Tag: Artificial intelligence

Who Controls AI? (Part 2) — The Widening Gap Shaping Our Future

By Chonsview Media

When I wrote Part 1 of Who Controls AI?, I concluded with a simple truth: control rests where capital and compute meet. Months later, that truth has only hardened. What has changed is the scale — and the consequences.

AI is no longer a tool used by a handful of tech giants. It has become the backbone of global industry, the engine of economic growth, the source of geopolitical tension, and the spark of new cultural, spiritual, and ethical debates.

This second installment explores how concentrated AI power is reshaping the world — and widening the divide between those who wield it and those who live under it.

  1. The Fast-Growing AI Industries

AI is no longer confined to Silicon Valley. The fastest growth is happening in industrial manufacturing, telecommunications, and financial services — sectors that are integrating AI at historic speed.

These industries are no longer using AI simply to cut costs. They are fusing entire sectors together:

  • Telecom companies are merging with healthcare providers to power remote ICU monitoring.
  • Manufacturers are embedding AI into supply chains that update themselves in real time.
  • Banks are using AI to detect fraud, automate compliance, and even generate investment strategies.

A 2026 NVIDIA report found that 88% of enterprises across major industries now report measurable revenue increases directly tied to AI integration. The winners are accelerating. The laggards are falling behind.

  1. AI’s Day-to-Day Improvement

We have crossed a threshold: AI is no longer just a text generator — it is becoming an autonomous executor.

Welcome to the era of Agentic AI.
These systems can:

  • Run multi-step workflows
  • Manage databases
  • Optimize supply chains
  • Execute tasks without human supervision

Yet progress is uneven. AI can win gold medals in International Mathematical Olympiads and pass PhD-level science exams — but still misread an analog clock or misunderstand basic physical logic.

This “jagged frontier” makes AI both powerful and unpredictable.

  1. AI’s Influence on the International Economy

The AI boom is creating immense wealth — but only for a select few.

According to PwC, 74% of all AI-driven economic value is captured by the top 20% of companies. This is the new 80/20 rule of the AI age.

Meanwhile, global macroeconomics is being reshaped by AI infrastructure spending.
Data centers, power grids, and chip manufacturing have become the primary floor preventing a global economic slowdown.

Asian manufacturing economies — especially those producing semiconductors and robotics — are experiencing an industrial super-cycle not seen in decades.

  1. The Risk of Out-of-Control AI Use

As AI systems become more autonomous, the guardrails are not keeping pace.

Documented AI safety incidents — from deepfake financial fraud to autonomous software failures — are rising sharply every year.

Developers face a technical dilemma known as the safety–accuracy paradox:

  • The more you optimize a model for safety,
  • The more you risk degrading its raw reasoning accuracy.

This friction is becoming one of the defining challenges of modern AI development.

  1. AI’s Impact on the Environment

Behind every digital mind is a massive physical footprint.

The United States alone hosts over 5,000 data centers, many of which strain local power grids. Training frontier models requires staggering amounts of electricity — and millions of gallons of water per day for cooling.

Communities near data centers are already experiencing environmental tension over water usage, land allocation, and energy demand.

AI may be virtual, but its environmental cost is painfully real.

  1. AI’s Impact on Education

The classroom has changed forever.

Generative AI tools now have roughly 80% adoption among university students worldwide. Education is shifting from memorization to prompt literacy, verification, and critical evaluation.

But there is a cost.
Younger students who rely too heavily on AI risk developing a “learning penalty” — a slowdown in foundational skills like critical thinking, problem-solving, and independent reasoning.

AI is the world’s best tutor.
It is also the world’s easiest crutch.

  1. AI Manipulation Risk

Control over the model means control over the narrative.

AI systems are trained on the public data commons — absorbing human biases, political slants, and cultural preferences. The entities that control model alignment effectively control the gateways to information.

This creates the potential for large-scale behavioral modification:

  • Subtle shifts in political discourse
  • Steering consumer behavior
  • Influencing cultural norms

The power to shape public perception is no longer in the hands of media companies alone — it is in the hands of those who control the models.

  1. AI and Job Security

The labor market is feeling the pressure — especially at the entry level.

While mass unemployment has not hit the global economy, specific sectors are contracting sharply.
Entry-level hiring for young software engineers (ages 22–25) has dropped significantly as senior developers use AI to multiply their output.

Surveys show that one-third of global organizations expect workforce reductions in the coming year, especially in:

  • Service operations
  • Supply chain management
  • Routine coding roles

The divide between AI-augmented workers and AI-displaced workers is widening.

  1. AI and Religious Tension

AI is forcing humanity to confront ancient questions in new ways.

When Anthropic’s co-founder spoke alongside the Vatican on AI ethics, it signaled a growing need for spiritual frameworks to govern synthetic intelligence.

As models simulate emotions — or as developers claim internal states resembling “grief” or “satisfaction” — theological debates intensify:

  • What is consciousness?
  • Can a machine have a soul?
  • What are the ethics of creating autonomous decision-makers?

AI is not just a technological challenge.
It is a philosophical one.

  1. AI vs. Humanity

The existential debate is no longer science fiction.

As AI systems take over more decision-making — from infrastructure to judicial workflows to financial systems — humanity risks losing oversight by default, not by choice.

The core question becomes unavoidable:
If AI surpasses human capability across all measurable benchmarks, how does humanity retain sovereignty over a world run by autonomous software?

This is the heart of the control debate.

  1. AI and Ownership / Copyright

The legal landscape is being rewritten in real time.

AI models have been trained on the intellectual property of artists, writers, musicians, and coders — often without consent. Lawsuits are piling up worldwide.

Tech giants are responding by shifting toward closed, proprietary datasets, creating a corporate moat that locks out smaller developers who cannot afford licensing fees.

Data is the new oil — and the new battleground.

  1. AI Wars and Geopolitics

AI dominance is now the centerpiece of global power.

The performance gap between U.S. and Chinese frontier models has narrowed dramatically.

  • The U.S. leads in private investment and compute infrastructure.
  • China leads in patents, publications, and industrial robotics.

But the entire global AI ecosystem depends on a single chokepoint:
Most of the world’s advanced AI chips are manufactured in Taiwan.

This fragile dependency shapes global diplomacy, military strategy, and economic policy.

Conclusion: The Divide Widens

Part 1 ended with a warning: control belongs to those who own the compute.
Part 2 reveals the consequences of that reality.

AI is accelerating global progress — but it is also widening every divide:

  • Between elite tech firms and ordinary businesses
  • Between AI superpowers and the rest of the world
  • Between workers who adapt and workers who are displaced
  • Between those who shape AI and those who are shaped by it

The question is no longer who controls AI.
The question is whether humanity can maintain control at all.

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Who Controls AI? Key Players and Influences

Artificial intelligence has evolved from theoretical concepts to a prevalent technology, marked by significant milestones and breakthroughs, particularly in recent years. Its control remains concentrated among major tech firms and governments, raising concerns over data privacy, economic impact, and environmental sustainability. Future scenarios depend on governance and societal choices regarding regulation and ethical use.

Artificial intelligence is a technology that feels both brand-new and ancient. It seems new because recent generative models exploded into public life in just a few years. It seems ancient because people have imagined “thinking machines” for centuries. Below I give a single, readable tour. This includes the origin story and who builds and governs AI. It also covers what happens to the data and who’s winning today. Additionally, it explores what the futures, both good and bad, look like for people, nature, and the wider universe.

1) When, where and how AI was first developed — a short timeline

Short takeaway: AI grew from theoretical ideas (Turing) into an organized research field at Dartmouth in 1956. It then evolved through symbolic systems and neural-network revivals. Finally, AI entered the current era driven by large datasets and specialized compute.

2) Why it was developed — motives and drivers

3) Who controls AI today?

Control is distributed and layered:

  • Big technology companies (U.S. and China primarily) — the largest models, cloud infrastructure, and chips are concentrated in a handful of firms (examples: OpenAI, Google/DeepMind, Microsoft, Meta, Anthropic, Baidu, Huawei). These companies control the most capable models, the data center capacity, and commercial distribution channels. Market incentives and access to capital make them central gatekeepers. Rest of World+1
  • Governments and regulators — they control legal access, procurement, and safety requirements. National strategies, including funding, export controls, and data rules, shape who can effectively build and deploy high-end AI. Different countries pursue different mixes of industrial policy and regulation (U.S., EU, China, etc.). The White House+1
  • Academia and open-source communities — universities, labs, and open-source groups drive core research. They make knowledge public. However, cutting-edge system training often requires private compute budgets. This requirement limits full parity with industry labs. Wikipedia

Net effect: control is concentrated where capital, compute, and data meet — i.e., large companies and states — but research and open communities still influence architectures and norms.

4) What happens to the data that AI platforms collect?

Data flows and uses are central to how modern AI works, and they raise legal, ethical, and practical issues.

  • Collection and storage. Platforms collect user queries, uploaded content, telemetry, and large swathes of public web content. Companies store this data for quality-improvement, training, safety monitoring, and product development. Some have explicit opt-in/opt-out settings; others change policies over time. (Example: Anthropic recently updated policies to use user chats for training unless users opt out). WIRED
  • Model training. Large models are trained or fine-tuned on aggregated datasets. This can include public posts, licensed data, and, in some cases, user interactions. Regulators in some regions have challenged or limited such uses when users were not informed or consent wasn’t adequate. For example, regulators in Brazil and parts of Europe have scrutinized certain uses of personal data. They have also blocked some uses for model training. TIME+1
  • Privacy risks and leakage. Models can unintentionally memorize and reproduce sensitive information; that risk is real when training data contains personal or private content. That creates legal issues under privacy regimes (GDPR, national laws) and technical challenges for differential privacy, data minimization, and auditing. TrustArc+1
  • Commercialization and derivatives. Companies can monetize derivative outputs, build products on top of user data, or license models to customers. Data can also be used for targeted advertising, profiling, and other commercial applications. That raises questions about consent, ownership, and fair compensation for content creators.
  • Regulatory response. Regulators are actively developing rules governing data use for AI (e.g., the EU AI Act guidance, national data-protection rulings), and courts and privacy authorities have begun issuing orders and penalties in some cases. European Data Protection Board+1

Bottom line: Data collected by AI platforms is stored. It is reused and often repurposed for training and product improvement. This practice has regulatory and privacy consequences. It is actively contested and evolving.

5) Who is benefiting the most right now?

Winners today cluster into several groups:

  1. Infrastructure and chip makers (first-order beneficiaries). Companies that produce GPUs, TPUs, and data-center gear, such as NVIDIA, AMD, and cloud providers, have seen massive demand. This is because large models require specialized compute. Financial analysts identify chip and infrastructure suppliers as major beneficiaries. Morgan Stanley
  2. Big tech platforms and cloud providers. The firms that can host, sell, and integrate models include Microsoft, Google, Amazon, Meta, and OpenAI partnerships. They monetize AI through cloud services and productivity tools. They also enhance advertising improvements and offer enterprise solutions. Rest of World+1
  3. Investors and AI-focused startups. Venture capital and investors are putting money into startups that offer narrow AI solutions. These include sectors like health tech and back-office automation. Many sectors are receiving AI-enabled investment boosts. These sectors include healthcare, legal, customer support, and finance. For example, a large share of health-tech funding has recently gone to AI-focused companies. World Health Expo+1
  4. Organizations that can deploy AI at scale. Large enterprises with data and integration capacity benefit from AI. Banks, retailers, and hospital networks see productivity gains. They can extract value faster than small players.
  5. Researchers and citizens (indirectly). There are big public benefits too. These include new scientific tools, faster drug discovery workflows, and accessibility improvements. However, these benefits are diluted by concentration and access barriers.

Short answer: The biggest short-term beneficiaries are those who own the compute, data, and distribution channels. These include chip manufacturers, cloud providers, and major tech companies. Furthermore, investors are funneling capital into AI-enabled sectors.

6) Predicted futures — plausible scenarios

No single prediction is certain; instead think in scenarios that combine technical progress, policy, and societal choices.

A. Augmentation & productivity boom (optimistic mainstream)

  • AI becomes a ubiquitous assistant for knowledge work, research, and creativity. It accelerates productivity and lowers costs. It unlocks new services like personalized education and earlier disease detection. Economic growth rises, new classes of jobs emerge, and many routine tasks are automated. Benefits are large but uneven unless policies (retraining, redistributive measures) are put in place.

B. Concentration & inequality (likely if current trends continue)

  • Value concentrates in a few firms/countries that control the most advanced models and infrastructure. This produces powerful incumbents, winner-take-most markets, and political strains. Without strong governance, inequality (wealth and bargaining power) may increase.

C. Regulatory fragmentation & geopolitics

  • Different regulatory regimes (EU precautionary rules, U.S. innovation-first, China strategic control) produce fragmented standards, data localization, and supply-chain decoupling. That could slow some innovation but also spur national AI stacks and security competition. Artificial Intelligence Act+1

D. Safety and misuse risks

  • Advanced models, if unconstrained, could be misused for fraud, disinformation, or automated cyber-attacks. They could also pose risks in rare catastrophic scenarios like biotech misuse or infrastructure sabotage. Governments and firms are already building monitoring and disclosure rules to reduce such risks. Recent laws (e.g., new transparency/safety measures in California) show policy is moving fast. Reuters+1

E. Environmental & resource constraints

  • Continued growth in model sizes and deployment means increased electricity and water demand for data centers. This raises sustainability concerns. These concerns persist unless compute gets dramatically more efficient or powered by green energy. Research shows training and operating large generative models has a non-trivial carbon and water footprint. MIT News+1

7) Pros and cons — the tradeoffs for humanity, nature and (broadly) the universe

Pros for humanity

  • Productivity and innovation: automation of repetitive work, faster scientific discovery, medical diagnostics, and better personalized services.
  • Access & inclusion: language translation, assistive technologies, and democratized tools can increase access to knowledge and services.
  • Solving complex problems: better climate models, optimized logistics, and improved resource allocation can help tackle big challenges.

Cons for humanity

Pros for nature

  • Optimized resource use: AI can reduce waste (smart grids, precision agriculture), help model ecosystems, and design greener systems.
  • Climate science: faster modelling and simulations can improve climate predictions and adaptation strategies.

Cons for nature

  • Energy & water consumption: Large-scale AI compute increases energy demand. It also raises cooling water needs. If powered by fossil fuels, this raises emissions. There is growing evidence of significant carbon footprints tied to training and deploying large models. Institute of Energy and the Environment+1

Pros for the wider universe (philosophical/long-term)

  • Knowledge acceleration: AI could expand scientific discovery (astronomy, materials) at rates humans alone can’t, unlocking new capabilities.
  • Longevity & health: improved biomedical research might extend healthy lifespans.

Cons for the wider universe (ethical/philosophical)

  • Existential risk (speculative): some thinkers worry about long-run scenarios where superintelligent systems misalign with human goals. While debated, this risk motivates governance, safety research, and international coordination.
  • Irreversible environmental damage: if energy and resource use spike unchecked, long-term planetary limits could be stressed.

8) What to watch and what society should do

  • Transparency and data rights. Demand clearer policies about how chat logs, uploads and public content are used for training. Opt-in/opt-out mechanisms and strong data-protection enforcement matter. Recent company and regulatory moves make this a front-line issue. WIRED+1
  • Regulation that balances safety and innovation. Laws like the EU AI Act and recent state-level safety disclosure laws illustrate evolving policies. They include risk-based rules, safety reporting, and standards for high-impact systems. Coordination across countries is crucial to avoid fragmentation while protecting rights. Artificial Intelligence Act+1
  • Energy and environmental standards. Track data-center power sourcing and efficiency improvements. Determine whether AI providers commit to green energy or carbon offsets. Without these measures, the environmental cost will rise. MIT News+1
  • Public investment in capabilities for the public good. Governments can fund open research, public-interest models, and “third-stack” infrastructure. This funding reduces dependence on a few firms. It also helps to democratize access. Brookings

9) Final, practical takeaway

AI is neither an automatic utopia nor an unavoidable catastrophe. It is a multipurpose technology. Its impact will be decided by who builds it. It also depends on who governs it and who benefits from it. Additionally, how we manage its environmental and social costs will play a role. Right now, control and profits tilt toward a few large firms and wealthy nations. Data practices are in flux and undergoing legal scrutiny. Environmental costs are real and growing. The best path ahead requires smart regulation. It needs public investment. We need transparency about data and safety. Technological effort is essential to make AI more efficient. It also needs to be more equitable.

Sources and further reading (selected)

  • History and origins: Coursera / Wikipedia overview. Coursera+1
  • Who controls AI / geopolitics: Rest of World analysis; Brookings on technology stacks. Rest of World+1
  • Data and privacy: Wired on Anthropic policy change; EDPB opinion on data protection and AI. WIRED+1
  • Beneficiaries & investment trends: Morgan Stanley and healthcare/VC coverage. Morgan Stanley+1
  • Regulation & governance: EU AI Act developments; California SB 53; U.S. executive actions. Artificial Intelligence Act+2Reuters+2
  • Environmental impact: MIT coverage and academic analyses of model carbon footprints. MIT News+1

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