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The Future of AI Software Development

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Re: The Future of AI Software Development

#111

Post by xboxnolifes »

newsoftheday wrote: post #29
chadash wrote: post #11

> Will LLMs be cheaper than humans once the subsidies for tokens go away? At this point we have little visibility to what the true cost of tokens is now, let alone what it will be in a few years time. It could be so cheap that we don’t care how many tokens we send to LLMs, or it could be high enough that we have to be very careful. We do have some idea. Kimi K2 is a relatively high performing open source model. Peopl…

> a cost that a (somewhat wealthy) hobbyist can afford $20,000 is a lot to drop on a hobby. We're probably talking less than 10%, maybe less than 5% of all hobbyists could afford that.

Up front, yeah. But people with hobbies on the more expensive end can definitely put out 4k a year. Im thinking like people who have a workshop and like to buy new tools and start projects.

Re: The Future of AI Software Development

#112

Post by layer8 »

3vidence wrote: post #101
skydhash wrote: post #97

Earlier quoted context omitted.

A lot of people don’t care about software other than the fact that the ones they use work well. They don’t want to create it, to maintain it, or to upgrade it. That’s what the IT department is for.

This seems like a big HN / VC bubble thing thinking that average people are interested in software at all... they really aren't. People want to open Netflix / YT / TikTok, open instagram, scroll reddit, take pictures, order stuff online, etc. Then professionals in fields want to read / write emails, open drawings, CADs, do tax returns, etc. If anything overall interest in software seems to be going down for the avera…

It just means that they want the software they use to work well, even if they aren’t particularly aware that what they use is software.

Re: The Future of AI Software Development

#113

Post by selridge »

PaulHoule wrote: post #94
noelsusman wrote: post #89

Earlier quoted context omitted.

This is the thing a lot of skeptics aren't grappling with. Software engineering as a profession is mostly about building software that can operate at scale. If you remove scale from the equation then you can remove a massive chunk of the complexity required to build useful software. There are a ton of recipe management apps out there, and all of them are more complex than I really need. They have to be, because other…

Tools for the non-professional developer to put their skills on wheels have always been part of the equation since we've had microcomputers if not minicomputer, see https://en.wikipedia.org/wiki/VisiCalc

But they’ve always basically required that you essentially become a programmer at the end of the day in order to get those benefits. The spreadsheet is probably the largest intruder in this ecosystem, but that’s only the case. If you don’t think that operating a spreadsheet is programming. It is.

What people are describing is that Normies can now do the kinds of things that only wizards with PERL could do in the 90s. The sorts of things that were always technically possible with computers if you were a very specific kind of person are now possible with computers for everyone else.

Re: The Future of AI Software Development

#114

Post by sosodev »

lambda wrote: post #39
chadash wrote: post #11

> Will LLMs be cheaper than humans once the subsidies for tokens go away? At this point we have little visibility to what the true cost of tokens is now, let alone what it will be in a few years time. It could be so cheap that we don’t care how many tokens we send to LLMs, or it could be high enough that we have to be very careful. We do have some idea. Kimi K2 is a relatively high performing open source model. Peopl…

You don't even need to go this expensive. An AMD Ryzen Strix Halo (AI Max+ 395) machine with 128 GiB of unified RAM will set you back about $2500 these days. I can get about 20 tokens/s on Qwen3 Coder Next at an 8 bit quant, or 17 tokens per second on Minimax M2.5 at a 3 bit quant. Now, these models are a bit weaker, but they're in the realm of Claude Sonnet to Claude Opus 4. 6-12 months behind SOTA on something that…

I was testing the 4-bit Qwen3 Coder Next on my 395+ board last night. IIRC it was maintaining around 30 tokens a second even with a large context window.

I haven't tried Minimax M2.5 yet. How do its capabilities compare to Qwen3 Coder Next in your testing?

I'm working on getting a good agentic coding workflow going with OpenCode and I had some issues with the Qwen model getting stuck in a tool calling loop.

Re: The Future of AI Software Development

#115

Post by louiereederson »

simonw wrote: post #58
louiereederson wrote: post #55

Earlier quoted context omitted.

Referring to my earlier comment, you need to have a model for how to account for training costs. If Anthropic stops training models now, what happens to their revenues and margins in 12 months? There's a difference between running inference and running a frontier model company.

Training costs are fixed. You spend $X-bn training a model and that single model then benefits all of your customers. Inference costs grow with your users. Provided you are making a profit on that inference you can eventually cover your training costs if you sign up enough paying customers. If you LOSE money on inference every new customer makes your financial position worse.

Your ability to sign up enough customers is directly related to your ability to sustain training costs. The model runs have a short lifespan. They may serve many customers at a given point in time per run, but in order to serve those customers over time you need to continually spend on training.

I think your mental model for an LLM vendor is similar to a foundry (i.e. TSMC). They spend a bunch of R&D on developing leading edge nodes and build foundries. That in your mental model would be similar to training costs.

My point is the correct mental model is more like (but not exactly like) a SaaS company, ironically. SaaS unit economics are a function of gross margin, churn and acquisition costs, i.e. Revenue x gross margin / churn - CAC. My point is some element (maybe the entirety) of training costs are more like CAC than they are like TSMC's R&D and capex. The question to ask to test this view is: is what happens to OpenAI or Anthropic revenue in 2027 or 2028 if they stop spending on training today? My view is it'll drop precipitously. This implies churn is very high. It is true that training costs can be spread over customers though, so the analogy breaks down there, but I think it is a better mental model than the foundry one.

Re: The Future of AI Software Development

#116

Post by simonw »

louiereederson wrote: post #115
simonw wrote: post #58

Earlier quoted context omitted.

Training costs are fixed. You spend $X-bn training a model and that single model then benefits all of your customers. Inference costs grow with your users. Provided you are making a profit on that inference you can eventually cover your training costs if you sign up enough paying customers. If you LOSE money on inference every new customer makes your financial position worse.

Your ability to sign up enough customers is directly related to your ability to sustain training costs. The model runs have a short lifespan. They may serve many customers at a given point in time per run, but in order to serve those customers over time you need to continually spend on training. I think your mental model for an LLM vendor is similar to a foundry (i.e. TSMC). They spend a bunch of R&D on developing le…

Yes, you need to continue spending money on training. But you don't need to spend MORE money on training just because you signed up more customers.

Re: The Future of AI Software Development

#117

Post by lm28469 »

chadash wrote: post #11

> Will LLMs be cheaper than humans once the subsidies for tokens go away? At this point we have little visibility to what the true cost of tokens is now, let alone what it will be in a few years time. It could be so cheap that we don’t care how many tokens we send to LLMs, or it could be high enough that we have to be very careful. We do have some idea. Kimi K2 is a relatively high performing open source model. Peopl…

90% of companies would go bankrupt in a year if you replaced their engineering team with execs talking to k2...

Re: The Future of AI Software Development

#118

Post by Copenjin »

wseqyrku wrote: post #96

> When I began in software in the 1980s I was dismissed as an “object guy” by database folks and as a “data modeler” by object folks. I've since been dismissed as a “patterns guy”, “agile guy”, “architecture guy”, “java guy”, “ruby guy”, and “anti-architect guy”. I'm now a past-it gray-beard surviving on drinking the intellectual blood of my younger colleagues. It's tasty. I don't think you can find that level of ego…

He forgot the "micro-shit" guy. At least he is not uncle Bob but not really much far from it. Slop well before AI.

Re: The Future of AI Software Development

#119

Post by PaulHoule »

selridge wrote: post #113
PaulHoule wrote: post #94

Earlier quoted context omitted.

Tools for the non-professional developer to put their skills on wheels have always been part of the equation since we've had microcomputers if not minicomputer, see https://en.wikipedia.org/wiki/VisiCalc

But they’ve always basically required that you essentially become a programmer at the end of the day in order to get those benefits. The spreadsheet is probably the largest intruder in this ecosystem, but that’s only the case. If you don’t think that operating a spreadsheet is programming. It is. What people are describing is that Normies can now do the kinds of things that only wizards with PERL could do in the 90s.…

That's partially true.

Languages like BASIC and Python have always been useful to people for whom programming is a part-time thing. Sure you have to learn something but it is not like learning assembly or C++.

On the other hand, it is notorious that people who don't know anything about programming can accomplish a little bit with LLM tools and then they get stuck.

It's part of what is so irksome about the slop blog posts about AI coding that HN is saturated with now. If you've accomplished something with AI coding it is because of: (1) your familiarity with the domain you're working in and (2) your general knowledge about how programming environments work. With (1) and (2) you can recognize the different between a real solution and a false solution and close the gap when something "almost works". Without it, you're going to go around in circles at best. People are blogging as if their experience with prompting or their unscientific experiments about this model and that model were valuable but they're not, (1) and (2) are valuable, anything specific about AI coding 2026-02-18 will be half-obsolete on 2026-02-19; so of course they face indifference.

Re: The Future of AI Software Development

#120

Post by trentnix »

lm28469 wrote: post #117
chadash wrote: post #11

> Will LLMs be cheaper than humans once the subsidies for tokens go away? At this point we have little visibility to what the true cost of tokens is now, let alone what it will be in a few years time. It could be so cheap that we don’t care how many tokens we send to LLMs, or it could be high enough that we have to be very careful. We do have some idea. Kimi K2 is a relatively high performing open source model. Peopl…

90% of companies would go bankrupt in a year if you replaced their engineering team with execs talking to k2...

Most execs I've worked with couldn't tell their engineering team what they wanted with any specificity. That won't magically get any better when they talk to an LLM.

If you can't write requirements an engineering team can use, you won't be able to write requirements for the robots either.

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