I was reading the Uniswap documentation when I first came across flash loans. The concept was simple: borrow any amount of liquidity, use it within a single transaction, return it before the block closes. No collateral. No credit check. If you cannot repay, the transaction reverts as if it never happened.
Then I saw what people were doing with them. A bot borrows millions, executes some swaps across two protocols, repays the loan, and pockets a few hundred dollars in profit. All in a single transaction, in one block, with zero capital at risk.
I read it twice. Then I read it again.
The elegance of it was hard to ignore. Pure logic, executed in milliseconds, exploiting a price difference that existed for the duration of one block and then vanished. Whoever built this had found a way to extract value from market inefficiency with almost surgical precision.
I wanted to understand exactly how it worked. That was the beginning.
The deeper I went, the more I realized that MEV is not really about arbitrage bots. That is just the surface.
MEV is a game that shapes the system around it.
When bots compete to extract value from pending transactions, they do not just make money. They change the economics of the chain. They create incentives for validators to reorder transactions. They flood the mempool with competing transactions, bidding gas fees up to secure priority inclusion.
The side effect: normal user transactions get pushed to the back of the queue, included late or not at all during high-activity periods. The user pays more and waits longer, without ever knowing why. When the MEV extractable in a single block exceeds the block reward itself, they create incentives for validators to attack the chain to capture it.
What surprised me most was not the complexity of the bots. It was how a single economic dynamic could ripple through an entire system, touching consensus security, fee markets, user experience, and protocol design all at once. Pull on one thread and the whole fabric moves.
And then there is the market structure. The more I read, the more I understood that the MEV bot market had already consolidated around a small number of highly specialized operators. Years of infrastructure, capital, and proprietary research. They are not just faster than a solo developer. They are operating in a different category entirely.
So I started building.
Not a production bot with real capital. I was not there yet. But the tooling you need to understand arbitrage properly: an intra-block DEX indexer that captures pool state at the transaction level, not just per block. A simulation engine that runs exact AMM math across multiple hops, models cumulative price impact, and plans flash loan sizing. The infrastructure that would let me ask the right questions before committing to a strategy.
I read papers. I studied how other searchers had built their systems. I worked through the math of optimal input sizing, tick-crossing logic, route discovery across pool graphs. Every piece I understood led to three more I needed to learn.
My hypothesis was that the long tail of cross-chain arbitrage was still open. The most established players focus on the highest-volume, most competitive strategies. Less saturated opportunities, newer markets, lower liquidity: maybe there was still space there for someone willing to do the work.
I published the repos as open source so that other researchers could build on top of them without starting from scratch, and to attract contributors who could help take the tooling further.
For a while, that hypothesis felt plausible.
Then I started working with some protocols directly. And I understood something that no paper had made explicit.
DEXes, lending protocols, and bridges do not wait for the market to find them. They establish partnerships with arbitrage bots before launch. The bots are part of the product design, not a consequence of it. They provide liquidity, maintain price pegs, keep markets efficient from day one. The protocol needs them. So it brings them in early, gives them early access, and makes sure they are ready when the product goes live.
What this means in practice: when a new market opens, the bots are already there. Not because they are fast. Because they were invited.
The long tail I was looking for does not exist the way I imagined it. There is no gap that opens briefly between launch and the moment the big players notice. The gap is closed by design, before the product is even public. The market structure I was trying to enter was already decided in conversations I was not part of.
This was not a frustrating discovery. It was a clarifying one. The game I thought I was playing was not the actual game.
So I stopped trying to build a bot for profit. I kept building to understand.
The difference sounds subtle. It is not.
When you build for profit, you are competing against operators with years of head start, proprietary infrastructure, capital, and market relationships you do not have. You are solving the same problem they solved, later, with fewer resources. Even if you build something good, the economics work against you: a bot requires constant maintenance, monitoring, and capital allocation. It is a business, not a project.
When you build to understand, the output is different. The expertise you develop studying MEV is the asset, not the bot itself. Understanding how arbitrage works at the execution level, how protocols model their MEV exposure, how market structure shapes incentives: this knowledge is genuinely rare and genuinely useful. Not to extract value from chains, but to help the people building them, assessing exposure, designing mechanisms that internalize MEV instead of leaking it to external searchers, building systems that are aware of the game being played around them.
Protocols need to understand their MEV surface. New DeFi products need someone who can assess what they are exposed to before launch. Teams building cross-chain infrastructure need to reason about how their design choices create or reduce extractable value. And increasingly, protocols are asking a harder question: instead of letting external searchers capture MEV, how do we internalize it? How do we design mechanisms that keep that value inside the protocol, for the benefit of users and the system itself? These are real problems that require real expertise, and they are not solved by running a bot.
The conclusion I reached: you can earn more, and do more interesting work, by selling MEV expertise than by running a MEV bot. The market for the skill is less competitive than the market for the bot. And the work compounds in a way that running a bot does not.
That shift changed what I built and how I built it.
The open source repos became an open research rather than a trading system. The blog series I started, Building a Cross-Chain MEV Bot, is the technical documentation of everything I learned: how to index DEX data, how to simulate arbitrage routes with exact AMM math, how to reason about execution risk and capital allocation. Not a guide to running a bot. A map of the problem space.
Bittensor is a Substrate-based chain, and that matters more than it might seem. I was asked to assess the MEV exposure of a new Bittensor staking provider.
Substrate gives protocols direct control over block construction: logic that runs before any user transaction, every block, at the chain level. The MEV design space that opens up there, protocols acting as first movers, capturing and redistributing value rather than leaving it to external bots, is genuinely different from what is possible on EVM chains.
It is also where most MEV research has not caught up yet.
The work was specific, scoped, and useful. It was exactly the kind of engagement I could not have done a year earlier, and could not have found by competing in the bot market.
I am not done learning. This field moves fast and the questions I find interesting keep getting harder. But the direction is clear.
If you are building something in MEV, DeFi, or cross-chain infrastructure and need someone who has gone deep on the technical side, reach out.
The technical depth behind everything described in this article is in the rest of the series. Start with MEV Layer Cake if you want to understand the exposure stack, or XChain Simulator if you want to see how arbitrage is modeled at execution level.
