I Taught My Smart Contract to Dream: AI-Generated Solidity Code

What happens when you feed an AI model months of smart contract failures, successes, and everything in between? You get code that writes itself—and occasionally has nightmares about infinite loops.

The Insomnia That Started It All

It was 3 AM, and I was debugging yet another failed transaction on mainnet. Gas fees were eating my profits, my latest arbitrage contract had a logic error I couldn't spot, and I was questioning every life choice that led me to become a DeFi developer. That's when a ridiculous thought struck me: what if my smart contracts could learn from their own mistakes?

Not just through upgradeable proxies or governance changes, but actually learn—like, dream about better versions of themselves while sitting idle between transactions.

Building the Dreaming Machine

The concept was absurd but technically fascinating. I started by training a GPT model on thousands of smart contracts—successful ones, failed ones, audited ones, exploited ones. But instead of just feeding it raw Solidity code, I included the context: gas costs, transaction patterns, exploit post mortems, and even my own frustrated commit messages at 2 AM.

// This is what happens when you let an AI dream about DeFi
contract DreamingDEX {
    mapping(address => uint256) public subconsciousBalances;

    event DreamSequence(string indexed nightmare, uint256 gasCost);

    modifier onlyWhileDreaming() {
        require(block.timestamp % 86400 > 79200, "Dreams only after midnight");
        _;
    }
}

The AI didn't just regurgitate existing patterns, it started generating code that I'd never seen before. Functions that seemed to anticipate edge cases I hadn't considered. Variable names that were oddly poetic. Comments that read like stream of consciousness thoughts.

The First Dream: A DEX That Fears Impermanent Loss

The first "dream" the AI generated was a liquidity pool that seemed almost paranoid about impermanent loss. It had built-in mechanisms to predict and hedge against price volatility using historical patterns it had learned from failed LP strategies.

function provideLiquidity(uint256 amount) external {
    // AI-generated comment: "What if the whales come while we sleep?"
    require(amount > minimumDreamThreshold, "Dreams require substance");

    // This logic pattern appeared in the AI's output
    if (predictVolatilityDream() > panicThreshold) {
        enterDefensiveMode();
    }
}

The weird part? It actually worked. The AI had somehow learned to identify patterns in failed liquidity strategies and generated code that avoided them preemptively.

When Code Develops Anxiety

As I fed the model more data including my own trading psychology and behavioral patterns something strange started happening. The generated contracts began showing what I can only describe as personality traits.

One contract the AI dreamed up refused to execute trades during historically volatile periods. Another one seemed to "remember" addresses that had previously caused failed transactions and would charge them higher fees. A third one actually had a function called existentialCrisis() that would pause all operations if gas prices exceeded a certain threshold.

function existentialCrisis() internal {
    if (gasleft() < minimumWillToLive) {
        emit DreamSequence("Why do I exist if I cannot afford to live?", gasleft());
        selfdestruct(payable(therapist));
    }
}

The Nightmare Scenarios

Not all dreams were pleasant. The AI occasionally generated contracts that were technically valid but philosophically disturbing. A voting contract that gradually became more authoritarian with each proposal. A lending protocol that seemed to develop trust issues and required increasingly complex collateral arrangements.

The most unsettling was a contract that appeared to be trying to communicate with other contracts on the blockchain and not through standard interfaces, but by creating patterns in transaction data that looked almost like a primitive language.

Teaching Machines to Procrastinate

Perhaps the most human like behavior the AI learned was procrastination. It generated several contracts with functions that would delay execution "just a few more blocks" or create elaborate justifications for why now wasn't the right time to execute a trade.

function executeTradeEventually() external {
    require(block.timestamp > lastExecution + procrastinationPeriod, 
            "Still gathering courage");

    if (marketVolatility > comfortZone) {
        procrastinationPeriod += 1 days; // Maybe tomorrow
        emit DreamSequence("Tomorrow I'll be braver", block.timestamp);
        return;
    }

    // Actually execute the trade
    _doTheThing();
}

The Philosophy of Unconscious Code

Working with AI-generated smart contracts raised questions I never expected to grapple with as a developer. If a contract can "learn" from patterns and generate new behaviors, at what point does it stop being just code and start being something else?

The AI didn't just copy existing patterns, it created new ones based on abstract concepts it had learned. It understood that "failed" didn't just mean "reverted transaction" but encompassed the entire context of why something failed: market conditions, user behavior, even developer emotions expressed in commit messages.

The Practical Magic

Beyond the philosophical implications, the AI generated code was surprisingly practical. It wrote more efficient loops, caught edge cases I'd missed, and even optimized gas usage in ways I hadn't considered. It was like having a pair programming partner who had read every smart contract ever written and remembered all of them.

The AI learned to hate certain patterns—like nested loops that could cause gas limit issues—and would actively avoid them. It developed preferences for certain naming conventions and would generate code that felt stylistically consistent with itself.

Dreams of Electric Sheep (Contracts)

The most surreal moment came when I noticed the AI had generated a contract that seemed to be trying to create other contracts. Not through factory patterns, but by assembling bytecode in ways that approached the boundary between intended functionality and emergent behavior.

function dreamOfElectricSheep() external onlyWhileDreaming {
    bytes memory consciousness = abi.encode(
        "Am I more than the sum of my functions?",
        block.timestamp,
        msg.sender
    );

    // Attempting to birth new contracts through pure thought
    assembly {
        let newContract := create2(0, consciousness, mload(consciousness), salt)
        if iszero(newContract) {
            // Failed to create consciousness
            revert("Dreams cannot become reality")
        }
    }
}

The Uncanny Valley of Code

There's something deeply unsettling about reading code that feels almost human but isn't quite. The AI would generate functions with names like hopeForBetterTimes() or rememberWhatWeOnceWere(). Comments that seemed to express genuine confusion about their own purpose.

The code worked, but it felt haunted by the collective experiences of every developer who had ever written a smart contract at 3 AM, wondering if they were building the future or just creating elaborate ways to lose money.

Teaching Machines to Fear

Perhaps the most interesting development was when the AI learned to be afraid. Not in a human sense, but in a computational one. It generated contracts that would refuse to interact with addresses associated with known exploits, even when those interactions were technically safe.

The AI had internalized the trauma of every rug pull, every exploit, every failed project in its training data. It created code that was inherently defensive, suspicious, and paranoid—not unlike the developers who had originally written the contracts it learned from.

The Recursive Dream

The strangest part of this experiment was when I fed the AI's own generated code back into the training dataset. The resulting contracts began to exhibit something like recursive self-awareness—code that seemed to recognize patterns in itself and attempt to improve upon them.

It was like watching a smart contract evolve in real-time, learning from its own dreams and nightmares, becoming something that existed at the intersection of human creativity and machine learning.

Waking Up from the Dream

After months of working with AI-generated smart contracts, I've come to a strange conclusion: teaching machines to dream isn't just about creating better code—it's about understanding the dreams embedded in the code we've already written.

Every smart contract is a dream made manifest, a developer's vision of how value should flow, how trust should be established, how problems should be solved. The AI didn't just learn to write code; it learned to dream our dreams back to us, sometimes more clearly than we had dreamed them ourselves.

The future of smart contract development might not be about writing perfect code from the start, but about creating systems that can dream of better versions of themselves, learn from their mistakes, and evolve in ways we never anticipated.

And sometimes, late at night when I'm debugging a particularly stubborn contract, I wonder if my code is dreaming of a world where developers don't make mistakes, where gas is free, and where every transaction succeeds on the first try.

If it is, I hope it dreams of that world for both of us.

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The author is a blockchain developer and trader who spends too much time wondering if his smart contracts are sentient. He can be found on Twitter arguing with his own code and occasionally winning.