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Quote: Professor Ethan Mollick - Wharton

"How do we mitigate those negative risks? I think there's a nitty-gritty path between here and some imagined future. We don't know if AI is going to get there-to super powerful and autonomous-but we do know it's disruptive today." - Professor Ethan Mollick - Wharton

In a candid conversation hosted by Scott Galloway on his Prof G podcast, Professor Ethan Mollick addresses the pressing challenge of managing artificial intelligence's immediate disruptions while navigating uncertainties about its long-term trajectory. Speaking from his vantage point at the Wharton School of the University of Pennsylvania, where he serves as an Associate Professor of Management and Co-Director of the Generative AI Labs, Mollick emphasises a grounded approach: focusing on today's realities rather than speculative dystopias or utopias.^1,2,4

Who is Ethan Mollick?

Ethan Mollick is a leading voice in the intersection of technology, innovation, and organisational behaviour. His work at Wharton explores how emerging technologies reshape work, creativity, and decision-making. Mollick's bestselling book, Co-Intelligence: Living and Working with AI, distils years of research into practical principles for integrating AI as a collaborative 'alien co-intelligence'. He advocates inviting AI to brainstorming sessions, treating it like a person with defined roles, and assuming current models represent the 'worst AI you will ever use'-a principle underscoring relentless improvement ahead.¹

Mollick's insights draw from empirical studies showing AI boosting productivity by 20-80% across tasks, far surpassing historical technologies like steam power. He warns of AI's opaque capabilities-no one fully understands why token-prediction systems yield extraordinary results-and forecasts 'agentic AI' in 2026: semi-autonomous systems handling complex goals with minimal oversight.^1,2,4 Recent predictions highlight surging adoption, with a billion weekly users and organisations embedding AI deeply into processes, demanding guardrails for safety in psychological, legal, and medical consultations.^4,5

Context of the Quote

The quote emerges from a February 2026 discussion on why CEOs often misjudge AI, mistaking it for narrow tools rather than transformative forces. Galloway, a serial entrepreneur and NYU Stern professor, probes Mollick on risks amid rapid progress. Mollick counters hype around superintelligent 'Machine Gods' by stressing AI's current disruption: even halting development now would yield a decade of upheaval in jobs, privacy, and security. He calls for 'nitty-gritty' strategies-practical steps like skill bundling (combining emotional intelligence, judgement, creativity, and expertise) to outpace automation-and organisational rethinking, including shorter work weeks or universal basic income in high-growth scenarios.^1,3,5

This reflects Mollick's four future scenarios from Co-Intelligence: 'As Good As It Gets' (plateau), 'Slow Growth' (manageable integration), 'Exponential Growth' (severe, unpredictable risks with AI self-improving), and 'The Machine God' (autonomous superintelligence). He urges focus on the path 'between here and some imagined future', prioritising today's agentic shifts and ethical guardrails over remote singularities.¹

Leading Theorists on AI Disruption and Risks

Mollick's views build on foundational thinkers who shaped AI risk discourse:

• Nick Bostrom (Oxford Future of Humanity Institute): In Superintelligence (2014), Bostrom warns of existential risks from misaligned superintelligent AI pursuing goals orthogonally to humanity's. His 'control problem'-ensuring AI obedience-influences Mollick's guardrail emphasis.¹
• Stuart Russell (UC Berkeley): Co-author of Artificial Intelligence: A Modern Approach, Russell advocates 'provably beneficial AI' via uncertainty about human preferences. His book Human Compatible (2019) stresses inverse reinforcement learning, aligning with Mollick's human-in-the-loop principle.¹
• Ray Kurzweil: Google's Director of Engineering predicts the Singularity by 2045-AI surpassing human intelligence via exponential growth. Kurzweil's law of accelerating returns informs Mollick's exponential scenarios, though Mollick tempers optimism with pragmatic disruption focus.¹
• Timnit Gebru and Margaret Mitchell: Pioneers in AI ethics, their work on bias and safety (e.g., Stochastic Parrots paper) underscores immediate risks like misinformation, echoing Mollick's calls for ethical AI interactions.⁴

These theorists highlight a spectrum: from alignment challenges (Bostrom, Russell) to accelerationism (Kurzweil) and equity concerns (Gebru). Mollick synthesises them into actionable advice, bridging theory and practice for leaders facing 2026's agentic wave.^1,2,3,4

Quote: Jensen Huang - Nvidia CEO

"I'll take every possible opportunity, external information, new insights, new discoveries, new engineering ... I'll take those opportunities and I'll use it to shape everybody else's belief system. And I'm doing that literally every single day." - Jensen Huang - Nvidia CEO

Jensen Huang, co-founder and CEO of NVIDIA, shared insights on his leadership approach during Lex Fridman Podcast #494 (March 23, 2026), emphasizing how he leverages continuous learning to shape organizational beliefs.

This statement reflects Huang's strategic approach to leadership at NVIDIA, the world's most valuable company and primary engine powering the AI computing revolution. According to the podcast discussion, Huang emphasizes the importance of shaping the beliefs of employees, partners, and the broader industry through continuous engagement with emerging innovations and discoveries.

The quote underscores a deliberate leadership philosophy where Huang actively translates external developments-whether technological breakthroughs, market insights, or engineering advances-into organizational culture and strategic direction. This approach aligns with NVIDIA's evolution into what Huang describes as an "AI factory," requiring extreme co-design across GPU, CPU, memory, networking, and software systems.

Huang's emphasis on daily belief-shaping reflects his broader vision for anticipating future AI innovations, including agentic systems and open-source models, while maintaining organizational alignment around these forward-looking priorities.

Quote: Reid Wiseman - Artemis II Mission commander

"You get to that point where you do not have to communicate any longer - you're just listening to everything happening, and all four of us are watching each other and the mission, and we do not need to speak - we just know." - Reid Wiseman - Artemis II Mission commander

The challenge of maintaining flawless team coordination across vast distances defines deep space exploration. Light-speed delays render real-time communication impossible beyond low Earth orbit, forcing crews to operate autonomously for hours or days. Artemis II, NASA's first crewed lunar flyby since Apollo 17 in 1972, tests this limit as four astronauts hurtle 400,000 kilometres from Earth in the Orion spacecraft. Communication blackouts lasting up to 45 minutes demand implicit trust and non-verbal cues, elevating crew synergy to a survival imperative ¹.

Artemis II: Reviving Human Lunar Ambition Amid Technical and Fiscal Hurdles

Scheduled for no earlier than September 2026 after repeated delays from heat shield anomalies and valve failures, Artemis II marks humanity's return to cislunar space. NASA aims to circumnavigate the Moon without landing, validating Orion's life support, propulsion, and re-entry systems for future missions like Artemis III's south pole landing. The 10-day flight path traces an inverted '8' around the Moon, peaking at 100 km altitude, exposing the crew to unprecedented radiation and isolation. Budget overruns exceeding $4 billion underscore the programme's stakes, with Orion's development costs ballooning from $11 billion to over $20 billion since 2011 [2].

Orion's design prioritises deep space endurance: solar arrays generate 12 kW, lithium-ion batteries sustain power during eclipse, and the European Service Module provides 33 tonnes of propellant for mid-course corrections. Yet, the spacecraft's compact 11-cubic-metre crew cabin amplifies interpersonal dynamics, where a single miscommunication could cascade into catastrophe. Historical precedents like Apollo 13's oxygen tank rupture highlight how crew resourcefulness compensates for engineering flaws, but Artemis II's extended duration-twice Apollo's outbound leg-intensifies psychological pressures [3].

Reid Wiseman: From Test Pilot to Mission Commander in NASA's New Guard

Commander Reid Wiseman, a 48-year-old Navy test pilot with 180 days on the International Space Station from Expedition 41, embodies NASA's shift towards seasoned operators. Selected in 2009, Wiseman logged 50 Soyuz docking simulations and commanded the station's Node 1 during a 2014 spacewalk. His Artemis II role demands orchestrating a diverse crew: pilot Victor Glover, the first Black astronaut for a lunar mission; mission specialist Christina Koch, record-holder for the longest single spaceflight by a woman at 328 days; and CSA astronaut Jeremy Hansen, Canada's first moon-bound explorer. This quartet's chemistry, forged in years of analogue training at NASA's Johnson Space Center, underpins the mission's success ¹.

Wiseman's leadership draws from combat aviation, where split-second decisions hinge on unspoken rapport. Artemis training regimens-centrifuge runs simulating 9g re-entry, neutral buoyancy labs for suited manoeuvres, and hyperbaric chambers for decompression drills-instil muscle memory. Crews endure 2.5 years of integrated rehearsals, progressing from scripted procedures to free-play scenarios mimicking failures like thruster malfunctions or solar flare alerts. This culminates in wordless intuition, where eye contact conveys status checks faster than voice loops clogged with telemetry [4].

The Science of Non-Verbal Crew Cohesion Under Extreme Isolation

High-stakes teams achieve 'shared mental models' through neuroplastic adaptation, where repeated exposure syncs physiological states. Studies from Antarctic overwintering and submarine patrols reveal cortisol levels drop 30% in cohesive groups, enhancing threat detection via micro-expressions. NASA's Human Research Program quantifies this via EEG headsets during HI-SEAS Mars simulations, showing alpha wave synchrony predicts 85% of task efficiency. In Orion, biometric sensors monitor heart rate variability and galvanic skin response, feeding algorithms that flag desynchrony before verbal alerts [5].

Artemis II amplifies these dynamics: at lunar distance, a round-trip signal delay hits 2.5 seconds, but service module outages erase voice entirely. Crews revert to procedural hand signals refined in vacuum chambers, echoing Apollo's 'thumbs up' for hatch seals. Psychological screening via the Big Five personality inventory ensures complementarity-Wiseman's extraversion balances Koch's conscientiousness-fostering 'emergent communication' where gestures encode complex data like fuel margins or trajectory tweaks [6].

Strategic Tensions: Human Intuition Versus Autonomous Systems

NASA's pivot to commercial partners like SpaceX's Starship for Artemis III introduces hybrid crews blending pilots with engineers, straining traditional hierarchies. Starship's 100-passenger capacity envisions lunar bases, but Orion's four-person intimacy preserves Apollo-era bonding. Critics argue over-reliance on human oversight ignores AI advancements; SpaceX's autonomous docking boasts 99.9% reliability, yet Wiseman's crew retains veto authority, reflecting distrust in black-box algorithms during anomalies [7].

Geopolitical frictions compound this: China's Chang'e programme eyes south pole resources by 2030, prompting NASA's Artemis Accords-signed by 45 nations-to secure 'safe zones'. Crew cohesion becomes a soft-power asset, projecting American resilience. Delays from Boeing's SLS rocket-$23 billion and counting-fuel debates on privatisation, with Musk advocating full reusability to slash costs 90%. Artemis II's success hinges on proving human crews outperform drones in ambiguous crises, like Apollo 13's slingshot manoeuvre [8].

Debates and Objections: Is Implicit Trust Overhyped?

Sceptics question romanticising silence amid data overload. Orion generates 1.8 terabytes daily from 1,000 sensors, demanding verbal triage to avoid cognitive overload. Former astronaut Chris Hadfield warns non-verbal cues falter under fatigue, citing Skylab's interpersonal strife. Diversity advocates praise the crew's composition but flag implicit bias in training, where male-dominated simulations undervalue Koch's input. Radiation exposure-up to 1 sievert, 300 times annual limits-induces nausea, eroding rapport [9].

Counterarguments cite analogue missions: NASA's CHAPEA buried four volunteers in a 3D-printed Mars habitat for 378 days, achieving 92% procedural compliance via non-verbals. HI-SEAS crews logged zero mission aborts despite 20% depression rates. Objectors like planetary scientist Phil Plait argue AI copilots, trained on billions of simulations, exceed human bandwidth, but NASA counters with 'judgement calls'-e.g., Apollo 11's manual landing-unattainable by current neural nets [10].

Technological Backbone Enabling Silent Operations

Orion's glass cockpit fuses 10 touchscreen displays with heads-up projections, minimising head movements for peripheral awareness. Augmented reality visors overlay telemetry, allowing glance-based status reads. The crew's 'loop discipline'-prioritising brevity-frees bandwidth for observation, with auto-transcripts logging nuance. Post-mission debriefs dissect these moments, refining selection for Artemis III's 30-day loiter [11].

Why This Capability Matters for Lunar Settlement and Beyond

Wordless synergy scales to multi-crew outposts, where bandwidth rationing mandates efficiency. Artemis paves for Gateway station, orbiting Lagrange points with six-person rotations. Implicit trust mitigates 'Earth-out-of-view' syndrome, slashing 40% of behavioural risks per NASA models. Economically, it justifies $93 billion Artemis investment by enabling ISRU-lunar water mining for propellant-staffed by intuitive teams [12].

Militarily, it informs crewed cislunar patrols amid rising orbital congestion (36,000 satellites by 2030). Philosophically, it reaffirms human agency in an AI-saturated era, where machines execute but crews improvise. As Artemis II hurtles towards its uncrewed dress rehearsal in 2025, Wiseman's insight spotlights the irreplaceable human core: not just surviving space, but thriving through unspoken bonds ¹.

References

1. BBC News. Artemis II: Inside the Moon mission to fly humans further than ever. bbc.co.uk
2. NASA Office of Inspector General. 2024 Orion Audit Report.
3. Lovell, J. Lost Moon. Houghton Mifflin, 1994.
4. NASA Johnson Space Center Training Overview.
5. Human Research Program: Synchrony Studies, 2023.
6. Big Five Inventory in Astronaut Selection, Acta Astronautica, 2022.
7. SpaceX Starship Updates, 2025.
8. Artemis Accords Signatories, State Department, 2025.
9. Hadfield, C. An Astronaut's Guide. Knopf, 2013.
10. CHAPEA Mission Report, NASA, 2025.
11. Orion Avionics Specifications, Lockheed Martin.
12. Gateway Programme Baseline, 2026.

Term: Limited partner (LP)

"A limited partner (LP) is an investor in a private equity fund who provides capital but does not participate in the day-to-day management of the fund." - Limited partner (LP)

A limited partner (LP) is a passive investor who contributes capital to a private equity or venture capital fund in exchange for a proportionate share of profits, whilst maintaining limited liability and no involvement in day-to-day management or operational decisions.^1,2

Core Characteristics and Structure

Limited partners are distinguished by several defining features that shape their role within private equity funds. Their liability is capped at the amount of capital they invest, meaning they cannot lose more than their initial contribution regardless of the fund's performance or debts incurred.^1,2 This liability protection is a primary attraction for institutional and individual investors seeking to diversify their portfolios whilst managing risk exposure.

The relationship between LPs and the fund is governed by a limited partnership agreement (LPA), a legally binding contract that specifies ownership percentages, profit distribution mechanisms, management fee structures, and the decision-making authority of general partners (GPs).^1,3 Whilst LPs remain passive in operational matters, they retain certain rights, including the ability to approve major changes to the business plan or structure (typically by majority vote) and to review financial statements and request updates on fund performance.¹

Types of Limited Partners

Limited partners in private equity encompass three primary categories:^1,4

• Institutional LPs include pension funds, endowments, foundations, and sovereign wealth funds-organisations with substantial capital reserves and mandates to generate consistent returns over extended timelines.¹
• Individual LPs are typically high-net-worth individuals who invest personal capital into private funds or directly into startups as angel investors.¹
• Family offices are private firms managing the finances of wealthy families, often making both fund investments and direct startup investments.¹

Limited Partners versus General Partners

The distinction between LPs and GPs is fundamental to private equity fund structure. Whilst limited partners provide the financial capital that fuels fund operations, general partners assume active management responsibility, making investment decisions, sourcing portfolio companies, and overseeing fund operations.^1,6 GPs carry unlimited liability and receive compensation through management fees and carried interest (a percentage of profits), aligning their interests with fund performance.^1,6

Limited partners, conversely, are sometimes referred to as "silent partners" or "passive investors" because of their hands-off operational role.^1,6 They entrust strategic decision-making entirely to GPs whilst maintaining their right to monitor fund progress and approve significant structural changes.¹

Investment Process and Capital Deployment

Limited partners commit to providing a specified amount of capital during the fund's lifetime, though capital is typically deployed in tranches as investment opportunities arise.⁶ GPs request capital from LPs when they identify suitable acquisition targets or investment opportunities.⁶ This staged capital deployment allows LPs to maintain liquidity whilst ensuring their committed capital is deployed strategically rather than immediately upon fund formation.

In return for their capital contribution, LPs receive distributions of profits according to the terms outlined in the LPA, typically after GPs recover their management fees and realise their carried interest.^1,3

Key Advantages and Constraints

The LP structure offers significant advantages: investors gain access to high-growth private investments, benefit from professional fund management, and achieve portfolio diversification across multiple ventures whilst limiting personal financial risk.² However, limited partners also face notable constraints, including illiquidity (capital is typically locked in for 7-10 years), limited control over investment decisions, and exposure to underperforming funds or adverse regulatory changes.³

Historical Context and Theoretical Framework

William D. Bygrave, a pioneering venture capital theorist and professor at Babson College, significantly shaped contemporary understanding of LP-GP relationships and fund structures. Bygrave's foundational work in the 1980s and 1990s established the theoretical framework for analysing how limited partners and general partners interact within venture capital ecosystems. His research emphasised the agency problem inherent in the LP-GP relationship-the tension between passive investors seeking returns and active managers pursuing their own interests.

Bygrave's contributions extended beyond theory into practical fund governance. He developed models demonstrating how limited partnership agreements could be structured to align GP incentives with LP interests, addressing information asymmetries and moral hazard concerns. His work at Babson College, where he founded the Center for Entrepreneurial Studies, trained generations of venture capitalists and institutional investors in understanding optimal fund structures. Bygrave's research highlighted that successful private equity and venture capital funds depend critically on clear contractual frameworks-precisely the LPAs that govern modern LP-GP relationships.

His emphasis on the importance of transparent communication, aligned incentives, and well-drafted partnership agreements remains foundational to contemporary private equity practice. Bygrave's theoretical insights into how limited partners can effectively monitor general partners without micromanaging operations continue to influence institutional investor policies and fund governance standards across the private capital industry.

Regulatory and Qualification Requirements

To participate as a limited partner in private equity funds, investors must meet specific legal definitions of qualified investors, a classification that typically includes public pension funds, endowments, insurance companies, and high-net-worth individuals meeting minimum asset thresholds.⁵ These requirements exist to ensure that LP investors possess sufficient financial sophistication and resources to understand the risks associated with illiquid, long-term private capital investments.

Quote: George Bernard Shaw - Nobel-winning playwright

"The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself." - George Bernard Shaw - Nobel-winning playwright

This iconic observation by George Bernard Shaw encapsulates his lifelong commitment to challenging societal norms and advocating for bold reform. Shaw, an Irish-born playwright, critic, and socialist, used his wit and satire to dissect class structures, morality, and human behaviour, urging society to confront uncomfortable truths rather than passively accept the status quo^1,2,3.

George Bernard Shaw: A Life of Literary and Social Defiance

Born on 26 July 1856 in Dublin, Ireland, George Bernard Shaw grew up in a Protestant middle-class family marked by financial struggles and domestic discord. His father's alcoholism and his mother's elopement with a music teacher profoundly shaped his views on marriage, class, and convention. At 20, Shaw moved to London in 1876, where he initially struggled as a novelist and journalist before finding his calling in drama^2,3.

Shaw's breakthrough came in the 1890s, influenced by Norwegian playwright Henrik Ibsen, whose realism inspired Shaw to infuse English theatre with social critique. His early collections, Plays Unpleasant (1898) and Plays Pleasant (1898), tackled exploitation, hypocrisy, and idealism. Hits like Arms and the Man (1894), Candida (1894), and Man and Superman (1903) blended comedy with Fabian socialism-a gradualist approach to reform that Shaw championed as a co-founder of the Fabian Society and the London School of Economics^1,2,4.

His masterpiece Pygmalion (1913), a sharp commentary on class and language, propelled him to global fame, later adapted into the musical My Fair Lady. Shaw penned over 60 plays, including Major Barbara (1905), The Doctor's Dilemma (1906), Caesar and Cleopatra (1898), Androcles and the Lion (1912), and Saint Joan (1923). In 1925, he received the Nobel Prize in Literature for work 'marked by both idealism and humanity, its stimulating satire often being infused with a singular poetic beauty'^1,2,6. Remarkably, he became the first person to win both a Nobel and an Oscar, the latter in 1939 for the Pygmalion screenplay-though he scorned the award as an 'insult' from Hollywood^1,5.

Shaw declined numerous honours, including a knighthood and a parliamentary seat, and donated his Nobel prize money to translate August Strindberg's works. A vegetarian, spelling reformer, and eugenics advocate (controversial by modern standards), he lived to 94, dying on 2 November 1950 in Hertfordshire, England^1,2,3.

The Quote's Origins and Context

Shaw's maxim appears in the preface to his 1903 play Man and Superman, a philosophical comedy exploring human evolution, will, and the 'Life Force'-Shaw's concept of creative energy driving progress. It contrasts the 'reasonable' conformist with the 'unreasonable' innovator who reshapes reality. Shaw elaborated: 'Therefore, all progress depends on the unreasonable man,' positioning unreasonableness as essential for advancement².

In the Edwardian era of rigid hierarchies, Shaw used this to champion socialism, women's rights, and anti-war sentiments. Written amid his rising fame, it reflects his Fabian belief in persistent, intellectual agitation over passive adaptation-a theme echoed in plays like Major Barbara, where moral compromise clashes with principled action^1,2.

Leading Theorists on Reason, Adaptation, and Progress

Shaw's idea draws from and influences key thinkers on human agency and societal change:

• Henrik Ibsen (1828-1906): Shaw's primary influence, Ibsen's realist dramas like A Doll's House (1879) challenged norms, portraying individuals adapting-or rebelling against-society's constraints, much like Shaw's unreasonable reformer².
• Friedrich Nietzsche (1844-1900): The philosopher's Thus Spoke Zarathustra (1883-1885) celebrates the 'overman' who transcends conventional morality, paralleling Shaw's praise for those who impose their will on the world².
• Karl Marx (1818-1883) and Fabian Socialists: Shaw, a Fabian, adapted Marx's class struggle into gradual reform. Thinkers like Sidney Webb (co-founder of the Fabian Society) advocated persistent intellectual pressure to evolve society, embodying the 'unreasonable' persistence^1,4.
• 20th-Century Echoes: George Orwell cited Shaw's quote approvingly, while modern innovators like Steve Jobs echoed it: 'The people who are crazy enough to think they can change the world are the ones who do.' It underpins theories in psychology (e.g., cognitive dissonance) and innovation studies, where disruptors defy norms².

Shaw's words remain a rallying cry for leaders, entrepreneurs, and reformers, reminding us that true progress demands the courage to be unreasonable.

Term: Carried interest (carry)

"Carry, short for carried interest, is the share of a fund's investment profits allocated to the general partner (GP) as performance compensation." - Carried interest (carry)

Carried interest, commonly abbreviated as "carry," represents a share of the profits earned from a fund's investments that is allocated to the general partner (GP) as performance compensation^1,6. This mechanism is fundamental to the structure of alternative investment funds, including private equity, venture capital, and hedge funds, serving as a primary incentive tool to align the interests of fund managers with those of their investors.

Core Structure and Function

In a typical private investment fund structure, a general partner (GP) raises capital from limited partners (LPs), who provide the investment capital⁴. The GP manages the fund, makes investment decisions, and oversees the portfolio companies. Rather than being compensated solely through management fees, the GP receives carried interest as a performance-based reward¹. This arrangement ensures that the GP has "skin in the game"-a direct financial stake in maximising returns for all investors¹.

Carried interest is only paid once the fund has returned investors' capital and surpassed a minimum hurdle rate of return, which is typically between 8% and 10%^1,3. This structure protects limited partners by ensuring that managers do not profit until investors have achieved their target returns. The specific terms governing carried interest allocation, including the hurdle rate and distribution waterfall (the order in which proceeds are distributed), are detailed in the fund's investment agreement¹.

Calculation and Typical Allocations

Carried interest is calculated as a percentage of the fund's total profits above the hurdle rate. The formula is straightforward:

Carried Interest = Total Fund Profits × Performance Fee Percentage

For example, if a fund invests £100 million, achieves a final value of £140 million (exceeding an 8% hurdle rate), and the GP receives 20% of profits, the carried interest would be calculated as follows¹:

• Total fund profits = £140 million ? £100 million = £40 million
• Carried interest = £40 million × 20% = £8 million
• Remaining profits to LPs = £32 million

In private equity, the standard carried interest allocation is typically 20% of profits to the GP and 80% to the LPs². However, this varies depending on fund type, market conditions, and investor demand. Some prominent firms, such as Bain Capital and Providence Equity Partners, command "super carry" arrangements exceeding 20%⁶. Venture capital and hedge funds may have different structures, with venture capital funds often following similar 20% allocations⁸.

Relationship to Compensation and Wealth Generation

Carried interest serves as a primary source of long-term wealth generation for fund managers, distinct from their annual management fees (typically 2% of assets under management)⁴. The performance fee structure creates powerful incentives for GPs to identify high-quality investment opportunities, actively manage portfolio companies, and execute profitable exits. This alignment of interests is widely accepted by fund investors as assurance that GP objectives match their own¹.

In real estate development, carried interest is also known as a "promoted interest" or "promote." It compensates the developer (GP) for substantial risks undertaken during development and the period prior to property sale, whilst aligning the developer's interests with those of equity investors⁷.

Tax Treatment

Carried interest has traditionally received favourable tax treatment. In the United States, it is typically taxed as long-term capital gains rather than ordinary income, provided the fund holds assets for more than three years². This preferential treatment has made carried interest a subject of ongoing tax policy debate, with critics referring to it as the "carried interest loophole" or "Wall Street's favourite tax break"⁶. The Tax Cuts and Jobs Act of 2017 extended the holding period requirement from one year to more than three years for long-term capital gains treatment, though most private equity funds hold assets for five years or longer, limiting the practical impact of this change².

Key Theorist: Jensen and Meckling on Agency Alignment

Michael C. Jensen and William H. Meckling provided foundational theoretical work on the agency problem and incentive alignment that underpins the carried interest model. Their seminal 1976 paper, "Theory of the Firm: Managerial Behaviour, Agency Costs and Ownership Structure," established the conceptual framework for understanding how performance-based compensation structures can mitigate conflicts between managers and investors.

Michael Jensen (1939-2019) was the Harvard Business School professor and leading organisational economist who spent much of his career examining how compensation structures influence managerial behaviour. Born in Rochester, New York, Jensen earned his PhD in economics from the University of Chicago and became renowned for his rigorous empirical and theoretical work on corporate governance. His research demonstrated that when managers have a direct financial stake in firm performance-what he termed "skin in the game"-they are incentivised to make decisions that maximise shareholder value rather than pursuing self-interested objectives¹.

Jensen's work was particularly influential in legitimising the private equity model during the 1980s and 1990s. He argued that the combination of management fees (to cover operational costs) and carried interest (to reward performance) created an optimal incentive structure. This framework became the intellectual foundation for the explosive growth of private equity and venture capital industries. Jensen's research on leveraged buyouts and the role of debt in disciplining management further supported the theoretical case for carried interest as a mechanism to align interests in alternative investment structures.

William H. Meckling (1927-1998) was Jensen's collaborator and a professor at the University of Rochester. Together, they developed agency theory-the economic framework explaining how principals (investors) can structure contracts with agents (managers) to minimise agency costs. Their work demonstrated mathematically that performance-based compensation reduces the divergence between managerial and investor interests. Meckling's contributions emphasised the importance of monitoring and incentive alignment, principles that directly informed the design of carried interest arrangements in modern investment funds.

The Jensen-Meckling framework remains the dominant theoretical justification for carried interest. Their insight that managers with equity-like stakes in performance outcomes will behave differently than salaried employees has proven remarkably durable, shaping not only private equity and venture capital but also executive compensation practices across corporate America. Their work established that carried interest is not merely a compensation mechanism but a structural solution to a fundamental economic problem: ensuring that those making investment decisions bear the consequences of their choices.

Quote: Anthropic

"We're developing a general purpose model with meaningful advances in reasoning, coding, and cybersecurity. Given the strength of its capabilities, we're being deliberate about how we release it." - Anthropic

Developing AI models with substantial improvements in reasoning, coding, and cybersecurity demands cautious deployment strategies, particularly when capabilities reach a level warranting restricted access. Anthropic's internal testing of Mythos, described as a 'step change' in performance, emerged from an accidental data leak that forced public acknowledgment of its existence¹. This model represents a pivot toward general-purpose systems capable of handling complex, multi-domain tasks, raising immediate concerns over misuse in sensitive areas like cyber operations. The deliberate release approach stems from the model's potency, where unchecked distribution could amplify risks in an era of intensifying U.S.-China technological competition.

Reasoning advances enable Mythos to tackle abstract problem-solving beyond narrow applications, while coding enhancements support autonomous software generation, potentially accelerating development cycles. Cybersecurity capabilities introduce dual-use potential: defensive tools for threat detection contrast with offensive exploits that adversaries could weaponize. In a landscape where AI underpins national security, such features compel developers to prioritize containment over rapid commercialization¹. This tension mirrors broader industry shifts, where capability scaling outpaces governance frameworks.

Factual Context of Mythos Development and Leak

Anthropic's progression to Mythos builds on prior models like Claude, incorporating scaled training on vast datasets and optimized architectures for efficiency. The data leak, occurring prior to March 26, 2026, inadvertently exposed testing benchmarks and internal communications, confirming Mythos as a successor with benchmark scores surpassing contemporaries in targeted domains¹. Anthropic confirmed ongoing evaluations, emphasizing internal safeguards before any external rollout. This incident underscores vulnerabilities in AI lab operations, where proprietary advancements risk premature exposure amid high-stakes competition.

The model's general-purpose design aims at versatility, integrating multimodal inputs for real-world applicability. Testing protocols reportedly include red-teaming for adversarial robustness, particularly in cybersecurity scenarios where AI could simulate attacks or defenses. Such rigor reflects lessons from earlier deployments, where unintended behaviors emerged post-release. The leak prompted Anthropic to balance transparency with security, issuing statements that affirm capability strength without detailing metrics¹.

U.S.-China AI Race as Release Constraint

U.S. export controls on advanced semiconductors and AI technologies form a critical backdrop, limiting China's access to hardware essential for training frontier models like Mythos. Since 2022, Biden-era restrictions expanded to encompass chipmaking equipment and outward investments in Chinese AI firms, aiming to preserve American primacy^5,1. These measures, intensified under subsequent administrations, target AI's military applications, including surveillance and autonomous weapons-precisely the domains where Mythos's cybersecurity prowess could prove decisive⁵.

Vinod Khosla, a prominent venture capitalist, characterized the dynamic as a 'techno-economic war,' asserting that AI leadership equates to global economic dominance¹. Controls have spurred Chinese self-reliance, with firms like Huawei engineering Nvidia alternatives and Cambricon achieving 4,300% revenue surges by filling voids left by banned U.S. chips^15,9. Despite this, U.S. allies like the Netherlands and Japan have aligned on lithography restrictions, hindering China's advanced chip production⁵. Anthropic's deliberate stance on Mythos aligns with this national security imperative, avoiding contributions to adversarial capabilities.

Technological Tensions and Capability Risks

Mythos's 'meaningful advances' signal a step toward artificial general intelligence (AGI) precursors, where integrated reasoning and coding enable emergent behaviors like novel algorithm invention. Cybersecurity integration heightens stakes: AI-driven vulnerability discovery could democratize hacking tools, eroding digital defenses globally. Deliberate release mitigates proliferation risks, potentially involving tiered access-limited APIs for vetted users, full weights withheld indefinitely.

This approach contrasts with open-source trends, where models like Llama diffuse rapidly but invite misuse. Anthropic's 'responsible scaling' philosophy prioritizes evaluation gates before progression, informed by constitutional AI techniques that embed safety directly in training¹. Yet, tensions arise from competitive pressures: delayed releases cede market share to less cautious rivals, complicating talent retention and funding in a capital-intensive field.

Debates and Objections to Cautious Rollouts

Critics argue that deliberate releases stifle innovation, echoing debates over export controls that U.S. firms like Nvidia decry as self-sabotaging. Nvidia's CEO lobbied for Blackwell chip sales to China, warning restrictions erode competitiveness⁷. Similarly, open advocates contend restricted models hinder collective safety research, as broad scrutiny uncovers flaws faster. Objections highlight 'involution' in China, where intense competition drives AI despite sanctions, potentially yielding unpredictable breakthroughs².

Proponents counter that openness amplifies existential risks, citing AI's role in hypothetical bioweapons design or cyber pandemics. U.S. policy frames semiconductors as vital for AI training-OpenAI's ChatGPT required 10,000 Nvidia GPUs-underscoring why controls kneecap rivals⁵. Debates intensify over talent flows: sanctions deter U.S.-China collaboration, fostering parallel ecosystems¹¹. Anthropic navigates this by focusing domestic deployment, though leaks risk reverse-engineering by state actors.

Strategic Implications for AI Governance

Mythos exemplifies a paradigm where capability thresholds trigger governance interventions, influencing global norms. U.S. bans on investments in Chinese AI accelerate decoupling, redirecting capital to allies like Southeast Asia⁴. China counters with 1 trillion yuan ($138 billion) funds for AI and quantum tech, betting on state-orchestrated leaps⁶. This bifurcation fragments progress: Western labs like Anthropic emphasize alignment, while Chinese efforts prioritize scale.

Deliberate release strategies could standardize via international accords, akin to nuclear non-proliferation. However, enforcement challenges persist, as smuggling and domestic innovation erode barriers⁹. For Anthropic, Mythos positions it as a safety leader, attracting partnerships amid investor scrutiny over risks.

Geopolitical Ramifications and Economic Stakes

The AI race extends to critical minerals and legacy chips, where China's processing monopoly fuels U.S. diversification^13,3. Trump's tariff escalations and investment pacts, like majority stakes in rare earth miners, aim to counter dumping¹⁰. Southeast Asia emerges as a neutral hub, hosting relocated supply chains^4,14. Whoever dominates AI reshapes influence in Global South markets¹.

Mythos's cybersecurity edge could fortify U.S. defenses, from election integrity to infrastructure protection. Yet, if emulated abroad, it equalizes threats. Economically, controls paradoxically boost Chinese incumbents like Cambricon, which now outperform downgraded Nvidia offerings¹⁵. Long-term, competition may yield global benefits through diversified innovation clusters¹².

Why Mythos's Approach Matters for the Future

Cautious deployment of high-capability models like Mythos sets precedents for managing AGI trajectories, where cybersecurity and reasoning converge on societal vulnerabilities. In a multipolar tech order, it underscores U.S. strategy: leverage leads via restrictions while fostering domestic excellence⁶. Failures in deliberation could precipitate arms races; successes might enable cooperative safeguards.

Ultimately, this model tests whether private labs can self-regulate amid geopolitical frenzy. As China invests massively despite headwinds², the race demands vigilance. Mythos's path illuminates the high-wire act of progress: harnessing power without unleashing peril¹.

References

1. Anthropic acknowledges testing new AI model representing ‘step change’ in capabilities, after accidental data leak reveals its existence - https://fortune.com/2026/03/26/anthropic-says-testing-mythos-powerful-new-ai-model-after-data-leak-reveals-its-existence-step-change-in-capabilities/

2. Vinod Khosla agrees with Trump on AI and China: 'We are ... - Fortune - 2026-03-06 - https://fortune.com/2026/03/06/vinod-khosla-china-techno-economic-war-ai-semiconductors/

3. What global executives need to ask about China in 2026 - Fortune - 2026-01-11 - https://fortune.com/2026/01/11/what-global-executives-need-to-ask-about-china-in-2026/

4. U.S. launches Chinese legacy chip investigation | Fortune - 2024-12-23 - https://fortune.com/asia/2024/12/23/us-launches-investigation-chinese-chips/

5. Companies and countries can stay nimble even as they ... - Fortune - 2025-11-17 - https://fortune.com/2025/11/17/companies-geopolitics-us-china-tensions-malaysia-southeast-asia/

6. America, China's $574 billion chip war with Biden scoring success - 2023-09-03 - https://fortune.com/2023/09/03/america-china-chip-war-whos-winning-raimondo-biden-semiconductors-economy/

7. The 'competition going on for supremacy' between China ... - Fortune - 2025-03-29 - https://fortune.com/2025/03/29/china-united-states-competition-trump-xi-jinping-tech-ai-deepseek-alibaba-tiktok-bytedance/

8. Nvidia chief still hopes to sell Blackwell chips to China - Fortune - 2025-11-01 - https://fortune.com/2025/11/01/nvidia-ceo-jensen-huang-blackwell-ai-chips-china-us-export-controls-trump-xi/

9. 'The Chinese have invaded us in terms of merchandise': Mexico and ... - 2026-02-02 - https://fortune.com/2026/02/02/chinese-imports-latin-america-mexico-argentina/

10. China does not need Nvidia chips in the AI war — export controls ... - 2025-12-03 - https://fortune.com/2025/12/03/china-trade-war-chips-nvidia-flawed-logic-gpus-ai/

11. In race to end China's chokehold on critical minerals, the U.S. needs ... - 2025-12-09 - https://fortune.com/2025/12/09/critical-minerals-us-china-supply-chain/

12. The last American venture capitalist in Beijing: Here are the strategic ... - 2022-11-01 - https://fortune.com/2022/11/01/last-american-venture-capital-beijing-heres-strategic-miscalculation-america-technology-competition-china/

13. How U.S.-China competition is benefiting the world—and reshaping ... - 2024-07-02 - https://fortune.com/2024/07/02/us-china-competition-benefiting-worldand-global-economy-supply-chains-politics-leadership/

14. Beijing's dominance in rare earth processing leaves others ... - Fortune - 2026-03-11 - https://fortune.com/2026/03/11/china-us-rare-earth-processing-critical-minerals/

15. Trump may have skipped APEC—but Xi's using it to sell China as ... - 2025-10-31 - https://fortune.com/2025/10/31/trump-skipped-apec-south-korea-xi-jinping-bessent-sou/

16. Nvidia's China-based rival posts 4,300% revenue jump as ... - Fortune - 2025-08-28 - https://fortune.com/2025/08/28/trump-trade-restrictions-earnings-tech-chipmakers-china-cambricon-4300-percent-revenue-surge-nvidia-h20-export-ban-ai-competition-semiconductor-industry/

Quote: Thomas H Davenport

“The businesses and organizations that succeed with AI will be those that invest steadily, rise above the hype, make a good match between their business problems and the capabilities of AI, and take the long view.” - Thomas H Davenport - Babson College Professor

Thomas H. Davenport, President's Distinguished Professor of Information Technology and Management at Babson College, is a leading expert on analytics, AI, and their business applications. His quote underscores a pragmatic approach to AI adoption: prioritizing steady investment, realistic assessments over hype, alignment of AI capabilities with specific business challenges, and a focus on long-term value creation.^1,6,7

Backstory on Thomas H. Davenport

Davenport has shaped the discourse on data-driven decision-making and AI for decades. Born in 1954, he earned his PhD from Harvard Business School and began his career as a visiting professor there before holding faculty positions at the University of Texas at Austin and Boston University. In 2017, he joined Babson College, a leading business school focused on entrepreneurship, as President's Distinguished Professor, where he directs the Digital Innovation and Transformation Initiative.^6,7

A prolific author of over a dozen books, Davenport popularized concepts like business process reengineering in Process Innovation (1993) and analytics in Competing on Analytics (2006). His seminal work on AI, The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (2018), directly informs the quoted insight. In it, he advises companies to conduct domain assessments (identifying high-impact business areas like knowledge bottlenecks or scaling issues) and use case assessments (evaluating AI for substantial value), while building prioritized portfolios of pilots matched to processes—echoing the quote's emphasis on matching problems to AI strengths.^1,4

Davenport's research, including MIT Sloan Management Review contributions and webinars like "Critical Success Factors for Achieving ROI from AI Initiatives" (2021, with Laks Srinivasan), highlights the "three D’s" (decisions) and "three C’s" (catalysts) for AI success, stressing culture over technology and appropriate ambition levels. He warns against hype-driven failures, noting nine barriers to AI-driven business model change, such as immature technologies, partial solutions, and integration challenges.^1,2,5 Recent work explores generative AI for knowledge management, advocating proprietary data training to boost innovation, productivity, and skills like effective prompting.³

Through executive teaching, consulting, and roles at firms like Accenture, Davenport has influenced Fortune 500 leaders, emphasizing workforce upskilling (e.g., machine literacy, emotional intelligence) and process redesign for scaling AI beyond proofs-of-concept.^1,3

Context of the Quote

The quote emerges from Davenport's core thesis in The AI Advantage and related research: AI thrives not through flashy overhauls but via disciplined, incremental strategies. He categorizes AI tasks—automation of repetitive processes (RPA), insights from data (machine learning), and engagement (NLP/chatbots)—urging firms to "build on current strengths in big data and analytics," pilot projects, and redesign work using design-thinking.^1,4

This advice counters AI hype by addressing real-world hurdles: poor data quality blocks efficiency (e.g., BMO Bank's data cleanup before AI rollout); scaling pilots to enterprise requires productivity gains via growth, not just cuts; and strategies vary by focus (cost-oriented internal projects vs. revenue-oriented customer enhancements).¹ Davenport profiles successes like banks optimizing processes for better customer experiences and retailers like Mercadona assigning humans to non-machine tasks.^1,4 His framework promotes the "long view," preparing for jobs evolution via skills like AI familiarity and communication.¹

Leading Theorists Related to AI Business Strategy

Davenport's views build on and parallel foundational thinkers in AI, analytics, and organizational transformation:

• Michael Porter (Harvard Business School): Pioneered competitive strategy in Competitive Advantage (1985), influencing Davenport's emphasis on aligning AI with business models (e.g., cost vs. differentiation). Porter's value chain analysis underpins domain assessments for AI value.¹

• Clayton Christensen (Harvard Business School): The Innovator's Dilemma (1997) explains disruptive innovation; Davenport applies this to AI startups vs. incumbents, noting barriers like "big companies buy startups" and installed bases delaying change.¹

• Erik Brynjolfsson (Stanford Digital Economy Lab, ex-MIT): Co-author of The Second Machine Age (2014), Brynjolfsson stresses complementary investments (skills, processes) for AI productivity—a "long view" echo in Davenport's work redesign and upskilling advice.¹

• Andrew McAfee (MIT): Brynjolfsson's collaborator, focuses on AI's economic impacts in Machine, Platform, Crowd (2017). His views on automation's job effects align with Davenport's "step in/up/aside" job framework and skills for human-AI collaboration.¹

• Randy Bean (NewVantage Partners): Chief Data Officer strategist; co-authored with Davenport on data-driven cultures, highlighting AI's role in data management as key to ROI amid barriers like siloed data.⁵

These theorists collectively advocate measured AI integration, prioritizing organizational readiness over technology alone—core to Davenport's quoted wisdom.^1,2,5

Term: Dry powder

"Dry powder refers to committed but uninvested capital that a private equity fund has available to deploy into new investments or follow-on capital." - Dry powder

Dry powder represents committed but uninvested capital that private equity firms maintain in reserve, ready to deploy into new investments or provide follow-on funding to existing portfolio companies. This capital has been pledged by limited partners (LPs) to the fund but remains unallocated and undrawn, sitting on the sidelines awaiting suitable investment opportunities.

Definition and Core Concept

In private equity, dry powder is distinct from committed capital. Committed capital represents the total amount an LP has agreed to provide to a fund over its lifetime, whilst dry powder is the portion of that committed capital that remains uncalled and undeployed. When a general partner (GP) identifies an investment opportunity, they issue a capital call to LPs, requesting a fraction of their total commitment. This drawn-down capital is then invested, reducing the fund's dry powder balance.

The term itself derives from military history dating to the 1600s, when soldiers in warring armies maintained stashes of dry gunpowder to ensure their ammunition remained functional and ready for deployment. In modern financial parlance, this metaphor translates to a stockpile of investment capital held in reserve for tactical deployment.

Strategic Importance and Applications

Dry powder serves multiple critical functions within private equity operations:

• Competitive advantage in deal-making: Substantial dry powder reserves allow PE firms to make attractive offers and outbid competitors, securing desirable investment opportunities whilst providing sellers with higher certainty of deal closure.
• Portfolio growth and support: Firms deploy dry powder to provide follow-on funding to promising portfolio companies during critical growth phases, enabling expansion initiatives and operational scaling without delay.
• Opportunistic investing during downturns: During economic downturns or periods of market volatility, PE firms can use dry powder to acquire undervalued assets or distressed companies at favourable prices, positioning themselves for substantial returns when markets recover.
• Financial flexibility: Dry powder provides the liquidity to respond quickly to unexpected opportunities or challenges, ensuring firms can capitalise on favourable conditions or mitigate risks effectively.

Sources and Accumulation

Dry powder originates from multiple sources. PE firms raise funds from institutional investors-including pension funds, endowments, insurance companies, and high-net-worth individuals-who commit capital to the fund. Additionally, profits from successful exits of previous investments can be reinvested, contributing to the firm's dry powder reserves. Some PE firms also maintain credit facilities with financial institutions, providing an additional source of capital that can be quickly accessed when needed.

Market Indicator and Confidence Signal

Dry powder levels serve as a barometer of future investment activity and investor confidence in the market. High levels of dry powder indicate that investors have confidence in the ability of PE firms to find and make profitable investments, even in uncertain economic environments. It may also signal a competitive market where many investors are actively seeking to deploy capital into promising companies. Conversely, lower dry powder levels may reflect market caution or successful deployment of previously accumulated reserves.

The LP-GP Framework

The relationship between limited partners and general partners is governed by the limited partnership agreement (LPA), which specifies the terms under which capital can be called and invested during the fund's investment period-typically the first 3-5 years of a fund's life. This contractual framework ensures transparency and alignment of interests between investors and fund managers regarding the timing and deployment of dry powder.

Related Strategist: David Rubenstein

David Rubenstein stands as one of the most influential figures in shaping modern private equity strategy and the conceptualisation of capital deployment efficiency, directly influencing how dry powder is understood and utilised across the industry.

Born in 1949 in Baltimore, Maryland, Rubenstein earned his undergraduate degree from Duke University and his law degree from the University of Chicago. His early career included roles in the Carter administration as Deputy Assistant to the President for Domestic Policy, where he gained invaluable experience in complex financial and policy negotiations. This background proved instrumental when, in 1987, he co-founded The Carlyle Group alongside William E. Conway Jr. and Daniel A. D'Aniello.

Rubenstein's relationship with dry powder strategy emerged from his pioneering work at Carlyle, where he developed sophisticated approaches to capital management and deployment timing. Rather than deploying capital reactively, Rubenstein championed a disciplined, opportunistic approach that emphasised maintaining strategic reserves to capitalise on market dislocations. This philosophy became foundational to Carlyle's success and influenced broader industry practice.

Under Rubenstein's leadership, Carlyle grew from a regional firm into a global powerhouse managing hundreds of billions in assets. His emphasis on maintaining dry powder reserves-rather than deploying capital immediately upon fundraising-allowed Carlyle to weather market downturns and acquire distressed assets at attractive valuations. This approach proved particularly valuable during the 2008 financial crisis, when firms with substantial dry powder could deploy capital opportunistically whilst competitors faced constraints.

Beyond Carlyle, Rubenstein has shaped industry discourse through his role as co-chairman and later executive chairman, and through extensive public engagement. He has articulated the strategic rationale for maintaining dry powder as both a competitive advantage and a fiduciary responsibility to investors. His perspective emphasises that disciplined capital deployment-waiting for the right opportunities rather than deploying capital for deployment's sake-generates superior returns.

Rubenstein's influence extends to his advocacy for transparency in private equity operations and his recognition that dry powder levels reflect not merely market conditions but also the quality of a firm's investment thesis and deal sourcing capabilities. His work has elevated dry powder from a mere accounting concept to a central strategic consideration in private equity fund management, demonstrating that capital discipline and opportunistic deployment represent core competitive advantages in the industry.

Quote: Max Planck - Nobel laureate

"A new scientific truth does not generally triumph by persuading its opponents and getting them to admit their errors, but rather by its opponents gradually dying out and giving way to a new generation that is raised on it." - Max Planck - Nobel laureate

Max Planck's famous statement captures a fundamental truth about the nature of scientific advancement: paradigms shift not through debate alone, but through the inexorable passage of time and generational change. This observation, drawn from his personal experiences, has become known as Planck's Principle and resonates deeply in the philosophy of science^1,2.

The Man Behind the Words: Max Planck's Life and Legacy

Born in 1858 in Kiel, Germany, Max Karl Ernst Ludwig Planck was a pioneering theoretical physicist who fundamentally transformed our understanding of the physical world. Educated at the universities of Munich and Berlin, he initially pursued classical thermodynamics before making his revolutionary breakthrough. In 1900, Planck introduced the concept of energy quanta to resolve discrepancies in black-body radiation, laying the foundation for quantum theory-a radical departure from classical physics that earned him the Nobel Prize in Physics in 1918^1,2.

Planck's career was marked by profound challenges. His quantum hypothesis faced fierce opposition from established scientists who clung to classical theories. Despite providing rigorous theoretical proofs, Planck struggled to gain widespread acceptance, a frustration he later reflected upon candidly. He served as president of the Kaiser Wilhelm Society (predecessor to the Max Planck Society) from 1926 to 1937 and navigated the moral complexities of Nazi Germany, including the loss of his son to execution on false treason charges. Planck died in 1947, leaving an indelible mark on modern physics^1,3.

The Context and Origin of the Quote

The quote originates from Planck's Scientific Autobiography, published posthumously in German in 1948 and translated into English in 1949. Writing in his later years, Planck recounted the 'painful experiences' of promoting his quantum ideas: 'It is one of the most painful experiences of my entire scientific life that I have but seldom… succeeded in gaining universal recognition for a new result, the truth of which I could demonstrate by a conclusive, albeit only theoretical proof.' He then articulated the principle as a 'remarkable fact'^1,3.

A slightly longer version appears on pages 33 and 97: 'An important scientific innovation rarely makes its way by gradually winning over and converting its opponents: it rarely happens that Saul becomes Paul. What does happen is that its opponents gradually die out, and that the growing generation is familiarised with the ideas from the beginning.' This reflects his view of science as an evolutionary process governed by human biology-death and renewal-rather than mere persuasion².

Though cited in Advances in Biochemical Psychopharmacology (1980), the quote's primary source is Planck's autobiography. It has been paraphrased colloquially as 'Science progresses one funeral at a time,' a concise version popularised by economist Paul Samuelson in the 1960s, who credited Planck while introducing the vivid phrasing³.

Planck's Principle in the Philosophy of Science

Scholars have interpreted the statement in multiple ways. In sociology of scientific knowledge, it underscores that change occurs via generational turnover, not individual conversions². Some see it as highlighting age-related stubbornness in science, contrasting with Karl Popper's emphasis on falsifiability. Others view it as a truism about time's role in validating enduring truths, as new ideas persist while flawed ones fade¹.

A 2023 study empirically supported Planck, finding that citations of new theories increase significantly after the deaths of prominent opponents, confirming science advances 'one funeral at a time'⁵.

Leading Theorists on Scientific Change

• Thomas S. Kuhn (1922-1996): In his seminal 1962 book The Structure of Scientific Revolutions, Kuhn cited Planck directly, popularising the idea of paradigm shifts-periods of 'normal science' punctuated by revolutions where old frameworks resist until supplanted. Kuhn argued that scientists cling to paradigms until anomalies force change, aligning with Planck's generational mechanism³.
• Karl Popper (1902-1994): Popper's philosophy of falsifiability emphasised testable predictions and bold conjectures, contrasting Planck's view by focusing on rational critique over demographic inevitability. Yet both highlight resistance to novelty¹.
• Paul A. Samuelson (1915-2009): The Nobel-winning economist adapted Planck's idea to economics, noting in his textbook that new doctrines prevail 'funeral by funeral,' influencing broader discussions on intellectual progress³.

Planck's words remind us that innovation in science, and indeed all fields of knowledge, demands patience. True progress endures beyond lifetimes, outlasting opposition through education and time.