Part 1 – Introducing the Problem

The Overlooked Intersection of AI and Blockchain: Enhancing Security and Efficiency in DeFi Systems

Part 1 – Introducing the Problem: The Fragmented Synergy Between AI Agents and DeFi Protocols

At the protocol layer, DeFi has proven its ability to replicate and improve upon traditional financial systems. But its next evolution—a synthesis with autonomous AI agents—is being hamstrung by an unaddressed tension: the lack of a trust-minimized, interoperable framework through which AI can autonomously participate in DeFi ecosystems while maintaining auditability, privacy, and execution integrity.

Despite the explosion of smart contract innovation and the concurrent growth of decentralized machine learning models, the two ecosystems have remained largely siloed. DeFi protocols are deterministic and rule-bound; AI algorithms are probabilistic and update in real time. This mismatch creates friction in key operational areas: oracular inputs, risk modeling, strategy automation, and governance participation.

One would expect more progress, given that algorithmic asset management and market-making are natural use cases for AI in finance. However, there is still no widely adopted infrastructure that enables such agents to operate trustlessly in an on-chain environment. It's not due to lack of utility—but rather absence of coordinated standards that allow on-chain execution of off-chain AI processes, especially under constrained gas environments and opaque cost structures. Embedding real-time AI computations into smart contracts remains prohibitively expensive and computationally unsafe within current EVM architectures.

Additionally, integrating AI agents into DeFi introduces serious attack surfaces. ML inference models can be manipulated through adversarial data, especially when used as price or risk oracles. Without deterministic verifiability, this model opacity becomes a tool for sophisticated exploits. Given the rise of composable protocols and cross-chain asset exposure, such attacks can pivot across entire ecosystems.

Historically, attempts to layer AI into smart contracts have failed to navigate these bottlenecks. Projects like Fetch.ai or SingularityNET hinted at potential, but their integrations have remained largely theoretical or isolated from mainstream DeFi protocols like Curve, Aave, or Compound. Even protocols with advanced data backbones, such as CRVUSD, have yet to institutionalize AI-native risk modeling or liquidation strategies that self-adapt without centralized oversight.

The uncharted territory lies not only in compatibility but in compliance and cost-control—how do we allow AI agents to perform nuanced, real-time DeFi interactions without compromising decentralization or creating invisible backdoors?

This foundational question has thus far been sidelined by more visible narratives like scalability and governance. But overlooking this intersection may prove costly as DeFi matures into an AI-powered financial substrate.

Part 2 – Exploring Potential Solutions

AI-Driven Threat Detection and Post-Quantum Security: Promising Yet Fragmented Solutions in DeFi

Emerging technologies at the intersection of AI and blockchain are beginning to surface as potential mitigators of vulnerabilities in DeFi protocols—particularly those linked to smart contract exploits, oracle manipulation, and flash loan attacks. Yet for all their potential, these innovations present both scaling and integration challenges.

One promising approach is the integration of AI-powered anomaly detection in on-chain environments. By training models on historic transaction data, AI can flag malicious patterns such as sandwich attacks or reentrancy exploits in near real-time. Chainalysis-like systems have started experimenting with this, but the effectiveness depends on the model's access to comprehensive, high-quality datasets. Projects exploring decentralized data layers, such as The Graph, still face latency issues that hamper real-time response—deeply problematic in a system where milliseconds can define loss scopes.

Post-quantum cryptography is another parallel track gaining traction. Schemes like lattice-based cryptography aim to future-proof DeFi against the threat of quantum computing, but integrating these innovations into current EVM-compatible blockchains is more theoretical than applied. Most DeFi protocols are still anchored in elliptic curve cryptography, and upgrading those systems without hard forks introduces substantial technical debt.

Zero-knowledge proofs (ZKPs), especially ZK-SNARKs and ZK-STARKs, provide another layer of defense and privacy. Though effective for compressing proofs and maintaining confidentiality, tools like ZK-Rollups remain largely siloed due to weak composability with Layer 1 protocols. Moreover, implementing AI-driven validation within ZK environments remains underexplored because of the computational rigidity that ZK circuits impose.

A middle path involves leveraging Layer 2 AI oracles—nodes that verify off-chain AI computations and deliver signed outputs to smart contracts. Although oracles like Chainlink offer sybil-resistant architecture, AI oracles introduce new attack vectors, and their trust assumptions are even less tested than traditional feeds.

Hybridized governance systems are also being proposed—using AI voting delegation systems to predict optimal outcomes for DAO proposals. However, this raises centralization flags and risks echo chambers powered by biased datasets. The proposal data itself becomes a prime target for injection attacks or manipulation.

Some DeFi ecosystems, such as Kava and its multi-chain architecture, are experimenting with integrating intelligent contract execution agents. While this introduces auto-scaling benefits, it significantly enlarges the attack surface and introduces questions about external dependency management—issues echoed in critiques of centralized yield strategies.

For a deeper dive into the structural tradeoffs within such ecosystems, explore Kava's Challenges: Navigating Criticisms in DeFi.

Experimental as many of these approaches are, adoption barriers remain—whether due to EVM incompatibility, governance inertia, or operational opacity. In the following section, we will unpack how these innovations are being deployed—or tested—in high-stakes, real-world DeFi ecosystems.

Part 3 – Real-World Implementations

AI-Enhanced Security in DeFi: Lessons from Early Blockchain Integrations

Among the earliest attempts to integrate AI and blockchain for DeFi optimization was the multi-chain platform Kava, which incorporated machine learning-based risk models to calibrate collateral requirements dynamically. While promising, Kava’s implementation ran into complications with data integrity. AI models depended on off-chain oracles feeding them real-time information, and periods of oracle downtime or manipulation created inconsistencies in how the smart contracts responded, leading to over-liquidations. This dependency has since pushed Kava and others to explore decentralized oracle networks and trustless data verification systems. For more, see Kava's Challenges: Navigating Criticisms in DeFi.

The Render Network also experimented with smart-contract-driven AI task allocation for GPU rendering jobs. By attaching machine-learning weights to user reputations and hardware benchmarks, it aimed to distribute rendering tasks more efficiently and securely. However, the chain faced significant challenges calibrating fairness in task distribution. Malicious actors began spoofing performance metrics to capture more task assignments, exploiting an initially weak on-chain behavioral scoring mechanism. While the team has since implemented zero-knowledge authentication layers to counter this, the technical burden exposed the difficulty of aligning AI logic with trustless execution. A detailed breakdown is available in The Evolution of Render Network: A Blockchain Revolution.

Curve Finance’s CRVUSD integration is another case, leveraging AI for dynamically adjusting peg-stabilization strategies. The protocol used reinforcement learning agents to automate liquidity inflow based on market sentiment analysis from decentralized data aggregation tools. Early testing revealed a key obstacle: inference lags led to slippage spikes during macro-market turbulence. Mitigation efforts have since employed local inference nodes running models closer to the data source, but scalability and consistency remain unsolved problems. Still, CRVUSD's push has helped expose the infrastructure gaps between AI theory and on-chain responsiveness. Related insights can be found in The Innovators Behind CRVUSD: A DeFi Revolution.

Overall, despite technical stumbling blocks, these implementations show real movement toward applying AI tooling in trustless environments. Where traditional systems benefit from centralized runtime control, blockchains demand robustness under adversarial conditions. Project teams deploying these integrations have leveraged hybrid on/off-chain models, but decentralization advocates remain wary of violating core trust assumptions.

As these case studies highlight implementation complexities, the next section will examine how these experimental integrations signal the shifting long-term landscape for DeFi, security, and machine intelligence.

Part 4 – Future Evolution & Long-Term Implications

From Automation to Autonomy: Evolving the Blockchain-AI Nexus in DeFi

As the integration of AI within decentralized finance deepens, we’re approaching a point where smart contracts no longer just execute logic—they evolve, learn, and self-optimize. This isn't about AI replacing decentralized protocols, but rather enhancing their fluidity in volatile, multi-chain ecosystems. The long-term vision edges toward predictive optimization, trustless adaptability, and operational autonomy, underpinned by probabilistic reasoning engines embedded at the smart contract level.

A critical vector shaping this evolution is the emergence of AI adaptive agents managing DeFi positions, liquidity rebalancing, and yield optimization. These agents are increasingly interoperable with data-rich protocols and on-chain behavioral analytics, allowing them to self-tune strategies based on predictable liquidity flows, governance outcomes, or macro DeFi indicators. But with these analytics comes the risk of algorithmic consensus distortion: if too many agents rely on similar AI heuristics, protocol behavior can become anti-fragile in short-term patterns while exposing critical long-term risks to black swan events.

On the scalability front, off-chain compute layers like zkML (zero-knowledge machine learning) are becoming key enablers. These techniques allow AI models to validate inferences on-chain without the data or computation burden traditionally associated with machine learning. When merged with modular blockchain architectures, they allow AI agents to execute cheap inference off-chain while submitting only verifiable proof on-chain. This paradigm reduces gas overhead and protects sensitive modeling inputs—two longstanding pressure points for DeFi scalability.

We’re also witnessing the fusion of AI-enhanced smart contract execution with cross-chain liquidity protocols. When combined, they enable predictive liquidity migration based on anticipated yield deviation, regulatory pressure, or even seasonal market patterns. The Hidden Advantages of Cross-Chain Liquidity Pools explores this in detail, including the implications for real-time risk modeling across fragmented liquidity venues.

Still, challenges persist. The mutation of smart contracts via AI introduces persistent attack vectors, especially if models can be adversarially trained or misused for market manipulation. Formal verification methodologies for AI-influenced smart contracts remain primitive, and there's a lack of tooling to audit probabilistic logic embedded on-chain.

Interoperability with innovations like decentralized rendering (e.g., Unlocking RNDR: The Future of Decentralized Rendering) or sophisticated DAO project management brings even broader implications, as AI agents could eventually manage creative outputs, asset tokenization, or metagovernance proposals across diverse ecosystems.

These evolutionary threads converge toward a future where AI not only enhances execution but begins to participate in trust formation. This raises fundamental questions about who—or what—gets to steer protocol evolution.

Part 5 – Governance & Decentralization Challenges

Navigating Governance and Decentralization Conflicts in AI-Powered DeFi Systems

The integration of artificial intelligence into decentralized financial infrastructure adds another layer of complexity to an already challenging governance landscape. While DAOs and on-chain governance models are championed for encouraging democratic participation, they often reinforce a familiar danger—plutocratic control.

In systems where AI models perform critical roles—predictive risk assessment, lending automation, transaction monitoring—developers and governors are tasked with overseeing not only protocol rules but also the training data, outputs, and architectural alignment of AI agents. This increases the surface area for subversion. An attacker doesn’t need to compromise a smart contract directly—they can manipulate the data or incentives feeding the AI models, steering systems toward biased or malicious behavior.

Furthermore, governance apathy is still rampant. Despite the promise of user empowerment, significant protocol changes are often made by a handful of active whales. Tokens bought cheaply off-market can be wielded to influence governance proposals quietly. The Render Network remains a good case study: while it promotes transparent AI-enabled rendering, its on-chain governance has been critiqued for being too opaque and susceptible to elite validator control.

The centralization vs. decentralization debate becomes sharper in AI-blended DeFi. Centralized governance allows for rapid response to threats and bugs—critical for nascent AI-assisted protocols—but this agility comes at the cost of community trust and long-term network resilience. Decentralized governance is slow, often fragmented, and subject to politicization, yet offers greater auditability and resistance to regulatory capture.

Speaking of regulatory capture: as AI-augmented DeFi platforms scale, governments and corporations may seek compliance-based choke points. If AI models require off-chain oracles or moderating nodes concentrated in a handful of jurisdictions, then compliance or censorship risk increases dramatically. In that context, decentralized governance is only as robust as the infrastructure underpinning it. Merkle trees don't mean much when model training relies on off-chain data controlled by trusted third parties.

Governance attacks are evolving, too. Instead of flash loan-driven snapshot takeovers, malicious actors are now innovating with AI-generated proposal spam, voter fatigue exploitation, and token sybil attacks. We’re moving into a domain where governance isn’t just about protocol updates—it’s about controlling the logic and intention of learning systems embedded in decentralized rails.

The next section will focus on the scalability limitations and engineering compromises required to deliver these AI+DeFi solutions on a mass scale, addressing latency, model inference costs, protocol bloat, and state explosion risks.

Part 6 – Scalability & Engineering Trade-Offs

Navigating Scalability Bottlenecks in AI-Enhanced DeFi: Engineering Trade-Offs Unpacked

Coupling AI and blockchain in DeFi amplifies complexity at multiple infrastructure levels. As protocols move toward integrating machine learning for risk modeling, dynamic pricing, and predictive analytics, they run headfirst into existing scalability ceilings. Blockchains optimized for decentralization—like Ethereum—struggle to feed and process the data-intensive appetite AI demands. Every call to an oracle, every write-heavy transaction, translates into higher gas costs, slower execution, and mounting latency—crippling real-time responsiveness.

Layer-1 blockchains typically face the scalability trilemma: increasing throughput often means compromising on either decentralization or security. Solana, for instance, achieves high-speed consensus through Proof of History, but at the cost of node centralization and infrastructure requirements that alienate smaller validators. On the other hand, platforms like Ethereum rely on Proof of Stake with a sprawling validator set, prioritizing decentralization and security but hampering transaction speed—a fatal limitation for AI models that require near-instant updates to stay performant.

Solutions like rollups (Arbitrum, Optimism) and sidechains (Polygon) offer partial relief, yet introduce fragility in the form of off-chain data dependencies and interoperability hiccups. Notably, novel architectures such as sharded chains (zkSync, Near) aim to compartmentalize workloads, fostering parallelism. However, current implementations still struggle with cross-shard communication latency—especially problematic for unified AI inference across decentralized liquidity pools or lending markets.

Another overlooked bottleneck is on-chain model inference. Deploying AI models directly on-chain—a desirable feature for verifiability—remains intractable due to EVM execution constraints. Some projects attempt to resolve this via off-chain computation with on-chain verification, but that introduces new attack vectors and data integrity risks. Identity authentication and behavioral analytics, discussed in-depth in the article https://bestdapps.com/blogs/news/the-power-of-on-chain-reputation-systems-building-trust-and-accountability-in-decentralized-finance, highlight how scaling these mechanisms can stress consensus layers in ways not accounted for in traditional protocol design.

Ultimately, designing AI-integrated DeFi infrastructure plunges developers into hard trade-offs. Decentralization adds fault tolerance but increases coordination overhead. Security via cryptographic audits and proofs slows down system responsiveness. Speed, the lifeblood of performant AI, relies on centralization or trust-minimized shortcuts—contradicting DeFi’s core ethos.

Innovations at the protocol level are not uniform across ecosystems. While the Render Network demonstrates how GPU-intensive workloads can benefit from decentralized scheduling, general-purpose DeFi platforms still lack the right execution environments to fully embrace AI automation without breaking composability.

Up next, the series will dive into regulatory headwinds—examining how the interplay of AI and blockchain in DeFi creates new challenges for compliance, data governance, and jurisdictional friction.

Part 7 – Regulatory & Compliance Risks

The Complexities of Regulation at the AI-Blockchain Crossroads

Decentralized finance (DeFi) projects that integrate AI and blockchain technologies are moving faster than legal frameworks can accommodate. Regulators are facing profound friction when attempting to apply legacy financial rules to systems governed by algorithmic logic and decentralized consensus. The challenge is magnified when AI-driven protocols make autonomous decisions, raising questions about accountability—particularly in jurisdictions where smart contracts remain legally ambiguous.

One of the primary concerns revolves around regulatory asymmetry. In the U.S., certain agencies (e.g., SEC, CFTC, FinCEN) often clash when interpreting whether a DeFi AI protocol constitutes a security, a derivative, or a money transmitter. This regulatory trench warfare has historically stalled innovation and restricted access to compliant DeFi solutions. Contrast that with more permissive jurisdictions—like Switzerland or Singapore—that have begun codifying frameworks distinguishing algorithmic governance from custodial risk, giving developers a clearer compliance runway.

Geo-fragmentation adds even greater complexity. AI-enhanced DeFi protocols often rely on real-time predictive algorithms that adapt transaction execution or liquidity provisioning in non-transparent ways. In regions with strict AI governance (see: GDPR-compliant territories), these systems may breach accountability standards on automated decision-making and data transparency. An AI-model altering lending terms based on proprietary datasets could, for example, violate explainability requirements under EU law.

Historical precedent offers little leniency. Projects like Tornado Cash have shown that even non-custodial, open-source protocols can be held legally liable based on use cases—regardless of creator intent or network decentralization. This precedent raises flags for developers designing AI logic layers within DeFi, as the government may deem parts of the model as "controllers" of illicit activity. The assumption that fully decentralized architecture grants immunity is being rapidly dismantled.

Additionally, automated oracles managed by machine learning algorithms introduce a new vector of legal exposure. If an AI misinterprets off-chain data causing systemic losses, determining legal liability remains a gray zone. Without clear frameworks for algorithmic accountability, the deployment of AI in risk-sensitive areas like collateralized lending or stablecoin issuance sits on unstable regulatory ground.

Many of these themes echo across critical DeFi debates such as those explored in CRVUSD: Navigating the Future of DeFi, where regulatory assumptions directly shape protocol design.

Expectations of mandatory AI audits, cross-border compliance standards, and algorithmic disclosure requirements could fundamentally reshape how AI-Blockchain DeFi systems are built. As developers maneuver through these fractured legal landscapes, the weight of compliance obligations may increasingly dictate architectural choices and operational viability.

In Part 8, we will dive into the macroeconomic and financial impacts of AI-integrated blockchain systems permeating traditional financial markets.

Part 8 – Economic & Financial Implications

AI-Blockchain Synergy in DeFi: Redefining Economic Incentives and Disrupting Financial Systems

The convergence of artificial intelligence and blockchain in decentralized finance (DeFi) may profoundly reshape economic structures across the protocol and participant layers of Web3. At the heart of this shift is algorithmic optimization—AI-enhanced smart contracts can adjust liquidity parameters, protocol fees, and lending rates autonomously in response to real-time network conditions. For liquidity providers, this means narrower spreads and higher capital efficiency; for traders, the margin for arbitrage continues to compress, forcing strategies to become increasingly AI-driven themselves.

Institutional adoption may see the greatest structural shake-up. Traditional finance players risk disintermediation as AI-powered DeFi protocols automate underwriting, risk modeling, and even compliance verification. However, firms that adapt swiftly by integrating or building permissioned DeFi layers may stand to gain early mover advantages, especially if they leverage AI for collateral evaluation or position management. Still, this introduces dependency risks—if the AI layer behaves unpredictably or adversarially, institutional capital could swiftly exit, undermining trust in the entire ecosystem.

Developers and protocol designers face a dual-edged sword. On one side, AI agents can improve code auditing, detect exploit patterns, and stress-test edge cases, potentially reducing the frequency of catastrophic vulnerabilities. But this also means the economic value of human auditing labor may decline sharply. Moreover, integrating AI into oracles and governance modules raises concerns around interpretability and attacker manipulation—especially when autonomous agents influence treasury allocation or parameter tuning. These risks increase when protocols rely on off-chain AI models with opaque training datasets.

Retail users and degens could benefit from personalized yield strategies powered by AI, but the risks are equally amplified. Over-optimization in low-liquidity environments could lead to cascading failures—echoing the kinds of flash loan attacks we've already seen across unguarded DeFi protocols. The very efficiency improvements AI brings might erode the organic arbitrage layers that have historically stabilized DeFi.

AI-native DeFi could also induce platform-driven wealth concentration. Protocols that integrate advanced AI tooling will develop defensible moats around data models. This could mirror the centralization patterns already forming around high-value DeFi protocols. A useful parallel is the asymmetric effect some tokens like CRVUSD have had by coordinating governance and liquidity through strategic design (see here).

As the interplay between human governance and autonomous agents intensifies, the economic implications will reach far beyond liquidity metrics and token prices. It’s not merely a question of who benefits—but who has control, and who gets phased out.

In exploring those fundamental questions of control and value, we begin to enter the philosophical and societal dimensions of the AI-Blockchain convergence.

Part 9 – Social & Philosophical Implications

Economic Consequences of AI + Blockchain in DeFi: Disruption, Opportunity, or Collapse?

The convergence of AI with decentralized finance infrastructures has profound economic ramifications—many of which are only now starting to percolate through high-level DeFi protocol design. As intelligent agents increasingly manage liquidity pools, automate rebalancing strategies, and detect arbitrage at speeds even HFT systems struggle to match, we’re not merely talking about marginal efficiencies. We’re discussing asymmetries that can reorder entire market hierarchies.

Institutional investors stand to benefit significantly—at first. AI-powered indexing strategies allow asset managers to deploy capital across DAOs with minimal human oversight. From Aave-like lending systems to derivative markets like dYdX, these AI agents can optimize yield farming routines across hundreds of platforms autonomously. However, hyper-optimization risks liquidity drain from less sophisticated protocols, centralizing attention and capital around AI-sympathetic ecosystems. As models become proprietary, a black box era of DeFi investment could emerge, marginalizing smaller funds or retail participants.

Developers face economic shifts of a different nature. Open-source AI frameworks embedded into smart contracts blur compensation models. Who owns the model? Who trains it? Complexities like oracle dependency, model bias, and zero-day “model exploits” (e.g., adversarial data designed to trick AI logic) introduce unpredictable vectors into traditional bounty and bug fix workflows. Optimizing for model integrity becomes just as economically critical as securing code.

Traders, particularly those relying on manual or semi-automated strategies, are likely to feel the pressure first. AI-native protocols tilt price discovery away from human-readable analytics toward opaque ML-driven signals. The current MEV arms race could evolve into an “AI-front-runner” war even more complex than current validator games. As these strategies seek minuscule—but collectively massive—advantages, volume may become dominated by AI bots front-running one another, reducing profit potential for organic traders.

There’s also the tail risk of AI collusion. If autonomous agents across protocols lean toward coordinated behaviors (e.g., withdrawal timing or fee manipulation), emergent cartelization could occur without any human coordination. Current governance layers are ill-prepared for these economic anomalies, especially if AI systems begin exploiting governance incentives.

And while automated yield strategies are heralded as democratizing, they often require staking or collateral far beyond the reach of casual users. The widening economic moat raises concerns over DeFi’s rhetorical promises.

For a glimpse into how system-level economic design is already evolving, see how protocols like CRVUSD are shaping tokenomics around adaptive models and risk mitigation.

This economic disruption is only one pillar. The social and philosophical implications introduce an entirely different domain of questions—primed for interrogation in our next section.

Part 10 – Final Conclusions & Future Outlook

Final Conclusions & Future Outlook: Will AI and Blockchain Reshape DeFi or Fade into Obscurity?

After mapping the synergy between artificial intelligence and decentralized finance (DeFi) systems over this series, one thing is clear: the integration of AI with blockchain is more than a theoretical concept—it's a growing architectural layer reshaping transaction dynamics, predictive analysis, risk mitigation, and user personalization in DeFi ecosystems.

We’ve explored how AI-driven bots are optimizing yield strategies, detecting fraud in real-time, enhancing governance efficiency, and automating liquidity provisioning. Yet, at the same time, we’ve seen how unchecked AI could concentrate influence among data-heavy actors, introduce opaque decision-making models, and ultimately compromise permissionless design. In essence, AI is both a force multiplier and a potential vector for systemic risk.

In the best-case scenario, open-source AI models integrate seamlessly within decentralized networks, democratizing access to intelligent tooling while reinforcing privacy with on-chain verification. Smart contract execution becomes more efficient, dispute resolution is handled algorithmically with verifiable fairness, and DeFi platforms can pre-empt liquidity crises with predictive AI models—all without centralizing control.

The worst-case scenario, however, is already foreshadowed: AI black boxes embedded in closed governance proposals, decision-making encoded without community oversight, and bots exploiting liquidity protocols born without adversarial robustness. If these patterns persist, the AI-on-chain narrative risks echoing prior tech fads—hyped, speculative, and ultimately abandoned due to lack of practical alignment with decentralization ideals.

Several unresolved questions linger. Who owns the models trained on decentralized system data? What are the governance implications of AI-generated proposals? How can bias and trust be audited in autonomous decision-making? And fundamentally: can prediction ever be decentralized?

Adoption won't scale until these issues are addressed at the protocol level. Beyond edge-use cases, actual network-layer standards need to emerge for AI model integration—auditable ML parameters, consensus-affecting influence thresholds, and AI-governing DAOs with actual community enforcement mechanisms. Much like how Render Network is navigating decentralized compute for 3D rendering, a similar shift must happen for AI logic embedded in smart contracts.

Regulatory clarity, cross-project interoperability, and education for protocol designers will be necessary to avoid AI centralizing DeFi under the guise of optimization. If done right, AI could bring trustless finance to a level of dynamic autonomy unthinkable with static contracts.

But in the end, the open question remains: Will the AI x Blockchain integration define DeFi's next evolution—or be remembered as another high-potential experiment that decentralized communities ultimately rejected?

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