A Novel AI-augmented framework for Quadratic governance and resource allocation- Allo OS

[JoyMutheu]

Original Doc

Code files

1. Executive Summary

Decentralized Autonomous Organizations (DAOs) are evolving beyond simple capital allocation mechanisms into intelligence-driven capital markets. We propose a novel AI-augmented, and cryptographically secure framework for Allo.Capital to manage capital allocation, governance, and ecosystem growth efficiently and equitably. The foundation of this system integrates:

1. AI-Weighted Quadratic Voting & Funding – Dynamic governance where AI-enhanced risk scoring modifies participant influence to mitigate Sybil attacks and ensure reputation-weighted decision-making.

2. Multi-Layered Tokenized Intelligence Staking – Incorporating financial capital, intellectual contributions, and reputation staking to ensure expert-driven governance.

3. AI-Optimized Treasury Management – Using Monte Carlo risk simulations, AI-driven trend prediction, and stochastic volatility modeling to prevent treasury depletion and optimize capital flow.

4. Predictive Governance Intelligence – Leveraging natural language processing (NLP), reinforcement learning, and historical trend analysis for real-time sentiment mapping and decision automation.

5. Zero-Knowledge Sybil Resistance & Privacy-Preserving Compliance – Implementing ZK-proofs and decentralized identity verification to secure capital flows and regulatory adherence.

6. AI-Powered Builder Incentives – Adaptive token issuance that responds to developer demand via dynamic supply control algorithms.

By aligning with Allo.Capital’s existing 0.5% protocol fee, 5% service fees, and token swap mechanisms, this proposal ensures long-term financial sustainability while enhancing decentralization and efficiency.

2. AI-Augmented Governance Model

2.1 AI-Weighted Quadratic Voting & Dynamic Influence Scoring

Traditional quadratic voting (QV) suffers from Sybil vulnerabilities and capital dominance by large holders. To counteract this, we introduce:

AI-assisted Sybil-resistant voter scoring.

Dynamic governance weight recalibration.

Historical contribution-adjusted influence metrics.

Quadratic Voting Equation

Incorporating AI adjustment:

Quadratic voting (QV) is a mechanism designed to balance majority influence while ensuring minority voices are represented. The cost of casting votes grows quadratically rather than linearly, preventing whales from disproportionately dominating governance.

In Allo.Capital, quadratic voting will be applied within fund allocation, governance decisions, and community-driven proposals. Contributors will receive governance tokens based on their past impact and staking. When voting on research grants, protocol updates, or funding rounds, participants will allocate their voting power using QV, ensuring that high-impact builders receive meaningful support without centralized influence. This mechanism will be integrated with AI-based reputation scoring, adjusting voting weights dynamically to prevent Sybil attacks and strengthen meritocratic decision-making.

This will ensure that trusted, high-impact participants have more governance power independent of financial capital. Quadratic matching coefficients are dynamically weighted based on contributor on-chain reputation (ZK-verified). Privacy-preserving allocation validation using zk-SNARKs ensures sybil resistance without sacrificing anonymity.

2.2 Multi-Layered Tokenized Intelligence Staking

Beyond traditional staking mechanisms, we propose three-dimensional staking:

1. Financial Staking – Standard native token stake.

2. Intellectual Capital Staking – Knowledge contributions evaluated via peer-reviewed smart contracts.

3. Reputation Staking – Verifiable contributions weighted by network-assessed expertise

The Multi-Layered Tokenized Intelligence Staking mechanism in Allo.Capital enhances governance by integrating financial, intellectual, and reputation-based staking. Financial staking operates traditionally, allowing users to lock native tokens for governance power and rewards. Intellectual capital staking enables contributors to tokenize research, models, and insights as peer-reviewed smart contracts, ensuring decision-making is data-driven. Reputation staking assigns governance weight based on AI-driven reputation scores, leveraging verifiable contributions and expertise. This system prevents governance capture by wealth alone, aligning decision-making with proven intelligence and knowledge-backed insights, fostering a high-trust, meritocratic DAO ecosystem.

3. AI-Optimized Treasury Management & Risk Modeling

3.1 Monte Carlo Simulations for Fund Distribution

Monte Carlo methods help model treasury risk by simulating multiple future scenarios.

Mathematical expression:

AI-Optimized Treasury Management & Risk Modeling leverages Monte Carlo simulations to enhance fund distribution strategies. By simulating thousands of possible economic scenarios, this approach predicts risk-adjusted capital flows, ensuring optimal liquidity allocation across various funding initiatives. The model incorporates Allo’s fee structures (0.5% on capital flows, 5% on services, token swaps), dynamically adjusting fund distributions based on real-time market conditions, project performance, and ecosystem volatility. This AI-driven method minimizes treasury depletion risks, enhances long-term sustainability, and aligns resource allocation with Allo’s strategic goals, ensuring capital efficiency and decentralized financial resilience.

4. Builder Incentives & Tokenized Reputation Staking

To align with builder incentives, we introduce a three-token system:

1. $ALLO (Governance Token) – Primary governance weight.

2. $INTELLIGENCE (INTL) – Tied to verifiable knowledge contributions.

3. $BUILDER Credits – Developer-focused staking incentives.

Mathematical Model for Adaptive Token Issuance
Formula:

This mathematical model for token issuance will help Allo.Capital by ensuring sustainable growth, incentivized participation, and efficient resource allocation while maintaining economic stability.

1. Sustainable Supply & Demand: The bonding curve model dynamically adjusts token issuance based on real-time capital inflows and burns, preventing oversupply or deflation. This keeps Allo’s ecosystem liquid while maintaining scarcity-driven value accrual.

2. Efficient Capital Allocation: By integrating quadratic funding, Allo.Capital ensures fair and decentralized funding distribution, prioritizing projects with broad-based community support rather than a few whales dominating allocations.

3. Treasury Optimization & Fee Utilization: The adaptive burn mechanism ensures that a portion of fees (from Allo’s 0.5% capital flow fees and 5% service fees) is used to regulate token supply, preventing inflation while maintaining sufficient liquidity for ecosystem incentives.

4. Incentivized Participation: The staking and governance mechanisms tied to token issuance encourage long-term commitment, ensuring builders, researchers, and contributors remain actively engaged in Allo’s development.

By implementing this model, Allo.Capital can efficiently scale, align incentives across stakeholders, and create a self-sustaining decentralized financial infrastructure, positioning itself as a leader in next-generation DAO governance and funding.

5. Zero-Knowledge (ZK) Sybil Resistance & Compliance

5.1 Privacy-Preserving Identity Verification

We use ZK-proofs for decentralized identity verification.
Mathematical formula:

Zero-Knowledge (ZK) Sybil Resistance & Compliance will significantly enhance Allo.Capital’s governance security, ensuring fair participation, privacy, and regulatory compliance without compromising decentralization.

By implementing ZK-proofs for decentralized identity verification, Allo can verify the uniqueness of participants without exposing sensitive personal data. This prevents Sybil attacks, where malicious actors create multiple fake identities to manipulate voting or funding allocations. Additionally, ZK-proofs can enforce compliance with jurisdictional regulations by proving attributes (e.g., citizenship, accreditation status) without revealing unnecessary details. This ensures equitable governance, trustless verification, and enhanced capital distribution integrity, making Allo a secure and future-proof decentralized funding ecosystem.

6. Integration with Allo.Capital Infrastructure

6.1 Alignment with Fee Model

We align with Allo.Capital’s protocol fees:

0.5% fee on capital flows – Treasury revenue stream.

5% service fees – Directly funds research and builder grants.

Token swaps – Captures long-term value from successful projects.

Revenue Projection Formula

This model will allow Allo.Capital to forecast revenue with precision, ensuring sustainable treasury growth and continuous reinvestment into the ecosystem. By factoring in capital inflows, service fees, and tokenized equity swaps, Allo can optimize treasury diversification and maintain long-term financial resilience, reinforcing its position as a self-sustaining funding DAO.

7. Value Capture & Distribution Strategy

This proposal introduces a multi-layered revenue model combining protocol fees, tokenized equity, and AI-optimized fund distribution, reinforcing Allo’s mission to streamline decentralized capital allocation. The 0.5% protocol fee on capital flows and 5% service fees serve as the primary revenue sources, while token swaps from funded projects provide exposure to high-growth initiatives, creating long-term treasury appreciation.

The proposed framework also emphasizes an AI-driven, multi-stakeholder reward mechanism using quadratic staking models to balance governance power between funders, builders, and intelligence contributors. The Multi-Layered Tokenized Intelligence Staking system ensures equitable reward allocation, allowing participants to stake financial, intellectual, and reputation-based capital. Additionally, Monte Carlo simulations for fund allocation help optimize risk-adjusted resource distribution, ensuring capital is directed towards high-impact, scalable projects. The Zero-Knowledge Sybil Resistance system further secures governance and funding integrity, preventing manipulation while maintaining decentralization and compliance. By integrating these mechanisms, Allo.Capital can efficiently allocate capital, drive innovation, and scale sustainably.

8. Community OS & Governance Mechanism

The proposed Community OS and governance mechanism for Allo.Capital is built on a multi-layered, reputation-weighted decision-making model leveraging Quadratic Voting (QV), Tokenized Intelligence Staking, and Zero-Knowledge Sybil Resistance. Governance participants, including funders, builders, and intelligence contributors, will engage in stake-weighted voting, where financial, intellectual, and reputation-based staking determines influence. This ensures that decision-making power is distributed based on verifiable expertise and contribution rather than mere token holdings. Quadratic Voting mitigates centralization risks by ensuring that large token holders do not dominate governance, promoting a balanced, meritocratic decision-making structure. The governance framework is modular and AI-optimized, integrating predictive analytics for proposal impact assessments and using ZK-verified decentralized identities to prevent Sybil attacks. Additionally, AI-driven sentiment analysis ensures governance decisions align with ecosystem priorities, making Allo.Capital a scalable, decentralized, and high-integrity funding protocol.

9. Technical Feasibility & Smart Contract Architecture

Implementation Stack:

EVM-compatible smart contracts (Solidity, Cairo)

ZK-Proof enabled intelligence verification (zkSNARKs, zk-STARKs)

AI Model Deployment (LLMs for governance, RL for treasury)

On-Chain Reputation System (Soulbound NFT-based knowledge credentials)

10. Roadmap & Implementation

Phase	Key Deliverables
Q1 2025	AI governed quadratic Voting , smart contract audits
Q2 2025	Treasury risk modeling, capital deployment simulations
Q3 2025	Multilayered governance, ZK-sybil deployment
Q4 2025	Full AI driven research commons and funding intelligence

11. Conclusion

The proposed Allo.Capital DAO Governance and Resource Allocation Framework introduces a novel, scalable, and decentralized approach to funding, research, and innovation. By evolving beyond traditional capital allocation to holistic resource allocation, the framework enables sustainable long-term growth while maintaining immediate feasibility. The intelligence pillar, supported by on-chain reputation mechanisms, quadratic staking, and dynamic capital flows, ensures that funding decisions are data-driven, transparent, and aligned with Allo.Capital’s vision.

This proposal aligns with Allo.Capital’s strategic vision by balancing decentralization with efficiency, integrating cutting-edge financial mechanisms, and fostering a sustainable governance ecosystem. By implementing next-generation DAO design principles, this model ensures that Allo.Capital remains at the forefront of decentralized finance, research, and funding allocation in 2025 and beyond.

  Notes & Considerations (NB)

1. Intelligence-Based Coordination vs. Traditional Governance
   Quadratic voting (QV) should not merely be a mechanism for decision-making but a way to channel collective intelligence rather than aggregate uninformed preferences. AI should not function as a centralized decision-maker (CEO model) but as a tool to surface and distribute knowledge, enhancing the collective intelligence of participants. Governance mechanisms should shift away from pure token-based weight toward knowledge/information-driven governance.

2. Farcastle Raids as a DAO Coordination Mechanism
   The castle/raid structure provides a flexible model:
   Castle: A persistent governance framework handling meta-level coordination.
   Raids: Temporary, autonomous product teams focused on specific value accrual deliverables that split dividends to contributors and funders.
   This allows for rapid capital allocation while maintaining adaptive team structures.

3. AI as an Intelligence Amplifier, Not a Decision-Maker
   AI must enhance human decision-making rather than replacing it. UX/UI should be designed to make governance joyful and frictionless, emphasizing communication over rigid voting. The system should incentivize radical dissensus over majoritarian decision-making, avoiding the search for the mean/average opinion.

4. Zero-Governance Capital Allocation
   Governance should be emergent from function, rather than an imposed structure.
   Capital should be allocated based on performance metrics rather than rigid governance votes. The model should allow for autonomous steward selection at the meta-level while ensuring decentralized coordination.

5. Strategic Integration of AI & Quadratic Voting
   AI-enhanced quadratic voting must remain transparent, ensuring real-time recalibration of voting weights without slowing down governance cycles. AI should not function as a gatekeeper but as a facilitator of collective intelligence, ensuring trust in capital allocation decisions.

6. Decentralized Stewardship for Long-Term Sustainability
   Stewardship models should fit the function they serve rather than imposing predefined structures. Governance should enable curation, valuation, and liquidity provisioning, aligning incentives across funders, researchers, and product teams.

how my proposal merges with allo os
My proposal for a Next-Gen AI-Augmented Capital & Resource Allocator aligns seamlessly with AlloOS’s vision of creating a modular, programmable capital allocation system. While AlloOS provides the essential infrastructure for onchain organizations to deploy capital efficiently, my proposal enhances this by integrating AI-powered intelligence and automation. This AI-driven layer optimizes funding distribution, enhances decision-making, and streamlines capital/resource deployment through smart agents that execute governance-aligned strategies. By embedding real-time data processing and predictive modeling, this proposal significantly improves AlloOS’s core allocation logic.

One of the key areas where my proposal integrates into AlloOS is through an AI-powered funding intelligence layer. Currently, DAOs and funders struggle with data-driven insights when allocating capital. My solution introduces AI-powered analytics that assess historical funding data, impact metrics, and governance signals to recommend optimized allocation models, such as quadratic funding, retroactive rewards, and AI-assisted budgeting. This AI Allocator Engine can function as a plug-in module within AlloOS, providing funders with actionable insights to maximize impact and efficiency.

Another major enhancement is the AI-agent-driven capital deployment mechanism. Traditional funding models rely on manual governance votes and static allocation rules, often leading to inefficiencies. My proposal introduces autonomous AI agents that continuously adjust fund distribution based on real-time performance data and impact forecasting. These agents interact directly with AlloOS’s smart contracts, ensuring that capital flows dynamically, adapting to ecosystem needs and funding efficiency models in a way that manual governance cannot match.

Additionally, my proposal enhances governance-integrated funding strategies by incorporating AI-enhanced governance-weighted allocation models. Instead of relying solely on static voting mechanisms, this system dynamically weighs governance votes based on treasury health, funding milestones, and community engagement. AI-driven governance enables adaptive allocation strategies that respond to real-time data, making capital distribution more transparent, efficient, and impact-focused. By embedding this directly into AlloOS’s funding marketplace, DAOs gain a more agile and responsive capital allocation mechanism.

By integrating my AI-augmented proposal into AlloOS, we create an intelligent, automated, and data-driven funding ecosystem. This AI-powered capital allocator ensures smarter fund distribution, reduced inefficiencies, and increased transparency, positioning AlloOS as the premier AI-enhanced funding infrastructure in Web3. My structured roadmap for MVP development, governance optimization, and full AI-agent deployment ensures that this integration is executed effectively, transforming AlloOS into the most advanced AI-powered capital allocation system available.

References
Onchain Capital Allocation Handbook- Kevin Owocki

[RFC] [DRAFT] AlloOS - A multitool for onchain capital allocation

OWOCKI NOTE - this is a rough draft document. Only for feedback at this point. Do not take too seriously just yet. [image] A multitool for onchain capital allocation TLDR Unified Capital Allocation Hub: AlloOS is designed to consolidate fragmented capital allocation tools into one interoperable platform, streamlining funding distribution, governance, and community-driven finance. User & Developer Empowerment: The platform offers an AI-powered interface to simplify launching funding campaigns…

[deltajuliet]

Thanks for this @JoyMutheu - I hope that I added in the photos in the correct order. Porting from submissions to the forum is tricky. Have a couple of notes to bring the convo here:

There’s a lot to like here, and I appreciate the forward-thinking nature of the design. Below are a few key thoughts and areas where I’d love to see further clarity:

1. Quadratic Voting

You mention that quadratic voting (QV) will be used for fund allocation, governance, and proposals. Can you provide concrete examples of where QV would be most effective? For instance, would it apply to:

• Allocating research funding based on collective community preference?
• Treasury spending decisions, where AI-adjusted weights ensure fair influence?
• Governance proposals where reputation-adjusted voting prevents dominance by large holders?

It would be helpful to understand how this would work in practice and whether AI-based influence recalibration would be transparent to voters as well as not slow down speed from proposal → funding

2. AI-Based Reputation - How Is Influence Scored?**

The AI-enhanced governance model suggests that historical contributions, verified activity, and on-chain interactions will determine voting weights. Can you elaborate on how this reputation is quantified? Some additional considerations:

• Will reputation decay over time if a participant is inactive?
• How does the system differentiate between different types of contributions (e.g., governance participation vs. technical development vs. capital investment)?
• Is there an appeals process for reputation scoring? Would a guild own that or could we also do that in a decentralized manner?

Would love to see more details on how quadratic matching coefficients are dynamically weighted based on on-chain identity verification.

3. Intellectual Capital Staking

The concept of tokenized knowledge contributions is intriguing. Could you provide an example of how this would work in practice? For instance:

• Would researchers submit smart contracts representing their findings, which are then peer-reviewed and assigned tokenized value? What would be the overhead of them to do that?
• How does the staking mechanism validate the credibility of research?
• Would intellectual capital staking confer governance power, or would it primarily be a funding mechanism?

4. AI-Treasury Optimization

• Phase 1: AI-assisted forecasting for capital allocation recommendations.
• Phase 2: Automated treasury modeling with limited execution authority.
• Phase 3: Full AI-managed treasury with DAO oversight.

I’d love to hear thoughts on how we ensure transparency in AI decisions and whether there would be human override mechanisms in edge cases.

5. Zero-Knowledge Sybil Resistance vs. Passport Integration

The proposal suggests full ZK-proof identity verification. Would it be possible to simplify this by integrating a solution like Gitcoin Passport instead?

• A lightweight approach could use Passport for governance participation while keeping ZK-proofs optional for higher-stakes decision-making.
• This might reduce the technical burden while still preventing Sybil attacks.

Would love to hear your thoughts on this tradeoff.

6. Moving Away from ‘Grants’ Toward ‘Investments’

The proposal still uses the term ‘grants,’ but the broader structure of Allo.Capital leans toward an investment model where capital allocation provides future upside for the DAO.

• Would funding agreements include token swaps, revenue-sharing, or convertible stakes?
• How can we ensure the DAO captures long-term value rather than purely distributing funds.

[JoyMutheu]

Hi @deltajuliet thanks for the compliments. I have addressed your questions below:

1. Quadratic Voting – Where It’s Most Effective
   I believe Quadratic voting (QV) can be most effective when applied to decision-making areas where stakeholder influence needs to be balanced. This prevents plutocratic dominance while still allowing expertise-weighted input. Below are concrete applications:
   Research Funding Allocation: Instead of allowing the largest token holders to dictate funding, QV ensures broad community input, where smaller but passionate contributors have an outsized impact relative to their stake. This aligns incentives toward funding high-impact, widely-supported projects rather than those backed by large investors.
   Treasury Spending Decisions with AI-Adjusted Weights: AI adjusts voting power dynamically based on historical contribution patterns, ensuring that experienced participants who have successfully forecasted outcomes receive adjusted voting influence. This mitigates capital inefficiencies and prevents manipulation by temporary large-stake holders.
   Governance Proposals with Reputation-Adjusted Voting: A Reputation-Weighted QV model prevents whales from dominating governance decisions. AI calibrates weights based on verified expertise (e.g., researchers get more weight on scientific proposals, developers on technical upgrades).
   To ensure transparency and speed to voters:
   All AI recalibrations are verifiable on-chain, with audit trails available for community scrutiny.
   The AI model runs parallel simulations before finalizing weight adjustments, minimizing latency between proposal submission and execution.
   By integrating AI-governed QV with transparent weight adjustments, Allo.Capital will ensure efficient, fair, and fraud-resistant funding allocations.

2. AI-Based Reputation – How Influence Is Scored
   Reputation in Allo.Capital is quantified through an AI-weighted scoring system, incorporating multiple on-chain and off-chain factors:
   Historical Contributions – Scored based on DAO participation, treasury proposal accuracy, and governance involvement.
   On-Chain Interactions – Verified contract deployments, GitHub commits, governance votes, liquidity provision, and knowledge-sharing.
   Capital Investment vs. Technical Development vs. Governance – AI assigns different weights:
   Governance Engagement: Weighted based on accuracy of past votes relative to outcomes.
   Technical Development: Verified GitHub contributions and smart contract audits increase scores.
   Capital Investment: Investment-based weight is non-linear to prevent whale dominance.
   Decay Mechanism:
   Reputation decays over time if a user is inactive, ensuring ongoing contributions are necessary to maintain influence. Decay follows a logarithmic time function, preventing abrupt drops.
   Appeals Process:
   A decentralized guild-based review mechanism allows users to challenge AI-assigned scores.
   All appeals and AI-based adjustments are stored on-chain for full transparency.
   This dynamic reputation model ensures meritocratic influence, rather than wealth-based governance.

3. Intellectual Capital Staking – Practical Implementation
   I introduce the concept of Intellectual Capital Staking (ICS) which extends beyond traditional financial staking by tokenizing verifiable knowledge contributions. It can be applied like this:
   Researchers submit smart contracts encapsulating research insights (e.g., new cryptographic protocols, AI models). These contracts undergo peer-reviewed staking, where domain experts validate their credibility before assigning tokenized value.
   Validation Overhead:
   Smart contracts undergo AI-assisted verification and peer-reviewed staking, minimizing spam.
   Decentralized review nodes curate high-value research, preventing malicious staking.
   Governance vs. Funding Power:
   ICS participants receive governance weight proportional to their verified contributions.
   High-impact research receives retroactive funding, rewarding contributors based on measurable impact (e.g., citation indexing, on-chain usage).
   This framework will decentralize R&D funding at Allo while ensuring governance remains expertise-driven.

4. AI-Treasury Optimization – Phased Implementation
   AI-driven treasury management progresses through three distinct phases:
   Phase 1 – AI-Assisted Forecasting

AI runs Monte Carlo simulations and predictive analytics on fund allocation scenarios.
DAO members receive AI-generated insights, improving fund distribution accuracy.
Phase 2 – Semi-Automated Execution

AI automates low-risk treasury functions (e.g., yield optimization, hedging strategies) while keeping high-risk decisions under DAO control.
A human oversight committee intervenes in edge cases.
Phase 3 – Fully AI-Managed Treasury

AI executes treasury allocations dynamically, adjusting investments based on risk models.
DAO retains override capabilities through time-locked multisigs, ensuring human intervention is possible in critical scenarios.
Ensuring Transparency in AI decisions:
AI-driven decisions are logged on-chain, allowing DAO members to audit treasury flows.
Real-time risk assessment dashboards provide transparency into AI modeling assumptions.
This hybrid AI-human model enhances efficiency, risk mitigation, and accountability.

5. Zero-Knowledge Sybil Resistance vs. Gitcoin Passport
   Allo.Capital’s ZK-based identity verification ensures strong Sybil resistance without compromising privacy. This framework can be simplified through the following ways:
   Gitcoin Passport for Low-Stakes Governance:

Ensures low-friction participation without requiring full ZK verification.
Balances accessibility with security for casual governance.
ZK-Proofs for High-Stakes Decisions:

Required for treasury proposals, research funding, and reputation staking.
Prevents identity spoofing while maintaining on-chain privacy.
This hybrid approach optimizes security while maintaining usability.

6. Moving from Grants to Investments – Capturing Long-Term Value
   Allo.Capital moves away from traditional grant-based funding towards investment-driven capital allocation:
   Funding Agreements with Token Swaps & Revenue Sharing

Projects receiving funding commit to future token swaps or revenue-sharing agreements.
Funding agreements include programmable revenue splits, ensuring ongoing value accrual.
Convertible Stakes & NFT-Based Participation Rights

Researchers and builders receive NFT-based governance stakes, tied to project milestones.
This aligns incentives while maintaining liquid participation mechanisms.
I believe combining the proposed investment-based models with long-term value capture will ensure Allo.Capital maximizes sustainability while ensuring the DAO benefits from its funding decisions.

Also, @owocki @thedevanshmehta I’d really appreciate if you take a look at my proposal. Thanks

[owocki]

trying to grok how all the pieces fit together here. i could use a system diagram or visual overview to see how everything fits together.

Gall’s Law is a principle that states complex systems that work have evolved from simpler systems that worked. im wondering if there is a simple version of this that we can evolve more complexity from over time…