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> Price increases: As VC funding dries up and companies face pressure to turn a profit, we'll likely see sharp increases in the cost of AI services. I disagree

> Price increases: As VC funding dries up and companies face pressure to turn a profit, we'll likely see sharp increases in the cost of AI services. I disagree. Yes, the VC funding will dry up, but hardware and algorithmic advances will decrease running costs by equal amounts. Lack of a moat will prevent companies recouping past expenses, since any who try will be outcompeted by new market entrants who don't have those past expenses.

jsheard 2y ago
This presumes that the SOTA models won't keep growing to fill any efficiency gains made. The models of today will get cheaper to run, sure, but surely the expectation is that AI will do a lot more than it does today in order to justify the staggering investments going into it?
sigmoid10 2y ago
>Lack of a moat will prevent companies recouping past expenses I think we'll see a lot of vendor lock in by introducing service complexity. We are already at a point where the hassle of switching from one big API provider to another is not always worth the hassle for a small reduction in price or a tiny improvement on some benchmarks. AI will go the same route as all SaaS products until we have GAI-level stuff running locally on affordable consumer hardware. And there's a lot of money to be made until then.
fsiefken 2y ago
Yes, the new Ryzen APU's might have 128G, great for running LLMs. https://videocardz.com/181601/amd-testing-strix-halo-apu-with-128gb-memory-config https://videocardz.com/181601/amd-testing-strix-halo-apu-wit... Perhaps some other party will come up with Groq like performant LPUs and LLMs will get more precise and efficient. I read that more and more people are running their LLMs locally.
dscottboggs 2y ago
Do you have some numbers to back that up or just vibes?
deleted 2y ago
[deleted]
cosmicradiance 2y ago
> Yes, the VC funding will dry up, but hardware and algorithmic advances will decrease running costs by equal amounts. The author said the same - "The current reliance on expensive GPU clusters may give way to more specialized, efficient AI hardware. This could help mitigate some cost pressures." As with the cloud, prices increase once competition dries out. Even if hardware becomes cheap, the services will cost more because shrewd and greedy boards run most businesses.
nertirs 2y ago
I would even argue, that a lot of AI services in the future will be close to free for the public. That is because in a lot of cases, the data received from user interactions is more valuable, than the data generated by the AI service.
vincent_s 2y ago
1. These companies have burned through billions in VC funding. Just breaking even won't be enough - they'll need significant profits to justify those investments. 2. We have at least one real-world example suggesting profitability is challenging. Microsoft is reportedly still losing money on GitHub Copilot, even after years of operation and many paying customers.
- londons_explore 2y ago
  > These companies have burned through billions in VC funding. These companies, yes. But new startups will come, spend only millions, and achieve the same thing. No moat.

jsheard 2y ago

This presumes that the SOTA models won't keep growing to fill any efficiency gains made. The models of today will get cheaper to run, sure, but surely the expectation is that AI will do a lot more than it does today in order to justify the staggering investments going into it?

sigmoid10 2y ago

>Lack of a moat will prevent companies recouping past expenses I think we'll see a lot of vendor lock in by introducing service complexity. We are already at a point where the hassle of switching from one big API provider to another is not always worth the hassle for a small reduction in price or a tiny improvement on some benchmarks. AI will go the same route as all SaaS products until we have GAI-level stuff running locally on affordable consumer hardware. And there's a lot of money to be made until then.

fsiefken 2y ago

Yes, the new Ryzen APU's might have 128G, great for running LLMs. https://videocardz.com/181601/amd-testing-strix-halo-apu-with-128gb-memory-config https://videocardz.com/181601/amd-testing-strix-halo-apu-wit... Perhaps some other party will come up with Groq like performant LPUs and LLMs will get more precise and efficient. I read that more and more people are running their LLMs locally.

dscottboggs 2y ago

Do you have some numbers to back that up or just vibes?

deleted 2y ago

[deleted]

cosmicradiance 2y ago

> Yes, the VC funding will dry up, but hardware and algorithmic advances will decrease running costs by equal amounts. The author said the same - "The current reliance on expensive GPU clusters may give way to more specialized, efficient AI hardware. This could help mitigate some cost pressures." As with the cloud, prices increase once competition dries out. Even if hardware becomes cheap, the services will cost more because shrewd and greedy boards run most businesses.

nertirs 2y ago

I would even argue, that a lot of AI services in the future will be close to free for the public. That is because in a lot of cases, the data received from user interactions is more valuable, than the data generated by the AI service.

vincent_s 2y ago

1. These companies have burned through billions in VC funding. Just breaking even won't be enough - they'll need significant profits to justify those investments. 2. We have at least one real-world example suggesting profitability is challenging. Microsoft is reportedly still losing money on GitHub Copilot, even after years of operation and many paying customers.

londons_explore 2y ago
> These companies have burned through billions in VC funding. These companies, yes. But new startups will come, spend only millions, and achieve the same thing. No moat.