AI co-pilots automate the boilerplate of governance, allowing contributors to focus on core protocol logic and incentive design. This mirrors the shift from manual smart contract coding to frameworks like Foundry and Hardhat.
the-state-of-web3-education-and-onboarding
Blog
The Future of Proposal Drafting: AI Co-pilots and Collective Intelligence
AI is not replacing DAO contributors; it's refactoring the governance stack. Human effort shifts from drafting boilerplate to high-leverage strategic alignment and simulation, unlocking scalable collective intelligence.
introduction
THE PARADIGM SHIFT
Introduction
AI co-pilots are transforming proposal drafting from a manual, adversarial process into a structured, data-driven negotiation.
Collective intelligence emerges from structured debate, not unstructured forums. Platforms like Commonwealth and Tally are evolving from simple voting interfaces into coordination layers that capture institutional knowledge.
The future is composable proposals, where modules for treasury management, parameter tuning, and security audits are assembled like DeFi legos. This creates a standardized language for governance, similar to ERC-20 for tokens.
Evidence: Snapshot's integration with tools like Safe for execution and OpenZeppelin for security audits demonstrates the move towards a modular, verifiable proposal stack.
thesis-statement
THE FUTURE OF GOVERNANCE
The Core Argument: Drafting is a Commodity, Alignment is the Asset
AI commoditizes proposal drafting, forcing DAOs to compete on the higher-order function of aligning and executing collective intelligence.
AI commoditizes proposal drafting. Tools like ChatGPT and specialized DAO co-pilots will produce competent first drafts for free, collapsing the value of pure writing skill. The bottleneck shifts from document creation to strategic synthesis and stakeholder analysis.
The asset is alignment, not text. A DAO's competitive edge is its ability to forge consensus on complex, multi-stakeholder decisions. This requires understanding nuanced trade-offs between groups like LPs, token holders, and core developers, which AI cannot yet navigate.
Collective intelligence becomes the moat. Protocols like Optimism with its Citizens' House or Arbitrum's multi-tiered governance are building institutional processes to channel community sentiment into executable mandates. This operational layer is defensible.
Evidence: The rise of on-chain voting platforms like Tally and Snapshot demonstrates the market's focus is on execution and participation, not document genesis. Their value is in structuring the decision, not drafting the proposal.
market-context
THE PROPOSAL PARADOX
The Current State: Why DAO Proposal Systems Are Broken
Current DAO governance is a high-friction, low-signal bottleneck, where participation is a privilege of the well-resourced and well-connected.
01
The Information Asymmetry Problem
Proposals are drafted in a vacuum by small, expert teams, creating a massive knowledge gap for the average voter. This leads to blind delegation or low-quality signaling.
- 90%+ of token holders lack the time/expertise to analyze complex proposals.
- Voting becomes a popularity contest, not a merit-based evaluation.
- Critical flaws are discovered post-vote, after funds are already allocated.
<10%
Informed Voters
>80%
Delegated Power
02
The Participation Tax
The cognitive and financial cost of creating a serious proposal is prohibitive. You need legal, technical, and economic expertise just to draft a first pass.
- Drafting a comprehensive proposal can take 100+ hours of specialized labor.
- Requires upfront capital for audits and simulations before a single vote.
- This creates a moat for insiders and whales, stifling grassroots innovation.
$50K+
Avg. Draft Cost
Weeks
Time to Draft
03
The Static Document Trap
Proposals are static PDFs or forum posts, not interactive, data-rich objects. Voters can't simulate outcomes, query assumptions, or stress-test financial models in real-time.
- No ability to run Monte Carlo simulations on treasury payouts.
- Cannot model second-order effects on protocol security (e.g., slashing risks).
- Feedback loops are slow, happening over weeks in disjointed forum threads.
0
Live Simulations
Static
Data Model
04
The Sybil-Resistance vs. Meritocracy Conflict
Current systems optimize for Sybil-resistance (1 token = 1 vote) at the expense of meritocratic input. The most knowledgeable community members have no more formal influence than a passive whale.
- Reputation systems like SourceCred are bolted-on, not native.
- Expertise (e.g., a core dev's technical review) is drowned out by token weight.
- Creates perverse incentives where lobbying whales is more effective than building consensus.
Token-Weighted
Voting Power
Ignored
Expert Signal
AI-ASSISTED GOVERNANCE
The Proposal Funnel: From Ideation to Execution
Comparison of proposal drafting methodologies, from manual processes to AI co-pilots and collective intelligence platforms.
| Core Metric / Capability | Manual Drafting (Status Quo) | AI Co-pilot (e.g., GovGPT, OpenBB) | Collective Intelligence (e.g., Commonwealth, Tally) |
|---|---|---|---|
Average Drafting Time | 5-10 days | < 4 hours | 2-5 days |
Pre-Submission Sentiment Analysis | |||
Automated On-Chain Impact Simulation | |||
Cross-Protocol Precedent Integration | |||
Real-time Collaborative Editing | |||
Structured Bounty System for Edits | |||
Average Cost per Proposal Draft | $500-$5k+ | $50-$200 | $0-$100 (crowdsourced) |
Integration with Snapshot, Tally, etc. |
deep-dive
THE WORKFLOW
The New Stack: AI Co-pilot → Human Refiner → On-Chain Simulator
Proposal drafting evolves from a manual, error-prone process into a deterministic, simulation-backed pipeline.
AI Co-pilots generate initial drafts by ingesting governance history and forum sentiment. Tools like ChatGPT-4o and specialized agents from OpenAI or Anthropic parse thousands of posts to synthesize coherent proposals, eliminating the blank-page problem.
Human experts become strategic refiners, not writers. Their role shifts to auditing AI logic, injecting political nuance, and ensuring alignment with the DAO's long-term incentive structures, a task where human judgment remains superior.
On-chain simulators validate proposals before a vote. Platforms like Gauntlet and Chaos Labs run agent-based simulations against forked mainnet states, stress-testing economic parameters and exposing unintended consequences in a sandbox.
This stack creates a feedback loop where simulation data trains the next generation of AI co-pilots. The result is a continuously improving governance model that reduces human bias and increases proposal success rates.
protocol-spotlight
THE FUTURE OF PROPOSAL DRAFTING
Protocols Building the Infrastructure
Governance is broken. AI co-pilots and collective intelligence platforms are automating the grunt work to surface higher-signal decisions.
01
The Problem: Proposal Sprawl
DAO governance is paralyzed by low-quality proposals and voter fatigue. Manual drafting creates a massive coordination tax, with >80% of proposals failing due to poor framing or incomplete analysis.
- Signal-to-Noise Crisis: Voters drown in unstructured forums and repetitive drafts.
- Execution Risk: Poorly scoped proposals lead to failed on-chain execution and wasted gas.
- Participation Collapse: Low-quality discourse drives away key stakeholders.
>80%
Proposal Failure Rate
-90%
Voter Attention
02
The Solution: AI Co-pilots (e.g., OpenDevin, GPT Engineer)
Specialized AI agents ingest forum discussions, historical data, and code to draft context-aware, executable proposals. They turn intent into structured, on-chain-ready code.
- Automated Drafting: Converts community sentiment into a formal TEMPCHECK or RFC in minutes, not weeks.
- Risk Simulation: Models financial and technical impact using protocols like Gauntlet and Chaos Labs.
- Code Generation: Directly outputs Aragon, Tally, or custom governance module code, reducing dev overhead.
10x
Drafting Speed
-70%
Dev Time
03
The Solution: Collective Intelligence Platforms (e.g., Commonwealth, Snapshot X)
Platforms that structure discourse into actionable intelligence, using prediction markets and sentiment aggregation to pre-filter proposals.
- Sentiment Aggregation: Tools like Karma and Boardroom quantify support before an expensive on-chain vote.
- Prediction Markets: Platforms like Polymarket and Metagovernance gauge proposal success probability, creating a financial stake in good governance.
- Delegation 2.0: AI-enhanced delegate platforms (Lobby, Agents) allow for context-specific, proposal-type delegation instead of blind trust.
50%+
Higher Signal
~24h
Consensus Lead Time
04
The Problem: Security & Sybil Attacks
Automated drafting and voting amplifies the threat surface. AI-generated proposals can contain malicious code, and sybil-resistant identity remains unsolved.
- Adversarial AI: Bad actors use the same tools to craft deceptive proposals that appear legitimate.
- Identity Layer Gap: Without robust Proof-of-Personhood (Worldcoin, BrightID) or stake-weighted systems, AI-driven participation is gamed.
- Verification Overhead: Auditing AI-generated code requires new tooling from firms like Certik and OpenZeppelin.
$2B+
Governance Exploits
High
Attack Surface
05
The Solution: On-Chain Execution Frameworks
Proposals are not just text; they are executable programs. Frameworks like Ethereum's EIPs, Cosmos SDK, and Optimism's Governor ensure the drafted intent translates faithfully to on-chain state changes.
- Formal Verification: Integrates with tools like Certora to mathematically prove proposal correctness before execution.
- Gas Optimization: AI co-pilots simulate transactions using Tenderly or Blocknative to minimize costs.
- Composability: Drafts are modular components that can be reused across DAOs, creating a library of governance primitives.
99.9%
Execution Accuracy
-40%
Gas Costs
06
The Endgame: Autonomous Governance
The convergence of AI drafting, collective intelligence, and secure execution creates a flywheel for on-chain political systems. DAOs evolve into self-optimizing entities.
- Continuous Integration: Proposals are automatically tested, simulated, and deployed in a CI/CD pipeline for governance.
- Dynamic Parameters: AI agents (like Gauntlet) continuously adjust protocol parameters via automated proposals based on real-time data.
- Meta-Governance: The system itself is upgraded via proposals it helps draft, creating a recursively self-improving governance layer.
1000x
Decision Throughput
Near-Zero
Human Latency
counter-argument
THE SIGNAL VS. NOISE FILTER
Steelman: Does This Just Create AI-Generated Garbage?
AI co-pilots will not replace human governance but will enforce a new standard of rigor, filtering out low-signal proposals before they waste collective attention.
AI enforces proposal rigor. It mandates structured data, executable code snippets, and quantified impact analysis, moving governance beyond persuasive prose. This mirrors the shift from forum debates to on-chain execution seen in Compound's Governor or Aave's governance v3.
The filter creates scarcity. By raising the minimum viable quality, AI reduces proposal volume but increases the average signal-to-noise ratio. This counters the governance fatigue plaguing protocols like Uniswap and MakerDAO.
Evidence: Current AI tools like OpenAI's GPT-4 and specialized agents from Molecule DAO already parse complex scientific proposals, demonstrating the model's ability to surface technical merit over rhetoric.
risk-analysis
THE AI GOVERNANCE PARADOX
The Bear Case: Centralization, Sybil Attacks, and Model Collapse
AI co-pilots promise efficiency but introduce new, systemic risks that could undermine decentralized governance.
01
The Centralized Intelligence Problem
AI models are trained on centralized data, creating a single point of failure and ideological capture. Governance becomes a reflection of the training data's biases, not the community's will.
- Risk: Proposals converge on patterns from OpenAI or Anthropic datasets.
- Outcome: DAOs lose sovereignty to the latent preferences of Silicon Valley.
1-2
Model Providers
100%
Training Set Control
02
Sybil Attacks at LLM-Scale
Automated proposal generation lowers the cost of attack, enabling malicious actors to flood governance with plausible, AI-generated spam.
- Vector: Generate 10,000+ unique, context-aware proposals to drown out legitimate discourse.
- Target: Overwhelm human voters and automated Snapshot/Tally interfaces, causing voter apathy.
$1
Cost per 1k Proposals
10x
Attack Scale
03
On-Chain Model Collapse
If AI drafts proposals and the community rubber-stamps them, the governance data loop becomes self-referential. The model trains on its own output, leading to degenerate, inbred proposals.
- Mechanism: Similar to GPT training on synthetic data; quality and diversity collapse.
- Result: Protocol innovation stalls, converging on a narrow, potentially broken local optimum.
3-5
Cycles to Degradation
-90%
Proposal Diversity
04
The Oracle Manipulation Endgame
AI co-pilots will rely on oracles (e.g., Chainlink, Pyth) for real-world data to draft proposals. This creates a new attack surface: manipulate the oracle, manipulate governance.
- Attack: Feed false price or event data to trigger specific, harmful proposal generation.
- Amplification: A single oracle flaw can auto-generate economically devastating proposals across multiple DAOs simultaneously.
1
Oracle Feed
N
Protocols Affected
05
The Verification Arms Race
The solution to AI spam is more AI: verifier models to audit proposal drafts. This leads to a computationally expensive, opaque arms race between generator and verifier AIs.
- Cost: Governance overhead skyrockets with $MM in compute costs for model inference.
- Opacity: Decisions hinge on the black-box reasoning of dueling neural networks, eroding transparency.
100x
Compute Cost
0%
Human Readability
06
Loss of Legitimacy & Forking
When governance is perceived as AI-controlled, community legitimacy evaporates. The only recourse is a hard fork, splitting the network and its liquidity.
- Precedent: See Ethereum/Ethereum Classic or Uniswap/SushiSwap dynamics.
- Trigger: A controversial, AI-drafted proposal passes, revealing the "AI Overlord" reality, causing a -50%+ TVL exit.
1
Catalytic Event
-50%
TVL Exit
future-outlook
THE AUTOMATION PIPELINE
The 24-Month Outlook: From Assistance to Autonomous Agents
AI will evolve from a drafting assistant into an autonomous agent that negotiates and executes governance proposals.
AI transitions from co-pilot to pilot. Current tools like OpenAI's GPT-4 and Anthropic's Claude assist with drafting. The next phase involves agents that autonomously research on-chain data, simulate proposal outcomes using platforms like Gauntlet or Chaos Labs, and draft optimized proposals.
Collective intelligence creates superior proposals. A single AI agent has limited perspective. Future systems will deploy agent swarms that debate each other, simulating a DAO's diverse stakeholders. This adversarial process surfaces edge cases and optimizes for network utility, not just passability.
Autonomous execution closes the feedback loop. The final stage is agents that not only draft but also on-chain execution. Using intent-based frameworks like UniswapX or secure bridges like Across, an agent can bundle a governance vote with its required cross-chain actions, making proposals self-implementing.
Evidence: MakerDAO's Endgame Plan explicitly outlines a path to AI-assisted governance, with concrete benchmarks for AI-driven analysis of vault risk parameters and collateral portfolios, providing a real-world roadmap.
takeaways
THE FUTURE OF PROPOSAL DRAFTING
TL;DR for Time-Poor Builders
Governance is broken. AI co-pilots and collective intelligence tools are emerging to fix it.
01
The Problem: The Signal-to-Noise Ratio is Abysmal
Governance forums are flooded with low-effort posts and unstructured debate, making it impossible to find the actionable signal. This leads to voter apathy and plutocratic outcomes.
- >90% of forum posts are noise, not signal.
- Voter participation often falls below 5% for complex proposals.
- Discovery of consensus is manual and slow, taking weeks.
<5%
Participation
>90%
Noise
02
The Solution: AI Co-pilots (e.g., Tally's Governor, Commonwealth AI)
LLMs are being fine-tuned to act as protocol-specific drafting assistants, turning fragmented ideas into structured, on-chain executable proposals.
- Automates legal and code boilerplate, reducing drafting time from days to hours.
- Simulates proposal impact using historical data and forked testnets.
- Generates plain-language summaries to boost voter comprehension.
10x
Drafting Speed
-70%
Errors
03
The Solution: Collective Intelligence Platforms (e.g., Boardroom, Agora)
These platforms structure discussion into phases (Idea → Temp Check → RFC) and use prediction markets & sentiment analysis to surface true community consensus before an on-chain vote.
- Quantifies sentiment across forums, Discord, and social media.
- Uses futarchy-like markets to predict proposal success probability.
- Creates a canonical record of decision rationale for future reference.
50%+
Higher Signal
~80%
Predictive Accuracy
04
The Problem: Security is an Afterthought
Proposals are drafted by humans who miss edge cases, leading to catastrophic on-chain exploits post-execution (see Tornado Cash governance attack). Manual audits are too slow for weekly governance cycles.
- ~$1B+ lost to governance exploits historically.
- Audit timelines (2-4 weeks) are incompatible with agile governance.
- Formal verification is absent from the drafting process.
$1B+
Exploit Value
4+ weeks
Audit Lag
05
The Solution: On-Chain Simulation & Formal Verification
Tools like Chaos Labs and OpenZeppelin Defender allow for automated, fork-based simulation of proposals. Emerging tools integrate formal verification directly into the drafting interface.
- Simulates 1000+ state permutations in minutes on a forked mainnet.
- Automatically flags reentrancy, slippage, and privilege escalation risks.
- Generates a verifiable proof of safe execution for voters.
1000x
Test Coverage
~5 min
Simulation Time
06
The Future: Autonomous Proposal Markets
The end-state is a proposal futures market where anyone can stake on the passage of an idea. The most funded ideas are auto-drafted by AI, simulated, and sent on-chain—creating a continuous, capital-efficient innovation engine.
- Aligns economic incentives with protocol improvement.
- Democratizes agenda-setting beyond core dev teams.
- **Creates a liquid market for governance attention.
24/7
Innovation Cycle
Market-Driven
Agenda
ENQUIRY
Get In Touch
today.
Our experts will offer a free quote and a 30min call to discuss your project.
NDA Protected
Confidentiality24h Response
Time to ValueDirectly to Engineering Team
No Sales Fluff10+
Protocols Shipped
$20M+
TVL Overall