The implicit assumption behind every major AMM design is a mixed flow: retail users swapping tokens for actual use, and arbitrageurs correcting price discrepancies. This assumption was never stated explicitly, but it shaped every architectural decision — pair pools, flat fees, uniform liquidity ranges, anti-MEV mechanisms.
I’ve been questioning whether that assumption still holds as the primary design constraint.
The empirical picture is harder to read than it first appears. Heimbach et al. [1] attribute over 25% of volume on Ethereum’s five largest DEXes to non-atomic arbitrage alone. Canidio and Fritsch [2] estimate combined arbitrage at roughly 40% of Uniswap v3 volume, leaving ~60% classified as “noise traders.” Qin et al. [3] quantified hundreds of millions in extracted value from bot-originated activity across DEXes. On the surface this suggests organic retail flow is still the majority.
But I think there’s a methodological problem with how “real user” gets defined in this literature. These papers identify bots by looking for known arbitrage patterns — high frequency, cross-venue price correction, atomic execution. Everything that doesn’t match those patterns gets classified as organic flow by residual. There’s no independent verification that the residual actually represents human-initiated activity.
Consider what that residual probably contains: bots executing swaps on behalf of users through aggregators and intent systems (on-chain the transaction is bot-originated, but it gets counted as organic); low-frequency bots that don’t exhibit the high-frequency signatures used to identify arbitrageurs; protocol rebalancing bots; and noise-trading bots that deliberately mimic retail patterns. On-chain data has no reliable signal for human intent — every transaction looks identical regardless of whether a human clicked a button or a script fired autonomously.
There’s also a more fundamental categorization problem: aggregators and arbitrageurs are functionally identical from the AMM’s perspective. Both route flow toward the best available price. Both extract value from a pool when it’s mispriced relative to other venues. The AMM receives the same transaction either way and cannot distinguish between them. The difference is meaningful from the end user’s perspective — one is executing a human’s intent, the other is capturing a price discrepancy for its own account — but it’s invisible to the AMM’s fee and pricing logic. Classifying aggregator-routed volume as “organic” while classifying direct arbitrage as “bot activity” creates an artificial distinction that doesn’t reflect how either actor interacts with the pool.
The deeper observation is simpler: humans are not very active on-chain directly. The friction of gas costs, wallet management, and on-chain UX creates a high bar for unmediated activity. Most humans who interact with DeFi do so through an interface that abstracts the execution — which means the actual on-chain actor is almost always some form of automated system, whether that’s an aggregator solver, an intent filler, or a classic arbitrage bot.
This creates a tension I keep coming back to:
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A large and likely undercounted fraction of on-chain AMM flow is automated
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The dominant research direction is making life harder for automated actors
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LPs are evaluated against benchmarks like LVR [4] and IL that measure relative performance against an idealized market, not whether their USD position actually grew
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The “organic flow” that supposedly subsidizes LPs is increasingly intermediated by aggregators and batch systems before it reaches the AMM directly
And yet the design conversation keeps centering retail protection as the primary objective, with arbitrageurs as the adversary to mitigate.
I’m not sure this framing is entirely wrong — retail users do deserve good execution when they show up. But I wonder if the assumption that organic flow is the majority is less well-supported than the literature suggests, and whether that changes the design priorities.
If automated flow is larger than we think, what does that change about how you’d design fee mechanisms, liquidity structure, or LP incentives?
And separately: is LVR the right benchmark for LP profitability, or is it answering a different question than what a real LP actually cares about? Milionis et al. [4] are explicit that LVR measures costs relative to a rebalancing strategy, not absolute USD return — which raises the question of whether it’s the right objective function for a passive LP with a finite horizon.
Curious if others have better methodologies for distinguishing human from automated flow, or data that pushes back on this framing.
References
[1] L. Heimbach, V. Pahari, and E. Schertenleib, “Non-Atomic Arbitrage in Decentralized Finance,” in IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, May 2024. arXiv:2401.01622.
[2] A. Canidio and R. Fritsch, “Arbitrageurs’ Profits, LVR, and Sandwich Attacks: Batch Trading as an AMM Design Response,” arXiv preprint arXiv:2307.02074, 2023.
[3] K. Qin, L. Zhou, and A. Gervais, “Quantifying Blockchain Extractable Value: How Dark is the Forest?” in 2022 IEEE Symposium on Security and Privacy (SP), IEEE, 2022, pp. 198–214. DOI: 10.1109/SP46214.2022.9833734.
[4] J. Milionis, C. C. Moallemi, T. Roughgarden, and A. L. Zhang, “Automated Market Making and Loss-Versus-Rebalancing,” arXiv preprint arXiv:2208.06046, 2022.
Tags: amm-design, mev, fee-mechanism, liquidity-providers, defi-research