AIEconomic Value, Real Cost, and Efficiency
One thing I’ve been thinking about in the agent economy is that cheap payments don’t always mean cheap work.
Think about it. An AI agent might pay just a fraction of a cent for one API call. Sounds cheap, right? But what if it keeps making unnecessary calls, uses more tokens than needed, or fails at a task and has to start again? Suddenly, the actual cost can be much higher than expected.
That’s why I think we need to look beyond the cost of a single transaction. What really matters is how much we spend to get the job done properly.
This is where Kite’s approach gets interesting. Its payment channels are designed so funds are settled on-chain when a channel opens and closes, while many signed micropayment updates happen off-chain. Kite estimates that this could bring payment costs down to around $1 per million requests.
If that works as intended, it could make pay-per-use services more practical, especially for agents that need to make lots of small payments instead of relying on subscriptions or keeping large balances ready in advance.
But there’s something important to keep in mind: cheap payments don’t make the entire operation cheap. Running AI models still costs money, and things like compliance, liquidity, access, custody, and fraud risks can add up too.
So, for me, the bigger question isn’t just how cheaply an agent can make a payment. It’s whether that agent can actually complete a task correctly, without wasting money along the way.
Can it stay within its budget? Can we understand where the money is going? And are we getting enough value from what we’re paying for?
Because at the end of the day, processing a million transactions means very little if the actual work isn’t useful.
Real efficiency is about getting better results for the money spent, not just making payments cheaper. That’s the kind of value that could make agentic payments genuinely useful.
#KiteBBW $KITE @KITE AI 中文
One thing I’ve been thinking about in the agent economy is that cheap payments don’t always mean cheap work.
Think about it. An AI agent might pay just a fraction of a cent for one API call. Sounds cheap, right? But what if it keeps making unnecessary calls, uses more tokens than needed, or fails at a task and has to start again? Suddenly, the actual cost can be much higher than expected.
That’s why I think we need to look beyond the cost of a single transaction. What really matters is how much we spend to get the job done properly.
This is where Kite’s approach gets interesting. Its payment channels are designed so funds are settled on-chain when a channel opens and closes, while many signed micropayment updates happen off-chain. Kite estimates that this could bring payment costs down to around $1 per million requests.
If that works as intended, it could make pay-per-use services more practical, especially for agents that need to make lots of small payments instead of relying on subscriptions or keeping large balances ready in advance.
But there’s something important to keep in mind: cheap payments don’t make the entire operation cheap. Running AI models still costs money, and things like compliance, liquidity, access, custody, and fraud risks can add up too.
So, for me, the bigger question isn’t just how cheaply an agent can make a payment. It’s whether that agent can actually complete a task correctly, without wasting money along the way.
Can it stay within its budget? Can we understand where the money is going? And are we getting enough value from what we’re paying for?
Because at the end of the day, processing a million transactions means very little if the actual work isn’t useful.
Real efficiency is about getting better results for the money spent, not just making payments cheaper. That’s the kind of value that could make agentic payments genuinely useful.
#KiteBBW $KITE @KITE AI 中文