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Comment by swatcoder | Hacker News Reader

Everybody's got their own calculus about how competitive their space is and what this tech can do for them, but some might be best off dancing around lock-in by being careful about what they use from OpenAI and how tightly they integrate with it.

This is very early in the maturity cycle for this tech. The options that will be available for private inference and fine tuning, for cloud-gpu/timeshare inference and fine tuning, and for competing hosted solutions are going to vastly different as months go by. What looks like squeezing value out of OpenAI today might look a lot like technical debt and frustrating lock-in a year from now.

That's what they're hoping you chase after, and if your product is defined by this technology, maybe that's what you have to do. But if you're just thinking about feature opportunities for a more robust product, judiciousness could pay off better than rushing. For now.

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"What looks like squeezing value out of OpenAI today might look a lot like technical debt and frustrating lock-in a year from now."

Just wanted to highlight this as such a great, concise way to look at the Buy vs Build with pretty much any cloud service, thanks!

Seems right to me. That's why it's good to build with extendability in mind to allow switching easily in the future if needed.

While everything you say mighy be true, it's also irrelevant.

Time to market is more important, you build users you get an edge, you can swap models later on (as long as you own the data).

Almost nobody sharecropping their way to an MVP has a product except at the sufferance of the company doing the work for them - regardless of which one they switch to later.

There's very little there there in most of these folks.

(For the few where there is, though, I agree with you.)

The Phind CEO talked in an interview about how their own model is already out ahead of ChatGPT 4 for the target use case of their search engine in some cases, and increasingly matching it on general searches: https://www.latent.space/p/phind#details

I use it instead of Bing Chat now for cases where I really need a search engine and Google is useless. Mainly because it's faster, but I also like not having to open another browser.

Do you build on AWS AI services then? Or any other cloud provider? The outcome is the same, right? Technical lock in, cost risks, integration maintenance, etc.

The key line in my comment, for emphasis:

> This is very early in the maturity cycle for this tech.

Think about what value you get out of the services and what migration might look like. If you are making simple completion or chat calls with a clever prompt, then migration will probably be trivial when the time comes. Those features are the commodity that everyone will be offering and you'll be able to shop around for the ideal solution as alternatives become competitive.

Alternately, if you're handing OpenAI a ton of data for them to opaquely digest for fine tuning with no egress tools, or having them accumulate lots of other critical data with no egress, you're obviously getting yourself locked in.

The more features you use, the more idiosyncratic those features are, the more non-transferable those features are, and the more deeply you integrate those features, the more risk you're taking. So you want to consider whether the reward is worth that risk.

Different projects will legitimately have different answers for that.

While I agree with you, as a happy GPT4 plus customer, I'm worried about the inevitable enshittification downhill roll that will eventually ensue.

Once marketing gets in charge of product, it's doomed. And I can't think of a product startup that it hasn't happened to. Particularly with this type of growth, at some point, the suits start to out number the techies 10:1.

This is why openeness and healthy competition is primordial.

It's not marketing, it's economics.

If you set money on fire -- eventually there's a time when you need to stop doing that.

I think the parent is more talking about the other common situation where organizations start focusing on maximizing profits, rather than just working towards a basic profitability. Eg, Google Maps API pricing comes to mind.

Yes, OpenAI might be (we don't know how much) burning through their $5B capital/Azure credits now, but I think the `turbo` models are starting to addressing this as well. And $20/month from a large user base can also add up pretty quick.

You do see VC bloat up company sizes for what could have been a very profitable small to medium sized private business, without enshittefication, had they not hired dozens more people.

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