Big AI is accelerating the metacrisis What can we do?
đ Original Paper Info
- Title: Big AI is accelerating the metacrisis What can we do?- ArXiv ID: 2512.24863
- Date: 2025-12-31
- Authors: Steven Bird
đ Abstract
The world is in the grip of ecological, meaning, and language crises which are converging into a metacrisis. Big AI is accelerating them all. Language engineers are playing a central role, persisting with a scalability story that is failing humanity, supplying critical talent to plutocrats and kleptocrats, and creating new technologies as if the whole endeavour was value-free. We urgently need to explore alternatives, applying our collective intelligence to design a life-affirming future for NLP that is centered on human flourishing on a living planet.đĄ Summary & Analysis
1. **Key Contributions**: - Analyzes the three crises (ecological, meaning, language) caused by LLMs and how they interact. - Argues for establishing protected spaces in academia to discuss problems with LLMs, balancing technological progress with social responsibility. - Emphasizes that language technology can play a crucial role in sustaining human life on Earth and proposes that linguists should be aware of the political and value-laden nature of their work.-
Simple Explanation with Metaphors:
- The paper describes large language models as “demons destroying forests,” emphasizing the need for protected areas to mitigate these negative impacts.
- It argues for a safe space to discuss LLM issues, likening it to a ‘room where doctors debate patient health in hospitals.’
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Sci-Tube Style Script:
- “Hello everyone, today we’re looking at how large language models (LLMs) impact human society and the Earth’s ecosystem. First, LLMs put significant environmental stress by operating data centers. Second, they undermine social meanings, leading to a crisis of truth and meaning. Finally, LLMs severely affect minority language communities, necessitating more equitable and responsible approaches.”
đ Full Paper Content (ArXiv Source)
Large Language Models and Generative AI have become âa source of great fascinationâ leading to âwildly hyperbolic theories of the virtual realmâ , a new so-called âIntelligent Age defined by advancements in knowledge, health, culture and societal welfareâ . This AI Gold Rush has become a âsilent nuclear holocaust in our information ecosystemâ .
âBig AIâ â the corporations, the state capture, the âvarious kinds of automation sold as AIâ â is escalating global crises which are now reaching tipping point. Six of nine planetary boundaries have been breached . There is a real prospect of ecosystem, economic, and geopolitical collapse . Big AI is now fuelling this system while stoking itself. Big AI is accelerating the metacrisis (cf. Fig. 1).
Our professional body, the Association for Computational Linguistics (ACL), is possibly the largest publisher of LLM research. Authors warrant that their work complies with the ACL Code of Ethics, âunderstanding that the public good is the paramount considerationâ . How are we to reconcile our professional obligation with the harms caused by the technologies we are creating?
To speak out is to take the ACL Code seriously. Yet earlier versions of this article were three times rejected from ACL venues: Unempirical! Courts controversy! Underestimates GenAI! A political pamphlet! No facts were contested. It seems that the problem is not with truth but with truth-telling. Kind people advised me not to criticise but to participate, saying that this AI-driven future is inevitable.
If nothing else, we agree that language is key, only not language as sequence data but language as humanityâs greatest technology for sustaining our common life. AI scales, not through huge LLMs run in Big AIâs polluting data centres, but through amplifying the social, leveraging the exponential possibilities offered by human networks.
None of the above is to blame individuals. âWe are all complicit. Weâve allowed the âmarketâ to define what we value so that the redefined common good seems to depend on profligate lifestyles that enrich the sellers while impoverishing the soul and the earthâ . Brilliant language engineers have been recruited into structures that convert public goods like corpora into private goods like LLMs, while extracting knowledge and behaviour through deception, exploitation, and theft . How do we operate instead as professionals, dedicated to the public good as paramount?
Cascading Crises
LLMs are implicated in three crises
When it comes to LLMs, three crises are particularly significant thanks to the way language constitutes our common life: our stewardship of the planet; the wellbeing of our communities; and the diversity of our cultures. We consider each in turn.
Ecological crisis:
The world is experiencing a cascade of crises encompassing climate, pollution, and biodiversity, linked to heatwaves, flash floods, drought and wildfires . To this, Big AIâs data centres add excessive greenhouse gas emissions, water usage, e-waste, and critical minerals . Societal collapse is a plausible outcome due to: diminishing returns from adding complexity to social structures; the possibility of environmental perturbations that surpass the limits of adaptation; and the way risks propagate through complex systems .
Meaning crisis:
Big AIâs attention economy is linked to social media addiction . LLMs perpetuate harmful classifications . LLMs undermine critical thinking, knowledge diversity, and democracy . LLMs generate fake news, education, and healthcare , lacking access to truth or social norms . The result is a crisis of truth and meaning.
Language crisis:
Minoritised speech communities experience economic and cultural alienation, displacement, and genocide leading to language extinction . The problems are sociopolitical and not fixed by language technologies, with their epistemic harms . Claims for the universal applicability of technologies like speech recognition and machine translation neglect the realities that: (a)Â most of the worldâs population is multilingual, already using a few dozen contact languages for information access and economic participation; (b)Â the 90% of the worldâs languages outside the most populous are generally non-bounded, non-homogeneous, non-written, and non-standardised .
LLMs amplify crisis interactions
Ecological crisis $`\leftrightarrow`$ meaning crisis:
The ecological crisis feeds the meaning crisis when LLM content uses eco-anxiety to capture attention, and doomscrolling numbs eco-anxiety . In the reverse direction, LLM content on social media narcotises dysfunction and apathy, making it harder for communities to unite in the face of the ecological crisis .
Meaning crisis $`\leftrightarrow`$ language crisis:
The meaning crisis feeds the language crisis when the avalanche of attention-grabbing LLM content from dominant languages crowds out local languages, and when attention capture leads to non-participation in local lifeworlds . In the reverse direction, language loss undermines the place of elders, disrupts knowledge transmission, and harms wellbeing and cognition . The data free-for-all violates Indigenous (and human) sovereignty, stoking both crises .
Language crisis $`\leftrightarrow`$ ecological crisis:
The language crisis feeds the ecological crisis when it undermines the capacity of indigenous communities to take care of their storied ancestral lands which are rich in species diversity; and when it accelerates the loss of medicinal knowledge as a resource for human wellbeing . In the reverse direction, mining and climate disasters intensified by data centres displace people from their lands, while climate change and pandemics decimate linguistic communities, and loss of ecological diversity undermines cultures which depend on plant and animal species .
The metacrisis:
The worldâs crises are interconnected systems , drawing together in what has been called the polycrisis or the metacrisis , âa complex system of interrelated, varied, and multi-layered crisesâ . As shown, Big AI, its LLMs, and data centres are all implicated. On top of this, Big AI accelerates itself through the phenomenon of AI hype .
In short, Big AI is accelerating the metacrisis.
A Sober Assessment
Big AI will not govern itself
Big AI interest in ethics functions to minimise regulatory oversight , shaping governments and academia in âa relational outcome of entangled dynamics between design decisions, norms, and powerâ , cf. Figure 1. Big AI purports to âsolveâ the ethical problems of AI with more AI, thanks to âquantitative notions of fairness [that] funnel our thinking into narrow silosâ , and leading to the devastating absence of the rule of law in the virtual world .
Politically-motivated âphilanthropyâ and ethics washing function to maintain a deregulated space, in what is known as the âmirage of algorithmic governanceâ , a âfaçade that justifies deregulation, self-regulation or market driven governanceâ . Big AI puts moneymaking before public safety . âThe idea that economic growth and the pursuit of profit should be tempered in the interests of individual well-being and less unequal societies is anathema to those in the vanguard of economic libertarianismâ .
There are many initiatives to regulate Big AI , but âfully characterizing the social and environmental impacts of Gen-AI is complex and hinders targeted regulationsâ . âIt will be far from straightforward to implement [AI ethics frameworks] in practice to constrain the behaviour of those with disproportionate power to shape AI development and governanceâ . This is no reason not to try.
The benefits do not justify the harms
In a consequentialist moment we might be tempted to dismiss the manifold harms of Big AI (§2) considering the grandiose promises: from âeliminating poverty to establishing sustainable cities and communities and providing quality education for allâ . While waiting for those benefits, we can still consider the quality of the science.
Sequence models are far removed from natural language . Much work is superficial and fashion-driven, with SOTA-chasing and endless âtables with numbersâ . Bias is generally understood âas though it is a bug to be fixed rather than a feature of classification itselfâ . The actors are âdeafeningly male and white and technoheroicâ , valorising technical novelty over all else . AI isnât working, or even helpful, for most people on the planet .
Only a minority of researchers can access SOTA tools, and they must use exponentially more resources for only linear performance gains . Review processes allow industry âresearchâ to leverage the prestige of conference publication into reputational benefits for private companies . In spite of its manifold harms (§2), LLM research receives scholarly recognition . Big AI is free to âpolice its own use of artificial intelligence [leading to] the creation of a prominent conference on âFairness, Accountability, and Transparencyâ [sponsored by] Google, Facebook, and Microsoftâ . This is the Big Tobacco playbook all over again .
The scalability story is a myth
Data centres cannot keep growing on a planet facing climate catastrophe . AI safety is inherently not scalable , and so we see a futile quest where guardrails are piled on top of monitoring systems on top of mitigations in a perpetual game of Whac-a-Mole. Big AIâs dirty secret is the annotation sweatshops located in the âhidden outposts of AIâ . âThe myth of AI as affordable and efficient depends on layers of exploitation, including the extraction of mass unpaid labor to fine-tune the AI systems of the richest companies on earthâ .
Despair is not an option
It is tempting to despair. The long-awaited traction of NLP wasnât meant to be like this. Yet ârestoration is a powerful antidote to despair. Restoration offers concrete means by which humans can once again enter into positive, creative relationship with the more-than-human world, meeting responsibilities that are simultaneously material and spiritual. Itâs not enough to grieve. Itâs not enough to just stop doing bad thingsâ .
What Can We Do?
1. Public good as the paramount consideration.
The ACL Code applies to the conduct of its members, not just our publications. It is not enough to assert that âsomeoneâs going to do it anywayâ (the myth of technological inevitability; ), or that âmy contribution is but a small cog in a large machineâ (the problem of many hands; ), or that âmy work is connecting the world for goodâ (the myth that universal technology artefacts solve social problems; ).
Faced with the metacrisis (§2), the great challenge of our time is to ârebuild our societies, starting from whatâs right in front of us: our areas of influenceâ . This is fundamentally a communal activity: âAll of our flourishing is mutualâ .
2. Protect NLP/ACL from corporate capture.
We need to address the inequity that exists when people with Big AI sponsorship have a disproportionate opportunity to make SOTA contributions . We also need to recognise that the public good principle of the ACL is not aligned with actors who âunderstand AI as a commercial product that should be kept as a closely guarded secret, and used to make profits for private companiesâ .
There is a prima facie conflict of interest when Big AI sponsors our professional bodies and when Big AI employees serve as office-bearers. Whether real or perceived, such conflicts must be declared and managed, as we navigate the âinternal battle between those in charge of the business, which [needs] to be on the right side of power, and the independent editorial hierarchy, which [needs] to be responsible to the peopleâ .
3. Cultivate actual natural language processing.
The ACL has the liberty to re-assert the scope of computational linguistics in its calls for papers, centering the phenomenon of natural human language , and to use evaluation âas a force to drive changeâ .
4. Establish protected spaces for critical NLP.
Review processes should ensure that scholarly contributions are not rejected simply for challenging the status quo. This includes research on power dynamics and injustices , which may encounter friction from reviewers who want to guard the space for ideologically safe contributions.
5. Articulate a vision for life-sustaining research.
What is our vision for language technology in the context of human flourishing on a living planet? New conference themes, workshops, and journal special issues are a start. However, I believe we need to promote new framings, methods, and evaluations, e.g.: the principles of data feminism ; the 10 Point Plan to Address the Information Crisis ; community-centric approaches ; the Ethics of Care ; decolonising methods ; Benderâs proposals for resisting dehumanisation ; and Senâs Capability Approach . Language engineers need to be made aware of the political and value-laden nature of their work .
6. Leadership with public statements & policies.
In view of the harms of LLMs and the imperative to understand the public good as paramount, the ACL could provide informational statements and develop policy positions . This would serve ACL Ends âto represent computational linguistics to foundations and government agencies worldwideâ and âto provide information on computational linguistics to the general publicâ.
7. Fundamental shift in perception and values.
Taking action is difficult unless we are ânurtured by deeply held values and ways of seeing ourselves and the worldâ . Only then can we stop seeing social problems as opportunities for universal technologies , resist technofeudalism and the âAI Arms Raceâ , replace our quest for efficiency with âvalues such as autonomy, creativity, ethics, slowness, carefulnessâ , supplant extractivist thinking with abundant intelligences , and embrace natural language as humanityâs greatest technology for enacting our agency and sustaining our common life.
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