By Dmitry LopatikIn the first article, I argued that Software-Defined Vehicles (SDVs) are not simply an AI or software engineering challenge. They represent a much bigger business transformation. Building the software is only one part of the equation. The harder…
Not every technology shift changes software architecture. Many simply introduce another layer into the stack. A new framework replaces an older one. A faster database improves performance. A different cloud provider reduces infrastructure costs. Engineering teams adopt the technology, adjust…
Walk into almost any engineering organization today, and you’ll hear a familiar claim.“We’re becoming AI-native.”Ask what that actually means and the answers vary wildly.“We use GitHub Copilot.”“Our developers use ChatGPT.”“We’ve automated code reviews.”“We’re experimenting with AI agents.”None of those are…
Every CTO is under pressure to have an AI strategy.The result is predictable. AI quickly becomes part of every product roadmap.Sometimes that’s exactly the right decision. Sometimes it isn’t. The difficult part is that both scenarios often look identical in…
Every outsourcing relationship looks good during the first three months. The kickoff workshop goes well. Governance meetings are regular. The first sprint is delivered on time. Everyone is optimistic.The real test begins much later. Six months in, deadlines start slipping.…
Software engineering leadership is caught in a quiet, frustrating trap.Walk into almost any quarterly business review or executive board meeting, and you’ll witness the exact same script playing out. CFOs keep demanding a line-by-line accounting of R&D spend. At the…
AI won’t fix broken DevOps. But it will expose it.For years, DevOps has promised us the ultimate trifecta: faster releases, seamless collaboration, and rock-solid software. Naturally, organizations bought in. They poured massive budgets into CI/CD pipelines, embraced infrastructure as code,…
For years, software testing meant one thing: automation.Organizations poured money into frameworks, built massive regression suites, and measured success by a single metric: What percentage of our tests are automated? It was a massive leap forward from the days of…
The software engineering landscape is experiencing a profound structural dislocation. For decades, technology leaders leaned on quantifiable metrics to measure the output of engineering teams. They relied heavily on lines of code generated, pull request counts, or basic sprint velocity.…
The corporate world is currently gripped by an obsession with artificial intelligence algorithms. Executive boards look at generative models, predictive analytics, and automated decision engines as the definitive competitive advantages of the modern era. Yet, a silent crisis unfolds behind…