#termmax @TermMax It’s the same $1 million collateral—why, in @TermMax , can you borrow different amounts? Don’t just look at “value.”
At first, I looked at @TermMaxFi’s MLTV and assumed it was no different from the LTV numbers in other DeFi protocols.
All it does is set a maximum borrowing ratio for the protocol.
But when I break down its Market structure further, I realized that MLTV here plays a far more complex role than a simple risk-control metric.
@TermMaxFi’s lending isn’t about dumping all assets into a single pool and quoting users based on total liquidity.
Different Markets correspond to specific Collateral Tokens, Debt Tokens, maturity times, and MLTV.
In other words, when a user opens a position, what the protocol defines first isn’t “how much you have,” but:
What asset you’re putting up—and, within this particular Market, how much debt it can support at most.
The difference is actually significant.
Even if ETH, PT, or other yield-bearing assets have the same $1 million value, their price behavior, liquidity, and term structure can be completely different.
So @TermMaxFi embeds the maximum borrowing capacity into the parameter system of each specific Market, while GT records the final collateral-debt relationship the user creates.
Then the FT, maturity settlement, and liquidation all unfold around the debt structure of that specific Market.
Only after seeing this did I realize MLTV’s real importance isn’t simply about “limiting how much you can borrow.”
It’s really drawing a clear credit boundary for each kind of asset.
Before, I always thought DeFi’s core competitive edge was improving capital efficiency.
But @TermMaxFi’s design made me think: the higher the capital efficiency, the less the protocol can simply treat different assets as the same kind of risk.
A truly mature on-chain credit market might not keep raising LTV.
Instead, it answers a more fundamental question first: for different assets, what kinds of distinct credit rules should they have? #TermMax
At first, I looked at @TermMaxFi’s MLTV and assumed it was no different from the LTV numbers in other DeFi protocols.
All it does is set a maximum borrowing ratio for the protocol.
But when I break down its Market structure further, I realized that MLTV here plays a far more complex role than a simple risk-control metric.
@TermMaxFi’s lending isn’t about dumping all assets into a single pool and quoting users based on total liquidity.
Different Markets correspond to specific Collateral Tokens, Debt Tokens, maturity times, and MLTV.
In other words, when a user opens a position, what the protocol defines first isn’t “how much you have,” but:
What asset you’re putting up—and, within this particular Market, how much debt it can support at most.
The difference is actually significant.
Even if ETH, PT, or other yield-bearing assets have the same $1 million value, their price behavior, liquidity, and term structure can be completely different.
So @TermMaxFi embeds the maximum borrowing capacity into the parameter system of each specific Market, while GT records the final collateral-debt relationship the user creates.
Then the FT, maturity settlement, and liquidation all unfold around the debt structure of that specific Market.
Only after seeing this did I realize MLTV’s real importance isn’t simply about “limiting how much you can borrow.”
It’s really drawing a clear credit boundary for each kind of asset.
Before, I always thought DeFi’s core competitive edge was improving capital efficiency.
But @TermMaxFi’s design made me think: the higher the capital efficiency, the less the protocol can simply treat different assets as the same kind of risk.
A truly mature on-chain credit market might not keep raising LTV.
Instead, it answers a more fundamental question first: for different assets, what kinds of distinct credit rules should they have? #TermMax