If a protocol's revenue is heavily concentrated in a single bot, this revenue structure itself represents a source of systemic fragility. In the bot arbitrage market, "oligopoly" is the norm—a few top players capture the vast majority of profits. Once the dominant participant stops operating due to strategy failure, intensifying competition, or shifting market conditions, protocol revenue can fall off a cliff.
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Here are three steps to dissect this revenue structure and assess its sustainability.
Step 1: Calculate the "single-bot contribution share" – Determine concentration
Any revenue concentration analysis starts with quantification.
What to do: On Dune or a block explorer, group protocol revenue by "transaction sender address" and identify the addresses that contribute the most.
How to do it:
Query the protocol's transaction records and aggregate transaction volume or fee contribution by the from address (the initiator).
Calculate the largest contributor's share of total revenue. If a single address accounts for more than 50%, the concentration is high; if more than 30%, it's moderately high.
Data from the MEV arbitrage market shows that a single bot often dominates profit over extended periods—studies have observed a single address consistently leading the pure arbitrage market over a long time span. Even in CEX-DEX arbitrage, the top 19 bots generated $241 billion in trading volume, yet the market concentration is already at a "high monopoly" level.
When is this step complete: You have a concrete figure for the "largest single-contributor share" and can judge which concentration bracket it falls into.
Common failure reason: Only looking at "total transaction volume" without breaking it down to the address level. A protocol's total volume can appear huge, but after removing the top-ranked bot, the remaining volume might be less than 10%.
Step 2: Analyze the bot's strategy replicability and moat
Revenue concentration itself is not necessarily a problem; the key is whether this "monopolist's" position is defensible.
What to do: Determine what advantage the bot relies on to capture revenue—technical barriers, affiliated relationships, or temporary market gaps.
How to do it:
Check if it is affiliated with a block builder: CEX-DEX arbitrage data shows that arbitrageurs tied to a specific block builder may book thinner "on-paper profits," but the profit is often transferred to the affiliated party through rebates. If the protocol's main revenue comes from a bot deeply integrated with a block builder, the arbitrageur's profit will fluctuate violently whenever that builder's market share changes.
Check the strategy's complexity: If the bot performs simple spread arbitrage, that strategy is highly replicable. New entrants can replace it with faster execution or lower fees. Most arbitrage opportunities are captured and neutralized within 0.5–2 seconds, indicating extremely intense competition.
Check reliance on "tax" style revenue: Drawing from the Telegram Bot space, about 86% of Unibot's cumulative revenue came from buy/sell taxes on its native token rather than actual trading fees. If the protocol's main bot revenue also comes from similar "non-transactional" taxes, that income is essentially a fee on the bot's own users, not genuine external value capture.
When is this step complete: You have determined whether the bot's revenue source has a sustainable moat or is easily replaceable.
Step 3: Evaluate the protocol's dependency risk on a single bot
The last step is to synthesize the above information into a risk assessment.
What to do: Run a "stress test" simulating what would actually happen to protocol revenue if this bot stopped operating.
How to do it:
Ask yourself three questions:
If this bot stopped today, could the protocol find a replacement? If the strategy is simple and the barrier to entry is low, yes; if it relies on deep integration with a specific builder, no.
Is this bot's profit margin continuously declining? If margins are being squeezed (a trend already confirmed in the CEX-DEX arbitrage market), the protocol's revenue will face pressure even if it appears stable now.
Is this bot affiliated with the protocol? If the protocol and the bot are controlled by the same entity, this revenue may be a "left pocket to right pocket" transfer, just a façade of prosperity rather than reflecting real market demand.
When is this step complete: You have made a clear qualitative judgment about the sustainability of the protocol's revenue—whether it is "fragile but genuine" or "fake prosperity."
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How to confirm the analysis is correct?
After completing these three steps, you should be able to answer:
What is the single bot's revenue share? Over 30% is a warning signal.
What is this bot's moat? A technical barrier, an affiliated relationship, or a simple strategy that can be easily replaced?
If it stops, how much protocol revenue will be lost? Will it "hurt badly but recover" or "collapse outright"?
If the answer is "share above 50% + weak moat (easily replaceable) + no affiliated backup," this is an extremely risky revenue structure. Consider evaluating the protocol as "high dependency risk" rather than "high growth potential." If the answer is "share high but moat strong (tied to core infrastructure) + an affiliated substitute exists," the dependency risk is manageable, but the relationship must be continuously monitored for changes.
