Speaking during an interview on CNBC’s “Squawk on the Street” segment earlier this week, CEO of cybersecurity giant Palo Alto Networks Nikesh Arora implored the tech industry to lower the cost of AI.
During the segment, the chief executive argued that the cost to use large language models (LLMs) has to drop by 20 percent by 2027 — and 90 percent by 2028 — for the tech to be useful to enterprises.
“We need to see the pricing for AI come down,” Arora said.
Depends entirely on whether or not Moore’s law has hit a physical limit yet.
If we’re still doubling computational every X years for the same cost, we’d be able to see same models cheaper. If we’re however finally hitting physical limits, then yeah. It won’t be cheaper and we can’t just smash more context in it like there’s no tomorrow.
Then the most likely breakthrough would have to be DNA storage (tri point storage, rather than bi point storage) and then we’d need fast read/write to DNA storage. As that’d theoretically allow for more context for the LLMs, context is the biggest bottleneck
Moore’s law has been dead for more than a decade by now
Sort of, we still get improvements but they are a fraction of what they used to be