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Quote: Dr Eric Schmidt - Ex-Google CEO

“The win will be teaming between a human and their judgment and a supercomputer and what it can think.” - Dr Eric Schmidt - Former Google CEO

Dr Eric Schmidt is recognised globally as a principal architect of the modern digital era. He served as CEO of Google from 2001 to 2011, guiding its evolution from a fast-growing startup into a cornerstone of the tech industry. His leadership was instrumental in scaling Google’s infrastructure, accelerating product innovation, and instilling a model of data-driven culture that underpins contemporary algorithms and search technologies. After stepping down as CEO, Schmidt remained pivotal as Executive Chairman and later as Technical Advisor, shepherding Google’s transition to Alphabet and advocating for long-term strategic initiatives in AI and global connectivity.

Schmidt’s influence extends well beyond corporate leadership. He has played policy-shaping roles at the highest levels, including chairing the US National Security Commission on Artificial Intelligence and advising multiple governments on technology strategy. His career is marked by a commitment to both technical progress and the responsible governance of innovation, positioning him at the centre of debates on AI’s promises, perils, and the necessity of human agency in the face of accelerating machine intelligence.

Context of the Quotation: Human–AI Teaming

Schmidt’s statement emerged during high-level discussions about the trajectory of AI, particularly in the context of autonomous systems, advanced agents, and the potential arrival of superintelligent machines. Rather than portraying AI as a force destined to replace humans, Schmidt advocates a model wherein the greatest advantage arises from joint endeavour: humans bring creativity, ethical discernment, and contextual understanding, while supercomputers offer vast capacity for analysis, pattern recognition, and iterative reasoning.

This principle is visible in contemporary AI deployments. For example:

• In drug discovery, AI systems can screen millions of molecular variants in a day, but strategic insights and hypothesis generation depend on human researchers.
• In clinical decision-making, AI augments the observational scope of physicians—offering rapid, precise diagnoses—but human judgement is essential for nuanced cases and values-driven choices.
• Schmidt points to future scenarios where “AI agents” conduct scientific research, write code by natural-language command, and collaborate across domains, yet require human partnership to set objectives, interpret outcomes, and provide oversight.
• He underscores that autonomous AI agents, while powerful, must remain under human supervision, especially as they begin to develop their own procedures and potentially opaque modes of communication.

Underlying this vision is a recognition: AI is a multiplier, not a replacement, and the best outcomes will couple human judgement with machine cognition.

Relevant Leading Theorists and Critical Backstory

This philosophy of human–AI teaming aligns with and is actively debated by several leading theorists:

• Stuart Russell
  Professor at UC Berkeley, Russell is renowned for his work on human-compatible AI. He contends that the long-term viability of artificial intelligence requires that systems are designed to understand and comply with human preferences and values. Russell has championed the view that human oversight and interpretability are non-negotiable as intelligence systems become more capable and autonomous.
• Fei-Fei Li
  Stanford Professor and co-founder of AI4ALL, Fei-Fei Li is a major advocate for “human-centred AI.” Her research highlights that AI should augment human potential, not supplant it, and she stresses the critical importance of interdisciplinary collaboration. She is a proponent of AI systems that foster creativity, support decision-making, and preserve agency and dignity.
• Demis Hassabis
  Founder and CEO of DeepMind, Hassabis’s group famously developed AlphaGo and AlphaFold. DeepMind’s work demonstrates the principle of human–machine teaming: AI systems solve previously intractable problems, such as protein folding, that can only be understood and validated with strong human scientific context.
• Gary Marcus
  A prominent AI critic and academic, Marcus warns against overestimating current AI’s capacity for judgment and abstraction. He pursues hybrid models where symbolic reasoning and statistical learning are paired with human input to overcome the limitations of “black-box” models.
• Eric Schmidt’s own contributions reflect active engagement with these paradigms, from his advocacy for AI regulatory frameworks to public warnings about the risks of unsupervised AI, including “unplugging” AI systems that operate beyond human understanding or control.

Structural Forces and Implications

Schmidt’s perspective is informed by several notable trends:

• Expansion of infinite context windows: Models can now process millions of words and reason through intricate problems with humans guiding multi-step solutions, a paradigm shift for fields like climate research, pharmaceuticals, and engineering.
• Proliferation of autonomous agents: AI agents capable of learning, experimenting, and collaborating independently across complex domains are rapidly becoming central; their effectiveness maximised when humans set goals and interpret results.
• Democratisation paired with concentration of power: As AI accelerates innovation, the risk of centralised control emerges; Schmidt calls for international cooperation and proactive governance to keep objectives aligned with human interests.
• Chain-of-thought reasoning and explainability: Advanced models can simulate extended problem-solving, but meaningful solutions depend on human guidance, interpretation, and critical thinking.

Summary

Eric Schmidt’s quote sits at the intersection of optimistic technological vision and pragmatic governance. It reflects decades of strategic engagement with digital transformation, and echoes leading theorists’ consensus: the future of AI is collaborative, and its greatest promise lies in amplifying human judgment with unprecedented computational support. Realising this future will depend on clear policies, interdisciplinary partnership, and an unwavering commitment to ensuring technology remains a tool for human advancement—and not an unfettered automaton beyond our reach.

Quote: Dr. Fei-Fei Li - Stanford Professor - world-renowned authority in artificial intelligence

“I do think countries all should invest in their own human capital, invest in partnerships and invest in their own technological stack as well as the business ecosystem... I think not investing in AI would be macroscopically the wrong thing to do.” - Dr. Fei-Fei Li - Stanford Professor - world-renowned authority in artificial intelligence

The statement was delivered during a high-stakes panel discussion on artificial superintelligence, convened at the Future Investment Initiative in Riyadh, where nation-state leaders, technologists, and investors gathered to assess their strategic positioning in the emerging AI era. Her words strike at the heart of a dilemma facing governments worldwide: how to build national AI capabilities whilst avoiding the trap of isolationism, and why inaction would be economically and strategically untenable.

Context: The Geopolitical Stakes of AI Investment

The Historical Moment

Dr. Li's statement comes at a critical juncture. By late 2024 and into 2025, artificial intelligence had transitioned from speculative technology to demonstrable economic driver. Estimates suggested AI could generate between $15 trillion and $20 trillion in economic value globally by 2030—a figure larger than the current gross domestic product of most nations. This windfall is not distributed evenly; rather, it concentrates among early movers with capital, infrastructure, and talent. The race is on, and the stakes are existential for national competitiveness, employment, and geopolitical influence.

In this landscape, a nation that fails to invest in AI capabilities risks profound economic displacement. Yet Dr. Li is equally clear: isolation is counterproductive. The most realistic path forward combines three pillars:

• Human Capital: The talent to conceive, build, and deploy AI systems
• Partnerships: Strategic alliances, particularly with leading technological ecosystems (the US hyperscalers, for instance)
• Domestic Technological Infrastructure: The local research bases, venture capital, regulatory frameworks, and business ecosystems that enable sustained innovation

This is not a counsel of surrender to Silicon Valley hegemony, but rather a sophisticated argument about comparative advantage and integration within global technological networks.

Dr. Fei-Fei Li: The Person and Her Arc

Early Life and Foundational Values

Dr. Fei-Fei Li's perspective is shaped by her personal trajectory. Born in Chengdu, China, she emigrated to the United States at age fifteen, settling in New Jersey where her parents ran a small business. This background infuses her thinking: she understands both the promise of technological mobility and the structural barriers that constrain developing economies. She obtained her undergraduate degree in physics from Princeton University in 1999, with high honours, before pursuing doctoral studies at the California Institute of Technology, where she worked across computer science, electrical engineering, and cognitive neuroscience, earning her PhD in 2005.

The ImageNet Revolution

In 2007, whilst at Princeton, Dr. Li embarked on a project that would reshape artificial intelligence. Observing that cognitive psychologist Irving Biederman estimated humans recognise approximately 30,000 object categories, Li conceived ImageNet: a massive, hierarchically organised visual database. Colleagues dismissed the scale as impractical. Undeterred, she led a team (including Princeton professors Jia Deng, Kai Li, and Wei Dong) that leveraged Amazon Mechanical Turk to label over 14 million images across 22,000 categories.

By 2009, ImageNet was published. More critically, the team created the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), an annual competition that invited researchers worldwide to develop algorithms for image classification. This contest became the crucible in which modern deep learning was forged. When Geoffrey Hinton's group achieved a breakthrough using convolutional neural networks in 2012, winning the competition by a decisive margin, the deep learning revolution was catalysed. ImageNet is now recognised as one of the three foundational forces in the birth of modern AI.

What is instructive here is that Dr. Li's contribution was not merely technical but infrastructural: she created a shared resource that democratised AI research globally. Academic groups from universities across continents—not just Silicon Valley—could compete on equal footing. This sensibility—that progress depends on enabling distributed talent—runs through her subsequent work.

Career Architecture and Strategic Leadership

Following her Princeton years, Dr. Li joined Stanford University in 2009, eventually becoming the Sequoia Capital Professor of Computer Science—a title of singular prestige. From 2013 to 2018, she directed Stanford's Artificial Intelligence Lab (SAIL), one of the world's premier research institutes. Her publications exceed 400 papers in top-tier venues, and she remains one of the most cited computer scientists of her generation.

During a sabbatical from Stanford (January 2017 to September 2018), Dr. Li served as Vice President and Chief Scientist of AI/ML at Google Cloud. Her mandate was to democratise AI technology, lowering barriers for businesses and developers—work that included advancing products like AutoML, which enabled organisations without deep AI expertise to deploy machine learning systems.

Upon returning to Stanford in 2019, she became the founding co-director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), an explicitly multidisciplinary initiative spanning computer science, social sciences, humanities, law, and medicine—all united by the conviction that AI must serve human flourishing, not vice versa.

Current Work and World Labs

Most recently, Dr. Li co-founded and serves as chief executive officer of World Labs, an AI company focused on spatial intelligence and generative world models. This venture extends her intellectual agenda: if large language models learn patterns over text, world models learn patterns over 3D environments, enabling machines to understand, simulate, and reason about physical and virtual spaces. For robotics, healthcare simulation, autonomous systems, and countless other domains, this represents the next frontier.

Recognition and Influence

Her standing is reflected in numerous accolades: election to the National Academy of Engineering, the National Academy of Medicine (2020), and the American Academy of Arts and Sciences (2021); the Intel Lifetime Achievement Innovation Award in 2023; and inclusion in Time magazine's 100 Most Influential People in AI. She is colloquially known as the "Godmother of AI." In 2023, she published a memoir, The Worlds I See: Curiosity, Exploration and Discovery at the Dawn of AI, which chronicles her personal journey and intellectual evolution.

Leading Theorists and Strategic Thinkers: The Landscape of AI and National Strategy

The backdrop to Dr. Li's statement includes several strands of thought about technology, development, and national strategy:

Economic and Technological Diffusion

• Erik Brynjolfsson and Andrew McAfee (The Second Machine Age, Machine Platform Crowd): These MIT researchers have articulated how technological revolutions create winners and losers, and that policy choices—not technology alone—determine whether gains are broadly shared. They underscore that without intentional intervention, automation and AI tend to concentrate wealth and opportunity.
• Dani Rodrik (Harvard economist): Rodrik's work on "premature demonetarisation" and structural transformation highlights the risks faced by developing economies when technological progress accelerates faster than institutions can adapt. His analysis supports Dr. Li's argument: countries must actively build capacity or risk being left behind.
• Mariana Mazzucato (University College London): Mazzucato's research on the entrepreneurial state emphasises that breakthrough innovations—including AI—depend on public investment in foundational research, education, and infrastructure. Her work buttresses the case for public and private sector partnership.

Artificial Intelligence and Cognition

• Geoffrey Hinton, Yann LeCun, and Yoshua Bengio: The triumvirate of deep learning pioneers recognised that neural networks could scale to superhuman performance in perception and pattern recognition, yet have increasingly stressed that current approaches may be insufficient for general intelligence. Their candour about limitations supports a measured, long-term investment view.
• Stuart Russell (UC Berkeley): Russell has been a prominent voice calling for AI safety and governance frameworks to accompany capability development. His framing aligns with Dr. Li's insistence that human-centred values must guide AI research and deployment.

Geopolitics and Technology Competition

• Michael Mazarr (RAND Corporation): Mazarr and colleagues have analysed great-power competition in emerging technologies, emphasising that diffusion of capability is inevitable but the pace and terms of diffusion are contestable. Nations that invest in talent pipelines and partnerships will sustain influence; those that isolate will atrophy.
• Kai-Fu Lee: The Taiwanese-American venture capitalist and author (AI Superpowers) has articulated how the US and China are in a competitive race, but also how smaller nations and regions can position themselves through strategic partnerships and focus on applied AI problems relevant to their economies.
• Eric Schmidt (former Google CEO): Schmidt, who participated in the same FII panel as Dr. Li, has emphasised that geopolitical advantage flows to nations with capital markets, advanced chip fabrication (such as Taiwan's TSMC), and deep talent pools. Yet he has also highlighted pathways for other nations to benefit through partnerships and focused investment in particular domains.

Human-Centred Technology and Inclusive Growth

• Timnit Gebru and Joy Buolamwini: These AI ethics researchers have exposed how AI systems can perpetuate bias and harm marginalised communities. Their work reinforces Dr. Li's emphasis on human-centred design and inclusive governance. For developing nations, this implies that AI investment must account for local contexts, values, and risks of exclusion.
• Turing Award recipients and foundational figures (such as Barbara Liskov on systems reliability, and Leslie Valiant on learning theory): Their sustained emphasis on rigour, safety, and verifiability underpins the argument that sustainable AI development requires not just speed but also deep technical foundations—something that human capital investment cultivates.

Development Economics and Technology Transfer

• Paul Romer (Nobel laureate): Romer's work on endogenous growth emphasises that ideas and innovation are the drivers of long-term prosperity. For developing nations, this implies that investment in research capacity, education, and institutional learning—not merely adopting foreign technologies—is essential.
• Ha-Joon Chang: The heterodox development economist has critiqued narratives of "leapfrogging" technology. His argument suggests that nations building indigenous technological ecosystems—through domestic investment in research, venture capital, and entrepreneurship—are more resilient and capable of adapting innovations to local needs.

The Three Pillars: An Unpacking

Dr. Li's framework is sophisticated precisely because it avoids two traps: technological nationalism (the fantasy that any nation can independently build world-leading AI from scratch) and technological fatalism (the resignation that small and medium-sized economies cannot compete).

Human Capital

The most portable, scalable asset a nation can develop is talent. This encompasses:

• Education pipelines: From primary through tertiary education, with emphasis on mathematics, computer science, and critical thinking
• Research institutions: Universities, national laboratories, and research councils capable of contributing to fundamental and applied AI knowledge
• Retention and diaspora engagement: Policies to keep talented individuals from emigrating, and mechanisms to attract expatriate expertise
• Diversity and inclusion: As Dr. Li has emphasised through her co-founding of AI4ALL (a nonprofit working to increase diversity in AI), innovation benefits from diverse perspectives and draws from broader talent pools

Partnerships

Rather than isolating, Dr. Li advocates for strategic alignment:

• North-South partnerships: Developed nations' hyperscalers and technology firms partnering with developing economies to establish data centres, training programmes, and applied research initiatives. Saudi Arabia and the UAE have pursued this model successfully
• South-South cooperation: Peer learning and knowledge exchange among developing nations facing similar challenges
• Academic and research collaborations: Open-source tools, shared benchmarks (as exemplified by ImageNet), and collaborative research that diffuse capability globally
• Technology licensing and transfer agreements: Mechanisms by which developing nations can access cutting-edge tools and methods at affordable terms

Technological Stack and Ecosystem

A nation cannot simply purchase AI capability; it must develop home-grown institutional and commercial ecosystems:

• Open-source communities: Participation in and contribution to open-source AI frameworks (PyTorch, TensorFlow, Hugging Face) builds local expertise and reduces dependency on proprietary systems
• Venture capital and startup ecosystems: Policies fostering entrepreneurship in AI applications suited to local economies (agriculture, healthcare, manufacturing)
• Regulatory frameworks: Balanced approaches to data governance, privacy, and AI safety that neither stifle innovation nor endanger citizens
• Domain-specific applied AI: Rather than competing globally in large language models, nations can focus on AI applications addressing pressing local challenges: medical diagnostics, precision agriculture, supply-chain optimisation, or financial inclusion

Why Inaction Is "Macroscopically the Wrong Thing"

Dr. Li's assertion that not investing in AI would be fundamentally mistaken rests on several converging arguments:

Economic Imperatives

AI is reshaping productivity across sectors. Nations that fail to develop internal expertise will find themselves dependent on foreign technology, unable to adapt solutions to local contexts, and vulnerable to supply disruptions or geopolitical pressure. The competitive advantage flows to early movers and sustained investors.

Employment and Social Cohesion

While AI will displace some jobs, it will create others—particularly for workers skilled in AI-adjacent fields. Nations that invest in reskilling and education can harness these transitions productively. Those that do not risk deepening inequality and social fracture.

Sovereignty and Resilience

Over-reliance on foreign AI systems limits national agency. Whether in healthcare, defence, finance, or public administration, critical systems should rest partly on domestic expertise and infrastructure to ensure resilience and alignment with national values.

Participation in Global Governance

As AI governance frameworks emerge—whether through the UN, regional bodies, or multilateral forums—nations with substantive technical expertise and domestic stakes will shape the rules. Those without will have rules imposed upon them.

The Tension and Its Resolution

Implicit in Dr. Li's statement is a tension worth articulating: the world cannot support 200 competing AI superpowers, each building independent foundational models. Capital and talent are finite. Yet neither is the world a binary of a few AI leaders and many followers. The resolution lies in specialisation and integration:

• A nation may not lead in large language models but excel in robotics for agriculture
• It may not build chips but pioneer AI applications in healthcare or education
• It may not host hyperscaler data centres but contribute essential research in AI safety or fairness
• It will necessarily depend on global partnerships whilst developing sovereign capacity in domains critical to its citizens

This is neither capitulation nor isolation, but rather a mature acceptance of global interdependence coupled with strategic autonomy in domains of national importance.

Conclusion: The Compass for National Strategy

Dr. Li's counsel, grounded in decades of research leadership, industrial experience, and global perspective, offers a compass for policymakers navigating the AI era. Investment in human capital, strategic partnerships, and home-grown technological ecosystems is not a luxury or academic exercise—it is fundamental to national competitiveness, prosperity, and agency. The alternative—treating AI as an external force to be passively absorbed—is indeed "macroscopically" mistaken, foreclosing decades of economic opportunity and surrendering the right to shape how this powerful technology serves human flourishing.

Quote: Dr. Fei-Fei Li - Stanford Professor - world-renowned authority in artificial intelligence

“I think robotics has a long way to go... I think the ability, the dexterity of human-level manipulation is something we have to wait a lot longer to get. ” - Dr. Fei-Fei Li - Stanford Professor - world-renowned authority in artificial intelligence

While AI has made dramatic progress in perception and reasoning, the physical manipulation and dexterity seen in human hands is far from being matched by machines.

Context of the Quote: The State and Limitations of Robotics

Dr. Li’s comment was made against the backdrop of accelerating investment and hype in artificial intelligence and robotics. While AI systems now master complex games, interpret medical scans, and facilitate large-scale automation, the field of robotics—especially with respect to dexterous manipulation and embodied interaction in the real world—remains restricted by hardware limitations, incomplete world models, and a lack of general adaptability.

• Human dexterity involves fine motor control, real-time feedback, and a deep understanding of spatial and causal relationships. As Dr. Li emphasises, current robots struggle with tasks that are mundane for humans: folding laundry, pouring liquids, assembling diverse objects, or improvising repairs in unpredictable environments.
• Even state-of-the-art robot arms and hands, controlled by advanced machine learning, manage select tasks in highly structured settings. Scaling to unconstrained, everyday environments has proven exceedingly difficult.
• The launch of benchmarks such as the BEHAVIOR Challenge by Stanford, led by Dr. Li’s group, is a direct response to these limitations. The challenge simulates 1,000 everyday tasks across varied household environments, aiming to catalyse progress by publicly measuring how far the field is from truly general-purpose, dexterous robots.

Dr. Fei-Fei Li: Biography and Impact

Dr. Fei-Fei Li is a world-renowned authority in artificial intelligence, best known for foundational contributions to computer vision and the promotion of “human-centred AI”. Her career spans:

• Academic Leadership: Professor of Computer Science at Stanford University; founding co-director of the Stanford Institute for Human-Centered AI (HAI).
• ImageNet: Li created the ImageNet dataset, which transformed machine perception by enabling deep neural networks to outperform previous benchmarks and catalysed the modern AI revolution. This advance shaped progress in visual recognition, autonomous systems, and accessibility technologies.
• Human-Centred Focus: Dr. Li is recognised for steering the field towards responsible, inclusive, and ethical AI, ensuring research aligns with societal needs and multidisciplinary perspectives.
• Spatial Intelligence and Embodied AI: A core strand of her current work is in spatial intelligence—teaching machines to understand, reason about, and interact with the physical world with flexibility and safety. Her venture World Labs is pioneering this next frontier, aiming to bridge the gap from words to worlds.
• Recognition: She was awarded the Queen Elizabeth Prize for Engineering in 2025—alongside fellow AI visionaries—honouring transformative contributions to computing, perception, and human-centred innovation.
• Advocacy: Her advocacy spans diversity, education, and AI governance. She actively pushes for multidisciplinary, transparent approaches to technology that are supportive of human flourishing.

Theoretical Foundations and Leading Figures in Robotic Dexterity

The quest for human-level dexterity in machines draws on several fields—robotics, neuroscience, machine learning—and builds on the insights of leading theorists:

The Challenge: Why Robotics Remains “a Long Way to Go”

• Embodiment: Unlike pure software, robots operate under real-world physical constraints. Variability in object geometry, materials, lighting, and external force must be mastered for consistent human-like manipulation.
• Generalisation: A robot that succeeds at one task often fails catastrophically at another, even if superficially similar. Human hands, with sensory feedback and innate flexibility, effortlessly adapt.
• World Modelling: Spatial intelligence—anticipating the consequences of actions, integrating visual, tactile, and proprioceptive data—is still largely unsolved. As Dr. Li notes, machines must “understand, navigate, and interact” with complex, dynamic environments.
• Benchmarks and Community Efforts: The BEHAVIOR Challenge and open-source simulators aim to provide transparent, rigorous measurement and accelerate community progress, but there is consensus that true general dexterity is likely years—if not decades—away.

Conclusion: Where Theory Meets Practice

While AI and robotics have delivered astonishing advances in perception, narrowly focused automation, and simulation, the dexterity, adaptability, and common-sense reasoning required for robust, human-level robotic manipulation remain an unsolved grand challenge. Dr. Fei-Fei Li’s work and leadership define the state of the art—and set the aspirational vision for the next wave: embodied, spatially conscious AI, built with a profound respect for the complexity of human life and capability. Those who follow in her footsteps, across academia and industry, measure their progress not against hype or isolated demonstrations, but against the demanding reality of everyday human tasks.

Quote: Dr. Fei-Fei Li - Stanford Professor - world-renowned authority in artificial intelligence

“That ability that humans have, it's the combination of creativity and abstraction. I do not see today's AI or tomorrow's AI being able to do that yet.” - Dr. Fei-Fei Li - Stanford Professor - world-renowned authority in artificial intelligence

Dr. Li’s statement came amid wide speculation about the near-term prospects for artificial general intelligence (AGI) and superintelligence. While current AI already exceeds human capacity in specific domains (such as language translation, memory recall, and vast-scale data analysis), Dr. Li draws a line at creative abstraction—the human ability to form new concepts and theories that radically change our understanding of the world. She underscores that, despite immense data and computational resources, AI does not demonstrate the generative leap that allowed Newton to discover classical mechanics or Einstein to reshape physics with relativity. Dr. Li insists that, absent fundamental conceptual breakthroughs, neither today’s nor tomorrow’s AI can replicate this synthesis of creativity and abstract reasoning.

About Dr. Fei-Fei Li

Dr. Fei-Fei Li holds the title of Sequoia Capital Professor of Computer Science at Stanford University and is a world-renowned authority in artificial intelligence, particularly in computer vision and human-centric AI. She is best known for creating ImageNet, the dataset that triggered the deep learning revolution in computer vision—a cornerstone of modern AI systems. As the founding co-director of Stanford’s Institute for Human-Centered Artificial Intelligence (HAI), Dr. Li has consistently championed the need for AI that advances, rather than diminishes, human dignity and agency. Her research, with over 400 scientific publications, has pioneered new frontiers in machine learning, neuroscience, and their intersection.

Her leadership extends beyond academia: she served as chief scientist of AI/ML at Google Cloud, sits on international boards, and is deeply engaged in policy, notably as a special adviser to the UN. Dr. Li is acclaimed for her advocacy in AI ethics and diversity, notably co-founding AI4ALL, a non-profit enabling broader participation in the AI field. Often described as the "godmother of AI," she is an elected member of the US National Academy of Engineering and the National Academy of Medicine. Her personal journey—from emigrating from Chengdu, China, to supporting her parents’ small business in New Jersey, to her trailblazing career—is detailed in her acclaimed 2023 memoir, The Worlds I See.

Remarks on Creativity, Abstraction, and AI: Theoretical Roots

The distinction Li draws—between algorithmic pattern-matching and genuine creative abstraction—addresses a foundational question in AI: What constitutes intelligence, and is it replicable in machines? This theme resonates through the works of several canonical theorists:

• Alan Turing (1912–1954): Regarded as the father of computer science, Turing posed the question of machine intelligence in his pivotal 1950 paper, “Computing Machinery and Intelligence”. He proposed what we call the Turing Test: if a machine could converse indistinguishably from a human, could it be deemed intelligent? Turing hinted at the limits but also the theoretical possibility of machine abstraction.
• Herbert Simon and Allen Newell: Pioneers of early “symbolic AI”, Simon and Newell framed intelligence as symbol manipulation; their experiments (the Logic Theorist and General Problem Solver) made some progress in abstract reasoning but found creative leaps elusive.
• Marvin Minsky (1927–2016): Co-founder of the MIT AI Lab, Minsky believed creativity could in principle be mechanised, but anticipated it would require complex architectures that integrate many types of knowledge. His work, especially The Society of Mind, remained vital but speculative.
• John McCarthy (1927–2011): While he named the field “artificial intelligence” and developed the LISP programming language, McCarthy was cautious about claims of broad machine creativity, viewing abstraction as an open challenge.
• Geoffrey Hinton, Yann LeCun, Yoshua Bengio: Fathers of deep learning, these researchers demonstrated that neural networks can match or surpass humans in perception and narrow problem-solving but have themselves highlighted the gap between statistical learning and the ingenuity seen in human discovery.
• Nick Bostrom: In Superintelligence (2014), Bostrom analysed risks and trajectories for machine intelligence exceeding humans, but acknowledged that qualitative leaps in creativity—paradigm shifts, theory building—remain a core uncertainty.
• Gary Marcus: An outspoken critic of current AI, Marcus argues that without genuine causal reasoning and abstract knowledge, current models (including the most advanced deep learning systems) are far from truly creative intelligence.

Synthesis and Current Debates

Across these traditions, a consistent theme emerges: while AI has achieved superhuman accuracy, speed, and recall in structured domains, genuine creativity—the ability to abstract from prior knowledge to new paradigms—is still uniquely human. Dr. Fei-Fei Li, by foregrounding this distinction, not only situates herself within this lineage but also aligns her ongoing research on “large world models” with an explicit goal: to design AI tools that augment—but do not seek to supplant—human creative reasoning and abstract thought.

Her caution, rooted in both technical expertise and a broader philosophical perspective, stands as a rare check on techno-optimism. It articulates the stakes: as machine intelligence accelerates, the need to centre human capabilities, dignity, and judgement—especially in creativity and abstraction—becomes not just prudent but essential for responsibly shaping our shared future.

Quote: Dr Eric Schmidt - Ex-Google CEO

“I worry a lot about ... Africa. And the reason is: how does Africa benefit from [AI]? There's obviously some benefit of globalisation, better crop yields, and so forth. But without stable governments, strong universities, major industrial structures - which Africa, with some exceptions, lacks - it's going to lag.” - Dr Eric Schmidt - Former Google CEO

Dr Eric Schmidt’s observation stems from his experience at the highest levels of the global technology sector and his acute awareness of both the promise and the precariousness of the coming AI age. His warning about Africa’s risk of lagging in AI adoption and benefit is rooted in today’s uneven technological landscape and long-standing structural challenges facing the continent.

About Dr Eric Schmidt

Dr Eric Schmidt is one of the most influential technology executives of the 21st century. As CEO of Google from 2001 to 2011, he oversaw Google’s transformation from a Silicon Valley start-up into a global technology leader. Schmidt provided the managerial and strategic backbone that enabled Google’s explosive growth, product diversification, and a culture of robust innovation. After Google, he continued as Executive Chairman and Technical Advisor through Google’s restructuring into Alphabet, before transitioning to philanthropic and strategic advisory work. Notably, Schmidt has played significant roles in US national technology strategy, chairing the US National Security Commission on Artificial Intelligence and founding the bipartisan Special Competitive Studies Project, which advises on the intersections of AI, security, and economic competitiveness.

With a background encompassing leading roles at Sun Microsystems, Novell, and advisory positions at Xerox PARC and Bell Labs, Schmidt’s career reflects deep immersion in technology and innovation. He is widely regarded as a strategic thinker on the global opportunities and risks of technology, regularly offering perspective on how AI, digital infrastructure, and national competitiveness are shaping the future economic order.

Context of the Quotation

Schmidt’s remark appeared during a high-level panel at the Future Investment Initiative (FII9), in conversation with Dr Fei-Fei Li of Stanford and Peter Diamandis. The discussion centred on “What Happens When Digital Superintelligence Arrives?” and explored the likely economic, social, and geopolitical consequences of rapid AI advancement.

In this context, Schmidt identified a core risk: that AI’s benefits will accrue unevenly across borders, amplifying existing inequalities. He emphasised that while powerful AI tools may drive exceptional economic value and efficiencies—potentially in the trillions of dollars—these gains are concentrated by network effects, investment, and infrastructure. Schmidt singled out Africa as particularly vulnerable: absent stable governance, strong research universities, or robust industrial platforms—critical prerequisites for technology absorption—Africa faces the prospect of deepening relative underdevelopment as the AI era accelerates. The comment reflects a broader worry in technology and policy circles: global digitisation is likely to amplify rather than repair structural divides unless deliberate action is taken.

Leading Theorists and Thinking on the Subject

The dynamics Schmidt describes are at the heart of an emerging literature on the “AI divide,” digital colonialism, and the geopolitics of AI. Prominent thinkers in these debates include:

• Professor Fei-Fei Li
  A leading AI scientist, Dr Li has consistently framed AI’s potential as contingent on human-centred design and equitable access. She highlights the distinction between the democratisation of access (e.g., cheaper healthcare or education via AI) and actual shared prosperity—which hinges on local capacity, policy, and governance. Her work underlines that technical progress does not automatically result in inclusive benefit, validating Schmidt’s concerns.
• Kate Crawford and Timnit Gebru
  Both have written extensively on the risks of algorithmic exclusion, surveillance, and the concentration of AI expertise within a handful of countries and firms. In particular, Crawford’s Atlas of AI and Gebru’s leadership in AI ethics foreground how global AI development mirrors deeper resource and power imbalances.
• Nick Bostrom and Stuart Russell
  Their theoretical contributions address the broader existential and ethical challenges of artificial superintelligence, but they also underscore risks of centralised AI power—technically and economically.
• Ndubuisi Ekekwe, Bitange Ndemo, and Nanjira Sambuli
  These African thought leaders and scholars examine how Africa can leapfrog in digital adoption but caution that profound barriers—structural, institutional, and educational—must be addressed for the continent to benefit from AI at scale.
• Eric Schmidt himself has become a touchstone in policy/tech strategy circles, having co-chaired the US National Security Commission on Artificial Intelligence. The Commission’s reports warned of a bifurcated world where AI capabilities—and thus economic and security advantages—are ever more concentrated.

Structural Elements Behind the Quote

Schmidt’s remark draws attention to a convergence of factors:

• Institutional robustness
  Long-term AI prosperity requires stable governments, responsive regulatory environments, and a track record of supporting investment and innovation. This is lacking in many, though not all, of Africa’s economies.
• Strong universities and research ecosystems
  AI innovation is talent- and research-intensive. Weak university networks limit both the creation and absorption of advanced technologies.
• Industrial and technological infrastructure
  A mature industrial base enables countries and companies to adapt AI for local benefit. The absence of such infrastructure often results in passive consumption of foreign technology, forgoing participation in value creation.
• Network effects and tech realpolitik
  Advanced AI tools, data centres, and large-scale compute power are disproportionately located in a few advanced economies. The ability to partner with these “hyperscalers”—primarily in the US—shapes national advantage. Schmidt argues that regions which fail to make strategic investments or partnerships risk being left further behind.

Summary

Schmidt’s statement is not simply a technical observation but an acute geopolitical and developmental warning. It reflects current global realities where AI’s arrival promises vast rewards, but only for those with the foundational economic, political, and intellectual capital in place. For policy makers, investors, and researchers, the implication is clear: bridging the digital-structural gap requires not only technology transfer but also building resilient, adaptive institutions and talent pipelines that are locally grounded.

Quote: Trevor McCourt - Extropic CTO

“We need something like 10 terawatts in the next 20 years to make LLM systems truly useful to everyone... Nvidia would need to 100× output... You basically need to fill Nevada with solar panels to provide 10 terawatts of power, at a cost around the world’s GDP. Totally crazy.” - Trevor McCourt - Extropic CTO

Trevor McCourt, Chief Technology Officer and co-founder of Extropic, has emerged as a leading voice articulating a paradox at the heart of artificial intelligence advancement: the technology that promises to democratise intelligence across the planet may, in fact, be fundamentally unscalable using conventional infrastructure. His observation about the terawatt imperative captures this tension with stark clarity—a reality increasingly difficult to dismiss as speculative.

Who Trevor McCourt Is

McCourt brings a rare convergence of disciplinary expertise to his role. Trained in mechanical engineering at the University of Waterloo (graduating 2015) and holding advanced credentials from the Massachusetts Institute of Technology (2020), he combines rigorous physical intuition with deep software systems architecture. Prior to co-founding Extropic, McCourt worked as a Principal Software Engineer, establishing a track record of delivering infrastructure at scale: he designed microservices-based cloud platforms that improved deployment speed by 40% whilst reducing operational costs by 30%, co-invented a patented dynamic caching algorithm for distributed systems, and led open-source initiatives that garnered over 500 GitHub contributors.

This background—spanning mechanical systems, quantum computation, backend infrastructure, and data engineering—positions McCourt uniquely to diagnose what others in the AI space have overlooked: that energy is not merely a cost line item but a binding physical constraint on AI's future deployment model.

Extropic, which McCourt co-founded alongside Guillaume Verdon (formerly a quantum technology lead at Alphabet's X division), closed a $14.1 million Series Seed funding round in 2023, led by Kindred Ventures and backed by institutional investors including Buckley Ventures, HOF Capital, and OSS Capital. The company now stands at approximately 15 people distributed across integrated circuit design, statistical physics research, and machine learning—a lean team assembled to pursue what McCourt characterises as a paradigm shift in compute architecture.

The Quote in Strategic Context

McCourt's assertion that "10 terawatts in the next 20 years" is required for universal LLM deployment, coupled with his observation that this would demand filling Nevada with solar panels at a cost approaching global GDP, represents far more than rhetorical flourish. It is the product of methodical back-of-the-envelope engineering calculation.

His reasoning unfolds as follows:

From Today's Baseline to Mass Deployment:
A text-based assistant operating at today's reasoning capability (approximating GPT-5-Pro performance) deployed to every person globally would consume roughly 20% of the current US electrical grid—approximately 100 gigawatts. This is not theoretical; McCourt derives this from first principles: transformer models consume roughly 2 × (parameters × tokens) floating-point operations; modern accelerators like Nvidia's H100 operate at approximately 0.7 picojoules per FLOP; population-scale deployment implies continuous, always-on inference at scale.

Adding Modalities and Reasoning:
Upgrade that assistant to include video capability at just 1 frame per second (envisioning Meta-style augmented-reality glasses worn by billions), and the grid requirement multiplies by approximately 10×. Enhance the reasoning capability to match models working on the ARC AGI benchmark—problems of human-level reasoning difficulty—and the text assistant alone requires a 10× expansion: 5 terawatts. Push further to expert-level systems capable of solving International Mathematical Olympiad problems, and the requirement reaches 100× the current grid.

Economic Impossibility:
A single gigawatt data centre costs approximately $10 billion to construct. The infrastructure required for mass-market AI deployment rapidly enters the hundreds of trillions of dollars—approaching or exceeding global GDP. Nvidia's current manufacturing capacity would itself require a 100-fold increase to support even McCourt's more modest scenarios.

Physical Reality Check:
Over the past 75 years, US grid capacity has grown remarkably consistently—a nearly linear expansion. Sam Altman's public commitment to building one gigawatt of data centre capacity per week alone would require 3–5× the historical rate of grid growth. Credible plans for mass-market AI acceleration push this requirement into the terawatt range over two decades—a rate of infrastructure expansion that is not merely economically daunting but potentially physically impossible given resource constraints, construction timelines, and raw materials availability.

McCourt's conclusion: the energy path is not simply expensive; it is economically and physically untenable. The paradigm must change.

Intellectual Foundations: Leading Theorists in Energy-Efficient Computing and Probabilistic AI

Understanding McCourt's position requires engagement with the broader intellectual landscape that has shaped thinking about computing's physical limits and probabilistic approaches to machine learning.

Geoffrey Hinton—Pioneering Energy-Based Models and Probabilistic Foundations:
Few figures loom larger in the theoretical background to Extropic's work than Geoffrey Hinton. Decades before the deep learning boom, Hinton developed foundational theory around Boltzmann machines and energy-based models (EBMs)—the conceptual framework that treats learning as the discovery and inference of complex probability distributions. His work posits that machine learning, at its essence, is about fitting a probability distribution to observed data and then sampling from it to generate new instances consistent with that distribution. Hinton's recognition with the 2023 Nobel Prize in Physics for "foundational discoveries and inventions that enable machine learning with artificial neural networks" reflects the deep prescience of this probabilistic worldview. More than theoretical elegance, this framework points toward an alternative computational paradigm: rather than spending vast resources on deterministic matrix operations (the GPU model), a system optimised for efficient sampling from complex distributions would align computation with the statistical nature of intelligence itself.

Michael Frank—Physics of Reversible and Adiabatic Computing:
Michael Frank, a senior scientist now at Vaire (a near-zero-energy chip company), has spent decades at the intersection of physics and computing. His research programme, initiated at MIT in the 1990s and continued at the University of Florida, Florida State, and Sandia National Laboratories, focuses on reversible computing and adiabatic CMOS—techniques aimed at reducing the fundamental energy cost of information processing. Frank's work addresses a deep truth: in conventional digital logic, information erasure is thermodynamically irreversible and expensive, dissipating energy as heat. By contrast, reversible computing minimises such erasure, thereby approaching theoretical energy limits set by physics rather than by engineering convention. Whilst Frank's trajectory and Extropic's diverge in architectural detail, both share the conviction that energy efficiency must be rooted in physical first principles, not merely in engineering optimisation of existing paradigms.

Yoshua Bengio and Chris Bishop—Probabilistic Learning Theory:
Leading researchers in deep generative modelling—including Bengio, Bishop, and others—have consistently advocated for probabilistic frameworks as foundational to machine learning. Their work on diffusion models, variational inference, and sampling-based approaches has legitimised the view that efficient inference is not about raw compute speed but about statistical appropriateness. This theoretical lineage underpins the algorithmic choices at Extropic: energy-based models and denoising thermodynamic models are not novel inventions but rather a return to first principles, informed by decades of probabilistic ML research.

Richard Feynman—Foundational Physics of Computing:
Though less directly cited in contemporary AI discourse, Feynman's 1982 lectures on the physics of computation remain conceptually foundational. Feynman observed that computation's energy cost is ultimately governed by physical law, not engineering ingenuity alone. His observations on reversibility and the thermodynamic cost of irreversible operations informed the entire reversible-computing movement and, by extension, contemporary efforts to align computation with physics rather than against it.

Contemporary Systems Thinkers (Sam Altman, Jensen Huang):
Counterintuitively, McCourt's critique is sharpened by engagement with the visionary statements of industry leaders who have perhaps underestimated energy constraints. Altman's commitment to building one gigawatt of data centre capacity per week, and Huang's roadmaps for continued GPU scaling, have inadvertently validated McCourt's concern: even the most optimistic industrial plans require infrastructure expansion at rates that collide with physical reality. McCourt uses their own projections as evidence for the necessity of paradigm change.

The Broader Strategic Narrative

McCourt's remarks must be understood within a convergence of intellectual and practical pressures:

The Efficiency Plateau:
Digital logic efficiency, measured as energy per operation, has stalled. Transistor capacitance plateaued around the 10-nanometre node; operating voltage is thermodynamically bounded near 300 millivolts. Architectural optimisations (quantisation, sparsity, tensor cores) improve throughput but do not overcome these physical barriers. The era of "free lunch" efficiency gains from Moore's Law miniaturisation has ended.

Model Complexity Trajectory:
Whilst small models have improved at fixed benchmarks, frontier AI systems—those solving novel, difficult problems—continue to demand exponentially more compute. AlphaGo required ~1 exaFLOP per game; AlphaCode required ~100 exaFLOPs per coding problem; the system solving International Mathematical Olympiad problems required ~100,000 exaFLOPs. Model miniaturisation is not offsetting capability ambitions.

Market Economics:
The AI market has attracted trillions in capital precisely because the economic potential is genuine and vast. Yet this same vastness creates the energy paradox: truly universal AI deployment would consume resources incompatible with global infrastructure and economics. The contradiction is not marginal; it is structural.

Extropic's Alternative:
Extropic proposes to escape this local minimum through radical architectural redesign. Thermodynamic Sampling Units (TSUs)—circuits architected as arrays of probabilistic sampling cells rather than multiply-accumulate units—would natively perform the statistical operations that diffusion and generative AI models require. Early simulations suggest energy efficiency improvements of 10,000× on simple benchmarks compared to GPU-based approaches. Hybrid algorithms combining TSUs with compact neural networks on conventional hardware could deliver intermediate gains whilst establishing a pathway toward a fundamentally different compute paradigm.

Why This Matters Now

The quote's urgency reflects a dawning recognition across technical and policy circles that energy is not a peripheral constraint but the central bottleneck determining AI's future trajectory. The choice, as McCourt frames it, is stark: either invest in a radically new architecture, or accept that mass-market AI remains perpetually out of reach—a luxury good confined to the wealthy and powerful rather than a technology accessible to humanity.

This is not mere speculation or provocation. It is engineering analysis grounded in physics, economics, and historical precedent, articulated by someone with the technical depth to understand both the problem and the extraordinary difficulty of solving it.

Quote: Stephen Schwartzman - Blackstone Founder

“You have to be very gentle around people. If you're in a leadership position, people hear your words amplified. You have to be very careful what you say and how you say it. You always have to listen to what other people have to say. I genuinely want to know what everybody else thinks.” - Stephen Schwarzman - Blackstone Founder

“You have to be very gentle around people. If you're in a leadership position, people hear your words amplified. You have to be very careful what you say and how you say it. You always have to listen to what other people have to say. I genuinely want to know what everybody else thinks.” - Stephen Schwarzman - Blackstone Founder

Quote: Stephen Schwartzman - Blackstone Founder

“I always felt that somebody was only capable of one super effort to create something that can really be consequential. There are so many impediments to being successful. If you're on the field, you're there to win, and to win requires an enormous amount of practice - pushing yourself really to the breaking point.” - Stephen Schwarzman - Blackstone Founder

Stephen A. Schwarzman is a defining figure in global finance and alternative investments. He is Chairman, CEO, and Co-Founder of Blackstone, the world’s largest alternative investment firm, overseeing over $1.2 trillion in assets.

Backstory and Context of the Quote

Stephen Schwarzman’s perspective on effort, practice, and success is rooted in over four decades building Blackstone from a two-person start-up to an institution that has shaped capital markets worldwide. The referenced quote captures his philosophy: that achieving anything truly consequential demands a singular, maximal effort—a philosophy he practised as Blackstone’s founder and architect.

Schwarzman began his career in mergers and acquisitions at Lehman Brothers in the 1970s, where he met Peter G. Peterson. Their complementary backgrounds—a combination of strategic vision and operational drive—empowered them to establish Blackstone in 1985, initially with just $400,000 in seed capital and a big ambition to build a differentiated investment firm. The mid-1980s financial environment, marked by booming M&A activity, provided fertile ground for innovation in buyouts and private markets.

From the outset, Schwarzman instilled a culture of rigorous preparation and discipline. A landmark early setback—the unsuccessful investment in Edgecomb Steel—became a pivotal learning event. It led Schwarzman to institutionalise robust investment committees, open and adversarial (yet respectful) debate, and a relentless process of due diligence. This learning loop, focused on not losing money and fact-based challenge culture, shaped Blackstone’s internal systems and risk culture for decades to come.

His attitude to practice, perseverance, and operating at the limit is not merely rhetorical—it is Blackstone’s operational model: selecting complex assets, professionalising management, and adding value through operational transformation before timing exits for maximum advantage. The company’s strict approval layers, multi-stage risk screening, and exacting standards demonstrate Schwarzman’s belief that only by pushing to the limits of endurance—and addressing every potential weakness—can lasting value be created.

In his own words, Schwarzman attributes success not to innate brilliance but to grit, repetition, and the ability to learn from failure. This is underscored by his leadership style, which evolved towards being gentle, clear, and principled, setting high standards while building an enduring culture based on integrity, decency, and open debate.

About Stephen A. Schwarzman

• Born in 1947 in Philadelphia, Schwarzman studied at Yale University (where he was a member of Skull and Bones) and earned an MBA from Harvard Business School.
• Blackstone, which he co-founded in 1985, began as an M&A boutique and now operates across private equity, real estate, credit, hedge funds, infrastructure, and life sciences, making it a recognised leader in global investment management.
• Under Schwarzman’s leadership, Blackstone institutionalised patient, active ownership—acquiring, improving, and timing the exit from portfolio companies for optimal results while actively shaping industry standards in governance and risk management.
• He is also known for his philanthropy, having signed The Giving Pledge and contributed significantly to education, arts, and culture.
• His autobiography, What It Takes: Lessons in the Pursuit of Excellence, distils the philosophy underpinning his business and personal success.
• Schwarzman’s role as a public intellectual and advisor has seen him listed among the “World’s Most Powerful People” and “Time 100 Most Influential People”.

Leading Theorists and Intellectual Currents Related to the Quote

The themes embodied in Schwarzman’s philosophy—singular effort, practice to breaking point, coping with setbacks, and building institutional culture—draw on and intersect with several influential theorists and schools of thought in management and the psychology of high achievement:

• Anders Ericsson (Deliberate Practice): Ericsson’s research underscores that deliberate practice—extended, focused effort with ongoing feedback—is critical to acquiring expert performance in any field. Schwarzman’s stress on “enormous amount of practice” parallels Ericsson’s findings that natural talent is far less important than methodical, sustained effort.
• Angela Duckworth (Grit): Duckworth’s work on “grit” emphasises passion and perseverance for long-term goals as key predictors of success. Her research supports Schwarzman’s belief that breaking through obstacles—and continuing after setbacks—is fundamental for consequential achievement.
• Carol Dweck (Growth Mindset): Dweck demonstrated that embracing a “growth mindset”—seeing failures as opportunities to learn rather than as endpoints—fosters resilience and continuous improvement. Schwarzman’s approach to institutionalising learning from failure at Blackstone reflects this theoretical foundation.
• Peter Drucker (Management by Objectives and Institutional Culture): Drucker highlighted the importance of clear organisational goals, continuous learning, and leadership by values for building enduring institutions. Schwarzman’s insistence on codifying culture, open debate, and aligning every decision with the brand reflects Drucker’s emphasis on the importance of system and culture in organisational performance.
• Jim Collins (Built to Last, Good to Great): Collins’ research into successful companies found a common thread of fanatical discipline, a culture of humility and rigorous debate, all driven by a sense of purpose. These elements are present throughout Blackstone’s governance model and leadership ethos as steered by Schwarzman.
• Michael Porter (Competitive Strategy): Porter’s concept of sustained competitive advantage through unique positioning and strategic differentiation is echoed in Blackstone’s approach—actively improving operations rather than simply relying on market exposure, and committing to ‘winning’ through operational and structural edge.

Summary

Schwarzman’s quote is not only a personal reflection but also a distillation of enduring principles in high achievement and institutional leadership. It is the lived experience of building Blackstone—a case study in dedication, resilience, and the institutionalisation of excellence. His story, and the theoretical underpinnings echoed in his approach, provide a template for excellence and consequence in any field marked by complexity, competition, and the need for sustained, high-conviction effort.

Quote: Trevor McCourt - Extropic CTO

“If you upgrade that assistant to see video at 1 FPS - think Meta’s glasses... you'd need to roughly 10× the grid to accommodate that for everyone. If you upgrade the text assistant to reason at the level of models working on the ARC AGI benchmark... even just the text assistant would require around a 10× of today’s grid.” - Trevor McCourt - Extropic CTO

The quoted remark by Trevor McCourt, CTO of Extropic, underscores a crucial bottleneck in artificial intelligence scaling: energy consumption outpaces technological progress in compute efficiency, threatening the viability of universal, always-on AI. The quote translates hard technical extrapolation into plain language—projecting that if every person were to have a vision-capable assistant running at just 1 video frame per second, or if text models achieved a level of reasoning comparable to ARC AGI benchmarks, global energy infrastructure would need to multiply several times over, amounting to many terawatts—figures that quickly reach into economic and physical absurdity.

Backstory and Context of the Quote & Trevor McCourt

Trevor McCourt is the co-founder and Chief Technology Officer of Extropic, a pioneering company targeting the energy barrier limiting mass-market AI deployment. With multidisciplinary roots—a blend of mechanical engineering and quantum programming, honed at the University of Waterloo and Massachusetts Institute of Technology—McCourt contributed to projects at Google before moving to the hardware-software frontier. His leadership at Extropic is defined by a willingness to challenge orthodoxy and champion a first-principles, physics-driven approach to AI compute architecture.

The quote arises from a keynote on how present-day large language models and diffusion AI models are fundamentally energy-bound. McCourt’s analysis is rooted in practical engineering, economic realism, and deep technical awareness: the computational demands of state-of-the-art assistants vastly outstrip what today’s grid can provide if deployed at population scale. This is not merely an engineering or machine learning problem, but a macroeconomic and geopolitical dilemma.

Extropic proposes to address this impasse with Thermodynamic Sampling Units (TSUs)—a new silicon compute primitive designed to natively perform probabilistic inference, consuming orders of magnitude less power than GPU-based digital logic. Here, McCourt follows the direction set by energy-based probabilistic models and advances it both in hardware and algorithm.

McCourt’s career has been defined by innovation at the technical edge: microservices in cloud environments, patented improvements to dynamic caching in distributed systems, and research in scalable backend infrastructure. This breadth, from academic research to commercial deployment, enables his holistic critique of the GPU-centred AI paradigm, as well as his leadership at Extropic’s deep technology startup.

Leading Theorists & Influencers in the Subject

Several waves of theory and practice converge in McCourt’s and Extropic’s work:

1. Geoffrey Hinton (Energy-Based and Probabilistic Models):
Long before deep learning’s mainstream embrace, Hinton’s foundational work on Boltzmann machines and energy-based models explored the idea of learning and inference as sampling from complex probability distributions. These early probabilistic paradigms anticipated both the difficulties of scaling and the algorithmic challenges that underlie today’s generative models. Hinton’s recognition—including the Nobel Prize for work on energy-based models—cements his stature as a theorist whose footprints underpin Extropic’s approach.

2. Michael Frank (Reversible Computing)
Frank is a prominent physicist in reversible and adiabatic computing, having led major advances at MIT, Sandia National Laboratories, and others. His research investigates how the physics of computation can reduce the fundamental energy cost—directly relevant to Extropic’s mission. Frank’s focus on low-energy information processing provides a conceptual environment for approaches like TSUs to flourish.

3. Chris Bishop & Yoshua Bengio (Probabilistic Machine Learning):
Leaders like Bishop and Bengio have shaped the field’s probabilistic foundations, advocating both for deep generative models and for the practical co-design of hardware and algorithms. Their research has stressed the need to reconcile statistical efficiency with computational tractability—a tension at the core of Extropic’s narrative.

4. Alan Turing & John von Neumann (Foundations of Computing):
While not direct contributors to modern machine learning, the legacies of Turing and von Neumann persist in every conversation about alternative architectures and the physical limits of computation. The post-von Neumann and post-Turing trajectory, with a return to analogue, stochastic, or sampling-based circuitry, is directly echoed in Extropic’s work.

5. Recent Industry Visionaries (e.g., Sam Altman, Jensen Huang):
Contemporary leaders in the AI infrastructure space—such as Altman of OpenAI and Huang of Nvidia—have articulated the scale required for AGI and the daunting reality of terawatt-scale compute. Their business strategies rely on the assumption that improved digital hardware will be sufficient, a view McCourt contests with data and physical models.

Strategic & Scientific Context for the Field

• Core problem: The energy that powers AI is reaching non-linear scaling—mass-market AI could consume a significant fraction or even multiples of the entire global grid if naively scaled with today’s architectures.
• Physics bottlenecks: Improvements in digital logic are limited by physical constants: capacitance, voltage, and the energy required for irreversible computation. Digital logic has plateaued at the 10nm node.
• Algorithmic evolution: Traditional deep learning is rooted in deterministic matrix computations, but the true statistical nature of intelligence calls for sampling from complex distributions—as foregrounded in Hinton’s work and now implemented in Extropic’s TSUs.
• Paradigm shift: McCourt and contemporaries argue for a transition to native hardware–software co-design where the core computational primitive is no longer the multiply–accumulate (MAC) operation, but energy-efficient probabilistic sampling.

Summary Insight

Trevor McCourt anchors his cautionary prognosis for AI’s future on rigorous cross-disciplinary insights—from physical hardware limits to probabilistic learning theory. By combining his own engineering prowess with the legacy of foundational theorists and contemporary thinkers, McCourt’s perspective is not simply one of warning but also one of opportunity: a new generation of probabilistic, thermodynamically-inspired computers could rewrite the energy economics of artificial intelligence, making “AI for everyone” plausible—without grid-scale insanity.

Quote: Alex Karp - Palantir CEO

“The idea that chips and ontology is what you want to short is batsh*t crazy.” - Alex Karp -Palantir CEO

Alex Karp, co-founder and CEO of Palantir Technologies, delivered the now widely-circulated statement, “The idea that chips and ontology is what you want to short is batsh*t crazy,” in response to famed investor Michael Burry’s high-profile short positions against both Palantir and Nvidia. This sharp retort came at a time when Palantir, an enterprise software and artificial intelligence (AI) powerhouse, had just reported record earnings and was under intense media scrutiny for its meteoric stock rise and valuation.

Context of the Quote

The remark was made in early November 2025 during a CNBC interview, following public disclosures that Michael Burry—of “The Big Short” fame—had taken massive short positions in Palantir and Nvidia, two companies at the heart of the AI revolution. Burry’s move, reminiscent of his contrarian bets during the 2008 financial crisis, was interpreted by the market as both a challenge to the soaring “AI trade” and a critique of the underlying economics fueling the sector’s explosive growth.

Karp’s frustration was palpable: not only was Palantir producing what he described as "anomalous" financial results—outpacing virtually all competitors in growth, cash flow, and customer retention—but it was also emerging as the backbone of data-driven operations across government and industry. For Karp, Burry’s short bet went beyond traditional market scepticism; it targeted firms, products (“chips” and “ontology”—the foundational hardware for AI and the architecture for structuring knowledge), and business models proven to be both technically indispensable and commercially robust. Karp’s rejection of the “short chips and ontology” thesis underscores his belief in the enduring centrality of the technologies underpinning the modern AI stack.

Backstory and Profile: Alex Karp

Alex Karp stands out as one of Silicon Valley’s true iconoclasts:

• Background and Education: Born in New York City in 1967, Karp holds a philosophy degree from Haverford College, a JD from Stanford, and a PhD in social theory from Goethe University Frankfurt, where he studied under and wrote about the influential philosopher Jürgen Habermas. This rare academic pedigree—blending law, philosophy, and critical theory—deeply informs both his contrarian mindset and his focus on the societal impact of technology.
• Professional Arc: Before founding Palantir in 2004 with Peter Thiel and others, Karp had forged a career in finance, running the London-based Caedmon Group. At Palantir, he crafted a unique culture and business model, combining a wellness-oriented, sometimes spiritual corporate environment with the hard-nosed delivery of mission-critical systems for Western security, defence, and industry.
• Leadership and Philosophy: Karp is known for his outspoken, unconventional leadership. Unafraid to challenge both Silicon Valley’s libertarian ethos and what he views as the groupthink of academic and financial “expert” classes, he publicly identifies as progressive—yet separates himself from establishment politics, remaining both a supporter of the US military and a critic of mainstream left and right ideologies. His style is at once brash and philosophical, combining deep skepticism of market orthodoxy with a strong belief in the capacity of technology to deliver real-world, not just notional, value.
• Palantir’s Rise: Under Karp, Palantir grew from a niche contractor to one of the world’s most important data analytics and AI companies. Palantir’s products are deeply embedded in national security, commercial analytics, and industrial operations, making the company essential infrastructure in the rapidly evolving AI economy.

Theoretical Background: ‘Chips’ and ‘Ontology’

Karp’s phrase pairs two of the foundational concepts in modern AI and data-driven enterprise:

• Chips: Here, “chips” refers specifically to advanced semiconductors (such as Nvidia’s GPUs) that provide the computational horsepower essential for training and deploying cutting-edge machine learning models. The AI revolution is inseparable from advances in chip design, leading to historic demand for high-performance hardware.
• Ontology: In computer and information science, “ontology” describes the formal structuring and categorising of knowledge—making data comprehensible, searchable, and actionable by algorithms. Robust ontologies enable organisations to unify disparate data sources, automate analytical reasoning, and achieve the “second order” efficiencies of AI at scale.

Leading theorists in the domain of ontology and AI include:

• John McCarthy: A founder of artificial intelligence, McCarthy’s foundational work on formal logic and semantics laid groundwork for modern ontological structures in AI.
• Tim Berners-Lee: Creator of the World Wide Web, Berners-Lee developed the Semantic Web, championing knowledge structuring via ontologies—enabling data to be machine-readable and all but indispensable for AI’s next leap.
• Thomas Gruber: Known for his widely cited definition of ontology in AI as “a specification of a conceptualisation,” Gruber’s research shaped the field’s approach to standardising knowledge representations for complex applications.

In the chip space, the pioneering work of:

• Jensen Huang: CEO and co-founder of Nvidia, drove the company’s transformation from graphics to AI acceleration, cementing the centrality of chips as the hardware substrate for everything from generative AI to advanced analytics.
• Gordon Moore and Robert Noyce: Their early explorations in semiconductor fabrication set the stage for the exponential hardware progress that enabled the modern AI era.

Insightful Context for the Modern Market Debate

The “chips and ontology” remark reflects a deep divide in contemporary technology investing:

• On one side, sceptics like Burry see signs of speculative excess, reminiscent of prior bubbles, and bet against companies with high valuations—even when those companies dominate core technologies fundamental to AI.
• On the other, leaders like Karp argue that while the broad “AI trade” risks pockets of overvaluation, the engine—the computational hardware (chips) and data-structuring logic (ontology)—are not just durable, but irreplaceable in the digital economy.

With Palantir and Nvidia at the centre of the current AI-driven transformation, Karp’s comment captures not just a rebuttal to market short-termism, but a broader endorsement of the foundational technologies that define the coming decade. The value of “chips and ontology” is, in Karp’s eyes, anchored not in market narrative but in empirical results and business necessity—a perspective rooted in a unique synthesis of philosophy, technology, and radical pragmatism.