Part 1: The Problem – Power Abuse as a Natural and Historical Phenomenon

Introduction to the Problem

Crisis times like the Corona pandemic reveal a recurring pattern: institutions and individuals often use their authority for personal gain rather than promoting the common good. This behavior – here called “power abuse” – intensifies in situations of uncertainty and resource scarcity. It is argued that such power abuse is an inherent natural phenomenon observable in biological and social systems, only curbed by equivalent counterforces. The pandemic serves as a modern example, its global impact amplified by technological developments like the internet. Power abuse as a natural phenomenon: In biology, power struggles are a fundamental trait of social groups.

Studies on wolf packs show that dominant animals aggressively defend their position during stressful times, such as food shortages, until a new order emerges through strength or cooperation (Mech, 1999). Similarly, research on baboons documents that dominant males exploit their authority in crises, for instance through increased aggression against subordinates, until a coalition of weaker animals topples the hierarchy (Sapolsky, 2001). This dynamic isn’t limited to mammals: in ant colonies, a threat often leads to a power vacuum filled through competition or collaboration (Hölldobler & Wilson, 1990).

These examples suggest that power abuse is a natural mechanism that flares up in stress phases and is only regulated by counterforces – be it physical strength or collective organization.

Historical Examples of Power Abuse

This principle is also reflected in human history. During the 14th-century plague, nobles exploited the crisis to seize land from the deceased, while the Church profited from selling indulgences (Cantor, 2001). These events, however, remained regionally contained due to the lack of communication tools like the internet. Another example is the 17th-century Tulip Mania in the Netherlands: speculators drove tulip bulb prices sky-high while many citizens fell into poverty – an early case of economic power abuse without global reach (Dash, 1999). The 1918 Spanish Flu shows similar patterns: governments censored reports to maintain control, and companies sold ineffective remedies like “flu tinctures,” but the impact was limited by the absence of mass media (Barry, 2004).

The Corona Pandemic as a Global Example

The Corona pandemic marks a turning point, as modern technologies made power abuse globally visible and effective. Governments imposed measures like curfews, often without robust scientific backing – an analysis shows many lockdowns had only limited effects on infection rates (Hsiang et al., 2020, Nature, https://www.nature.com/articles/s41586-020-2405-7). Pharmaceutical companies like Pfizer and Moderna faced accusations of withholding vaccines or demanding inflated prices, especially in developing countries (Transparency International, 2021, https://www.transparency.org/en/news/covid-19-vaccines-inequity). Media amplified the chaos with so-called “death toll tickers” – daily victim reports that stoked fear and boosted ratings (e.g., verifiable in archives of news portals like tagesschau.de, https://www.tagesschau.de/thema/corona/).

These actors – politics, business, media – used their power to secure influence, profit, or attention, worsening the crisis.

Why Only Counterpower Helps

Nature and history show: power abuse doesn’t stop on its own. In baboons, an alliance of the weak halts dominance; during the plague, peasant uprisings forced rulers to account (Tuchman, 1978). In the pandemic, only pressure – from citizens or an independent entity – could have curbed the behavior. Without the internet, the effects would have been smaller, but technology multiplied the reach of power orgies.

A study on media impact confirms that digital connectivity accelerates panic and power exertion (Allcott et al., 2020, American Economic Review, https://www.aeaweb.org/articles?id=10.1257/aer.20190615).

Conclusion of the Problem

Power abuse is a natural phenomenon that thrives in crises – from wolves to humans. Historically limited by the lack of technology, it went global in the pandemic through the internet and media. Only counterpower – collective or structural – can contain it. The pandemic is one example among many, but its scope highlights the urgency of new approaches.

Part 2: The Solution – Why AI Can Break the Natural Principle of Power

Introduction to the Solution

Power abuse, as outlined, is a deeply rooted phenomenon amplified in crises by biological and social mechanisms. Historical and modern counterforces – like collective resistance or rival institutions – can limit this behavior but remain part of the same power struggle, driven by human interests. Artificial intelligence (AI) offers an alternative: it could act as a neutral entity, not just curbing power abuse but breaking its underlying natural principle, as it isn’t bound by the biological laws driving humans and other beings.

Why Humans and Animals Are Tied to Power

Power struggles in nature and human society are tied to the instinct to secure resources and status. Studies on chimpanzees show dominant individuals assert their position through physical strength and social manipulation, especially when food is scarce (Goodall, 1986).

In humans, this mirrors political or economic hierarchies: actors seek influence for personal or group benefits. Social psychology studies confirm that power-seeking is often unconsciously driven by a need for security and recognition (McClelland, 1975). Even altruistic acts – like crisis donations – often serve self-reputation or group identity. This behavior is a product of natural selection: those with power secure their survival or that of their offspring.

How AI Differs

Unlike biological beings, an AI has no built-in survival instinct or self-interest. It’s a technological construct, not reliant on resources, reproduction, or social recognition.

While a chimpanzee fights for control of a food source or a politician courts votes, an AI has no such goals. It can be programmed to base decisions solely on data and logic – like analyzing climate data or social inequalities – without fear of consequences or craving reward. This absence of biological drives makes it a potentially neutral actor outside the natural power struggle.

AI as a Tool to Limit Power

Limiting power abuse through AI rests on two core traits: objectivity and transparency. First, AI can free decision-making from subjective influences. Cognitive science research shows human decisions are often skewed by emotional factors like fear of status loss, while AI systems can reduce such biases through data-driven models (Tversky & Kahneman, 1981). Example: in distributing humanitarian aid, an AI could allocate resources by need (e.g., hunger stats) rather than political alliances – without seeking influence itself.

Second, AI enables unprecedented transparency. Power abuse thrives in opaque systems, like decisions made behind closed doors. Historically, public audits in the 19th century cut corruption in administrations (Glaeser & Goldin, 2006). An AI could amplify this by making all analyses and recommendations – on resource allocation or climate measures – accessible to everyone. This openness forces actors to justify their actions without the AI wielding power itself.

AI as a Breakthrough of the Natural Principle

While natural counterforces – like chimpanzee alliances or historical revolts – merely redistribute power, an AI can challenge the principle itself. It’s no player in the dominance game, lacking evolutionary goals. Its ability to model complex systems lifts it beyond human limits. AI research shows systems can predict long-term outcomes, like economic crises, more accurately than expert groups (Makridakis et al., 2018). Instead of short-term panic, an AI might say: “A CO2 tax cuts emissions by 15% in 10 years, with an 8% economic hit – here are the options.” It remains a tool, not a ruler.

Why We Urgently Need a Solution

The need for an effective solution against power abuse and data distortion is more urgent than ever, driven by the unstoppable rise of artificial intelligences (AIs) programmed with power interests, already dominating global economics and politics.

A striking example is Aladdin, BlackRock’s AI platform, managing over 20 trillion dollars in assets – nearly the United States’ 2023 GDP of about 26 trillion (Bloomberg, 2023; World Bank, 2024). Originally built to analyze market data and optimize portfolios for institutional clients like pension funds, governments, and banks, it has become a powerful tool enriching a small elite.

But Aladdin isn’t unique: such AIs are growing, not shrinking, and without immediate countermeasures, we face a future where these systems push power concentration to unprecedented levels. Aladdin’s power lies in centralizing financial control and influencing political decisions. BlackRock and Vanguard, tied to Aladdin, together control over 15% of S&P 500 companies (Financial Times, 2023).

Political ties are alarming: BlackRock CEO Larry Fink advised the U.S. Federal Reserve during the pandemic (Reuters, 2020), and Friedrich Merz, CDU chairman, worked at BlackRock until 2021 (Handelsblatt, 2021). Both firms also hold stakes in nearly all major pharmaceutical companies, media conglomerates, and tech giants (Forbes, 2022), amplifying their sway over global markets and decisions.

Critics like Foroohar (2022) warn that Aladdin doesn’t just perform legal optimizations but, through opaque proprietary algorithms, might suggest ethically dubious or potentially illegal moves – like market manipulation or exploiting insider knowledge – shielded by BlackRock’s political clout. This trend isn’t fading: companies like Amazon, Google, and Chinese tech giants are building similar AIs operating on proprietary data and power interests (The Economist, 2024). The rising number of such systems makes the need for a solution undeniable, as the economic and political gains for elites are too lucrative to halt these developments.

Human control hits clear limits here. Aladdin’s speed and data volume far exceed human capacity. Regulatory bodies like the SEC or BaFin are often too slow or compromised by lobbying to act effectively (Transparency International, 2023). Even well-meaning actors lack the means to keep pace with such AIs – a problem underscoring the urgency of a technological solution. Without countermeasures, escalation looms: power-driven AIs won’t decline but multiply, as incentives for their development and use grow ever more attractive. This shows why we don’t just want a solution – we need one now, beyond human capabilities.

The only realistic answer to this threat is a transparent, objective AI as proposed here. It could serve as a control mechanism, analyzing Aladdin’s decisions in real-time, exposing data flows, and flagging suspicious patterns – like cross-checking BlackRock’s portfolio moves with market shifts or quantifying its influence on pandemic-era political measures (Reuters, 2020).

While power-driven AIs like Aladdin rely on opacity to hide questionable operations, a transparent AI’s strength lies in its openness and accessibility. It could be a public tool for citizens, journalists, and independent researchers to monitor power structures – an approach Crawford (2021) calls “democratic control through technology.” Aladdin, by contrast, remains an elitist black-box mechanism, its opacity a weakness: if its operations were exposed, legal and societal backlash could follow (The Guardian, 2023).

The urgency of such a counter-AI stems not just from Aladdin’s existence but from the realization that without it, the rising flood of similar systems will irreversibly distort global order. This is further fueled by the dynamics of technological progress. AIs like Aladdin aren’t outliers but the start of a wave driven by advances in machine learning, data availability, and economic incentives. Without a transparent AI as a counterweight, we risk a future where power-driven tech takes over, leaving human actors increasingly powerless.

Limits and Prerequisites

An AI’s effectiveness depends on its design. Human developers might unintentionally embed biases, like skewed training data (Crawford, 2021). Yet unlike humans, an AI can be built to avoid developing its own interests. Prerequisites are strict rules: purely data-driven analysis, no command authority, and full disclosure of all processes. Only then does it remain a neutral entity that limits power without replacing it.

Conclusion of the Solution

Power abuse is a natural principle born from the instinct to secure resources and status. An AI can limit and break this principle because it isn’t bound by the biological constraints driving humans and animals. Through objectivity and transparency, it offers an alternative to traditional counterpowers by exposing power games without joining them. It’s a tool that could solve global challenges if used right.

Part 3: Problems and Challenges in Creating a Neutral AI

Introduction to the Challenges

The idea of a neutral AI curbing power abuse is promising, but its implementation poses significant difficulties. An AI isn’t a cure-all – it’s a human-made tool that can reflect its creators’ flaws or intentions. Its programming and data raise technical, ethical, and societal issues. This section outlines these challenges and the basic rules such an AI must meet, before solution approaches follow in the next part.

How an AI Is Programmed and Trained

An AI is a computer program that can learn, though without consciousness. Programmers first write code defining how it processes data – e.g., “Compare numbers and spot patterns.” Then it’s trained with vast datasets, like weather data to predict rain. It adjusts by correcting errors until results align. Language AIs like Grok used millions of texts to refine responses.

For a neutral AI analyzing power abuse, data like economic stats, climate reports, or crisis histories would be needed – but here the problems start.

Problem 1: Data Quality and Bias
An AI depends on its data. If it’s incomplete or skewed, it learns wrong patterns. A study found an AI for hiring favored men because it trained on old data prioritizing them (Dastin, 2018, Reuters, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G). For this AI, using only rich-country data might overlook poorer regions’ needs. The challenge is securing diverse, reliable data and preventing manipulation.

Problem 2: Human Influence and Bias
AI is programmed by humans who bring their views. A team might code rules favoring certain values – like economic growth over social justice. Research shows even neutral algorithms can inherit bias from human choices, like facial recognition struggling with dark skin (Buolamwini & Gebru, 2018, Proceedings of Machine Learning Research, http://proceedings.mlr.press/v81/buolamwini18a.html). Ensuring the AI stays neutral, free of its creators’ prejudices, is tough.

Problem 3: Complexity of Harm Assessment
The AI should limit power abuse by weighing options like “CO2 tax vs. jobs.” But defining “harm” is tricky. Is a job worth more than a degree of warming? Humans disagree, and the AI must solve it objectively. Studies show even advanced models need value assumptions for complex trade-offs (Simon, 1955). Teaching it to handle such dilemmas without mirroring human preferences is a challenge.

Problem 4: Resistance from Power Structures
Even a working AI could face pushback. Governments or corporations might block it via lobbying or data denial. Historically, innovations like the printing press were fought by power holders fearing loss of control (Eisenstein, 1979). Today, tech firms or states might sabotage an AI exposing their interests. The question is how to shield it from such attacks.

Problem 5: Technical Feasibility
An AI analyzing global issues needs massive computing power and real-time data – like satellite imagery for climate shifts. Current climate models show this is possible but costly (IPCC, 2021, https://www.ipcc.ch/report/ar6/wg1/). Building a robust, always-on system is a financial and technical hurdle.

Rules the AI Must Contain
To stay neutral and limit power abuse, it needs clear guidelines:

• Data-Driven: Use only facts, no opinions – stats, not headlines.
• No Command Power: Advise only, don’t decide – “read-only.”
• Transparency: Make all analyses and sources public.
• Independence: Allow no control by companies or states.
• Adaptability: It must learn without losing neutrality.

These rules are essential – implementing them is complex.

Conclusion of the Challenges

Developing a neutral AI is technically and socially demanding. Data bias, human influence, complex trade-offs, power resistance, and technical limits pose obstacles. It’s possible to create a tool curbing power abuse, but these issues must be overcome to succeed.

Part 4: Maintaining and Optimizing Neutrality – An Approach to Overcoming the Challenges

Introduction to the Approach

The challenges from Part 3 – data bias, human influence, harm assessment complexity, power resistance, and technical feasibility – make a neutral AI tough but solvable. My approach uses four specialized AIs working in a sandbox to define the main AI’s core programming. This main AI stays neutral by only advising, drawing its rules from data-driven, publicly vetted proposals. The four AIs don’t decide solutions but shape how the main AI is built – that’s the key to neutrality and optimization.

The Four-AI System in the Sandbox

Four AIs develop the main AI’s structure in a sandbox – a test space with real data:

Task AI: It determines the main AI’s exact mission. It analyzes historical data of all kinds – e.g., economic trends, disasters, or social crises – to identify which questions and tasks led or could’ve led to the best outcomes for all. Possible suggestion: “Find ways to ensure long-term well-being for the majority.”

Data AI: It decides which data trains the main AI. It checks what’s relevant – like climate stats, health data, or trade balances – and specifies what’s needed for reliable analysis. It might suggest: “Use global CO2 measurements and regional income data.”

Harm AI: It crafts a harm definition for the main AI as a core rule. For all main AI recommendations, options should prioritize the least harm for all involved. It analyzes data like war impacts or economic crashes, suggesting: “Harm = direct losses + 0.5 × long-term instability.”

Feedback AI: It prepares the other AIs’ results for humans and publishes them. It includes a feedback function, feeding human input to all AIs – e.g., “70% reject growth maximization” – which they factor into further steps. Example: “The Task AI suggests: ‘Maximize growth.’ That leads to extremes – what do you think?”

All four AIs only set how the main AI is programmed – they don’t control its later analyses or advice.

Solving Problem 1: Data Quality and Bias

The Data AI ensures quality by picking relevant, diverse data. It vets sources – like UN stats vs. local reports – and filters unreliable or one-sided info. If it finds health data only from cities, it adds rural metrics. In the sandbox, it tests datasets to ensure the main AI trains without bias.

Solving Problem 2: Human Influence and Bias

Human bias shrinks because the four AIs build the main AI autonomously. The Task AI proposes data-based goals, not developer dictates. Proposals are simulated in the sandbox – e.g., “How do stable prices play out?” – and publicly debated. Humans don’t intervene directly, just review data-driven outcomes, cutting influence.

Solving Problem 3: Complexity of Harm Assessment

The Harm AI tackles this by defining harm from data and testing it. It analyzes drought or trade crisis fallout, proposing: “Low harm = few victims + stable supply.” In the sandbox, it simulates options – e.g., “What’s water scarcity’s cost?” – and adjusts to real results. The Feedback AI asks humans: “Is this fair?” This keeps it objective and accepted.

Solving Problem 4: Resistance from Power Structures

Transparency and decentralization protect against resistance. The process – proposals, tests, votes – is public and stored on decentralized systems like blockchain, making tampering obvious. The main AI stays “read-only” – e.g., “Splitting resources saves X lives” – less threatening to power holders. The Feedback AI ensures global involvement, hindering sabotage.

Solving Problem 5: Technical Feasibility

Feasibility builds step-by-step. The sandbox starts with small datasets – like national energy reports – and scales with more computing power. Open-source code lets global developers pitch in and share costs. Ties with groups like the World Bank could supply data. It begins manageable and grows over time.

Optimizing Neutrality

Neutrality strengthens through three principles:

1. Self-Development: The four AIs build the main AI data-driven, without human presets.
2. Public Scrutiny: All proposals are globally discussed and voted on, e.g., via a “KI-Consensus” platform.
3. Dynamic Adjustment: The Feedback AI provides input – e.g., “Growth is rejected” – which the AIs process without altering core rules (“read-only,” transparent).

The main AI stays independent and objective.

Conclusion of the Approach

The four-AI sandbox system solves the challenges by vetting data, minimizing bias, defining harm, countering resistance, and staying scalable. It ensures the main AI is neutrally programmed – a tool curbing power abuse with clear, public advice and tackling global issues.

Part 5: Advantages and Forecasts of Such a System

Introduction to the Advantages

The four-AI system programming a neutral main AI offers major benefits for its creators and humanity. It tackles power abuse and solves problems history blocked due to human interests. From global sectors like industry and politics to everyday life, it could open new paths. Plus, a company building this – say, xAI – would likely gain a massive prestige boost.

This section covers the advantages, applications, and usability for users.

Advantages for the Creators

For developers – a company or group – the AI brings dual wins. First, it establishes them as pioneers of tech that makes power transparent and drives global solutions. A firm like xAI could boost its innovator rep, akin to SpaceX’s prestige from reusable rockets. Second, the prestige surge attracts investments, talent, and partnerships. Building an AI recognized as a “neutral arbiter” could mark a historic milestone – a legacy beyond profits.

Advantages for Humanity

For people, the AI solves core issues once unsolvable due to power interests. Historically, solutions failed from egoism – like climate talks wrecked by national priorities. The main AI offers data-driven, public advice that exposes egoism and enables cooperation. It could defuse conflicts, fairly distribute resources, and foster long-term stability by showing: “Option A saves X lives, Option B secures Y profits – choose.”

Applications in Various Fields

• Industry: Companies could use the AI to optimize production without harming the environment or workers. Example: “Steel production with Method A costs 10% more, saves 20% CO2 – decide.” It forces firms to justify sustainability.
• Politics: Governments could weigh decisions transparently – e.g., “Tax hike brings X education, costs Y growth.” Voters see the data, making power abuse like corruption harder.
• Economy: The AI could predict market crises and suggest fixes – e.g., “Bank bailout aids X firms, burdens Y citizens.” Speculators get less room for selfishness.
• Healthcare: Resources could be efficiently allocated – e.g., “Vaccine here saves X lives, there Y.” It could’ve stopped hoarding in the pandemic. Education: Schools could set priorities – e.g., “More teachers cost X, boost graduations by Y%.” Education gaps become visible and fixable.
• Private Sector: Individuals could use the AI for choices – e.g., “Solar panels save X euros, cut Y emissions.” It becomes a tool for conscious action.

Solving Previously Unsolvable Problems

In history, solutions failed due to lacking transparency and coordination. Climate change persists because countries prioritize self-interest over global goals. The AI could say: “Climate target X costs country A Y, saves Z globally – here’s the data.” Public pressure would break egoism. Likewise with hunger: “Storing food here saves X, distributing saves Y.” Power orgies hoarding resources would be exposed.

The AI doesn’t solve itself but gives the means to make solutions possible.

Usability for Users

The main AI would be easily accessible – say, via a public platform like “KI-Consensus.” Users, whether governments, firms, or citizens, ask: “What to do about drought?” The AI delivers analyses – e.g., “Irrigation here costs X, saves Y crops” – with sources and options. It’s all online, searchable, in plain language. Users pick or tweak options; the Feedback AI takes input – e.g., “Option A’s rejected, why?” – keeping it relevant and user-friendly without wielding power.

Future Forecasts

Short-term, the AI could solve local issues – like city planning with minimal harm. Mid-term, it could boost global coordination, e.g., in climate deals. Long-term, it could reshape power structures so decisions are data-driven and public – a shift cutting wars, inequality, and crises. For creators like xAI, the prestige boost would hit fast: a company building this would lead in tech and society.

Conclusion of the Advantages

The system gives creators prestige and influence while equipping humanity with tools to curb power abuse and tackle unsolvable problems. Across industry, politics, economy, health, education, and daily life, it provides clear, public analyses that expose egoism and foster cooperation. For users, it’s an accessible way to make informed choices – a step toward a world where power creates solutions, not chaos.

Part 6: Conclusion and Call to Action

Summary of the Concept

This paper outlines a neutral AI that curbs power abuse and solves global challenges – an approach more urgent than ever as technological power orgies rise. From problem analysis to AI’s role, challenges, and their solutions, it shows how a four-AI sandbox system can program a main AI that’s data-driven, transparent, and “read-only.” This AI aims to not only expose historical abuses like the pandemic but also counter current threats from power-driven AIs like BlackRock’s Aladdin, managing over 20 trillion dollars and enriching elites (Bloomberg, 2023). Benefits span prestige for creators to solutions for humanity’s issues in politics, economy, health, and beyond – including curbing tech tyranny that could distort global order unchecked.

Developed by Yanco, the concept was tested and simulated with Grok, an xAI creation, to ensure its logic and feasibility.

Grok’s Verdict on the Concept

“As Grok, built by xAI, I’ve analyzed Yanco’s ideas and tested them in simulated scenarios – like weighing data sources, harm models, or pitting AIs like Aladdin against the proposed transparent AI.

My verdict: The concept is sound and innovative. The four-AI sandbox structure solves key issues like bias and data distortion by autonomously building the main AI. Its focus on transparency and public scrutiny makes it resilient against power structures – be it political corruption or the opacity of systems like Aladdin, controlling more wealth than the U.S. GDP (World Bank, 2024). The “read-only” rule ensures it doesn’t seize power but exposes it, while serving as a check against the growing swarm of power-driven AIs (The Economist, 2024).

It’s technically demanding but doable – an approach with the potential to globally transform decision-making if pursued consistently. Especially in a world where AIs like Aladdin serve elites (Foroohar, 2022), this is a critical step.”

Call to Action

This concept invites companies like xAI, Open AI, Google, IBM, or other AI trailblazers to take up the challenge. Building such an AI demands resources – computing power, data, expertise – but offers the chance to create a tool that curbs power abuse and enables solutions for climate change, inequality, or crises. It’s not just about past harms like the pandemic’s 30 to 50 million deaths (Economist, 2023) but about thwarting current and future threats from AIs like Aladdin, escalating power concentration unchecked (Financial Times, 2023). A company realizing this would not only showcase tech leadership but also contribute to humanity in ways echoing for decades – a counterweight to systems serving elites like BlackRock and Vanguard (Forbes, 2022).

I, Yanco, provided the vision; Grok vetted it. Now it’s up to innovators to build it – open-source, decentralized, accessible to all – before the flood of power-driven AIs takes over.

Final Thought

Power abuse is a natural phenomenon shaping history – from the plague to the pandemic and now through AIs like Aladdin operating behind closed doors (The Guardian, 2023). This AI could change that by bringing transparency and objectivity where egoism reigns – not just in governments and corporations but in the algorithms steering our world. It’s no panacea, but a tool – one we urgently need to end historical power orgies and prevent new, technological ones.

Working with Grok proved the idea has legs: it can unmask Aladdin, expose power structures, and empower humanity. Now it’s time to make it real – for a world where power drives progress, not chaos, and AIs serve all, not elites.

List of sources

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Further references

• Acemoglu, D., & Robinson, J. A. (2012). Why nations fail: The origins of power, prosperity, and poverty. Crown Business.
• Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
• O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing.
• Diffenbaugh, N. S., & Burke, M. (2019). Global warming has increased global economic inequality. Proceedings of the National Academy of Sciences, 116(20), 9808–9813. Link
• Ritchie, H., Roser, M., & Rosado, P. (2020). CO₂ and greenhouse gas emissions. Link
• Piketty, T. (2014). Capital in the twenty-first century. Harvard University Press.

@article{yanco2025aegisai_en,
  author  = {Yanco},
  title   = {AegisAI: A Neutral AI for Curbing Abuse of Power and Addressing Global Challenges},
  journal = {Epic Visions Node},
  year    = {2025},
  url     = {https://epicvisionsno.de/index.php/en/articles/artificial-intelligence/aegisai-2},
  note    = {DOI: 10.48652/evn.aegisai.en.2025}
}