The data from enterprise customers is clear but conflicted. While 94% of customers say they’re spending more on AI this year, they’re doing so with budget constraints that will steal from other initiatives. As well, the choice of where customers plan to run generative AI is split almost exactly down the middle in terms of public cloud vs. on-premises/edge. Further complicating matters, developers report the experiences in the public cloud with respect to feature richness and velocity of innovation has been outstanding. At the same time, organizations express valid concerns about IP leakage, compliance, legal risks and cost that will limit their use of the public cloud. 

In this Breaking Analysis we’ll share the most recent data and thinking around the adoption of large language models and address the factors to consider when thinking about how the market will evolve. As always, we’ll share the latest ETR data to shed new light on key issues customers face balancing risk with time to value.

Google memo - we have no moat and neither does OpenAI

https://www.semianalysis.com/p/google-we-have-no-moat-and-neither

Janelle Teng - AI in the Cloud article on Substack:

https://nextbigteng.substack.com/p/ai-model-layer-the-new-frontline-of-cloud-wars

A16z on the economics of AI:

https://a16z.com/2023/08/03/the-economic-case-for-generative-ai-and-foundation-models/

Wall St Journal Article citing AWS, Google, MSFT, Dell & HPE POV

https://www.wsj.com/articles/the-ai-boom-is-here-the-cloud-may-not-be-ready-1a51724d?reflink=mobilewebshare_permalink

Technalysis GenAI study of 1,000 ITDMS:

https://www.technalysisresearch.com/downloads/TECHnalysis%20Research%20Generative%20AI%20in%20Enterprise%20Survey%20Highlights.pdf

AWS Outposts at the edge with Sagemaker - Circa 2021

https://aws.amazon.com/blogs/machine-learning/machine-learning-at-the-edge-with-aws-outposts-and-amazon-sagemaker/