955: Nested Learning, Spatial Intelligence and the AI Trends of 2026, with Sadie St. Lawrence - SuperDataScience Podcast Recap
Podcast: SuperDataScience Podcast
Published: 2026-01-06
Duration: 1 hr 9 min
Guests: Sadie St. Lawrence
Summary
AI expert Sadie St. Lawrence outlines key trends in AI for 2026, including the rise of specialized industry models and the significance of spatial intelligence. She reflects on the accuracy of her 2025 predictions and discusses the potential for AI operations to become a new job category.
What Happened
The episode kicks off with Sadie St. Lawrence and Jon Krohn discussing how AI capability costs have plummeted, making intelligence exponentially cheaper. The same level of AI capability that cost $100 a year ago now costs just $1, altering how we view future AI developments. Reflecting on 2025 predictions, Sadie highlights how agentic AI became a dominant trend, with AI engineering skills surpassing traditional data science skills in demand.
The conversation transitions to the SuperDataScience Awards, where Google is celebrated for its comeback with the integration of AI into their search and G Suite products. NVIDIA is recognized as a market leader due to its strong position in AI hardware, underscored by their partnership with Sadie's Human Machine Collaboration Institute (HMCI) to create research ecosystems in California.
Sadie's first prediction for 2026 identifies the emergence of more specialized industry models, akin to breakthroughs like AlphaFold, which will target specific domains. She foresees the advancement of continual and nested learning, enabling AI models to keep updating and learning, thus bridging the gap between AI and human intelligence.
Spatial intelligence and physical AI are expected to gain traction, with emphasis on utilizing 3D environments and new data sources. This will likely lead to innovative applications and practices in AI, further pushing the boundaries of what's possible in technology.
A return to fundamental research is anticipated as scaling is no longer seen as the sole path to breakthroughs in AI. This shift in focus could lead to unexpected discoveries that redefine AI capabilities.
Finally, Sadie predicts the rise of AI operations (AIOps), a new job category focusing on GPU management and model orchestration, similar to the role of DevOps in software development. This shift will require new skills and could open up significant employment opportunities in the AI sector.
Key Insights
- AI capability costs have drastically reduced, with the same level of capability that cost $100 a year ago now costing just $1, significantly altering the landscape for future AI developments.
- NVIDIA has strengthened its position as a market leader in AI hardware through its partnership with the Human Machine Collaboration Institute to create research ecosystems in California.
- The rise of AI operations (AIOps) is predicted to create a new job category focusing on GPU management and model orchestration, similar to the role of DevOps in software development.
- A shift towards fundamental research in AI is anticipated as scaling is no longer viewed as the sole path to breakthroughs, potentially leading to unexpected discoveries that redefine AI capabilities.