Author: Cecilia (Qi) Feng, Founder of Their Foundry (30 January 2026)

Why We Wrote This (2-minute Overview)

TL;DR

Crypto markets are downstream of macro regimes: macro/policy are the exogenous weather, on-chain plumbing is the endogenous engine, and stress posture is the control system (absorb vs. transmit shocks)—so as macro weather turns more discontinuous, market resilience becomes a first-order design problem.
Core finding (Oct 2025 ~$19B cascade): today’s plumbing is pro-cyclical and shock-transmitting (Smithian)—without a system-scale backstop, optional liquidity retreats, risk engines liquidate, and prices set by thin liquidity trigger positive-feedback cascades, amid limited circuit-breaks, segmentation, or risk labeling.
Adoption implication: for mass-scale institutional + retail use, resilience—i.e., continued market functioning under stress (liquidity continuity, orderly clearing, and bounded collateral dynamics) becomes a design constraint, not a nice-to-have.
Constraint diagnosis: current stabilization capacity is still local and ad hoc, while tail events are fast and cross-venue—so the binding problem is system-level coordination (shared triggers, capacity, standardized terms, credible loss allocation).
Contribution: we propose Rule-Bound, Conditional Coordination (RBCC) as shared language and a reference pattern to make cross-venue/cross-ecosystem stabilization designs comparable, discussable, and transparent for market-structure stewards before the next storm—with explicit guardrails to surface trade-offs and limit moral hazard (since any shock-absorption layer reshapes incentives and reallocates tail risk rather than eliminating it).

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Introduction

Institutional participation in blockchain and crypto is already underway: capital, infrastructure, and compliance rails are being built in parallel. That shift quietly changes the design question. A market can be philosophically committed to market clearing and still face adoption constraints at mass scale if, in tail regimes, its stress dynamics can push liquidity and collateral behavior beyond tolerable bounds. The question is not whether volatility exists, but how a system behaves when extreme volatility is forced upon it by shocks nobody controls.

The October 2025 ~$19B liquidation cascade—one of the most significant flash-crash events in crypto history—offers a timely stress test. The crypto community has already produced excellent post-mortems on specific venues and protocol mechanics, and we view those as important building blocks. At Their Foundry, our goal is to complement those efforts by surfacing a missing boundary condition: macro and liquidity regimes, and how they interact with the design philosophy embedded in on-chain systems. Put differently, this event provides an empirical window into system reflexes—how liquidity, collateral constraints, and automatic risk engines behave when macro “weather” turns hostile.

A simple metaphor helps anchor the mental model. Imagine you’re driving a car through a certain kind of weather. The weather is the macro regime and policy environment. The engine is the on-chain leverage and liquidation machinery. And the car’s control system reflects the system’s stress-response philosophy—whether stress is transmitted outward through forced liquidations and liquidity withdrawal (Smithian), or partially damped through some counter-cyclical reflex (Keynesian). While macro conditions and policy shocks are exogenous inputs, what can be designed are the market’s plumbing and its reflex circuits.

Now place the flash crash in that frame. You’re already driving under cloudy skies (a strained macro backdrop), and then a storm hits (the tariff shock). The engine is running hot—high leverage paired with automated liquidations—while the control system is tuned to purge leverage quickly rather than absorb shocks. The system still reaches a new equilibrium, but the ride is violent: the tires lose grip on the road, passengers get jolted, and the cabin ends up covered in spilled coffee.

Accordingly, we use the ~$19B flash crash as an empirical stress test to show how macro and liquidity regimes interact with on-chain design philosophy—and why, under exogenous shocks, pro-cyclical plumbing can push market functioning (liquidity, collateral, and clearing dynamics) beyond tolerable bounds for mass-scale institutional and retail participation. In today’s rapidly shifting geopolitical chessboard, the macro “weather” is becoming more discontinuous and harder to forecast; resilience therefore becomes a design constraint, not a nice-to-have.

This paper’s contribution is threefold:

Diagnose how a largely Smithian stress response behaves under cross-venue tail regimes when macro and liquidity conditions tighten—linking external shocks to on-chain reflexes in liquidity, collateral, and liquidation plumbing.
Review existing stabilization and backstop mechanisms in crypto markets—and surface why they tend to be local, episodic, and hard to generalize across venues under stress (e.g., coordination frictions, fragmented governance, and surplus risk-capacity constraints).
Clarify what a counter-cyclical “Keynesian reflex” could mean in the crypto context at the level of market plumbing, and offer a concrete, bounded reference pattern—rule-bound, conditional coordination—as an explicitly non-exhaustive middle path to structure discussion and enable apples-to-apples comparisons across stabilization designs for market-structure stewards.

The paper proceeds as follows: We first establish a stress-response spectrum and its trade-offs, then trace the flash crash through a macro-to-on-chain transmission model. Next, we examine the on-chain yield stack and collateral plumbing to surface recurring stress modes in shock regimes, and review existing stabilization tools and why they do not generalize to cross-venue stress. Finally, we use rule-bound, conditional coordination as an illustrative middle pattern to structure industry discussion—explicitly non-exhaustive.

Market Design Philosophy

In order to clearly distinguish market design philosophies and their corresponding stress responses under an adverse macro-policy shock, we briefly compare the fiat macroeconomy and the crypto economy. They sit at opposite poles of a broader design spectrum, representing two internally coherent—yet competing—approaches (Figure 1). On one end is Keynesian intervention, where volatility is dampened through institutional reflexes and balance-sheet capacity. On the other end is Smithian self-correction, where equilibrium is restored through the collective behavior of decentralized market participants maximizing self-interest, enforced by rule-based liquidation.

In practice, this is the difference between a system built to absorb stress versus one built to expel it. The flash crash revealed what happens when invisible hands meet automated liquidation bots—under a macro regime that makes liquidity scarce.

Figure 1. Pro-Cyclical vs. Counter-Cyclical System Design Spectrum (Source: Their Foundry)

The Keynesian Reflex — Counter-Cyclical Shock Absorber

In the traditional economy, the Federal Reserve serves as an institutionalized reflex circuit designed to dampen volatility. It has a dual mandate—price stability and maximum employment— with two primary tools of interest-rate policy and balance-sheet operations.

Specifically for balance sheet operations, quantitative easing (QE) and quantitative tightening (QT) are expressions of liquidity elasticity: QE expands bank reserves by purchasing Treasuries and agency mortgage-backed securities (MBS), while QT reduces reserves by allowing those holdings to mature or, in limited cases, to be sold. Though QE and QT affect reserves rather than household money directly, they transmit through portfolio-balance (rebalancing), expectations/signaling, and credit channels to shape broader financial conditions.

In a well-functioning Keynesian system, the reflex is designed to be counter-cyclical: it leans against panic and restrains excess with the Fed’s balance sheet functioning as a public shock absorber—expanding when private balance sheets contract, and tightening as conditions normalize. The goal is not to eliminate cycles, but to prevent self-reinforcing spirals that threaten financial stability and employment.

The Smithian Machine — Pro-Cyclical Shock Transmitter

Crypto’s market architecture largely mirrors a Smithian reflex: it is governed mainly by incentive-compatible rules, while discretionary interventions—where they occur—tend to be local, episodic, and institution-specific rather than system-wide. Market venues and protocols set constraints (margin rules, liquidation thresholds, auction formats); autonomous actors—market makers, arbitrageurs, liquidators—compete to enforce them because doing so is profitable. In calm regimes, this competition supports market efficiency: spreads stay tight, mispricings are arbitraged quickly, and leverage is constrained by enforceable risk limits.

Under stress, the same design becomes mechanically pro-cyclical. When uncertainty rises, rational actors protect their own balance sheets: spreads widen, quotes retreat, and risk limits tighten. Because liquidity provision is largely discretionary when it is most expensive, the system’s primary stabilization mechanism is not a backstop but rule-bound liquidation and deleveraging. The system preserves solvency constraints, but often by transmitting stress into prices rather than cushioning it. In design terms, it behaves as a shock transmitter more than a shock absorber.

Prior work—analysis from the Bank for International Settlements (BIS)—has highlighted DeFi’s structural fragilities, especially collateral-driven pro-cyclicality and the absence of a lender-of-last-resort backstop under stress. We arrive at a compatible diagnosis from first principles by tracing how liquidity provision and the on-chain yield stack behave as risk appetite tightens. Our focus here is on how these fragilities interact with macro/liquidity regimes and market-design philosophy during cross-venue tail events—using the ~$19B episode as an empirical stress test.

The $19 Billion Flash Crash: A Chain of Transmission

Recall the car metaphor: macro and liquidity are the weather, on-chain plumbing is the engine, and design philosophy is the control system. The flash crash was the product of those layers colliding—policy shocks and a tightening liquidity regime changed the “weather” just as leverage and automated risk controls amplified the move through the system. Figure 2 sketches this transmission chain end to end. Intuitively, if any element of the cocktail (policy stance, U.S. macro backdrop, risk appetite, on-chain leverage, auto-liquidation mechanics, or the presence or absence of a liquidity shock absorber) had been altered, the outcome could have looked different as well. In the following section, we walk through the macro-to-on-chain transmission chain—how the shock propagated into a liquidation cascade.

Figure 2. Transmission Channels of a Policy Shock (Source: Their Foundry)

Macro & Policy

(Corresponds to Steps 1–2 in Figure 2.)

The recent deleveraging unfolded along a now-familiar macro-to-on-chain sequence. At the time of the event (October 10-11, 2025), the U.S. economy had already shown signs of strain: core inflation was still stuck around ~2.6-2.7%, real 10-year yields hovered near 1.7–1.8%, and unemployment drifted toward the mid-4% range (Figure 2. Step 1) — a stagflation-flavored mix of sticky prices and cooling labor demand. The underlying macro series—core inflation, unemployment, policy rates, and real yields—are shown in Figures 3–7.

Against this backdrop, the Federal Reserve was in quantitative tightening mode: the balance sheet had been shrinking since 2022, and although the pace of runoff had recently been tapered, the net effect was still to drain reserves and risk appetite from the system and prevent market overheating. Policy rates had stopped rising and were already edging down from their peak, but remained clearly restrictive in real terms.

In practice, the Fed was walking a narrow ridge between its dual mandates, leaning toward inflation containment over full employment. With policy rates high in real terms and balance-sheet runoff (QT) ongoing, the stance functions as a brake on market—cooling demand and price pressures while tightening financial conditions, with the trade-off of slower growth and a softening labor market. Liquidity isn’t necessarily already gone, but the ground is primed: when the tariff shock hits and risk appetite reverses, conditions can quickly tip into a liquidity drought. For risk assets—and especially leveraged on-chain structures—that sets the stage for a meta-level liquidity squeeze (Figure 2, Step 2).

Figure 3. Core Inflation is ~2.6% (Source: U.S. Bureau of Labor Statistics)

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Macro Charts (Figure 4-7): US Core CPI, US Unemployment Rate, US Effective Federal Funds Rate, US 10-Year Real Treasury Yield

Equity Pricing

(Corresponds to Steps 3–6′ in Figure 2.)

Sustained high real yields and a softening labor market weighed on demand and compressed forward earnings expectations (Figure 2, Step 3). In valuation terms, this raised the discount rate applied to future cash flows, lowering the present value of risk assets. As financial conditions tightened, equity pricing—fundamentally a reflection of discounted future cash flows (DCF)—became increasingly sensitive to small changes in growth or rate assumptions, priming markets for an outsized reaction once a policy shock arrived.

Then the shock came abruptly: the announcement of a 100 percent tariff on Chinese imports (Figure 2. Policy Shock, red arrow). Though not a mechanical cause, it acted as a catalyst by raising uncertainty and adding new downside risks to the growth and inflation path. A tariff of that magnitude implied higher input costs, supply-chain friction, and potential wage spillovers — all of which compressed corporate profitability and kept discount rates elevated (Figure 2. Step 4’-5’). In market terms, it forced a repricing of risk across both equities and crypto (Figure 2. Step 6’), where assets trade largely on forward expectations and discount-rate/liquidity conditions—especially in crypto, where cash-flow anchoring is thinner and prices are more regime- and liquidity-sensitive.

Crypto Pricing & Violent Deleveraging

(Corresponds to Steps 3–8 in Figure 2.)

In traditional markets, valuation frameworks such as DCF anchor prices to projected earnings. In crypto, however, pricing remains predominantly reflexive — driven by supply-demand imbalances, liquidity cycles, and collective confidence in future utility rather than realized fundamentals. Token valuations respond less to income streams and more to meta-liquidity conditions (Figure 2. Step 4 pink path): funding availability, leverage ratios, and sentiment feedback loops. When macro tightening constrains liquidity and risk appetite simultaneously, these reflexive mechanisms amplify volatility instead of absorbing it — setting the stage for an automated deleveraging cascade—this is Smithian reflex in motion.

Prices fell across major trading venues. Because most on-chain leverage is over-collateralized, falling collateral prices mechanically grind down the collateralization ratio: the same debt is now backed by less market value. Once that ratio dropped below each protocol’s liquidation threshold, positions flipped from “safe” to “forcibly unwound.” Liquidation bots executed their mandates (Figure 2. Steps 5–7), repaying debt, seizing collateral, and selling it—often at a discount or via auction—to close underwater positions. Each forced sale pushed market prices lower, dragging more positions through their thresholds and widening the circle of liquidation. Within hours, a reflexive deleveraging cascade was underway—an automated purge of leverage that restored solvency by force rather than by fresh capital (Figure 2. Step 8).

On-Chain Yield Complex: Where Income Meets Short-Convexity

We focus on on-chain yield because, under stress, the “yield stack” often functions like embedded short convexity: strategies that earn carry in calm regimes can become mechanically correlated once volatility rises and liquidity thins. The reason is structural: many yield primitives are financed on overlapping collateral reservoirs and governed by mark-to-market risk engines, while the liquidity that sets reference prices is largely private and discretionary.

The yield market—crypto’s income layer—now sits on a material TVL base and leverage loops across: (i) staking and liquid-staking rewards, (ii) lending/borrowing and stablecoin carry (including “delta-neutral” loops), and (iii) basis/funding carry in perpetuals (often paired with spot or LST collateral). Across these components, the shared dependencies are the macro “weather” (policy shocks, real yields, global risk appetite) and a microstructure reality: when stress hits, marks can be set by the thinnest liquidity at precisely the worst moment (Figure 2, Pink Zone).

From Carry to Forced Deleveraging

The October episode showed how quickly this stack flipped from “carry” to forced deleveraging once marks were set by thin order books and margin engines repriced collateral. The tariff shock hit an already fragile macro and liquidity backdrop just as markets were still positioned for calm. Risk appetite reversed abruptly: major cryptoassets sold off, implied volatility spiked, and wrapped and synthetic assets that serve as core collateral in leveraged stacks / portfolio-margin positioning (including delta-neutral carry) experienced local dislocations on major venues.

As reference prices were set by spot market—and against a macro backdrop of shrinking risk appetite—market makers stepped back, thinning liquidity. With few participants willing to catch the falling knife, prices only gradually re-anchored to fundamentals later in the session. From the perspective of “yield” users, positions that had looked like safe carry or delta-neutral loops suddenly behaved like levered short-gamma: lending TVL stepped down (reflecting both mark-to-market and some balance-sheet contraction), liquid staking collateral lost headroom, and perp open interest was forcibly cut as margin engines repriced collateral (Figures 8–12). Once marks rolled over, the system reverted to the liquidation logic described in Crypto Pricing & Violent Deleveraging (Figure 2, Pink Section).

Figure 8. Total Liquidations on Crypto-derivatives Exchanges, Daily (Source: CoinGlass)

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On-Chain Data Charts At Flash Crash (Figure 9-12): Lending TVL, Liquid Staking TVL, Perpetual Open Interest, Perpetual Trading Volume

Recurring Stress Modes in Shock-Transmitting Regimes

This dynamic raises three structural questions: liquidity backstops, risk pooling, and price governance under stress. The first is who funds the bid in true stress. In the current crypto setup, liquidity is mostly private and pro-cyclical: market makers and arbitrageurs are free to step away when uncertainty is highest, and no actor has a mandate to buy precisely when marks look worst. In game-theoretic terms it is a classic prisoner’s dilemma: each actor rationally de-risks because they can’t count on others to stay in, even though everyone would be better off if liquidity remained. Hence, self-protection—pulling risk, widening spreads, withdrawing quotes—is individually rational, but collectively it deepens the crash. The outcome is incentive-compatible with a Smithian, profit-maximizing market design under stress: private liquidity is optional, and therefore pro-cyclical in the tail.

The second is how risk is shared and transmitted. Many venues and protocols rely on shared collateral pools and cross-margining: different risk assets sitting in the same “reservoir,” connected by wide pipes. Crucially, without a robust way to surface, label and price risk, the contents of each “reservoir” are opaque—making meaningful segmentation difficult because participants can’t reliably tell what is being mixed with what. When one asset or venue’s pricing becomes dislocated, liquidations and ADL (auto-deleveraging) can propagate stress across users and products rather than containing it. In the reservoir metaphor, if one basin is contaminated and all valves are open, pollution spreads system-wide instead of being trapped locally. This design maximizes efficiency in calm regimes but leaves little capacity to localize stress when shocks arrive.

The third is what price source the system chooses to believe. Today, a large share of risk engines lean on spot-based marks from specific venues. Moving toward redemption- or fundamental-value references—for example, using protocol-level redemption value or robust cross-venue oracles—is one possible upgrade. But it does not remove risk; it reallocates tail risk from order-book microstructure to issuer/protocol mechanics, custody & settlement constraints, governance discretion, and oracle integrity.

Viewed through this lens, the “on-chain yield stack” is best understood as a set of building blocks. The short-convexity profile emerges when those blocks are mixed, looped, and financed on overlapping collateral reservoirs—often without robust risk labeling or segmentation—so that carry structures that look diversified in calm regimes become mechanically correlated under stress. When spot-based marks gap and margin systems reprice collateral, positions that once appeared distinct can converge into the same defensive behavior: cutting risk, selling collateral, and amplifying downside volatility.

Stepping back, these are not idiosyncratic blowups—they are predictable outcomes of a Smithian, optional-liquidity design under stress. In the tail, private liquidity can step away, while rule-bound risk engines continue to enforce self-protection through margining, liquidation, and ADL. The question that follows is whether to accept that pro-cyclical reflex as the default—or to introduce counter-cyclical coordination and stabilization mechanisms that can localize stress transmission, keep markets functional, and anchor stress expectations as shocks arrive.

Stress Response Spectrum and Context-Dependent Design: From Self-Correction to Shock Absorption

Contextual Stress Posture & Baseline Questions

The October flash crash offers an empirical study of a largely Smithian machine operating under hostile macro conditions. That interaction—macro shock colliding with today’s largely Smithian market plumbing—matters more as we enter 2026 amid an active shift in the world order (e.g., Canada and China trade deal, intensifying EU–US friction on Greenland/tariff, executive-branch subpoena threat against the Fed chair, Venezuela, among others).

In the flash-crash episode, shrinking risk appetite plus mechanical liquidation created reflexive dynamics: falling prices hardened expectations of further downside, while automatic margining, shared collateral reservoirs, and looping strategies synchronized deleveraging as liquidity retreated. The outcome resembles a hard-landing form of self-correction with limited obligation or capacity to cushion the path. In principle, voluntary, discretionary private backstops could emerge (e.g., J. P. Morgan’s bank syndicate in 1907), but they depend on coordination, surplus risk capacity, and confidence—so they are episodic rather than a reliable systemic response at scale.

The stress posture is contextual. As stress episodes have real distributional consequences for participants, it is important to clarify the design trade-offs that shape those outcomes. Crypto-native users often operate in high-volatility environments with higher context and risk tolerance. In that setting, whether a fully Smithian stress response is appropriate usually emerges from community norms and risk tolerance. As participation broadens, however, the tolerance envelope changes: institutional and retail adoption typically requires systems that not only function as designed in calm regimes, but also cushion shocks to keep uncertainty within tolerable bounds and preserve continuity of market functioning for everyday use. If the industry aims to power mass-market financial infrastructure—spanning both blockchain execution layers and on-chain DeFi primitives—at the scale of billions of users and trillions of dollars, then stress behavior becomes a practical design constraint. Hence the crucial questions for market-structure stewards become:

Is a purist Smithian baseline the most suitable default for mass-market institutional and retail adoption?
Do backstops exist?
If so, are they rule-triggered or discretionary—and under what conditions?
What costs do they impose, and who bears them?

Backstop Geometry

To examine the other end of the design spectrum, it helps to look at the Keynesian reflex embedded in fiat market plumbing. Faced with the same combination of policy shock and evaporating liquidity, the Federal Reserve could respond via rate cuts, by slowing QT, and—if funding markets seize—through large-scale repo operations or other liquidity facilities to stabilize market functioning. Its balance sheet—mostly sovereign and agency securities denominated in its own currency, under a stable political regime—gives it both the scale and the mandate to absorb shocks rather than transmit them. In market stress episodes, these holdings are typically more resilient than risk assets—often benefiting from flight-to-quality—supporting the Fed’s ability to act counter-cyclically (though in some shocks even Treasuries can sell off).

Figure 13. Backstop Geometry: Public Balance Sheets vs. Crypto Foundations (Source: Their Foundry)

Crypto foundations, in their current form, are generally chartered to steward a specific ecosystem, and are not typically mandated—or equipped—to act as a system-wide backstop with an explicit financial-stability toolkit or expandable balance sheet (Figure 13). Their reserves are often meaningfully correlated with the ecosystem’s risk asset (the native token), with varying degrees of diversification into USD/stables/other assets. As a result, discretionary stabilization capacity can become constrained precisely when stress is system-wide, because reserve value and market conditions can deteriorate together—an echo of the pro-cyclical short convexity dynamics observed elsewhere in on-chain market structure.

Coordination further constrains feasibility. Even when individual actors have the willingness to respond, many stress episodes are cross-venue and macro-driven, propagating faster than any single ecosystem’s toolkit can reliably address in isolation. In those regimes, the binding question becomes whether there are shared triggers and common terms—so expectations, timing, and scope can align across venues—because foundation treasuries are measured in billions, not trillions, and deploying meaningful liquidity into rapidly thinning markets is operationally difficult and may have limited effectiveness if financed primarily by selling correlated assets. By the time action is feasible, market depth may already be impaired, and the signaling value of any intervention can be uncertain.

In fiat systems, tail risk is ultimately socialized through the public balance sheet; in today’s crypto systems, tail risk is often crystallized rapidly in prices through market plumbing (margining and liquidation). The October flash crash made that contrast visible in a single, compressed episode.

Feedback-Loop Mechanics

Beyond differences in backstop (counter-cyclical) capacity, market expectations—and therefore market behavior—also diverge sharply across the two ends of the design spectrum (Figure 1). These dynamics are reflexive in Soros’s sense: they link perception to reality, but the sign of the feedback differs. In Keynesian-style systems, policy backstops tend to create negative feedback: when markets panic, authorities try to anchor market expectations by signaling that funding and collateral values will not be allowed to spiral unchecked. When that commitment is credible—and paired with constraints—it can become self-reinforcing, stabilizing funding conditions and reducing the amount of intervention ultimately required; when it is perceived as an unconditional safety net, it can also encourage risk-taking and moral hazard. In crypto’s largely rule-based market plumbing, stress more often produces positive feedback: when stress hits, expectations can flip from “liquidity will be there” to “liquidity will disappear,” so participants widen spreads and pull quotes—books thin, risk limits tighten, and margin/liquidation mechanics activate, pushing prices down and validating the original fear.

Thought Experiment: What a Keynesian Reflex Might Look Like in Crypto

In the prior section, we compared backstop capacity at two ends of the design spectrum: a fully Smithian crypto market versus a fully Keynesian fiat market. The comparison surfaced a central constraint: today’s crypto toolkits are still in the process of maturing toward a system-scale capacity to manage cross-venue, macro-driven shocks, given localized mandates, limited balance sheets, and coordination that is often informal and hard to operationalize at scale. In that setup, expectations can turn positively reflexive—beliefs about vanishing liquidity and further downside feed directly into behavior that amplifies the shock. And since macro is the weather—hard to predict and impossible to control—flash crashes are best read as a cocktail of shifting macro/geopolitical conditions interacting with a Smithian machine. If crypto is moving toward institutional and mass-market use, it may be beneficial to design explicitly for resilience in these tail regimes. We can’t make it stop raining, but we can carry an umbrella.

A Keynesian Reflex in Crypto Is a Coordination Problem

Crypto’s binding constraint is coordination at the system level: cross-venue shocks often propagate faster than any single venue or ecosystem can stabilize, while backstop capacity and authority remain fragmented. If the cryptoeconomy ever sought a Keynesian-style reflex—an endogenous, counter-cyclical capacity to cushion large-scale shocks—it would likely require a system-level coordination layer that can align actions across venues at the moment stress arrives: timing, incentives, and loss allocation. Given fragmented authority and balance sheets, the workable design space is neither pure self-correction nor open-ended, discretionary intervention, but a bounded middle (Figure 14.) that can scale beyond ad hoc or purely informal arrangements. Historically, stabilization has often emerged through coordination (via states and multi-party institutions such as the IMF, BIS, or OPEC), but crypto’s institutional constraints are different. The aim here is to offer a clear mental framework—grounded in incentives, credibility, and governance constraints—to enable a more productive industry discussion of what bounded, coordinated stabilization could plausibly look like as crypto moves toward institutional and mass-market adoption.

Figure 14. Rule-Bound Conditional Coordination as a System Stress Response (Source: Their Foundry)

Rule-Bound Conditional Coordination (RBCC)

(Corresponds to Figure 14.)

Definition. In the crypto context, a “Keynesian reflex” refers to design choices that dampen pro-cyclical spirals during system-wide, cross-venue stress by coordinating liquidity, incentives, and expectations across key participants.

Conceptually, it can be described by the non-exhaustive properties in Figure 15. In what follows, we use rule-bound, conditional coordination as a concrete reference pattern for that bounded middle (a coordination layer): shared, pre-defined triggers and standardized terms that activate only under specified stress conditions via transparent mechanisms. Because crypto’s plumbing is fast and automatic while crisis coordination is slow, the system needs ex ante coverage for common cascade patterns—so responses can be activated predictably and audited ex post, rather than improvised through open-ended authority.

Figure 15. Rule-Bound Conditional Coordination (RBCC): Core Properties (Source: Their Foundry)

Scope. Importantly, this framing is not about “defending prices” in the sense of targeting or sustaining a particular market level. Rather, it clarifies where a stabilization impulse would operate: in the market’s plumbing—liquidity provision, collateral and margin mechanics, and the incentive-and-expectations dynamics that shape behavior under stress. The goal is to preserve market functioning when private liquidity retreats, preventing thin books and rigid risk engines from mechanically amplifying cascades, and possibly reducing cliff effects embedded in funding and collateral rules. Price discovery still occurs and losses still clear; the coordination layer is bounded to the transmission channels of stress, not discretionary price targeting.

While Rule-Bound Conditional Coordination (RBCC) is the coordination layer, three enabling building blocks make it operational:

First, a pooled anti-reflexive reserve: ex ante resources designed to be weakly correlated to the system’s risk assets and mutualized to support cross-venue stress response under transparent contribution rules. The precise funding model can vary, but the goal is consistent: maintain deployable capacity when native-token liquidity and market depth deteriorate.
Second, conditional liquidity facilities that make coordination incentive-compatible rather than charitable: access is subject to transparent eligibility criteria, support is bounded by caps and duration, and terms are priced explicitly (haircuts, fees/penalty rates, collateral requirements), so assistance is conditional and compensated—not open-ended.
Third, a menu of mechanism-level dampeners—circuit breakers, risk isolation, volatility-aware liquidation throttles, time-based gates, and orderly auction design—that can be referenced as shared “risk hygiene” standards to localize spillovers and reduce cliff effects when stress propagates through shared collateral and margin plumbing. Many of these controls already exist in some protocol designs; the missing layer is system-level intent and coordination over shared triggers, minimum standards, and stress-time rules.

Moral hazard and Governance

Any counter-cyclical coordination layer reshapes behavior ex ante. If participants come to expect that severe stress will be met with some form of liquidity facilities, leverage, maturity transformation, and correlated exposures can expand until the coordination layer becomes endogenous to the system’s own risk-taking. In that sense, a Keynesian reflex does not eliminate tail risk—it reallocates it. A Smithian system concentrates tail risk in prices and liquidations; a Keynesian-style coordination layer can shift part of that tail risk into governance: rule-setting, enforcement, bounded edge-case judgment, and the credibility and fairness of access under stress.

The binding questions are therefore governance and incentive questions, not purely technical ones: how access is governed and defined (eligibility), what triggers apply, on what facility terms, and how losses are ultimately allocated. In crypto—where coordination can be highly effective for protocol upgrades, but shared standards for cross-venue stress response are still nascent—surfacing these questions early is part of the work. The point here is to present the trade-offs with intellectual honesty: if a system aspires to dampen cascade dynamics, the design inevitably raises questions of eligibility, loss allocation, and legitimacy. A Keynesian reflex is thus a trade—and ultimately a collective choice about which tail risks the ecosystem is willing to absorb, and where it prefers those risks to sit. It can dampen violent feedback loops, but only if explicit guardrails prevent incentives, power, and accountability from becoming a new transmission channel of instability.

Closing Reflections

The ~$19 billion flash crash is an empirical stress test of how a largely Smithian market structure behaves under cross-venue tail conditions. In a system-wide storm—macro and policy shocks interacting with thin liquidity—fragmented backstops and slow, informal coordination can become part of the transmission channel.

For markets that aspire to institutional and mass-market reliability, the first important step is to clarify the design question: should any counter-cyclical reflex exist at the system level to preserve basic market functioning in tail regimes—and if so, on what terms? This paper’s contribution is to make that question legible by surfacing how Smithian structures can purge risk violently under macro shock, by contextualizing what a Keynesian reflex could mean in crypto market plumbing, and by offering a concrete, bounded example—rule-bound, conditional coordination—so the industry can debate stabilization designs with shared definitions and comparable reference points.

Before any system-wide coordination is feasible, however, the industry and market-structure stewards will benefit from shared language: common definitions, reference cases, and a clear map of the design space. The work ahead is architectural: defining shared stress triggers, risk labels, and coordination primitives that can scale beyond any single venue or ecosystem.

Disclosures & Disclaimers: This document is provided by the author and Their Foundry (a strategic tooling and research studio) for informational and research purposes only. Information is provided “as is” as of the publication date and may be incomplete, inaccurate, or become outdated; views expressed are the author’s own and may change. Nothing herein constitutes investment, legal, tax, or financial advice, nor an offer, solicitation, or recommendation to buy or sell any asset or to engage in any investment strategy. Any collaboration, sponsorship, or commercial discussions are separate from this research and subject to mutually agreed terms. Certain information and figures are sourced from third-party materials believed to be reliable; they have not been independently verified and no representation is made regarding their accuracy or completeness. References to specific assets, venues, protocols, or market participants—including within charts, screenshots, and citations—are included solely for attribution and analytical context and are not intended to imply endorsement, affiliation, or any commercial relationship, unless explicitly disclosed. Readers should conduct their own independent diligence and assume full responsibility for any decisions made based on this material. The author may, from time to time, hold positions in digital assets and may engage in advisory, research, or commercial work related to the topics discussed.