11–20 of 89 posts
Hang on to your wallet, you ain't seen nothing yet. The true cost of AI won't be revealed until after a large portion of the customer base has become "hooked" on it.
That combined with the relatively easy switching costs. It doesn't bode well for AI companies seeking to create a walled garden.
Hang on to your wallet, you ain't seen nothing yet. The true cost of AI won't be revealed until after a large portion of the customer base has become "hooked" on it.
I'm not sure I agree. Beyond the Anthropic world, we see 1. The GitHub Copilot pricing change that started a week ago already change buying decisions. 2. Small name open weight providers selling at what I assume, and hear through grape vine, is a profitable place. Claude is over priced for what you get, and if headline is true, expensive to run. I do wonder if their API pricing is profitable. That's the word on the s…
My own impression based on inference prices for deepseek or other "open" models in the 1T range (including providers like DeepInfra with no obvious reason to subsidize their API costs) is that Anthropic is offering subscriptions at cost (on average, power users are a bit more expensive, casual users more profitable) and making good profit on API pricing. Profit that then is spent on model training, marketing and development, for an overall negative bottom line
Edit: in case it gets changed: current headine is "Anthropic/OpenAI may be spending more than $1000 for every $100 you pay them"
If this is the true cost of AI then the future might be dedicated extension cards for computers that hardcode entire models + weights. Downside: you need to buy a new one for each model. Upside: insanely fast inference and zero subscription cost, only one time purchase cost. Once a certain open source model gets good enough this might become viable. Right now the landscape is still shifting too fast. State of the art…
the world has long since moved on from that business model, unfortunately.
So we're in the $6 Uber ride era of AI still?
I’d say we are at the peak of inflated expectations, exactly where they want things to be for the IPOs as they unload their bags on the public. There will be an absolutely massive crash that will destroy the stock market, you’ll finally be able to buy ram and a hard drive again, and then AI will really take your job in the plateau of productivity that comes much later.
That’s probably because only a handful of companies manufacture GPUs, and they’re still expensive. I think that will change over time as competition increases. LLMs are also still in a relatively early stage. We’re already seeing models become both smaller and more capable—for example, GPT-4 compared to Qwen3-30B, which can outperform GPT-4 in many tasks while using significantly less compute. So if this trend contin…
GPUs that can run everything from Crysis to CUDA are a harder engineering problem to solve than creating a chip that's optimized for inference. Not to mention that inference is an excellent first step towards a full, competitive GPU as well.
Hang on to your wallet, you ain't seen nothing yet. The true cost of AI won't be revealed until after a large portion of the customer base has become "hooked" on it.
* Hardware improvements will reduce costs
* Model training improvements (read: more efficient model training) will reduce costs
* Better models will reduce costs (more inference for less hardware time while keeping quality constant)
* Tooling and platform will stabilize—less need to dump money into applications and backend systems because they will become mature—also improvements in AI efficiency and quality will lower the cost of maintenance and future feature development
* Energy buildout will stabilize (we will eventually have enough energy supply to meet AI demand)
* Chips market will stabilize (chip supply will catch up to AI demand, lowering the hardware costs)
So we're in the $6 Uber ride era of AI still?
I just hope local llms keep getting better and ways to make them run faster on consumer devices improves