IMO, if you depend on AI to make a living, I'd invest in hardware for local inference, and learn on how to effectively make a living using AI inference you control, on hardware you control. Sure, economically speaking it's way cheaper to use one of these heavily subsidised services (for now), and their models are faster and more capable, but if your livelihood depends on AI inference, and you are renting AI inference, you are a being a serf of the tokenlord. And your livelihood depends on the whims of the tokenlord. They can increase rent prices, they can decide you can no longer do whatever you are doing, and you have no recourse, because you are dependant on them to make a living.
There are a lot of things in my toolchain pre-AI that I did not own and relied on to make a living. Mobile developers are in even worse shape, and iOS developers doubly so. The idea we were somehow less beholden before AI, I think, is silly.
None of us can wholly do our trades without support. Local inference is a fun idea, but you'll be out-competed by the serfs, as you call them.
You listing things that make developers dependent doesn't mean they weren't less dependent before. Now they have all those things, and more.
It is debatable whether local inference is a competitive disadvantage. One key advantage is consistent performance. No unexpected model downgrades or yanks, no silly safeguards imposed, and no quotas is a lot of advantage.
And by the way, it is not an either-or decision. You can use local inference as your daily driver while still leaning on frontier models when you get stuck.
It's okay to be a prepper, but you don't have to be. Assuming access to internet, water, electricity and increasingly AI is a entirely fine way to live. It might not be anthropic which you will want to use but current capability models will be abundant and access readily available.
I don't read that recommendation as prepping. If I were to start an earthworks business I likely would rent heavy equipment at first, but once I get the business going I'd likely begin to buy my own machines.
That's not prepping in the sense of having a bunker full of canned beans. Its taking control of a key piece of my business, and likely saving money in the long run.
I don't think that analogy holds up. Owning heavy equipment requires lots of capital, and only makes sense if you keep utilization high. Even large companies will rent or lease equipment if it's something they use infrequently.
A large company might own their equipment, but an individual operator probably won't. So it might make sense for some large software companies to own their LLM hardware, but it probably won't make economic sense for individuals.
Of course the economics are different in different industries. Trucking owner operators account for ~15% of truckers, but buying a rig is six figures against 5-6 figure income. Buying a mac mini is 4 figures against a 6 figure income, so maybe lots of people will do it even if it's not economically optimal.
This may be a difference in location or urban vs rural? I live in a more rural area and many people I've hired over the years own their equipment (well, if having a loan on it counts). That goes for tractors obviously, but similarly for wheel loaders, excavators, etc that they use for hired work.
> once I get the business going I'd likely begin to buy my own machines
only when it's cheaper than to continue renting it in your calculations
same goes for renting vs buying a house, or anything.
the breakeven point here would come when they stop subsidizing the subscriptions, or when local ai gets to run on basically everything, making it ~free (sans electricity)
Except there's a huge gulf of self-hosting and using API hosts - no way you can reach the economics of a shared host. Privacy is a problem but you can chose who you host with and where it's hosted (which jurisdiction).
When privacy/compliance really starts to matter it's up to the client/business to provide you with tooling - you're not running that on your own hardware anyway.
So the local AI for individuals is just a hobby/gimmick at this point not a rational decision. Self-hosting for business is a different story.
I'm not sure. The problem with the cloud llm's is they are complete black boxes that change frequently and randomly day by day.
If you run Qwen 3.8 on your own hardware, every single day, it's the exact same model running in the exact same way.
Yes, it's nowhere near as "smart" as the cloud based models. But it's consistent.
So the workflows and "ways of working" you create will work mostly similar day to day.
With Claude/OpenAI you frequently find days where the models are useless, and days when they are out of this world.
So I guess the choice comes down to:
1. Randomly the smartest thing on the planet with unpredictable rate limits that is mostly amazing, but frequently messes with your workflows
2. A really good local coding model that is consistent every day with no rate limits
I'm not sure. My gut feeling is maybe the right answer is a mix of both.
Gambling on the biggest models, hoping they are working smart that day, when planning or doing very complex work. Then doing most of the tasks/daily work using local models??
You can run any open model on a shared API host via OpenRouter and pin to which host you want to go for the quant/privacy/etc. mix you care about. You can pay them directly if you don't want the OpenRouter overhead - but the convenience of switching, having one invoice, etc. is worth it IMO
It's not closed hosted models vs open local models, it's hosted open models vs local open models where the math doesn't work for local LLMs.
The only local inference use-case I can think of is porn generation (because most providers don't want to deal with it) and illegal shit like hacking to minimize the tracing.
And if you're super paranoid - but honestly giving sensitive info to LLMs in any scenario is a gamble.
If you game and can use your GPU I guess then it works as well but models that fit into a gaming GPU suck too much to bother IMO.
This is pretty terrible advice when there are dozens of AI inference providers out there serving great models with significantly more cost effectiveness than you'd get from buying your own hardware.
IMO, if you depend on AI to make a living, I'd invest in hardware for local inference, and learn on how to effectively make a living using AI inference you control, on hardware you control. Sure, economically speaking it's way cheaper to use one of these heavily subsidised services (for now), and their models are faster and more capable, but if your livelihood depends on AI inference, and you are renting AI inference, you are a being a serf of the tokenlord. And your livelihood depends on the whims of the tokenlord. They can increase rent prices, they can decide you can no longer do whatever you are doing, and you have no recourse, because you are dependant on them to make a living.