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I get it - goals often feel like extra homework. But I’ve found they don’t have to be. Done right, they can keep you focused, accelerate your learning, and guide better decisions.

I was in the room at this year’s Pragmatic Summit when Laura Tacho dropped the numbers: nearly all developers use AI coding assistants, over a quarter of production code is AI-written - and yet productivity gains haven’t budged past 10%.

That number is expected to rise to 65% within two years. Yet 96% of developers, according to this Sonar research, say they don’t fully trust AI-generated code.

With AI building features, teams must shift from doing tasks to orchestrating them - PMs guide intent, engineers oversee systems, designers review output live, and QA builds self-healing processes.

When teams don’t share information, clash on priorities, or follow unclear processes, even simple tasks can drag on for months.

Building with LLMs is nothing like traditional software. If we want something that actually works in production, we have to test it, monitor it, and keep iterating on real customer data.

We’re past just making better tools: machines will do the repetitive coding, and humans will focus on guiding, judging, and architecture.

Ex-Tesla AI director now programs mostly in plain English with AI, calling it the biggest workflow change in 20 years.

Until now, AI could only advise and plan, never act. But MCP is finally letting it bridge the gap between thinking and doing.

Software teams don’t implode because of bad code - they implode because someone really needed to win the argument about it.

Building an app with AI feels easy - until real users arrive.

Infobip Shift 2026 is heading to Zadar! Join us September 13-15, 2026, at Kresimir Cosic Hall - and yes, the call for speakers is officially open.

We’re writing more code than ever thanks to AI - but remembering how it all works a year later is quickly becoming the real hard problem.