Why low cost fully open source local LLMs matter for policy, research and industry
The contemporary debate on artificial intelligence is polarized. On one side, industry leaders predict the imminent arrival of superintelligence and massive cognitive acceleration. On the other, critics reduce large language models to sophisticated text prediction engines devoid of understanding. Evidence from practice and research suggests a more nuanced reality, one that should guide strategic choices in AI development.
Large language models do not possess consciousness or an inner life. Yet they demonstrably capture structure, meaning and regularities in data through compression in high dimensional spaces. This functional understanding enables them to perform tasks once thought to require human level cognition. The question, therefore, is not whether AI “thinks” like humans, but how societies choose to develop and deploy these systems.
At the research level, priorities must shift away from sheer scale toward efficiency, openness and scientific accountability. Performance gains from ever larger models are flattening, while costs in energy and data acquisition rise. Open research into smaller, more efficient architectures, continual learning and interpretability offers higher social returns. Open source LLMs provide a shared experimental substrate, enabling reproducibility and cumulative progress rather than siloed breakthroughs.
At the governmental level, AI should be treated as critical digital infrastructure. Relying exclusively on proprietary cloud based models creates strategic dependencies, legal uncertainty and long term fiscal risk. Low cost fully open source local LLMs allow public administrations to deploy AI on premises or in sovereign clouds, ensuring data protection, auditability and policy control. Public funding should preferentially support open models and shared datasets, maximizing reuse across sectors.
At the business level, competitive advantage will increasingly derive from domain specific adaptation rather than access to closed APIs. Companies that integrate local LLMs into their workflows gain control over costs, latency and data governance. Open models lower entry barriers for startups and SMEs, fostering innovation ecosystems instead of reinforcing monopolies. Custom fine tuning on local data enables higher quality outcomes than generic global models.
The broader implication is political. AI development framed solely as a race toward superintelligence risks replicating past technological bubbles. Framed instead as a collective effort to understand and deploy powerful tools responsibly, it can strengthen knowledge economies and democratic governance. Fully open source local LLMs embody this alternative trajectory: technically robust, economically sustainable and aligned with public values.
The future of artificial intelligence will not be decided only in hyperscale data centers. It will be shaped by choices about openness, locality and collective investment in knowledge as a commons.
Sources:
- The Case That A.I. Is Thinking, The New Yorker. Analysis of understanding and compression in LLMs. https://www.newyorker.com/magazine/2025/11/10/the-case-that-ai-is-thinking
- Kanerva, P., Sparse Distributed Memory. Foundational theory on high dimensional representations. https://web.stanford.edu/~kanerva/sdm.html
- Deep Learning, Ian Goodfellow et al. The Deep Learning textbook to help students and practitioners enter the field of machine learning. https://www.deeplearningbook.org
- European Commission, Open Source Software Strategy 2020–2023. Policy framework for open technologies. https://commission.europa.eu/open-source-strategy