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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.

Quote: David Solomon - Goldman Sachs CEO

“Generally speaking people hate change. It’s human nature. But change is super important. It’s inevitable. In fact, on my desk in my office I have a little plaque that says 'Change or die.' As a business leader, one of the perspectives you have to have is that you’ve got to constantly evolve and change.” - David Solomon - Goldman Sachs CEO

The quoted insight comes from David M. Solomon, Chief Executive Officer and Chairman of Goldman Sachs, a role he has held since 2018. It was delivered during a high-profile interview at The Economic Club of Washington, D.C., 30 October 2025, as Solomon reflected on the necessity of adaptability both personally and as a leader within a globally significant financial institution.

“We have very smart people, and we can put these [AI] tools in their hands to make them more productive... By using AI to reimagine processes, we can create operating efficiencies that give us a scaled opportunity to reinvest in growth.” - David Solomon - Goldman Sachs CEO

David Solomon, Chairman and CEO of Goldman Sachs, delivered the quoted remarks during an interview at the HKMA Global Financial Leaders’ Investment Summit on 4 November 2025, articulating Goldman’s strategic approach to integrating artificial intelligence across its global franchise. His comments reflect both personal experience and institutional direction: leveraging new technology to drive productivity, reimagine workflows, and reinvest operational gains in sustainable growth, rather than pursuing simplistic headcount reductions or technological novelty for its own sake.

Backstory and Context of the Quote

David Solomon’s statement arises from Goldman Sachs' current transformation—“Goldman Sachs 3.0”—centred on AI-driven process re-engineering. Rather than employing AI simply as a cost-cutting device, Solomon underscores its strategic role as an enabler for “very smart people” to magnify their productivity and impact. This perspective draws on his forty-year career in finance, where successive waves of technological disruption (from Lotus 1-2-3 spreadsheets to cloud computing) have consistently shifted how talent is leveraged, but have not diminished its central value.

The immediate business context is one of intense change: regulatory uncertainty in cross-border transactions, rebounding capital flows into China post-geopolitical tension, and a high backlog of M&A activity, particularly for large-cap US transactions. In this environment, efficiency gains from AI allow frontline teams to refocus on advisory, origination, and growth while adjusting operational models at a rapid pace. Solomon’s leadership style—pragmatic, unsentimental, and data-driven—favours process optimisation, open collaboration, and the breakdown of legacy silos.

About David Solomon

Background:

• Born in Hartsdale, New York, in 1962; educated at Hamilton College with a BA in political science, then entered banking.
• Career progression: Held senior roles at Irving Trust, Drexel Burnham, Bear Stearns; joined Goldman Sachs in 1999 as partner, eventually leading the Financing Group and serving as co-head of the Investment Banking Division for a decade.
• Appointed President and COO in 2017, then CEO in October 2018 and Chairman in January 2019, succeeding Lloyd Blankfein.
• Brought a reputation for transformative leadership, advocating modernisation, flattening hierarchies, and integrating technology across every aspect of the firm’s operations.

Leadership and Culture:

• Solomon is credited with pushing through “One Goldman Sachs,” breaking down internal silos and incentivising cross-disciplinary collaboration.
• He has modernised core HR and management practices: implemented real-time performance reviews, loosened dress codes, and raised compensation for programmers.
• Personal interests—such as his sideline as DJ D-Sol—underscore his willingness to defy convention and challenge the insularity of Wall Street leadership.

Institutional Impact:

• Under his stewardship, Goldman has accelerated its pivot to technology—automating trading operations, consolidating platforms, and committing substantial resources to digital transformation.
• Notably, the current “GS 3.0” agenda focuses on automating six major workflows to direct freed capacity into growth, consistent with a multi-decade productivity trend.

Leading Theorists and Intellectual Lineage of AI-Driven Productivity in Business

Solomon’s vision is shaped and echoed by several foundational theorists in economics, management science, and artificial intelligence:

1. Clayton Christensen

• Theory: Disruptive Innovation—frames how technological change transforms industries not through substitution but by enabling new business models and process efficiencies.
• Relevance: Goldman Sachs’ approach to using AI to reimagine workflows and create new capabilities closely mirrors Christensen’s insights on sustaining versus disruptive innovation.

2. Erik Brynjolfsson & Andrew McAfee

• Theory: Race Against the Machine, The Second Machine Age—chronicled how digital automation augments human productivity and reconfigures the labour market, not just replacing jobs but reshaping roles and enhancing output.
• Relevance: Solomon’s argument for enabling smart people with better tools directly draws on Brynjolfsson’s proposition that the best organisational outcomes occur when firms successfully combine human and machine intelligence.

3. Michael Porter

• Theory: Competitive Advantage—emphasised how operational efficiency and information advantage underpin sustained industry leadership.
• Relevance: Porter’s ideas connect to Goldman’s agenda by showing that AI integration is not just about cost, but about improving information processing, strategic agility, and client service.

4. Herbert Simon

• Theory: Bounded Rationality and Decision Support Systems—pioneered the concept that decision-making can be dramatically improved by systems that extend the cognitive capabilities of professionals.
• Relevance: Solomon’s claim that AI puts better tools in the hands of talented staff traces its lineage to Simon’s vision of computers as skilled assistants, vital to complex modern organisations.

5. Geoffrey Hinton, Yann LeCun, Yoshua Bengio

• Theory: Deep Learning—established the contemporary AI revolution underpinning business process automation, language models, and data analysis at enterprise scale.
• Relevance: Without the breakthroughs made by these theorists, AI’s current generation—capable of augmenting financial analysis, risk modelling, and operational management—could not be applied as Solomon describes.

Synthesis and Strategic Implications

Solomon’s quote epitomises the intersection of pragmatic executive leadership and theoretical insight. His advocacy for AI-integrated productivity reinforces a management consensus: sustainable competitive advantage hinges not just on technology, but on empowering skilled individuals to unlock new modes of value creation. This approach is echoed by leading researchers who situate automation as a catalyst for role evolution, scalable efficiency, and the ability to redeploy resources into higher-value growth opportunities.

Goldman Sachs’ specific AI play is therefore neither a defensive move against headcount nor a speculative technological bet, but a calculated strategy rooted in both practical business history and contemporary academic theory—a paradigm for how large organisations can adapt, thrive, and lead in the face of continual disruption.

Quote: Satya Nadella - Microsoft CEO

“At scale, nothing is a commodity. We have to have our cost structure, supply-chain efficiency, and software efficiencies continue to compound to ensure margins. Scale - and one of the things I love about the OpenAI partnership - is it’s gotten us to scale. This is a scale game.” - Satya Nadella - Microsoft CEO

Satya Nadella has been at the helm of Microsoft since 2014, overseeing its transformation into one of the world’s most valuable technology companies. Born in Hyderabad, India, and educated in electrical engineering and computer science, Nadella joined Microsoft in 1992, quickly rising through the ranks in technical and business leadership roles. Prior to becoming CEO, he was best known for driving the rapid growth of Microsoft Azure, the company’s cloud infrastructure platform—a business now central to Microsoft’s global strategy.

Nadella’s leadership style is marked by systemic change—he has shifted Microsoft away from legacy, siloed software businesses and repositioned it as a cloud-first, AI-driven, and highly collaborative tech company. He is recognised for his ability to anticipate secular shifts—most notably, the move to hyperscale cloud computing and, more recently, the integration of advanced AI into core products such as GitHub Copilot and Microsoft 365 Copilot. His background—combining deep technical expertise with rigorous business training (MBA, University of Chicago)—enables him to bridge both the strategic and operational dimensions of global technology.

This quote was delivered in the context of Nadella’s public discussion on the scale economics of AI, hyperscale cloud, and the transformative partnership between Microsoft and OpenAI (the company behind ChatGPT, Sora, and GPT-4/5/6) on the BG2 podcast, 1st November 2025 In this conversation, Nadella outlines why, at the extreme end of global tech infrastructure, nothing remains a “commodity”: system costs, supply chain and manufacturing agility, and relentless software optimisation all become decisive sources of competitive advantage. He argues that scale—meaning not just size, but the compounding organisational learning and cost improvement unlocked by operating at frontier levels—determines who captures sustainable margins and market leadership.

The OpenAI partnership is, from Nadella’s perspective, a practical illustration of this thesis. By integrating OpenAI’s frontier models deeply (and at exclusive scale) within Azure, Microsoft has driven exponential increases in compute utilisation, data flows, and the learning rate of its software infrastructure. This allowed Microsoft to amortise fixed investments, rapidly reduce unit costs, and create a loop of innovation not accessible to smaller or less integrated competitors. In Nadella’s framing, scale is not a static achievement, but a perpetual game—one where the winners are those who compound advantages across the entire stack: from chip supply chains through to application software and business model design.

Theoretical Foundations and Key Thinkers

The quote’s themes intersect with multiple domains: economics of platforms, organisational learning, network effects, and innovation theory. Key theoretical underpinnings and thinkers include:

Scale Economics and Competitive Advantage

• Alfred Chandler (1918–2007): Chandler’s work on the “visible hand” and the scale and scope of modern industrial firms remains foundational. He showed how scale, when coupled with managerial coordination, allows firms to achieve durable cost advantages and vertical integration.
• Bruce Greenwald & Judd Kahn: In Competition Demystified (2005), they argue sustainable competitive advantage stems from barriers to entry—often reinforced by scale, especially via learning curves, supply chains, and distribution.

Network Effects and Platform Strategy

• Jean Tirole & Marcel Boyer: Tirole’s work on platform economics shows how scale-dependent markets (like cloud and AI) naturally concentrate—network effects reinforce the value of leading platforms, and marginal cost advantage compounds alongside user and data scale.
• Geoffrey Parker, Marshall Van Alstyne, Sangeet Paul Choudary: In their research and Platform Revolution, these thinkers elaborate how the value in digital markets accrues disproportionately to platforms that achieve scale—because transaction flows, learning, and innovation all reinforce one another.

Learning Curves and Experience Effects

• The Boston Consulting Group (BCG): In the 1960s, Bruce Henderson’s concept of the “experience curve” formalised the insight that unit costs fall as cumulative output grows—the canonical explanation for why scale delivers persistent cost advantage.
• Clayton Christensen: In The Innovator’s Dilemma, Christensen illustrates how technological discontinuities and learning rates enable new entrants to upend incumbent advantage—unless those incumbents achieve scale in the new paradigm.

Supply Chain and Operations

• Taiichi Ohno and Shoichiro Toyoda (Toyota Production System): The industrial logic that relentless supply chain optimisation and compounding process improvements, rather than static cost reduction, underpin long-run advantage, especially during periods of rapid demand growth or supply constraint.

Economics of Cloud and AI

• Hal Varian (Google, UC Berkeley): Varian’s analyses of cloud economics demonstrate the massive fixed-cost base and “public utility” logic of hyperscalers. He has argued that AI and cloud converge when scale enables learning (data/usage) to drive further cost and performance improvements.
• Andrew Ng, Yann LeCun, Geoffrey Hinton: Pioneer practitioners in deep learning and large language models, whose work established the “scaling laws” now driving the AI infrastructure buildout—i.e., that model capability increases monotonically with scale of data, compute, and parameter count.

Why This Matters Now

Organisations at the digital frontier—notably Microsoft and OpenAI—are now locked in a scale game that is reshaping both industry structure and the global economy. The cost, complexity, and learning rate needed to operate at hyperscale mean that “commodities” (compute, storage, even software itself) cease to be generic. Instead, they become deeply differentiated by embedded knowledge, utilisation efficiency, supply-chain integration, and the ability to orchestrate investments across cycles of innovation.

Nadella’s observation underscores a reality that now applies well beyond technology: the compounding of competitive advantage at scale has become the critical determinant of sector leadership and value capture. This logic is transforming industries as diverse as finance, logistics, pharmaceuticals, and manufacturing—where the ability to build, learn, and optimise at scale fundamentally redefines what was once considered “commodity” business.

In summary: Satya Nadella’s words reflect not only Microsoft’s strategy but a broader economic and technological transformation, deeply rooted in the theory and practice of scale, network effects, and organisational learning. Theorists and practitioners—from Chandler and BCG to Christensen and Varian—have analysed these effects for decades, but the age of AI and cloud has made their insights more decisive than ever. At the heart of it: scale—properly understood and operationalised—remains the ultimate competitive lever.

Quote: David Solomon - Goldman Sachs CEO

“Generally speaking people hate change. It’s human nature. But change is super important. It’s inevitable. In fact, on my desk in my office I have a little plaque that says 'Change or die.' As a business leader, one of the perspectives you have to have is that you’ve got to constantly evolve and change.” - David Solomon - Goldman Sachs CEO

The quoted insight comes from David M. Solomon, Chief Executive Officer and Chairman of Goldman Sachs, a role he has held since 2018. It was delivered during a high-profile interview at The Economic Club of Washington, D.C., 30 October 2025, as Solomon reflected on the necessity of adaptability both personally and as a leader within a globally significant financial institution.

His statement is emblematic of the strategic philosophy that has defined Solomon’s executive tenure. He uses the ‘Change or die’ principle to highlight the existential imperative for renewal in business, particularly in the context of technological transformation, competitive dynamics, and economic disruption.

Solomon’s leadership at Goldman Sachs has been characterised by deliberate modernisation. He has overseen the integration of advanced technology, notably in artificial intelligence and fintech, implemented culture and process reforms, adapted workforce practices, and expanded strategic initiatives in sustainable finance. His approach blends operational rigour with entrepreneurial responsiveness – a mindset shaped both by his formative years in high-yield credit markets at Drexel Burnham and Bear Stearns, and by his rise through leadership roles at Goldman Sachs.

His remark on change was prompted by questions of business resilience and the need for constant adaptation amidst macroeconomic uncertainty, regulatory flux, and the competitive imperatives of Wall Street. For Solomon, resisting change is an instinct, but enabling it is a necessity for long-term health and relevance — especially for institutions in rapidly converging markets.

About David M. Solomon

• Born 1962, Hartsdale, New York.
• Hamilton College graduate (BA Political Science).
• Early career: Irving Trust, Drexel Burnham, Bear Stearns.
• Joined Goldman Sachs as a partner in 1999, advancing through financing and investment banking leadership.
• CEO from October 2018, Chairman from January 2019.
• Known for a modernisation agenda, openness to innovation and talent, commitment to client service and culture reform.
• Outside finance: Philanthropy, board service, and a second career as electronic dance music DJ “DJ D-Sol”, underscoring a multifaceted approach to leadership and personal renewal.

Theoretical Backstory: Leading Thinkers on Change and Organisational Adaptation

Solomon’s philosophy echoes decades of foundational theory in business strategy and organisational behaviour:

Charles Darwin (1809–1882)
While not a business theorist, Darwin’s principle of “survival of the fittest” is often cited in strategic literature to emphasise the adaptive imperative — those best equipped to change, survive.

Peter Drucker (1909–2005)
Drucker, regarded as the father of modern management, wrote extensively on innovation, entrepreneurial management and the need for “planned abandonment.” He argued, “The greatest danger in times of turbulence is not the turbulence; it is to act with yesterday’s logic.” Drucker’s legacy forms a pillar of contemporary change management, advising leaders not only to anticipate change but to institutionalise it.

John Kotter (b. 1947)
Kotter’s model for Leading Change remains a classic in change management. His eight-step framework starts with establishing a sense of urgency and is grounded in the idea that successful transformation is both necessary and achievable only with decisive leadership, clear vision, and broad engagement. Kotter demonstrated that people’s resistance to change is natural, but can be overcome through structured actions and emotionally resonant leadership.

Clayton Christensen (1952-2020)
Christensen’s work on disruptive innovation clarified how incumbents often fail by ignoring, dismissing, or underinvesting in change — even when it is inevitable. His concept of the “Innovator’s Dilemma” remains seminal, showing that leaders must embrace change not as an abstract imperative but as a strategic necessity, lest they be replaced or rendered obsolete.

Rosabeth Moss Kanter
Kanter’s work focuses on the human dynamics of change, the importance of culture, empowerment, and the “innovation habit” in organisations. She holds that the secret to business success is “constant, relentless innovation” and that resistance to change is deeply psychological, calling for leaders to engineer positive environments for innovation.

Integration: The Leadership Challenge

Solomon’s ethos channels these frameworks into practical executive guidance. For business leaders, particularly in financial services and Fortune 500 firms, the lesson is clear: inertia is lethal; organisational health depends on reimagining processes, culture, and client engagement for tomorrow’s challenges. The psychological aversion to change must be managed actively at all levels — from the boardroom to the front line.

In summary, the context of Solomon’s quote reflects not only a personal credo but also the consensus of generations of theoretical and practical leadership: only those prepared to “change or die” can expect to thrive and endure in an era defined by speed, disruption, and relentless unpredictability.

Quote: Andrej Karpathy - Ex-OpenAI, Ex-Tesla AI

“[With AI] we're not building animals. We're building ghosts or spirits.” - Andrej Karpathy - Ex-OpenAI, Ex-Tesla AI

Andrej Karpathy, renowned for his leadership roles at OpenAI and Tesla’s Autopilot programme, has been at the centre of advances in deep learning, neural networks, and applied artificial intelligence. His work traverses both academic research and industrial deployment, granting him a panoramic perspective on the state and direction of AI.

When Karpathy refers to building “ghosts or spirits,” he is drawing a conceptual line between biological intelligence—the product of millions of years of evolution—and artificial intelligence as developed through data-driven, digital systems. In his view, animals are “baked in” with instincts, embodiment, and innate learning capacities shaped by evolution, a process unfolding over geological timeframes. By contrast, today’s AI models are “ghosts” in the sense that they are ethereal, fully digital artefacts, trained to imitate human-generated data rather than to evolve or learn through direct interaction with the physical world. They lack bodily instincts and the evolutionary substrate that endows animals with survival strategies and adaptation mechanisms.

Karpathy describes the pre-training process that underpins large language models as a form of “crappy evolution”—a shortcut that builds digital entities by absorbing the statistical patterns of internet-scale data without the iterative adaptation of embodied beings. Consequently, these models are not “born” into the world like animals with built-in survival machinery; instead, they are bootstrapped as “ghosts,” imitating but not experiencing life.

The Cognitive Core—Karpathy’s Vision for AI Intelligence

Karpathy’s thinking has advanced towards the critical notion of the “cognitive core”: the kernel of intelligence responsible for reasoning, abstraction, and problem-solving, abstracted away from encyclopaedic factual knowledge. He argues that the true magic of intelligence is not in the passive recall of data, but in the flexible, generalisable ability to manipulate ideas, solve problems, and intuit patterns—capabilities that a system exhibits even when deprived of pre-programmed facts or exhaustive memory.

He warns against confusing memorisation (the stockpiling of internet facts within a model) with general intelligence, which arises from this cognitive core. The most promising path, in his view, is to isolate and refine this core, stripping away the accretions of memorised data, thereby developing something akin to a “ghost” of reasoning and abstraction rather than an “animal” shaped by instinct and inheritance.

This approach entails significant trade-offs: a cognitive core lacks the encyclopaedic reach of today’s massive models, but gains in adaptability, transparency, and the capacity for compositional, creative thought. By foregrounding reasoning machinery, Karpathy posits that AI can begin to mirror not the inflexibility of animals, but the open-ended, reflective qualities that characterise high-level problem-solving.

Karpathy’s Journey and Influence

Karpathy’s influence is rooted in a career spent on the frontier of AI research and deployment. His early proximity to Geoffrey Hinton at the University of Toronto placed him at the launch-point of the convolutional neural networks revolution, which fundamentally reshaped computer vision and pattern recognition.

At OpenAI, Karpathy contributed to an early focus on training agents to master digital environments (such as Atari games), a direction in retrospect he now considers premature. He found greater promise in systems that could interact with the digital world through knowledge work—precursors to today’s agentic models—a vision he is now helping to realise through ongoing work in educational technology and AI deployment.

Later, at Tesla, he directed the transformation of autonomous vehicles from demonstration to product, gaining hard-won appreciation for the “march of nines”—the reality that progressing from system prototypes that work 90% of the time to those that work 99.999% of the time requires exponentially more effort. This experience informs his scepticism towards aggressive timelines for “AGI” and his insistence on the qualitative differences between robust system deployment and controlled demonstrations.

The Leading Theorists Shaping the Debate

Karpathy’s conceptual framework emerges amid vibrant discourse within the AI community, shaped by several seminal thinkers:

Sutton’s “bitter lesson” posits that scale and generic algorithms, rather than domain-specific tricks, ultimately win—suggesting a focus on evolving animal-like intelligence. Karpathy, however, notes that current development practices, with their reliance on dataset imitation, sidestep the deep embodiment and evolutionary learning that define animal cognition. Instead, AI today creates digital ghosts—entities whose minds are not grounded in physical reality, but in the manifold of internet text and data.

Hinton and LeCun supply the neural and architectural foundations—the “cortex” and reasoning traces—while both Karpathy and their critics note the absence of rich, consolidated memory (the hippocampus analogue), instincts (amygdala), and the capacity for continual, self-motivated world interaction.

Why “Ghosts,” Not “Animals”?

The distinction is not simply philosophical. It carries direct consequences for:

• Capabilities: AI “ghosts” excel at pattern reproduction, simulation, and surface reasoning but lack the embodied, instinctual grounding (spatial navigation, sensorimotor learning) of animals.
• Limitations: They are subject to model collapse, producing uniform, repetitive outputs, lacking the spontaneous creativity and entropy seen in human (particularly child) cognition.
• Future Directions: The field is now oriented towards distilling this cognitive core, seeking a scalable, adaptable reasoning engine—compact, efficient, and resilient to overfitting—rather than continuing to bloat models with ever more static memory.

This lens sharpens expectations: the way forward is not to mimic biology in its totality, but to pursue the unique strengths and affordances of a digital, disembodied intelligence—a spirit of the datasphere, not a beast evolved in the forest.

Broader Significance

Karpathy’s “ghosts” metaphor crystallises a critical moment in the evolution of AI as a discipline. It signals a turning point: the shift from brute-force memorisation of the internet to intelligent, creative algorithms capable of abstraction, reasoning, and adaptation.

This reframing is shaping not only the strategic priorities of the most advanced labs, but also the philosophical and practical questions underpinning the next decade of AI research and deployment. As AI becomes increasingly present in society, understanding its nature—not as an artificial animal, but as a digital ghost—will be essential to harnessing its strengths and mitigating its limitations.

Quote: Sholto Douglas - Anthropic

“People have said we’re hitting a plateau every month for three years... I look at how models are produced and every part could be improved. The training pipeline is primitive, held together by duct tape, best efforts, and late nights. There’s so much room to grow everywhere.” - Sholto Douglas - Anthropic

Sholto Douglas made the statement during a major public podcast interview in October 2025, coinciding with Anthropic’s release of Claude Sonnet 4.5—at the time, the world’s strongest and most “agentic” AI coding model. The comment specifically rebuts repeated industry and media assertions that large AI models have reached a ceiling or are slowing in progress. Douglas argues the opposite: that the field is in a phase of accelerating advancement, driven both by transformative hardware investment (“compute super-cycle”), new algorithmic techniques (particularly reinforcement learning and test-time compute), and the persistent “primitive” state of today’s AI engineering infrastructure.

He draws an analogy with early-stage, improvisational systems: the models are held together “by duct tape, best efforts, and late nights,” making clear that immense headroom for improvement remains at every level, from training data pipelines and distributed infrastructure to model architecture and reward design. As a result, every new benchmark and capability reveals further unrealised opportunity, with measurable progress charted month after month.

Douglas’s deeper implication is that claims of a plateau often arise from surface-level analysis or the “saturation” of public benchmarks, not from a rigorous understanding of what is technically possible or how much scale remains untapped across the technical stack.

Sholto Douglas: Career Trajectory and Perspective

Sholto Douglas is a leading member of Anthropic’s technical staff, focused on scaling reinforcement learning and agentic AI. His unconventional journey illustrates both the new talent paradigm and the nature of breakthrough AI research today:

• Early Life and Mentorship: Douglas grew up in Australia, where he benefited from unusually strong academic and athletic mentorship. His mother, an accomplished physician frustrated by systemic barriers, instilled discipline and a systemic approach; his Olympic-level fencing coach provided a first-hand experience of how repeated, directed effort leads to world-class performance.
• Academic Formation: He studied computer science and robotics as an undergraduate, with a focus on practical experimentation and a global mindset. A turning point was reading the “scaling hypothesis” for AGI, convincing him that progress on artificial general intelligence was feasible within a decade—and worth devoting his career to.
• Independent Innovation: As a student, Douglas built “bedroom-scale” foundation models for robotics, working independently on large-scale data collection, simulation, and early adoption of transformer-based methods. This entrepreneurial approach—demonstrating initiative and technical depth without formal institutional backing—proved decisive.
• Google (Gemini and DeepMind): His independent work brought him to Google, where he joined just before the release of ChatGPT, in time to witness and help drive the rapid unification and acceleration of Google’s AI efforts (Gemini, Brain, DeepMind). He co-designed new inference infrastructure that reduced costs and worked at the intersection of large-scale learning, reinforcement learning, and applied reasoning.
• Anthropic (from 2025): Drawn by Anthropic’s focus on measurable, near-term economic impact and deep alignment work, Douglas joined to lead and scale reinforcement learning research—helping push the capability frontier for agentic models. He values a culture where every contributor understands and can articulate how their work advances both capability and safety in AI.

Douglas is distinctive for his advocacy of “taste” in AI research, favouring mechanistic understanding and simplicity over clever domain-specific tricks—a direct homage to Richard Sutton’s “bitter lesson.” This perspective shapes his belief that the greatest advances will come not from hiding complexity with hand-crafted heuristics, but from scaling general algorithms and rigorous feedback loops.

Intellectual and Scientific Context: The ‘Plateau’ Debate and Leading Theorists

The debate around the so-called “AI plateau” is best understood against the backdrop of core advances and recurring philosophical arguments in machine learning.

The “Bitter Lesson” and Richard Sutton

• Richard Sutton (University of Alberta, DeepMind), one of the founding figures in reinforcement learning, crystallised the field’s “bitter lesson”: that general, scalable methods powered by increased compute will eventually outperform more elegant, hand-crafted, domain-specific approaches.
• In practical terms, this means that the field’s recent leaps—from vision to language to coding—are powered less by clever new inductive biases, and more by architectural simplicity plus massive compute and data. Sutton has also maintained that real progress in AI will come from reinforcement learning with minimal task-specific assumptions and maximal data, computation, and feedback.

Yann LeCun and Alternative Paradigms

• Yann LeCun (Meta, NYU), a pioneer of deep learning, has maintained that the transformer paradigm is limited and that fundamentally novel architectures are necessary for human-like reasoning and autonomy. He argues that unsupervised/self-supervised learning and new world-modelling approaches will be required.
• LeCun’s disagreement with Sutton’s “bitter lesson” centres on the claim that scaling is not the final answer: new representation learning, memory, and planning mechanisms will be needed to reach AGI.

Shane Legg, Demis Hassabis, and DeepMind

• DeepMind’s approach has historically been “science-first,” tackling a broad swathe of human intelligence challenges (AlphaGo, AlphaFold, science AI), promoting a research culture that takes long-horizon bets on new architectures (memory-augmented neural networks, world models, differentiable reasoning).
• Demis Hassabis and Shane Legg (DeepMind co-founders) have advocated for testing a diversity of approaches, believing that the path to AGI is not yet clear—though they too acknowledge the value of massive scale and reinforcement learning.

The Scaling Hypothesis: GW’s Essay and the Modern Era

• The so-called “scaling hypothesis”—the idea that simply making models larger and providing more compute and data will continue yielding improvements—has become the default “bet” for Anthropic, OpenAI, and others. Douglas refers directly to this intellectual lineage as the critical “hinge” moment that set his trajectory.
• This hypothesis is now being extended into new areas, including agentic systems where long context, verification, memory, and reinforcement learning allow models to reliably pursue complex, multi-step goals semi-autonomously.

Summing Up: The Current Frontier

Today, researchers like Douglas are moving beyond the original transformer pre-training paradigm, leveraging multi-axis scaling (pre-training, RL, test-time compute), richer reward systems, and continuous experimentation to drive model capabilities in coding, digital productivity, and emerging physical domains (robotics and manipulation).

Douglas’s quote epitomises the view that not only has performance not plateaued—every “limitation” encountered is a signpost for further exponential improvement. The modest, “patchwork” nature of current AI infrastructure is a competitive advantage: it means there is vast room for optimisation, iteration, and compounding gains in capability.

As the field races into a new era of agentic AI and economic impact, his perspective serves as a grounded, inside-out refutation of technological pessimism and a call to action grounded in both technical understanding and relentless ambition.

Quote: Julian Schrittwieser - Anthropic

“The talk about AI bubbles seemed very divorced from what was happening in frontier labs and what we were seeing. We are not seeing any slowdown of progress.” - Julian Schrittwieser - Anthropic

Those closest to technical breakthroughs are witnessing a pattern of sustained, compounding advancement that is often underestimated by commentators and investors. This perspective underscores both the power and limitations of conventional intuitions regarding exponential technological progress.

Context of the Quote

Schrittwieser delivered these remarks in a 2025 interview on the MAD Podcast, prompted by widespread discourse on the so-called ‘AI bubble’. His key contention is that debate around an AI investment or hype “bubble” feels disconnected from the lived reality inside the world’s top research labs, where the practical pace of innovation remains brisk and outwardly undiminished. He outlines that, according to direct observation and internal benchmarks at labs such as Anthropic, progress remains on a highly consistent exponential curve: “every three to four months, the model is able to do a task that is twice as long as before completely on its own”.

He draws an analogy to the early days of COVID-19, where exponential growth was invisible until it became overwhelming; the same mathematical processes, Schrittwieser contends, apply to AI system capabilities. While public narratives about bubbles often reference the dot-com era, he highlights a bifurcation: frontier labs sustain robust, revenue-generating trajectories, while the wider AI ecosystem might experience bubble-like effects in valuation. But at the core—the technology itself continues to improve at a predictably exponential rate well supported by both qualitative experience and benchmark data.

Schrittwieser’s view, rooted in immediate, operational knowledge, is that the default expectation of a linear future is mistaken: advances in autonomy, reasoning, and productivity are compounding. This means genuinely transformative impacts—such as AI agents that function at expert level or beyond for extended, unsupervised tasks—are poised to arrive sooner than many anticipate.

Profile: Julian Schrittwieser

Julian Schrittwieser is one of the world’s leading artificial intelligence researchers, currently based at Anthropic, following a decade as a core scientist at Google DeepMind. Raised in rural Austria, Schrittwieser’s journey from an adolescent fascination with game programming to the vanguard of AI research exemplifies the discipline’s blend of curiosity, mathematical rigour, and engineering prowess. He studied computer science at the Vienna University of Technology, before interning at Google.

Schrittwieser was a central contributor to several historic machine learning milestones, most notably:

• AlphaGo, the first program to defeat a world champion at Go, combining deep neural networks with Monte Carlo Tree Search.
• AlphaGo Zero and AlphaZero, which generalised the approach to achieve superhuman performance without human examples, through self-play—demonstrating true generality in reinforcement learning.
• MuZero (as lead author), solving the challenge of mastering environments without even knowing the rules in advance, by enabling the system to learn its own internal, predictive world models—an innovation bringing RL closer to complex, real-world domains.
• Later work includes AlphaCode (code synthesis), AlphaTensor (algorithmic discovery), and applied advances in Gemini and AlphaProof.

At Anthropic, Schrittwieser is at the frontier of research into scaling laws, reinforcement learning, autonomous agents, and novel techniques for alignment and safety in next-generation AI. True to his pragmatic ethos, he prioritises what directly raises capability and reliability, and advocates for careful, data-led extrapolation rather than speculation.

Theoretical Backstory: Exponential AI Progress and Key Thinkers

Schrittwieser’s remarks situate him within a tradition of AI theorists and builders focused on scaling laws, reinforcement learning (RL), and emergent capabilities:

Leading Theorists and Historical Perspective

These thinkers converge on several key observations directly reflected in Schrittwieser's view:

• Exponential Capability Curves: Consistent advances in performance often surprise those outside the labs due to our poor intuitive grasp of exponentiality—what Schrittwieser terms a repeated “failure to understand the exponential”.
• Scaling Laws and Reinforcement Learning: Improvements are not just about larger models, but ever-better training, more reliable reinforcement learning, agentic architecture, and robust reward systems—developments Schrittwieser's work epitomises.
• Novelty and Emergence: Historically, theorists doubted whether neural models could go beyond sophisticated mimicry; the “Move 37” moment (AlphaGo’s unprecedented move in Go) was a touchstone for true machine creativity, a theme Schrittwieser stresses remains highly relevant today.
• Bubbles, Productivity, and Market Cycles: Mainstream financial and social narratives may oscillate dramatically, but real capability growth—observable in benchmarks and direct use—has historically marched on undeterred by speculative excesses.

Synthesis: Why the Perspective Matters

The quote foregrounds a gap between external perceptions and insider realities. Pioneers like Schrittwieser and his cohort stress that transformative change will not follow a smooth, linear or hype-driven curve, but an exponential, data-backed progression—one that may defy conventional intuition, but is already reshaping productivity and the structure of work.

This moment is not about “irrational exuberance”, but rather the compounding product of theoretical insight, algorithmic audacity, and relentless engineering: the engine behind the next wave of economic and social transformation.

Quote: Andrej Karpathy - Ex-OpenAI, Ex-Tesla AI

“AI is so wonderful because there have been a number of seismic shifts where the entire field has suddenly looked a different way. I've maybe lived through two or three of those. I still think there will continue to be some because they come with almost surprising regularity.” - Andrej Karpathy - Ex-OpenAI, Ex-Tesla AI

Andrej Karpathy, one of the most recognisable figures in artificial intelligence, has spent his career at the epicentre of the field’s defining moments in both research and large-scale industry deployment.

Karpathy’s background is defined by deep technical expertise and a front-row seat to AI’s rapid evolution. Having completed his PhD at Stanford and held pivotal research positions, he worked alongside Geoffrey Hinton at the University of Toronto during the early surge of deep learning. His career encompasses key roles at Tesla, where he led the Autopilot vision team, and at OpenAI, contributing to some of the world’s most prominent large language models and generative AI systems. This vantage point has allowed him to participate in, and reflect upon, the discipline’s “seismic shifts”.

Karpathy’s narrative has been shaped by three inflection points:

• The emergence of deep neural networks from a niche field to mainstream AI, spearheaded by the success of AlexNet and the subsequent shift of the research community toward neural architectures.
• The drive towards agent-based systems, with early enthusiasm for reinforcement learning (RL) and game-based environments (such as Atari and Go). Karpathy himself was cautious about the utility of games as the true path to intelligence, focusing instead on agents acting within the real digital world.
• The rise of large language models (LLMs)—transformers trained on vast internet datasets, shifting the locus of AI from task-specific systems to general-purpose models with the ability to perform a broad suite of tasks, and in-context learning.

His reflection on these ‘regular’ paradigm shifts arises from lived experience: "I've maybe lived through two or three of those. I still think there will continue to be some because they come with almost surprising regularity." These moments recalibrate assumptions, redirect research priorities, and set new benchmarks for capability. Karpathy’s practical orientation—building “useful things” rather than targeting biological intelligence or pure AGI—shapes his approach to both innovation and scepticism about hype.

Context of the Quote
In his conversation with podcaster Dwarkesh Patel, Karpathy elaborates on the recurring nature of breakthroughs. He contrasts AI’s rapid, transformative leaps with other scientific fields, noting that in machine learning, scaling up data, compute, and novel architectures can yield abrupt improvements—yet each wave often triggers both excessive optimism and later recalibration. A major point he raises is the lack of linearity: the field does not “smoothly” approach AGI, but rather proceeds via discontinuities, often catalysed by new ideas or techniques that were previously out of favour or overlooked.

Karpathy relates how, early in his career, neural networks were a marginal interest and large-scale “representation learning” was only beginning to be considered viable by a minority in the community. With the advent of AlexNet, the landscape shifted overnight, rapidly making previous assumptions obsolete. Later, the pursuit of RL-driven agents led to a phase where entire research agendas were oriented toward gameplay and synthetic environments—another phase later superseded by the transformer revolution and language models. Karpathy reflects candidly on earlier missteps, as well as the discipline’s collective tendency to over- or under-predict the timetable and trajectory of progress.

Leading Theorists and Intellectual Heritage
The AI revolutions Karpathy describes are inseparable from the influential figures and ideas that have shaped each phase:

• Geoffrey Hinton: Hailed as the “godfather of AI”, Hinton was instrumental in deep learning’s breakthrough, advancing techniques for training multilayered neural networks and championing representation learning against prevailing orthodoxy.
• Yann LeCun: Developed convolutional neural networks (CNNs), foundational for computer vision and the 2010s wave of deep learning success.
• Yoshua Bengio: Co-architect of the deep learning movement and a key figure in developing unsupervised and generative models.
• Richard Sutton: Principal proponent of reinforcement learning, Sutton articulated the value of “animal-like” intelligence: learning from direct interaction with environments, reward, and adaptation. Sutton’s perspective frequently informs debates about the relationship between model architectures and living intelligence, encouraging a focus on agents and lifelong learning.

Karpathy’s own stance is partly a pragmatic response to this heritage: rather than pursuing analogues of biological brains, he views the productive path as building digital “ghosts”—entities that learn by imitation and are shaped by patterns in data, rather than evolutionary processes.

Beyond individual theorists, the field’s quantum leaps are rooted in a culture of intellectual rivalry and rapid intellectual cross-pollination:

• The convolutional and recurrent networks of the 2010s pushed the boundaries of what neural networks could do.
• The development and scaling of transformer-based architectures (as in Google’s “Attention is All You Need”) dramatically changed both natural language processing and the structure of the field itself.
• The introduction of algorithms for in-context learning and large-scale unsupervised pre-training marked a break with hand-crafted representation engineering.

The Architecture of Progress: Seismic Shifts and Pragmatic Tension
Karpathy’s insight is that these shifts are not just about faster hardware or bigger datasets; they reflect the field’s unique ecology—where new methods can rapidly become dominant and overturn accumulated orthodoxy. The combination of open scientific exchange, rapid deployment, and intense commercialisation creates fertile ground for frequent realignment.

His observation on the “regularity” of shifts also signals a strategic realism: each wave brings both opportunity and risk. New architectures (such as transformers or large reinforcement learning agents) frequently overshoot expectations before their real limitations become clear. Karpathy remains measured on both promise and limitation—anticipating continued progress, but cautioning against overpredictions and hype cycles that fail to reckon with the “march of nines” needed to reach true reliability and impact.

Closing Perspective
The context of Karpathy’s quote is an AI ecosystem that advances not through steady accretion, but in leaps—each driven by conceptual, technical, and organisational realignments. As such, understanding progress in AI demands both technical literacy and historical awareness: the sharp pivots that have marked past decades are likely to recur, with equally profound effects on how intelligence is conceived, built, and deployed.

Quote: Jonathan Ross - CEO Groq

“The countries that control compute will control AI. You cannot have compute without energy.” - Jonathan Ross - CEO Groq

Jonathan Ross stands at the intersection of geopolitics, energy economics, and technological determinism. As founder and CEO of Groq, the Silicon Valley firm challenging Nvidia's dominance in AI infrastructure, Ross articulated a proposition of stark clarity during his September 2025 appearance on Harry Stebbings' 20VC podcast: "The countries that control compute will control AI. You cannot have compute without energy."

This observation transcends technical architecture. Ross is describing the emergence of a new geopolitical currency—one where computational capacity, rather than traditional measures of industrial might, determines economic sovereignty and strategic advantage in the 21st century. His thesis rests on an uncomfortable reality: artificial intelligence, regardless of algorithmic sophistication or model architecture, cannot function without the physical substrate of compute. And compute, in turn, cannot exist without abundant, reliable energy.

The Architecture of Advantage

Ross's perspective derives from direct experience building the infrastructure that powers modern AI. At Google, he initiated what became the Tensor Processing Unit (TPU) project—custom silicon that allowed the company to train and deploy machine learning models at scale. This wasn't academic research; it was the foundation upon which Google's AI capabilities were built. When Amazon and Microsoft attempted to recruit him in 2016 to develop similar capabilities, Ross recognised a pattern: the concentration of advanced AI compute in too few hands represented a strategic vulnerability.

His response was to establish Groq in 2016, developing Language Processing Units optimised for inference—the phase where trained models actually perform useful work. The company has since raised over $3 billion and achieved a valuation approaching $7 billion, positioning itself as one of Nvidia's most credible challengers in the AI hardware market. But Ross's ambitions extend beyond corporate competition. He views Groq's mission as democratising access to compute—creating abundant supply where artificial scarcity might otherwise concentrate power.

The quote itself emerged during a discussion about global AI competitiveness. Ross had been explaining why European nations, despite possessing strong research talent and model development capabilities (Mistral being a prominent example), risk strategic irrelevance without corresponding investment in computational infrastructure and energy capacity. A brilliant model without compute to run it, he argued, will lose to a mediocre model backed by ten times the computational resources. This isn't theoretical—it's the lived reality of the current AI landscape, where rate limits and inference capacity constraints determine what services can scale and which markets can be served.

The Energy Calculus

The energy dimension of Ross's statement carries particular weight. Modern AI training and inference require extraordinary amounts of electrical power. The hyperscalers—Google, Microsoft, Amazon, Meta—are each committing tens of billions of dollars annually to AI infrastructure, with significant portions dedicated to data centre construction and energy provision. Microsoft recently announced it wouldn't make certain GPU clusters available through Azure because the company generated higher returns using that compute internally rather than renting it to customers. This decision, more than any strategic presentation, reveals the economic value density of AI compute.

Ross draws explicit parallels to the early petroleum industry: a period of chaotic exploration where a few "gushers" delivered extraordinary returns whilst most ventures yielded nothing. In this analogy, compute is the new oil—a fundamental input that determines economic output and strategic positioning. But unlike oil, compute demand doesn't saturate. Ross describes AI demand as "insatiable": if OpenAI or Anthropic received twice their current inference capacity, their revenue would nearly double within a month. The bottleneck isn't customer appetite; it's supply.

This creates a concerning dynamic for nations without indigenous energy abundance or the political will to develop it. Ross specifically highlighted Europe's predicament: impressive AI research capabilities undermined by insufficient energy infrastructure and regulatory hesitance around nuclear power. He contrasted this with Norway's renewable capacity (80% wind utilisation) or Japan's pragmatic reactivation of nuclear facilities—examples of countries aligning energy policy with computational ambition. The message is uncomfortable but clear: technical sophistication in model development cannot compensate for material disadvantage in energy and compute capacity.

Strategic Implications

The geopolitical dimension becomes more acute when considering China's position. Ross noted that whilst Chinese models like DeepSeek may be cheaper to train (through various optimisations and potential subsidies), they remain more expensive to run at inference—approximately ten times more costly per token generated. This matters because inference, not training, determines scalability and market viability. China can subsidise AI deployment domestically, but globally—what Ross terms the "away game"—cost structure determines competitiveness. Countries cannot simply construct nuclear plants at will; energy infrastructure takes decades to build.

This asymmetry creates opportunity for nations with existing energy advantages. The United States, despite higher nominal costs, benefits from established infrastructure and diverse energy sources. However, Ross's framework suggests this advantage is neither permanent nor guaranteed. Control over compute requires continuous investment in both silicon capability and energy generation. Nations that fail to maintain pace risk dependency—importing not just technology, but the capacity for economic and strategic autonomy.

The corporate analogy proves instructive. Ross predicts that every major AI company—OpenAI, Anthropic, Google, and others—will eventually develop proprietary chips, not necessarily to outperform Nvidia technically, but to ensure supply security and strategic control. Nvidia currently dominates not purely through superior GPU architecture, but through control of high-bandwidth memory (HBM) supply chains. Building custom silicon allows organisations to diversify supply and avoid allocation constraints that might limit their operational capacity. What applies to corporations applies equally to nations: vertical integration in compute infrastructure is increasingly a prerequisite for strategic autonomy.

The Theorists and Precedents

Ross's thesis echoes several established frameworks in economic and technological thought, though he synthesises them into a distinctly contemporary proposition.

Harold Innis, the Canadian economic historian, developed the concept of "staples theory" in the 1930s and 1940s—the idea that economies organised around the extraction and export of key commodities (fur, fish, timber, oil) develop institutional structures, trade relationships, and power dynamics shaped by those materials. Innis later extended this thinking to communication technologies in works like Empire and Communications (1950) and The Bias of Communication (1951), arguing that the dominant medium of a society shapes its political and social organisation. Ross's formulation applies Innisian logic to computational infrastructure: the nations that control the "staples" of the AI economy—energy and compute—will shape the institutional and economic order that emerges.

Carlota Perez, the Venezuelan-British economist, provided a framework for understanding technological revolutions in Technological Revolutions and Financial Capital (2002). Perez identified how major technological shifts (steam power, railways, electricity, mass production, information technology) follow predictable patterns: installation phases characterised by financial speculation and infrastructure building, followed by deployment phases where the technology becomes economically productive. Ross's observation about current AI investment—massive capital expenditure by hyperscalers, uncertain returns, experimental deployment—maps cleanly onto Perez's installation phase. The question, implicit in his quote, is which nations will control the infrastructure when the deployment phase arrives and returns become tangible.

W. Brian Arthur, economist and complexity theorist, articulated the concept of "increasing returns" in technology markets through works like Increasing Returns and Path Dependence in the Economy (1994). Arthur demonstrated how early advantages in technology sectors compound through network effects, learning curves, and complementary ecosystems—creating winner-take-most dynamics rather than the diminishing returns assumed in classical economics. Ross's emphasis on compute abundance follows this logic: early investment in computational infrastructure creates compounding advantages in AI capability, which drives economic returns, which fund further compute investment. Nations entering this cycle late face escalating barriers to entry.

Joseph Schumpeter, the Austrian-American economist, introduced the concept of "creative destruction" in Capitalism, Socialism and Democracy (1942)—the idea that economic development proceeds through radical innovation that renders existing capital obsolete. Ross explicitly invokes Schumpeterian dynamics when discussing the risk that next-generation AI chips might render current hardware unprofitable before it amortises. This uncertainty amplifies the strategic calculus: nations must invest in compute infrastructure knowing that technological obsolescence might arrive before economic returns materialise. Yet failing to invest guarantees strategic irrelevance.

William Stanley Jevons, the 19th-century English economist, observed what became known as Jevons Paradox in The Coal Question (1865): as technology makes resource use more efficient, total consumption typically increases rather than decreases because efficiency makes the resource more economically viable for new applications. Ross applies this directly to AI compute, noting that as inference becomes cheaper (through better chips or more efficient models), demand expands faster than costs decline. This means the total addressable market for compute grows continuously—making control over production capacity increasingly valuable.

Nicholas Georgescu-Roegen, the Romanian-American economist, pioneered bioeconomics and introduced entropy concepts to economic analysis in The Entropy Law and the Economic Process (1971). Georgescu-Roegen argued that economic activity is fundamentally constrained by thermodynamic laws—specifically, that all economic processes dissipate energy and cannot be sustained without continuous energy inputs. Ross's insistence that "you cannot have compute without energy" is pure Georgescu-Roegen: AI systems, regardless of algorithmic elegance, are bound by physical laws. Compute is thermodynamically expensive—training large models requires megawatts, inference at scale requires sustained power generation. Nations without access to abundant energy cannot sustain AI economies, regardless of their talent or capital.

Mancur Olson, the American economist and political scientist, explored collective action problems and the relationship between institutional quality and economic outcomes in works like The Rise and Decline of Nations (1982). Olson demonstrated how established interest groups can create institutional sclerosis that prevents necessary adaptation. Ross's observations about European regulatory hesitance and infrastructure underinvestment reflect Olsonian dynamics: incumbent energy interests, environmental lobbies, and risk-averse political structures prevent the aggressive nuclear or renewable expansion required for AI competitiveness. Meanwhile, nations with different institutional arrangements (or greater perceived strategic urgency) act more decisively.

Paul Romer, the American economist and Nobel laureate, developed endogenous growth theory, arguing in works like "Endogenous Technological Change" (1990) that economic growth derives from deliberate investment in knowledge and technology rather than external factors. Romer's framework emphasises the non-rivalry of ideas (knowledge can be used by multiple actors simultaneously) but the rivalry of physical inputs required to implement them. Ross's thesis fits perfectly: AI algorithms can be copied and disseminated, but the computational infrastructure to deploy them at scale cannot. This creates a fundamental asymmetry that determines economic power.

The Historical Pattern

History provides sobering precedents for resource-driven geopolitical competition. Britain's dominance in the 19th century rested substantially on coal abundance that powered industrial machinery and naval supremacy. The United States' 20th-century ascendance correlated with petroleum access and the industrial capacity to refine and deploy it. Oil-dependent economies in the Middle East gained geopolitical leverage disproportionate to their population or industrial capacity purely through energy reserves.

Ross suggests we are witnessing the emergence of a similar dynamic, but with a critical difference: AI compute is both resource-intensive (requiring enormous energy) and productivity-amplifying (making other economic activity more efficient). This creates a multiplicative effect where compute advantages compound through both direct application (better AI services) and indirect effects (more efficient production of goods and services across the economy). A nation with abundant compute doesn't just have better chatbots—it has more efficient logistics, agricultural systems, manufacturing processes, and financial services.

The "away game" concept Ross introduced during the podcast discussion adds a critical dimension. China, despite substantial domestic AI investment and capabilities, faces structural disadvantages in global competition because international customers cannot simply replicate China's energy subsidies or infrastructure. This creates opportunities for nations with more favourable cost structures or energy profiles, but only if they invest in both compute capacity and energy generation.

The Future Ross Envisions

Throughout the podcast, Ross painted a vision of AI-driven abundance that challenges conventional fears of technological unemployment. He predicts labour shortages, not mass unemployment, driven by three mechanisms: deflationary pressure (AI makes goods and services cheaper), workforce opt-out (people work less as living costs decline), and new industry creation (entirely new job categories emerge, like "vibe coding"—programming through natural language rather than formal syntax).

This optimistic scenario depends entirely on computational abundance. If compute remains scarce and concentrated, AI benefits accrue primarily to those controlling the infrastructure. Ross's mission with Groq—creating faster deployment cycles (six months versus two years for GPUs), operating globally distributed data centres, optimising for cost efficiency rather than margin maximisation—aims to prevent that concentration. But the same logic applies at the national level. Countries without indigenous compute capacity will import AI services, capturing some productivity benefits but remaining dependent on external providers for the infrastructure that increasingly underpins economic activity.

The comparison Ross offers—LLMs as "telescopes of the mind"—is deliberately chosen. Galileo's telescope revolutionised human understanding but required specific material capabilities to construct and use. Nations without optical manufacturing capacity could not participate in astronomical discovery. Similarly, nations without computational and energy infrastructure cannot participate fully in the AI economy, regardless of their algorithmic sophistication or research talent.

Conclusion

Ross's statement—"The countries that control compute will control AI. You cannot have compute without energy"—distils a complex geopolitical and economic reality into stark clarity. It combines Innisian materialism (infrastructure determines power), Schumpeterian dynamism (innovation renders existing capital obsolete), Jevonsian counterintuition (efficiency increases total consumption), and Georgescu-Roegen's thermodynamic constraints (economic activity requires energy dissipation).

The implications are uncomfortable for nations unprepared to make the necessary investments. Technical prowess in model development provides no strategic moat if the computational infrastructure to deploy those models remains controlled elsewhere. Energy abundance, or the political will to develop it, becomes a prerequisite for AI sovereignty. And AI sovereignty increasingly determines economic competitiveness across sectors.

Ross occupies a unique vantage point—neither pure academic nor disinterested observer, but an operator building the infrastructure that will determine whether his prediction proves correct. Groq's valuation and customer demand suggest the market validates his thesis. Whether nations respond with corresponding urgency remains an open question. But the framework Ross articulates will likely define strategic competition for the remainder of the decade: compute as currency, energy as prerequisite, and algorithmic sophistication as necessary but insufficient for competitive advantage.