AI Bubble: It Settles Where Precision Isn't Needed

Pavel Golubev did not enter data science through engineering. Arabic history introduced text analysis; financial analytics in Geneva added statistics. In Russia he worked on Renault supply chains, then industrial consulting at EY and Accenture, where factory floors revealed what slides hide.

Courses, projects, and Open Data Science enabled his move into ML and built a network. It led Pavel to Dubai and Reaktor, where he framed problems, negotiated data, wrote code, and delivered. Aviation and manufacturing showed that a model is useless without data, explicit value, and willingness to change a process.

The pandemic closed Reaktor's Dubai office, so Pavel moved to the Netherlands to lead fraud analytics at Beat. Latin America brought fraud, armed crime, and computer vision; a Tesla fleet and scattered initiatives exposed the gap between funded growth and efficiency. Beat's failure sharpened how he evaluates products and employers.

For Pavel, AI starts with a problem: speeding up one step does not create ROI unless the workflow changes. His Microsoft studio builds industrial solutions for Azure, while he moved from management to a Principal IC role. Multidisciplinary, forward-deployed teams move engineers closer to customers but demand broad skills and clear ownership.