What are Bottlenecks to Scaling AI?
Widescale AI adoption will require overcoming a handful of hurdles, including power demand, the need for synthetic data, and the use of distributed training.
AI applications are already being integrated into existing applications, aiding software development and other early adopters. What will the next phase of AI integration look like? Owen Hyde, Managing Director and Technology Equity Research Analyst at Jennison Associates, discusses how AI can increase productivity in areas such as customer service and why Agentic systems will be crucial as AI applications evolve.
Agentic systems are very likely to become a key part of this technology, and that’s really what’s going to drive the next level of AI applications over time.
The quality of infrastructure supporting the digital economy will help determine which companies, countries and regions emerge as winners in the AI revolution.
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Widescale AI adoption will require overcoming a handful of hurdles, including power demand, the need for synthetic data, and the use of distributed training.
The location of data centers housing AI workloads can be vitally important. Find out why.
Discover why some of the largest hyperscale companies choose to lease rather than build data centers.