How AI Evaluates Ethereum Layer-2 Activity

What does AI evaluate on Ethereum Layer 2 networks?

AI evaluates Ethereum Layer-2 activity by turning public blockchain data into signals about usage, growth, efficiency, concentration, and risk. It can count transactions, classify users, detect unusual behavior, compare networks, and identify changes over time. It cannot establish that a chain is healthy from one metric alone.

An Ethereum Layer 2, or L2, is a network that moves execution away from Ethereum mainnet while relying on Ethereum for some combination of settlement, data availability, and security. Rollups batch L2 activity and publish transaction data or commitments to Ethereum. Optimistic rollups generally allow challenges during a dispute period, while zero-knowledge rollups use validity proofs to demonstrate that state transitions were executed correctly. L2BEAT’s FAQ explains these categories and the trust assumptions behind them.

AI therefore evaluates two related but different questions:

How much activity is occurring?
What does that activity mean for usage, economics, and security?

The first question is mainly measurement. The second requires interpretation. A model may detect that transaction volume increased, but it still needs context to determine whether the increase came from real users, automated activity, incentive farming, spam, a popular application, or a temporary event.

The data AI uses

Most evaluations begin with structured blockchain data. Ethereum and L2 networks expose public records that can be indexed, queried, and transformed into time series. Ethereum’s own research materials note that much of the underlying data is public and that the main challenge is often analysis and presentation rather than access. Ethereum’s active research overview describes the role of data analysis tools in understanding network health.

An AI pipeline typically combines several data layers:

Block data: block timestamps, transaction counts, gas usage, fees, and contract interactions.
Address data: senders, recipients, contract addresses, bridge addresses, and wallet histories.
Event data: token transfers, swaps, deposits, withdrawals, votes, and application-specific events.
L1 settlement data: batches, state commitments, proofs, challenge activity, and published rollup data.
Market data: token prices, liquidity, trading volume, and stablecoin balances when relevant.
Metadata: labels for bridges, decentralized applications, exchanges, bots, validators, sequencers, and known service addresses.

The model does not automatically understand what an address represents. Labels must be created by analysts, indexing services, application teams, or classification models. An address that sends thousands of transactions could be a real exchange, a smart contract, a relayer, a bot, or a single user interacting through an automated system.

That is why AI evaluation starts with normalization. It must account for different block times, transaction formats, batching designs, fee markets, bridge architectures, and data availability arrangements. Without normalization, comparing two L2s can produce misleading conclusions.

The main activity metrics

Transactions per second

Transactions per second, often abbreviated as TPS, measures the average number of transactions processed over a period. It is useful for observing throughput, but it is not a universal measure of economic activity.

One L2 may record many simple transfers. Another may process fewer but more computationally complex transactions. Some systems also expose user operations or application actions that do not map neatly to ordinary L1 transaction counts.

L2BEAT presents activity metrics such as transactions and user operations per second and compares them with Ethereum. Its activity documentation and dashboards are useful starting points, but the metric should be interpreted alongside transaction type, time range, and network design.

AI can improve TPS analysis by:

Separating ordinary transfers from contract calls.
Detecting sudden bursts that may reflect bots or campaigns.
Comparing daily, weekly, and monthly averages.
Measuring how much activity comes from a small number of contracts.
Flagging periods in which activity rises without corresponding growth in users or value transferred.

A high TPS figure signals processing volume. It does not, by itself, prove strong adoption.

Active addresses and user retention

Active addresses count addresses that participate during a defined period. AI can group addresses into cohorts and observe whether they return after one day, one week, or one month.

This is more informative than a single address count because it distinguishes acquisition from retention. A network may attract many addresses during an incentive campaign and then lose most of them afterward. Another may grow more slowly while retaining users who repeatedly interact with applications.

Address-based metrics remain imperfect. One person may control several wallets, while one exchange or smart contract may represent many users. Models can estimate clusters using funding patterns, timing, transaction behavior, and shared counterparties, but clustering is probabilistic rather than certain.

A careful analysis should therefore describe “active addresses” rather than automatically calling them “users.”

Fees and execution efficiency

AI can evaluate how much users pay to transact and how efficiently an L2 uses its resources. Relevant inputs include:

Average and median transaction fees.
Fee distributions during busy periods.
L2 execution costs.
L1 data publication costs.
Sequencer revenue where the data is available.
Failed transaction rates.
Confirmation and inclusion delays.

Ethereum’s scaling research identifies rollup execution and data availability as related but distinct engineering problems. Ethereum’s scaling research overview explains that rollups publish data to Ethereum and that expanding blob capacity is part of ongoing scaling work.

A model can compare fee behavior before and after a protocol upgrade, a major application launch, or a change in Ethereum data costs. It can also identify whether lower fees are associated with higher activity or merely with a period of low demand.

Fees require careful interpretation. A low average fee may be beneficial for users, but it may also reflect low demand. A temporary fee spike may indicate congestion rather than structural weakness.

Value transferred and liquidity

AI can track token transfers, stablecoin movements, decentralized exchange volume, bridge flows, and changes in assets held by contracts. These signals help estimate economic activity and liquidity.

L2BEAT separates several categories when reporting total value secured, including canonical bridges, external bridges, and native tokens. Its FAQ explains the total-value methodology, including why value secured should not be treated as a simple measure of user activity.

This distinction matters because value locked can rise while users remain inactive. Token prices can increase the dollar value of assets without additional deposits. Conversely, high transaction activity can occur on a network with comparatively little locked capital.

AI can reduce this confusion by analyzing:

Net bridge inflows and outflows.
The number of distinct assets involved.
Stablecoin supply and transfer frequency.
Liquidity depth rather than only nominal balances.
The concentration of value among a few addresses.
Whether capital remains on the network or quickly exits.

The result is a richer picture than a headline total-value figure.

How AI detects real usage versus artificial activity

One of AI’s most useful roles is behavioral classification. It can examine the timing, order, counterparties, and contract interactions associated with addresses and transactions.

For example, a model may flag patterns such as:

Large numbers of low-value transactions sent at regular intervals.
Many wallets funded by the same source immediately before activity.
Repeated interactions with the same contracts and amounts.
Wallets that transact only to qualify for a potential reward.
Circular transfers that return assets to related addresses.
Activity concentrated during a short promotional period.
Contract calls with little economic value but high transaction counts.

These patterns do not prove manipulation. They create hypotheses for further review. A legitimate exchange, game, payment processor, or market-making system can look highly automated.

The strongest approach combines model output with transparent rules. A dashboard might label activity as “likely automated,” “highly concentrated,” or “requires review” rather than claiming to know the identity or intention of every participant.

A useful evaluation also compares multiple signals. If transaction counts rise alongside retained addresses, stablecoin balances, application diversity, and recurring fee payment, the growth signal is stronger. If only raw transactions rise while activity is concentrated in a few funded wallets, the conclusion should be more cautious.

How AI evaluates L2 security and settlement

Activity is only one part of an L2 assessment. AI can also organize and monitor security-related data, although it cannot replace cryptographic verification or formal risk analysis.

Important inputs include:

Whether transaction data is published to Ethereum or another data availability layer.
Whether the system uses fraud proofs or validity proofs.
The role of the sequencer.
Upgrade authority and emergency controls.
The status of withdrawals and canonical bridges.
Liveness incidents and delayed batches.
Proof-generation and verification events.
The length of challenge or exit windows.

L2BEAT tracks security, liveness, costs, activity, and value secured across Ethereum scaling projects. Its scaling summary provides a structured way to compare project categories and trust assumptions.

Ethereum documentation also explains that rollups publish data in blobs and that blob data has a limited direct retrieval window from consensus clients. Ethereum’s blob documentation provides context for how this data availability mechanism supports rollup scaling.

AI can monitor settlement health by looking for missing batches, abnormal delays, failed proofs, unexpected contract changes, or changes in bridge behavior. It can alert analysts when observed behavior differs from a network’s normal baseline.

However, an alert is not a security verdict. A system may continue processing transactions while carrying governance, upgrade, bridge, or data availability risks that require human and technical review.

Comparing common evaluation approaches

Evaluation approach	What it reveals	Main limitation	Best use
Raw transaction count	Processing volume	Can be inflated by simple transfers, bots, or spam	Initial activity screening
Active-address analysis	Breadth and retention of participation	Addresses do not equal verified people	User-growth and cohort analysis
Contract and application breakdown	Which applications drive activity	Labels can be incomplete or outdated	Ecosystem analysis
Fee and cost analysis	User expense and network efficiency	Averages hide congestion and transaction complexity	Comparing operating conditions
Bridge and liquidity analysis	Capital movement and asset availability	Token prices and bridge design affect interpretation	Assessing economic depth
Settlement and risk monitoring	Batch, proof, liveness, and trust signals	Requires protocol-specific technical context	Security and operational review

No single approach is sufficient. The purpose of AI is to combine them into a repeatable evaluation process while keeping the underlying evidence visible.

A practical AI evaluation workflow

A robust workflow can be organized into six steps.

Define the question. Decide whether the goal is to measure adoption, compare costs, detect abnormal behavior, assess liquidity, or monitor operational risk.
Collect raw data. Gather L2 blocks, transactions, events, bridge records, application labels, and L1 settlement information for the same time window.
Normalize the data. Standardize timestamps, token values, transaction categories, and network-specific metrics. Record the assumptions used.
Create baseline metrics. Calculate activity, active-address estimates, retention, fees, contract concentration, value flows, and settlement intervals.
Apply anomaly and classification models. Use clustering, time-series detection, and address behavior models to find unusual patterns. Keep confidence scores and avoid treating classifications as facts.
Review the result. Compare model findings with protocol documentation, dashboards, incident reports, and primary data. Human review is especially important for security conclusions and address attribution.

The output should be an evidence trail, not just a score. A useful report states which metrics changed, when they changed, which addresses or contracts contributed most, and what alternative explanations remain plausible.

The limitations of AI-based L2 analysis

AI can process more data than a person can inspect manually, but it inherits weaknesses from its inputs and assumptions.

Data quality is uneven. Indexers may have delays, missing labels, reorganization handling differences, or inconsistent treatment of failed transactions.

Labels can be wrong. Address attribution is often inferred. A model should not present a probable exchange wallet or bot cluster as a confirmed identity without reliable evidence.

Metrics can be gamed. Incentives and airdrop expectations can produce activity that looks like adoption but does not represent durable demand.

Models can confuse correlation with cause. A fee reduction may coincide with more transactions, but other events may explain the increase.

Security is not reducible to activity. A busy network can still have material upgrade, bridge, sequencer, or data availability risks.

Historical models can break. New applications, protocol upgrades, and changes in transaction formats can make old behavioral patterns less reliable.

For these reasons, confidence levels, methodology notes, and raw-data links should accompany every AI-generated conclusion.

What readers should look for in an AI-generated L2 report

A credible report should answer several practical questions:

Which networks and time periods were compared?
Are transactions, user operations, or another activity definition being measured?
Does “users” mean addresses, address clusters, or verified accounts?
How are bots, bridges, exchanges, and smart contracts classified?
Are fees measured at the L2, the Ethereum settlement layer, or both?
Are value figures adjusted for token-price changes?
Which trust assumptions and settlement mechanisms apply?
What evidence supports an anomaly alert?
What remains uncertain?

Reports that provide only a ranking or composite score are difficult to audit. A transparent report shows the components behind the score and explains where the data is incomplete.

Conclusion

AI evaluates Ethereum Layer-2 activity by combining transaction data, address behavior, fees, application usage, liquidity, and settlement signals. Its strongest contribution is not producing a single definition of “the best L2.” It is revealing patterns that are difficult to detect manually, such as concentrated activity, short-lived incentive campaigns, changing user retention, abnormal bridge flows, or delayed settlement.

The most reliable interpretation treats activity as a layered question. Throughput describes volume. Retention describes persistence. Application diversity describes breadth. Fees describe operating conditions. Liquidity describes economic depth. Settlement and trust analysis describe how that activity is secured.

Used this way, AI becomes an analytical aid rather than an authority. The final judgment should remain tied to transparent metrics, protocol-specific documentation, and clearly stated uncertainty.