From handshakes to algorithms: how machine-driven hard trust is reshaping business

The conditions that sustained this model are now shifting. A decade of financial shocks and governance failures has exposed how fragile personality-driven systems can be.

Trust based on judgement is expensive to maintain, slow to scale and vulnerable to the incentives and blind spots of the people at its centre. When conditions tighten, it evaporates quickly, leaving behind disputes and legal complexity that are costly to unwind.

In its place, a different model is taking shape, one that relies less on discretion and more on design. Call it hard trust.

Here, outcomes are determined not by individual judgement but by system architecture. Rules are embedded into code, execution is automatic and enforcement is built into the system itself rather than applied after the fact.

The clearest illustration is in finance, where a ‘smart contract’ does not wait for approval or interpretation. If its predefined conditions are met, it executes. If they’re not, it does not. There is no negotiating with code and therefore no scope for delay driven by personality or circumstance.

Traditional financial arrangements are full of what might be called soft variables: intent, interpretation, timing and influence. These introduce flexibility, which can be useful, but they also introduce friction. They create room for negotiation, but equally for dispute.

Algorithmic systems remove this ambiguity by replacing the question of whether a counterparty will act with whether the system permits them to act at all. Trust shifts away from individuals towards structure.

There is a useful analogy in physics. Systems that depend on a few critical nodes tend to fail disproportionately when those nodes weaken. Economic systems behave similarly. If too much depends on a few decision-makers, fragility could accumulate until a single misjudgement or misaligned incentive hits outcomes. We have seen this play out often enough.

Automation and decentralization are, in this sense, less ideological choices than engineering responses. They distribute risk, standardize execution and limit the damage any one actor can cause.

That said, current hard trust models are opaque, which means users must trust the intentions and competence of their developers.

And they can fail spectacularly, as they did in the Terra/Luna crash of 2022, where the architecture was flawed, or in the flash crash of 6 May 2010, where algorithmic trading briefly erased 9% of the Dow index. Accenture dropped to a penny that day from around $40, before recovering. But while the practical challenges of shifting trust from institutions to algorithms are formidable, the direction is clear.

None of this implies that humans are being removed from the system. Their role is being repurposed. Judgement will move upstream into design and architecture, while execution gets increasingly automated. The key question is whether the system designed by people can be trusted to behave as intended under pressure.

For business leaders and policymakers, the implications are practical. Evaluating an institution now requires more than assessing its leadership or its balance sheet. It would demand scrutiny of the underlying logic of the system itself, including how decisions are made, how rules are enforced and how failure is handled.

Systems that rely heavily on discretion may appear flexible but often conceal hidden fragilities. Those built on clear, automated rules can seem rigid yet prove more resilient over time.

Markets are beginning to adjust accordingly. Personality is being discounted even as protocol attracts a premium.

This is a recognition of the limits of human capability, that’s all. People are highly effective at designing systems but less reliable at operating them flawlessly over long periods, particularly under stress.

So-called trustless systems must not invite cynicism. They are expressions of ambition, an assumption that human ingenuity is best applied to building frameworks that do not require exceptional individuals to function well.

The handshake, once a marker of honour, is giving way to the algorithm. What the algorithm lacks in warmth, it compensates with consistency. This transition will not be free of friction. Systems that promise certainty can encode new risks, particularly if their assumptions are poorly understood.

Code reflects its creators, including their blind spots. Failures, when they occur, may be harder to interpret even when they are easier to trace. The task has not changed. Risk needs to be visible and containable. That is what trust has always been about.

The author is co-founder of Siana Capital, a venture fund manager.