How AI Tools Generate Technical Debt in IoT Systems, and What to Do About It
Four ways AI assistants create IoT technical debt, backed by studies, and three disciplines to contain it: review, no-go zones, refactoring.
Founder & Engineering Leader
And the engineering teams behind them. Fifteen-plus years across software architecture, cloud, data, Industrial IoT and AI. Founder of Sivense, incorporated in the US and Europe, with 90 engineers across Europe, from Portugal to Ukraine.
Founder, Sivense · Forbes Technology Council · Fast Company Impact Council

Technology matters when it survives contact with the real world.
For more than 15 years, I’ve worked across software engineering, architecture, data, cloud, AI and Industrial IoT.
I’ve built products, scaled an engineering organization from 10 to 90 people, and worked on systems deployed in complex enterprise and industrial environments.
Today, I’m focused on a simple question:
How do we turn rapidly evolving technology into reliable products, scalable engineering organizations and measurable business outcomes?
AI is an analyst, not an executor. Blast radius, rollback and visibility decide what it may touch, and judgment still has to be trained.
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Heroics don't scale. Delegate outcomes, keep the decision, and build ways to see what is really happening once you can't hold the context yourself.
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Refactoring as ROI, training as investment, data retention as a cost line: engineering decisions in the language of the P&L.
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Bearings, vibration spectra, ten billion records a day. What predictive maintenance teaches about data, models and the people who read them.
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Four ways AI assistants create IoT technical debt, backed by studies, and three disciplines to contain it: review, no-go zones, refactoring.
Three filters, blast radius, rollback and visibility, decide which IoT DevOps tasks AI may touch. Production access stays human.
What broke when his company grew from 10 to 90 people, and the four management shifts that fixed it.
AI erodes the entry-level work that builds engineering judgment. Four practices to keep juniors thinking instead of parroting output.
Why burnout is a management problem, with the story of keeping a 90-person team working after the invasion of Ukraine.
Why technical leaders over-control, a five-step delegation system, and the team that shrank a 16 TB database to 1 TB on its own.
How a PdM pipeline turns bearing vibration into FFT spectra and alerts, plus six infrastructure steps an IT team must prepare.
Worked on technology deployed at Fortune Global 500 companies in logistics, manufacturing and automotive.
Sivense
Advanced software engineering for complex products.
We build software, AI, data, cloud and connected systems where strong engineering execution matters: systems that have to keep working for years, in front of real users, real data and real machines.
Sivense is incorporated in the US and Europe. Engineering teams work across Europe, from Portugal to Ukraine, in EU time zones with US East Coast overlap.
I speak about AI, engineering leadership, complex systems and building technology organizations.
I'm always interested in thoughtful conversations about technology, engineering organizations, AI and ambitious products.
Get in touch opens LinkedIn in a new tab. Email reaches [email protected].