Shuffling the Deck to Deal Fair

When the Game Stops Looking Fair

The issue wasn’t simply about bad actors. At its core, SwishSwap’s system relied on deterministic ordering—processing trades in a way that looked neutral but consistently advantaged certain users. Those with faster connections, closer to validator nodes, or better access to routing infrastructure had a persistent edge.

Hailey dug into the data and found a pattern: during peak hours and high-volatility moments, slippage on top trading pairs spiked, often in ways that couldn’t be explained by market conditions alone. In practice, the “neutral” system wasn’t neutral at all. It had baked-in structural biases that ordinary users could never hope to overcome.

This created a troubling picture. Traders were already shifting their orderflow toward private channels and specialized wallets that promised “protection.” The very activity that had fueled SwishSwap’s growth was fragmenting, and with it, the company’s control over execution quality.

Competing Demands Close In

Inside SwishSwap, the problem was pulling Hailey in multiple directions. Engineering leaders argued that fairness interventions might slow the system and risk throughput targets. Product managers worried that delaying solutions would further erode customer satisfaction. Legal and compliance counsel saw the writing on the wall: regulators were increasingly sensitive to fairness and execution integrity, and SwishSwap’s “see-no-evil” posture wasn’t going to last much longer.

Meanwhile, the external environment was getting harsher. Bots were becoming more sophisticated—leveraging every nuance of network latency to front-run user trades. Competitors were making loud claims about protecting orderflow, even if their solutions were half-measures. And within the community, moderators fielded a rising tide of complaints about unfair treatment—echoing through Discord and X/Twitter threads.

Hailey faced a high-stakes balancing act: how to restore trust in execution quality without compromising the very performance metrics that defined SwishSwap’s competitive edge.

The Silent Cost of Doing Nothing

The easiest response might have been to downplay the issue and hope the community moved on. But Hailey knew the consequences of inaction would be severe.

If traders continued to feel disadvantaged, they would trade less, or worse, migrate entirely to platforms perceived as fairer. That meant daily active users would decline, acquisition costs would rise, and the long-term loyalty that SwishSwap relied on would evaporate. Liquidity providers, too, would eventually leave. They were already seeing adverse selection: their positions were routinely being picked off by opportunistic strategies—leading to losses that incentives barely covered.

Then there was the looming specter of regulation. Words like “market integrity” and “best execution” weren’t confined to traditional finance anymore. If SwishSwap failed to show credible improvements, it risked not only reputational damage but also being held up as an example of negligence in industry hearings or policy debates.

Strategically, the bigger danger was drift. Competitors willing to invest in fairness would capture the narrative—presenting themselves as safer, more trustworthy venues for both retail and institutional flow. SwishSwap’s differentiation (hard-won through years of liquidity bootstrapping) would erode in the face of claims it could neither match nor disprove.

The hidden cost of ignoring these dynamics wasn’t just slippage or bad press; it was the slow but certain unraveling of SwishSwap’s position in the market. For Hailey, it was clear: fairness in execution wasn’t a “nice-to-have.” It was the next battleground for trust, growth, and long-term survival.

Reframing Execution as a Strategic Advantage

Hailey’s breakthrough came when she stopped framing the issue as a defensive problem and started seeing it as a strategic opportunity. Rather than asking, “How do we stop users from leaving?” she asked, “How can SwishSwap lead the industry in provable fairness?”

That shift in mindset opened the door to a very different type of solution. Instead of patchwork measures (like tweaking fee incentives or outsourcing “protection” to third-party RPC providers), SwishSwap could build fairness into the core of its transaction ordering. The concept, rooted in cutting-edge research, was deceptively simple: introduce randomness where it is fair to randomize, and determinism where time differences truly matter. This is exactly what the Secret Random Oracle (SRO) provides, based on a recently published research from Microsoft, Cornell, and UW.

With the SRO integrated into SwishSwap’s ordering process, trades that were otherwise indistinguishable would have no baked-in advantage based on network proximity or hardware speed. At the same time, trades that clearly arrived earlier would still be respected. For Hailey, this wasn’t just a technical fix; it was a chance to rewrite the rules of the game, positioning SwishSwap as the first mover in auditable, measurable fairness.

The strategy was clear: SwishSwap needed to adopt SRO-based ordering as a core product feature, and set objectives and key results (OKRs) that would demonstrate progress to users, liquidity providers, and potential institutional partners. Hailey envisioned OKRs that included measurable improvements in fairness metrics, reduced sandwiching incidence, and stronger execution outcomes—all without compromising system reliability or throughput.

Translating Strategy into Concrete Steps

Turning vision into execution required careful planning. Hailey understood that this wasn’t something the engineering team could simply switch on overnight. The solution had to be implemented methodically, with evidence gathered at every step to support both internal decision-making and external messaging.

Her first move was to commission a baseline analysis. Before any fairness enhancements could be deployed, SwishSwap needed data on its current state: how often equally timed trades were ordered unfairly, how frequently sandwiching occurred, and what latency looked like in practice. These metrics would become the yardstick against which future gains were measured.

Next came the architecture decision. The SRO could be implemented in two ways: through secure hardware enclaves that generated randomness quickly but depended on a trusted vendor, or through cryptographic functions that eliminated hardware reliance but required more computational effort. This wasn’t just an engineering choice; it was a governance decision that would shape how stakeholders perceived the trustworthiness of SwishSwap’s fairness claims.

Once the architecture was chosen, Hailey insisted on a phased rollout. In the first stage, the system would run in “shadow mode”—collecting data on what fairness would look like if SRO had been active, without affecting live transactions. If that proved stable, the next step would be a limited deployment on mid-tier trading pools (large enough to matter but not so critical that failure would be catastrophic). Only after success at that stage would the system scale to top-tier pools, with clear kill-switches in place to mitigate risks.

Hailey also saw the importance of ecosystem partnerships. Wallets and RPC providers increasingly acted as gatekeepers of orderflow, and many marketed their own fairness protections. If SwishSwap could integrate SRO guarantees directly into these partners’ routing logic, it could reclaim flow that was drifting away. Beyond the technical mechanics, this meant forging commercial relationships that signaled confidence in SwishSwap’s execution quality.

Finally, Hailey knew that transparency would make or break the initiative. She pushed for governance and disclosure practices that went beyond the norm: publishing a fairness policy, releasing periodic audits of the SRO system, and proactively sharing metrics with the community. By putting data in the public eye, SwishSwap could transform fairness from a hidden vulnerability into a marketable differentiator.

This was not just about fixing an operational flaw. It was about reshaping SwishSwap’s identity… from being seen as another commodity exchange to being recognized as the fairness leader in a crowded, competitive space. For Hailey, the adoption of SRO wasn’t a technical experiment; it was a bold move to reclaim control of execution quality, align with evolving regulatory expectations, and create a first-mover advantage that competitors would struggle to match.

Turning Fairness into Tangible Outcomes

Once the rollout plan was in motion, Hailey’s focus shifted to the tangible outcomes that stakeholders would care about most. For traders, the promise was straightforward: a trading experience where the execution price better aligned with the quoted price, and where predatory patterns like sandwiching became noticeably rarer. This wasn’t about perfection (markets will always have volatility), but about restoring trust that the system itself wasn’t tilted against the average user.

Liquidity providers, too, had something to gain. By reducing the ability of opportunistic bots to consistently exploit order sequencing, SwishSwap could protect LP positions from toxic flow. Over time, this meant providers could commit liquidity with greater confidence—reducing the churn and incentive arms race that had become increasingly expensive.

For the platform itself, the expected benefits went beyond operational performance. By publishing verifiable fairness metrics and independent audits, SwishSwap could elevate its brand narrative from “a place to trade” to “a place that protects.” That subtle but powerful distinction opened doors to aggregator partnerships, wallet integrations, and institutional design pilots. In an industry where credibility is currency, fairness was now part of the value proposition.

Measuring Progress with Clear Lenses

Hailey insisted that success had to be observable and graded. She encouraged her team to think in terms of good, better, and best (not as slogans, but as practical checkpoints).

A “good” outcome meant pilot pools showed measurable improvements in fairness metrics, with latency still within user-acceptable thresholds. This would demonstrate that the concept worked in production without jeopardizing reliability.

A “better” outcome meant those improvements carried through to the top trading pools, accompanied by a visible drop in sandwiching patterns and a lift in user sentiment. At this stage, the community should begin to recognize that execution quality wasn’t just holding steady but improving—giving SwishSwap a competitive edge.

The “best” outcome, however, was more ambitious: fairness improvements that were sustained network-wide, backed by independent verification, and cited by institutional partners as a reason to route orderflow to SwishSwap. At that level, fairness wasn’t just a technical feature; it was a commercial differentiator, a reason for new capital and long-term loyalty to flow into the ecosystem.

Lessons for the Broader Organization

The journey left Hailey and her colleagues with lessons that reached beyond the technical domain. The first was that fairness itself is a product feature. Just as users once demanded lower fees or faster confirmations, they were now beginning to value verifiable fairness as part of the overall experience. What began as a risk mitigation exercise had become a way to differentiate.

Another lesson was the importance of trade-off management. Adding randomness inevitably introduced small delays, but those milliseconds were not wasted; they were an investment in trust. By framing latency budgets as part of a deliberate fairness strategy, Hailey was able to shift the internal conversation from “how fast can we go?” to “how fairly can we operate without undermining performance?”

Governance also emerged as a theme. Choosing between a hardware-based SRO and a cryptographic version wasn’t simply an engineering matter; it was a statement of values. Whichever route SwishSwap chose, the key was to document the rationale, disclose it openly, and own it as part of the brand identity.

The team also learned that measurement and messaging are inseparable. Without clear dashboards and independent verification, fairness claims would sound hollow. But once numbers were published and verified, they became powerful tools to attract partners and reassure users.

Finally, Hailey recognized that fairness required a cultural shift. Engineering, product, legal, business development, and community teams all had to buy in. Fairness could not live as a niche technical project—it had to be embraced as a company-wide commitment. Only then could SwishSwap transform a technical fix into a durable strategic advantage.

In the end, the story of SwishSwap was not just about building a new ordering mechanism. It was about reimagining fairness as both an operational necessity and a growth strategy—showing that the companies willing to take fairness seriously would be the ones to define the next chapter of decentralized finance.

Further Reading

• Mallari, M. (2025, September 12). Fair Play or Foul Play? Ordering a Better Blockchain Game. AI-First Product Management by Michael Mallari. https://michaelmallari.bitbucket.io/research-paper/fair-play-or-foul-play-ordering-a-better-blockchain-game/