Great questions are the predicate to scalable execution

When I was a new bench scientist, I was often plagued by what I called the “hand of crap”: failed protein purifications, contaminated cell lines, botched experiments. It wasn’t just bad luck or inexperience. It was mindset. A mindset rooted in scarcity, distrust, and control.

I hated handing off experiments. No one else could possibly “see” what I saw or do it “right.” I believed that if I didn’t do the science myself, it wasn’t real. And so, I spun my wheels, designing experiments that were fundamentally flawed because the question itself was bad. I was, frankly, full of shit.

A few years later, after a humbling and productive stint at a biotech startup in NYC, I started my PhD at the Francis Crick Institute in London. It was there that everything shifted.

I learned that the “hand of crap” isn’t a curse it’s a consequence of weak ideas. Strong hypotheses yield informative outcomes, regardless of which way the data goes. Great scientists design experiments where every outcome tells you something valuable. And more importantly, they choose problems that require minimal manipulation of nature.

This was liberating.

Suddenly, I wasn’t worried about breaking the system. The biology just was. Whether I’d just flown back from Paris or finished a gym session, the phenomena were robust and reproducible.

I’m writing up a manuscript on how the rulebook of spatiotemporal tumor heterogeneity is underwritten by Newtonian physics.

This confidence had a compounding effect. I started trusting the Crick’s core facilities—plasmid sequencing, FACS, histopathology, microscopy, even mouse husbandry. I wasn’t relying on other postdocs or grad students to scale my work. I was leveraging world-class infrastructure to move faster. My role was to think deeply, coordinate precisely, and execute smartly.

As the projects grew, so did the quality of my collaborators. They weren’t generalists. They were niche technology experts—people who had built custom tools that could be integrated into my workflow. It was no accident. The Crick was built for this kind of velocity. Their Scientific Technology Platforms (STPs) were low on bureaucracy and high on readiness. Execution was just… fast.

Having come from industry, I saw what many didn’t: the Crick was structured like a biotech company. The core facilities weren’t just helpful, they were essential to increasing DBTL (Design, Build, Test, Learn) cycles. Scientists who understood this system who knew how to orchestrate across this distributed network were winning. Fast.

And if you don’t believe me, just look at the publication record.