alt Hacker NewsI'm a consultant and I've already worked for multiple AI companies that shut down over the last two years. I think the main problem is that they all are relying on the big token providers and trying to capture value with various services.
At some point the token costs will increase so much, it will put all of these AI companies out of business.
Don't get me wrong. AI is definitely useful. The high costs are currently being subsidized by VC and other investors and unless there is a major breakthrough, it's just not going to work in the long-term.
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Why assume token costs will increase as opposed to become commoditized?
Not to mention if you make a successful value add the token provider will add it to their own harness and they're not paying the retail price they charge you for the tokens they consume.
Are you seeing companies that are in over their heads because they don't have enough domain knowledge?
I think that's always been an issue in tech, but it feels like AI exacerbates the issue. I see people starting companies and services where their primary domain knowledge seems to be whatever their AI tells them. It's sometimes enough to get off the ground, but I wonder how long it takes for customers/clients who actually need the domain knowledge to bail.
yea this is something I've been wondering about. I'm not too keen on the finance aspect, but I understand that the big players aren't turning a profit. OpenAI is still losing money. I've seen some discussion related to increased token costs. If that continues, isn't dependence on their services a risk?
I believe this to be completely mistaken, because the bang-for-buck is getting better. Luna on max thinking is very capable and builds you a feature for 20 cents.
Its just that in a gold rush, there are few winners and many losers. There used to be hundreds of car companies in the beginning, only a dozen or so survived long term.
GPT4 was more expensive than Astra. And by the time it didn't even have cache. Tokens are so cheap and so commoditized today that people already forgot how things were in the earlier days of LLMs.