My career was borne out of proximity to material excess. My father, a Polish-American immigrant, ran a small gallery with other Polish artists in Greenwich, CT, a well-known enclave of East Coast wealth. He sold paintings, painted furniture and home murals. My mother helped run the shop. We lived modestly, in a working-class town nearby. I sometimes accompanied my dad to see clients. His battered pickup would roll through wrought-iron gates onto lush grounds, where neatly trimmed hedges framed grand manors. Their owners emerged onto cobblestone driveways, dressed and groomed like people I saw in glossy magazines with thick pages. They stood in stark contrast to my dad’s throwaway clothes and paint-encrusted hands. To a young kid, this was success—not their achievements, just their picturesque lifestyles. Many were investment bankers, and my career choice was as simple as that.
Fast forward to college graduation: I started a job in mergers and acquisitions, with dreams of first class flights in Italian suits. While I wore a suit every day, it wasn’t to broker deals in Monaco, but to use Excel in an office. As I compiled hundreds of spreadsheets, I pondered their role in society. A common justification for M&A is that it makes companies and markets more efficient. Academic research tends to find that it doesn’t reliably increase efficiency. I decided to move on, in search of clearer impact. That said, there’s a silver lining to starting your career in investment banking. Much like greyhound training, they dangle a lure, the cash bonuses, which push you into a high tempo. Even when the incentive disappears, a brisk pace remains.
After leaving finance, I found myself questioning many things, including the nature of knowledge itself —so I joined a cognition lab that studied how people form causal beliefs. I was specifically interested in how we track the frequency of causal events - like observing an animal act friendly in 9 out of 10 encounters, and determining how that’s encoded into intuition. While the research felt promising, I struggled to see how publishing would matter - cognition journals rarely influence the zeitgeist. I soon became more interested in how scientific ideas could reach mainstream markets. Digital media’s interactive nature was quite suitable for scientific exploration, so I decided to learn more about it.
I moved to NYC to join a digital agency. It was a fun introduction to the city, as we worked for several landmarks like Ellis Island and Lincoln Center. These projects were particularly focused on creative, which made me think a lot about the tradeoff between content quality and quantity. I began to suspect that artisanry was naively left behind in the old world as it gave way to the new. So I started chiseling away at web assets like they were renaissance sculptures, in hopes that refinement would draw crowds. And it did, engagement improved with quality. But content was only one side of media, and I left to deepen my understanding of data analysis.
My next role was at a media analytics firm founded by senior Obama campaign staffers. They framed campaigns as a series of math problems to be solved with computer scripts. So for example, when selecting audiences and TV slots for an ad, an algorithm estimated which combination drove the highest lift for the lowest cost. This was followed by statistically isolating a campaign’s causal impact, which can become arbitrarily complex. After seeing how effective and academically boundless advertising science was in national politics, I decided to explore it further in the corporate sector.
I moved to a larger ad agency where my work centered on writing machine learning algorithms. When I finished a script I felt proud, like a mechanic gazing upon a glistening, suspended engine. It never roared, but performance metrics flashed on screens, and guided the next round of tune-ups. It was easy to get absorbed in this improvement cycle, and lose sight of how much agency life revolved around sales. Initiatives often required approval from numerous stakeholders. That meant stepping away from the technical work, and spending time building trust. Sometimes that involved projecting competence, but it was often simply a matter of time - like quietly sitting and listening to someone’s complaints.
While at the agency one of my accounts - a US grocery conglomerate - wanted to build an in-house ad network for themselves and their suppliers. I joined the network to lead its machine learning practice. While there, a core challenge was scaling predictive models across diverse vendor ecosystems. Developing prediction software across multiple environments required minding their vast constraint sets while pushing for accuracy. The process was not unlike tourbillon watchmaking, where a rotating cage is built around the watch’s timekeeping mechanism to increase its precision. Each cage must be carefully calibrated around the mechanism it carries, which brings with it a host of physical constraints. Like tourbillon builds, minding the constraints of prediction software required monastic patience, but was deeply satisfying once everything began to tick.
Causal Art
At this point I had developed a broad set of skills in science and engineering, and wanted to apply them towards a good cause. So I started building an app that increases mainstream engagement with scientific content. While scientific education is upstream of more direct efforts like sending mosquito nets to villages, I think it’s important. Many of the issues facing modern society, like climate change, are complex. My hope is that when people grasp this complexity, they’ll make better decisions about it. Stay tuned!
By Luke Berszakiewicz
March 31, 2026