The “AI Fights” of 2025 Are Cooling—But the Real Competition Moves in 2026

Author: L.Hartwell

Affiliation: Independent Researcher

People often talked about AI in 2025 as a series of "fights." These included fights over rules, lawsuits over data and copyright, geopolitical disputes over chips and cloud capacity, and fierce competition among companies to release models that could do more and more. This article contends that numerous conflicts did not "conclude" in 2025 but rather transformed—from vociferous, headline-oriented confrontations to more subdued institutionalisation. The article elucidates the volatility and convergence within the AI sector in 2025 through the lens of Bourdieu’s field theory, world-systems analysis, and institutional isomorphism. Actors endeavoured to safeguard their positions in a swiftly evolving field while concurrently emulating each other’s governance practices, safety protocols, and compliance frameworks. The paper employs a structured qualitative review of the 2024–2025 policy debates, industry reporting, technical trend literature, and organisational disclosures to construct a thematic map of the prevailing conflict arenas of the year. The results show that the "AI fights" can be grouped into four main areas of conflict: (1) legitimacy and trust, (2) control of data and cultural production, (3) control of compute and supply chains, and (4) control of standards for responsible deployment. The article predicts that by 2026, "competition by institutional design" will be the norm. This means that an advantage will come less from the size of the model and more from: agentic systems that can do multi-step work, verifiable governance, enterprise integration, and the ability to work across regulatory blocs. The paper concludes that in 2026, organisations that can turn technical skills into recognised authority, which is a kind of symbolic capital, will probably be rewarded. They will also need to deal with global inequality in access to computing, language resources, and infrastructure.

Keywords: AI governance; competition; regulation; copyright; compute geopolitics; institutional theory; agentic systems

Introduction

People started to call AI development a conflict in 2025. People talked about "wars" of models, "arms races" of computers, and "legal battles" over training data. These metaphors were not made up. They talked about how quickly the AI field was growing and how unsure its rules still were. Organizations and governments were not merely building tools; they were negotiating who gets to define what AI is, what it should do, and what counts as acceptable risk.

Yet if we step back, 2025 also looks like a year where the most dramatic confrontations began to cool. Not because the underlying tensions disappeared, but because the ecosystem started to stabilize into recognizable institutions: compliance teams, audit language, safety benchmarks, procurement guidelines, and sector-specific governance templates. Public fights became less chaotic, while private bargaining increased.

This paper answers two questions:

1. What did the “AI fights” of 2025 actually represent at a structural level?

2. What are we likely to see in 2026 as those conflicts shift into rules, routines, and organizational forms?

To keep the discussion practical, the article uses simple, human-readable English while maintaining a Scopus-style structure. Theoretical framing comes from:

• Bourdieu’s field theory (competition for capital and position),

• World-systems analysis (core/periphery dynamics in global AI infrastructure), and

• Institutional isomorphism (why organizations become similar under pressure).

The argument is straightforward: 2025 was a year of contested legitimacy. Actors fought to control attention, legal definitions, and supply chains. In 2026, advantage will increasingly come from the ability to operate as a “trusted institution” across different regulatory and geopolitical contexts—while delivering measurable value through reliable AI systems.

Background

1) Bourdieu: AI as a Field of Power and Capital

Bourdieu describes social life as organized into fields—structured spaces of competition where actors struggle for resources and status. Each field has its own “currency,” which Bourdieu calls forms of capital:

• Economic capital: money, compute budgets, market share.

• Cultural capital: expertise, research capability, talent, and know-how.

• Social capital: alliances, partnerships, access to networks and distribution channels.

• Symbolic capital: legitimacy, reputation, trust, and the power to define what is “responsible” or “innovative.”

Applied to AI in 2025, the “fights” can be read as struggles over symbolic capital as much as technical performance. When firms publish safety frameworks, release transparency reports, join standards initiatives, or emphasize “responsible AI,” they are not only reducing risk. They are also competing to be seen as the rightful leaders of the field.

Two details matter here. First, symbolic capital is scarce and unstable during rapid technological change. Second, actors with economic power often try to convert it into symbolic legitimacy. In 2025, we saw many attempts to turn compute dominance into moral authority (“we are the safe and responsible builders”). Meanwhile, critics—authors, artists, civil society, and some regulators—contested that legitimacy by challenging training practices, labor impacts, and information integrity.

2) World-Systems: Core, Semi-Periphery, and AI Infrastructure

World-systems theory argues that the global economy is shaped by unequal relations between a core (high-tech, high-capital regions), a periphery (resource-providing, low-bargaining regions), and a semi-periphery (hybrid zones that both depend on and compete with the core). In AI, the equivalent structure is visible in:

• Concentration of advanced compute and cloud infrastructure,

• Concentration of frontier model research,

• Unequal access to high-quality training data and language resources,

• Export controls, supply-chain restrictions, and dependency on specific chip ecosystems.

From this view, the “AI fights” of 2025 were not only corporate rivalries. They were also global negotiations about who gets to build, who gets to buy, and who must accept dependency. AI capability became tied to national and regional strategies, especially where compute supply chains and cloud access intersected with security narratives.

3) Institutional Isomorphism: Why Everyone Started to Look Alike

DiMaggio and Powell’s institutional isomorphism explains why organizations in the same environment become similar. They identify three mechanisms:

• Coercive isomorphism: pressure from laws, regulators, procurement rules, and powerful buyers.

• Mimetic isomorphism: copying peers when uncertainty is high (“best practice” imitation).

• Normative isomorphism: shared professional standards driven by experts, auditors, and credentialed communities.

In 2025, these pressures grew quickly. Even organizations that disliked regulation often adopted similar language: risk categories, audit readiness, alignment policies, security controls, and model governance checklists. This reduced the appearance of conflict (“we all support responsible AI”) while moving battles into subtler arenas: definitions, enforcement, technical measurement, and cross-border compliance.

Method

Research Design

This article uses a structured qualitative synthesis (similar to an integrative review) rather than an experiment. The aim is explanatory: to interpret what “AI fights” meant socially and institutionally, and to forecast plausible 2026 dynamics.

Data Sources and Sampling Logic

The analysis draws on four categories of materials published or discussed widely during 2024–2025:

1. Policy and governance texts (regulatory frameworks, risk management standards, government strategy documents).

2. Industry disclosures (model cards, safety reports, transparency notes, corporate policy statements).

3. Academic and technical literature on foundation models, AI governance, and socio-technical risk.

4. Synthesis reports from consulting and research organizations tracking AI adoption.

Sampling favored texts that were (a) repeatedly referenced in professional discourse and (b) representative of different stakeholder positions (industry, government, civil society, research). Because the article is written for publication without external links, sources are listed as standard references at the end.

Analytical Procedure

The research applied a thematic coding approach:

• Step 1: Identify recurring “fight arenas” (regulation, IP/data, compute geopolitics, trust/safety, labor and adoption).

• Step 2: Map each arena to theoretical lenses (field competition, core/periphery relations, isomorphism).

• Step 3: Extract patterns of “resolution” (where conflict cooled) versus “migration” (where conflict moved into new forms).

• Step 4: Build a 2026 outlook based on observed institutional trajectories (compliance maturity, enterprise integration, agentic systems, evaluation regimes).

Limitations

This is not a predictive model with quantified probabilities. It is a theory-informed synthesis. Forecasting is presented as reasoned expectation, not certainty. Also, “AI fights” is an interpretive label—useful for organizing discourse but not a precise category.

Analysis

Arena 1: The Legitimacy Fight—Who Gets to Define “Responsible AI”?

By 2025, many stakeholders agreed AI was valuable, but disagreed about acceptable trade-offs. This produced a legitimacy struggle:

• Firms sought legitimacy through safety teams, transparency language, and claims of responsible development.

• Governments sought legitimacy by promising protection: privacy, security, consumer rights, and national competitiveness.

• Creators and civil society sought legitimacy by highlighting harms: unauthorized use of work, bias, misinformation, labor displacement, and surveillance concerns.

• Enterprises sought legitimacy through procurement discipline: demanding auditability, security, and contractual clarity.

Using Bourdieu, we can say actors competed for symbolic capital by positioning themselves as guardians of the public interest. In practice, that meant:

• producing governance rituals (reports, principles, oversight boards),

• shaping risk vocabulary (“high-risk,” “general purpose,” “frontier,” “dual-use”),

• and defining what counts as evidence of safety (benchmarks, red-teaming, incident reporting).

The 2025 “fight” cooled when organizations realized that legitimacy must be operational, not just rhetorical. Enterprises began to ask: “Can we audit this system? Can we control data flows? Can we explain decisions? Can we ensure reliability?” The fight moved from grand debates to implementation details.

Isomorphism explains why corporate governance statements began to resemble one another. Under regulatory uncertainty, organizations copied templates that appeared “safe” and “professional.” Over time, these templates became market requirements.

What this sets up for 2026: legitimacy will be increasingly measured by verifiability—not only what organizations claim, but what they can prove.

Arena 2: The Data and Copyright Fight—Cultural Production as a New Bargaining Space

AI systems depend on data, and generative AI depends heavily on creative and informational content. In 2025, conflicts around training data became more visible in courts and public debate. The underlying question was not only legal; it was economic and cultural:

• Who owns the past cultural record?

• Who is allowed to learn from it at scale?

• What compensation—if any—is owed to creators and publishers?

• How should consent work in an era of web-scale training?

From a world-systems lens, we also see unequal bargaining power. Creators, small publishers, and institutions in less wealthy regions often lack resources to negotiate or litigate. Meanwhile, large firms can treat legal risk as a cost of innovation.

In Bourdieu’s terms, this is a conflict over cultural capital (knowledge, content, artistry) and its conversion into economic capital (commercial AI products). Creators argued that AI firms were extracting value without fair exchange. AI firms argued that learning from existing material is part of innovation and that outputs are “transformative.”

By late 2025, the “fight” began to shift toward market-making: licensing deals, dataset governance, opt-out/opt-in systems, provenance tracking, and content authenticity tools. Even when the legal landscape remained unsettled, organizations increasingly acted as if they needed a stable pipeline of high-quality, permissioned data—especially for enterprise and public-sector uses.

What this sets up for 2026: growth in data rights management, provenance standards, licensing intermediaries, and a stronger divide between “open web training” and “contracted training.”

Arena 3: The Compute and Supply-Chain Fight—AI Capability as Geopolitical Infrastructure

In 2025, the most important constraint was not imagination; it was compute. Advanced AI relies on chips, energy, cooling, networking, and cloud-scale operations. This made AI a strategic asset, and strategic assets trigger geopolitical bargaining.

World-systems theory helps explain why the global AI map looks uneven:

• Core actors control key chip design ecosystems, high-end manufacturing, and hyperscale cloud.

• Semi-periphery actors try to build domestic capacity or become regional hubs.

• Periphery regions often become sites of extraction (minerals, data labeling labor, or data generation) while lacking control over AI infrastructure.

In this context, the “AI fights” of 2025 were also about dependency. Regions and firms asked:

• Can we access advanced compute reliably?

• Are we exposed to export restrictions or procurement bans?

• Can we build local capacity, or must we rent it from the core?

The fight cooled in public discourse because organizations turned toward pragmatic strategies: multi-cloud approaches, model efficiency, smaller specialized models, and on-device inference to reduce dependency. But these are not equal solutions. Efficiency helps, yet frontier capability still demands scale. This means the core retains structural advantage.

What this sets up for 2026: stronger “compute realism.” Organizations will compete on efficiency, but geopolitical blocs will still matter. Expect more investment in regional AI infrastructure, sovereign cloud narratives, and energy-aware AI engineering.

Arena 4: The Standards Fight—Benchmarks, Audits, and the Politics of Measurement

When a technology becomes powerful, measurement becomes political. In 2025, it was no longer enough to claim a model was “safe” or “accurate.” Stakeholders demanded evidence.

But what counts as evidence?

• Benchmarks can be gamed.

• Safety tests can be selective.

• Real-world performance depends on context.

• Harm is often social, not only technical.

Institutional isomorphism again matters. Once audit language enters procurement, organizations start aligning to what auditors can check. This produces a predictable pattern: what gets measured gets managed, and what gets managed becomes the definition of “responsible.”

This creates a subtle “fight” that will intensify in 2026: a struggle over evaluation regimes. Competing groups will promote different measurement systems:

• Developers may prefer capability benchmarks and controlled red-team results.

• Regulators may prefer documentation, incident reporting, and lifecycle controls.

• Enterprises may prefer reliability, security, and liability clarity.

• Civil society may prefer transparency, discrimination testing, and impact assessment.

In Bourdieu’s terms, controlling benchmarks is a way to accumulate symbolic capital: the authority to declare what counts as “good AI.”

What this sets up for 2026: expansion of independent evaluation, standardized reporting, third-party audits, and sector-specific testing (finance, health, education, public services).

Arena 5: The Workplace and Adoption Fight—From “Can It?” to “Should We?” to “How Do We Control It?”

A major shift in 2025 was that AI became less of a novelty and more of an operational concern. The central question changed:

• Early phase: “Can the model do it?”

• 2025 phase: “Should we deploy it?”

• Late 2025 into 2026: “How do we control it at scale?”

Enterprises increasingly treated AI not as a single tool but as a socio-technical system: it changes workflows, incentives, accountability, and skills. This created conflict between:

• productivity ambitions and risk governance,

• speed of innovation and compliance,

• experimentation and the need for consistent quality.

Institutional pressures pushed organizations toward new roles: AI risk officers, model governance committees, secure deployment pipelines, and internal policies about data and prompts. This is a form of normative isomorphism: professional communities (security, compliance, audit, procurement) impose their standards on AI teams.

What this sets up for 2026: deeper integration into business processes, paired with stronger controls. AI will be “everywhere,” but increasingly boxed into governed channels.

Findings

From the analysis, five findings summarize how the “AI fights” of 2025 cooled and transformed.

Finding 1: The Fights Did Not End—They Institutionalized

The core conflicts of 2025 persisted, but moved from public confrontation to organizational routines: compliance programs, licensing negotiations, procurement checklists, and evaluation frameworks. The visible “war” narrative softened as institutions absorbed the conflict.

Finding 2: Symbolic Capital Became a Competitive Asset

Beyond model capability, the winners of 2025 were those who gained trust: in enterprises, in government relationships, and in public discourse. In Bourdieu’s terms, symbolic capital became convertible into contracts, access, and policy influence.

Finding 3: Global Inequality in Compute Became More Structuring Than Model Design

Even as model optimization improved, the global distribution of compute continued to shape who could train frontier systems, who could deploy them cheaply, and who could build local ecosystems. World-systems dynamics remained central.

Finding 4: Isomorphism Produced Convergence in Governance Language

Organizations increasingly sounded alike: safety commitments, risk frameworks, transparency templates. This reduced chaos but also created “governance theater” risks—performing compliance without genuine control.

Finding 5: The Center of Gravity Shifted Toward Systems, Not Models

By late 2025, the most important advances were not only about larger models, but about systems: tools, orchestration, retrieval, agents, security, monitoring, and human-in-the-loop processes. This shift accelerates in 2026.

What We Will See in 2026

Based on 2025 dynamics, the following developments are likely in 2026.

1) The Rise of Agentic AI as the New Competitive Frontier

In 2026, the biggest excitement will likely come from AI systems that can plan, act, and verify—not just generate text. “Agents” will be marketed as digital workers that can execute multi-step tasks: scheduling, procurement support, customer workflows, document handling, and internal analytics.

But agents increase risk: they can take actions, trigger transactions, and propagate errors. This will push governance from “model safety” to “system safety,” including permissions, sandboxing, monitoring, and rollback mechanisms.

2) A Stronger Split Between Consumer AI and Governed Enterprise AI

Consumer tools will remain fast-moving and experimental. Enterprise AI will become more conservative: controlled data environments, strict access rules, contractual warranties, and auditable logs. Expect a “two-speed AI world.”

3) Compliance as a Product Feature, Not a Legal Afterthought

In 2026, compliance will become a selling point: documentation quality, audit-ready reports, risk classification support, and built-in safety controls. Firms that treat compliance as design—not paperwork—will gain market share in regulated industries.

4) Content Provenance and Authenticity Systems Will Expand

As deepfakes and synthetic media become more common, provenance will matter more for journalism, education, and public trust. The next fight will be over which provenance standards become dominant and who controls verification infrastructure.

5) Efficiency Engineering Will Become a Mainstream Strategy

With compute constrained and energy costs visible, 2026 will reward efficient architectures, compression, retrieval-augmented approaches, and smaller specialized models. This also supports broader access in semi-periphery regions.

6) The Geography of AI Will Matter More—Regulatory and Geopolitical Blocs

Organizations will increasingly design deployment strategies around blocs: data rules, model obligations, export restrictions, and sector regulations. Global firms will need “compliance choreography”: aligning product behavior with multiple regimes without fragmenting into chaos.

7) The Quiet Return of Human Skill as Differentiator

Paradoxically, as AI becomes more capable, organizations will rediscover the value of human judgment: domain expertise, ethics, security, and operational discipline. The most successful deployments will invest in training, change management, and accountability—turning human capability into organizational resilience.

Conclusion

The "AI fights" of 2025 were real, but the way they ended is best seen as a shift into a more structured phase. Using Bourdieu, we can see that there is competition for capital and legitimacy in a field that is growing quickly. World-systems analysis shows us how global inequality in computing and infrastructure affects both opportunity and dependence. We can use institutional isomorphism to understand why organisations adopted similar governance practices when there was uncertainty about the rules.

The main question in the competition in 2026 will probably be: Who can make AI systems that are not only powerful, but also easy to control, check, and trust across borders?The next step is less about big releases and more about designing institutions, such as systems engineering, evaluation regimes, licensing markets, compliance-by-construction, and responsible integration into work.

In short, the fights in 2025 didn't go away; they grew up. And 2026 will be the year when being mature really pays off.

Hashtags

#ArtificialIntelligence #AIGovernance #TechPolicy #DigitalEconomy #InnovationManagement #FutureOfWork #AI2026

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