they are very adept at doing a negative cash flow.
this post was submitted on 22 Sep 2026
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Tech companies are expected to be run at a loss for quite some time before they start being profitable.
They were founded in 2015 and "officially" launched GPT in 2022 to the public. If they make profits by 2032, that would be in line with how other tech companies established themselves.
For example, Spotify wasn't profitable until 2024. They launched in 2008.
So, I don't know why them not being profitable is what everyone is talking about. They're not expected to be profitable until 2030 at the earliest. And every serious investor know this.
Yes I'm very fun at parties. But it seems that a lot of people just do not understand the expected timelines of tech companies.
Okay but the world had enough money to bankroll Spotify for 20 years. It doesn't have the kind of money OpenAI needs.
And Spotify was barely making a loss AND had a lot of revenue. They were just putting more capital into infrastructure, etc. instead of profits.
Proprietary frontier LLMs in companies riddled with highly suspect business practices.
My local LLM setup has absolutely been making the equivalence of positive cashflow for me since gemma 4 and qwen 3.8: I need less total time for several groups of annoying (but necessary) tasks, which means my productivity is up and I can use the rest as my own free time.
I need less total time for several groups of annoying (but necessary) tasks
Such as? Just curious what people use LLMs for.
- Answer questions (with references) about source code and documentation
- Add clear-bounded features to a pre-existing codebase
- Transpile between programming languages
Stuff I can do myself perfectly well, but the results - after postprocessing - are indistinguishable from me having done so. Even when accounting for everything (including hallucinations) the time savings are real: Renewables-powered electricity is cheaper than my attention. By a lot.
Caveat: This only works if you're already a domain expert and if you do it exclusively for too long, you might be at risk of ceasing to be such an expert. Don't use it to try and do the thinking for you.
I handle so many text files in my job and LLMs help make the scripts for me to handle these text files. I know it is important to at least know stuff like regex but I am not a superhuman. It is difficult for me to heavily specialize in scripting while also specialize in the technicalities of my job. To help show why it is difficult to specialize scripting, I work with hardware description languages which have a different syntax from scripting languages. Handling different languages causes confusion on the proper syntax even with a basic if else statement because the syntax is different between languages.
Aside from this, it can also help me automate describing what the sequences I created in the file for easy documentation. I just have to instruct the "patterns" it needs to look for. I just need to proofread the output and do minor fixes.
at this rate Winamp might get more profitable than OpenAI and it is free.
But isn't OpenAI just burning billions of moneys already? In that case Winamp is already more profitable
believe it or not but there was a time when Winamp used to be AOL's money drain
Your local grocery store is more profitable than OpenAI. In fact you are more profitable than OpenAI.
OpenAI owes hundreds of billions of dollars, with a revenue a fraction of that. They will never become profitable.
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So you're saying we should be pumping all our savings and pension money in WinRar?
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