The case for working on hard problems

There’s a gravitational pull toward easy problems. They’re well-defined, have clear solutions, and offer quick wins. The feedback loop is tight. Ship something, see results, move on.

Hard problems don’t work that way. They resist quick answers. They require sitting with uncertainty, sometimes for a long time. The path forward isn’t obvious, and the timeline isn’t predictable.

So why pursue them?

Because easy problems are crowded. When something is straightforward, everyone can do it. Competition is fierce, margins are thin, and differentiation is temporary. The advantage disappears as soon as someone else figures out the same solution.

Hard problems filter for patience and depth. Fewer people attempt them. Fewer still persist through the ambiguity. The ones who do build something that’s genuinely difficult to replicate - not because of secrets, but because of the accumulated understanding that comes from working through complexity.

The value of research isn’t just the answer. It’s everything you learn on the way there.

R&D is often treated as a cost center - something to minimize or outsource. But for organizations working on hard problems, research is the core of the business. It’s where insight compounds. It’s where the next generation of capability comes from.

This doesn’t mean ignoring practicality. The goal isn’t to research forever without shipping. It’s to balance exploration with application - to let deep work inform what you build, and let building inform what you explore next.

The hardest problems often sit at the intersection of multiple disciplines. Security and machine learning. Cryptography and systems design. Theory and implementation. Progress requires fluency across boundaries, not just depth in one silo.

Easy problems will always be there. They’ll always feel more urgent. But the work that matters most is usually the work that takes the longest to pay off.

That’s where we choose to spend our time.