeply in OSS (+100x multipler) bragging about the +2x multiplier they got in exchange.

Cthulhu_ 1y ago

> But often prompting is more complex than programming something. I'd challenge this one; is it more complex, or is all the thinking and decision making concentrated into a single sentence or paragraph? For me, programming something is taking a big high over problem and breaking it down into smaller and smaller sections until it's a line of code; the lines of code are relatively low effort / cost little brain power. But in my experience, the problem itself and its nuances are only defined once all code is written. If you have to prompt an AI to write it, you need to define the problem beforehand. It's more design and more thinking upfront, which is something the development community has moved away from in the past ~20 years with the rise of agile development and open source. Techniques like TDD have shifted more of the problem definition forwards as you have to think about your desired outcomes before writing code, but I'm pretty sure (I have no figures) it's only a minority of developers that have the self-discipline to practice test-driven development consistently. (disclaimer: I don't use AI much, and my employer isn't yet looking into or paying for agentic coding, so it's chat style or inline code suggestions)

sksisoakanan 1y ago
The issue with prompting is English (or any other human language) is nowhere near as rigid or strict a language as a programming language. Almost always an idea can be expressed much more succinctly in code than language. Combine that with when you’re reading the code it’s often much easier to develop a prototype solution as you go and you end up with prompting feeling like using 4 men to carry a wheelbarrow instead of having 1 push it.
- michaelfeathers 1y ago
  I think we are going to end up with common design/code specification language that we use for prompting and testing. There's always going to be a need to convey the exact semantics of what we want. If not, for AI then for the humans who have to grapple with what is made.
  - rerdavies 1y ago
    Sounds like "Heavy process". "Specifying exact semantics" has been tried and ended up unimaginably badly.

echelon 1y ago

> The better I am at solving a problem, the less I use AI assistants. Yes, but you're expensive. And these models are getting better at solving a lot of business-relevant problems. Soon all business-relevant problems will be bent to the shape of the LLM because it's cost-effective.

onemoresoop 1y ago
You're forgetting how much money is being burned in keeping these LLMs cheap. Remember when Uber was a fraction of the cost of a cab? Yeah, those days didn't last.
- a4isms 1y ago
  I have been in this industry since the mid 80s. I can't tell you how many people worry that I can't handle change because as a veteran, I must cling to what was. Meanwhile, of course, the reason I am still in the industry is because of my plasticity. Nothing is as it was for me, and I have changed just about everything about how I work multiple times. But what does stay the same all this time are people and businesses and how we/they behave. Which brings me to your comment. The comparison to Uber drivers is apt, and to use a fashionable word these days, the threat to people and startups alike is "enshittification." These tools are not sold, they are rented. Should a few behemoths gain effective control of the market, we know from history that we won't see these tools become commodities and nearly free, we'll see the users of these tools (again, both people and businesses) squeezed until their margins are paper-thin. Back when articles by Joel Spolsky regularly hit the top page of Hacker News, he wrote "Strategy Letter V:" https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/ https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/ The relevant takeaway was that companies try to commoditize their complements, and for LLM vendors, every startup is a complement. A brick-and-mortar metaphor is that of a retailer in a mall. If you as a retailer are paying more in rent than you're making, you are "working for the landlord," just as if you are making less than 30% of profit on everything you sell or rent through Apple's App Store, you're working for Apple. I once described that as "Sharecropping in Apple's Orchard," and if I'm hesitant about the direction we're going, it's not anything about clinging to punch cards and ferromagnetic RAM, it's more the worry that it's not just a question of programmers becoming enshittified by their tools, it's also the entire notion of a software business "Sharecropping the LLM vendor's fields." We spend way too much time talking about programming itself and not enough about whither the software business if its leverage is bound to tools that can only be rented on terms set by vendors. -------- I don't know for certain where things will go or how we'll get there. I actually like the idea that a solo founder could create a billion-dollar company with no employees in my lifetime. And I have always liked the idea of software being "Wheels for the Mind," and we could be on a path to that, rather than turning humans into "reverse centaurs" that labour for the software rather than the other way around. Once upon a time, VCs would always ask a startup, "What is your Plan B should you start getting traction and then Microsoft decides to compete with you/commoditize you by giving the same thing away?" That era passed, and Paul Graham celebrated it: https://paulgraham.com/microsoft.html https://paulgraham.com/microsoft.html Then when startups became cheap to launch—thank you increased tech leverage and cheap money and YCombinator industrializing early-stage venture capital—the question became, "What is your moat against three smart kids launching a competitor?" Now I wonder if the key question will bifurcate: 1. What is your moat against somebody launching competition even more cheaply than smart kids with YCombinator's backing, and; 2. How are you insulated against the cost of load-bearing tooling for everything in your business becoming arbitrarily more expensive?