Scaling AI Technology
The conversation highlights the shift from proof of concept to production in AI technology, emphasizing the growing demand from enterprises eager to adopt these innovations. As organizations transition from small-scale tests to broader implementations, the focus is on creating scalable, economically viable models that can handle extensive user bases without exorbitant costs. Aidan stresses the importance of balancing model complexity with practical usability for the market.In this clip
From this podcast
Decoder with Nilay Patel
AI will make money sooner than you think, says Cohere CEO Aidan Gomez
Related Questions
How scalable are the "done for you" and "done with you" models?
Is it true that when the cost of trying new technologies goes to zero, deciding what to build becomes the bottleneck, and that knowing which model output is merely plausible and which is actually good is not a commodity skill, making taste in technology a defining advantage, as discussed in the episode LLMs to agents: The Beauty & Perils of Investing in GenAI // VC Panel // Agents in Production and the clip Enterprise Innovation Challenges?