Exploring how AI and cryptocurrency converge through decentralized AI networks, on-chain machine learning, and practical implementation strategies
We're witnessing a pivotal moment in technology where two of the most transformative innovations of the 21st century—artificial intelligence and blockchain—are beginning to converge in meaningful ways. While AI has revolutionized how we process information and make decisions, and crypto has reimagined digital ownership and decentralized systems, their intersection promises something entirely new: decentralized AI networks that combine the computational power of machine learning with the transparency, immutability, and distributed nature of blockchain technology.
This convergence isn't just theoretical. We're seeing real projects emerge that leverage blockchain to democratize AI training, create verifiable on-chain inference, and build decentralized marketplaces for AI compute and models. But why now? The timing is driven by several factors: the astronomical costs of centralized AI infrastructure, growing concerns about AI model bias and control, and the maturation of blockchain technology to handle computationally intensive workloads.
In this post, we'll explore how decentralized AI networks work, examine current implementations, understand the technical and economic challenges, and look ahead to what this convergence means for the future of both industries.
The current AI landscape is dominated by a handful of tech giants—OpenAI, Google, Anthropic, Meta—who control:
This centralization creates several problems:
Blockchain technology offers potential solutions:
zkML uses zero-knowledge proofs to verify that an AI inference was computed correctly without revealing the model weights or input data.
How it works:
Example projects:
// Simplified zkML verification contract
contract ZKMLVerifier {
mapping(bytes32 => bool) public verifiedInferences;
function verifyInference(
bytes32 modelHash,
bytes calldata input,
bytes calldata output,
bytes calldata proof
) external returns (bool) {
// Verify ZK proof
bool valid = zkVerifier.verify(proof, [modelHash, hash(input), hash(output)]);
if (valid) {
bytes32 inferenceId = keccak256(abi.encodePacked(modelHash, input, output));
verifiedInferences[inferenceId] = true;
emit InferenceVerified(inferenceId, modelHash);
}
return valid;
}
}These platforms create two-sided marketplaces connecting GPU providers with AI compute consumers.
Architecture:
Leading projects:
# Example: Requesting compute on a decentralized network
from decentralized_compute import ComputeMarket
market = ComputeMarket(network="io.net")
# Define compute requirements
job = {
"type": "training",
"model": "transformer",
"dataset": "ipfs://Qm...",
"epochs": 100,
"gpu_type": "A100",
"gpu_count": 8,
"max_price": "100 tokens/hour"
}
# Submit job to marketplace
job_id = market.submit_job(job)
# Monitor progress
while not market.is_complete(job_id):
status = market.get_status(job_id)
print(f"Progress: {status.progress}%")
time.sleep(60)
# Retrieve trained model
model_cid = market.get_result(job_id)
print(f"Model saved to IPFS: {model_cid}")Federated learning trains models across decentralized devices without centralizing data. Blockchain adds coordination and incentives.
Process:
Projects:
AI agents that operate autonomously on-chain, making decisions and executing transactions.
Use cases:
// Example: On-chain AI agent for DeFi
contract AITradingAgent {
address public modelVerifier;
uint256 public treasuryBalance;
struct TradeDecision {
address token;
uint256 amount;
bool isBuy;
bytes32 modelOutput;
bytes zkProof;
}
function executeTrade(TradeDecision calldata decision) external {
// Verify the trade decision came from the AI model
require(
IZKVerifier(modelVerifier).verifyDecision(
decision.modelOutput,
decision.zkProof
),
"Invalid AI decision proof"
);
// Execute trade
if (decision.isBuy) {
buyToken(decision.token, decision.amount);
} else {
sellToken(decision.token, decision.amount);
}
emit TradeExecuted(decision.token, decision.amount, decision.isBuy);
}
}What it is: A decentralized machine intelligence network where miners compete to produce valuable intelligence, and validators assess quality.
How it works:
Technical innovation:
Results:
What it is: Tools and infrastructure for creating zero-knowledge proofs of machine learning computations.
Technical approach:
Use cases:
Example workflow:
# Convert PyTorch model to ONNX
python convert_to_onnx.py --model model.pt --output model.onnx
# Generate EZKL proof
ezkl gen-settings -M model.onnx
ezkl calibrate-settings -M model.onnx -D input.json
ezkl compile-circuit -M model.onnx
ezkl setup -M model.onnx
ezkl prove -M model.onnx -D input.json --proof-path proof.json
ezkl verify --proof-path proof.json --settings-path settings.jsonWhat it is: Decentralized data exchange with compute-to-data functionality.
Architecture:
Economic model:
Real-world usage:
Problem: Cryptographic proofs and blockchain verification add significant computational cost.
Current solutions:
Future directions:
Problem: Decentralization requires transparency, but model weights are valuable IP.
Solutions:
Problem: Training quality AI requires large, high-quality datasets that may not be freely available.
Solutions:
Problem: How do decentralized networks coordinate model updates, handle disputes, and evolve?
Solutions:
1. Compute payments:
2. Staking and quality signals:
3. Governance:
4. Access rights:
5. Mining rewards:
For a decentralized AI network to be sustainable:
# Example token economics for decentralized inference network
class InferenceNetworkEconomics:
def calculate_rewards(self,
inference_quality: float, # 0-1 score
response_time: float, # seconds
stake_amount: float, # tokens staked
network_utilization: float # 0-1
) -> float:
# Base reward for completing inference
base_reward = 10.0
# Quality multiplier (higher quality = more rewards)
quality_multiplier = 1 + inference_quality
# Speed bonus (faster = more rewards)
speed_bonus = max(0, 2.0 - response_time / 10.0)
# Stake multiplier (more skin in the game = more rewards)
stake_multiplier = 1 + math.log(1 + stake_amount) / 10
# Network utilization adjustment (higher utilization = higher rewards)
utilization_multiplier = 1 + network_utilization * 0.5
total_reward = (
base_reward *
quality_multiplier *
(1 + speed_bonus) *
stake_multiplier *
utilization_multiplier
)
return total_rewardPrerequisites:
Step 1: Train a simple model
import torch
import torch.nn as nn
class SimpleClassifier(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(10, 20)
self.fc2 = nn.Linear(20, 2)
def forward(self, x):
x = torch.relu(self.fc1(x))
return self.fc2(x)
model = SimpleClassifier()
# Train your model here...
torch.save(model.state_dict(), 'model.pth')Step 2: Convert to ONNX
import torch.onnx
dummy_input = torch.randn(1, 10)
torch.onnx.export(
model,
dummy_input,
"model.onnx",
input_names=['input'],
output_names=['output']
)Step 3: Generate ZK proof with EZKL
# Install EZKL
curl https://hub.ezkl.xyz/install.sh | bash
# Generate settings
ezkl gen-settings -M model.onnx
# Calibrate settings
ezkl calibrate-settings -M model.onnx -D input.json
# Compile circuit
ezkl compile-circuit -M model.onnx -S settings.json --compiled-circuit model.ezkl
# Setup (generate proving/verification keys)
ezkl setup -M model.ezkl
# Generate proof
ezkl prove -M model.ezkl -D input.json -W witness.json --proof-path proof.json
# Verify proof
ezkl verify --proof-path proof.json --settings-path settings.json --vk-path vk.keyStep 4: Deploy verifier on-chain
# Generate Solidity verifier contract
ezkl create-evm-verifier --vk-path vk.key --sol-code-path Verifier.sol
# Deploy using Hardhat/Foundry
forge create Verifier --private-key $PRIVATE_KEY --rpc-url $RPC_URLStep 1: Set up a Bittensor node
# Install Bittensor
pip install bittensor
# Create wallet
btcli wallet create --wallet.name my_wallet --wallet.hotkey my_hotkey
# Register on a subnet (costs TAO)
btcli subnet register --netuid 1 --wallet.name my_wallet --wallet.hotkey my_hotkeyStep 2: Run a miner
import bittensor as bt
import torch
# Create miner
class MyMiner(bt.Miner):
def forward(self, synapse: bt.Synapse) -> bt.Synapse:
# Implement your AI logic here
# For text generation:
prompt = synapse.text
response = my_llm.generate(prompt)
synapse.text = response
return synapse
# Start mining
miner = MyMiner(
wallet=bt.wallet(name="my_wallet", hotkey="my_hotkey"),
netuid=1
)
miner.run()Step 1: Install Akash CLI
curl https://raw.githubusercontent.com/akash-network/node/master/install.sh | shStep 2: Create deployment manifest
# deploy.yaml
version: "2.0"
services:
ml-training:
image: pytorch/pytorch:latest
expose:
- port: 8888
as: 80
to:
- global: true
env:
- "DATASET_URL=https://..."
command:
- "python"
- "train.py"
resources:
gpu:
units: 1
attributes:
vendor:
nvidia:
- model: "rtx4090"
cpu:
units: 4
memory:
size: 16Gi
storage:
size: 100Gi
profiles:
compute:
ml-training:
resources:
cpu:
units: 4
memory:
size: 16Gi
gpu:
units: 1
storage:
size: 100Gi
placement:
akash:
pricing:
ml-training:
denom: uakt
amount: 1000
deployment:
ml-training:
akash:
profile: ml-training
count: 1Step 3: Deploy
# Create certificate
akash tx cert create client --from my-wallet --chain-id akashnet-2
# Create deployment
akash tx deployment create deploy.yaml --from my-wallet --chain-id akashnet-2
# View bids
akash query market bid list --owner $(akash keys show my-wallet -a)
# Accept bid and create lease
akash tx market lease create --dseq <DSEQ> --provider <PROVIDER> --from my-wallet
# Get service status
akash provider lease-status --dseq <DSEQ> --provider <PROVIDER>Step 1: Setup
npm install @coinbase/agentkitStep 2: Create AI agent
import { AgentKit, CoinbaseWallet } from '@coinbase/agentkit';
import OpenAI from 'openai';
const wallet = await CoinbaseWallet.create();
const agentkit = new AgentKit({
wallet,
network: 'base-mainnet'
});
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
async function runAgent(userPrompt: string) {
const tools = agentkit.getTools();
const response = await openai.chat.completions.create({
model: "gpt-4",
messages: [
{ role: "system", content: "You are an AI agent that can execute crypto transactions." },
{ role: "user", content: userPrompt }
],
tools: tools.map(tool => ({
type: "function",
function: {
name: tool.name,
description: tool.description,
parameters: tool.parameters
}
}))
});
// Execute tool calls
for (const toolCall of response.choices[0].message.tool_calls || []) {
const result = await agentkit.executeTool(
toolCall.function.name,
JSON.parse(toolCall.function.arguments)
);
console.log(`Executed ${toolCall.function.name}:`, result);
}
}
// Example: AI agent that manages a DeFi portfolio
await runAgent("Swap 0.1 ETH for USDC on Uniswap and provide liquidity");Projects to explore:
Learning resources:
Developer communities:
The convergence of AI and crypto represents more than just a technological trend—it's a fundamental reimagining of how we build, deploy, and govern artificial intelligence. By combining blockchain's decentralization, transparency, and token incentives with AI's computational power and intelligence, we're creating systems that are:
The challenges are real: computational overhead, coordination complexity, regulatory uncertainty. But the potential is too significant to ignore. We're moving from an era where AI is controlled by a handful of corporations to one where it's a public good, accessible to all, verifiable by anyone, and governed by communities.
Whether you're a developer, researcher, investor, or simply curious about the future of technology, now is the time to engage with this convergence. Build on these platforms, contribute to the protocols, participate in the networks. The future of AI will be decentralized—and you can help shape it.
What will you build?
Want to discuss decentralized AI? Find me on Twitter @ishanrathi or reach out via email.
Interested in working together on crypto x AI projects? I'm available for consulting and collaboration. Get in touch →