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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‘That doesn’t exist’: The Quiet, Chaotic End of Elon Musk’s DOGE

Published

3 days ago

November 24, 2025

Abdul Rahman

DOGE is dead. Following a statement from OPM Director Scott Kupor that the agency “doesn’t exist”, we analyse how Musk’s “chainsaw” approach failed to survive Washington.

If T.S. Eliot were covering the Trump administration, he might note that the Department of Government Efficiency (DOGE) ended not with a bang, but with a bureaucrat from the Office of Personnel Management (OPM) politely telling a reporter, “That doesn’t exist.”

Today, November 24, 2025, marks the official, unceremonious end of the most explosive experiment in modern governance. Eight months ahead of its July 2026 deadline, the agency that promised to “delete the mountain” of federal bureaucracy has been quietly dissolved. OPM Director Scott Kupor confirmed the news this morning, stating the department is no longer a “centralised entity.”

It is a fittingly chaotic funeral for a project that was never built to last. DOGE wasn’t an agency; it was a shock therapy stunt that mistook startup velocity for sovereign governance. And as of today, the “Deep State” didn’t just survive the disruption—it absorbed it.

Table of Contents

The Chainsaw vs. The Scalpel

In January 2025, Elon Musk stood on a stage brandishing a literal chainsaw, promising to slice through the red tape of Washington. It was great television. It was terrible management.

The fundamental flaw of DOGE was the belief that the U.S. government operates like a bloatware-ridden tech company. Musk and his co-commissioner Vivek Ramaswamy applied the “move fast and break things” philosophy to federal statutes that require public comment periods and congressional oversight.

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For a few months, it looked like it was working. The unverified claims of “billions saved” circulated on X (formerly Twitter) daily. But you cannot “bug fix” a federal budget. When the “chainsaw” met the rigid wall of administrative law, the blade didn’t cut—it shattered. The fact that the agency is being absorbed by the OPM—the very heart of the federal HR bureaucracy—is the ultimate irony. The disruptors have been filed away, likely in triplicate.

The Musk Exodus: A Zombie Agency Since May

Let’s be honest: DOGE didn’t die today. It died in May 2025.

The moment Elon Musk boarded his jet back to Texas following the public meltdown over President Trump’s budget bill, the soul of the project evaporated. The reported Trump-Musk feud over the “Big, Beautiful Bill”—which Musk criticized as a debt bomb—severed the agency’s political lifeline.

For the last six months, DOGE has been a “zombie agency,” staffed by true believers with no captain. While the headlines today focus on the official disbanding, the reality is that Washington’s immune system rejected the organ transplant half a year ago. The remaining staff, once heralded as revolutionaries, are now quietly updating their LinkedIns or engaging in the most bureaucratic act of all: transferring to other departments.

The Human Cost of “Efficiency”

While we analyze the political theatre, we cannot ignore the wreckage left in the wake of this experiment. Reports indicate over 200,000 federal workers have been displaced, either through the aggressive layoffs of early 2025 or the “voluntary” buyouts that followed.

These weren’t just “wasteful” line items; they were safety inspectors, grant administrators, and veteran civil servants. The federal workforce cuts impact will be felt for years, not in money saved, but in phones that go unanswered at the VA and permits that sit in limbo at the EPA.

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Conclusion: The System Always Wins

The absorption of DOGE functions into the OPM and the transfer of high-profile staff like Joe Gebbia to the new “National Design Studio” proves a timeless Washington truth: The bureaucracy is fluid. You can punch it, scream at it, and even slash it with a chainsaw, but it eventually reforms around the fist.

Musk’s agency is gone. The Department of Government Efficiency news cycle is over. But the regulations, the statutes, and the OPM remain. In the battle between Silicon Valley accelerationism and D.C. incrementalism, the tortoise just beat the hare. Again.

Frequently Asked Questions (FAQ)

Why was DOGE disbanded ahead of schedule?

Officially, the administration claims the work is done and functions are being “institutionalized” into the OPM. However, analysts point to the departure of Elon Musk in May 2025 and rising political friction over the aggressive nature of the cuts as the primary drivers for the early closure.

Did DOGE actually save money?

It is disputed. While the agency claimed to identify hundreds of billions in savings, OPM Director Scott Kupor and other officials have admitted that “detailed public accounting” was never fully verified. The long-term costs of severance packages and rehiring contractors may offset initial savings.

What happens to DOGE employees now?

Many have been let go. However, select high-level staff have been reassigned. For example, Joe Gebbia has reportedly moved to the “National Design Studio,” and others have taken roles at the Department of Health and Human Services (HHS).

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AI

Nvidia Earnings Power AI Boom, Stock Faces Pressure

Published

1 week ago

November 18, 2025

Abdul Rahman

NVDA earnings beat expectations, fueling AI momentum, but Nvidia stock price shows investor caution.

Nvidia’s latest earnings report has once again underscored its central role in the global AI revolution. The chipmaker, whose GPUs power everything from generative AI models to advanced data centers, posted blockbuster results that exceeded Wall Street expectations. Yet, despite the strong NVDA earnings, the Nvidia stock price slipped, reflecting investor caution amid sky-high valuations and intense competition. According to Yahoo Finance, the company’s results remain one of the most closely watched indicators of AI’s commercial trajectory.

Table of Contents

Key Earnings Highlights

For the fourth quarter of fiscal 2025, Nvidia reported record revenue of $39.3 billion, up 78% year-over-year. Data center sales, driven by surging demand for AI infrastructure, accounted for $35.6 billion, a 93% increase from the prior yearNVIDIA Newsroom. Earnings per share came in at $0.89, up 82% year-over-year.

On a full-year basis, Nvidia delivered $130.5 billion in revenue, more than doubling its performance from fiscal 2024. This growth cements Nvidia’s dominance in the AI hardware market, where its GPUs remain the backbone of large language models, autonomous systems, and enterprise AI adoption.

Expert and Market Reactions

Analysts on Yahoo Finance’s Market Catalysts noted that while Nvidia consistently beats estimates, its stock often reacts negatively due to lofty expectations. Antoine Chkaiban of New Street Research emphasized that five of the past eight earnings beats were followed by declines in Nvidia stock, as investors reassess valuations.

Investor sentiment remains mixed. On one hand, Nvidia’s results confirm its unrivaled position in AI infrastructure. On the other, concerns about sustainability, competition from rivals like AMD, and potential regulatory scrutiny weigh on market psychology.

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NVDA Stock Price Analysis

Following the earnings release, NVDA stock price fell nearly 3%, closing at $181.08, down from a previous close of $186.60. Despite the dip, Nvidia shares remain up almost 28% over the past yearBenzinga, reflecting long-term confidence in its AI-driven growth story.

The volatility highlights a recurring theme: Nvidia’s earnings power is undeniable, but investor sentiment is sensitive to valuation risks. With a trailing P/E ratio above 50, the stock is priced for perfection, leaving little margin for error.

Forward-Looking AI Implications

Nvidia’s earnings reaffirm that AI is not just a technological trend but a revenue engine reshaping the semiconductor industry. The company’s GPUs are embedded in every layer of AI innovation—from cloud hyperscalers to startups building generative AI applications.

Looking ahead, analysts expect Nvidia’s revenue to continue climbing, with consensus estimates projecting EPS growth of more than 40% next year. However, the company must navigate challenges including supply chain constraints, intensifying competition, and geopolitical risks tied to chip exports.

Outlook

Nvidia’s latest earnings report demonstrates the company’s unmatched leverage in the AI economy. While NVDA earnings continue to impress, the Nvidia stock price reflects investor caution amid high expectations. For long-term shareholders, the trajectory remains promising: Nvidia is positioned as the indispensable supplier of AI infrastructure, a role that will likely define both its market value and the broader tech landscape.

In the months ahead, Nvidia’s ability to balance innovation with investor confidence will determine whether its stock can sustain momentum. As AI adoption accelerates globally, Nvidia’s role as the sector’s bellwether remains unchallenged.

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Business

5 Disruptive AI Startups That Prove the LLM Race is Already Dead

Published

1 week ago

November 17, 2025

Abdul Rahman

The trillion-dollar LLM race is over. The true disruption will be Agentic AI—autonomous, goal-driven systems—a trend set to dominate TechCrunch Disrupt 2025.

When OpenAI’s massive multimodal models were released in the early 2020s, the entire tech world reset. It felt like a gold rush, where the only currency that mattered was GPU access, trillions of tokens, and a parameter count with enough zeroes to humble a Fortune 500 CFO. For years, the narrative has been monolithic: bigger models, better results. The global market for Large Language Models (LLMs) and LLM-powered tools is projected to be worth billions, with worldwide spending on generative AI technologies forecast to hit $644 billion in 2025 alone.

This single-minded pursuit has created a natural monopoly of scale, dominated by the five leading vendors who collectively capture over 88% of the global market revenue. But I’m here to tell you, as an investor on the ground floor of the next wave, that the era of the monolithic LLM is over. It has peaked. The next great platform shift is already here, and it will be confirmed, amplified, and debated on the hallowed stage of TechCrunch Disrupt 2025.

The future of intelligence is not about the model’s size; it’s about its autonomy. The next billion-dollar companies won’t be those building the biggest brains, but those engineering the most competent AI Agents.

Table of Contents

🛑 The Unspoken Truth of the Current LLM Market

The current obsession with ever-larger LLMs—models with hundreds of billions or even trillions of parameters—has led to an industrial-scale, yet fragile, ecosystem. While adoption is surging, with 67% of organisations worldwide reportedly using LLMs in some capacity in 2025, the limitations are becoming a structural constraint on true enterprise transformation.

We are seeing a paradox of power: models are capable of generating fluent prose, perfect code snippets, and dazzling synthetic media, yet they fail at the most basic tenets of real-world problem-solving. This is the difference between a hyper-literate savant and a true executive.

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Here is the diagnosis, informed by the latest ai news and deep-drives:

The Cost Cliff is Untenable: Training a state-of-the-art frontier model still requires a multi-billion-dollar fixed investment. For smaller firms, the barrier is staggering; approximately 37% of SMEs are reportedly unable to afford full-scale LLM deployment. Furthermore, the operational (inference) costs, while dramatically lower than before, remain a significant drag on gross margins for any scaled application.
The Reliability Crisis: A significant portion of users, specifically 35% of LLM users in one survey, identify “reliability and inaccurate output” as their primary concerns. This is the well-known “hallucination problem.” When an LLM optimizes for the most probable next word, it does not optimise for the most successful outcome. This fundamentally limits its utility in high-stakes fields like finance, healthcare, and engineering.
The Prompt Ceiling: LLMs are intrinsically reactive. They are stunningly sophisticated calculators that require a human to input a clear, perfect equation to get a useful answer. They cannot set their own goals, adapt to failure, or execute a multi-step project without continuous, micro-managed human prompting. This dependence on the prompt limits their scalability in true automation.

We have reached the point of diminishing returns. The incremental performance gain of going from 1.5 trillion parameters to 2.5 trillion parameters is not worth the 27% increase in data center emissions and the billions in training costs. The game is shifting.

🔮 The TechCrunch Disrupt 2025 Crystal Ball: The Agentic Pivot

My definitive prediction for TechCrunch Disrupt 2025 is this: The main stage will not be dominated by the unveiling of a new, larger foundation model. It will be dominated by startups focused entirely on Agentic AI.

What is Agentic AI?

Agentic AI systems don’t just generate text; they act. They are LLMs augmented with a planning module, an execution engine (tool use), persistent memory, and a self-correction loop. They optimise for a long-term goal, not just the next token. They are not merely sophisticated chatbots; they are autonomous problem-solvers. This is the difference between a highly-trained analyst who writes a report and a CEO who executes a multi-quarter strategy.

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Here are three fictional, yet highly plausible, startup concepts poised to launch this narrative at TechCrunch Disrupt’s Startup Battlefield:

1. Stratagem

The Pitch: “We are the first fully autonomous, goal-seeking sales development agent (SDA) for B2B SaaS.”
The Agentic Hook: Stratagem doesn’t just write cold emails. A human simply inputs the goal: “Close five $50k+ contracts in the FinTech vertical this quarter.” The Agentic AI then autonomously:
- Reasons: Breaks the goal into steps (Targeting $\rightarrow$ Outreach $\rightarrow$ Qualification $\rightarrow$ Hand-off).
- Acts: Scrapes real-time financial data to identify companies with specific growth signals (a tool-use capability).
- Self-Corrects: Sends initial emails, tracks engagement, automatically revises its messaging vector (tone, length, value prop) for non-responders, and books a qualified meeting directly into the human sales rep’s calendar.
- The LLM is now a component, not the core product.

2. Phage Labs

The Pitch: “We have decoupled molecular synthesis from human-led R&D, leveraging multi-agent systems to discover novel materials.”
The Agentic Hook: This startup brings the “Agent Swarm” model to material science. A scientist inputs the desired material properties (e.g., “A polymer with a tensile strength 15% higher than Kevlar and 50% lighter”). A swarm of specialised AI Agents then coordinates:
- The Generator Agent proposes millions of novel molecular structures.
- The Simulator Agent runs millions of physics-based tests concurrently in a cloud environment.
- The Refiner Agent identifies the 100 most promising candidates, and most crucially, writes the robotics instructions to synthesise and test the top five in a wet lab.
- The system operates 24/7, with zero human intervention until a successful material is confirmed.

3. The Data-Moat Architectures (DMA)

The Pitch: “We eliminate the infrastructure cost of LLMs by orchestrating open-source models with proprietary data moats.”
The Agentic Hook: This addresses the cost problem head-on. The core technology is an intelligent Orchestrator Agent. Instead of relying on a single, expensive, trillion-parameter model, the Orchestrator intelligently routes complex queries to a highly efficient network of smaller, specialized, open-source models (e.g., one for code, one for summarization, one for RAG queries). This dramatically reduces latency and inference costs while achieving a higher reliability score than any single black-box LLM. By routing a question to the most appropriate, fine-tuned, and low-cost model, they are fundamentally destroying the Big Tech LLM moat.

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🏆 Why TechCrunch is the Bellwether

The shift from the LLM race to Agentic AI is a classic platform disruption—and a debut at Tech Crunch is still the unparalleled launchpad. Why? Because the conference isn’t just about technology; it’s about market validation.

History is our guide. Companies that launched at TechCrunch Disrupt didn’t just have clever tech; they had a credible narrative for how they would fundamentally change human behaviour, capture mindshare, and dominate a market. The intensity of the Startup Battlefield 200, where over 200 hand-selected, early-stage entrepreneurs compete, forces founders to distil their vision into a five-minute pitch that is laser-focused on value.

This focus is the very thing that the venture capital community is desperate for right now. Investors are no longer underwriting the risk of building a foundational LLM—that race is lost to a handful of giants. They are now hunting for the applications that will generate massive ROI on top of that infrastructure. When a respected publication like techcrunch.com reports on a debut, it signals to the world’s most influential VCs—who are all in attendance—that this isn’t science fiction; it’s a Series A waiting to happen.

The successful TechCrunch Disrupt 2025 startup will not have a “better model.” It will have a better system—a goal-driven Agent that can execute, self-correct, and deliver measurable business outcomes without constant human hand-holding. This is the transition from AI as a fancy word processor to AI as a hyper-competent, autonomous employee.

Conclusion: The Era of Doing

For years, the LLM kings have commanded us with the promise of intelligence. We’ve been wowed by their ability to write sonnets, simulate conversations, and generate images. But a truly disruptive technology doesn’t just talk about solving a problem; it solves it.

The Agentic AI revolution marks the transition from the Era of Talking to the Era of Doing.

The biggest LLM is now just a powerful but inert, brain—a resource to be leveraged. The true innovation is in the nervous system, the memory, and the self-correction loop that transforms that raw intelligence into measurable, scalable, and autonomous value.

Will this new era, defined by goal-driven, Agentic AI, be the one that finally breaks the LLM monopoly and truly disrupts Silicon Valley? Let us know your thoughts below.

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