Today's Cheap AI Services Won't Last

> 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.

I have pretty much the opposite take: years from now we'll tell our grandchildren that the AI APIs we used to use were paid BY THE TOKEN!

We're in the dialup days of AI, where capabilities are in the hands of very few companies because hardware and training costs are prohibitively expensive. Sure, the apps we use are heavily subsidized by investment funding and the competition is very fierce. I'll also concede that 99% of the AI startups today will fail. But that doesn't mean only the 1% will be left: new ones will continuously enter the arena, compete for attention, and to do that they'll need to lower their prices. All the while, hardware costs will decrease, and incumbents like NVIDIA will inevitably grow stagnant and others will come to eat their lunch. It's the circle of (business) life.

> The current landscape bears a striking resemblance to the early days of cloud computing. Fifteen years ago, it was commonplace for individuals and businesses to pay less than $10 a month for shared hosting or a VPS. This pricing model seemed set in stone, with costs expected to continue declining.

A Digital Ocean VPS starts at $4 a month. It's not so much that we've lost the cheap part of the cloud, it's that the big cloud providers figured out how to do enterprise sales and capture the part of the market that was already overpriced and wasn't sophisticated enough to optimize itself.

This was a huge concern in 2022, before open models with useful IQ levels were released. Today, it's hard to imagine prohibitive prices at the end-user level, because it's hard to envision an application that won't eventually have a "good enough" open/free on-device inference implementation.

Two caveats: application-specific patents are still possible (and many torpedo patents are undoubtedly en route right now) and this argument might not hold at the middleman level (where wrapper apps might get their margins squeezed out.)

I feel that a specialized AI becomes too niche to be useful for the common folks.

A general LLM model, that is "jack of all and master of none" may remain the go-to choice for the masses. These systems leave the last bits of intelligence to be filled by humans.

Of course. At current trend its going to consume ridiculous amount of electricity for no good reason (and make some more CO2). Funny that 60W old school light bulb is bad bad bad, but 400W gfx card is ok and armies of 3kW servers to produce bs responses to queries are also ok. Consuming ridiculous kilowatts to produce even more bs video is also "no problemo". Hm. Poor artists, I can see why they're furious about this.

I think AI services will be generally free because there is too much juicy data that can be mined and a risk someone might just make it easy to make your own AI "server" so to speak.

You guys are paying for AI services?

The models are literally free, just run them yourself