51–60 of 156 posts
I think the title on HN doesn't reflect all that is in TFA, but rather the linked article[0]. Fowler's article is interesting tho. I do like the idea that "all code is tech debt", and we shouldn't want to produce more of it than we need. But it's also worth remembering that debt is not bad per se, buying a house with a mortgage is also debt and can be a good choice for many reasons. [0]: https://thenewstack.io/ai-vel…
What's with the editorialized title? The text is actually about the Thoughtworks Future of Software Development retreat.
> LLMs are eating specialty skills. There will be less use of specialist front-end and back-end developers as the LLM-driving skills become more important than the details of platform usage. Will this lead to a greater recognition of the role of Expert Generalists? Or will the ability of LLMs to write lots of code mean they code around the silos rather than eliminating them? This is one of the most interesting questi…
I've faced the same but my conclusion is the opposite. In the past 6 months, all my code has been written by claude code and gemini cli. I have written code backend, frontend, infrastructure and iOS. Considering my career trajectory all of this was impossible a couple of years ago. But the technical debt has been enormous. And I'll be honest, my understanding of these technologies hasn't been 'expert' level. I'm 100%…
We’ve been trying to build well engineered, robust, scalable systems because software had to be written to serve other users.
But LLMs change that. I have a bunch of vibe coded command lines tools that exactly solve my problems, but very likely would make terrible software. The thing is, this program only needs to run on my machine the way I like to use it.
In a growing class of cases bespoke tools are superior to generalized software. This historically was not the case because it took too much time and energy to maintain these things. But today if my vibe coded solution breaks, I can rebuild it almost instantly (because I understand the architecture). It takes less time today to build a bespoke tool that solved your problem than it does to learn how to use existing software.
There’s still plenty of software that cannot be replaced with bespoke tools, but that list is shrinking.
> 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…
What is up with all this nonsense about token subsidies? Dario in his recent interview with Dwarkesh made it abundantly clear that they have substantial inference margins, and they use that to justify the financing for the next training run. Chinese open source models are dirt cheap, you can buy $20 worth of kimi-k2.5 on opencode and spam it all week and barely make a dent. Assuming we never got bigger models, but ha…
There exist a large number of people who are absolutely convinced that LLM providers are all running inference at a loss in order to capture the market and will drive the prices up sky high as soon as everyone is hooked. I think this is often a mental excuse for continuing to avoid engaging with this tech, in the hope that it will all go away.
There's a difference between running inference and running a frontier model company.
> 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…
Earlier quoted context omitted.
There exist a large number of people who are absolutely convinced that LLM providers are all running inference at a loss in order to capture the market and will drive the prices up sky high as soon as everyone is hooked. I think this is often a mental excuse for continuing to avoid engaging with this tech, in the hope that it will all go away.
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.
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.
What is up with all this nonsense about token subsidies? Dario in his recent interview with Dwarkesh made it abundantly clear that they have substantial inference margins, and they use that to justify the financing for the next training run. Chinese open source models are dirt cheap, you can buy $20 worth of kimi-k2.5 on opencode and spam it all week and barely make a dent. Assuming we never got bigger models, but ha…
There exist a large number of people who are absolutely convinced that LLM providers are all running inference at a loss in order to capture the market and will drive the prices up sky high as soon as everyone is hooked. I think this is often a mental excuse for continuing to avoid engaging with this tech, in the hope that it will all go away.
What people probably get messed up on as being the loss leader is likely generous usage limits on flat rate subscriptions.
For example GitHub Copilot Pro+ comes with 1500 premium requests a month. That's quite a lot and it's only $39.00. (Requests ~ Prompts).
For some time they were offering Opus 4.6 Fast at 9x billing (now raised to 30x).
That was upto 167 requests of around ~128k context for just $39. That ridiculous model costs $30/$150 Mtok so you can easily imagine the economics on this.