The Grassroots Revolution: Local AI Meetups Rewriting the Rules

In a packed auditorium at Vancouver's H.R. MacMillan Space Centre on a crisp October evening, 250 people gathered not for a corporate product launch or venture capital showcase, but for something far more radical: a community meetup about artificial intelligence. There were no slick keynotes from Big Tech executives, no million-dollar demos. Instead, artists sat alongside researchers, students chatted with entrepreneurs, and someone's homemade algorithm competed for attention with discussions about whether AI could help preserve Indigenous languages.

This wasn't an anomaly. Across the globe, from San Francisco to Accra, from Berlin to Mumbai, a quiet revolution is reshaping how ordinary people engage with one of the most consequential technologies of our time. Local AI meetups and skill-sharing events are proliferating at unprecedented rates, creating grassroots networks that challenge the notion that artificial intelligence belongs exclusively to elite universities and trillion-dollar corporations. These gatherings are doing something remarkable: they're building alternative governance structures, developing regional toolchains, establishing ethical norms, and launching cooperative projects that reflect local values rather than Silicon Valley's priorities.

The numbers tell part of the story. Throughout 2024, Vancouver's grassroots AI community alone hosted 13 monthly meetups attracting over 2,000 total attendees. Data Science Connect, which began as a grassroots meetup in 2012, has evolved into the world's largest data and AI community, connecting more than 100,000 data practitioners and executives. Hugging Face, the open-source AI platform, drew over 5,000 people to what its CEO called potentially “the biggest AI meetup in history” in San Francisco. But beyond attendance figures lies something more profound: these communities are fundamentally reimagining who gets to shape AI's future.

The Vancouver Model

The Vancouver AI community's journey offers a masterclass in grassroots organising. What started with 80 people crammed into a studio in January 2024 grew to monthly gatherings of 250-plus at the Space Centre by year's end. But the community's significance extends far beyond headcount. As organisers articulated in their work published in BC Studies Journal, they built “an ecosystem where humans matter more than profit.”

This philosophy manifests in practical ways. Monthly meetups deliberately avoid the pitch-fest atmosphere that dominates many tech gatherings. Instead, they create what one regular attendee describes as “high energy, low pressure: a space where AI isn't just code but culture.” The format spotlights people “remixing AI with art, community, and some serious DIY spirit.” Researchers present alongside artists; established professionals mentor students; technical workshops sit comfortably next to philosophical debates about algorithmic accountability.

The impact is measurable. The community generated over £7,500 in hackathon prizes throughout 2024, incubated multiple startups, and achieved something perhaps more valuable: it spawned autonomous sub-communities. Surrey AI, Squamish AI, Mind AI & Consciousness, AI & Education, and Women in AI all emerged organically as participants recognised the model's value and adapted it to their specific contexts and interests. This wasn't top-down franchise expansion but genuine grassroots proliferation, what the organisers call “de facto grassroots ecosystem emerging from below.”

By August 2024, the community formalised its structure as the BC AI Ecosystem Association, a nonprofit that could sustain and scale the work whilst maintaining its community-first ethos. The move illustrates a broader pattern: successful grassroots AI communities often evolve from informal gatherings to structured organisations without losing their foundational values.

The Skills Revolution

Traditional AI education follows a familiar path: university degrees, corporate training programmes, online courses consumed in isolation. Community meetups offer something fundamentally different: peer-to-peer learning embedded in social networks, hands-on experimentation, and knowledge exchange that flows in multiple directions simultaneously.

Research on AI collaboration reveals striking differences between casual tool users and what it terms “strategic AI collaborators.” The latter group, which often emerges from active community participation, approaches AI “as a creative partner or an entire team with a range of specialised skills.” They're 1.8 times more likely than simple AI users to be seen as innovative teammates. More tellingly, strategic collaborators take the 105 minutes per day they save through AI tools and reinvest it in deeper work: learning new skills and generating new ideas. Those in the most advanced collaboration category report that AI has increased their motivation and excitement about work.

Community meetups accelerate this evolution from user to collaborator. In Vancouver, participants don't just attend talks; they contribute to hackathons, collaborate on projects, and teach each other. At Hugging Face's massive San Francisco gathering, attendees weren't passive consumers of information but active contributors to open-source projects. The platform's Spaces feature enables developers to create and host interactive demos of their models, with underlying code visible to everyone, transforming AI development “from a black-box process into an open, educational experience.”

The career impact is substantial. In 2024, nearly 628,000 job postings demanded at least one AI skill, with the percentage of all job postings requiring AI skills increasing from 0.5 percent in 2010 to 1.7 percent in 2024. More dramatically, job postings mentioning AI increased 108 percent between December 2022 and December 2024. Yet whilst two-thirds of leaders say they wouldn't hire someone without AI skills, only 39 percent of users have received AI training from their companies. The gap drives professionals towards self-directed learning, often through community meetups and collaborative projects.

LinkedIn data shows a 142-fold increase in members adding AI skills like Copilot and ChatGPT to their profiles and a 160 percent increase in non-technical professionals using learning courses to build AI aptitude. Community meetups provide the social infrastructure for this self-directed education, offering not just technical knowledge but networking opportunities, mentorship relationships, and collaborative projects that build portfolios.

From Weekend Projects to Real-World Impact

If regular meetups provide the consistent social fabric of grassroots AI communities, hackathons serve as pressure cookers for rapid innovation. Throughout 2024, community-organised AI hackathons demonstrated remarkable capacity to generate practical solutions to pressing problems.

Meta's Llama Impact Hackathon in London brought together over 200 developers across 56 teams, all leveraging Meta's open-source Llama 3.2 model to address challenges in healthcare, clean energy, and social mobility. The winning team developed Guardian, an AI-powered triage assistant designed to reduce waiting times and better allocate resources in accident and emergency departments through intelligent patient intake and real-time risk assessments. The top three teams shared a £38,000 prize fund and received six weeks of technical mentorship to further develop their projects.

The Gen AI Agents Hackathon in San Francisco produced DataGen Framework, led by an engineer from Lucid Motors. The project addresses a critical bottleneck in AI development: creating synthetic datasets to fine-tune smaller language models, making them more useful without requiring massive computational resources. The framework automates generation and validation of these datasets, democratising access to effective AI tools.

Perhaps most impressively, India's The Fifth Elephant Open Source AI Hackathon ran from January through April 2024, giving participants months to work with mentors on AI applications in education, accessibility, creative expression, scientific research, and languages. The theme “AI for India” explicitly centred local needs and contexts. Ten qualifying teams presented projects on Demo Day, with five prizes of ₹100,000 awarded across thematic categories.

These hackathons don't just produce projects; they build ecosystems. Participants form teams that often continue collaborating afterwards. Winners receive mentorship, funding, and connections that help transform weekend prototypes into sustainable ventures. Crucially, the problems being solved reflect community priorities rather than venture capital trends.

From Global South to Global Solutions

Nowhere is the power of community-driven AI development more evident than in projects emerging from the Global South, where local meetups and skill-sharing networks are producing solutions that directly address regional challenges whilst offering models applicable worldwide.

Darli AI, developed by Ghana-based Farmerline, exemplifies this approach. Launched in March 2024, Darli is a WhatsApp-accessible chatbot offering expert advice on pest management, crop rotation, logistics, and fertiliser application. What makes it revolutionary isn't just its functionality but its accessibility: it supports 27 languages, including 20 African languages, allowing farmers to interact in Swahili, Yoruba, Twi, and many others.

The impact has been substantial. Since creation, Darli has aided over 110,000 farmers across Ghana, Kenya, and other African nations. The platform has handled 8.5 million interactions and calls, with more than 6,000 smartphone-equipped farmers engaging via WhatsApp. The Darli Helpline currently serves 1 million listeners receiving real-time advice on everything from fertilisers to market access. TIME magazine recognised Darli as one of 2024's 200 most groundbreaking inventions.

Farmerline's approach offers lessons in truly localised AI. Rather than simply translating technical terms, they focused on translating concepts. Instead of “mulching,” Darli uses phrases like “putting dead leaves on your soil” to ensure clarity and understanding. This attention to how people actually communicate reflects deep community engagement rather than top-down deployment.

As Farmerline CEO Alloysius Attah explained: “There are millions of farmers in rural areas that speak languages not often supported by global tech companies. Darli is democratising access to regenerative farming, supporting farmers in their local languages, and ensuring lasting impact on the ground.”

Similar community-driven innovations are emerging across the Global South. Electric South collaborates with artists and creative technologists across Africa working in immersive media, AI, design, and storytelling technologies through labs, production, and distribution. The organisation convened African artists to develop responsible AI policies specifically for the African extended reality ecosystem, creating governance frameworks rooted in African values and contexts.

Building Regional Toolchains

Whilst Big Tech companies release flagship models and platforms designed for global markets, grassroots communities are building regional toolchains tailored to local needs, languages, and contexts. This parallel infrastructure represents one of the most significant long-term impacts of community-led AI development.

The open-source movement provides crucial foundations. LAION, a nonprofit organisation, provides datasets, tools, and models to liberate machine learning research, encouraging “open public education and more environment-friendly use of resources by reusing existing datasets and models.” LF AI & Data, a Linux Foundation initiative, nurtures open-source AI and data projects “like a greenhouse, growing them from seed to fruition with full support and resources.”

These global open-source resources enable local customisation. LocalAI, a self-hosted, community-driven, local OpenAI-compatible API, serves as a drop-in replacement for OpenAI whilst running large language models on consumer-grade hardware with no GPU required. This democratises access to AI capabilities for communities and organisations that can't afford enterprise-scale infrastructure.

Regional communities are increasingly developing specialised tools. ComfyUI, an open-source visual workflow tool for image generation launched in 2023 and maintained by community developers, turns complex prompt engineering and model management into a visual drag-and-drop experience specifically designed for the Stable Diffusion ecosystem. Whilst not tied to a specific geographic region, its community-driven development model allows local groups to extend and customise it for particular use cases.

The Model Context Protocol, supported by GitHub Copilot and VS Code teams alongside Microsoft's Open Source Programme Office, represents another community-driven infrastructure initiative. Nine sponsored open-source projects provide frameworks, tools, and assistants for AI-native workflows and agentic tooling, with developers discovering “revolutionary ways for AI and agents to interact with tools, codebases, and browsers.”

These toolchains matter because they provide alternatives to corporate platforms. Communities can build, modify, and control their own AI infrastructure, ensuring it reflects local values and serves local needs rather than maximising engagement metrics or advertising revenue.

Community-Led Governance

Perhaps the most crucial contribution of grassroots AI communities is the development of ethical frameworks and governance structures rooted in lived experience rather than corporate PR or regulatory abstraction.

Research on community-driven AI ethics emphasises the importance of bottom-up approaches. In healthcare, studies identify four community-driven approaches for co-developing ethical AI solutions: understanding and prioritising needs, defining a shared language, promoting mutual learning and co-creation, and democratising AI. These approaches emphasise “bottom-up decision-making to reflect and centre impacted communities' needs and values.”

One framework advocates a “sandwich approach” combining bottom-up processes like community-driven design and co-created shared language with top-down policies and incentives. This recognises that purely grassroots efforts face structural barriers whilst top-down regulation often misses crucial nuances of local contexts.

In corporate environments, a bottom-up, self-service ethical framework developed in collaboration with data and AI communities alongside senior leadership demonstrates how grassroots approaches can scale. Conceived as a “handbook-like” tool enabling individual use and self-assessment, it familiarises users with ethical questions in the context of generative AI whilst empowering use case owners to make ethically informed decisions.

For rural AI development, ethical guidelines developed in urban centres often “miss critical nuances of rural life.” Salient values extend beyond typical privacy and security concerns to include community self-reliance, ecological stewardship, preservation of cultural heritage, and equitable access to information and resources. Participatory methods, where community members contribute to defining ethical boundaries and priorities, prove essential for ensuring AI development aligns with local values and serves genuine needs.

UNESCO's Ethical Impact Assessment provides a structured process helping AI project teams, in collaboration with affected communities, identify and assess impacts an AI system may have. This model of ongoing community involvement throughout the AI lifecycle represents a significant departure from the “deploy and hope” approach common in commercial AI.

Community-based organisations face particular challenges in adopting AI ethically. Recent proposals focus on designing frameworks tailored specifically for such organisations, providing training, tools, guidelines, and governance systems required to use AI technologies safely, transparently, and equitably. These frameworks must be “localised to match cultural norms, community rights, and workflows,” including components such as fairness, transparency, data minimisation, consent, accessibility, bias audits, accountability, and community participation.

The Seattle-based AI governance working group suggests that developers should be encouraged to prioritise “social good” with equitable approaches embedded at the outset, with governments, healthcare organisations, and technology companies collaborating to form AI governance structures prioritising equitable outcomes.

Building Inclusive Communities

Gender diversity in AI remains a persistent challenge, with women significantly underrepresented in technical roles. Grassroots communities are actively working to change this through dedicated meetups, mentorship programmes, and inclusive spaces.

Women in AI Club's mission centres on “empowering, connecting, and elevating women in the AI space.” The organisation partners with industry leaders to provide experiential community programmes empowering women to excel in building their AI companies, networks, and careers. Their network connects female founders, builders, and investors throughout their AI journey.

Women in AI Governance (WiAIG) focuses specifically on governance challenges, providing “access to an unparalleled network of experts, thought leaders, and change-makers.” The organisation's Communities and Leadership Networks initiative fosters meaningful connections and professional support systems whilst creating opportunities for collective growth and visibility.

These dedicated communities provide safe spaces for networking, mentorship, and skill development. At NeurIPS 2024, the Women in Machine Learning workshop featured speakers who are women or nonbinary giving talks on their research, organised mentorship sessions, and encouraged networking. Similar affinity groups including Queer in AI, Black in AI, LatinX in AI, Disability in AI, Indigenous in AI, Global South in AI, Muslims in ML, and Jews in ML create spaces for communities defined by various axes of identity.

The Women+ in Data/AI Festival 2024 in Berlin celebrated “inclusivity and diversity in various tech communities” by organising a tech summer festival creating opportunities for technical, professional, and non-technical conversations in positive, supportive environments. Google's Women in AI Summit 2024 explored Gemini APIs and Google AI Studio, showcasing how the community builds innovative solutions.

These efforts recognise that diversity isn't just about fairness; it's about better AI. Systems developed by homogeneous teams often embed biases and blind spots. Community-led initiatives bringing diverse perspectives to the table produce more robust, equitable, and effective AI.

From Local to International

Whilst grassroots AI communities often start locally, successful ones frequently develop regional and even international connections, creating networks that amplify impact whilst maintaining local autonomy.

The Young Southeast Asian Leaders Initiative (YSEALI) AI FutureMakers Regional Workshop, running from September 2024 to December 2025 with awards ranging from £115,000 to £190,000, brought together participants aged 18-35 interested in leveraging AI technology to address economic empowerment, civic engagement, education, and environmental sustainability. This six-day workshop in Thailand exemplifies how regional cooperation can pool resources, share knowledge, and tackle challenges too large for individual communities.

ASEAN finalised the ASEAN Responsible AI Roadmap under the 2024 Digital Work Plan, supporting implementation of the ASEAN AI Guide for policymakers and regulators. Key initiatives include the ASEAN COSTI Tracks on AI 2024-2025, negotiations for the ASEAN Digital Economy Framework Agreement, and establishment of an ASEAN AI Working Group. Updates are expected for the draft Expanded ASEAN Guide on AI Governance and Ethics for Generative AI in 2025.

At the APEC level, policymakers and experts underscored the need for cooperative governance, with Ambassador Carlos Vasquez, 2024 Chair of APEC Senior Officials' Meeting, stating: “APEC can serve as a testing ground, an incubator of ideas, where we can explore and develop strategies that make technology work for all of us.”

The Cooperative AI Foundation represents another model of regional and international collaboration. During 2024, the Foundation funded proposals with a total budget of approximately £505,000 for cooperative AI research. They held the Concordia Contest at NeurIPS 2024, followed by release of an updated Concordia library for multi-agent evaluations developed by Google DeepMind.

These regional networks allow communities to share successful models. Vancouver's approach inspired Surrey AI, Squamish AI, and other sub-communities. Farmerline's success in Ghana provides a template for similar initiatives in other African nations and beyond. Cross-border collaboration, as one report notes, “will aid all parties to replicate successful local AI models in other regions of the Global South.”

Beyond Attendance Numbers

Quantifying the impact of grassroots AI communities presents challenges. Traditional metrics like attendance figures and number of events tell part of the story but miss crucial qualitative outcomes.

Career advancement represents one measurable impact. LinkedIn's Jobs on the Rise report highlights AI consultant, machine learning engineer, and AI research scientist among the fastest-growing roles. A Boston Consulting Group study found that companies successfully scaling AI report creating three times as many jobs as they've eliminated through AI implementation. Community meetups provide the skills, networks, and project experience that position participants for these emerging opportunities.

Project launches offer another metric. The Vancouver community incubated multiple startups throughout 2024. Hackathons produced Guardian (the A&E triage assistant), DataGen Framework (synthetic dataset generation), and numerous other projects that continued development beyond initial events. The Fifth Elephant hackathon in India resulted in at least five funded projects continuing with ₹100,000 awards.

Skills development shows measurable progress. Over just three years (2021-2024), the average job saw about one-third of its required skills change. Community participation helps professionals navigate this rapid evolution. Research on AI meeting analytics platforms like Read.ai demonstrates how data-driven insights enable tracking participation, analysing sentiment, and optimising collaboration, providing models for measuring community engagement.

Network effects prove harder to quantify but equally important. When Vancouver's single community fractured into specialised sub-groups, it demonstrated successful knowledge transfer and model replication. When Data Science Connect grew from a grassroots meetup to a network connecting over 100,000 practitioners, it created a resource pool far more valuable than the sum of individual members.

Perhaps most significantly, these communities influence broader AI development. Open-source projects sustained by community contributions provide alternatives to proprietary platforms. Ethical frameworks developed through participatory processes inform policy debates. Regional toolchains demonstrate that technological infrastructure need not flow exclusively from Silicon Valley to the world but can emerge from diverse contexts serving diverse needs.

The Limits of Grassroots Power

Despite remarkable achievements, grassroots AI communities face persistent challenges. Sustainability represents a primary concern. Volunteer-organised meetups depend on individual commitment and energy. Organisers face burnout, particularly as communities grow and administrative burdens increase. Vancouver's evolution to a nonprofit structure addresses this challenge but requires funding, governance, and professionalisation that can tension with grassroots ethos.

Resource constraints limit what communities can achieve. Whilst open-source tools democratise access, cutting-edge AI development still requires significant computational resources. Training large models remains out of reach for most community projects. This creates asymmetry: corporations can deploy massive resources whilst communities must work within tight constraints.

Representation and inclusion remain ongoing struggles. Despite dedicated efforts like Women in AI and various affinity groups, tech communities still skew heavily towards already privileged demographics. Geographic concentration in major tech hubs leaves vast populations underserved. Language barriers persist despite tools like Darli demonstrating what's possible with committed localisation.

Governance poses thorny questions. How do communities make collective decisions? Who speaks for the community? How are conflicts resolved? As communities scale, informal consensus mechanisms often prove inadequate. Formalisation brings structure but risks replicating hierarchies and exclusions that grassroots movements seek to challenge.

The relationship with corporate and institutional power creates ongoing tensions. Companies increasingly sponsor community events, providing venues, prizes, and speakers. Universities host meetups and collaborate on projects. Governments fund initiatives. These relationships provide crucial resources but raise questions about autonomy and co-optation. Can communities maintain independent voices whilst accepting corporate sponsorship? Do government partnerships constrain advocacy for regulatory reform?

Moreover, as one analysis notes, historically marginalised populations have been underrepresented in datasets used to train AI models, negatively impacting real-world implementation. Community efforts to address this face the challenge that creating truly representative datasets requires resources and access often controlled by the very institutions perpetuating inequity.

New Models of AI Development

Despite challenges, grassroots communities are pioneering collaborative approaches to AI development that point towards alternative futures. These models emphasise cooperation over competition, commons-based production over proprietary control, and democratic governance over technocratic decision-making.

The Hugging Face model demonstrates the power of open collaboration. By making models, datasets, and code freely available whilst providing infrastructure for sharing and remixing, the platform enables “community-led development as a key driver of open-source AI.” When innovations come from diverse contributors united by shared goals, “the pace of progress increases dramatically.” Researchers, practitioners, and enterprises can “collaborate in real time, iterate quickly, share findings, and refine models and tools without the friction of proprietary boundaries.”

Community-engaged data science offers another model. Research in Pittsburgh shows how computer scientists at Carnegie Mellon University worked with residents to build technology monitoring and visualising local air quality. The collaboration began when researchers attended community meetings where residents suffering from pollution from a nearby factory shared their struggles to get officials' attention due to lack of supporting data. The resulting project empowered residents whilst producing academically rigorous research.

Alaska Native healthcare demonstrates participatory methods converging with AI technology to advance equity. Indigenous communities are “at an exciting crossroads in health research,” with community engagement throughout the AI lifecycle ensuring systems serve genuine needs whilst respecting cultural values and sovereignty.

These collaborative approaches recognise, as one framework articulates, that “supporting mutual learning and co-creation throughout the AI lifecycle requires a 'sandwich' approach” combining bottom-up community-driven processes with top-down policies and incentives. Neither purely grassroots nor purely institutional approaches suffice; sustainable progress requires collaboration across boundaries whilst preserving community autonomy and voice.

The Future of Grassroots AI

As 2024 demonstrated, grassroots AI communities are not a temporary phenomenon but an increasingly essential component of how AI develops and deploys. Several trends suggest their growing influence.

First, the skills gap between institutional AI training and workforce needs continues widening, driving more professionals towards community-based learning. With only 39 percent of companies providing AI training despite two-thirds of leaders requiring AI skills for hiring, meetups and skill-sharing events fill a crucial gap.

Second, concerns about AI ethics, bias, and accountability are intensifying demands for community participation in governance. Top-down regulation and corporate self-governance both face credibility deficits. Community-led frameworks grounded in lived experience offer legitimacy that neither purely governmental nor purely corporate approaches can match.

Third, the success of projects like Darli AI demonstrates that locally developed solutions can achieve global recognition whilst serving regional needs. As AI applications diversify, the limitations of one-size-fits-all approaches become increasingly apparent. Regional toolchains and locally adapted models will likely proliferate.

Fourth, the maturation of open-source AI infrastructure reduces barriers to community participation. Tools like LocalAI, ComfyUI, and various Model Context Protocol implementations enable communities to build sophisticated systems without enterprise budgets. As these tools improve, the scope of community projects will expand.

Finally, the fragmentation of Vancouver's single community into specialised sub-groups illustrates a broader pattern: successful models replicate and adapt. As more communities demonstrate what's possible through grassroots organising, others will follow, creating networks of networks that amplify impact whilst maintaining local autonomy.

The Hugging Face gathering that drew 5,000 people to San Francisco, dubbed the “Woodstock of AI,” suggests the cultural power these communities are developing. This wasn't a conference but a celebration, a gathering of a movement that sees itself as offering an alternative vision for AI's future. That vision centres humans over profit, cooperation over competition, and community governance over technocratic control.

Rewriting the Future, One Meetup at a Time

In Vancouver's Space Centre, in a workshop in rural Ghana, in hackathon venues from London to Bangalore, a fundamental rewriting of AI's story is underway. The dominant narrative positions AI as emerging from elite research labs and corporate headquarters to be deployed upon passive populations. Grassroots communities are authoring a different story: one where ordinary people actively shape the technologies reshaping their lives.

These communities aren't rejecting AI but insisting it develop differently. They're building infrastructure that prioritises access over profit, creating governance frameworks that centre affected communities, and developing applications that serve local needs. They're teaching each other skills that traditional institutions fail to provide, forming networks that amplify individual capabilities, and launching projects that demonstrate alternatives to corporate AI.

The impact is already measurable in startups launched, careers advanced, skills developed, and communities empowered. But the deepest impact may be harder to quantify: a spreading recognition that technological futures aren't predetermined, that ordinary people can intervene in seemingly inexorable processes, that alternatives to Silicon Valley's vision not only exist but thrive.

From Vancouver's 250-person monthly gatherings to Darli's 110,000 farmers across Africa to Hugging Face's 5,000-person celebration in San Francisco, grassroots AI communities are demonstrating a crucial truth: the most powerful AI might not be the largest model or the slickest interface but the one developed with and for the communities it serves.

As one Vancouver organiser articulated, they're building “an ecosystem where humans matter more than profit.” That simple inversion, repeated in hundreds of communities worldwide, may prove more revolutionary than any algorithmic breakthrough. The future of AI, these communities insist, won't be written exclusively in corporate headquarters or government ministries. It will emerge from meetups, skill-shares, hackathons, and collaborative projects where people come together to ensure that the most transformative technology of our era serves human flourishing rather than extracting from it.

The revolution, it turns out, will be organised in community centres, broadcast over WhatsApp, coded in open-source repositories, and governed through participatory processes. And it's already well underway.

References & Sources

Tim Green

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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