The Deep Learning Revolution

From AlexNet to AI Agents (2012–2026)

An interactive history of how neural networks evolved from computer vision breakthroughs to autonomous software engineering agents.

💡 Scroll to zoom. Shift+Drag to select range.
Vision / DL
NLP / LLMs
Coding / Agents
Industry Impact
Note on Regions: The "Region" metric reflects the physical location of the research lab or team that drove the breakthrough (e.g., DeepMind in Europe, Google Brain Berlin for ViT), rather than the corporate headquarters of the parent company.

Why History Matters for Software Engineers

Understanding this timeline is crucial for modern engineers. We have moved from an era of manual feature engineering (pre-2012), to representation learning (2012-2021), to prompt engineering and copilot assistance (2022-2024), and now into the agentic workflow era (2025+).

The tools we use are no longer static compilers; they are probabilistic reasoning engines. To build robust systems today, engineers must understand the capabilities, limitations, and historical context of the models powering their workflows.