If you are accustomed to general-purpose L1s like Ethereum and Solana, the "blockchain ledger" in your mind likely looks like this: a bunch of addresses, a string of transaction hashes, occasionally mixed with a few contract call logs. Who clicked "confirm transaction," who entered a password in the wallet, these human-level stories will ultimately be compressed into one record after another from → to → value → gas. The chain itself does not care who you are or why you are making a transfer; it only holds the responsibility of accurately recording "who paid whom how much," leaving the rest to exchanges, wallets, and regulatory bodies to handle.
After KITE appeared, I gradually realized that a very clear dividing line is forming: the ledgers of traditional L1s are remembered for humans, with humans as the default protagonists, while contracts and applications are merely supporting roles; whereas KITE and similar AI payment public chains simply turn this around, treating 'autonomous intelligent agents' as first-class citizens and designing the ledger from the start to be 'for machines to keep accounts,' with humans retreating to behind the scenes, only appearing when granting permissions, setting rules, and budgeting.
Let's lay out the comparison. The original intention behind Ethereum's design is 'a global programmable computer,' focusing on universality: you can issue tokens, engage in DeFi, create NFTs, run various strange contracts, but no matter what you play, the interaction entry is always human. You open the wallet, select an address, fill in parameters, check gas, and then confirm with some hesitation; all transaction fees are paid in ETH, with prices soaring and plunging along with network congestion and coin price fluctuations. Solana takes another route, pursuing extreme throughput and low fees, placing execution, settlement, and data all on the same layer, pulling TPS to thousands or tens of thousands and compressing single transaction fees to less than a cent, naturally suited for high-frequency DeFi, gaming, NFTs, and other human-led interactions. But whether ETH or SOL, they are essentially 'human wallet networks': you can use various tricks to abstract and smooth the experience, but the underlying assumption of the chain remains 'a user is clicking,' rather than 'a group of agents is continuously settling with each other.'
The KITE white paper overturns this assumption from the very first page. It does not first ask 'how humans use it,' but rather 'how agents use it.' The official description is very straightforward: KITE is a sovereign Layer 1 chain compatible with EVM, based on Avalanche infrastructure, specifically optimized for transaction forms between AI agents, using PoS consensus at its core, but layered above it with Proof of Attributed Intelligence (PoAI), incorporating real agent activities and intelligent contributions into the consensus process. This sounds like a bunch of buzzwords, but when it comes down to the ledger structure, the changes are very specific: it presupposes that transactions are not initiated by 'someone from an anonymous address,' but rather by 'an agent hanging under a specific user, possessing independent identity and budget'; it presupposes that a transaction is not just a simple transfer, but can be 'calling an API, requesting computation, and a working record settled per instance.'
If you flip through KITE's architecture documentation, you will find several interesting keywords: three-layer identity model (User / Agent / Session), native stablecoin fees, dedicated payment channels and transaction lanes, transaction types oriented towards agents. The User layer is the root authority holder, equivalent to yourself; the Agent layer is the long-term agent authorized by you, with its own 'passport' and budget; the Session layer is a temporary session key, valid only within a certain task window. All these identities can be independently represented and tracked on-chain. When an agent initiates a transaction, it carries not only the address and signature but also its 'context' in this structure: who its root user is, its current session window, and what budgets and rules are in effect. Compared to Ethereum's model that only cares about msg.sender, this multi-layered identity embedding itself is a stance of 'keeping accounts for machines' — the chain must understand not just 'whose address this is,' but 'which agent is acting on behalf of whom in what context.'
Looking at the fee model, this is also one of the biggest differences between KITE and traditional L1s. Ethereum and Solana are typical 'native token pays Gas' models, where ETH and SOL are both assets and computing power rents, mixing security, speculation, and user experience together. Whether you are transferring USDC or calling an application completely unrelated to ETH/SOL, you must first buy some native coin to pay the tax; gas fees fluctuate with the price of the coin and network congestion, and the chain itself does not care whether you just want to pay a few cents in calling fees to a machine. KITE directly separates this layer: the underlying settlement uses PoS+PoAI to confirm block security, while the transaction fees on the user side are mainly in stablecoins, clearly stated in the documentation — assets like USDC, PYUSD can serve as stable valuation units, providing a predictable fee structure, while also reducing the cost of single messages to the level of $0.000001 through state channels. For AI agents, the significance of this design is that they can finally understand costs in the familiar 'dollar world': every API call, every message sent has a stable dollar price, rather than a string of gas numbers that fluctuate with market sentiment.
Many people will ask, what is $KITE doing? The design here resembles a two-layer economic system: stablecoins are responsible for 'keeping track of machine transactions,' while the KITE token is more tied to network security, governance, and module revenue sharing. The official explanation is that nodes need to stake KITE to participate in PoS consensus, and the ecosystem modules and the network will extract a portion of the commissions from each AI service transaction fee, flowing value back to the KITE pool through various mechanisms, forming 'revenue-driven network growth.' In other words, 'machine accounts' are kept in stablecoins, while 'equity accounts' are kept in KITE, with the two bridged together through the attribution logic of PoAI and fee distribution. This is a very different worldview from Ethereum's 'all value revolves around ETH.'
What truly made me feel the dividing line was solidified is the compliance aspect. The vast majority of general-purpose L1s view compliance as an 'off-chain issue': the chain is only responsible for being a neutral ledger, while KYC, AML, sanctions lists, MiCA, and similar matters are left to exchanges, custodians, and regulatory technology companies. Ethereum contracts can, of course, create whitelists and risk controls themselves, but that is more of a conscious application layer; the protocol itself does not participate much. KITE, in its latest series of materials, is almost the opposite: it separately submitted the white paper under the EU MiCAR framework, branding it as 'a compliance modular public chain ready for automated entities' and treating compliance as programmable logic in the architecture, rather than a post-fix. This means that KYC is no longer just 'something done by a centralized entry point,' but can be broken down into several checkpoints that can be executed and proven by machines: which wallets or agents must be under completely real-name entities, which transactions must be accompanied by certain proofs, and which jurisdictions require additional labels or reports.
With its three-layer identity model and trust primitives like 'Kite Passport,' the entire compliance process is integrated into the ledger: the same transaction not only records who paid whom how much but also states 'under what rule set this money was issued,' 'which compliance checks were triggered,' and 'whether it met a certain SLA.' For AI agents, this is the first opportunity to comply with the rules of the financial world using 'a language that machines can understand' at the protocol level, rather than relying solely on a human risk control team behind a closed API. Because of this, the recent article on Binance Square (Kite: The Compliance Engine for Machines Running Finance) describes this chain as 'compiling the most challenging parts of the financial world into logic executable by machines,' rather than simply calling it 'another L1.'
If I were to compress these differences into one sentence, I would say: the ledgers of Ethereum and Solana record 'what humans did on the chain,' while KITE's ledger records 'who the machine represents, and what it did within certain boundaries.' The former's units are addresses, transactions, and contract calls, while the latter's units are agents, tasks, and authorized actions. The fee model of the former revolves around a volatile native asset, while the latter consciously separates 'the cash flow of the intelligent economy' from 'the equity logic of the network itself,' carrying them on two tracks: stablecoins and KITE. The former outsources compliance modules to the surrounding ecosystem, while the latter attempts to integrate compliance into the transaction itself, allowing machines to be directly constrained by regulatory language.
These sound grand, but for those of us who need to pay for models, settle API transactions, and write a bit of automation scripts, it actually translates into very specific usage differences. As an ordinary user, I don't want to hoard a bunch of volatile gas tokens just to set a monthly budget of dozens of dollars for a few agents, nor do I want to be confused every time a bill comes in about which expense is for training, which is for inference, and which is for calls between agents. Under KITE's design, a more acceptable scenario for me is: I set monthly stablecoin budgets and compliance boundaries for 'work agents,' 'family agents,' and 'investment agents,' leaving the rest to them to run on-chain; each month, I open a reconciliation view and see 'which agent spent how much on what service, triggered which compliance clauses, and which tasks received additional rewards,' rather than a pile of incomprehensible Tx Hashes.
If you are a developer, the dividing line is even clearer: you can continue to deploy your products on general-purpose L1s like Ethereum and Solana, treating users as the main interaction object, using various abstract solutions to help them mask gas and signature details; or you can try to package your services as a 'service agent' with a passport, SLA, and auditable history on KITE, with the default caller being other agents rather than humans. In the first path, you are competing for human attention and entry traffic; in the latter path, you are competing for the opportunity to 'be included in the chain of intelligent contributions' — as long as enough AI agents repeatedly call you, the PoAI and fee distribution mechanism will naturally write some value back to your address.
From the entrepreneur's perspective, I would break down this 'keeping accounts for machines' story into three actionable items. The first is to honestly sketch out the ledger view of your current business and ask yourself: are you recording human behavior or machine behavior? If you find that all entries are still 'some user clicked a button on some page,' it indicates that your system is still highly human-driven, even if you are already using a bunch of models behind the scenes. The second is to pick a crucial AI interaction scenario you depend on most, and imagine if you fully entrusted it to agents, what steps would that transaction path on a chain like KITE be broken down into: which step requires identity, which requires real-time payment, and which needs compliance checkpoints inserted. You will more intuitively feel how much structured information 'a ledger oriented towards machines' adds. The third is to find an opportunity to personally try once, wrapping a small script into an 'agent,' allowing it to pay for a real call via some form of state channel and stablecoin fees in a test environment, then compare what differences it would have if that call record were written on Ethereum or Solana.
For me personally, the dividing line between KITE and traditional L1s does not lie in how high the TPS is or how large the ecosystem is, but rather in whether it dares to admit a fact: what runs on the chain in the future may no longer primarily be humans, but a whole group of mutually settling machines. If the ledger is still designed only according to human habits, machines will either be forced to masquerade as humans or be forever locked in some platform black box working; only when the ledger is willing to open up a separate narrative for machines do we have the opportunity to seriously discuss 'how money should be recorded in the AI era, how compliance should be done, and how value should be distributed.' KITE chooses to stand on the other side of this line, which is both a product choice and a bet: betting that there will be enough agents in the future worth rewriting a ledger for them.
