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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.

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Quote: Fyodor Dostoevsky – Russian novelist, essayist and journalist

“A man who lies to himself, and believes his own lies becomes unable to recognize truth, either in himself or in anyone else, and he ends up losing respect for himself and for others. When he has no respect for anyone, he can no longer love, and, in order to divert himself, having no love in him, he yields to his impulses, indulges in the lowest forms of pleasure, and behaves in the end like an animal. And it all comes from lying – lying to others and to yourself.” – Fyodor Dostoevsky – Russian novelist, essayist and journalist

Fyodor Mikhailovich Dostoevsky (November 11, 1821 – February 9, 1881) was a Russian novelist, essayist, and journalist who explored the depths of the human psyche with unflinching honesty. Born in Moscow to a family of modest means, Dostoevsky’s early life was marked by the emotional distance of his parents and an eventual tragedy when his father was murdered. He trained as a military engineer but pursued literature with relentless ambition, achieving early success with novels such as Poor Folk and The Double.

Dostoevsky’s life took a dramatic turn in 1849 when he was arrested for participating in a radical intellectual group. Sentenced to death, he faced a mock execution before his sentence was commuted to four years of hard labor in Siberia followed by military service. This harrowing experience, combined with his life among Russia’s poor, profoundly shaped his worldview and writing. His later years were marked by personal loss—the deaths of his first wife and his brother—and financial hardship, yet he produced some of literature’s greatest works during this time, including Crime and Punishment, The Idiot, Devils, and The Brothers Karamazov.

Dostoevsky’s writings are celebrated for their psychological insight and existential depth. He scrutinized themes of morality, free will, faith, and the consequences of self-deception—topics that continue to resonate in philosophy, theology, and modern psychology. His funeral drew thousands, reflecting his status as a national hero and one of Russia’s most influential thinkers.

Context of the Quote

The quoted passage is widely attributed to Dostoevsky, most notably appearing in The Brothers Karamazov, his final and perhaps most philosophically ambitious novel. The novel, published in serial form shortly before his death, wrestles with questions of faith, doubt, and the consequences of living a lie.

The quote is spoken by the Elder Zosima, a wise and compassionate monk in the novel. Zosima’s teachings in The Brothers Karamazov frequently address the dangers of self-deception and the importance of spiritual and moral honesty. In this passage, Dostoevsky is warning that lying to oneself is not merely a moral failing, but a fundamental corruption of perception and being. The progression—from dishonesty to self-deception, to the loss of respect for oneself and others, and ultimately to the decay of love and humanity—paints a stark picture of spiritual decline.

This theme is central to Dostoevsky’s work: characters who deceive themselves often spiral into psychological and moral crises. Dostoevsky saw truth—even when painful—as a prerequisite for authentic living. His novels repeatedly show how lies, whether to oneself or others, lead to alienation, suffering, and a loss of authentic connection.

Leading Theorists on Self-Deception

While Dostoevsky is renowned in literature for his treatment of self-deception, the theme has also been explored by philosophers, psychologists, and sociologists. Below is a brief overview of leading theorists and their contributions:

Philosophers

Søren Kierkegaard (1813–1855): The Danish philosopher explored the idea of existential self-deception, particularly in The Sickness Unto Death, where he describes how humans avoid the despair of being true to themselves by living inauthentic lives, what he calls “despair in weakness.”
Jean-Paul Sartre (1905–1980): In Being and Nothingness, Sartre popularized the concept of “bad faith” (mauvaise foi), the act of deceiving oneself to avoid the anxiety of freedom and responsibility. Sartre’s ideas are often seen as a philosophical counterpart to Dostoevsky’s literary explorations.
Friedrich Nietzsche (1844–1900): Nietzsche’s concept of “resentment” and the “will to power” also touches on self-deception, particularly how individuals and societies construct false narratives to justify their weaknesses or desires.

Psychologists

Sigmund Freud (1856–1939): Freud introduced the idea of defence mechanisms, such as denial and rationalization, as ways the psyche protects itself from uncomfortable truths—essentially systematizing the process of self-deception.
Donald Winnicott (1896–1971): The psychoanalyst discussed the “false self,” a persona developed to comply with external demands, often leading to inner conflict and emotional distress.
Erich Fromm (1900–1980): Fromm, like Dostoevsky, examined how modern society encourages escape from freedom and the development of “automaton conformity,” where individuals conform to avoid anxiety and uncertainty.

Modern Thinkers

Dan Ariely (b. 1967): The behavioural economist has shown experimentally how dishonesty often begins with small, self-serving lies that gradually erode ethical boundaries.
Robert Trivers (b. 1943): The evolutionary biologist proposed that self-deception evolved as a strategy to better deceive others, which ironically can make personal delusions more convincing.

Legacy and Insight

Dostoevsky’s insights into the dangers of self-deception remain remarkably relevant today. His work, together with that of philosophers and psychologists, invites reflection on the necessity of honesty—not just to others, but to oneself—for psychological health and authentic living. The consequences of failing this honesty, as Dostoevsky depicts, are not merely moral, but existential: they impact our ability to respect, love, and ultimately, to live fully human lives.

By placing this quote in context, we see not only the literary brilliance of Dostoevsky but also the enduring wisdom of his diagnosis of the human condition—a call to self-awareness that echoes through generations and disciplines.