A year ago, enterprises balked at the prospect of deploying AI. The dominant blocker : security. By using AI, would my company lose its data as employees passed sensitive queries to large language models?
Today, buyers are more familiar & have security options : deploying AI on virtual private cloud architectures, tools to delete data from cloud AI vendors, dedicated security tools for AI, & a panoply of open source alternatives.
ROI (return-on-investment) has replaced fear.
AI is expensive. What is my fancy GPU doing for the business? What revenue has AI increased? What cost has AI reduced? How much better is AI than existing software?
This pressure on performance is equally present in both the development of internal tools & the procurement of external software.
There are many causes : the significant capex required to rent GPUs, an economic backdrop where the labor markets are weak & the potential for recession lurks, & also the tremendous promises the industry has made on the back of AI.
In chatting with enterprise buyers, we hear consistent questions :

what performance improvement can I expect with an AI product compared to the one I currently have?
how much better is on AI product compared to its peers?
how can we reduce the operating expense or increase the margin of an internally built AI product?1

In some categories, the ROI is clear : call center & security center automation. In others, general productivity gains are harder to quantify & defend additional expense.
There’s a growing sentiment that AI buyers will enter a crestfallen phase where the promises of AI haven’t yet been met.
The greater the ROI a GPU can deliver, the more likely negative sentiment will be reversed. At the end of every AI investment conversation, the champion will have to answer the question : what has our GPU done for us today?

1 Smaller models are often the answer, especially as their quality begins to rival their larger counterparts because of new training techniques like distillation or over-training.
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