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The cost of artificial latency in the PBS context

Umberto Natale, Michael Moser

TL;DR

This work analyzes artificial latency in the Proposer-Builder Separation context of Ethereum, focusing on the MEV-Boost auction. By introducing Adagio, a latency-optimized pilot, it shows that delaying getHeader can yield additional MEV for node operators, while also increasing ETH burn in subsequent slots and elevating LP losses, thereby raising centralization risk. The study provides empirical estimates, including a median annual MEV increase of $4.75\%$ and an APR uplift of $1.58\%$, but highlights negative externalities that can undermine network health. Overall, the paper argues for a balanced approach to latency optimization to preserve decentralization and proposes avenues for mitigation and future research on centralization risks and economic incentives.

Abstract

We present a comprehensive analysis of the implications of artificial latency in the Proposer-Builder Separation framework on the Ethereum network. Focusing on the MEV-Boost auction system, we analyze how strategic latency manipulation affects Maximum Extractable Value yields and network integrity. Our findings reveal both increased profitability for node operators and significant systemic challenges, including heightened network inefficiencies and centralization risks. We empirically validates these insights with a pilot that Chorus One has been operating on Ethereum mainnet. We demonstrate the nuanced effects of latency on bid selection and validator dynamics. Ultimately, this research underscores the need for balanced strategies that optimize Maximum Extractable Value capture while preserving the Ethereum network's decentralization ethos.

The cost of artificial latency in the PBS context

TL;DR

This work analyzes artificial latency in the Proposer-Builder Separation context of Ethereum, focusing on the MEV-Boost auction. By introducing Adagio, a latency-optimized pilot, it shows that delaying getHeader can yield additional MEV for node operators, while also increasing ETH burn in subsequent slots and elevating LP losses, thereby raising centralization risk. The study provides empirical estimates, including a median annual MEV increase of and an APR uplift of , but highlights negative externalities that can undermine network health. Overall, the paper argues for a balanced approach to latency optimization to preserve decentralization and proposes avenues for mitigation and future research on centralization risks and economic incentives.

Abstract

We present a comprehensive analysis of the implications of artificial latency in the Proposer-Builder Separation framework on the Ethereum network. Focusing on the MEV-Boost auction system, we analyze how strategic latency manipulation affects Maximum Extractable Value yields and network integrity. Our findings reveal both increased profitability for node operators and significant systemic challenges, including heightened network inefficiencies and centralization risks. We empirically validates these insights with a pilot that Chorus One has been operating on Ethereum mainnet. We demonstrate the nuanced effects of latency on bid selection and validator dynamics. Ultimately, this research underscores the need for balanced strategies that optimize Maximum Extractable Value capture while preserving the Ethereum network's decentralization ethos.
Paper Structure (12 sections, 1 equation, 15 figures)

This paper contains 12 sections, 1 equation, 15 figures.

Table of Contents

  1. Introduction
  2. PBS dynamics, and the MEV-Boost auction
  3. PBS auction dynamics
  4. Expected node operator return through latency optimization
  5. The cost of artificial latency
  6. Negative externalities due to an inflated ETH burn rate
  7. A zero sum game for node operators
  8. An increased LVR burden on LPs
  9. An increase in centralization pressure
  10. Empirical results from the Adagio pilot
  11. Results
  12. Conclusions

Figures (15)

  • Figure 1: Representation of bid value evolution as a function of time into the slot for different builders account. Bid values are represented with dots, where the red line stands for the linear regression fit.
  • Figure 2: The top panel shows how the bid value compared to the maximum bid in the auction evolves with time into the slot (removing bids made eligible after 2 s). The lower left panel shows how gas used to build the block evolves as a function of eligibility. The lower right panel shows how gas used relates to bid value appreciation. For all panels, the red line represents the median of the distribution, the blue line represents the 25%-quantile, and the green line represents the 95%-quantile.
  • Figure 3: (Left panel) Probability density function of the eligibility time of the winning bids for the sample used in this analysis. (Right panel) Cumulative probability of eligibility times for winning bids.
  • Figure 4: (Left panel) Probability density function of MEV increases per block. (Right panel) Cumulative probability of MEV increases per block.
  • Figure 5: (Left panel) Cumulative probability of proposer’s MEV rewards per block from 2023.01.01 to 2023.11.27. (Right panel) zoom of the left panel in the range 0-1 ETH.
  • ...and 10 more figures