Across industries, rising compute expenses are often cited as a barrier to AI adoption — but leading companies are finding that cost is no longer the real constraint.
The tougher challenges (and the ones top of mind for many tech leaders)? Latency, flexibility and capacity.
At Wonder, for instance, AI adds a mere few cents per order; the food delivery and takeout company is much more concerned with cloud capacity with skyrocketing demands. Recursion, for its part, has been focused on balancing small and larger-scale training and deployment via on-premises clusters and the cloud; this has afforded the biotech company flexibility for rapid experimentation.
The companies’ true in-the-wild experiences highlight a broader industry trend: For enterprises operating AI at scale, economics aren’t the key decisive factor — the conversation has shifted from how to pay for AI to how fast it can be deployed and sustained.
AI leaders from the two companies recently sat down with Venturebeat’s CEO and editor-in-chief Matt Marshall as part of VB’s traveling AI Impact Series. Here’s what they shared. Wonder: Rethink what you assume about capacityWonder uses AI to power everything from recommendations to logistics — yet, as of now, reported CTO James Chen, AI adds just a few cents per order. Chen explained that the technology component of a meal order costs 14 cents, the AI adds 2 to 3 cents, although that’s “going up really rapidly” to 5 to 8 cents. Still, that seems almost immaterial compared to total operating costs.
Instead, the 100% cloud-native AI company’s main concern has been capacity with growing demand. Wonder was built with “the assumption” (which proved to be incorrect) that there would be “unlimited capacity” so they could move “super fast …