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PM edition. Issue number 1272

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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.

Term: Platform strategy

"Platform strategy refers to the acquisition of a core ("platform") company that serves as the foundation for subsequent bolt-on acquisitions, with the objective of creating value through scale, scope, and capability enhancement." - Platform strategy

A platform strategy is a structured acquisition approach where a private equity firm purchases a foundational company and subsequently acquires complementary businesses to create value through scale, operational synergies, and capability enhancement.

Core Definition and Mechanics

The platform strategy operates on a "buy and build" model. A private equity group identifies and acquires an initial platform company-typically a well-established business with EBITDA above £5-10 million, professional systems, experienced management, and significant market presence. This platform then serves as the anchor for subsequent acquisitions of smaller, related businesses known as "add-ons" or "bolt-ons." Over a typical investment period of 3-7 years, the platform consolidates multiple acquisitions, increasing in value before being sold to exit investors at a substantially higher valuation.

Strategic Rationale

Private equity firms favour platform strategies because they unlock rapid value creation, particularly in fragmented markets where no single dominant player exists. By consolidating smaller businesses under a strong platform, investors capture market share, drive operational improvements, and realise scale efficiencies that would be difficult for individual companies to achieve independently.

The approach also provides strategic entry points into new industries or geographies. For example, a PE firm might acquire a well-established regional healthcare provider, then use it as a base to expand across neighbouring markets through targeted acquisitions that fulfil specific strategic needs.

Value Creation Mechanisms

Platform strategies generate value through multiple channels:

• Shared Infrastructure: Consolidating functions such as HR, IT, legal, and finance across portfolio companies eliminates redundancies and reduces costs.
• Bulk Purchasing Power: Centralised vendor negotiations and bulk purchasing of software, materials, and services reduce per-unit costs significantly.
• Standardised Technology: A unified technology stack improves data visibility and operational efficiency across all portfolio companies.
• Cross-Company Learning: Insights and best practices from one company directly benefit others, accelerating growth across the portfolio.
• Operational Playbooks: Standardised business processes and procedures reduce trial-and-error inefficiencies and enable faster integration of add-ons.

Unlike traditional private equity scaling methods that rely on quick operational fixes or aggressive cost-cutting, platform strategies emphasise sustainable, long-term value creation. Companies operating under a PE platform strategy grow faster and exit at higher valuations because they are structured for enduring success rather than short-term gains.

Platform Company Characteristics

Successful platform companies typically possess:

• A strong, experienced management team with proven track records in the target industry
• Well-defined operational systems and repeatable processes
• Sufficient scale and capitalisation to support add-on acquisitions
• Positive cash flows and demonstrated growth potential
• Leadership capable of integrating new businesses effectively

Add-On Acquisition Strategy

Add-ons are selected strategically to fulfil specific operational or market needs. For instance, if the platform is a medical services company, a PE firm might acquire a manufacturer of medical equipment parts to eliminate external purchasing costs and create opportunities for further expansion. This strategic fit approach reduces risk and accelerates value creation compared to opportunistic acquisitions.

Value Realisation

Sustainable gains in platform private equity come from balancing organic initiatives-such as process improvement and leadership development-with inorganic expansion through targeted acquisitions. Key performance indicators including revenue growth, EBITDA improvement, and integration milestones measure progress. Case studies demonstrate that platforms executing thoughtful bolt-on strategies often double their enterprise value within several years.

Related Strategy Theorist: Henry Kravis

Biography and Contribution

Henry Roberts Kravis (born 1944) is an American financier and co-founder of Kohlberg Kravis Roberts & Co. (KKR), one of the world's most influential private equity firms. Born in Tulsa, Oklahoma, Kravis studied economics at Claremont McKenna College before earning an MBA from Columbia Business School. His career in finance began at Bear Stearns, where he worked under Jerome Kohlberg Jr., a pioneering figure in leveraged buyouts.

In 1976, Kravis and his cousin George Roberts founded KKR with Kohlberg, establishing what would become a transformative force in private equity. Throughout the 1980s and 1990s, KKR pioneered aggressive acquisition strategies, most famously the £24 billion leveraged buyout of RJR Nabisco in 1989-the largest LBO of its era. This transaction, detailed in the book "Barbarians at the Gate," exemplified the bold, transformative approach that defined Kravis's investment philosophy.

Relationship to Platform Strategy

Whilst Kravis is primarily known for pioneering leveraged buyouts and aggressive financial engineering, his strategic vision fundamentally shaped the evolution toward platform-based acquisition strategies. KKR's approach to portfolio management-building operational capabilities, integrating acquired companies, and creating synergies across holdings-established foundational principles that underpin modern platform strategies.

Kravis recognised early that sustainable value creation required more than financial leverage; it demanded operational excellence, strategic consolidation, and the ability to integrate disparate businesses into cohesive, high-performing entities. This philosophy directly influenced how contemporary private equity firms structure platform investments. By emphasising management quality, operational integration, and long-term value creation over pure financial arbitrage, Kravis's legacy shaped the transition from purely financial engineering to the strategic, operationally-focused platform strategies that dominate private equity today.

Under Kravis's leadership, KKR evolved from a leveraged buyout specialist into a diversified investment firm managing over £500 billion in assets globally. His emphasis on building world-class management teams and creating operational synergies across portfolio companies established the template that modern platform strategies follow. Today, platform strategies represent the maturation of principles Kravis championed: that private equity value creation stems from strategic consolidation, operational improvement, and the systematic integration of complementary businesses-not merely from financial leverage alone.

Quote: Albert Einstein - Nobel Laureate

"A man to whom it has been given to bless the world with a great creative idea has no need for the praise of posterity. His very achievement has already conferred a higher boon upon him." - Albert Einstein - Nobel Laureate

In 1948, Albert Einstein penned these words as a heartfelt tribute in "Max Planck in Memoriam," honouring the German physicist whose revolutionary ideas laid the foundation for quantum mechanics. The quote encapsulates Einstein's admiration for Max Planck, whom he regarded not merely as a colleague but as a towering figure whose creative insight transformed our understanding of the universe. Delivered in the shadow of World War II and amid the post-war reconstruction of science, this reflection underscores a timeless truth: true genius finds its reward in the idea itself, transcending the need for later acclaim.

The Context of the Quote

Max Planck passed away on 4 October 1947 at the age of 89, having endured personal tragedies including the loss of his first wife, two daughters, and two sons-one executed by the Nazis for his alleged involvement in the plot to assassinate Hitler. Einstein's memorial, published in 1948, was part of a collection celebrating Planck's life and work. At this time, Einstein, himself a Nobel Laureate in 1921 for his explanation of the photoelectric effect, was in exile in the United States, reflecting on the giants who shaped modern physics. The quote emerges from Einstein's deep respect for Planck's humility and the profound impact of his 1900 discovery of energy quanta, which challenged classical physics and birthed quantum theory^1,2,5.

Max Planck: The Man and His Monumental Achievement

Born in 1858 in Kiel, Germany, Max Planck initially pursued a career in thermodynamics, influenced by the second law and the works of Rudolf Clausius. By 1900, as professor at the University of Berlin, he grappled with the "black-body radiation" problem: classical theory predicted infinite energy at high frequencies (the "ultraviolet catastrophe"), clashing with experiments. Planck resolved this by proposing that energy is emitted in discrete packets, or "quanta," introducing his constant h in the formula E = hf, where f is frequency. This act, described by Einstein as the basis of twentieth-century physics, was not immediately embraced by Planck himself, who viewed it as a mathematical fix rather than a physical reality^2,5,8.

Planck's quantum hypothesis paved the way for Einstein's 1905 paper on the photoelectric effect, where light too behaves as particles (photons), earning Einstein his Nobel. Planck championed relativity, calling Einstein the "Copernicus of the twentieth century," and defended scientific truth amid political turmoil, remaining in Germany through both world wars². His philosophy emphasised that scientific truth triumphs not by persuasion but through generational change, as opponents fade away⁶. Einstein praised Planck's perseverance in seeking nature's "pre-established harmony"⁹.

Albert Einstein: The Philosopher-Physicist

Einstein (1879-1955), born in Ulm, Germany, revolutionised physics with special relativity (1905), general relativity (1915), and contributions to quantum theory, though he later critiqued its probabilistic nature, famously debating Planck and others on reality's foundations^1,3,4. His philosophy blended intuition, simplicity, and mathematical elegance: "purely mathematical construction enables us to find those concepts... that provide the key to the understanding of natural phenomena"¹. Einstein viewed Planck as a rare "mansion" in the "temple of science," driven by pure curiosity rather than utility or fame⁷. Their correspondence and mutual respect highlight a shared belief in science as a pursuit of profound order³.

Leading Theorists and the Dawn of Quantum Theory

The quote's themes of creativity and achievement resonate with quantum pioneers:

• Niels Bohr: Developed the atomic model incorporating quanta, founding complementarity to reconcile wave-particle duality.
• Werner Heisenberg: Formulated matrix mechanics and the uncertainty principle, shifting physics to probabilistic interpretations.
• Erwin Schrödinger: Introduced wave mechanics, equivalent to Heisenberg's, leading to the unified quantum formalism.
• Ludwig Boltzmann: Precursor via statistical mechanics; his entropy work influenced Planck's quantum leap².
• Ernst Mach and Wilhelm Ostwald: Positivists Einstein credited Planck with overcoming, proving atoms' reality through Brownian motion¹.

These figures, building on Planck's foundation, reshaped reality's depiction, echoing Einstein's conviction that great ideas bestow immortality on their creators^1,2.

Enduring Relevance

Today, Planck's constant underpins technologies from lasers to semiconductors, while Einstein's vision reminds us that science's highest rewards lie in discovery itself. This tribute bridges personal loss, scientific revolution, and philosophical depth, inspiring generations to pursue ideas that bless the world.

Quote: Jensen Huang - Nvidia CEO

"I think we've just reinvented the computer." - Jensen Huang - Nvidia CEO

In a profound reflection on the evolution of computing, NVIDIA CEO Jensen Huang articulated a paradigm shift during his interview on the Lex Fridman Podcast #494, stating, "I think we've just reinvented the computer." This remark, made in the context of advanced AI systems, underscores how modern computing has transitioned from mere data retrieval to generative intelligence capable of research, tool usage, and synthetic data creation.^1,2

Context of the Quote

Huang's statement emerged from a discussion on the architecture of future AI agents. He reasoned that these systems require access to ground truth data via file systems, the ability to conduct research, and integration with input/output subsystems and tools. This holistic view reveals computing's "deeply profound" implications, marking a reinvention where AI evolves beyond passive storage into active, context-aware generation.² Delivered on 23 March 2026, amid NVIDIA's ascent to a $4 trillion valuation, the quote captures the explosive growth of the 'token economy' - where AI produces 'token goods' like generated text, images, and code, turning computers from cost centres (akin to unprofitable warehouses) into revenue-generating factories.¹

Backstory on Jensen Huang

Born in Taiwan in 1963, Jensen Huang co-founded NVIDIA in 1993 with Chris Malachowsky and Curtis Priem, initially focusing on graphics processing units (GPUs) for gaming and visualisation. Facing near-bankruptcy in the late 1990s, Huang pivoted NVIDIA towards programmable shaders and IEEE-compliant FP32 floating-point precision, enabling GPUs for general-purpose computing.² The launch of CUDA in 2006 democratised this power, placing supercomputing capabilities in researchers' hands via PCs, outpacing rivals like OpenCL due to NVIDIA's massive install base.² Under Huang's leadership, NVIDIA dominated AI hardware, powering breakthroughs from deep learning to large language models. By 2026, as CEO of the world's most valuable company, Huang envisions computing's GDP share surging 100-fold, with AI achieving artificial general intelligence (AGI) today - defined as systems autonomously building profitable applications.^1,2

Leading Theorists in AI and Computing Reinvention

John McCarthy (1927-2011): Coined 'artificial intelligence' in 1956 at the Dartmouth Conference, pioneering Lisp and time-sharing systems. His vision of machines reasoning like humans laid foundational theory for AI's shift from rule-based to generative paradigms.²

Geoffrey Hinton: 'Godfather of deep learning', Hinton's backpropagation and neural network research in the 1980s, revitalised in 2012 via AlexNet (powered by NVIDIA GPUs), enabled the scaled training underpinning today's token-generating models.¹

Yann LeCun and Yoshua Bengio: With Hinton, the 'three musketeers' of AI advanced convolutional networks and generative adversarial networks (GANs), theorising self-supervised learning that allows AI to synthesise data - echoing Huang's 'token factory'.¹

Ilya Sutskever: Co-founder of OpenAI, his work on sequence transduction and reinforcement learning from human feedback (RLHF) birthed models like GPT, which Huang sees as reinventing computing through tool-augmented agency.²

These theorists' ideas converged with NVIDIA's hardware, propelling Huang's prophecy: every profession - from carpenters to plumbers - will programme via natural language, expanding coders from 30 million to 1 billion.¹

Implications for the AI Revolution

Huang predicts AI disruption for task-based roles but empowerment for purpose-driven innovators. Power challenges will be met with 'elegant degradation' data centres utilising grid redundancies. As barriers to AI entry drop to zero - simply ask, 'How do I use you?' - the reinvention promises unprecedented productivity, with trillion-dollar companies commonplace.¹

Quote: FundaAI

"TurboQuant is not another DeepSeek moment." - FundaAI

Term: Multiple on Invested Capital (MOIC)

"Multiple on Invested Capital (MOIC) measures the total value returned from an investment relative to the total equity capital invested, expressed as a simple multiple (e.g., 2.5×). In private equity, MOIC captures the absolute value creation of an investment without regard to the time taken to achieve it." - Multiple on Invested Capital (MOIC)

The Multiple on Invested Capital (MOIC) is a financial metric that measures the total value returned from an investment relative to the total equity capital invested, expressed as a simple multiple (for example, 2.5×).¹ In private equity, MOIC captures the absolute value creation of an investment without regard to the time taken to achieve it, making it one of the most commonly used performance indicators across the industry.³

Core Definition and Calculation

MOIC answers a fundamental question: how many times has the initial capital been multiplied?³ The metric is calculated using a straightforward formula:

\text = \frac{\text}{\text}

Alternatively expressed as:

\text = \frac{\text}{\text}

The total value of investment includes all cash received from the investment-such as dividends, profits, and eventual sale proceeds-as well as unrealised gains, which represent the potential future value of the investment if sold at current market rates.⁸

Practical Examples

A MOIC value of 2.0× indicates that a private equity fund has doubled its original investment.³ If a fund invested £1 million and received £3 million from the investment, the fund would have a MOIC of 3.0×.⁵ In a more substantial scenario, if a private equity fund invests £100 million in a company and realises £500 million in total value (both realised and unrealised), the MOIC would be 5.0×, indicating a fivefold return on the initial capital.⁸

Key Characteristics and Advantages

MOIC provides several distinct advantages as a performance metric:

• Simplicity and directness: MOIC tells investors in a straightforward manner whether and by how much their original investment has grown.³
• Time-agnostic measurement: Unlike the Internal Rate of Return (IRR), MOIC does not account for the time value of money or the duration of the investment, making it useful as a quick assessment tool.¹
• Comprehensive value capture: MOIC includes both realised returns (actual cash distributions) and unrealised gains (current market value of remaining holdings), providing a complete picture of value creation.²
• Comparative analysis: The metric enables investors to compare the performance of different investments and funds on a standardised basis.³

MOIC in Context: Related Metrics

Whilst MOIC is excellent for quickly assessing investment success, it is typically calculated alongside other performance metrics to provide a more holistic understanding:³

• Internal Rate of Return (IRR): Measures returns whilst accounting for the time value of money and the duration of the investment.
• Distributions to Paid-In Capital (DPI): Represents the amount paid out by a fund to investors in relation to their investments, focusing only on realised returns.¹
• Total Value to Paid-In (TVPI): Similar to MOIC but measures total capital actually paid in over time, including follow-on investments, rather than just initial capital.⁹
• Public Market Equivalent (PME): Compares private equity returns to equivalent public market investments.³

Interpreting MOIC Performance

A higher MOIC is perceived positively because it implies that investments are profitable and have generated substantial value.⁴ Conversely, a lower MOIC is viewed negatively, as it indicates that the investment may be unprofitable and investors risk not receiving their target return or even recouping their initial capital.⁴ In private equity practice, a MOIC of 2.0× or above is generally considered a strong outcome, though expectations vary by fund strategy and market conditions.

Terminology and Variations

The term MOIC is interchangeable with several other expressions commonly used in investment circles:⁴ "Multiple on Money" (MoM) and "cash-on-cash return" are synonymous terms that describe the same metric. This terminology consistency reflects the widespread adoption of MOIC across venture capital, private equity, and hedge fund sectors.⁶

David Rubenstein and the Professionalisation of Private Equity Metrics

The systematic use of MOIC and other standardised performance metrics in private equity owes much to David Rubenstein, co-founder of The Carlyle Group, who has been instrumental in professionalising the private equity industry since the 1980s. Rubenstein recognised that private equity required transparent, comparable metrics to attract institutional capital and build credibility with limited partners.

Born in 1949, Rubenstein earned his undergraduate degree from Duke University and his law degree from the University of Chicago. After working as a lawyer and in the White House during the Carter administration, he co-founded Carlyle in 1987 with William E. Conway Jr. and Daniel A. D'Aniello. At a time when private equity was largely opaque and driven by informal relationships, Rubenstein championed the adoption of standardised reporting metrics, including MOIC, IRR, and DPI, which became industry benchmarks.

Rubenstein's advocacy for transparency and rigorous performance measurement transformed private equity from a relatively closed industry into one that could attract substantial institutional investment from pension funds, endowments, and sovereign wealth funds. His emphasis on clear, quantifiable metrics like MOIC enabled investors to compare fund performance objectively and hold managers accountable for value creation. Under his leadership, Carlyle grew to become one of the world's largest private equity firms, managing over £300 billion in assets, and his influence on industry standards remains profound. Rubenstein's belief that "you can't manage what you don't measure" became a guiding principle for the entire private equity sector, making MOIC and related metrics central to how the industry evaluates and communicates investment success.