Comment by dvt
9 months ago
It's clear to me that OpenAI is quickly realizing they have no moat. Even this obfuscation of the chain-of-thought isn't really a moat. On top of CoT being pretty easy to implement and tweak, there's a serious push to on-device inference (which imo is the future), so the question is: will GPT-5 and beyond be really that much better than what we can run locally?
I wonder if they'll be able to push the chain-of-thought directly into the model. I'd imagine there could be some serious performance gains achievable if the model could "think" without doing IO on each cycle.
In terms of moat, I think people underestimate how much of OpenAI's moat is based on operations and infrastructure rather than being purely based on model intelligence. As someone building on the API, it is by far the most reliable option out there currently. Claude Sonnet 3.5 is stronger on reasoning than gpt-4o but has a higher error rate, more errors conforming to a JSON schema, much lower rate limits, etc. These things are less important if you're just using the first-party chat interfaces but are very important if you're building on top of the APIs.
I don't understand the idea that they have no moat. Their moat is not technological. It's sociological. Most AI through APIs uses their models. Most consumer use of AI involves their models, or ChatGPT directly. They're clearly not in the "train your own model on your data in your environment" game, as that's a market for someone else. But make no mistake, they have a moat and it is strong.
> But make no mistake, they have a moat and it is strong.
Given that Mistral, Llama, Claude, and even Gemini are competitive with (if not better than) OpenAI's flagships, I don't really think this is true.
There are countless tools competitive with or better than what I use for email, and yet I still stick with my email client. Same is true for many, many other tools I use. I could perhaps go out of my way to make sure I'm always using the most technically capable and easy-to-use tools for everything, but I don't, because I know how to use what I have.
This is the exact dynamic that gives OpenAI a moat. And it certainly doesn't hurt them that they still produce SOTA models.
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Inertia is a hell of a moat.
Everyone building is comfortable with OpenAI's API, and have an account. Competing models can't just be as good, they need to be MUCH better to be worth switching.
Even as competitors build a sort of compatibility layer to be plug an play with OpenAI they will always be a step behind at best every time OpenAI releases a new feature.
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Doesn't that make it less of a moat? If the average consumer is only interacting with it through a third party, and that third party has the ability to switch to something better or cheaper and thus switch thousands/millions of customers at once?
Their moat is no stronger than a good UI/API. What they have is first mover advantage and branding.
LiteLLM proxies their API to all other providers and there are dozens of FOSS recreations of their UI, including ones that are more feature-rich, so neither the UI nor the API are a moat.
Branding and first mover is it, and it's not going to keep them ahead forever.
I don't see why on-device inference is the future. For consumers, only a small set of use cases cannot tolerate the increased latency. Corporate customers will be satisfied if the model can be hosted within their borders. Pooling compute is less wasteful overall as a collective strategy.
This argument can really only meet its tipping point when massive models no longer offer a gotta-have-it difference vs smaller models.
On-device inference will succeed the way Linux does: It is "free" in that it only requires the user to acquire a model to run vs. paying for processing. It protects privacy, and it doesn't require internet. It may not take over for all users, but it will be around.
This assumes that openly developed (or at least weight-available) models are available for free, and continue being improved.
Why would a non profit / capped profit company, one that prioritizes public good, want a moat? Tongue in cheek.
>there’s a serious push to on-device inference
What push are you referring to? By whom?
Based on their graphs of how quality scales well with compute cycles, I would expect that it would indeed continue to be that much better (unless you can afford the same compute locally).
Not much of a moat vs other private enterprise, though