Code, Cloud & AI

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In this episode, I sat down with Anton Sizikov from GitHub to talk about how AI coding tools have evolved from “smart autocomplete” into full-blown agentic workflows. We unpacked the early days of GitHub Copilot, when the biggest customer concerns were privacy, legal implications, and whether the tool could be trusted at all, and compared that with today’s reality of agents, context engineering, cloud-based workflows, and outcome-driven development. Anton shared how his own usage has changed over time -from manually reviewing every suggestion to orchestrating multiple agents in parallel - and why experienced users often get dramatically more value from these tools because they understand how to shape context, control cost, and build the right guardrails around them.

We also dug into the harder questions engineering teams are wrestling with now: how to measure ROI, how usage-based billing changes developer behavior, and why faster code generation doesn’t automatically mean better business outcomes. Anton explained why there is no universal playbook yet for rolling AI out at scale, why teams need to think more like product owners now that building is cheaper and faster, and why engineers who are still on the fence should start experimenting sooner rather than later. It was a thoughtful conversation about where AI-assisted software development is already delivering value, where it still creates friction, and what engineers need to learn to stay effective as these tools keep changing.

• Anton Sizikov on LinkedIn: https://nl.linkedin.com/in/sizikov
• Anton's Blog: https://blog.cloud-eng.nl/ 
• GitHub Copilot: https://github.com/features/copilot
• GitHub Models: https://docs.github.com/en/github-models/about-github-models
• Visual Studio Code: https://code.visualstudio.com/