Running locally for me is mainly about learning, maintaining control+privacy, and helping shift my coding+design process to leverage LLMs. I guess if you made me boil it down to a single word to justify the cost I would just say: tuition.
Sounds like we have similar boxes - mine has a 10 core CPU, 64 GB of ram, and a 2070 Super. My motherboard had two unused PCIe3x8 slots and doesn't support Blackwell GPUs. I bought a couple of brand new Ada generation RTX 2000s with 16GB of memory for under $1400 to get to 40 GB of VRAM. That will easily run Qwen3.6-27b at a 6-bit quantization and 80,000 token context size. It isn't fast (19-21 t/s), but using pi-coding-agent is fine.
Now, my instinct is that I am giving up SOTA performance on agentic coding with this setup and LLM. But the gap between my setup and SOTA commercial models is small enough that it doesn't matter to me.
On the one hand, sure, why not have a default install throw a bunch of bells+whistles via skills and extensions.
But I like pi precisely because it is so minimal. I want understand and work around the simplest possible agentic coding setup, find the sharp edges, maybe even improve my prompting ability. And doing all three with a locally hosted LLM.
At some point, if I don't understand the foundations, am I just punting on actually thinking about what I'm doing?
Of course, making individual choices about how to do agentic coding are precisely just making individual choices. People should do what makes them happy and productive.
This may be too naive, but I created a user on my linux box who doesn't have very many permissions. Then I sudo to that user, use firejail to start pi in a dev project directory, and let it have at it.
My projects are usually very limited with respect to external dependencies and that is part of prompts or markdown files describing various project goals, plans, and current state.
My operating theory is that this probably won't get my systems borked. I wasn't patient enough to dig deeper.
"Is this decline a distinct change from the recent behavior of the labor share in the U.S.? Along the two key dimensions we investigate, our answer is no. <later> ... and they provide little evidence that it will evolve differently from past episodes."
This conclusion seems to be against "this time is different" arguments. Should we be generally encouraged by similarity to past declines pre-2000 or bearish and think that there is more drop to come like the 2000-2007 and 2007-2019 periods they graph out?
I guess there is no way to predict other than check back in after time passes.
I used to agree with you but now do not. I now think the floor for this market is probably no worse than the annual revenue of cell phone plans in the US market. So say, $250 billion.
Now, that probably doesn't justify the valuations and hype being thrown around, but I think it gets at a real revenue number.
I also don't know how that number fits into the funding rounds already raised and VC dreams of IPOs for these two.
This isn't coming from deep analysis on a verifiable source, but I started asking people in my social circle (includes white-collar and blue-collar folks) about their LLM use. The biggest surprise in 2026 for me was that almost all of these people told me about regular (and sometimes sophisticated) use.
A more intriguing observation - I work on the side with high school students and have two college kids of my own. Their LLM usage (and their peers) is much, much lower than expected . . . that's a little counterintuitive given "popular" perceptions I read.
> I used to agree with you but now do not. I now think the floor for this market is probably no worse than the annual revenue of cell phone plans in the US market. So say, $250 billion.
I don't think we're talking about the same thing. I'm talking about what their IPO is going to do to their share price.
In any case, $250b revenue translates to, best case scenario, $50b profit. On an investment of $1t. It does not look good for those companies making up the $1t investment.
Gotcha. I'm past the point of having any confident thoughts about what happens to their share price at IPO.
What about the idea that there is a high likelihood that the potential share price for OpenAI and Anthropic are both going to be pretty divorced from a rational market price for either?
Interesting idea and reasonable number, but cell phones need a lot of infrastructure and they need interconnection. The risk here is that in the future a combination of near-sota open weights models optimised to use as little resources as possible and a reasonable drop in compute price, will make possible for small and tiny providers to compete with Anthropic/ OpenAI or even for people to run their own private models for most applications. Then large, expensive sota models would only be used for research and to answer the small subset of general user queries that need that kind of intelligence.
--what this means for the valuation of the AI companies
Probably nothing. Most users have no idea what an LLM is or how it runs. Anecdotally speaking, I see many LLM users default to whatever their day job provides to them. And even slightly more sophisticated users seem ok with paying for their openai or anthropic subscriptions.
Maybe we will see a small but dedicated group of open weight model users who prefer local llm, but everybody else will just consume from the big providers? The scenario might look something like OS choices today - a small, committed group of Linux users vs the vast majority of other users running Windows, MacOS, or Chrome?
Prices from OpenAI and Anthropic have really jumped in the past month. I work for a big giant company and our Github co-pilot costs increased as of today, June 1st. Our internal estimates are that our bill will double or triple. How much are we willing to pay? I don't know, but nobody wants to be "left behind".
I think there's actually a big market opportunity here. Somebody, like Dell or HP, should start selling turnkey on-prem LLM servers.
Appreciate the anecdote and your other comments on HN. But I strongly suspect you are incredibly atypical based on your background and previous work experience in ways that would tremendously down weight the probability that any part of your experience with recruiters would apply to even above average engineers.
"In 2023–24, Bachelor’s degree production fell 5.5% compared to the previous year across CS, CE, and I departments. Among departments reporting both years, the decrease was 4.3%. Despite this drop, production remains well above pre-pandemic levels and reflects continued strength following the post-2020 rebound. CS saw a 7.4% decrease and CE a 13.3% decrease."
But it also looks like enrollment in CS programs increased in 2024/2025:
"U.S. CS departments reported an increase in new majors per department of 12.8%"
"I personally dropped $20k on a high end desktop . . . "
This is where I think current hackers should be headed. I grew up with lots of family who were backyard mechanics, wrenching on cars and motorcycles. Their investment in tools made my occasional PC purchase look extremely affordable. Based on what I read, senior mechanics often have five-figure US dollar investments in tools. Of course, I guess high quality torque wrenches probably outlast current GPU chips? I'd hate to be stuck making a $10K investment every 24 months on a new GPU . . .
I have been renting GPU resources and running open weight models, but recently my preferred provider simply doesn't have hardware available. I'm now kicking myself a little for not simply making a big purchase last fall when prices were better.
Professional mechanics might do that, but a home mechanic can get very far one one $200 set, and then another $300 spent over years buying several useful things for each project.
I've replaced transmissions, head gaskets, and done all work for our family cars for two decades based on a Costco toolkit, and 20 trips to the autoparts store or Walmart when I needed something to help out.
Maybe I'm being a little forgetful that yes I bought a jack, and Jack stands, and have a random pipe as a breaker bar, and other odds and ends. But you can go very far for $1k as a DIYer.
Sounds like we have similar boxes - mine has a 10 core CPU, 64 GB of ram, and a 2070 Super. My motherboard had two unused PCIe3x8 slots and doesn't support Blackwell GPUs. I bought a couple of brand new Ada generation RTX 2000s with 16GB of memory for under $1400 to get to 40 GB of VRAM. That will easily run Qwen3.6-27b at a 6-bit quantization and 80,000 token context size. It isn't fast (19-21 t/s), but using pi-coding-agent is fine.
Now, my instinct is that I am giving up SOTA performance on agentic coding with this setup and LLM. But the gap between my setup and SOTA commercial models is small enough that it doesn't matter to me.