AI and the Transformation of State-Capital Dynamics

• 23 Feb 2026

This editorial is based on “As we contemplate possibilities of AI, it is wreaking enduring transformations in state-capital relations” which was published in The Indian Express on 21/02/2026. The editorial examines how Artificial Intelligence is fundamentally transforming state–capital relations by anchoring capital to infrastructure, data, and national security. It highlights the rise of techno-nationalism and the governance challenges posed to democracy, markets, and civil liberties.

For Prelims: IndiaAI Mission, Digital Public Infrastructure, DPDP Act 2023,Semiconductor Mission.

For Mains: How is AI reshaping the structure of state-capital relations, what issues arise from this reshaping, measures needed.

Artificial Intelligence is no longer merely a disruptive technology, it is reconfiguring the architecture of political economy itself. Unlike earlier waves of globalisation that thrived on mobile and footloose capital, AI is deeply infrastructure-heavy, territorially anchored, and data-intensive. This has altered the incentives of capital, drawing it closer to the state for regulation, security, and strategic support. Consequently, AI is catalysing a profound transformation in state–capital relations, marked by techno-nationalism, concentration of power, and the erosion of traditional market–state boundaries.

How is Artificial Intelligence Reshaping the Structure Of State–Capital Relations in India ?

• Techno-Nationalism & The Rise of Infrastructure Monopolies: India is moving from a passive regulator to an active co-investor in AI infrastructure, aligning state capacity with large domestic firms to build sovereign compute and foundational models. This techno-nationalist strategy seeks to reduce dependence on foreign Big Tech by fostering scale-intensive infrastructure monopolies, creating a close, strategic state–capital alliance driven by geopolitical and economic imperatives.  
  • Under the ₹10,372 crore IndiaAI Mission, the state has recently deployed over 38,000 GPUs at a subsidized rate of ₹65 per hour through strategic public-private partnerships. 
  • Concurrently, the ₹76,000 crore India Semiconductor Mission is incentivizing domestic champions, such as Tata Electronics' recent 2026 partnership with Qualcomm, to manufacture critical AI chips locally.
• Convergence of State Surveillance & Corporate Data Extraction: AI is erasing the divide between private data extraction and state authority, with firms relying on legal sanction to deploy mass analytics while the state increasingly outsources policing and governance to private algorithms. 
  • This symbiosis fuels surveillance capitalism, where corporate revenues are secured through state contracts and public security becomes dependent on proprietary tools. 
  • During the 2025 Ganesh Chaturthi in Pune, AI-enabled CCTV systems using private facial recognition software reportedly generated over 8 lakh behavioural alerts, while the February 2026 integration of AI into national criminal databases for predictive policing further institutionalised this state–corporate interdependence.
• Digital Public Infrastructure (DPI) & Informational Capitalism: The Indian state is redefining its economic role by architecting Digital Public Infrastructure (DPI) that democratizes foundational AI resources to catalyze private wealth creation. By nationalizing anonymized datasets and providing open-source models, the state actively dismantles the data moats of foreign tech monopolies to facilitate domestic informational capitalism. 
  • Consequently, the state transitions into an indispensable ecosystem enabler, ensuring that private sector AI innovation and capital accumulation are structurally dependent on state-maintained public goods. 
  • The government’s AIKosh platform actively drives this by offering over 7,500 high-quality datasets and 273 AI models as shared national resources for startups and enterprises. 
  • Additionally, Andhra Pradesh's February 2026 launch of the "Swadeshi AI Stack" in partnership with IBM and NxtGen exemplifies regional governments providing centralized digital goods for private commercial exploitation.

• Regulatory Capture & Pro-Innovation Policy Alliances: Private capital is heavily shaping India’s AI regulatory landscape by lobbying for "light-touch," pro-innovation frameworks that prioritize rapid market expansion over stringent rights-based safeguards. Rather than constraining tech monopolies like the European Union, the Indian state strategically aligns with capital, viewing deregulation as a necessary geopolitical compromise to attract global investment. 
  • This structural concession reveals a state-capital consensus where civil liability and privacy concerns are subordinated to the mutual goal of accelerating domestic technological supremacy. Validating this deregulatory alliance, the February 2026 India AI Impact Summit successfully secured over $250 billion in infrastructure investment commitments from both global and domestic tech conglomerates. 
  • Concurrently, the government's recently published AI Governance Guidelines maintain a strictly non-interventionist stance, reflecting industry demands for self-regulation and enabling a projected $32 billion domestic AI market by 2031.
• Algorithmic Governance & the Casualization of Labor: The state actively permits tech capital to utilize algorithmic management to casualize labor, systematically prioritizing frictionless capital accumulation over traditional worker protections. By refusing to mandate formal employment status for gig workers, the government structurally aligns with platform monopolies to suppress wage costs and bypass social security obligations. 
  • This consensus fundamentally restructures the industrial workforce, shifting the entire economic risk of platform capitalism onto atomized, algorithmically managed digital labor. Following the January 2026 nationwide gig worker strikes, the Ministry of Labour declined to classify platform workers as formal employees, allowing platforms to retain AI-driven piece-rate wages. 
  • Consequently, India's digital gig workforce, projected to exceed 23.5 million by 2030, remains structurally excluded from statutory provident funds and guaranteed minimum wages under current digital labor codes.
• Sovereign Venture Capitalism & Deep-Tech Protectionism: The Indian state is fundamentally blurring public finance and private enterprise by aggressively acting as a venture capitalist to insulate strategic AI innovation from foreign acquisition. By assuming the role of the ultimate underwriter of high-tech risk, the government structurally tethers private capital accumulation directly to sovereign geopolitical interests. 
  • This interventionist posture ensures that critical AI breakthroughs by private startups remain entirely captive to domestic state agendas rather than global financial markets. Through the newly operationalized ₹10,000 crore DeepTech Fund, the government has directly co-invested in over 12 indigenous AI startups focusing on dual-use technologies since late 2025.

• Militarisation of AI and the Emerging Military–Tech Complex: State–capital relations are increasingly militarised as governments move beyond traditional public sector defence manufacturing to engage agile private technology firms for AI-enabled capabilities. This shift is driven by the need for rapid innovation in areas such as autonomous systems, surveillance analytics, and counter-drone technologies, which conventional defence PSUs often struggle to deliver at pace.
  • As a result, national security architectures are becoming structurally dependent on private intellectual property, elevating specialised defence-tech firms to the status of strategic sovereign assets. 
  • Recent defence modernisation priorities and procurement reforms have facilitated greater participation of startups and private firms in AI-driven threat assessment, unmanned systems, and decision-support tools. 
  • This trend signals the gradual emergence of a domestic military–industrial–tech complex, where public defence objectives and private technological capital are increasingly fused, reshaping the balance between state authority and market power in the security domain.
• "GovTech" Privatization & Algorithmic Austerity: The state is increasingly outsourcing its core welfare architecture to private cloud and analytics capital to achieve fiscal consolidation through algorithmic optimization and austerity. By transforming citizen-welfare delivery into a lucrative "GovTech" procurement market, private firms directly profit from streamlining state expenditure and identifying administrative redundancies. 
  • This creates a structural dependency where the state’s fundamental capacity to govern and distribute resources is entirely mediated by proprietary, private-sector predictive models. 
  • The late-2025 integration of private AI analytics into Aadhaar-linked Public Distribution Systems generated over $1.8 billion annual AI revenue in continuous state contracts for IT majors like TCS and Infosys.

What Issues Arise from the AI-Driven State–Capital Nexus? 

• Regulatory Capture & Monopolization: The symbiotic alliance between governments and Big Tech fosters severe regulatory capture, effectively stifling grassroots innovation and market competition. As states increasingly rely on corporate capital to secure national AI supremacy, they enact protectionist policies that inherently shield existing tech monopolies. 
  • Consequently, public interest and ethical safeguards are systematically subverted to sustain the market dominance of a few elite behemoths. For instance, digital industry lobbying expenditures in the EU surged to €151 million by 2026 to dilute the AI Act's data protection clauses. 
  • Similarly, major US tech firms funded the $100 million "Leading the Future" Super PAC in 2025 to successfully block state-level AI regulations and secure federal regulatory moratoriums.
• Surveillance Capitalism & Digital Authoritarianism: The nexus accelerates the deployment of mass surveillance architectures, seamlessly merging corporate data extraction with state security apparatuses. Capital constructs hyper-invasive predictive models driven by profit, which authoritarian and democratic states alike weaponize to monitor citizens and crush political dissent. 
  • This convergence obliterates privacy rights, creating a digital panopticon where biometric tracking becomes a mandatory condition for societal participation. In Pakistan, state authorities have explored AI-driven facial progression models built from social media data to track marginalized groups like the Baloch community across generations. 
  • In the US, the FTC recently banned Rite Aid from using biased facial recognition security systems, highlighting how unchecked corporate surveillance tools lead to the wrongful targeting of minorities.
• Hyper-Militarization & Autonomous Warfare: Private capital is aggressively militarizing AI technology, blurring the line between civilian innovation and lethal autonomous weapons systems. 
  • Defense departments funnel billions into tech corporations to develop AI-powered target identification and command-and-control infrastructures, fundamentally altering the ethics of modern warfare. This privatized arms race lowers the threshold for lethal engagement, bypassing traditional geopolitical breakers that prevent catastrophic conflicts. 
  • The UN General Assembly recently adopted resolution 79/239 in late 2024 to address the severe international security risks posed by this rapid military-AI integration. A stark example is the documented use of AI systems like "Lavender" in the Middle East, where automated algorithms generated kill lists with human operators approving strikes in as little as 20 seconds.
• Unchecked Environmental Degradation: The state-capital race for AI supremacy actively ignores the catastrophic ecological footprint of hyperscale compute infrastructure. Governments grant massive subsidies and regulatory exemptions to corporate data centers, prioritizing techno-nationalism over binding international climate commitments. 
  • This insatiable demand for processing power exacerbates resource scarcity, plunging vulnerable regions into severe water and energy crises. The International Energy Agency projects global data center electricity demand will double to 945 terawatt-hours by 2030. 
  • Furthermore, water consumption by Indian data centers is slated to double to 358 billion liters by 2030, worsening the climate crisis in an already water-stressed nation.
• Geopolitical Bifurcation & The "Splinternet": The fusion of state strategy and technological capital is fracturing the global digital ecosystem into deeply polarized, sovereign tech blocs. Driven by techno-nationalism, superpowers are weaponizing semiconductor supply chains and hardware exports to starve geopolitical rivals of frontier compute capabilities. 
  • This zero-sum competition destroys the promise of an open, globally integrated internet, forcing third-party nations to choose sides in an algorithmic Cold War. The strategic divergence is stark, Washington leverages private-sector innovation and export controls via the $53 billion CHIPS Act, while Beijing pursues state-led self-reliance with goals exceeding 90% AI adoption by 2030. 
  • Consequently, global AI research is bifurcating, with China capturing over 40% of global AI citations in 2024, four times higher than the US individually.
• Digital Colonialism & Global South Exploitation: The AI nexus perpetuates a modern form of digital colonialism, where powerful state-backed tech conglomerates extract resources and data from the Global South. Developing nations are relegated to supplying cheap labor for data annotation and raw critical minerals for hardware, while the immense wealth generated by AI remains concentrated in Western and Chinese capitals. 
  • This structural inequality strips developing nations of their digital sovereignty, forcing them into perpetual technological dependency. Building a standard 2 kg computer requires extracting 800 kg of raw materials, heavily relying on unsustainable rare earth mining in the Global South to fuel northern AI hardware. 
  • Additionally, advanced tech ecosystems in Silicon Valley and Shenzhen hoard the trillions in projected AI-driven GDP growth, leaving developing nations marginalized and without equitable access to foundational models.
• Labor Disruption & Extreme Wealth Concentration: Corporate capital utilizes AI to aggressively automate labor and slash operational costs, while state policies fail to protect displaced workforces or redistribute the resulting productivity gains. 
  • This structural shift moves power away from human labor directly into the hands of computer-owners, fundamentally destabilizing the middle class and exacerbating socio-economic divides. 
  • Governments, heavily influenced by tech lobbying, actively resist implementing robust welfare nets or universal basic income, prioritizing corporate margins over societal stability. 
  • In the US alone, AI-related capex currently equates to approximately 0.8% of US GDP. Meanwhile, widespread commercial automation is accelerating job displacement across both blue-collar and white-collar sectors, sparking severe populist backlash and localized protests against rising inequality.
• Democratic Erosion & Algorithmic Propaganda: The alliance between state actors and digital platforms weaponizes information ecosystems, utilizing generative AI to manipulate public perception at an unprecedented scale. Capital prioritizes engagement-driven algorithms that amplify polarizing content, which states and political entities easily exploit to launch targeted disinformation campaigns and suppress democratic discourse. 
  • This synthetic manipulation of reality erodes institutional trust and dismantles the shared factual baseline required for functioning democracies. During recent global election cycles, deep fakes and algorithmic bias were heavily utilized to hyper-target voters, exploiting the vast troves of personal data collected by tech monopolies. 
  • AI lobbying groups like CCIA continue to push for the right to scrape sensitive political and demographic data without active consent, ensuring these systems remain highly effective manipulation engines.

What Measures are Required To Manage This Transformation?

• Sovereign Digital Public Infrastructure for AI: To prevent complete corporate capture of foundational AI models, governments must establish robust, publicly funded digital public infrastructure (DPI) dedicated to AI compute and datasets. This approach democratizes access by treating advanced computing clusters and high-quality training data as shared public utilities rather than exclusive proprietary assets. 
  • By offering subsidized access to sovereign GPU grids and culturally representative open-source datasets, states can empower local startups and researchers to build competitive models without relying on Big Tech ecosystems. 
  • Such a measure directly dismantles the high barriers to entry that currently define the frontier AI market, fostering a decentralized innovation landscape. Ultimately, building a public option for AI infrastructure guarantees technological sovereignty and ensures that critical digital development remains aligned with public interest rather than solely corporate margins.
• Implementation of Federated Data Trusts: Breaking the monopolistic grip on the raw material of AI requires the legal establishment of federated data trusts to govern data sharing and utilization. These independent, fiduciary-driven trusts would act as intermediaries between data subjects and AI developers, ensuring that data is accessed ethically, securely, and purely for authorized purposes. 
  • Operating on principles of data portability and interoperability, this framework mandates that dominant tech platforms share anonymized, high-value datasets with smaller market players through highly regulated APIs. 
  • This structural intervention eliminates the zero-sum nature of data hoarding, effectively neutralizing the massive network effects that shield incumbent monopolies from competition. By placing collective bargaining power back into the hands of a fiduciary, data trusts transform extractive surveillance practices into a balanced, consent-driven data economy.
• Ex-Ante Antitrust Enforcement for the AI Stack: Traditional competition laws are too reactive for the rapid evolution of the AI state-capital nexus, necessitating a shift toward aggressive, ex-ante antitrust frameworks. Regulators must proactively define and monitor the critical chokepoints of the AI technology stack, spanning from silicon manufacturing and cloud compute to foundational models and consumer applications. 
  • This measure involves strictly blocking vertical mergers and exclusive supply partnerships that allow dominant players to lock in ecosystem control and starve competitors of essential infrastructure. By legally enforcing structural separation between the layers of the AI supply chain, authorities can prevent conglomerates from self-preferencing their own downstream applications. 
  • Consequently, this proactive market-shaping ensures a level playing field where innovation is driven by merit and technological superiority rather than entrenched capital power.
• Embedding Techno-Legal Compliance by Design: The inherent complexity and opacity of neural networks demand that regulatory compliance is no longer treated as a post-development checklist but is hardcoded directly into the AI architecture. Policymakers must mandate a techno-legal governance framework where legal obligations, such as privacy preservation, bias mitigation, and transparency, are mathematically embedded into the model's training and deployment pipelines. 
  • This involves requiring developers to integrate automated audit trails, real-time anomaly detection, and explainability modules before a system can be commercially released. By merging rule-based legal conditioning with technical enforcement mechanisms, regulators can achieve continuous, automated oversight without suffocating the pace of innovation. 
  • This architectural shift ensures that frontier models remain intrinsically bound by societal safeguards, drastically reducing the risk of catastrophic failures or undetected systemic discrimination.
• Mandatory Algorithmic Impact Assessments: To counter the socio-economic risks generated by algorithmic decision-making, states must enforce mandatory Algorithmic Impact Assessments (AIAs) for all high-risk AI deployments. Modeled after environmental impact studies, these standardized assessments compel corporations to rigorously evaluate and document the potential consequences of their AI systems on marginalized communities, labor markets, and democratic institutions. 
  • Crucially, the assessment process must include mandatory consultations with diverse cross-functional teams, domain experts, and the public communities most likely to be affected by the technology. 
  • By forcing organizations to transparently justify their algorithmic choices and explicitly outline their risk-mitigation strategies, this measure pierces the veil of corporate secrecy. Institutionalizing this preemptive scrutiny transforms AI development from a rapid beta-testing endeavor into a deliberate, accountable, and socially responsible engineering practice.
• Graded Liability and Human-in-the-Loop Mandates: Addressing the accountability vacuum in autonomous systems requires the implementation of a graded, function-based liability regime paired with strict human-in-the-loop oversight mandates. This legal structure proportionally assigns legal and financial responsibility to AI developers, deployers, and importers based on the system's risk classificationand the specific function it performs. 
  • For applications operating in critical sectors like healthcare, criminal justice, or infrastructure, this measure legally mandates a human-in-the-loop workflow to review and approve machine-generated decisions. Implementing these operational fail-safes ensures that algorithmic hallucinations or biased outputs cannot automatically trigger life-altering actions without human contextual judgment and intervention. 
  • By aligning severe financial penalties with negligent algorithmic deployment, this measure economically incentivizes corporations to prioritize safety and precision over rapid market capture.
• Agile Regulatory Sandboxing for Sovereign Innovation: Navigating the tension between rigid state control and unchecked capitalist expansion requires the widespread adoption of agile, state-sponsored regulatory sandboxes. 
  • These controlled testing environments allow emerging startups to experiment with frontier AI applications under the direct, collaborative supervision of regulatory authorities. 
  • Within these sandboxes, companies receive temporary waivers from certain compliance burdens in exchange for granting regulators full transparency into their model's behavior, training protocols, and risk profiles. 
  • This dynamic feedback loop enables policymakers to craft precise, evidence-based rules derived from real-world technological capabilities rather than abstract, outdated legislative assumptions. Ultimately, this collaborative approach fosters a pro-innovation ecosystem that nurtures sovereign technological capabilities while simultaneously designing the exact guardrails needed to protect public safety.
• Harmonized Global Interoperability Standards: Because the AI-driven state-capital nexus operates fluidly across borders, managing its influence requires establishing binding, interoperable global governance standards. International coalitions must establish unified technical benchmarks for AI safety, content provenance, and data security to prevent corporations from exploiting regulatory arbitrage by relocating to permissive jurisdictions. 
  • This measure involves standardizing technical protocols such as cryptographic watermarking for synthetic media and universal incident reporting frameworks to track global algorithmic harms. Aligning international legal definitions and compliance architectures ensures that multinational tech conglomerates cannot play sovereign nations against one another to dilute ethical requirements. 
  • By forging a cohesive global regulatory net, states can collectively assert democratic dominance over borderless capital, ensuring that the trajectory of artificial intelligence serves a unified vision of human progress.

Conclusion:

Artificial Intelligence is not merely transforming production but reordering power between the state and capital. By anchoring capital to territory, infrastructure, and data, AI has dissolved traditional market–state boundaries. The emerging techno-nationalist compact promises strategic capacity but risks deepening inequality, surveillance, and democratic erosion. Managing this transformation demands proactive governance that subordinates technological power to constitutional values and public interest.

Drishti Mains Question 

Examine the rise of techno-nationalism in the age of Artificial Intelligence. How does it alter the logic of globalisation and sovereignty?

FAQs

1. What is techno-nationalism?

Alignment of state power and domestic capital to achieve technological sovereignty.

2. Why is AI different from earlier technologies?

It is capital-intensive, data-driven, and territorially anchored.

3. What is surveillance capitalism?

Profit-making through large-scale extraction and analysis of personal data.

4. What is Digital Public Infrastructure (DPI)?

State-built digital systems enabling private innovation at scale.

5. Why is AI governance difficult?

Because innovation, security, and rights often pull in opposite directions.

 

UPSC Civil Services Examination Previous Year Question (PYQ) 

Prelims:

Q. With the present state of development, Artificial Intelligence can effectively do which of the following? (2020)

1. Bring down electricity consumption in industrial units
2. Create meaningful short stories and songs
3. Disease diagnosis
4. Text-to-Speech Conversion
5. Wireless transmission of electrical energy

Select the correct answer using the code given below: 

(a) 1, 2, 3 and 5 only  

(b) 1, 3 and 4 only 

(c) 2, 4 and 5 only  

(d) 1, 2, 3, 4 and 5 

Ans: (b) 

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Mains:

Q. Introduce the concept of Artificial Intelligence (AI). How does Al help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare? (2023)