Delphi Labs presents: a Dynamic Interest Rate Model Using Control Theory
In this piece, we explore an alternative pricing solution which we believe to be more capital efficient and better suited to the dynamic crypto market. delphidigital.io/reports/dynami…
Lending OGs such as @compoundfinance and @AaveAave typically use a fixed price curve where the interest rate (IR) is determined according to the utilization rate of each money market.
While this model has proved useful and was a clever initial approach to the pricing problem, it has some limitations.
Specifically, it can be too rigid for the constantly evolving crypto market and cannot adjust to changes in external market conditions.
This rigidity can translate into 2 undesirable outcomes:
1⃣ Illiquidity in money markets where the IR doesn’t adjust quickly enough (i.e. when new yield farms pop up)
2⃣ underutilization of certain markets
So, how do we propose to solve this?
By incorporating control theory into the pricing mechanism.
Specifically, we propose using a PID controller that dynamically adjusts interest rates to target an optimal utilization within each money market. Let’s explore 👇
Simply put, the PID controller works as follows:
1⃣ It calculates the difference btwn the optimal utilization + the current utilization
2⃣ It adjusts the IR accordingly. All else being equal, the higher the difference, the higher the IR adjustment
3⃣ It repeats 1⃣ periodically
In contrast to the prevalent pricing model across DeFi, the PID model dynamically adjusts to market conditions.
Given that it doesn’t depend on a fixed curve, IRs within this model will keep adjusting whenever the current state is different than the desired one.
This model will be implemented by @mars_protocol, a lending protocol on top of @terra_money that’s currently being incubated by Delphi Labs.
If you’re a DeFi builder or part of a protocol and are interested in experimenting with this model, please reach out to us; we want to hear from you!
Special thanks to @euler_mab from @euler_xyz for his valuable help revising this report. The initial idea of using a PID Controller within the lending context came from his work in Euler XYZ.
Crypto x AI Month - Decentralized AI Training: Can It Dismantle Centralized Powerhouses?
Top experts @IridiumEagle, @DillonRolnick, @fenbielding, @johannes_hage debate how open-source and DeAI can rival tech giants, a panel hosted by @Shaughnessy119.
🗣 “What decentralization really gives us is the opportunity for a properly modular infrastructure where you could actually compose those models together.” - @fenbielding (@gensynai)
Timeboost, @arbitrum's new transaction sequencing policy, replaces first-come-first-serve with an auction for transaction priority.
Winners access an "express lane" with a 200ms advantage for transaction inclusion. This shifts competition from latency to strategic bidding and prediction.
Arbitrum uses first-come-first-serve (FCFS) transaction ordering based on sequencer arrival.
This enables fast blocks and reduces frontrunning, but it has drawbacks:
In the other hand, Timeboost auctions are held every minute for next-minute express lane control:
Auctioneer accepts bids for 45 seconds, with 15 seconds for resolution. Minimum bid: 3 $ARB or 0.001 $ETH (DAO decision). Limit: 5 bids per address per round. 👇
4 Potential Express lane strategies:
1️⃣ Predict MEV opportunities using price models for arbitrage and liquidations.
2️⃣ Capture real-time MEV during control period.
3️⃣ Establish secondary market by winning auctions and reselling rights.
4️⃣ Collaborate with others, sharing access when collective MEV exceeds individual extraction.
Timeboost shifts Arbitrum's MEV from speed to strategy via time-based auctions.
It generates DAO revenue, reduces spam, and allows flexible allocation.
This approach encourages strategic bidding, benefiting the ecosystem while enabling community-driven distribution.
In last year’s Infrastructure Year Ahead report, we outlined the evolving landscape of rollups in the section "The L2 Wars."
This year, we reflect on these past predictions in this new unlocked Alpha Feed 🔓
Key insights included 👇
🔹Blast Rollup: A complete distortion of rollup architecture, signaling the end of the “kumbaya” phase.
🔹Fragmented L2 Ecosystems: Increasing isolation with unique bridging, interoperability standards, and SDKs for new chains/L3s (e.g., Superchain, Orbit).
🔹Alt-DA for Scaling: Rollups rely on alternative DA to achieve scalability.
🔹Disruption of DA Value: Premium charges on DA would no longer be sustainable, as DA faces innovation.
🔹ETH’s New Bull Case: Ethereum's future as the global proof verification layer and money.
🔹Limited Value for DA Layers: DA layers would see restricted value accrual.
🔹Positive Outlook for L2 Tokens: Sequencing value could drive positive outcomes for L2 tokens.
These predictions have largely played out, with Ethereum's focus shifting back to scaling L1 to avoid losing value to rollups.
ETH has been going through somewhat of a social crisis, and these talking points above are now brought up ad nauseam. ⤵️
Reflecting on our report a year ago, "Solana The Monolith", @solana's modular components continue gaining value across platforms, affecting ETH & BTC.
As rollups promote modularity, Solana is at the forefront.👇
In the report, we explore how Solana is contributing to the growing landscape of modular blockchain components while also developing its internal demand through the creation of L2 solutions.
Previously, the SVM and Validator Client were closely integrated, which constrained innovation by requiring any changes to consider the entire system as a whole.
The 21st century is primed for a continued struggle: the state, the corporation, the elites vs. the network. The hierarchy and the hivemind. The Tower and the Square.
With the rapid advances in AI, perhaps the stakes have never been higher.
Of history's countless concentrations and diffusions of power, the next chapter is perhaps its most consequential.
Don’t miss out on @PonderingDurian lastest report ! Probably the best current read on Centralized AI, Crypto, and Open Source AI 👀👇 delphi.link/TheTowerAndSqu…
Your Size is Not Size
Google, Amazon, Meta, Microsoft, and Apple have over $400B in cash-like assets and generate >1.5% of GDP in annual operating income. The race for AI supremacy is only heating up.
The 4 hyperscalers (Excl. Apple) spent $467B in capex the last 3 years👇