Most people think markets are driven by models predicting the future. In practice, a huge amount of profit comes from understanding the plumbing — the systems that move orders, not the assets being traded.
If you look at firms like Jane Street from a builder’s perspective, what they’ve really done is engineer for microstructure:
Low-latency systems to react to order flow in microseconds
Smart routing logic that decides where an order should go before others can respond
Real-time pricing engines that constantly reconcile ETFs with underlying baskets
Infrastructure-level edge instead of thesis-level edge
This isn’t about predicting Tesla’s earnings. It’s about detecting when an ETF briefly diverges from its components and resolving that mismatch faster than anyone else.
From a systems design standpoint, this is fascinating.
You’re dealing with:
Distributed systems under extreme time constraints
Deterministic decision-making with probabilistic inputs
Massive throughput with near-zero tolerance for delay
Tight coupling between software, hardware, and physical location
And the key insight: the closer you get to the infrastructure layer, the more predictable the edge becomes.
At higher levels (macro, fundamentals), uncertainty dominates.
At lower levels (execution, routing, pricing), behavior becomes more mechanical — and therefore exploitable.
This is why “efficiency” in markets is nuanced. Yes, spreads tighten and prices converge faster. But that efficiency is implemented by systems designed to extract value from tiny, repeatable inconsistencies.
For builders, there’s a broader lesson here:
If you want durable edge, don’t just build on top of systems — understand how they work underneath. The biggest opportunities often aren’t in better predictions, but in better mechanisms.
Markets are just one example. The same pattern shows up everywhere infrastructure meets scale.
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