Statute of limitations kicks in at the moment of your awareness of the watch being fake. But, you and the plaintiff might dispute over the fact of when you learned the watch was fake. That’s exactly what this jury decision was about. Musk claimed he wasn’t aware of OpenAI’s for profit push until 2022. Altman claimed he was aware of it as far back as 2017 or 2019. The Jury looked at texts and emails and interviewed witnesses and decided that Musk was aware of it in 2019, which is more than 3 years before he filed the suit in 2024.
This is not true. API tokens are not sold at a loss, and hardware gets more efficient over time, so serving inference on the same model gets cheaper. LLAMA 3.1 405B parameters was $6/$12/M tokens in 2024, but in 2026 that same model is $3/$3/M tokens.
The most intelligent model at a given time is much larger than the previous, which is why token costs for GPT5.5 are higher than 5.4. But you should expect that 2 years from now, serving a GPT5.5 sized model will be cheaper than GPT5.5 today. You should expect it to be even cheaper to get an equally intelligent model 2 years from now, because distillation techniques are effective at reducing the necessary parameter count for the same benchmark scores.
So are they going to stop at GPT 5.5? This analysis only seems to be counting inference cost when the majority of the cost, and why they are burning through money, is the training.
That includes lunch, park bench, coffee shop to charge phone, etc, but yea. north brooklyn to coney island and back. ive only done it a few times , not 5 days a week
the most productive teams will be the ones that treat code as compiler output (which we never read)
legacy manual codebases which require human review will be the new "maintaining a FORTRAN mainframe". they'll stick around for longer than you'd expect (because they still work) , at legacy stagnant engineering companies
i disagree because i see code as the actual product of the thought behind it. it is after all a description of the intent of the programmer and programming language are what we use to communicate to machines
that said, we will see over the next few years who is right!
Product work can be counterintuitive. An engineer / PM might think that a design or feature “makes sense”, but you don’t actually know that unless you measure usage.
I was in school when GPT came out and there is a strong generational divide. It reminds me of when i was young teachers said you couldn’t use Wikipedia because it isn’t guaranteed to be correct, but we did anyway. Same thing with LLMs. It’s a faster way to do things so eventually everything will be done that way.
What backlash against Adobe? I think you are mistaking comment section consensus for reality. People on forums and social media complain, but the comment section consensus is often dead wrong!
There was no real backlash against Adobe. They added subscriptions and grew revenue. Some people grumbled online, but they paid, which means they don’t like the old model, they like the new one.
There is absolutely no monopoly in photo editing software. Entering this market is fairly easy with a new product. I wonder what market (in software or outside software) could you name as more competitive.
Anthropic is a great case study in why uptime doesn’t matter. The service is so valuable that you can have one nine uptime and add $9bil ARR in 3 months.
Yep. Enthusiasts are cheap, picky, and have no loyalty. They’re extremely political and are the only type of customer who will actually switch. Plus it’s a tiny market. You might eek out $50mil revenue after a decade, if you’re lucky.
