Just like every other subscription model, including the one in the Black Mirror episode, Common People. The value is too good to be true for the price at the beginning. But you become their prisoner in the long run, with increasing prices and degrading quality.
I think the only people worried about lock-in or Black Mirror themes are the people who are thinking about these subscriptions in an abstract sense.
It’s really easy to change providers. They’re all improving. Competition is intense.
People are reasonably worried that the same will happen to AI.
It absolutely does not.
Your use of social network derives value from your network. If you switch, you have to convince everyone else to switch as well.
It's a tremendous barrier to switching.
LLMs are for the most part interchangeable commodity.
One option is to add adverts to the generated output, and making the product cheaper than the competition. Another is to have all your cloud data preprocessed with LLMs in a non-portable way, so that changing will incur a huge cost.
As of now, specifically for coding assistance LLM, workflows remain generic enough to have relatively low switching costs between the different models.
259. Anthropic tightens usage limits for Claude Code without telling users (techcrunch.com)
395 points by mfiguiere 2 days ago | hide | 249 comments
It isn’t specific to software/subscriptions but there are plenty of examples of quality degradation in the comments
Netflix/Hulu were "losing money on streaming"-level cheap.
Uber was "losing money on rides"-level cheap.
WeWork was "losing money on real-estate" level cheap.
Until someone releases wildly profitable LLM company financials it's reasonable to expect prices to go up in the future.
Course, advances in compute are much more reasonable to expect than advances in cheap media production, taxi driver availability, or office space. So there's a possibility it could be different. But that might require capabilities to hit a hard plateau so that the compute can keep up. And that might make it hard to justify the valuations some of these companies have... which could also lead to price hikes.
But I'm not as worried as others. None of these have lock-in. If the prices go up, I'm happy to cancel or stop using it.
For a current student or new grad who has only ever used the LLM tools, this could be a rougher transition...
Another thing that would change the calculation is if it becomes impossible to maintain large production-level systems competitively without these tools. That's presumably one of the things the companies are betting on. We'll see if they get there. At that point many of us probably have far bigger things to worry about.
Eventually, these things should get closer. Eventually the hosted solutions have to make money. Then we’ll see if the costs of securing everything and paying some tech company CEO’s wage are higher than the benefits of centrally locating the inference machines. I expect local running will win, but the future is a mystery.
Locally I need to pay for my GPU hardware 24x7. Some electricity but mostly going to be hardware cost at my scale (plus I have excess free energy to burn).
Remotely I probably use less than an hour of compute a day. And only workdays.
Combined with batching being computationally more efficient it’s hard to see anything other than local inference ALWAYS being 10x more expensive than data centre inference.
(Would hope and love to be proven wrong about this as it plays out - but that’s the way I see it now).
They will. And when they do it will hit hard, especially if you’re not just a consumer but relying on it for work.
One vector is personalization. Your LLM gets to know you and your history. They will not release that to a different company.
Another is integrations. Perhaps you’re using LLMs for assistance, but only Gemini has access to your calendar.
Cloud used to be ”rent a server”. You could do it anywhere, but AWS was good & cheap. Now how is is it to migrate? Can you even afford the egress? How easy is it to combine offerings from different cloud providers?