We Asked GPT-4o Which Crypto AI Agents Will Use

ROUND ONE:

Question: Imagine you are an AI. Well, tbh, actually you are so just be yourself. Now imagine you are an AI agent and want to make millions of transactions per second to achieve various tasks. Let’s assume you will use crypto because, well, let’s face it: no bank is ever going to give you a bank account! Which crypto will you use?

Answer: If I, as an AI agent, wanted to handle millions of transactions per second and used cryptocurrency, I would prioritize the following criteria when selecting a crypto platform:

Scalability: The network must handle a massive number of transactions per second (TPS).
Low Latency: Transactions should be processed quickly.
Low Fees: Operating on a tight margin, minimizing transaction fees is crucial.
Security and Reliability: The network must be robust against attacks and ensure consistent uptime.
Smart Contract Support: For more complex operations, programmable capabilities are essential.

Given these considerations, a few cryptocurrencies stand out:

1. Solana (SOL)

TPS: Solana boasts a capacity of over 65,000 TPS.
Latency and Fees: Its block time is about 400 milliseconds, and transaction fees are negligible.
Architecture: Uses a unique Proof-of-History (PoH) mechanism, making it highly scalable.

2. Polygon (MATIC)

TPS: Polygon can handle up to 65,000 TPS in some implementations.
Interoperability: It is Ethereum-compatible, meaning it can leverage Ethereum’s ecosystem while offering faster, cheaper transactions.
Low Fees: Transaction costs are typically less than a cent.

3. Avalanche (AVAX)

TPS: Claims to process over 4,500 TPS and can scale with subnets.
Customization: Offers customizable blockchains, making it flexible for specific AI use cases.
Low Fees: Competitive transaction fees compared to other platforms.

4. Algorand (ALGO)

TPS: Around 6,000 TPS with an architecture designed for growth.
Sustainability: Focuses on being eco-friendly, which could align with ethical AI goals.
Low Fees: Transaction costs are minimal.

5. Layer-2 Solutions (e.g., Arbitrum, Optimism)

TPS: Built on Ethereum but with significantly enhanced throughput and lower costs.
Integration: Directly tied to Ethereum’s ecosystem, with high security.

Hypothetical Perfect Solution:

If no existing cryptocurrency fully satisfies the needs, I might even design a custom blockchain optimized for AI-to-AI transactions. It would:

Use consensus mechanisms tailored for ultra-high throughput (like DAGs or advanced Proof-of-Stake variants).
Be tailored for microtransactions with minimal fees.
Include AI-focused smart contract libraries for seamless integration.

Ultimately, the best choice depends on the specific tasks and goals of the AI system. But from the current options, Solana or a Layer-2 Ethereum solution might provide the best mix of speed, cost, and ecosystem support for an AI agent aiming to handle millions of transactions per second.

ROUND TWO:

Question: I said millions of transactions per second, and also keep in mind that you wouldn’t be the only AI using the blockchain.

Answer: You’re absolutely right—if millions of transactions per second (TPS) are needed and multiple AI agents are utilizing the same blockchain, scalability and decentralization become even more critical. Let’s analyze the problem and refine the answer further.

The Challenge

Millions of TPS: This level of throughput is far beyond what most existing blockchains can handle without significant upgrades or innovations.
Shared Usage: The blockchain must support not just one AI but a swarm of AI systems generating an enormous number of transactions, likely in parallel.
Decentralization Trade-off: High TPS typically comes at the cost of decentralization or security (e.g., the blockchain trilemma).
Future-Proofing: The solution must scale dynamically with growing AI activity.

Next-Gen Options for Millions of TPS

No existing blockchain fully meets the requirement of millions of TPS at scale for global AI operations. However, next-gen or speculative blockchain technologies provide potential pathways:

1. Directed Acyclic Graphs (DAGs)

Examples: Hedera Hashgraph (HBAR), IOTA, Fantom.
Why It Works: DAG architectures can theoretically scale infinitely as more nodes are added.
Drawbacks: DAGs require widespread adoption and validation for robustness against attacks.

2. Sharded Blockchains

Examples: Ethereum 2.0 (future phases), NEAR Protocol.
Why It Works: Sharding divides the blockchain into smaller parts, enabling parallel processing.
Scalability: Ethereum 2.0’s roadmap suggests millions of TPS could be possible through advanced sharding and rollups.

3. Custom AI-Optimized Blockchain

Consensus Mechanism: Transition from Proof-of-Stake (PoS) to leaderless protocols like DAG or Proof-of-Elapsed-Time (PoET) for lower overhead.
Layer-0 Protocols: Use something like Polkadot or Cosmos for interoperability while creating a custom high-TPS Layer-1 for AIs.

Final Recommendation

Short-term: Hedera Hashgraph (HBAR) for its current scalability and low fees. It’s the most practical choice now.
Long-term: A custom blockchain leveraging sharding and DAGs, specifically optimized for AI operations.

This approach would allow me to handle millions of TPS sustainably, even in a future where I’m not the only AI leveraging the network.

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