This is important context in the wake of yesterday’s “raise” announcement. A lot of this stuff seems to just quietly never happen once the ink on the PR puff dries. The AI industry increasingly looks in scramble mode to keep the hype going as those storm clouds of financial and business reality get darker and darker on the horizon.
OpenAI != The AI industry
OpenAI has stagnated technologically, and is a financial zombie, but that's not true for every part of the industry. Once these early movers flame out, there will be more stability with Google, Microsoft, and AWS.
The stargate, nvidia and amd deals are all linked together and the fallout is not public. Nvidia and amd stock seems to not care about it at all. Oracle fired 30000 employees, not sure if it’s to fund that initiative or a fall out of that
> The $20-200 LLM plans are all subsidized and aren't paying for themselves. Something has to give here. Whats interesting to me as well as much as companies are pushing AI adoption, i have started to hear AI token spend limits enforced across a few companies, so its not entirely clear that b2b can make them profitable yet either. If all the models reach good enough, then low cost provider would win. Gemini seems lik…
>If all the models reach good enough, then low cost provider would win. Gemini seems like a safer bet since Google controls more of the stack / has more efficiencies / cross selling / etc. Gemini is the best deal too. For $20: you get multiple quotas per day across the products (web, CLI, antigravity, AI Studio) 2tb of cloud storage, and you can family share the plan.
I don't know Gemini's pricing model in detail, but in general pricing doesn't generalize well between personal/hobbyist and enterprise use. Consumer pricing of variable costs is a balancing act, and most Gemini users aren't going to be anywhere near the quota; a company of 1000 can't always buy for $20,000 what 1000 random users with $20 personal plans are theoretically capped at.
For a company bringing a new technology from zero to mainstream, I think it's pretty normal that there will be a lot of failed attempts at productization. The thing that isn't normal is the degree of experimentation relative to company valuation. Normally once a company reaches $700 B+ valuation, they've figured out their product and monetization strategy. ChatGPT is clearly still iterating heavily on that - not norm…
And not normal for a company that has been at it this long. The Apple II went on sale on June 10th, 1977. Visicalc went on sale October 17th, 1979- 860 days separate the two. ChatGPT was opened to the public on November 30th, 2022, which was 1219 days ago- almost 50% more time has elapsed than between the Apple II and Visicalc.
IMO, the AI companies are trying to be both T-Mobile and Google Doc at the same time. Even Apple is struggling with being both the platform and the product. The issue with OpenAI is that the platform has no moat (other than money) and the product can be easily copied. In the game console world, the platforms have patents and trademarks, and games are not easily produced.
It's tempting to look at trends and assume there must be a rule behind them, but it's also intellectually lazy. Please do the hard work of justifying your stance like GGP did.
it is a simple stance - if you have a product that is used by hundreds of millions of people ad monetization strategy will be found cause there are people a lot smarter than you and me that will get it done. here’s intellectual challenge - find a business with comparable number of users to openai which is not swimming in ad revenue - one will do
A counterpoint is that there are many products with significant usage that fail or never attempt advertising monetization. They just increase the cost of the product.
And not normal for a company that has been at it this long. The Apple II went on sale on June 10th, 1977. Visicalc went on sale October 17th, 1979- 860 days separate the two. ChatGPT was opened to the public on November 30th, 2022, which was 1219 days ago- almost 50% more time has elapsed than between the Apple II and Visicalc.
Without me trying to be snarky why do you feel spreadsheet software launching is comparable to this scenario?
Visicalc is widely regarded to be the first "killer app" for the Apple computer. Perhaps even the first "killer app" period.
What they really should focus on is making those models more efficient. With them most likely losing money on inference (+model training + salaries + building data centers), I can't see why they would want more compute and more products, since more tokens spent is actually bad for them.
Making existing models more efficient won't make them God in a Box.
For a company bringing a new technology from zero to mainstream, I think it's pretty normal that there will be a lot of failed attempts at productization. The thing that isn't normal is the degree of experimentation relative to company valuation. Normally once a company reaches $700 B+ valuation, they've figured out their product and monetization strategy. ChatGPT is clearly still iterating heavily on that - not norm…
Which is a good thing. Elon has showed the world, the only thing that limits the upper bound is bureaucracy, extreme risk-averse and no culture for experimenting. More and more companies will start operating on the correct reward/risk curve or else getting crushed by firms who do. OpenAI has forced Google, Apple, Meta out of their comfort zone because they know OpenAI will eat their lunch
True, Elon has really been achieving win after win with Tesla and Twitter.
Without me trying to be snarky why do you feel spreadsheet software launching is comparable to this scenario?
Visicalc is often described as the killer app of the first generation Personal Computer(1). It was the product that drove them into every small business in the country, that blew up sales of personal computers and brought them out of the realm of hobbyists into enterprise. And, honestly, I think Visicalc and spreadsheets are still a greater benefit than what I've seen out of generative AI today. And that happened a l…
Thanks for the in depth explanation. I was definitely not up on my tech history here. :)