# Koray Kavukcuoglu on frontier models, coding agents, and building AGI

## Executive summary

Google DeepMind SVP Koray Kavukcuoglu discusses the ambitious journey toward Artificial General Intelligence (AGI), emphasizing that success relies on moving models from simple coding capabilities to full software engineering and agentic workflows. The discussion highlights the continuous progress of the Gemini model family (e.g., 3.7, Flash) through parallel research tracks and stresses that real-world user interaction is critical for guiding development toward AGI.

## Key takeaways

- AGI lacks a definitive test or benchmark: There is no single standardized test to determine if an AI has reached AGI; progress is measured by the overall journey and capability build-up, not a sudden threshold. (08:53)
- The focus shifted from coding to software engineering: A major breakthrough in model development was understanding that true intelligence requires more than just writing code; it involves mastering the full scope of 'software engineering,' including working with tools and functions, effectively turning the model into an agent. (02:48)
- Gemini 4 is positioned as a major research milestone: The team announced Gemini 4 as the most ambitious pre-training run to date, representing a significant step in combining multiple learnings and architectural improvements into one model. (03:17)
- User interaction is the guide for AGI development: The path to building AGI depends heavily on continuous user interaction and feedback, whether users are performing daily tasks (e.g., emails) or conducting advanced scientific research. This usage spectrum guides problem-solving efforts. (11:39)

## Technical details

- Model Architecture and Evolution: The Gemini model family utilizes parallel development tracks, progressing through versions like 3.5, 3.6, and 3.7. The Flash models are noted for their rapid iteration speed, allowing the team to quickly approach frontier capabilities. (02:19)
- Agentic Capabilities: The core technical shift involves developing 'agentic actions' and 'agentic workflows,' moving beyond simple code generation to complex, multi-step problem solving that mimics human software engineering practices. (02:48)
- Complexity of Domains: The difficulty of tasks has increased significantly from constrained environments (like Atari games) to real-world scenarios characterized by 'ambiguity' and 'depth of ambiguity.' Language processing is now much richer and more multi-domain than previous controlled action spaces. (15:08, 16:29)

## Practical implications

- Engineers should focus on building systems that integrate AI models into complex, multi-step workflows (agentic systems) rather than treating the model as a standalone code generator.
- The increasing complexity of real-world tasks means future development must prioritize handling ambiguity and open-ended interpretation.
- Continuous feedback loops from user deployment are critical for guiding the next generation of AI capabilities.

## Topics

Artificial General Intelligence (AGI), Large Language Models (LLMs), Agentic Systems, Deep Learning, Software Engineering Automation, Gemini, Google Developers

Source: https://www.youtube.com/watch?v=Rrr2gdbvNFU