The Tiny Revolution: The Moment AI Didn't See Coming
In a computing landscape dominated by the relentless pursuit of scale, where artificial intelligence laboratories compete to build ever-larger models measured in hundreds of billions of parameters, a research team at Samsung has just delivered a profound challenge to the industry's core assumptions. Their Tiny Recursive Model (TRM), weighing in at a mere 7 million parameters, has achieved something remarkable: it outperforms AI giants that are literally 100,000 times its size on complex reasoning tasks.
This isn't just an incremental improvement or a clever optimisation trick. It's a fundamental reconsideration of how artificial intelligence solves problems, and it arrives at a moment when the AI industry faces mounting questions about sustainability, accessibility, and the concentration of power among a handful of technology giants capable of funding billion-dollar training runs.
The implications ripple far beyond academic benchmarks. If small, specialised models can match or exceed the capabilities of massive language models on specific tasks, the entire competitive landscape shifts. Suddenly, advanced AI capabilities become accessible to organisations without access to continent-spanning data centres or nine-figure research budgets. The democratisation of artificial intelligence, long promised but rarely delivered, might finally have its breakthrough moment.
The Benchmark That Humbles Giants
To understand the significance of Samsung's achievement, we need to examine the battlefield where this David defeated Goliath: the Abstraction and Reasoning Corpus for Artificial General Intelligence, better known as ARC-AGI.
Created in 2019 by François Chollet, the renowned software engineer behind the Keras deep learning framework, ARC-AGI represents a different philosophy for measuring artificial intelligence. Rather than testing an AI's accumulated knowledge (what cognitive scientists call crystallised intelligence), ARC-AGI focuses on fluid intelligence: the ability to reason, solve novel problems, and adapt to new situations without relying on memorised patterns or vast training datasets.
The benchmark's puzzles appear deceptively simple. An AI system encounters a grid of coloured squares arranged in patterns. From a handful of examples, it must identify the underlying rule, then apply that reasoning to generate the correct “answer” grid for a new problem. Humans, with their innate pattern recognition and flexible reasoning abilities, solve these puzzles readily. State-of-the-art AI models, despite their billions of parameters and training on trillions of tokens, struggle profoundly.
The difficulty is by design. As the ARC Prize organisation explains, the benchmark embodies the principle of “Easy for Humans, Hard for AI.” It deliberately highlights fundamental gaps in AI's reasoning and adaptability, gaps that cannot be papered over with more training data or additional compute power.
The 2024 ARC Prize competition pushed the state-of-the-art score on the private evaluation set from 33 per cent to 55.5 per cent, propelled by frontier techniques including deep learning-guided program synthesis and test-time training. Yet even these advances left considerable room for improvement.
Then came ARC-AGI-2, released in 2025 as an even more demanding iteration designed to stress-test the efficiency and capability of contemporary AI reasoning systems. The results were humbling for the industry's flagship models. OpenAI's o3-mini-high, positioned as a reasoning-specialised system, managed just 3 per cent accuracy. DeepSeek's R1 achieved 1.3 per cent. Claude 3.7 scored 0.7 per cent. Google's Gemini 2.5 Pro, despite its massive scale and sophisticated architecture, reached only 4.9 per cent.
Samsung's Tiny Recursive Model achieved 7.8 per cent on ARC-AGI-2, and 44.6 per cent on the original ARC-AGI-1 benchmark. For perspective: a model smaller than most mobile phone applications outperformed systems that represent billions of dollars in research investment and require industrial-scale computing infrastructure to operate.
The Architecture of Efficiency
The technical innovation behind TRM centres on a concept its creators call recursive reasoning. Rather than attempting to solve problems through a single forward pass, as traditional large language models do, TRM employs an iterative approach. It examines a problem, generates an answer, then loops back to reconsider that answer, progressively refining its solution through multiple cycles.
This recursive process resembles how humans approach difficult problems. We don't typically solve complex puzzles in a single moment of insight. Instead, we try an approach, evaluate whether it's working, adjust our strategy, and iterate until we find a solution. TRM embeds this iterative refinement directly into its architecture.
Developed by Alexia Jolicoeur-Martineau, a senior researcher at the Samsung Advanced Institute of Technology AI Lab in Montreal, the model demonstrates that architectural elegance can triumph over brute force. The research revealed a counterintuitive finding: a tiny network with only two layers achieved far better generalisation than a four-layer version. This reduction in size appears to prevent the model from overfitting, the tendency for machine learning systems to memorise specific training examples rather than learning general principles.
On the Sudoku-Extreme dataset, TRM achieves 87.4 per cent test accuracy. On Maze-Hard, which tasks models with navigating complex labyrinths, it scored 85 per cent. These results demonstrate genuine reasoning capability, not pattern matching or memorisation. The model is solving problems it has never encountered before by understanding underlying structures and applying logical principles.
The approach has clear limitations. TRM operates effectively only within well-defined grid problems. It cannot handle open-ended questions, text-based tasks, or multimodal challenges that blend vision and language. It is, deliberately and by design, a specialist rather than a generalist.
But that specialisation is precisely the point. Not every problem requires a model trained on the entire internet. Sometimes, a focused tool optimised for a specific domain delivers better results than a general-purpose behemoth.
The Hidden Costs of AI Scale
To appreciate why TRM's efficiency matters, we need to confront the economics and environmental impact of training massive language models.
GPT-3, with its 175 billion parameters, reportedly cost between $500,000 and $4.6 million to train, depending on hardware and optimisation techniques. That model, released in 2020, now seems almost quaint. OpenAI's GPT-4 training costs exceeded $100 million according to industry estimates, with compute expenses alone reaching approximately $78 million. Google's Gemini Ultra model reportedly required $191 million in training compute.
These figures represent only direct costs. Training GPT-3 consumed an estimated 1,287 megawatt-hours of electricity, equivalent to powering roughly 120 average US homes for a year, whilst generating approximately 552 tonnes of carbon dioxide. The GPUs used in that training run required 1,300 megawatt-hours, matching the monthly electricity consumption of 1,450 typical American households.
The trajectory is unsustainable. Data centres already account for 4.4 per cent of all energy consumed in the United States. Global electricity consumption by data centres has grown approximately 12 per cent annually since 2017. The International Energy Agency predicts that global data centre electricity demand will more than double by 2030, reaching around 945 terawatt-hours. Some projections suggest data centres could consume 20 to 21 per cent of global electricity by 2030, with AI alone potentially matching the annual electricity usage of 22 per cent of all US households.
Google reported that its 2023 greenhouse gas emissions marked a 48 per cent increase since 2019, driven predominantly by data centre development. Amazon's emissions rose from 64.38 million metric tonnes in 2023 to 68.25 million metric tonnes in 2024. The environmental cost of AI's scaling paradigm grows increasingly difficult to justify, particularly when models trained at enormous expense often struggle with basic reasoning tasks.
TRM represents a different path. Training a 7-million-parameter model requires a fraction of the compute, energy, and carbon emissions of its giant counterparts. The model can run on modest hardware, potentially even edge devices or mobile processors. This efficiency isn't merely environmentally beneficial; it fundamentally alters who can develop and deploy advanced AI capabilities.
Democratisation Through Specialisation
The concentration of AI capability among a handful of technology giants stems directly from the resource requirements of building and operating massive models. When creating a competitive large language model demands hundreds of millions of dollars, access to state-of-the-art GPUs during a global chip shortage, and teams of world-class researchers, only organisations with extraordinary resources can participate.
This concentration became starkly visible in recent market share data. In the foundation models and platforms market, Microsoft leads with an estimated 39 per cent market share in 2024, whilst AWS secured 19 per cent and Google 15 per cent. In the consumer generative AI tools segment, Meta AI's market share jumped to 31 per cent in 2024, matching ChatGPT's share. Google's Gemini increased from 13 per cent to 27 per cent year-over-year.
Three companies effectively control the majority of generative AI infrastructure and consumer access. Their dominance isn't primarily due to superior innovation but rather superior resources. They can afford the capital expenditure that AI development demands. During Q2 of 2024 alone, technology giants Google, Microsoft, Meta, and Amazon spent $52.9 billion on capital expenses, with a substantial focus on AI development.
The open-source movement has provided some counterbalance. Meta's release of Llama 3.1 in July 2024, described by CEO Mark Zuckerberg as achieving “frontier-level” status, challenged the closed-source paradigm. With 405 billion parameters, Llama 3.1 claimed the title of the world's largest and most capable open-source foundation model. French AI laboratory Mistral followed days later with Mistral Large 2, featuring 123 billion parameters and a 128,000-token context window, reportedly matching or surpassing existing top-tier systems, particularly for multilingual applications.
These developments proved transformative for democratisation. Unlike closed-source models accessible only through paid APIs, open-source alternatives allow developers to download model weights, customise them for specific needs, train them on new datasets, fine-tune them for particular domains, and run them on local hardware without vendor lock-in. Smaller companies and individual developers gained access to sophisticated AI capabilities without the hefty price tags associated with proprietary systems.
Yet even open-source models measuring in the hundreds of billions of parameters demand substantial resources to deploy and fine-tune. Running inference on a 405-billion-parameter model requires expensive hardware, significant energy consumption, and technical expertise. Democratisation remained partial, extending access to well-funded startups and research institutions whilst remaining out of reach for smaller organisations, independent researchers, and developers in regions without access to cutting-edge infrastructure.
Small, specialised models like TRM change this equation fundamentally. A 7-million-parameter model can run on a laptop. It requires minimal energy, trains quickly, and can be modified and experimented with by developers without access to GPU clusters. If specialised models can match or exceed general-purpose giants on specific tasks, then organisations can achieve state-of-the-art performance on their particular use cases without needing the resources of a technology giant.
Consider the implications for edge computing and Internet of Things applications. The global edge computing devices market is anticipated to grow to nearly $43.03 billion by 2030, recording a compound annual growth rate of approximately 22.35 per cent between 2023 and 2030. Embedded World 2024 emphasised the growing role of edge AI within IoT systems, with developments focused on easier AI inferencing and a spectrum of edge AI solutions.
Deploying massive language models on edge devices remains impractical. The computational and storage demands of models with hundreds of billions of parameters far exceed what resource-constrained devices can handle. Even with aggressive quantization and compression, bringing frontier-scale models to edge devices requires compromises that significantly degrade performance.
Small specialised models eliminate this barrier. A model with 7 million parameters can run directly on edge devices, performing real-time inference without requiring cloud connectivity, reducing latency, preserving privacy, and enabling AI capabilities in environments where constant internet access isn't available or desirable. From industrial sensors analysing equipment performance to medical devices processing patient data, from agricultural monitors assessing crop conditions to environmental sensors tracking ecosystem health, specialised AI models can bring advanced reasoning capabilities to contexts where massive models simply cannot operate.
The Competitive Landscape Transformed
The shift towards efficient, specialised AI models doesn't merely democratise access; it fundamentally restructures competitive dynamics in the artificial intelligence industry.
Large technology companies have pursued a particular strategy: build massive general-purpose models that can handle virtually any task, then monetise access through API calls or subscription services. This approach creates powerful moats. The capital requirements to build competing models at frontier scale are prohibitive. Even well-funded AI startups struggle to match the resources available to hyperscale cloud providers.
OpenAI leads the AI startup landscape with $11.3 billion in funding, followed by Anthropic with $7.7 billion and Databricks with $4 billion. Yet even these figures pale beside the resources of their corporate partners and competitors. Microsoft has invested billions into OpenAI and now owns 49 per cent of the startup. Alphabet and Amazon have likewise invested billions into Anthropic.
This concentration of capital led some observers to conclude that the era of foundation models would see only a handful of firms, armed with vast compute resources, proprietary data, and entrenched ecosystems, dominating the market. Smaller players would be relegated to building applications atop these foundation models, capturing marginal value whilst the platform providers extracted the majority of economic returns.
The emergence of efficient specialised models disrupts this trajectory. If a small research team can build a model that outperforms billion-dollar systems on important tasks, the competitive moat shrinks dramatically. Startups can compete not by matching the scale of technology giants but by delivering superior performance on specific high-value problems.
This dynamic has historical precedents in software engineering. During the early decades of computing, complex enterprise software required substantial resources to develop and deploy, favouring large established vendors. The open-source movement, combined with improvements in development tools and cloud infrastructure, lowered barriers to entry. Nimble startups could build focused tools that solved specific problems better than general-purpose enterprise suites, capturing market share by delivering superior value for particular use cases.
We may be witnessing a similar transformation in artificial intelligence. Rather than a future where a few general-purpose models dominate all use cases, we might see an ecosystem of specialised models, each optimised for particular domains, tasks, or constraints. Some applications will continue to benefit from massive general-purpose models with broad knowledge and capability. Others will be better served by lean specialists that operate efficiently, deploy easily, and deliver superior performance for their specific domain.
DeepSeek's release of its R1 reasoning model exemplifies this shift. Reportedly requiring only modest capital investment compared to the hundreds of millions or billions typically spent by Western counterparts, DeepSeek demonstrated that thoughtful architecture and focused optimisation could achieve competitive performance without matching the spending of technology giants. If state-of-the-art models are no longer the exclusive preserve of well-capitalised firms, the resulting competition could accelerate innovation whilst reducing costs for end users.
The implications extend beyond commercial competition to geopolitical considerations. AI capability has become a strategic priority for nations worldwide, yet the concentration of advanced AI development in a handful of American companies raises concerns about dependency and technological sovereignty. Countries and regions seeking to develop domestic AI capabilities face enormous barriers when state-of-the-art requires billion-dollar investments in infrastructure and talent.
Efficient specialised models lower these barriers. A nation or research institution can develop world-class capabilities in particular domains without matching the aggregate spending of technology leaders. Rather than attempting to build a GPT-4 competitor, they can focus resources on specialised models for healthcare, materials science, climate modelling, or other areas of strategic importance. This shift from scale-dominated competition to specialisation-enabled diversity could prove geopolitically stabilising, reducing the concentration of AI capability whilst fostering innovation across a broader range of institutions and nations.
The Technical Renaissance Ahead
Samsung's Tiny Recursive Model represents just one example of a broader movement rethinking the fundamentals of AI architecture. Across research laboratories worldwide, teams are exploring alternative approaches that challenge the assumption that bigger is always better.
Parameter-efficient techniques like low-rank adaptation, quantisation, and neural architecture search enable models to achieve strong performance with reduced computational requirements. Massive sparse expert models utilise architectures that activate only relevant parameter subsets for each input, significantly cutting computational costs whilst preserving the model's understanding. DeepSeek-V3, for instance, features 671 billion total parameters but activates only 37 billion per token, achieving impressive efficiency gains.
The rise of small language models has become a defining trend. HuggingFace CEO Clem Delangue suggested that up to 99 per cent of use cases could be addressed using small language models, predicting 2024 would be their breakthrough year. That prediction has proven prescient. Microsoft unveiled Phi-3-mini, demonstrating how smaller AI models prove effective for business applications. Google introduced Gemma, a series of small language models designed for efficiency and user-friendliness. According to research, the Diabetica-7B model achieved 87.2 per cent accuracy, surpassing GPT-4 and Claude 3.5, whilst Mistral 7B outperformed Meta's Llama 2 13B across various benchmarks.
These developments signal a maturation of the field. The initial phase of deep learning's renaissance focused understandably on demonstrating capability. Researchers pushed models larger to establish what neural networks could achieve with sufficient scale. Having demonstrated that capability, the field now enters a phase focused on efficiency, specialisation, and practical deployment.
This evolution mirrors patterns in other technologies. Early mainframe computers filled rooms and consumed enormous amounts of power. Personal computers delivered orders of magnitude less raw performance but proved transformative because they were accessible, affordable, and adequate for a vast range of valuable tasks. Early mobile phones were expensive, bulky devices with limited functionality. Modern smartphones pack extraordinary capability into pocket-sized packages. Technologies often begin with impressive but impractical demonstrations of raw capability, then mature into efficient, specialised tools that deliver practical value at scale.
Artificial intelligence appears to be following this trajectory. The massive language models developed over recent years demonstrated impressive capabilities, proving that neural networks could generate coherent text, answer questions, write code, and perform reasoning tasks. Having established these capabilities, attention now turns to making them practical: more efficient, more accessible, more specialised, more reliable, and more aligned with human values and needs.
Recursive reasoning, the technique powering TRM, exemplifies this shift. Rather than solving problems through brute-force pattern matching on enormous training datasets, recursive approaches embed iterative refinement directly into the architecture. The model reasons about problems, evaluates its reasoning, and progressively improves its solutions. This approach aligns more closely with how humans solve difficult problems and how cognitive scientists understand human reasoning.
Other emerging architectures explore different aspects of efficient intelligence. Retrieval-augmented generation combines compact language models with external knowledge bases, allowing systems to access vast information whilst keeping the model itself small. Neuro-symbolic approaches integrate neural networks with symbolic reasoning systems, aiming to capture both the pattern recognition strengths of deep learning and the logical reasoning capabilities of traditional AI. Continual learning systems adapt to new information without requiring complete retraining, enabling models to stay current without the computational cost of periodic full-scale training runs.
Researchers are also developing sophisticated techniques for model compression and efficiency. MIT Lincoln Laboratory has created methods that can reduce the energy required for training AI models by 80 per cent. MIT's Clover software tool makes carbon intensity a parameter in model training, reducing carbon intensity for different operations by approximately 80 to 90 per cent. Power-capping GPUs can reduce energy consumption by about 12 to 15 per cent without significantly impacting performance.
These technical advances compound each other. Efficient architectures combined with compression techniques, specialised training methods, and hardware optimisations create a multiplicative effect. A model that's inherently 100 times smaller than its predecessors, trained using methods that reduce energy consumption by 80 per cent, running on optimised hardware that cuts power usage by 15 per cent, represents a transformation in the practical economics and accessibility of artificial intelligence.
Challenges and Limitations
Enthusiasm for small specialised models must be tempered with clear-eyed assessment of their limitations and the challenges ahead.
TRM's impressive performance on ARC-AGI benchmarks doesn't translate to general-purpose language tasks. The model excels at grid-based reasoning puzzles but cannot engage in conversation, answer questions about history, write creative fiction, or perform the myriad tasks that general-purpose language models handle routinely. Specialisation brings efficiency and performance on specific tasks but sacrifices breadth.
This trade-off is fundamental, not incidental. A model optimised for one type of reasoning may perform poorly on others. The architectural choices that make TRM exceptional at abstract grid puzzles might make it unsuitable for natural language processing, computer vision, or multimodal understanding. Building practical AI systems will require carefully matching model capabilities to task requirements, a more complex challenge than simply deploying a general-purpose model for every application.
Moreover, whilst small specialised models democratise access to AI capabilities, they don't eliminate technical barriers entirely. Building, training, and deploying machine learning models still requires expertise in data science, software engineering, and the particular domain being addressed. Fine-tuning a pre-trained model for a specific use case demands understanding of transfer learning, appropriate datasets, evaluation metrics, and deployment infrastructure. Smaller models lower the computational barriers but not necessarily the knowledge barriers.
The economic implications of this shift remain uncertain. If specialised models prove superior for specific high-value tasks, we might see market fragmentation, with different providers offering different specialised models rather than a few general-purpose systems dominating the landscape. This fragmentation could increase complexity for enterprises, which might need to manage relationships with multiple AI providers, integrate various specialised models, and navigate an ecosystem without clear standards or interoperability guarantees.
There's also the question of capability ceilings. Large language models' impressive emergent abilities appear partially due to scale. Certain capabilities manifest only when models reach particular parameter thresholds. If small specialised models cannot access these emergent abilities, there may be fundamental tasks that remain beyond their reach, regardless of architectural innovations.
The environmental benefits of small models, whilst significant, don't automatically solve AI's sustainability challenges. If the ease of training and deploying small models leads to proliferation, with thousands of organisations training specialised models for particular tasks, the aggregate environmental impact could remain substantial. Just as personal computing's energy efficiency gains were partially offset by the explosive growth in the number of devices, small AI models' efficiency could be offset by their ubiquity.
Security and safety considerations also evolve in this landscape. Large language model providers can implement safety measures, content filtering, and alignment techniques at the platform level. If specialised models proliferate, with numerous organisations training and deploying their own systems, ensuring consistent safety standards becomes more challenging. A democratised AI ecosystem requires democratised access to safety tools and alignment techniques, areas where research and practical resources remain limited.
The Path Forward
Despite these challenges, the trajectory seems clear. The AI industry is moving beyond the scaling paradigm that dominated the past several years towards a more nuanced understanding of intelligence, efficiency, and practical value.
This evolution doesn't mean large language models will disappear or become irrelevant. General-purpose models with broad knowledge and diverse capabilities serve important functions. They provide excellent starting points for fine-tuning, handle tasks that require integration of knowledge across many domains, and offer user-friendly interfaces for exploration and experimentation. The technology giants investing billions in frontier models aren't making irrational bets; they're pursuing genuine value.
But the monoculture of ever-larger models is giving way to a diverse ecosystem where different approaches serve different needs. Some applications will use massive general-purpose models. Others will employ small specialised systems. Still others will combine approaches, using retrieval augmentation, mixture of experts architectures, or cascaded systems that route queries to appropriate specialised models based on task requirements.
For developers and organisations, this evolution expands options dramatically. Rather than facing a binary choice between building atop a few platforms controlled by technology giants or attempting the prohibitively expensive task of training competitive general-purpose models, they can explore specialised models tailored to their specific domains and constraints.
For researchers, the shift towards efficiency and specialisation opens new frontiers. The focus moves from simply scaling existing architectures to developing novel approaches that achieve intelligence through elegance rather than brute force. This is intellectually richer territory, requiring deeper understanding of reasoning, learning, and adaptation rather than primarily engineering challenges of distributed computing and massive-scale infrastructure.
For society, the democratisation enabled by efficient specialised models offers hope of broader participation in AI development and governance. When advanced AI capabilities are accessible to diverse organisations, researchers, and communities worldwide, the technology is more likely to reflect diverse values, address diverse needs, and distribute benefits more equitably.
The environmental implications are profound. If the AI industry can deliver advancing capabilities whilst reducing rather than exploding energy consumption and carbon emissions, artificial intelligence becomes more sustainable as a long-term technology. The current trajectory, where capability advances require exponentially increasing resource consumption, is fundamentally unsustainable. Efficient specialised models offer a path towards an AI ecosystem that can scale capabilities without proportionally scaling environmental impact.
Beyond the Scaling Paradigm
Samsung's Tiny Recursive Model is unlikely to be the last word in efficient specialised AI. It's better understood as an early example of what becomes possible when researchers question fundamental assumptions and explore alternative approaches to intelligence.
The model's achievement on ARC-AGI benchmarks demonstrates that for certain types of reasoning, architectural elegance and iterative refinement can outperform brute-force scaling. This doesn't invalidate the value of large models but reveals the possibility space is far richer than the industry's recent focus on scale would suggest.
The implications cascade through technical, economic, environmental, and geopolitical dimensions. Lower barriers to entry foster competition and innovation. Reduced resource requirements improve sustainability. Broader access to advanced capabilities distributes power more equitably.
We're witnessing not merely an incremental advance but a potential inflection point. The assumption that artificial general intelligence requires ever-larger models trained at ever-greater expense may prove mistaken. Perhaps intelligence, even general intelligence, emerges not from scale alone but from the right architectures, learning processes, and reasoning mechanisms.
This possibility transforms the competitive landscape. Success in artificial intelligence may depend less on raw resources and more on innovative approaches to efficiency, specialisation, and practical deployment. Nimble research teams with novel ideas become competitive with technology giants. Startups can carve out valuable niches through specialised models that outperform general-purpose systems in particular domains. Open-source communities can contribute meaningfully to frontier capabilities.
The democratisation of AI, so often promised but rarely delivered, might finally be approaching. Not because foundation models became free and open, though open-source initiatives help significantly. Not because compute costs dropped to zero, though efficiency improvements matter greatly. But because the path to state-of-the-art performance on valuable tasks doesn't require the resources of a technology giant if you're willing to specialise, optimise, and innovate architecturally.
What happens when a graduate student at a university, a researcher at a non-profit, a developer at a startup, or an engineer at a medium-sized company can build models that outperform billion-dollar systems on problems they care about? The playing field levels. Innovation accelerates. Diverse perspectives and values shape the technology's development.
Samsung's 7-million-parameter model outperforming systems 100,000 times its size is more than an impressive benchmark result. It's a proof of concept for a different future, one where intelligence isn't synonymous with scale, where efficiency enables accessibility, and where specialisation defeats generalisation on the tasks that matter most to the broadest range of people and organisations.
The age of ever-larger models isn't necessarily ending, but its monopoly on the future of AI is breaking. What emerges next may be far more interesting, diverse, and beneficial than a future dominated by a handful of massive general-purpose models controlled by the most resource-rich organisations. The tiny revolution is just beginning.
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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