What does a winning MM team actually look like in 2026?

It seems many market-makers still depend on operational oversight while calling it automation. Real institutional liquidity isn't measured by how many people are sitting at the desk. It’s measured by how well your system holds up when the market breaks.

In many cases, large operational teams indicate that execution is still heavily manual, with automation covering only fragments of the process.

Every layer of human intervention is a point of failure. During market stress, the more hands on the keyboard, the higher the odds of delayed reactions and inconsistent execution. Real liquidity doesn't need a manual override.

We’ve seen it happen countless times. The moment the market goes parabolic, teams start scrambling to switch to manual mode. And every single time, the result is the same: while someone is busy agonizing over what to set, the algorithms are already bleeding money.

You can still find firms running simple delta-neutral structures with layers of manual control added on top. These models may survive stable conditions.

Stress is the real test.

Sharp drawdowns, liquidity contraction, and volatility spikes are where most systems begin to break down. This is usually where manual execution becomes a liability.

Sustainable liquidity cannot rely on reactive decision-making alone. It requires infrastructure built to adapt to market behavior in real time.

In the market-making industry, the hidden costs of operational reliance are often overlooked. We believe that any architecture dependent on human intervention is inherently disadvantaged during periods of high volatility. Our approach focuses on shifting the core of our operations from manual oversight to robust engineering. For us, system autonomy is not just an optimization - it is the prerequisite for institutional-grade reliability

That is the difference between supporting liquidity and engineering it.

The industry is already shifting toward quantitative infrastructure with minimal operational dependency.

The question for institutional partners today is 'How much discretionary risk and human-driven friction is embedded in your liquidity model?