Last year, when I rented a studio, the landlord couple was handling things separately—one took care of renovations while the other managed rentals. It was pretty inefficient.
Then we switched to a synchronized approach where tenant feedback directly influenced renovation decisions. Better renovation quality attracted better tenants, and the income was reinvested into further renovations.
Once those two lines synced up, the occupancy rate jumped from 40% to 90%.
@OpenLedger The dual flywheel in section six of the white paper works on the same logic. What are the two flywheels doing?
The AI flywheel operates like this: model developers propose ideas, gather data, fine-tune, and deploy. The higher the model quality, the more inference calls it gets, leading to greater attribution returns, which attracts even more high-quality data, further enhancing the model.
The blockchain flywheel has a different chain. Each time the model is called, it generates an immutable transaction record on-chain. With increased transaction volume, validator rewards go up, making the network more stable. This stability encourages developers to build models on it, leading to even more calls.
The only common trigger for both flywheels is: the model being called.
One inference call feeds both flywheels. The AI flywheel receives attribution data and revenue signals, while the blockchain flywheel gets transaction volume and validator incentives. The same action triggers two positive feedback loops; the white paper calls this synergy.
Cold start is the most ambiguous part.
Flywheels have a well-known issue: they need initial momentum to spin.
During the cold start phase, both flywheels are stationary, waiting for the other to move first. A sufficient number of models need to be running on the network, with enough real calls being made, for validators to have the incentive to participate and contributors to believe that returns will materialize.
The description of the cold start in section six of the white paper is the most vague, lacking specific guidance mechanisms or initial incentive plans. This is where the most pressing questions lie.
What to watch for after the mainnet goes live?
On-chain transaction volume and model inference call counts—watch if these two numbers are growing in sync.
If they rise together, it indicates that the dual flywheels are engaging. If one goes up while the other stays flat, it means the two chains haven't connected yet, and the flywheels are just spinning in isolation.
$OPEN What needs to be validated is whether the system can run on its own after the two lines engage. The landlord couple took a few months to verify this. #OpenLedger After the mainnet launch, we’ll get answers much quicker.
Then we switched to a synchronized approach where tenant feedback directly influenced renovation decisions. Better renovation quality attracted better tenants, and the income was reinvested into further renovations.
Once those two lines synced up, the occupancy rate jumped from 40% to 90%.
@OpenLedger The dual flywheel in section six of the white paper works on the same logic. What are the two flywheels doing?
The AI flywheel operates like this: model developers propose ideas, gather data, fine-tune, and deploy. The higher the model quality, the more inference calls it gets, leading to greater attribution returns, which attracts even more high-quality data, further enhancing the model.
The blockchain flywheel has a different chain. Each time the model is called, it generates an immutable transaction record on-chain. With increased transaction volume, validator rewards go up, making the network more stable. This stability encourages developers to build models on it, leading to even more calls.
The only common trigger for both flywheels is: the model being called.
One inference call feeds both flywheels. The AI flywheel receives attribution data and revenue signals, while the blockchain flywheel gets transaction volume and validator incentives. The same action triggers two positive feedback loops; the white paper calls this synergy.
Cold start is the most ambiguous part.
Flywheels have a well-known issue: they need initial momentum to spin.
During the cold start phase, both flywheels are stationary, waiting for the other to move first. A sufficient number of models need to be running on the network, with enough real calls being made, for validators to have the incentive to participate and contributors to believe that returns will materialize.
The description of the cold start in section six of the white paper is the most vague, lacking specific guidance mechanisms or initial incentive plans. This is where the most pressing questions lie.
What to watch for after the mainnet goes live?
On-chain transaction volume and model inference call counts—watch if these two numbers are growing in sync.
If they rise together, it indicates that the dual flywheels are engaging. If one goes up while the other stays flat, it means the two chains haven't connected yet, and the flywheels are just spinning in isolation.
$OPEN What needs to be validated is whether the system can run on its own after the two lines engage. The landlord couple took a few months to verify this. #OpenLedger After the mainnet launch, we’ll get answers much quicker.