The data from enterprise customers is clear but conflicted. While 94% of customers say they\u2019re spending more on AI this year, they\u2019re 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.\xa0
In this Breaking Analysis we\u2019ll 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\u2019ll 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
Technalysis GenAI study of 1,000 ITDMS:
AWS Outposts at the edge with Sagemaker - Circa 2021