How d/acc Transformed Key Domains Across Society

Governance

By 2035, governance is a hybrid of logically centralized state functions and politically decentralized community oversight. Nation-states still provide the baseline for constitutional law and international relations, but their power is balanced by a rich ecosystem of interoperable, semi-sovereign systems: neighborhood councils, civic DAOs, and thematic oversight networks. This architecture emerged less from ideology than from a practical need to deliver competent authority that citizens can trust, influence, and replace when traditional structures fail.

The key innovation is competitive governance, multiple overlapping jurisdictions and service providers that citizens can choose among, creating market-like pressures for effectiveness. Unlike traditional federalism, where individuals are locked into geographic monopolies, competitive governance lets residents participate in multiple networks at once, switching providers for functions like education, dispute resolution, or infrastructure based on performance. This is not privatization: these remain governance systems with democratic accountability, making binding collective decisions through voting and deliberation rather than consumer choice. Citizens retain voice and participation rights in each system, but also gain exit options between systems.

What’s in use in 2035

Competitive Local Governance: National laws set broad standards, while communities use forkable codebases (like Git for laws)¹ to manage local ordinances. Citizens can participate in multiple overlapping governance networks, creating competitive pressure for effective service delivery rather than mere participation.
AI-Enhanced Authority Systems: Rather than AI-assisted deliberation that burdens citizens with complex decisions, AI systems help elected leaders model policy outcomes, identify unintended consequences, and communicate decisions transparently. This enhances governmental competence without replacing democratic accountability.
Jurisdictional Routers: Protocols that mediate between state law and DAO bylaws, clarifying authority and handling disputes at the interface of centralized and decentralized systems. These systems are economically sustainable through transaction fees, creating self-funding infrastructure for managing jurisdictional complexity.
Safety-Netted DAOs: Civic organizations that retain temporary centralized controls (e.g., a “circuit breaker” council) to intervene in crises, with clear, time-locked paths toward greater decentralization.
Rapid Defensive Tech Deployment: Liability frameworks automatically shift to favor technologies that enhance collective security, during bioweapon threats, communities can instantly adopt new health monitoring without regulatory delays; during cyberattacks, defensive AI tools get fast-track approval through decentralized verification networks.
Attack-Resistant Information Infrastructure: When nation-states or AI systems launch coordinated disinformation campaigns, citizens access competing verification networks that stake reputation on accuracy, preventing any single point of epistemic failure from collapsing democratic decision-making.
Portable Digital Citizenship: Your verified identity and governance participation history transfers seamlessly between jurisdictions, when you move from Amsterdam’s housing DAO to Barcelona’s energy cooperative, your reputation, benefits, and voting rights migrate with you through interoperable protocols.

What made this possible

Pragmatic Decentralization Theory: A shift away from ideological purism toward a focus on fault tolerance, attack resistance, and measurable governance performance. The emphasis moved from maximizing participation to optimizing authority effectiveness and accountability.²
Systemic Failures: Failures of top-down institutions during global crises (pandemics, financial instability) created demand for resilient, anti-fragile alternatives, not total replacement.
Maturity of Tooling: Development of deliberation, versioning, and policy simulation tools that could deliver measurable outcomes, faster problem-solving, more responsive services, and policies that actually work, rather than the gridlock and symbolic politics of traditional systems.³
Frustration with Institutional Failure: Traditional governance systems consistently failed to deliver either meaningful participation or effective outcomes, creating demand for alternatives that could prove their worth through actual performance rather than procedural legitimacy.

Persistent frictions

The Interface Problem: Jurisdictional ambiguity creates constant tension over where state authority ends and community autonomy begins, especially in crises.
Authority Shopping vs. Collective Action: While exit rights discipline poor governance, they can undermine collective action problems that require sustained commitment from all participants, creating tension between individual choice and community resilience.
Cognitive Load: Citizens must navigate a hybrid identity, participating in both traditional civic processes and multiple decentralized governance contexts.
Legitimacy Asymmetry: State-level institutions and well-funded DAOs possess greater resources, creating a power imbalance in the hybrid ecosystem. Well-resourced communities can afford superior governance infrastructure and attract skilled administrators, potentially creating systematic inequality between areas with effective local authority and regions constrained by legacy institutional dysfunction.

Decentralized Infrastructure

By 2035, infrastructure is defined by economically sustainable resilience, but only after a decade of fierce resistance from entrenched interests. Utilities fought distributed energy with regulatory capture, fossil fuel companies lobbied to maintain grid dependency, and logistics giants used predatory pricing to crush local manufacturing networks. The transition succeeded not through cooperation, but because repeated centralized system failures made backup infrastructure financially necessary, insurance companies refused to cover businesses without resilience layers, and communities with distributed alternatives consistently outperformed those without during supply chain collapses.

Hyper-efficient global supply chains and national power grids remain the backbone of the economy, but are now complemented by decentralized infrastructure that generates revenue during normal operations while providing fault-tolerance during crises. Community microgrids profit from energy markets while providing backup power; local manufacturing hubs serve custom markets while maintaining emergency capacity; modular systems accommodate routine needs while scaling for crisis response. These systems won by creating continuous value rather than sitting idle, an economic model that proved more robust than the rent-seeking monopolies they replaced.

What’s in use in 2035

Revenue-Generating Microgrids: Solar and battery systems that profit from energy arbitrage, grid stabilization services, and demand response markets while providing automatic backup power during grid failures. They remain grid-connected for economic optimization but can “island” instantly during disruptions.
Dual-Use Manufacturing Networks: Community fab labs and 3D printing hubs that serve custom production markets (prototyping, repairs, specialty items) while maintaining open-source blueprints for essential goods (medical supplies, repair parts) to be activated during emergencies.
Resilience-Focused Food Systems: Local container farms and seed co-ops that supplement, rather than replace, large-scale agriculture, ensuring a baseline of food security.
Open-Source Infrastructure Blueprints: A global commons of designs for housing, water, and energy systems, allowing for rapid local deployment when needed.
Self-Funding Distributed Sensor Networks: Community-run environmental monitoring that generates revenue through data sales to research institutions, insurance companies, and government agencies while providing early warning systems for local hazards.

What made this possible

Economic Viability of Distributed Systems: Business models emerged that make community-scale infrastructure profitable through multiple revenue streams, creating sustainable financing for resilient systems without requiring public subsidy.
Cost Reductions in Key Tech: The declining cost of photovoltaics, additive manufacturing, and bioreactors made distributed systems economically competitive with centralized alternatives in many applications.
Failures of Hyper-Optimization: The fragility of just-in-time supply chains, exposed during multiple global crises, created a strong business and security case for alternative systems.
AI-Enabled System Optimization: Intelligent management systems that coordinate multiple operational modes and revenue streams, making complex dual-purpose infrastructure economically viable at community scale.

Persistent frictions

Optimization Tensions: Balancing profit maximization during normal operations with resilience capabilities creates ongoing design and operational trade-offs that require sophisticated management.
Standards Mismatch: Integrating decentralized, open-source hardware with proprietary, centralized systems creates significant technical and maintenance challenges.
Market Capture Risks: Successful dual-use infrastructure attracts acquisition offers from larger players, creating pressure to centralize or lose independence through vendor relationships.
Capability Inequality: Communities with better access to capital, technical expertise, and market opportunities can build more robust dual-use infrastructure, potentially creating systematic resilience gaps.

Science

By 2035, the scientific establishment is a diversified ecosystem with multiple career tracks and incentive structures. Legacy journals survive as prestige markers, but the center of gravity has shifted to open platforms where research is published immediately with embedded data, code, and real-time peer review. The core innovation is verifiable, attributable collaboration at scale, built to avoid past replication crises and gatekeeping bottlenecks, while defending against malicious actors who might exploit open research infrastructure.

Openness enables both unprecedented collaboration and potential misuse. The same federated networks that democratize discovery also lower barriers for those seeking to weaponize research. Resilience depends on distributed defenses, real-time monitoring, equipment-level attribution tracking, and rapid countermeasure manufacturing that can detect and neutralize threats faster than they emerge, though this remains uncertain and demands constant defensive R&D.

Scientists now pursue parallel careers across academia, prediction markets, open-source projects, and commercial applications, reducing career risk and creating competition between knowledge production systems. Federated learning lets labs train shared AI models without centralizing sensitive data, while attribution protocols on open platforms ensure contributors retain credit. Science has become more fault-tolerant; flawed conclusions can be rapidly challenged by a global network of independent actors, though this same verification network must also act as an early warning system for research with catastrophic misuse potential.

What’s in use in 2035:

Multi-Track Career Systems: Scientists maintain portfolios across traditional academia, prediction market validation, open-source contributions, and commercial applications, reducing career risk and institutional dependency.
Federated Research Networks: Global consortia that collaboratively train powerful AI discovery models without centralizing sensitive data.
Live, Versioned Papers: Research published on platforms with embedded data, models, and replication forums, moving beyond the static PDF.
Attribution-Based AIs: Tools that assist in hypothesis generation and analysis while meticulously tracking the provenance of data and ideas.
Prediction Market Science Tracks: Research programs where scientists stake reputation and funding on specific hypotheses, with market mechanisms validating findings through replication bets and outcome tracking.⁴
Hybrid Peer Review: A mix of traditional, anonymous expert review and open, reputation-staked validation from the broader scientific community.

What made this possible:

Advances in Privacy-Preserving AI: Federated learning and attribution-based control made it possible to collaborate without ceding control, resolving a key tension.
The Replication Crisis: A deep-seated cultural movement for transparency and reproducibility gained momentum after high-profile scientific failures, creating demand for alternative validation systems.
Portfolio Career Economics: Recognition that scientific careers are too risky when dependent on single institutions or funding sources, leading to systems that allow researchers to diversify across multiple validation mechanisms.
AI Meta-Science: Development of AI tools capable of auditing scientific papers for statistical errors, flawed reasoning, and data manipulation.

Persistent Frictions:

Credit and IP Disputes: Clashes between traditional, author-centric credit systems and the modular, contribution-based models of open networks.
Interface Friction: Reconciling the standards, timelines, and incentives of legacy journals and grant-awarding bodies with the faster, more fluid pace of decentralized science.
Risk of AI Monoculture: Over-reliance on a few dominant, foundation AI models for discovery could inadvertently stifle heterodox thinking.
The Audit Gap: Disparities in the ability to audit and validate complex AI models create a new form of scientific inequality between well-resourced and resource-constrained researchers.

Healthcare

By 2035, healthcare transformation has emerged primarily in agile mid-sized countries (Estonia, Singapore, Rwanda, South Korea) willing to experiment with regulatory frameworks, while legacy systems in the US and Western Europe adapt more slowly through plug-and-play technologies that don’t require institutional overhaul. The breakthrough wasn’t technical but regulatory and financial: countries that reformed payment models and licensing requirements enabled genuine innovation, while those that maintained fee-for-service reimbursement and restrictive professional licensing saw minimal change regardless of available technology.

The deeper shift is toward patient-controlled rather than community-controlled healthcare. While hospitals and clinics already operate at the physical local level, they don’t correspond to meaningful democratic communities, a hospital serves whoever lives within driving distance, not people who share governance or values. AI enables a different model: individualized health sovereignty where patients control their data, choose their care networks, and direct their treatment based on personal preferences rather than geographic accidents, but only in jurisdictions that reformed the underlying payment and regulatory structures that previously locked patients into provider networks.

What’s in use in 2035:

AI Value-Based Payment Models: The fundamental transformation, AI monitoring and prediction markets enable Robin Hanson-style “pay for health outcomes” rather than “pay for procedures performed.” This realigns the entire healthcare system away from perverse volume incentives, making preventive care profitable and reducing unnecessary interventions. Providers now earn more by keeping patients healthy rather than treating them frequently, fundamentally changing the incentive structure that drives all other healthcare decisions.
Patient-Controlled Health Data: Individuals control encrypted health records through frameworks like Attribution-Based Control, granting granular permissions for research or AI training while enabling seamless provider switching and price shopping, breaking data lock-in by hospital systems.
Federated AI Healthcare Networks: AI models train across hospitals and clinics without centralizing sensitive patient data, while personal AI health companions provide preventive coaching and coordinate care across multiple providers, keeping patients in control of decisions.⁵
Open-Source Medical Manufacturing & Crisis Response: Community bio-labs maintain blueprints for essential drugs and medical devices in a global commons, serving custom medicine markets during normal times while switching to emergency production during crises. During health emergencies, patients can instantly grant temporary data access to researchers worldwide while maintaining ownership, and distributed manufacturing networks activate countermeasures using pre-verified blueprints, cutting response time from months to days.
Decentralized Clinical Trials & Cross-Jurisdictional Protocols: Patient-owned data enables rapid, distributed clinical trials where participants retain control over their information while contributing to global research. Treatment protocols validated in agile countries (Estonia, Singapore) spread rapidly through open-source medical frameworks, allowing patients worldwide to access innovative treatments without waiting for local regulatory approval.

What made this possible:

Gradual Trust Building: AI in medicine was rolled out gradually, first as an assistant to doctors in centralized settings, building trust before being deployed in more autonomous local systems.⁶
Economic Models for Community Health: Development of sustainable business models that make community-controlled healthcare financially viable through membership fees, research partnerships, and preventive care savings.
Public Backlash Against Data Exploitation: A series of major health data breaches and controversies over its use by tech giants fueled demand for patient-controlled, decentralized alternatives that provide both privacy and economic benefit.
Advances in Encrypted AI:Homomorphic encryption and secure multiparty computation matured, making it possible to gain insights from health data without compromising privacy.

Persistent Frictions:

The Liability Gap: Determining legal and financial responsibility when a hybrid system of AI agents, local DAOs, and traditional hospitals fails is immensely complex.
Regulatory Lag: National regulators (like the FDA) struggle to create validation pathways that are rigorous enough for safety but flexible enough for decentralized, rapidly-iterating technologies.
Quality Control at the Edge: Ensuring consistent standards of care, sanitation, and validation across thousands of semi-autonomous health pods is a constant challenge.
The Interface Bottleneck and System Interoperability: Patients still face friction and data-loss when moving between the decentralized wellness layer and the centralized acute-care system.

Law & Justice

By 2035, legal systems have undergone strategic deregulation and competitive transformation rather than just adding decentralized layers on top of existing structures. The breakthrough wasn’t creating parallel systems, but dismantling regulatory monopolies that made legal services artificially scarce and expensive. The legal profession opened to competition, allowing non-lawyers to provide legal services, technology companies to offer compliance solutions, and AI systems to handle routine legal work, all while maintaining professional standards through market forces rather than guild protection.

The key innovation is regulatory arbitrage and competitive legal markets that drive efficiency rather than preserve institutional rents. Instead of just layering DAOs on top of slow courts, the system eliminated barriers that prevented legal innovation in the first place. Open-source legal frameworks, AI-powered dispute resolution platforms, and algorithmic contract enforcement compete directly with traditional services on speed, cost, and effectiveness.

What’s in use in 2035:

Competitive Arbitration Markets: Platforms like Kleros offer “fast, open and affordable justice for all” through blockchain-based dispute resolution, while traditional arbitration services compete by streamlining their processes. Mexican courts have already recognized and enforced Kleros arbitral awards, proving the model works within existing legal frameworks.⁷
Pareto-Optimal Negotiation Bots: AI systems designed to find mutually beneficial outcomes handle routine commercial negotiations. These systems focus on creating value for both sides and avoid perpetuating existing human biases by learning from outcomes rather than human behavior patterns.
Deregulated Legal Service Markets: Following Hadfield’s model of competitive legal markets, non-attorney providers offer specialized legal services, document preparation, compliance monitoring, contract analysis, at competitive rates. Traditional law firms compete by focusing on complex judgment-intensive work.
AI-Enhanced Efficiency Tools: AI systems automate document review, predict case outcomes, assist in legal research, and facilitate dispute resolution across the legal profession, dramatically reducing costs and case processing times.
Smart Contract Enforcement: Automated contract execution and enforcement reduces the need for intermediaries in routine commercial transactions, while complex agreements still require human interpretation.
Regulatory Sandboxes: Jurisdictions compete by offering experimental legal frameworks for new business models, creating race-to-the-top dynamics in regulatory innovation rather than protective guild systems.

What made this possible:

The Access to Justice Crisis: The prohibitive cost and slowness of legacy legal systems created immense demand for “good enough” alternatives for lower-stakes disputes.
Maturation of On-Chain Arbitration: Years of experimentation in the crypto space proved that decentralized courts could function effectively for specific, well-defined problems.
Competitive Pressure from Technology: AI tools demonstrated they could perform routine legal tasks faster and cheaper than human lawyers, making resistance to innovation economically unsustainable.
Regulatory Competition: Jurisdictions that deregulated legal services attracted businesses and investment, forcing others to follow suit or lose competitiveness.

Persistent Frictions:

The Sovereignty Conflict: Clashes are inevitable when a DAO’s ruling contradicts a state court’s judgment. The question of final authority remains a deep and unresolved tension.
The Enforcement Gap: Decentralized courts have no bailiffs or police. They are effective for digital assets but rely on the cooperation of legacy systems to enforce rulings in the physical world.
Code vs. Interpretation: The rigidity of smart-contract-based law can conflict with the need for nuanced, contextual human judgment, creating edge cases that neither system handles well.
Inequality of Access: While designed to be accessible, navigating these new legal layers requires a degree of technical and legal literacy that creates new barriers for some.
Quality Control in Open Markets: Ensuring competent service delivery without guild restrictions requires new forms of credentialing, reputation systems, and malpractice insurance that are still evolving.

Finance

By 2035, the financial landscape is a dynamic hybrid with differentiated currency roles. Centralized banking and national currencies provide the regulated backbone for wages, taxes, and large-scale commerce. Bitcoin serves as a widely-accepted store of value and settlement layer. Ethereum and programmable blockchains power smart contracts and AI agent economies. Alongside this, a robust decentralized financial (DeFi) ecosystem serves as a parallel system focused on innovation, censorship resistance, and transparent public goods funding protected by robust identity verification. It acts as both a competitive check on the power of traditional finance and a fault-tolerant alternative during times of instability.

Plural funding mechanisms like quadratic funding and retroactive rewards are used to allocate capital to projects that the traditional VC market overlooks; open-source software, local journalism, scientific research. These systems are now protected by proof-of-personhood protocols, social trust graphs, and cross-DAO verification to prevent Sybil attacks and reputation gaming.⁸ However, tension persists between standardized “impact” metrics and ultra-local needs that don’t map cleanly to algorithmic assessment.

The rise of autonomous AI agents transacting with programmable money creates inherently multipolar financial flows, as thousands of agents interact and negotiate rather than being controlled by centralized providers. This agent economy raises fundamental questions about human economic relevance: while agents may favor multipolarity through decentralized interactions, they could also outcompete humans in cooperation and transaction efficiency. The critical question becomes whether traditional human advantages, property rights, capital ownership, cultural judgment, will continue to provide meaningful leverage in an economy increasingly optimized by and for artificial agents.

What’s in use in 2035:

Public Goods Funding Protocols: Mechanisms like evolved versions of Optimism’s RetroPGF and Gitcoin Grants allocate billions annually, now protected by proof-of-personhood protocols, social trust graphs, and cross-DAO verification to prevent Sybil attacks and reputation gaming.
Differentiated Currency Roles: National fiat currencies handle wages, taxes, and large commerce. Bitcoin serves as a store of value and a settlement layer. Ethereum and programmable blockchains power smart contracts and AI agent economies. Local currencies tied to ecological health or civic participation provide backup mediums of exchange during national system disruptions. Rather than one currency winning, each serves distinct functions while maintaining system resilience through diversity.
Hybrid “TradFi-DeFi” Products: Regulated institutions offer products that give customers access to DeFi yields while managing the technical complexity and providing some consumer protection.
AI-Assisted Contribution Mapping: Systems that trace and weight non-market contributions (e.g., open-source code, care work) to inform retroactive reward distributions.
Self-Sovereign Identity and Web-of-Trust Credit: Individuals control their own identity data through self-sovereign identity systems, while credit assessment relies on web-of-trust networks where local communities and platforms validate reputation based on their own criteria. Rather than global reputation scores that impose a single definition of “good,” users build context-specific trust relationships—a stellar reputation in one community (like open-source development) may be irrelevant in another (like local farming cooperatives), preserving plurality and resisting authoritarian homogenization of values.
Creator Economy Without Platform Tax: Content creators receive direct payments through decentralized protocols, keeping 95% of revenue instead of losing 30% to platform fees, while fans can support creators across multiple platforms using the same wallet and reputation system.

What made this possible:

The Success of Early Experiments: The proven success of Gitcoin and optimism in the 2020s demonstrated that large-scale, transparent public goods funding was not just a theoretical idea.
Financial Crises and Censorship: Moments of high inflation, banking instability, or politically motivated financial censorship drove users to seek out censorship-resistant and non-custodial alternatives.
Gradual Integration: DeFi gained legitimacy not by replacing banks, but by building reliable, interoperable bridges to the traditional financial system (e.g., tokenized treasuries, compliant stablecoins).
A Cultural Shift Towards “Pluralism”: The idea that multiple, diverse systems are healthier than a single monoculture became a guiding principle in both finance and governance.

Persistent Frictions:

The Regulatory Interface: The constant push-and-pull between permissionless DeFi protocols and regulated national financial systems creates legal uncertainty and compliance friction.
Systemic Risk Contagion: The deep entanglement between DeFi and TradFi means that a crisis in one sector can now more easily spill over into the other.
Whale Capture vs. Plurality: Decentralized governance in finance is in a constant battle to resist capture by large token holders (“whales”) and maintain its pluralistic ideals.
The Usability Gap: Despite improvements, directly interacting with DeFi protocols remains more complex and risky for average users than using a traditional bank.

Climate/Energy

By 2035, energy and climate infrastructure is a hybrid system characterized by economically sustainable resilience. The large-scale, centralized grids and global energy markets remain the efficient backbone for baseline power. However, they are now complemented by a robust, decentralized layer of community-owned energy resources. The goal is not to replace the macro-grid but to make it fault-tolerant through systems that are profitable during normal times and resilient during systemic shocks, from extreme weather events to geopolitical disruptions.

Climate action has similarly evolved into a multi-scalar approach. Top-down international agreements and carbon markets provide a logically centralized framework, but they are complemented by a dense network of local, verifiable climate interventions that generate revenue while building resilience. This distributed layer succeeds because it serves multiple purposes simultaneously: community microgrids profit from energy arbitrage and grid services while providing backup power; local renewable projects serve commercial markets while building climate resilience; distributed sensor networks generate revenue through environmental data sales while providing early warning systems.⁹

What’s in use in 2035:

Revenue-Generating Microgrids: Solar and battery systems that profit from energy arbitrage, grid stabilization services, and demand response markets while providing automatic backup power during grid failures, creating sustainable financing for energy independence.
Tiered Climate Modeling: Global climate models set a baseline forecast, which is then refined with high-resolution, local data from civic sensor networks, allowing communities to accurately model their specific risks (e.g., fire, flood, heat).
AI-Driven Load Balancing: Sophisticated software manages the complex energy market, predicting demand and seamlessly shifting loads between the central grid, community batteries, and even electric vehicle fleets.
Verifiable Carbon Sequestration: Localized projects (e.g., biochar production from agricultural waste) are tracked on transparent, decentralized registries, allowing them to participate in larger carbon markets with a high degree of trust.
Profitable Energy Storage: Beyond national oil reserves, communities maintain local “energy buffers” like green hydrogen storage or charged battery banks, providing a distributed backup for critical infrastructure while earning revenue from grid services (frequency regulation, peak shaving, voltage support) during normal operations.

What made this possible:

Economic Incentives for Distributed Energy: Technology cost reductions and new revenue streams (grid services, peer-to-peer energy trading, carbon credits, micro-reactors) made distributed systems profitable rather than just environmentally beneficial.
Pragmatic Integration over Ideological Purity: The most successful projects focused not on going “off-grid” but on creating valuable services for the grid (e.g., selling battery capacity for stabilization), which funded their development.
Maturity of Coordination AI: The development of AI capable of managing the immense complexity of a hybrid grid was the key technical breakthrough that made the system viable without descending into chaos.
Cost Collapse of Resilience Tech: The falling prices of solar panels, batteries, and sensors made it economically feasible for municipalities, and even neighborhoods, to invest in their own backup power infrastructure.

Persistent Frictions:

Optimization Tensions: Balancing profit maximization during normal operations with resilience capabilities creates ongoing design and operational trade-offs that require sophisticated management systems.
The Control Dilemma: The AI platforms that coordinate the hybrid grid are immensely powerful. There are ongoing struggles over whether they should be governed by public utilities, private tech firms, or community DAOs, creating a new central point of failure and control.
Interoperability Challenges: Forcing thousands of different decentralized energy systems to speak the same language and adhere to the safety standards of the legacy national grid is a massive and continuous engineering effort.
Resilience Inequality: Well-resourced communities can afford robust, multi-day backup systems, while poorer regions remain vulnerable to grid failures, creating a stark divide between the “resilient” and the “brittle.”

Education

By 2035, education is a hybrid model responding to accelerating change and increasing human diversity in learning. Centralized public schools and universities remain the cornerstones for foundational knowledge, accredited degrees, and social development. This provides a crucial, logically centralized curriculum, but their traditional monopoly has been broken by rapid technological change that makes curricula obsolete within years rather than decades. A dynamic decentralized layer of AI tutors, peer-to-peer learning networks, and modular credentialing systems provides fault tolerance against outdated curricula and resistance to a one-size-fits-all approach to learning.

The key innovation is flexible educational authority that recognizes learning happens everywhere, through doing, creating, and real-world application, not just formal instruction. AI tutors operate in federated environments, personalizing learning for students without sending sensitive data to a central server. Learning DAOs and professional guilds offer micro-credentials for specific, market-relevant skills, which act as supplements for traditional degrees. Education is seen as a lifelong pursuit, and this hybrid system allows individuals to fluidly move between institutional learning and self-directed, community-verified skill acquisition.

What’s in use in 2035:

Adaptive Federated AI Tutors: Personalized learning aids that are co-developed by school districts and open-source communities, running locally to protect student privacy. These systems adjust to individual neurodiversity, learning disabilities, and cognitive preferences, enabling more effective learning.
Learn-by-Doing Modular Skill Credentials: Platforms where learners can earn verifiable credentials for specific skills (e.g., “Python for Data Analysis,” “AI Model Auditing”), issued by a mix of companies, DAOs, and guilds through real-world projects, open-source contributions, and peer-validated work.
Public Learning Simulators: Open-source games and simulations funded as public goods, designed to teach and reward complex systems thinking, civic literacy, and scientific reasoning.
Rapid Skill Adaptation Networks: Learning communities focused on helping people quickly acquire new competencies as technology and industries evolve, emphasizing meta-learning skills and adaptability over deep specialization in potentially obsolete fields.

What made this possible:

The Skills Gap Crisis: The slow pace of university curriculum updates compared to the rapid evolution of the job market created a massive demand for more agile, modular credentialing systems.
Privacy-Preserving Technology: The development of federated learning made personalized AI education possible without resorting to the surveillance-based models of early ed-tech.
Recognition of Learning Diversity: Growing awareness of neurodiversity, learning disabilities, and different learning styles drove demand for personalized educational approaches that traditional one-size-fits-all systems couldn’t accommodate effectively.
A Cultural Shift to “Competence over Credentials”: Employers began valuing verifiable skills and project portfolios alongside, and sometimes over, traditional degrees, legitimizing the decentralized layer.

Persistent Frictions:

The Legitimacy Gap: A persistent cultural and institutional bias exists in favor of traditional university degrees, especially in licensed professions like medicine and law.
Learning-by-Doing Limitations: While practical experience is valuable, some foundational theoretical knowledge still requires structured instruction, creating ongoing debates about the right balance between experiential and academic learning
Equity of Access: While seemingly open, access to the best AI tutors, learning networks, and high-reputation mentors is unevenly distributed, threatening to worsen educational inequality.
Epistemic Fragmentation: The risk that hyper-personalized, community-governed curricula could lead to educational silos that erode a shared foundation of civic and scientific knowledge.
Adaptation Fatigue: The constant need to learn new skills as technology evolves can be exhausting, particularly for older workers who built careers around deep expertise in now-obsolete fields.

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