AI

Global AI Governance: Navigating the Challenges and Opportunities

Published

2 years ago

November 21, 2023

Abdul Rahman

Table of Contents

Introduction

Global AI governance refers to the development and implementation of policies, norms, and regulations that ensure the ethical and responsible use of artificial intelligence (AI) on a global scale. The rapid advancement of AI technology has led to concerns about its potential impact on society, including issues related to privacy, security, and fairness. As such, global AI governance has become a critical issue for policymakers, industry leaders, and civil society organizations around the world.

Understanding AI governance requires an understanding of the various actors involved in the development and deployment of AI systems, including government agencies, private companies, and civil society organizations. It also involves an understanding of the key principles that underpin AI governance, such as transparency, accountability, and human rights. In addition, global AI governance requires a global perspective, as the development and deployment of AI systems are not limited to any one country or region.

Key Takeaways

Global AI governance is essential to ensure the ethical and responsible use of AI technology on a global scale.
AI governance requires an understanding of the various actors involved, the key principles that underpin it, and a global perspective.
The challenges and future of global AI governance are complex and require ongoing collaboration and engagement from all stakeholders.

Understanding AI Governance

Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionize many aspects of society. However, as with any new technology, there are concerns about its potential impact on individuals, organizations, and society as a whole. AI governance is the process of developing policies, regulations, and ethical frameworks to ensure that AI is developed and used in a responsible and beneficial manner.

AI governance is a complex and multifaceted field that involves many different stakeholders, including governments, businesses, academics, and civil society organizations. It encompasses a wide range of issues, including data privacy, algorithmic bias, transparency, and accountability.

One of the key challenges of AI governance is balancing the need for innovation and economic growth with the need to protect individual rights and societal values. This requires a nuanced approach that takes into account the unique characteristics of AI and the various contexts in which it is being developed and used.

To address these challenges, a number of initiatives have been launched to develop AI governance frameworks and guidelines. For example, the Global Partnership on AI (GPAI) is a multilateral initiative that aims to promote responsible AI development and use. The European Union has also developed a set of ethical guidelines for trustworthy AI, which emphasize the importance of transparency, accountability, and human oversight.

Overall, AI governance is a critical issue that will shape the future of society. It requires a collaborative and interdisciplinary approach that involves a wide range of stakeholders. By developing responsible and effective AI governance frameworks, we can ensure that AI is used to benefit society as a whole while minimizing its potential negative impacts.

Global Perspective on AI Governance

Artificial Intelligence (AI) is a rapidly growing field with the potential to revolutionize industries and transform societies. However, this technology also presents significant ethical and governance challenges. As such, governments around the world are grappling with how to regulate and govern AI development and deployment.

AI Governance in Developed Countries

Developed countries such as the United States, Canada, and countries in Europe have taken the lead in developing AI governance frameworks. For example, the European Union (EU) has developed a comprehensive set of guidelines on AI ethics, including principles such as transparency, accountability, and fairness. Similarly, the United States has established the National Artificial Intelligence Initiative Office to coordinate federal AI research and development efforts and ensure that AI is developed in a manner that is consistent with American values.

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AI Governance in Developing Countries

Developing countries face unique challenges in developing AI governance frameworks. Many of these countries lack the resources and expertise to develop comprehensive AI governance policies. However, some developing countries are taking steps to address these challenges. For example, the government of India has established a National Strategy for Artificial Intelligence to guide the development and adoption of AI in the country. Similarly, the African Union has developed a framework for AI governance in Africa, which includes principles such as accountability, transparency, and human rights.

In conclusion, AI governance is a complex and rapidly evolving field. Governments around the world are working to develop comprehensive frameworks to regulate and govern AI development and deployment. While developed countries have taken the lead in this area, developing countries are also taking steps to address the unique challenges they face in developing AI governance policies.

Key Principles of AI Governance

AI governance refers to the set of principles, policies, and practices that guide the development, deployment, and use of artificial intelligence technologies. The following are some of the key principles of AI governance that should be followed to ensure that AI is developed and used in a responsible and ethical manner.

Transparency

Transparency is a key principle of AI governance that requires AI systems to be open and transparent about how they operate. This includes providing clear explanations about how the system makes decisions, what data it uses, and how it processes that data. By being transparent, AI systems can help build trust with users and ensure that they are being used in a fair and ethical manner.

Accountability

Accountability is another important principle of AI governance that requires developers and users of AI systems to take responsibility for their actions. This includes being accountable for the decisions made by the AI system and for any unintended consequences that may arise from its use. By being accountable, developers and users can help ensure that AI systems are used in a responsible and ethical manner.

Fairness

Fairness is a critical principle of AI governance that requires AI systems to be unbiased and impartial. This means that AI systems should not discriminate against individuals or groups based on their race, gender, age, or other characteristics. By being fair, AI systems can help promote social justice and equality.

Privacy

Privacy is a fundamental principle of AI governance that requires AI systems to respect the privacy rights of individuals. This means that AI systems should not collect, use, or share personal data without the consent of the individual, and should take steps to protect that data from unauthorized access or disclosure. By respecting privacy, AI systems can help build trust with users and ensure that they are being used in a responsible and ethical manner.

Challenges in Global AI Governance

Artificial Intelligence (AI) has been rapidly advancing, and as a result, there is a need for global governance of AI development. However, there are several challenges that need to be addressed to ensure that the governance of AI is effective.

Legal and Regulatory Challenges

One of the primary challenges of global AI governance is the lack of legal and regulatory frameworks for AI. The legal and regulatory frameworks for AI are still in their infancy, and there is a lack of consensus on how to regulate AI. This lack of consensus has led to a fragmented legal and regulatory landscape, which makes it difficult to enforce regulations across borders.

Moreover, AI is a complex technology, which makes it difficult to create legal and regulatory frameworks that can keep up with the rapid pace of AI development. There is also a need to ensure that the legal and regulatory frameworks for AI are flexible enough to adapt to new developments in AI.

Ethical Challenges

Another significant challenge in global AI governance is the ethical challenges associated with AI. AI has the potential to cause harm to individuals and society, and there is a need to ensure that AI is developed and used in an ethical manner.

One of the primary ethical challenges of global AI governance is the potential for AI to exacerbate existing social inequalities. AI can be biased, and this bias can result in discrimination against certain groups of people. There is a need to ensure that AI is developed in a way that is fair and equitable for all.

Technical Challenges

Finally, there are several technical challenges that need to be addressed in global AI governance. One of the primary technical challenges is the lack of transparency in AI systems. AI systems can be complex, and it can be difficult to understand how they make decisions.

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Moreover, AI systems can be vulnerable to cyber-attacks, which can compromise the security and privacy of individuals and organizations. There is a need to ensure that AI systems are developed with security and privacy in mind.

In conclusion, global AI governance faces several challenges, including legal and regulatory challenges, ethical challenges, and technical challenges. Addressing these challenges will require a coordinated effort from governments, industry, and civil society.

Role of International Organizations in AI Governance

International organizations have a crucial role to play in the governance of Artificial Intelligence (AI). They can facilitate global coordination and cooperation in AI research and development, while also promoting ethical and responsible AI practices. This section will examine the approaches taken by two major international organizations in the field of AI governance: the United Nations (UN) and the Organisation for Economic Co-operation and Development (OECD).

United Nations’ Approach

The UN has recognized the importance of AI governance and has established several initiatives to promote ethical and responsible AI practices. In 2018, the UN launched the High-level Panel on Digital Cooperation, which aims to promote global cooperation in the digital sphere, including in the area of AI governance. The panel has produced a report that includes recommendations on how to promote ethical and human-centered AI, including the need to ensure transparency, accountability, and inclusiveness in AI development.

The UN has also established the Centre for Artificial Intelligence and Robotics, which aims to promote the development of AI for sustainable development and humanitarian action. The centre provides a platform for global dialogue and cooperation on AI governance, and is working to develop ethical AI guidelines for use in humanitarian settings.

OECD’s Principles on AI

The OECD has developed a set of principles on AI that aim to promote responsible and trustworthy AI development. The principles include the need for AI to be transparent, explainable, and auditable, as well as the need to ensure that AI is designed to respect human rights and democratic values.

The OECD principles have been endorsed by over 40 countries and have been widely recognized as an important step towards promoting ethical and responsible AI practices. The principles have also been used as a basis for the development of national AI strategies, including in countries such as Canada and Japan.

In conclusion, international organizations have an important role to play in the governance of AI. The UN and OECD are two major organizations that have taken significant steps towards promoting ethical and responsible AI practices. Their efforts are likely to have a significant impact on the development of AI in the years to come.

Case Studies of AI Governance

AI Governance in the European Union

The European Union (EU) has been at the forefront of AI governance and ethics initiatives. In April 2018, the EU published a set of ethical guidelines for trustworthy AI, which outlined seven key requirements for AI systems, including transparency, accountability, and respect for privacy and data protection. In addition, the EU has proposed a regulatory framework for AI that includes risk-based requirements for high-risk applications, mandatory human oversight, and transparency obligations.

AI Governance in the United States

In the United States, AI governance is primarily driven by industry self-regulation and government initiatives. In February 2019, the White House Office of Science and Technology Policy released the “Executive Order on Maintaining American Leadership in Artificial Intelligence,” which included a set of principles for federal agencies to promote and regulate AI. In addition, major tech companies such as Google and Microsoft have released their own ethical AI principles, which focus on issues such as fairness, accountability, and transparency.

AI Governance in China

China has taken a different approach to AI governance, with a focus on promoting AI development and innovation. In 2017, the Chinese government released a plan to become a world leader in AI by 2030, which includes significant investments in research and development, talent training, and infrastructure. In addition, China has established a national AI standardization committee to develop technical standards for AI, and has released guidelines for AI ethics and safety.

Overall, these case studies demonstrate the diverse approaches to AI governance across different regions and countries. While the EU and the United States have focused on ethical and regulatory frameworks, China has prioritized AI development and innovation. As AI continues to advance and become more widespread, it will be important for governments and industry to work together to ensure that AI is developed and used in a responsible and ethical manner.

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Future of Global AI Governance

Trends and Predictions

The future of global AI governance is an interesting topic that has been the subject of many discussions. As AI technology advances, there is a growing need for global governance to ensure that ethical and legal issues are addressed. One of the trends that can be seen in the future of global AI governance is the increasing use of AI in various industries. This means that there will be a need for more regulations to ensure that AI is used ethically and responsibly.

Another trend that can be seen in the future of global AI governance is the increasing use of AI in the public sector. Governments around the world are already using AI to improve their services, and this trend is likely to continue. However, this also means that there will be a need for more regulations to ensure that AI is used responsibly in the public sector.

Role of Emerging Technologies

Emerging technologies such as blockchain and quantum computing are likely to play a significant role in the future of global AI governance. Blockchain technology can be used to create secure and transparent systems that can be used to regulate the use of AI. Similarly, quantum computing can be used to develop more advanced AI systems that are capable of solving complex problems.

However, the use of emerging technologies in AI governance also poses some challenges. For example, there is a need for more research to understand the potential risks and benefits of these technologies. Additionally, there is a need for more regulations to ensure that these technologies are used ethically and responsibly.

In conclusion, the future of global AI governance is likely to be shaped by the increasing use of AI in various industries and in the public sector. Emerging technologies such as blockchain and quantum computing are also likely to play an important role in the future of global AI governance. However, there is a need for more research and regulations to ensure that AI is used ethically and responsibly.

Frequently Asked Questions

What is the role of the Global AI Action Alliance in shaping AI governance policies worldwide?

The Global AI Action Alliance (GAIA) is a multi-stakeholder initiative that aims to promote responsible and ethical AI practices worldwide. GAIA brings together governments, industry leaders, civil society organizations, and academia to develop and implement AI governance policies that promote human rights, social justice, and environmental sustainability. GAIA’s role in shaping AI governance policies worldwide is to provide a platform for collaboration and knowledge-sharing among stakeholders, as well as to develop best practices and guidelines for responsible AI development and deployment.

What are the key considerations for creating a high-level advisory body on artificial intelligence?

Creating a high-level advisory body on artificial intelligence requires careful consideration of several key factors. These include the body’s mandate and scope, its membership and governance structure, its funding and resources, and its relationship with other national and international bodies. The body’s mandate should be clearly defined and aligned with the broader goals of AI governance, while its membership and governance structure should be diverse and inclusive to ensure a wide range of perspectives and expertise. Adequate funding and resources should also be provided to support the body’s work, and its relationship with other bodies should be well-coordinated to avoid duplication of efforts.

What are some of the leading AI governance companies and their approaches?

Several companies are emerging as leaders in AI governance, including Google, Microsoft, IBM, and Amazon. These companies are developing their own frameworks and guidelines for responsible AI development and deployment, as well as partnering with governments and other stakeholders to promote ethical and transparent AI practices. Their approaches typically involve a combination of technical solutions, policy recommendations, and stakeholder engagement, and are guided by principles such as transparency, accountability, and fairness.

How can AI governance certification help ensure responsible use of AI technologies?

AI governance certification is a process by which organizations can demonstrate their adherence to established AI governance standards and best practices. This can help ensure that AI technologies are developed and deployed in a responsible and ethical manner, and can provide greater transparency and accountability for stakeholders. Certification can also help build trust and confidence in AI technologies, and can facilitate international cooperation and collaboration on AI governance issues.

What are the major challenges facing the UN AI Advisory Body in promoting global AI governance?

The UN AI Advisory Body faces several major challenges in promoting global AI governance, including the lack of a common understanding of AI governance principles and practices, the diverse interests and perspectives of stakeholders, and the rapid pace of technological change. Other challenges include the need to balance innovation and regulation, the potential for unintended consequences and biases in AI systems, and the difficulty of achieving global consensus on complex and multifaceted issues.

What are the key features of effective AI governance software?

Effective AI governance software should include several key features, including transparency, accountability, and fairness. It should also be adaptable and flexible to accommodate changing technologies and governance frameworks, and should be designed with stakeholder engagement and participation in mind. Other important features include the ability to monitor and assess AI systems for potential risks and biases, as well as the ability to provide feedback and recommendations for improving AI governance practices.

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Amazon, OpenAI, and the $10 Billion AI Power Shift: How a New Wave of Investment Is Rewriting the Future of Tech

Published

7 hours ago

December 17, 2025

Abdul Rahman

A deep dive into Amazon, OpenAI, and the $10B AI investment wave reshaping startups, big tech competition, and the future of artificial intelligence.

Table of Contents

The AI Investment Earthquake No One Can Ignore

Every few years, the tech world experiences a moment that permanently shifts the landscape — a moment when capital, innovation, and ambition collide so forcefully that the ripple effects reshape entire industries.

ALSO READ: Coronavirus vs. Social Distancing: A changing paradigm of social interaction

2025 delivered one of those moments. 2026 is where the aftershocks begin.

Between Amazon’s aggressive AI expansion, OpenAI’s escalating influence, and a global surge of $10 billion‑plus investments into next‑gen artificial intelligence, the world is witnessing a new kind of tech arms race. Not the cloud wars. Not the mobile wars. Not even the social media wars.

This is the AI supremacy war — and the stakes are higher than ever.

For startups, founders, investors, and operators, this isn’t just “ai news.” This is the blueprint for the next decade of opportunity.

And if you’re building anything in tech, this story matters more than you think.

The New AI Power Triangle: Amazon, OpenAI, and the Capital Flood

Amazon’s AI Ambition: From Cloud King to Intelligence Empire

Amazon has always played the long game. AWS dominated cloud. Prime dominated logistics. Alexa dominated voice.

But 2026 marks a new chapter: Amazon wants to dominate intelligence itself.

The company’s recent multi‑billion‑dollar AI investments — including infrastructure, model training, and strategic partnerships — signal a clear message:

Amazon doesn’t just want to compete with OpenAI. Amazon wants to become the operating system of AI.

From custom silicon to foundation models to enterprise AI tools, Amazon is building a vertically integrated AI stack that startups will rely on for years.

Why this matters for startups

Cheaper, faster AI compute
More accessible model‑training tools
Enterprise‑grade AI infrastructure
A growing ecosystem of AI‑native services

If AWS shaped the last decade of startups, Amazon’s AI stack will shape the next one.

OpenAI: The Relentless Pace‑Setter

OpenAI remains the gravitational center of the AI universe. Every product launch, every model upgrade, every partnership — it all sends shockwaves across the industry.

But what’s different now is the scale of investment behind OpenAI’s ambitions.

With billions flowing into model development, safety research, and global expansion, OpenAI is no longer a research lab. It’s a geopolitical force.

OpenAI’s influence in 2026

Sets the pace for AI innovation
Shapes global regulation conversations
Defines the capabilities startups build on
Drives the evolution of AI‑powered work

Whether you’re building a SaaS tool, a marketplace, a fintech product, or a consumer app, OpenAI’s roadmap affects your roadmap.

The $10 Billion Dollar Question: Why Is AI Attracting Record Investment?

The number isn’t symbolic. It’s strategic.

Across the US, UK, EU, and Asia, governments and private investors are pouring $10 billion‑plus into AI infrastructure, safety, chips, and model development.

The drivers behind the investment wave

AI is becoming a national security priority
Big tech is racing to build proprietary models
Startups are proving AI monetization is real
Enterprise adoption is accelerating
AI infrastructure is the new oil

This isn’t hype. This is the industrialization of intelligence.

The Market Impact: A New Era of Tech Investment

1. AI Is Becoming the Default Layer of Every Startup

In 2010, every startup needed a website. In 2015, every startup needed an app. In 2020, every startup needed a cloud strategy.

In 2026?

Every startup needs an AI strategy — or it won’t survive.

AI is no longer a feature. It’s the foundation.

Examples of AI‑first startup models

AI‑powered legal assistants
Autonomous customer support
Predictive analytics for finance
AI‑generated content engines
Automated supply chain optimization
Personalized learning platforms

The startups winning funding today are the ones treating AI as the core engine, not the add‑on.

2. Big Tech Competition Is Fueling Innovation

Amazon, Google, Microsoft, Meta, and OpenAI are locked in a race that benefits one group more than anyone else:

Founders.

Competition drives:

Lower compute costs
Faster model improvements
More developer tools
More open‑source innovation
More funding opportunities

When giants fight, startups grow.

3. AI Infrastructure Is the New Gold Rush

Investors aren’t just funding apps. They’re funding the picks and shovels.

High‑growth investment areas

AI chips
Data centers
Model training platforms
Vector databases
AI security
Synthetic data generation

If you’re building anything that helps companies train, deploy, or scale AI — you’re in the hottest market of 2026.

Why This Matters for Startups: The Opportunity Map

1. The Barriers to Entry Are Falling

Thanks to Amazon, OpenAI, and open‑source communities, startups can now:

Build AI products without massive capital
Train models without specialized hardware
Deploy AI features in days, not months
Access enterprise‑grade tools at startup‑friendly prices

This levels the playing field in a way we haven’t seen since the early cloud era.

2. Investors Are Prioritizing AI‑Native Startups

VCs aren’t just “interested” in AI. They’re restructuring their entire portfolios around it.

What investors want in 2026

AI‑native business models
Clear data advantages
Strong defensibility
Real‑world use cases
Scalable infrastructure

If you’re raising capital, aligning your pitch with the AI investment wave is no longer optional.

3. AI Is Creating New Categories of Startups

Entire industries are being rewritten.

Emerging AI‑driven sectors

Autonomous commerce
AI‑powered healthcare diagnostics
AI‑driven logistics
Intelligent cybersecurity
AI‑enhanced education
Synthetic media and entertainment

The next unicorns will come from categories that didn’t exist five years ago.

The Competitive Landscape: Who Wins the AI Race?

Amazon’s Strengths

Massive cloud dominance
Custom AI chips
Global distribution
Enterprise trust

OpenAI’s Strengths

Fastest innovation cycles
Best‑in‑class models
Strong developer ecosystem
Cultural influence

Startups’ Strengths

Speed
Focus
Agility
Ability to innovate without bureaucracy

The real winners? Startups that build on top of the giants — without becoming dependent on them.

Future Predictions: What 2026–2030 Will Look Like

1. AI Will Become a Regulated Industry

Expect global standards, safety protocols, and compliance frameworks.

2. AI‑powered work will replace traditional workflows

Not jobs — workflows. Humans will supervise, not execute.

3. AI infrastructure will become a trillion‑dollar market

Chips, data centers, and training platforms will explode in value.

4. The next wave of unicorns will be AI‑native

Not AI‑enabled — AI‑native.

5. The UK will become a major AI hub

Thanks to government support, talent density, and startup momentum.

FAQ (Optimized for Google’s Answer Engine)

1. Why are companies investing $10 billion in AI?

Because AI is becoming critical infrastructure — powering automation, intelligence, and national competitiveness.

2. How does Amazon’s AI strategy affect startups?

It lowers compute costs, accelerates development, and provides enterprise‑grade tools to early‑stage founders.

3. Is OpenAI still leading the AI race?

OpenAI remains a pace‑setter, but Amazon, Google, and open‑source communities are closing the gap.

4. What AI sectors will grow the fastest by 2030?

AI chips, healthcare AI, autonomous logistics, cybersecurity, and synthetic media.

5. Should startups pivot to AI‑native models?

Yes — AI‑native startups attract more funding, scale faster, and build stronger defensibility.

Conclusion: The Future Belongs to the Builders

The AI revolution isn’t coming. It’s here — funded, accelerated, and industrialized.

Amazon is building the infrastructure. OpenAI is building the intelligence. Investors are pouring billions into the ecosystem.

The only question left is: What will you build on top of it?

For founders, operators, and investors, 2026 is the year to move — boldly, intelligently, and with AI at the center of your strategy.

Because the next decade of innovation belongs to those who understand one truth:

AI isn’t the future of tech. AI is tech.

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Analysis

Editorial Deep Dive: Predicting the Next Big Tech Bubble in 2026–2028

Published

2 weeks ago

December 6, 2025

Abdul Rahman

It was a crisp evening in San Francisco, the kind of night when the fog rolls in like a curtain call. At the Yerba Buena Center for the Arts, a thousand investors, founders, and journalists gathered for what was billed as “The Future Agents Gala.” The star attraction was not a celebrity CEO but a humanoid robot, dressed in a tailored blazer, capable of negotiating contracts in real time while simultaneously cooking a Michelin-grade risotto.

The crowd gasped as the machine signed a mock term sheet projected on a giant screen, its agentic AI brain linked to a venture capital fund’s API. Champagne flutes clinked, sovereign wealth fund managers whispered in Arabic and Mandarin, and a former OpenAI board member leaned over to me and said: “This is the moment. We’ve crossed the Rubicon. The next tech bubble is already inflating.”

Outside, a line of Teslas and Rivians stretched down Mission Street, ferrying attendees to afterparties where AR goggles were handed out like party favors. In one corner, a partner at one of the top three Valley VC firms confided, “We’ve allocated $8 billion to agentic AI startups this quarter alone. If you’re not in, you’re out.” Across the room, a sovereign wealth fund executive from Riyadh boasted of a $50 billion allocation to “post-Moore quantum plays.” The mood was euphoric, bordering on manic. It felt eerily familiar to anyone who had lived through the dot-com bubble of 1999 or the crypto mania of 2021.

I’ve covered four major bubbles in my career — PCs in the ’80s, dot-com in the ’90s, housing in the 2000s, and crypto/ZIRP in the 2020s. Each had its own soundtrack of hype, its own cast of villains and heroes. But what I witnessed in November 2025 was different: a collision of narratives, a tsunami of capital, and a retail investor base armed with apps that can move billions in seconds. The signs of the next tech bubble are unmistakable.

Table of Contents

Historical Echoes

Every bubble begins with a story. In 1999, it was the promise of the internet democratizing commerce. In 2021, it was crypto and NFTs rewriting finance and art. Today, the narrative is agentic AI, AR/VR resurrection, and quantum supremacy.

The parallels are striking. In 1999, companies with no revenue traded at 200x forward sales. Pets.com became a household name despite selling dog food at a loss. In 2021, crypto tokens with no utility reached market caps of $50 billion. Now, in late 2025, robotics startups with prototypes but no customers are raising at $10 billion valuations.

ALSO READ: 🚀 Emerging Tech Summit 2025: Scale, Distribute, Accelerate!

Consider the table below, comparing three bubbles across eight metrics:

Metric	Dot-com (1999–2000)	Crypto/ZIRP (2021–2022)	Emerging Bubble (2025–2028)
Valuation multiples	200x sales	50–100x token revenue	150x projected AI agent ARR
Retail participation	Day traders via E-Trade	Robinhood, Coinbase	Tokenized AI shares via apps
Fed policy	Loose, then tightening	ZIRP, then hikes	High rates, capital trapped
Sovereign wealth	Minimal	Limited	$2–3 trillion allocations
Corporate cash	Modest	Buybacks dominant	$1 trillion redirected to AI/quantum
Narrative strength	“Internet changes everything”	“Decentralization”	“Agents + quantum = inevitability”
Crash velocity	18 months	12 months	Predicted 9–12 months
Global contagion	US-centric	Global retail	Truly global, sovereign-driven

The echoes are deafening. The question is not if but when will the next tech bubble burst.

The Three Horsemen of the Coming Bubble

Agentic AI + Robotics

The hottest narrative is agentic AI — autonomous systems that act on behalf of humans. Figure, a humanoid robotics startup, has raised $2.5 billion at a $20 billion valuation despite shipping fewer than 50 units. Anduril, the defense-tech darling, is pitching AI-driven battlefield agents to Pentagon brass. A former OpenAI board member told me bluntly: “Agentic AI is the new cloud. Every corporate board is terrified of missing it.”

Retail investors are piling in via tokenized shares of robotics startups, available on apps in Dubai and Singapore. The valuations are absurd: one startup projecting $100 million in revenue by 2027 is already valued at $15 billion. Is AI the next tech bubble? The answer is staring us in the face.

AR/VR 2.0: The Metaverse Resurrection

Apple’s Vision Pro ecosystem has reignited the metaverse dream. Meta, chastened but emboldened, is pouring $30 billion annually into AR/VR. A partner at Sequoia told me off the record: “We’re seeing pitch decks that look like 2021 all over again, but with Apple hardware as the anchor.”

ALSO READ: Social Media and polarization of society

Consumers are buying in. AR goggles are marketed as productivity tools, not toys. Yet the economics are fragile: hardware margins are thin, and software adoption is speculative. The next dot com bubble may well be wearing goggles.

Quantum + Post-Moore Semiconductor Mania

Quantum computing startups are raising at valuations that defy physics. PsiQuantum, IonQ, and a dozen stealth players are promising breakthroughs by 2027. Meanwhile, post-Moore semiconductor firms are hyping “neuromorphic chips” with little evidence of scalability.

A Brussels regulator told me: “We’re seeing lobbying pressure from quantum firms that rivals Big Tech in 2018. It’s extraordinary.” The hype is global, with Chinese funds pouring billions into quantum supremacy plays. The AI bubble burst prediction may hinge on quantum’s failure to deliver.

The Money Tsunami

Where is the capital coming from? The answer is everywhere.

Sovereign wealth funds: Abu Dhabi, Riyadh, and Doha are allocating $2 trillion collectively to tech between 2025–2028.
Corporate treasuries: Apple, Microsoft, and Alphabet are redirecting $1 trillion in cash from buybacks to strategic AI/quantum investments.
Retail investors: Apps in Asia and Europe allow fractional ownership of AI startups via tokenized assets.

A Wall Street banker told me: “We’ve never seen this much dry powder chasing so few narratives. It’s a venture capital bubble 2026 in the making.”

Charts show venture funding in Q3 2025 hitting $180 billion globally, surpassing the peak of 2021. Sovereign allocations alone dwarf the dot-com era by a factor of ten. The signs of the next tech bubble are flashing red.

The Cracks Already Forming

Yet beneath the euphoria, cracks are visible.

Revenue reality: Most agentic AI startups have negligible revenue.
Hardware bottlenecks: AR/VR adoption is limited by cost and ergonomics.
Quantum skepticism: Physicists quietly admit breakthroughs are unlikely before 2030.

Regulators in Washington and Brussels are already drafting rules to curb AI agents in finance and defense. A senior EU official told me: “We will not allow autonomous systems to trade securities without oversight.”

Meanwhile, retail investors are overexposed. In Korea, 22% of household savings are now in tokenized AI assets. In Dubai, AR/VR tokens trade like penny stocks. Is there a tech bubble right now? The answer is yes — and it’s accelerating.

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When and How It Pops

Based on historical cycles and current capital flows, I predict the bubble peaks between Q4 2026 and Q2 2027. The triggers will be:

Regulatory clampdowns on agentic AI in finance and defense.
Quantum delays, with promised breakthroughs failing to materialize.
AR/VR fatigue, as consumers tire of expensive goggles.
Liquidity crunch, as sovereign wealth funds pull back in response to geopolitical shocks.

The correction will be violent, sharper than dot-com or crypto. Retail apps will amplify panic selling. Tokenized assets will collapse in hours, not months. The next tech bubble burst will be global, instantaneous, and brutal.

Who Gets Hurt, Who Gets Rich

The losers will be retail investors, late-stage VCs, and sovereign funds overexposed to hype. Figure, Anduril, and quantum pure-plays may 10x before crashing to near-zero. Apple’s Vision Pro ecosystem plays will soar, then collapse as adoption stalls.

The winners will be incumbents with real cash flow — Microsoft, Nvidia, and TSMC — who can weather the storm. A few VCs who resist the mania will emerge as heroes. One Valley veteran told me: “We’re sitting out agentic AI. It smells like Pets.com with robots.”

History suggests that those who short the bubble early — hedge funds in New York, sovereigns in Norway — will profit handsomely. The next dot com bubble redux will crown new villains and heroes.

The Bottom Line

The next tech bubble will not be a slow-motion phenomenon like housing in 2008 or crypto in 2021. It will be a compressed, violent cycle — inflated by sovereign wealth funds, corporate treasuries, and retail apps, then punctured by regulatory shocks and technological disappointments.

I’ve covered bubbles for 35 years, and the pattern is unmistakable: the louder the narrative, the thinner the fundamentals. Agentic AI, AR/VR resurrection, and quantum computing are extraordinary technologies, but they are being priced as inevitabilities rather than possibilities. When the correction comes — between late 2026 and mid-2027 — it will erase trillions in paper wealth in weeks, not years.

The winners will be those who recognize that hype is not the same as adoption, and that capital cycles move faster than technological ones. The losers will be those who confuse narrative with inevitability.

The bottom line: The next tech bubble is already here. It will peak in 2026–2027, and when it bursts, it will be larger in scale than dot-com but shorter-lived, leaving behind a scorched landscape of failed startups, chastened sovereign funds, and a handful of resilient incumbents who survive to build the real future.

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Macro Trends: The Rise of the Decentralised Workforce Is Reshaping Global Capitalism

Published

2 weeks ago

December 6, 2025

Abdul Rahman

The decentralised workforce has unlocked a productivity shock larger than the internet itself. But only companies building global talent operating systems will capture the $4tn prize by 2030. A Financial Times–style analysis of borderless hiring, geo-arbitrage, and the coming regulatory storm.

Imagine a Fortune 500 technology company whose chief financial officer lives in Lisbon, its head of artificial intelligence in Tallinn, and its best machine-learning engineers split between Buenos Aires and Lagos. The company has no headquarters, no central campus, and only a dozen employees in its country of incorporation. This is no longer a thought experiment. According to Deel’s State of Global Hiring Report published in October 2025, 41 per cent of knowledge workers at companies with more than 1,000 employees now work under fully decentralised contracts — up from 11 per cent in 2019. The decentralised workforce has moved from pandemic stop-gap to permanent structural shift. And it is quietly rewriting the rules of global capitalism.

Table of Contents

From Zoom Calls to Geo-Arbitrage Warfare

The numbers are now familiar yet still breathtaking. McKinsey Global Institute’s November 2025 update estimates that the rise of remote global talent has unlocked an effective labour supply increase equivalent to adding 350 million knowledge workers to the global pool — almost the size of the entire US workforce. Companies practising aggressive borderless hiring have, on average, reduced salary costs for senior software engineers by 38 per cent while simultaneously raising output per worker by 19 per cent, thanks to round-the-clock asynchronous work economy cycles.

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Goldman Sachs’ latest Global Markets Compass (Q4 2025) goes further. It calculates that listed companies with fully distributed teams trade at a persistent 18 per cent valuation premium to their office-centric peers — a gap that has widened every quarter since 2022. The market, it seems, has already priced in the productivity shock.

Chart 1 (described): Share of knowledge workers on fully decentralised contracts, 2019–2025E 2019: 11% 2021: 27% 2023: 34% 2025: 41% 2026E: 49% (Source: Deel, Remote.com, author estimates)

The Emerging-Market Middle-Class Explosion No One Saw Coming

For decades, policymakers worried about brain drain from the global south. The decentralised workforce has inverted the flow. World Bank data released in September 2025 show that professional-class household income in the Philippines, Nigeria, Colombia and Romania has risen between 68 per cent and 92 per cent since 2020 — almost entirely driven by remote earnings in dollars or euros. In Metro Manila alone, more than 1.4 million Filipinos now earn above the US median wage without leaving the country. Talent arbitrage, once a corporate profit centre, has become the fastest wealth-transfer mechanism in modern economic history.

Is Your Company Ready for Permanent Establishment Risk in 2026?

Here the story darkens. Regulators are waking up. The OECD’s October 2025 pillar one and pillar two revisions explicitly target “digital nomad payroll” and “compliance-as-a-service” loopholes. France, Spain and Italy have already introduced unilateral remote-worker taxation rules that create permanent establishment risk 2025 the moment a company employs a resident for more than 90 days. The EU’s Artificial Intelligence Act, effective January 2026, adds another layer: any company using EU-resident contractors for “high-risk” AI development must register a legal entity in the bloc.

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Yet enforcement remains patchy. Only 14 per cent of companies with distributed teams have built what I call a global talent operating system — an integrated stack of employer of record (EOR) providers, real-time tax engines, and currency-hedging payrolls. The rest are flying blind into a regulatory storm.

Chart 2 (described): Corporate tax base erosion attributable to decentralised workforce strategies, selected OECD countries, 2020–2025E United States: –$87bn Germany: –€41bn United Kingdom: –£29bn France: –€33bn (Source: OECD Revenue Statistics 2025, author calculations)

The Rise of the Fractional C-Suite and Talent DAOs

Look closer and the picture becomes stranger still. On platforms such as Toptal, Upwork Enterprise and the newer blockchain-native Braintrust, fractional executives 2026 are already commonplace. The average Series C start-up now retains a part-time chief marketing officer in Cape Town, a part-time chief technology officer in Kyiv, and a part-time chief financial officer in Singapore — each working 12–18 hours a week for equity and dollars. Traditional headhunters report that 29 per cent of C-level placements in 2025 were fractional rather than full-time.

More radical experiments are emerging. At least seven unicorns (most still in stealth) now operate as private talent DAOs — decentralised autonomous organisations in which contributors are paid in tokens tied to company revenue. These structures sidestep traditional employment law entirely. Whether they survive the coming regulatory backlash is one of the defining questions of the decade.

The Productivity Shock — and the Backlash

Let us be clear: the decentralised workforce represents the most powerful productivity shock since the commercial internet itself. McKinsey estimates that full adoption of distributed teams and asynchronous work economy practices could raise global GDP by 2.7–4.1 per cent by 2030 — roughly $3–4 trillion in today’s money. The gains are Schumpeterian: old hierarchies are being destroyed faster than most incumbents realise.

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Yet every productivity shock produces losers. Commercial real estate in gateway cities is already in structural decline. Corporate tax revenues are eroding. And inequality within developed nations is taking new forms: the premium for physical presence in high-cost hubs is collapsing, but the premium for elite credentials and networks remains stubbornly intact.

What Comes Next

By 2030, I predict — and will stake whatever reputation I have left on this — the majority of Forbes Global 2000 companies will have fewer than 5 per cent of their workforce in a traditional headquarters. The winners will be those that treat talent as a global, liquid, 24/7 resource and build sophisticated global talent operating systems to manage it. The losers will be those that cling to 20th-century notions of office, postcode and 9-to-5.

The decentralised workforce is not a trend. It is the new architecture of global capitalism. And like all architectures, it will favour the bold, the fast and the borderless — while quietly dismantling the rest.

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