How Agentic AI and Hybrid Models Are Shaping the Future Workforce
Discover how agentic AI and hybrid AI models, including custom LLMs, are revolutionizing AI infrastructure and inferencing to leverage cutting-edge technology.
In a recent episode of the At the Edge podcast, SambaNova Systems cofounder and CEO Rodrigo Liang joined McKinsey Senior Partner Lareina Yee to discuss the transformative potential of agentic AI and hybrid AI models. The conversation highlighted the critical need for businesses to rethink their AI infrastructure to stay competitive in an AI-first world.
Rethinking AI Infrastructure
Liang emphasized the unprecedented scale of AI transformation, driven by the inefficiency of current computing infrastructure. McKinsey estimates a $5 trillion investment in data centers over the next five years to meet AI's demands. However, Liang pointed out challenges like power shortages and cooling needs, advocating for more efficient solutions like SambaNova's chip and API services.
The Hybrid AI Model
Businesses must adopt a hybrid approach, combining large language models (LLMs) with custom models tailored to specific operational needs. Liang stressed the importance of letting data dictate where AI models run, whether on-premises or in the cloud. This flexibility ensures scalability and cost-efficiency.
The Rise of Agentic AI
Agentic AI, which connects specialized small models into workflows, is poised to revolutionize enterprises faster than LLMs. Liang highlighted the importance of speed (time to first token) and cost efficiency in deploying these agents. For example, SambaNova's Llama 8B agents respond in 0.03 seconds, enabling real-time workflows.
Security and the S-Curve of AI Value
Security remains a top concern, especially in regulated industries like banking. Liang described an S-curve of AI value, where initial low-risk deployments (e.g., chatbots) yield minimal ROI, but transformative applications (e.g., automated regulatory reports) drive significant savings and efficiency.
Future Outlook: AI Inferencing and Robotics
Liang predicts a shift from AI training to inferencing, leveraging open-source models for customization. He also foresees widespread adoption of specialized robots in manufacturing and retail, with humanoid robots following later. Businesses should start by inventorying their operations and embracing hybrid AI to gain a competitive edge.
Global Accessibility and the SambaNova Legacy
Liang underscored the importance of making AI accessible globally, including native language support. The name SambaNova reflects the company's Brazilian roots and its mission to let technology flow seamlessly.
For more insights, listen to the full podcast here.
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About the Author
Dr. Lisa Kim
AI Ethics Researcher
Leading expert in AI ethics and responsible AI development with 13 years of research experience. Former member of Microsoft AI Ethics Committee, now provides consulting for multiple international AI governance organizations. Regularly contributes AI ethics articles to top-tier journals like Nature and Science.