Billions of years ago, in an ancient galaxy, two neutron stars began their final, fateful dance. Locked in a gravitational embrace, they spiraled closer, closer—until in a fraction of a second, they collided. The explosion was so violent it momentarily outshone entire galaxies. Gravitational waves erupted, rippling through space, carrying a message from the most extreme physics laboratory in the universe.
As a physics graduate student I studied these cosmic events, serving as program manager for a multi-institutional project aiming to make new instruments to measure them. Eight months into our satellite proposal cycle, I worked closely with our system engineering group to explore an audacious goal: could we launch a shoebox-sized satellite packed with instruments into a highly eccentric geostationary transfer orbit to observe and locate these rate events?
Our concept study was a labyrinth of scientific models, spacecraft research, and endless configurations. Spreadsheets multiplied, documents proliferated, code intertwined with Slack messages and Zoom meetings. After six months of intensive work, our final PowerPoint presentation revealed a humbling truth: our original ambitious concept wasn’t feasible. Instead, we would pivot to a simpler Low Earth Orbit mission with a more focused scientific objective.
This story isn’t unique. It’s the standard narrative of scientific missions during their formation phase. Engineering is inherently complex, and that complexity compounds as we push the boundaries of what’s possible. Modern large, distributed teams transform challenging design efforts into potential minefields of costly mistakes.
But what if this didn’t have to be the status quo? Will it always take 8 months to build a new satellite concept, or $1T and 20 years for a new stealth aircraft?
When ChatGPT emerged, it became clear that engineering’s future wasn’t bound by old paradigms. For the first time, we had technology capable of intelligently understanding our objectives and leveraging collective knowledge to solve novel problems. Imagine a machine that can think, ideate, and process information at a scale exponentially beyond human capacity.
This vision gave birth to Davinci—a tool designed to integrate the engineering process into a unified framework. It’s more than software; it’s a digital environment where requirements, code, documents, and CAD models converge. A collaborative space that democratizes system engineering, where every team member can work alongside an intelligent design partner.
Should building a satellite concept really consume eight months? A few years ago, I would have unequivocally said yes. That was simply engineering’s reality.
Today, my answer is different. We stand at the threshold of a new era—where ideas transform into reality not in months, but in days. Where human creativity is amplified, not replaced, by intelligent tools. Where the boundaries of innovation, and scientific discovery, are limited only by our imagination.
