Automating Beyond SWE
My strong sense is that Enterprise AI investment will continue even if Consumer AI spend slows.
The foregone cost savings are too great for companies to not deploy AI - at a minimum across the Software Engineering (SWE) function. Token spend increases will be more than offset by human capital spend decreases. Therefore, GPU production will continue.
My sense is also that CEOs are not close enough to LLMs to fully grasp what the ROI potential is across their companies. I am also not sure how many CTOs fully grasp LLMs’ potential to drive automation. You have to get in the weeds with LLMs to understand what each model is capable of, with which harnesses and tool sets etc.
My view is that there are huge cost saves to be had across Corporate Finance, Marketing, Customer service.. you name it.
As it relates to GPUs - chip companies can’t produce enough. I wonder if Tesla will acquire a piece of Intel? Tesla’s Terafab will take some time to pump out silicon at scale, INTC could help.
Last night we rolled out a new tool which turns Word docs and PDFs into markdown files, which are more quickly ingested and processed by LLMs. It’s really fast for example when you deploy a local instance of Kilby on a series of EPS call transcripts in markdown format.
You will see more services like Obsidian pop up that store your files locally in markdown format so that tools like Kilby can zip through them.