Oxford computer scientist and artificial intelligence researcher Michael Wooldridge says the rapid expansion of artificial intelligence is being shaped less by scientific inevitability than by competitive pressures among a small group of technology companies racing to avoid falling behind rivals.

In an interview discussing his latest book, Life Lessons from Game Theory: The Art of Thinking Strategically in a Complex World, Wooldridge argued that many of the current tensions surrounding artificial intelligence can be understood through the framework of game theory, particularly scenarios in which competitors continue escalating despite recognizing collective risks.

Wooldridge, a professor at the University of Oxford and one of Britain’s most prominent public communicators on artificial intelligence, said the industry increasingly resembles a strategic trap in which companies continue investing heavily in advanced systems because they believe competitors would gain advantage if they slowed development.

“We’ve got a small number of very wealthy companies that are busy pursuing AI, while at the same time saying that they are afraid that something’s going to go horribly wrong with it,” Wooldridge said. “So why are they busy pursuing it? Because they think if we back down and we don’t pursue it, somebody else will.

”The comments come amid intensifying global competition over artificial intelligence infrastructure, computing capacity and access to data. Major technology firms including OpenAI and Google DeepMind have expanded investments in large-scale machine learning systems, while governments in the United States, Europe and China are increasingly treating AI as a strategic industry tied to economic growth and national security.

Wooldridge said many of the core technologies underpinning today’s AI systems are not recent discoveries. He noted that key neural network techniques central to modern machine learning were developed by the mid-1980s, but computing power and data limitations prevented their wider deployment at the time.

“The only obstacle standing in the way of the AI revolution in the 1980s, really, was that computers weren’t powerful enough and we didn’t have enough data,” he said.He described the emergence of GPT-3 in 2020 as a turning point driven largely by scale rather than a fundamentally new scientific breakthrough.

According to Wooldridge, many researchers initially doubted whether simply expanding computational power and training data would substantially improve performance. He said the success of that approach surprised a significant portion of the research community.

OpenAI’s development strategy demonstrated that scaling existing methods could generate major commercial results, he said, although he cautioned against interpreting those advances as evidence that artificial general intelligence, or AGI, is imminent.Executives including Sam Altman and Demis Hassabis have publicly discussed the possibility of achieving human-level general intelligence within years. Wooldridge said those forecasts remain overly optimistic.

He argued that current systems still struggle with tasks requiring physical reasoning and adaptation in unfamiliar environments. While advanced chat systems can process complex linguistic queries, he said they remain unable to reliably perform many basic real-world activities that humans execute routinely.

“You can talk to ChatGPT about quantum mechanics in Latin,” Wooldridge said, “but at the same time, we don’t have AI that could come into your house, that it had never seen before, locate the kitchen and clear the dinner table.”Wooldridge said data availability may become one of the industry’s most significant constraints.

He noted that large language models already consume enormous quantities of text and digital material, creating pressure to secure new sources of information for future training cycles.“The whole of Wikipedia made up just 3% of GPT-3’s training data,” he said. “Where do you get 10 times more data from next time around?”That search for data, he argued, could reshape relationships between governments, corporations and individuals.

Wooldridge pointed to healthcare systems, wearable devices and online content creators as examples of potentially valuable data sources for future AI development.“The NHS is sitting on a huge amount of data about human beings,” he said. “That’s the most valuable kind of data imaginable.”He warned that commercial pressure to obtain increasingly detailed behavioral information could create incentives for broader surveillance and monitoring.

Wooldridge suggested future generations of online influencers may routinely agree to extensive data collection arrangements in exchange for visibility and commercial opportunity.The professor’s latest work focuses primarily on game theory, which he defines as the study of interactions between self-interested actors.

He said many geopolitical disputes, commercial rivalries and social conflicts can be interpreted through a relatively small number of strategic models.One recurring example in his analysis is the “game of chicken,” in which opposing sides continue escalating until one party backs down or both suffer severe consequences.

Wooldridge compared the framework to current tensions involving the United States and Iran, describing unpredictability as a recognized strategic tactic within game theory.“You’ve got two sides with ever-escalating threats against each other,” he said. “Somebody’s got to back down at some point.

”Wooldridge added that highly unpredictable behavior can complicate strategic decision-making because opponents struggle to assess likely responses and risks. Under such conditions, he said, game theory often encourages actors to prepare for worst-case outcomes.He also criticized what he described as a growing “zero-sum” political mindset in parts of modern public discourse.

In game theory, he said, zero-sum situations are not merely competitions where one side wins and another loses, but systems where actors are incentivized to maximize damage to opponents.“This zero-sum mentality is very damaging,” Wooldridge said.

“One of the important lessons from game theory is that, actually, the majority of interactions that we’re in are not zero-sum.”He linked that framework to populist political narratives that portray economic or social gains by one group as direct losses for another. As an alternative, Wooldridge highlighted the “Veil of Ignorance,” a philosophical model developed by political philosopher John Rawls in 1971.

The thought experiment asks individuals to design a society without knowing which position they themselves would ultimately occupy within it.Wooldridge said the model creates incentives for fairer social systems because participants must account for the possibility of ending up disadvantaged. He noted that former U.S. presidents Bill Clinton and Barack Obama had both expressed interest in Rawls’ ideas.

Despite concerns surrounding AI development, Wooldridge said he remains optimistic about technology and scientific inquiry. Growing up in rural Herefordshire, he taught himself programming after repeatedly visiting a local electronics shop that displayed a TRS-80 computer in its storefront during the early 1980s.

He later completed a doctorate in artificial intelligence and went on to publish more than 500 scientific papers and multiple books, while also presenting public lectures on the social implications of AI.

Asked whether students should avoid fields vulnerable to automation, Wooldridge rejected the idea that education should be driven solely by labor market forecasts.

“I didn’t get into computing because I thought it was going to give me a good job,” he said. “I got into it because I was just really interested in it.”

Over 70 teams—almost five times the number in 2025—are set to participate in the event, which will feature a more demanding course including paved slopes and parkland terrain designed to test improvements in durability, balance and battery performance.“It will certainly be interesting to see the progress in durability of components and battery lifetime compared to last year,” said Georg Stieler, Asia managing director at a technology consultancy.

He added that manufacturers continue to face pressure to balance product quality with cost as the technology evolves.Organizers said the race marks a shift from last year, when all participating robots were remotely controlled. In contrast, a significant share of entrants this year will rely on onboard sensors and algorithms to complete the course independently, highlighting gains in perception and decision-making systems.

Among the contenders is Tiangong Ultra, developed by the Beijing Innovation Center of Humanoid Robotics in collaboration with UBTech. The robot, which won last year’s race in 2 hours and 40 minutes, is expected to run fully autonomously this time, using sensor-based navigation and data-driven gait modeling.

Developers said achieving human-like running speeds presents significant technical challenges due to the limited time available for real-time perception and response. Training footage shared on Chinese social media shows some robots reaching speeds of up to 14 km per hour, though others displayed instability, with occasional falls and collisions.

China remains the dominant player in humanoid robotics deployment, accounting for more than 80% of the roughly 16,000 units installed globally in 2025, according to Counterpoint Research. By comparison, U.S.-based Tesla held about 5% of installations.

Domestic firms including AgiBot and Unitree each shipped over 5,000 units last year, with Unitree planning to scale annual production capacity to 75,000 robots.Despite rapid growth, industry experts say humanoid robots remain far from widespread commercial adoption in industrial environments, where precision, adaptability and complex task execution are required.

Current applications are largely limited to research, demonstrations and service roles such as interactive guides.“The reason our applications aren’t taking off is that the robots’ IQ is too low. The models are poor, their success rates are low,” said Tang Wenbin, founder of embodied intelligence startup Yuanli Lingji, speaking at a recent Beijing forum.

The Chinese government has identified embodied intelligence, or physical AI, as a strategic sector to enhance productivity and modernize manufacturing. Companies are investing heavily in data collection and machine learning, often using human workers equipped with sensors to train robotic systems.

UBTech said it expanded the number of humanoid robots deployed in factories from fewer than 10 in 2024 to more than 1,000 last year, and aims to launch 10,000 full-size units in 2026, including models tailored for commercial use, according to its chief business officer Michael Tam.

Humain, a Public Investment Fund-backed artificial intelligence firm, has partnered with US-based Turing to develop what they describe as the world’s first enterprise-scale marketplace for AI agents, marking a strategic step in Saudi Arabia’s efforts to expand its role in the global AI sector.

The platform, branded as Humain One, was unveiled at the FII Priority Summit in Miami. It is designed to function as a centralized marketplace where developers can publish AI agents and enterprises can deploy them across business operations. The initiative aligns with Saudi Arabia’s Vision 2030 strategy to diversify its economy and build advanced technology capabilities.

Humain Chief Executive Tareq Amin said the platform is intended to support a shift toward integrated AI systems operating across organizations. He emphasized that the next phase of artificial intelligence will involve multiple agents working in coordination rather than isolated tools performing discrete tasks.The marketplace allows businesses to access pre-built AI agents or develop customized ones tailored to internal workflows.

These agents can be deployed across departments such as finance, human resources, and operations, with the platform enabling coordination between them. According to Humain, the system is designed to simplify integration by ensuring that all agents operate within a unified infrastructure.

Saejong Lee, general manager of Humain One, described the platform as analogous to an application store for enterprises, but with a focus on operational automation and interconnectivity. He said the platform is structured to allow multiple agents to interact seamlessly, sharing data and executing tasks across functions without requiring separate systems.

Turing’s involvement includes contributing to the underlying architecture of the platform, particularly in areas such as model evaluation, fine-tuning, reasoning systems, and enterprise deployment. Jonathan Siddharth, chief executive and co-founder of Turing, said the collaboration aims to address technical challenges associated with scaling AI across complex organizational environments.

The platform introduces a standardized process for AI agent development and deployment. Developers first create and test agents in a controlled sandbox environment before deploying them into live systems. Once validated, agents are listed on the marketplace and can be installed automatically by enterprise users.

A key feature of Humain One is its unified operating environment, which is intended to reduce compatibility and security risks. All agents are subject to a certification process that includes automated testing, performance validation, and security checks. Certification records are made available to enterprise compliance teams to support governance requirements.

The platform also provides centralized management tools that allow organizations to monitor performance, control access, and define rules for data usage and agent permissions. This approach is designed to address a common challenge in enterprise AI adoption, where multiple tools from different vendors often operate in isolation.

Humain One supports multi-agent coordination, enabling different AI systems to collaborate on complex workflows. For example, one agent may handle invoice processing while another manages supplier communication, with data exchanged automatically under predefined rules.

This capability is expected to improve efficiency in operational processes that typically require manual coordination.The pricing structure for the platform is designed to accommodate different enterprise needs, offering options such as usage-based billing, pre-purchased computing capacity, and per-user licensing. In addition, the marketplace includes a revenue-sharing model that allows developers and organizations to monetize AI agents they publish.

The initiative forms part of a broader push by Humain to build large-scale AI infrastructure within Saudi Arabia. This includes investments in data center capacity and partnerships with global semiconductor firms such as Nvidia.

The company is also reported to have made a $3 billion investment in xAI, underscoring its ambitions to play a significant role in shaping the global AI ecosystem.Turing’s participation as both a development partner and the first US-based customer of the platform reflects the cross-border nature of the initiative.

The collaboration positions Saudi Arabia not only as a consumer of advanced technologies but also as a potential exporter of enterprise AI solutions.Industry participants view the emergence of AI agent marketplaces as a potential shift in how businesses adopt and scale artificial intelligence.

By standardizing deployment and enabling interoperability, such platforms could reduce barriers to entry for enterprises seeking to integrate AI into core operations.

For Saudi Arabia, the development of Humain One represents an effort to establish domestic technological infrastructure while attracting international partners.

The platform is expected to provide local businesses and government entities with a system aligned to national regulatory requirements, while offering global firms access to a growing regional market.

No official launch date has been announced.

Sam Altman’s trillion-dollar AI dream propels OpenAI into a new era of global innovation

OpenAI is once again at the center of the global technology conversation, as CEO Sam Altman unveiled a sweeping vision that could redefine the scale and scope of artificial intelligence development.

The company, already a trailblazer in the AI revolution, is restructuring its operations to accelerate innovation and attract large-scale investments that could power the next era of digital transformation.

Altman’s latest announcement signals a turning point for OpenAI — from a pioneering research lab to a technology powerhouse capable of driving trillion-dollar growth in global AI infrastructure.

With the new structure in place, OpenAI aims to raise significant public capital and build computing resources that can support unprecedented levels of AI development, accessibility, and deployment across industries.

According to Altman, OpenAI’s ambition is to develop up to 30 gigawatts of computing capacity, supported by an investment of $1.4 trillion. This plan reflects not only the growing demand for AI worldwide but also the company’s determination to scale efficiently and sustainably.

Each gigawatt, representing an enormous computing milestone, currently costs more than $40 billion — a figure that Altman believes will eventually drop by half as innovation advances and costs decline.

This forward-thinking approach aligns with OpenAI’s mission to make artificial intelligence a transformative force for good.

Altman’s focus is not just on scaling technology but also on ensuring that AI serves humanity by driving progress in healthcare, education, and global sustainability.

Under the newly formed OpenAI Foundation, the company plans to fund initiatives that use AI to cure diseases, improve infrastructure, and enhance resilience in the face of global challenges.

Altman’s strategic collaboration with industry leaders and governments reflects a deep understanding of the infrastructure and resources required to sustain AI’s growth.

Earlier this year, he announced a groundbreaking initiative called Stargate — a $500 billion project designed to expand data center capacity and accelerate AI innovation.

The project involves collaboration with leading technology companies such as Oracle, SoftBank, Nvidia, and CoreWeave, symbolizing the unification of expertise and vision to build the world’s most advanced AI ecosystem.

Standing alongside global leaders, Altman highlighted the project’s broader benefits beyond technological progress.

Stargate is expected to generate hundreds of thousands of new jobs, stimulate economic growth, and enhance global competitiveness in emerging digital industries.

He emphasized that AI has the potential to empower workers, boost productivity, and drive sustainable development, provided it is guided by ethical principles and responsible governance.

Altman’s trillion-dollar vision underscores the growing importance of large-scale computing infrastructure as the foundation of future AI applications.

By expanding its global footprint, OpenAI aims to ensure that nations, industries, and individuals can access advanced AI capabilities that were once confined to a few research institutions.

The restructuring is expected to open new doors for partnerships, public offerings, and innovation-driven collaborations worldwide.

The transformation of OpenAI into a large-scale, publicly capitalized enterprise reflects Altman’s belief that innovation requires both creative freedom and financial stability.

His leadership has redefined how technology companies balance research, commercial success, and global responsibility.

By envisioning a system capable of producing one gigawatt of compute every week, Altman has set an ambitious benchmark for the AI industry — one that mirrors his confidence in AI’s ability to revolutionize every sector, from energy and finance to healthcare and entertainment.

While the logistics of funding and implementation remain a challenge, Altman’s optimism has ignited excitement across the tech landscape.

His message is clear: the future of AI lies not only in technological breakthroughs but also in collective human effort, collaboration, and bold investment.

With OpenAI’s expanding ecosystem and growing partnerships, the company is poised to shape the direction of global AI innovation for decades to come.

As AI becomes an increasingly central part of modern life, OpenAI’s new chapter represents a fusion of vision, ambition, and responsibility.

Altman’s leadership embodies the belief that technology, when harnessed wisely, can empower societies, unlock creativity, and solve some of humanity’s greatest challenges.

His trillion-dollar AI dream is not just about scale — it is about shaping a future where intelligence, ethics, and innovation advance together for the greater good.