Subvolts take AI needs to learn from experience, moving beyond static systems to adapt over time. Apr 28, 2026 - Developers
- Understand the limitations of current AI models and the push towards systems that learn continuously.
- Business
- Learn about the next evolution in AI capabilities that could lead to more adaptive and responsive applications.
This episode offers a solid interview with an a16z partner on a critical AI research topic.
Why We Need Continual Learning
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Elena Burger interviews Malika Aubakirova on the AI + a16z podcast about the limitations of current AI systems in learning over time. They explore the concept of continual learning, contrasting it with in-context learning, and discuss how AI models might evolve to learn from ongoing experiences rather than remaining static.
Today's AI systems are like students who forget everything after a test. This episode discusses how AI could instead learn continuously, like a person who gets better with more experience. This matters because it could lead to smarter, more adaptable AI.
This episode explains why current AI struggles to learn continuously. It's for anyone interested in the future of AI development, particularly those following advancements in model architecture and learning paradigms. The discussion directly relates to ongoing research at frontier labs like Google Gemini and OpenAI, which are all pushing the boundaries of model capabilities and how they adapt to new information.
- Hosta16z
- GuestMalika Aubakirovapartner on the AI infrastructure team, a16z
- Episode Title: Why We Need Continual Learning
- Show: AI + a16z
- Length: 19 minutes
- Guest: Malika Aubakirova
- Guest Role: partner on the AI infrastructure team at a16z
Elena Burger speaks with Malika Aubakirova, partner on the AI infrastructure team at a16z, about why today’s AI systems struggle to learn over time. They discuss the limits of in-context learning, the case for continual learning, and how models may need to evolve from static systems into ones that learn from experience.
Resources:
Follow Malika on X: https://x.com/MaikaThoughts
Follow Elena on X: https://x.com/VirtualElena
Read more on Why We Need Continual Learning: https://a16z.com/why-we-need-continual-learning/
Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.
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- in-context learning
- Malika Aubakirova
- AI learning from examples given directly in the prompt, without changing the model itself.
- continual learning
- Malika Aubakirova
- AI systems that can learn and adapt over time from new data and experiences.
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Questions people ask
- What is continual learning?
- Continual learning refers to AI systems that can learn over time from new experiences, rather than remaining static.
- What are the limits of current AI learning?
- Today's AI systems struggle to learn effectively over extended periods, often hitting limits with methods like in-context learning.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to a16z.