What would a "legitimate competitor" look like to you?
Samsung and LG make high-end phones, and there are plenty of good personal computer vendors. And Windows is certainly a desktop OS that some people choose.
Apple doesn't offer any services unique to itself. It does offer a slick-looking and well-marketed "ecosystem" which is really just a bunch of different things that you could get from other vendors.
The consistency is that most left-wingers are pro environment and anti-corporation. So it makes perfect sense for them to oppose generative AI, which serves to enrich corporations and harm the environment.
Non-engineers are perfectly willing to volunteer their time to do drudgery. It's one of my opseng career's distinguishing specialties: I'll do drudgery rather than code when appropriate, rather than avoiding it or sulking about it (as was a common response at work for some number of decades!). Learned that lesson when I was 18 from an internship (where I completely failed to deliver any work product due to trying to code around the work). It's part of why I'm going into accounting: apparently having the stamina for dreary work is rare?!
Also look up double/triple data-entry systems, where you have multiple people enter the data and then flag and resolve differences. Won't protect you from your staff banding together to fuck you over with maliciously bad data, but it's incredibly effective to ensure people were Actually Working Their Blocks under healthy circumstances.
Pre-commit and pre-push hooks serve the purpose of keeping code isolated to a developer's machine. This is a recipe for disaster. You will run into situations where important work isn't accessible since a developer couldn't commit/push their code and the machine was lost or damaged. I've seen it happen.
Steam reached a new peak of 42 million concurrent players today [1]. An average/mid-tier gaming PC uses 0.2 kWh per hour [2]. 42 million * 0.2 gives 8,400,000 kWh per hour, or 8,400 MWh per hour.
By contrast, training GPT3 was estimated to have used 1,300 MWh of energy [3].
This does not account for training costs of newer models, nor inference costs. But we know inference costs are extraordinarily inexpensive and energy efficient [2]. The lowest estimate of energy cost for 1 hour of Steam's peak concurrent player count uses 6.5x more energy than all of the energy that went into training GPT3.
I was skeptical of the LLM energy use claim. I went looking for numbers on energy usage in a domain that most people do not worry about or actively perceive as a net negative. Gaming is a very big industry ($197 billion in 2025 [1], compare to the $252 billion in private AI investment for 2025 [2]) and mostly runs on the same hardware as LLMs. So it's a good gut check.
I have not seen evidence that LLM energy usage is out of control. It appears to be much less than gaming. But please feel free to provide sources that demonstrate this lie.
The question is whether claims of AI energy use have sustenance, or if there are other industries that should be more concerning. People are either truly concerned about the cost of energy or it's a misplaced excuse to reinforce their negative opinions.
I see no point in making this a numbers game. (Like, I was supposed to say "five" or something?)
Let's make it more of a category thing: when AI shows itself responsible for a new category of life-saving technique, like a cure for cancer or Alzheimer's, then I'd have to reconsider.
(And even then, it will be balanced against rising sea levels, extinctions, and other energy use effects.)
Search through github for commits authored by .edu, .ac.uk etc emails and spend a few days understanding what they’ve been building the past few years. Once you’ve picked your jaw off the floor, take another 10 minutes to appreciate that this is just the public code by some researchers, and is crumbs compared to what is being built right now behind closed doors.
Tenured professors are abdicating their teaching positions to work on startups. Commercial labs are pouring billions into tech that was unreachable just a few years ago. Academic labs are downscaling their interns 20x. Historically hermit companies are opening their doors to build partnerships in industry.
The scale of what is happening is difficult to comprehend.
Local LLMs that you can run on consumer hardware don't really do anything though. They are amusing, maybe you could use them for basic text search, but they don't have any real knowledge like the hosted ones do.
Gemma 3 27B, some smaller models in the 8-16B size range, and up to 32B can be run on hardware that fits in the "consumer" bracket. RAM is more expensive now, but most people can afford a machine with 32GB and maybe a small graphics card.
Small models don't have as much world knowledge as very large models (proprietary or open source ones), but it's not always needed. They still can do a lot of stuff. OCR and image captioning, tagging, following well-defined instructions, general chat, some coding, are all things local models do pretty well.
Using SmartSVN which makes life a fair bit better but still keeps this confusing terminology.
We'll be migrating to Git this year though so.
For reference, the codebase is over 20 years old, and includes binary dependencies like libraries. Makes it easy to compile old versions when needed, not so easy on the repository size...
Samsung and LG make high-end phones, and there are plenty of good personal computer vendors. And Windows is certainly a desktop OS that some people choose.
Apple doesn't offer any services unique to itself. It does offer a slick-looking and well-marketed "ecosystem" which is really just a bunch of different things that you could get from other vendors.