Because I’ve seen a lot of questions about how to use these models, I recorded a quick video showing how I use them on MacOS.
Local is not where the money is, it’s in cloud services and api usage fees.
They all tout OpenAI compatible APIs because OAI was the first mover. No real threat for incompatibility with OAI.
Plus these LLMs don’t have any kind of interface moat. It’s text in and text out.
> Plus these LLMs don't have any kind of interface moat.
The interface really has very little influence. Nobody in the enterprise world cares about the ChatGPT interface because they're all building features into their own products. The UI for ChatGPT has been copied ad nauseam - so if anyone really wanted something to look and feel the same it's already out there. Chat and visual modals are already there, so I'm curious how you think ChatGPT has an "interface moat"?
> Local private models are not a threat to openai.
There are lots of threats to AI. One of them being local models. Because if the OpenAI approach is to continue at their burn rate and hope that they will be the one and only I think they're very wrong. Small, targeted models provide for many more use cases than a bloated, expensive, generalized model. I would gather long term OpenAI either becomes a replacement for Google search or they, ultimately, fail. When I look around me I don't see many great implementations of any of this - mostly because many of them look and feel like bolt-ons to a foundational model that tries to do something slightly product specific. But even in those cases the confidence with which I'd put in these products today is of relatively low quality.
There is no interface moat. No reason for a happy openai user to ever leave openai, because they can enjoy all the local model tools with GPT.
I should’ve been more clear.
A business is worth the sum of future profits, discounted for time (because making money today is better than making money tomorrow). Negative profits today are fine as long as they are offset by future profits tomorrow. This should make intuitive sense.
And this is still true when the investment won't pay off for a long time. For example, governments worldwide provide free (or highly subsidized) schooling to all children. Only when the children become taxpaying adults, 20 years or so later, does the government get a return on their investment.
Most good things in life require a long time horizon. In healthy societies people plant trees that won't bear fruit or provide shade for many years.
I'm reminded of the Silicon Valley bit about no revenue https://youtu.be/BzAdXyPYKQo
It probably looks better to be not really trying for profitability and losing $5bn a year than trying hard and losing $4bn
I was really surprised when OpenAI started providing most of their good features for free. I am not a business person, but it seems crazy to me to not try for profitability, of at least being close to profitability. I would like to know what the competitors’ burn rates are also.
For API use, I think OpenAI’s big competition is Groq, serving open models like Llama 3.1.