Do you mean the marginal cost by the producer, or the cost on the consumer? I can't see the price of electricity falling much, and the demand curve is apparently exponential if the hype is to be believed.
DeepSeep V4 Pro is 99% cheaper than similarly performing models were 2 years ago (if such a model even existed).
Computing has always been about how to wring out more efficiency. The ENIAC was 150,000 watts, with 3 phase 240 volt power, and cost about $500,000.
My day to day laptop (a year old) is 35 watts, with 1 phase 20 volt power, and cost $1,000, so that's 99.98% less power consumption, 99.8% cheaper, and it has about 10 orders of magnitude more computing power, all on a time span of 80 years.
It died before AI came around and today's coding agents are somewhere upwards of twice as competent as whatever the state of the art of automatic coding was in 2020. 8I
A good chunk of that was one-time gains from shifting GPU and memory architectures to better match what LLMs need at scale as well as some algorithmic improvements. Most of the low-hanging architecture optimization has already been harvested. We'll certainly have more algorithmic gains but the consensus is they'll generally be smaller and less frequent.
There's always a chance we'll have some dramatic gains far larger than DeepSeek's optimizations a year ago, but it hasn't happened again yet at even that scale. It would be nice but I certainly wouldn't count on it.
Do you mean the marginal cost by the producer, or the cost on the consumer? I can't see the price of electricity falling much, and the demand curve is apparently exponential if the hype is to be believed.