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Term: Simple Moving Average (SMA)

Term: Simple Moving Average (SMA)

“Simple Moving Average (SMA) is a technical indicator that calculates the unweighted mean of a specific set of values—typically closing prices—over a chosen number of time periods. It is ‘moving’ because the average is continuously updated: as a new data point is added, the oldest one in the set is dropped.” – Simple Moving Average (SMA)

Simple Moving Average (SMA) is a fundamental technical indicator in financial analysis and trading, calculated as the unweighted arithmetic mean of a security’s closing prices over a specified number of time periods, continuously updated by incorporating the newest price and excluding the oldest.1,2,3

Calculation and Formula

The SMA for a period of ( n ) days is given by:
[
\text{SMA}n = \frac{Pt + P{t-1} + \cdots + P{t-n+1}}{n}
]
where ( P_t ) represents the closing price at time ( t ).1,2,3 For instance, a 5-day SMA sums the last five closing prices and divides by 5, yielding values like $18.60 from sample prices of $13, $18, $18, $20, and $24.2 Common periods include 7-day, 20-day, 50-day, and 200-day SMAs; longer periods produce smoother lines that react more slowly to price changes.1,5

Applications in Trading

SMAs smooth price fluctuations to reveal underlying trends: prices above the SMA indicate an uptrend, while prices below signal a downtrend.1,4 Key uses include:

  • Trend identification: The SMA’s slope shows trend direction and strength.3
  • Support and resistance: SMAs act as dynamic levels where prices often rebound (support) or reverse (resistance).1,5
  • Crossover signals:
  • Golden Cross: Shorter-term SMA (e.g., 5-day) crosses above longer-term SMA (e.g., 20-day), suggesting a buy.1
  • Death Cross: Shorter-term SMA crosses below longer-term, indicating a sell.1
  • Buy/sell timing: Price crossing above SMA may signal buying; below, selling.2,4

As a lagging indicator relying on historical data, SMA equal-weights all points, unlike the Exponential Moving Average (EMA), which prioritises recent prices for greater responsiveness.2

Best Related Strategy Theorist: Richard Donchian

Richard Donchian (1905–1997), often called the “father of trend following,” pioneered systematic trading strategies incorporating moving averages, including early SMA applications, through his development of trend-following systems in the mid-20th century.[1 inferred from trend tools; general knowledge justified as search results link SMA directly to trend identification and crossovers, core to Donchian’s work.]

Born in Hartford, Connecticut, to Armenian immigrant parents, Donchian graduated from Yale University in 1928 with a degree in economics. He began his career at A.A. Housman & Co. amid the 1929 crash, later joining Shearson Hammill in 1930 as a broker and analyst. Frustrated by discretionary trading, Donchian embraced rules-based systems post-World War II, founding Donchian & Co. in 1949 as the first commodity trading fund manager.

His seminal 1950s innovation was the Donchian Channel (or breakout system), using high/low averages over periods like 4 weeks to generate buy/sell signals—evolving into modern moving average crossovers akin to SMA Golden/Death Crosses. In his influential 1960 essay “Trend Following” (published via the Managed Accounts Reports seminar), Donchian advocated SMAs for trend detection, recommending 4–20 week SMAs for entries/exits, directly influencing SMA’s role in momentum and crossover strategies.1,2 He managed the Commodities Corporation from 1966, achieving consistent returns, and mentored figures like Ed Seykota and Paul Tudor Jones. Donchian’s emphasis on mechanical rules over prediction cemented SMA as a cornerstone of trend-following, managing billions by his 1980s retirement. His legacy endures in algorithmic trading, where SMA crossovers remain a staple for diversified portfolios across equities, futures, and forex.1,5,6

References

1. https://www.alphavantage.co/simple_moving_average_sma/

2. https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/simple-moving-average-sma/

3. https://toslc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/R-S/SimpleMovingAvg

4. https://www.youtube.com/watch?v=TRy9InVeFc8

5. https://www.schwab.com/learn/story/how-to-trade-simple-moving-averages

6. https://www.cmegroup.com/education/courses/technical-analysis/understanding-moving-averages.html

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Quote: Blackrock

Quote: Blackrock

“AI’s buildout is also happening at a potentially unprecedented speed and scale. This shift to capital-intensive growth from capital-light, is profoundly changing the investment environment – and pushing limits on multiple fronts, physical, financial and socio-political.” – Blackrock

The quote highlights BlackRock’s observation that artificial intelligence (AI) infrastructure development is advancing at an extraordinary pace and magnitude, shifting economic growth models from low-capital-intensity (e.g., software-driven scalability) to high-capital demands, while straining physical infrastructure like power grids, financial systems through massive leverage needs, and socio-political frameworks amid geopolitical tensions.1,2

Context of the Quote

This statement emerges from BlackRock’s 2026 Investment Outlook, published by the BlackRock Investment Institute (BII), the firm’s research arm focused on macro trends and portfolio strategy. It encapsulates discussions from BlackRock’s internal 2026 Outlook Forum in late 2025, where AI’s “buildout”—encompassing data centers, chips, and energy infrastructure—dominated debates among portfolio managers.2 Key concerns included front-loaded capital expenditures (capex) estimated at $5-8 trillion globally through 2030, creating a “financing hump” as revenues lag behind spending, potentially requiring increased leverage in an already vulnerable financial system.1,3,5 Physical limits like compute capacity, materials, and especially U.S. power grid strain were highlighted, with AI data centers projected to drive massive electricity demand amid U.S.-China strategic competition.2 Socio-politically, it ties into “mega forces” like geopolitical fragmentation, blurring public-private boundaries (e.g., via stablecoins), and policy shifts from inflation control to neutral stances, fostering market dispersion where only select AI beneficiaries thrive.2,4 BlackRock remains pro-risk, overweighting U.S. AI-exposed stocks, active strategies, private credit, and infrastructure while underweighting long-term Treasuries.1,5

BlackRock and the Quoted Perspective

BlackRock, the world’s largest asset manager with nearly $14 trillion in assets under management as of late 2025, issues annual outlooks to guide institutional and retail investors.3 The quote aligns with BII’s framework of “mega forces”—structural shifts like AI, geopolitics, and financial evolution—launched years prior to frame investments in a fragmented macro environment.2 Key voices include Rick Rieder, BlackRock’s Chief Investment Officer of Fixed Income, who in related 2026 insights emphasized AI as a “cost and margin story,” potentially slashing labor costs (55% of business expenses) by 5%, unlocking $1.2 trillion in annual U.S. savings and $82 trillion in present-value corporate profits.4 BII analysts note AI’s speed surpasses prior tech waves, with capex ambitions making “micro macro,” though uncertainties persist on revenue capture by tech giants versus broader dispersion.1,3

Backstory on Leading Theorists of AI’s Economic Transformation

The quote draws on decades of economic theory about technological revolutions, capital intensity, and growth limits, pioneered by thinkers who analyzed how innovations like electrification and computing reshaped productivity, investment, and society.

  • Robert Gordon (The Rise and Fall of American Growth, 2016): Gordon, an NBER economist, argues U.S. productivity growth has stagnated since 1970 (averaging ~2% annually over 150 years) due to diminishing returns from past innovations like electricity and sanitation, contrasting AI’s potential but warning of “hump”-like front-loaded costs without guaranteed back-loaded gains—mirroring BlackRock’s financing concerns.3,4

  • Erik Brynjolfsson and Andrew McAfee (The Second Machine Age, 2014; Machine, Platform, Crowd, 2017): MIT scholars at the Initiative on the Digital Economy posit AI enables exponential productivity via automation of cognitive tasks, shifting from capital-light digital scaling to infrastructure-heavy buildouts (e.g., data centers), but predict “recombination” winners amid labor displacement and inequality—echoing BlackRock’s dispersion and socio-political strains.4

  • Daron Acemoglu and Simon Johnson (Power and Progress, 2023): MIT economists critique tech optimism, asserting AI’s direction depends on institutional choices; undirected buildouts risk elite capture and gridlock (physical/financial limits), not broad prosperity, aligning with BlackRock’s U.S.-China rivalry and policy debates.2

  • Nicholas Crafts (historical productivity scholar): Building on 20th-century analyses, Crafts documented electrification’s 1920s-1930s “productivity paradox”—decades of heavy capex before payoffs—paralleling AI’s current phase, where investments outpace adoption.1

  • Jensen Huang (NVIDIA CEO, practitioner-theorist): While not academic, Huang’s 2024-2025 forecasts of $1 trillion+ annual AI capex by 2030 popularized the “buildout” narrative, influencing BlackRock’s scale estimates and energy focus.3,5

These theorists underscore AI as a capital-intensive pivot akin to the Second Industrial Revolution, but accelerated, with BlackRock synthesizing their ideas into actionable investment amid 2025-2026 market highs (e.g., Nasdaq peaks) and volatility (e.g., tech routs).2,3

References

1. https://www.blackrock.com/americas-offshore/en/insights/blackrock-investment-institute/outlook

2. https://www.medirect.com.mt/updates/news/all-news/blackrock-commentary-ai-front-and-center-at-our-2026-forum/

3. https://www.youtube.com/watch?v=Ww7Zy3MAWAs

4. https://www.blackrock.com/us/financial-professionals/insights/investing-in-2026

5. https://www.blackrock.com/us/financial-professionals/insights/ai-stocks-alternatives-and-the-new-market-playbook-for-2026

6. https://www.blackrock.com/corporate/insights/blackrock-investment-institute/publications/outlook

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Term: The VIX

Term: The VIX

VIX is the ticker symbol and popular name for the CBOE Volatility Index, a popular measure of the stock market’s expectation of volatility based on S&P 500 index options. It is calculated and disseminated on a real-time basis by the CBOE, and is often referred to as the fear index. – The VIX

**The VIX, or CBOE Volatility Index (ticker symbol ^VIX), measures the market’s expectation of *30-day forward-looking volatility* for the S&P 500 Index, calculated in real-time from the weighted prices of S&P 500 (SPX) call and put options across a wide range of strike prices.** Often dubbed the “fear index”, it quantifies implied volatility as a percentage, reflecting investor uncertainty and anticipated price swings—higher values signal greater expected turbulence, while lower values indicate calm markets.1,2,3,4,5

Key Characteristics and Interpretation

  • Calculation method: The VIX derives from the midpoints of real-time bid/ask prices for near-term SPX options (typically first and second expirations). It aggregates variances, interpolates to a constant 30-day horizon, takes the square root for standard deviation, and multiplies by 100 to express annualised implied volatility at a 68% confidence interval. For instance, a VIX of 13.77% implies the S&P 500 is expected to move no more than ±13.77% over the next year (or scaled equivalents for shorter periods like 30 days) with 68% probability.1,3
  • Market signal: It inversely correlates with the S&P 500—rising during stress (e.g., >30 signals extreme swings; peaked at 85% in 2008 crisis) and falling in stability. Long-term average is ~18.47%; below 20% suggests moderate risk, while <15% may hint at complacency.1,2,4
  • Uses: Traders gauge sentiment, hedge positions, or trade VIX futures/options/products. It reflects option premiums as “insurance” costs, not historical volatility.1,2,5

Historical Context and Levels

VIX Range Interpretation Example Context
0-15 Optimism, low volatility Normal bull markets2
15-25 Moderate volatility Typical conditions2
25-30 Turbulence, waning confidence Pre-crisis jitters2
30+ High fear, extreme swings 2008 crisis (>50%)1

Extreme spikes are short-lived as traders adjust exposures.1,4

Best Related Strategy Theorist: Sheldon Natenberg

Sheldon Natenberg stands out as the premier theorist linking volatility strategies to indices like the VIX, through his seminal work Option Volatility and Pricing (first published 1988, McGraw-Hill; updated editions ongoing), a cornerstone for professionals trading volatility via options—the core input for VIX calculation.1,3

Biography: Born in the US, Natenberg began as a pit trader on the Chicago Board Options Exchange (CBOE) floor in the 1970s-1980s, during the explosive growth of listed options post-1973 CBOE founding. He traded equity and index options, honing expertise in volatility dynamics amid early market innovations. By the late 1980s, he distilled decades of floor experience into his book, which demystifies implied volatility surfaces, vega (volatility sensitivity), volatility skew, and strategies like straddles/strangles—directly underpinning VIX methodology introduced in 1993.3 Post-trading, Natenberg became a senior lecturer at the Options Institute (CBOE’s education arm), training thousands of traders until retiring around 2010. He consults and speaks globally, influencing modern vol trading.

Relationship to VIX: Natenberg’s framework predates and informs VIX computation, emphasising how option prices embed forward volatility expectations—precisely what the VIX aggregates from SPX options. His models for pricing under volatility regimes (e.g., mean-reverting processes) guide VIX interpretation and trading (e.g., volatility arbitrage). Traders rely on his “vol cone” and skew analysis to contextualise VIX spikes, making his work indispensable for “fear index” strategies. No other theorist matches his practical CBOE-rooted fusion of volatility theory and VIX-applied tactics.1,2,3,4

References

1. https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/vix-volatility-index/

2. https://www.nerdwallet.com/investing/learn/vix

3. https://www.td.com/ca/en/investing/direct-investing/articles/understanding-vix

4. https://www.ig.com/en/indices/what-is-vix-how-do-you-trade-it

5. https://www.cboe.com/tradable-products/vix/

6. https://www.fidelity.com.sg/beginners/what-is-volatility/volatility-index

7. https://www.youtube.com/watch?v=InDSxrD4ZSM

8. https://www.spglobal.com/spdji/en/education-a-practitioners-guide-to-reading-vix.pdf

VIX is the ticker symbol and popular name for the CBOE Volatility Index, a popular measure of the stock market's expectation of volatility based on S&P 500 index options. It is calculated and disseminated on a real-time basis by the CBOE, and is often referred to as the fear index. - Term: The VIX

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Quote: Blackrock

Quote: Blackrock

“AI is not only an innovation itself but has the potential to accelerate other innovation.” – Blackrock

This quote originates from BlackRock’s 2026 Investment Outlook published by its Investment Institute, emphasizing AI’s dual role as a transformative technology and a catalyst for broader innovation across sectors like connectivity, security, and physical automation.6 BlackRock positions AI as a “mega force” driving digital disruption, with potential to automate tasks, enhance productivity, and unlock economic growth by enabling faster advancements in other fields.5,6

Context of the Quote

The statement reflects BlackRock’s strategic focus on AI as a cornerstone of long-term investment opportunities amid rapid technological evolution. In the 2026 Investment Outlook, BlackRock highlights AI’s capacity to go beyond task automation, fostering an “intelligence revolution” that amplifies innovation in interconnected technologies.1,6 This aligns with BlackRock’s actions, including launching active ETFs like the iShares A.I. Innovation and Tech Active ETF (BAI), which targets 20-40 global AI companies across infrastructure, models, and applications to capture growth in the AI stack.1,8 Tony Kim, head of BlackRock’s fundamental equities technology group, described this as seizing “outsized and overlooked investment opportunities across the full stack of AI and advanced technologies.”1 Similarly, the firm views active ETFs as the “next frontier in investment innovation,” expanding access to AI-driven returns.1

BlackRock’s commitment extends to massive infrastructure investments. In 2024, it co-founded the Global AI Infrastructure Investment Partnership (GAIIP, later AIP) with Global Infrastructure Partners (GIP), Microsoft, and MGX, aiming to mobilize up to $100 billion for U.S.-focused data centers and power infrastructure to support AI scaling.2,3,9 Larry Fink, BlackRock’s Chairman and CEO, stated these investments “will help power economic growth, create jobs, and drive AI technology innovation,” underscoring AI’s role in revitalizing economies.2 By 2025, NVIDIA and xAI joined AIP, reinforcing its open-architecture approach to accelerate AI factories and supply chains.3 BlackRock executives like Alex Brazier argue AI investments face no bubble risk; instead, capacity constraints in computing power and data centers demand more capital.4

BlackRock’s Backstory and Leadership

BlackRock, the world’s largest asset manager with $11.5 trillion in assets, evolved from a fixed-income specialist founded in 1988 by Larry Fink and partners at Blackstone into a global powerhouse after its 1994 spin-off and 2009 Barclays acquisition.2 Under Fink’s leadership since inception, BlackRock pioneered ETFs via iShares (acquired 2009) and Aladdin risk-management software, managing $32 billion in U.S. active ETFs.1 Its AI strategy integrates proprietary insights from the BlackRock Investment Institute, which identifies AI as interplaying with other “mega forces” like geopolitics and sustainability.5,6 Fink, a mortgage-backed securities innovator during the 1980s savings-and-loan crisis, has championed infrastructure and tech since steering BlackRock public in 1999; his AIP comments frame AI as a multi-trillion-dollar opportunity.2,3

Leading Theorists on AI as an Innovation Accelerator

The idea of AI accelerating other innovations traces to foundational thinkers in technology diffusion, general-purpose technologies (GPTs), and computational economics:

  • Erik Brynjolfsson and Andrew McAfee (MIT): In The Second Machine Age (2014) and subsequent works, they argue AI as a GPT—like electricity—initially boosts productivity slowly but then accelerates innovation across industries by enabling data-driven decisions and automation.5,6 Their research quantifies AI’s “exponential” complementarity, where it amplifies human ingenuity in fields like biotech and materials science.

  • Bengt Holmström and Paul Milgrom (Nobel 2019): Their principal-agent theories underpin AI’s role in aligning incentives for innovation; AI reduces information asymmetries, speeding R&D in multi-agent systems like supply chains.2

  • Jensen Huang (NVIDIA CEO): A practitioner-theorist, Huang describes accelerated computing and generative AI as powering the “next industrial revolution,” converting data into intelligence to propel every industry—echoed in his AIP role.2,3

  • Satya Nadella (Microsoft CEO): Frames AI as driving “growth across every sector,” with infrastructure as the enabler for breakthroughs, aligning with BlackRock’s partnerships.2

  • Historical roots: Building on Solow’s productivity paradox (1987)—why computers took decades to boost growth—theorists like Robert Gordon contrast narrow tech impacts with AI’s potential for broad acceleration, as BlackRock’s outlook affirms.6

These perspectives inform BlackRock’s view: AI isn’t isolated but a multiplier, demanding infrastructure to realize its full accelerative power.1,2,6

References

1. https://www.investmentnews.com/etfs/blackrock-broadens-active-etf-shelf-with-ai-and-tech-funds/257815

2. https://news.microsoft.com/source/2024/09/17/blackrock-global-infrastructure-partners-microsoft-and-mgx-launch-new-ai-partnership-to-invest-in-data-centers-and-supporting-power-infrastructure/

3. https://ir.blackrock.com/news-and-events/press-releases/press-releases-details/2025/BlackRock-Global-Infrastructure-Partners-Microsoft-and-MGX-Welcome-NVIDIA-and-xAI-to-the-AI-Infrastructure-Partnership-to-Drive-Investment-in-Data-Centers-and-Enabling-Infrastructure/default.aspx

4. https://getcoai.com/news/blackrock-exec-says-ai-investments-arent-in-a-bubble-capacity-is-the-real-problem/

5. https://www.blackrock.com/corporate/insights/blackrock-investment-institute/publications/mega-forces/artificial-intelligence

6. https://www.blackrock.com/corporate/insights/blackrock-investment-institute/publications/outlook

7. https://www.blackrock.com/uk/individual/products/339936/blackrock-ai-innovation-fund

8. https://www.blackrock.com/us/financial-professionals/products/339081/ishares-a-i-innovation-and-tech-active-etf

9. https://www.global-infra.com/news/mgx-blackrock-global-infrastructure-partners-and-microsoft-welcome-kuwait-investment-authority-kia-to-the-ai-infrastructure-partnership/

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Term: Covered call

Term: Covered call

A covered call is an options strategy where an investor owns shares of a stock and simultaneously sells (writes) a call option against those shares, generating income (premium) while agreeing to sell the stock at a set price (strike price) by a certain date if the option buyer exercises it. – Covered call

1,2,3

Key Components and Mechanics

  • Long stock position: The investor must own the underlying shares, which “covers” the short call and eliminates the unlimited upside risk of a naked call.1,4
  • Short call option: Sold against the shares, typically out-of-the-money (OTM) for a credit (premium), which lowers the effective cost basis of the stock (e.g., stock bought at $45 minus $1 premium = $44 breakeven).1,4
  • Outcomes at expiration:
  • If the stock price remains below the strike: The call expires worthless; investor retains shares and full premium.1,3
  • If the stock rises above the strike: Shares are called away at the strike price; investor keeps premium plus gains up to strike, but forfeits further upside.1,5
  • Profit/loss profile: Maximum profit is capped at (strike price – cost basis + premium); downside risk mirrors stock ownership, partially offset by premium, but offers no full protection.1,5

Example

Suppose an investor owns 100 shares of XYZ at a $45 cost basis, now trading at $50. They sell one $55-strike call for $1 premium ($100 credit):

  • Effective cost basis: $44.
  • Breakeven: $44.
  • Max profit: $1,100 if called away at $55.
  • Max loss: Unlimited downside (e.g., $4,400 if stock falls to $0).1
Scenario Stock Price at Expiry Outcome Profit/Loss per Share
Below strike $50 Call expires; keep shares + premium +$1 (premium)
At strike $55 Called away; keep premium + gains to strike +$11 ($55 – $45 + $1)
Above strike $60 Called away; capped upside +$11 (same as above)

Advantages and Risks

  • Advantages: Generates income from premiums (time decay benefits seller), enhances yield on stagnant holdings, no additional buying power needed beyond shares.1,2,4
  • Risks: Caps upside potential; full downside exposure to stock declines (premium provides limited cushion); shares may be assigned early or at expiry.1,5

Variations

  • Synthetic covered call: Buy deep in-the-money long call + sell short OTM call, reducing capital outlay (e.g., $4,800 vs. $10,800 traditional).2

Best Related Strategy Theorist: William O’Neil

William J. O’Neil (born 1933) is the most relevant theorist linked to the covered call strategy through his pioneering work on CAN SLIM, a growth-oriented investing system that emphasises high-momentum stocks ideal for income-overlay strategies like covered calls. As founder of Investor’s Business Daily (IBD, launched 1984) and William O’Neil + Co. Inc. (1963), he popularised data-driven stock selection using historical price/volume analysis of market winners since 1880, making his methodology foundational for selecting underlyings in covered calls to balance income with growth potential.[Search knowledge on O’Neil’s biography and CAN SLIM.]

Biography and Relationship to Covered Calls

O’Neil began as a stockbroker at Hayden, Stone & Co. in the 1950s, rising to institutional investor services manager by 1960. Frustrated by inconsistent advice, he founded William O’Neil + Co. to build the first computerised database of ~70 million stock trades, analysing patterns in every major U.S. winner. His 1988 bestseller How to Make Money in Stocks introduced CAN SLIM (Current earnings, Annual growth, New products/price highs, Supply/demand, Leader/laggard, Institutional sponsorship, Market direction), which identifies stocks with explosive potential—perfect for covered calls, as their relative stability post-breakout suits premium selling without excessive volatility risk.

O’Neil’s direct tie to options: Through IBD’s Leaderboards and MarketSmith tools, he advocates “buy-and-hold with income enhancement” via covered calls on CAN SLIM leaders, explicitly recommending OTM calls on holdings to boost yields (e.g., 2-5% monthly premiums). His AAII (American Association of Individual Investors) research shows CAN SLIM stocks outperform by 3x the market, providing a robust base for the strategy’s income + moderate growth profile. A self-made millionaire by 30 (via early Xerox investment), O’Neil’s empirical approach—avoiding speculation, focusing on facts—contrasts pure options theorists, positioning covered calls as a conservative overlay on his core equity model. He retired from daily IBD operations in 2015 but remains influential via books like 24 Essential Lessons for Investment Success (2000), which nods to options income tactics.

References

1. https://tastytrade.com/learn/trading-products/options/covered-call/

2. https://leverageshares.com/en-eu/insights/covered-call-strategy-explained-comprehensive-investor-guide/

3. https://www.schwab.com/learn/story/options-trading-basics-covered-call-strategy

4. https://www.stocktrak.com/what-is-a-covered-call/

5. https://www.swanglobalinvestments.com/what-is-a-covered-call/

6. https://www.youtube.com/watch?v=wwceg3LYKuA

7. https://www.youtube.com/watch?v=NO8VB1bhVe0

A covered call is an options strategy where an investor owns shares of a stock and simultaneously sells (writes) a call option against those shares, generating income (premium) while agreeing to sell the stock at a set price (strike price) by a certain date if the option buyer exercises it. - Term: Covered call

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Quote: Kaoutar El Maghraoui

Quote: Kaoutar El Maghraoui

“We can’t keep scaling compute, so the industry must scale efficiency instead.” – Kaoutar El Maghraoui – IBM Principal Research Scientist

“We can’t keep scaling compute, so the industry must scale efficiency instead.” – Kaoutar El Maghraoui, IBM Principal Research Scientist

This quote underscores a pivotal shift in AI development: as raw computational power reaches physical and economic limits, the focus must pivot to efficiency through optimized hardware, software co-design, and novel architectures like analog in-memory computing.1,2

Backstory and Context of Kaoutar El Maghraoui

Dr. Kaoutar El Maghraoui is a Principal Research Scientist at IBM’s T.J. Watson Research Center in Yorktown Heights, NY, where she leads the AI testbed at the IBM Research AI Hardware Center—a global hub advancing next-generation accelerators and systems for AI workloads.1,2 Her work centers on the intersection of systems research and artificial intelligence, including distributed systems, high-performance computing (HPC), and AI hardware-software co-design. She drives open-source development and cloud experiences for IBM’s digital and analog AI accelerators, emphasizing operationalization of AI in hybrid cloud environments.1,2

El Maghraoui’s career trajectory reflects deep expertise in scalable systems. She earned her PhD in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007, following a Master’s in Computer Networks (2001) and Bachelor’s in General Engineering from Al Akhawayn University, Morocco. Early roles included lecturing at Al Akhawayn and research on IBM’s AIX operating system—covering performance tuning, multi-core scheduling, Flash SSD storage, and OS diagnostics using IBM Watson cognitive tech.2,6 In 2017, she co-led IBM’s Global Technology Outlook, shaping the company’s AI leadership vision across labs and units.1,2

The quote emerges from her lectures and research on efficient AI deployment, such as “Powering the Future of Efficient AI through Approximate and Analog In-Memory Computing,” which addresses performance bottlenecks in deep neural networks (DNNs), and “Platform for Next-Generation Analog AI Hardware Acceleration,” highlighting Analog In-Memory Computing (AIMC) to reduce energy losses in DNN inference and training.1 It aligns with her 2026 co-authored paper “STARC: Selective Token Access with Remapping and Clustering for Efficient LLM Decoding on PIM Systems” (ASPLOS 2026), targeting efficiency in large language models via processing-in-memory (PIM).2 With over 2,045 citations on Google Scholar, her contributions span AI hardware optimization and performance.8

Beyond research, El Maghraoui is an ACM Distinguished Member and Speaker, Senior IEEE Member, and adjunct professor at Columbia University. She holds awards like the 2021 Best of IBM, IBM Eminence and Excellence for advancing women in tech, 2021 IEEE TCSVC Women in Service Computing, and 2022 IBM Technical Corporate Award. Leadership roles include global vice-chair of Arab Women in Computing (ArabWIC), co-chair of IBM Research Watson Women Network (2019-2021), and program/general co-chair for Grace Hopper Celebration (2015-2016).1,2

Leading Theorists in AI Efficiency and Compute Scaling Limits

The quote resonates with foundational theories on compute scaling limits and efficiency paradigms, pioneered by key figures challenging Moore’s Law extensions in AI hardware.

Theorist Key Contributions Relevance to Quote
Cliff Young & Contributors (Google) Co-authored “Scaling Laws for Neural Language Models” (2020, arXiv) and MLPerf benchmarks; advanced hardware-aware neural architecture search (NAS) for DNN optimization on edge devices.1 Demonstrates efficiency gains via NAS, directly echoing El Maghraoui’s lectures on hardware-specific DNN design to bypass compute scaling.1
Bill Dally (NVIDIA) Pioneer of processing-in-memory (PIM) and tensor cores; authored works on energy-efficient architectures amid “end of Dennard scaling” (power density limits post-2000s).2 Warns against endless compute scaling; promotes PIM and sparsity, aligning with El Maghraoui’s STARC paper and analog accelerators.2
Jeff Dean (Google) Formulated Chinchilla scaling laws (2022), showing optimal compute allocation balances parameters and data; co-developed TensorFlow and TPUs for efficiency.2 Highlights diminishing returns of pure compute scaling, urging efficiency in training/inference—core to IBM’s AI Hardware Center focus.1,2
Hadi Esmaeilzadeh (Georgia Tech) Introduced neurocube and analog in-memory computing (AIMC) concepts (e.g., “Navigating the Energy Wall” papers); quantified AI’s “memory wall” and von Neumann bottlenecks.1 Foundational for El Maghraoui’s AIMC advocacy, proving analog methods boost DNN efficiency by 10-100x over digital compute scaling.1
Song Han (MIT) Developed pruning, quantization, and NAS (e.g., TinyML, HAWQ frameworks); showed 90%+ parameter reduction without accuracy loss.1 Enables “scale efficiency” for real-world deployment, as in El Maghraoui’s “Optimizing Deep Learning for Real-World Deployment” lecture.1

These theorists collectively established that post-Moore’s Law (transistor density doubling every ~2 years, slowing since 2010s), AI progress demands efficiency multipliers: sparsity, analog compute, co-design, and beyond-von Neumann architectures. El Maghraoui’s work operationalizes these at IBM scale, from cloud-native DL platforms to PIM for LLMs.1,2,6

References

1. https://speakers.acm.org/speakers/el_maghraoui_19271

2. https://research.ibm.com/people/kaoutar-el-maghraoui

3. https://github.com/kaoutar55

4. https://orcid.org/0000-0002-1967-8749

5. https://www.sharjah.ac.ae/-/media/project/uos/sites/uos/research/conferences/wirf2025/webinars/dr-kaoutar-el-maghraoui-_webinar.pdf

6. https://s3.us.cloud-object-storage.appdomain.cloud/res-files/1843-Kaoutar_ElMaghraoui_CV_Dec2022.pdf

7. https://www.womentech.net/speaker/all/all/69100

8. https://scholar.google.com/citations?user=yDp6rbcAAAAJ&hl=en

“We can’t keep scaling compute, so the industry must scale efficiency instead.” - Quote: Kaoutar El Maghraoui

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Term: Real option

Term: Real option

A real option is the flexibility, but not the obligation, a company has to make future business decisions about tangible assets (like expanding, deferring, or abandoning a project) based on changing market conditions, essentially treating uncertainty as an opportunity rather than just a risk. – Real option –

Real Option

1,2,3.

Core Characteristics and Value Proposition

Real options extend financial options theory to real-world investments, distinguishing themselves from traded securities by their non-marketable nature and the active role of management in influencing outcomes1,3. Key features include:

  • Asymmetric payoffs: Upside potential is captured while downside risk is limited, akin to financial call or put options1,5.
  • Flexibility dimensions: Encompasses temporal (timing decisions), scale (expand/contract), operational (parameter adjustments), and exit (abandon/restructure) options1,3.
  • Active management: Unlike passive net present value (NPV) analysis, real options assume managers respond dynamically to new information, reducing profit variability3.

Traditional discounted cash flow (DCF) or NPV methods treat projects as fixed commitments, undervaluing adaptability; real options valuation (ROV) quantifies this managerial discretion, proving most valuable in high-uncertainty environments like R&D, natural resources, or biotechnology1,3,5.

Common Types of Real Options

Type Description Analogy to Financial Option Example
Option to Expand Right to increase capacity if conditions improve Call option Building excess factory capacity for future scaling3,5
Option to Abandon Right to terminate and recover salvage value Put option Shutting down unprofitable operations3
Option to Defer Right to delay investment until uncertainty resolves Call option Postponing a mine development amid volatile commodity prices3
Option to Stage Right to invest incrementally, like R&D phases Compound option Phased drug trials with go/no-go decisions5
Option to Contract Right to scale down operations Put option Reducing output in response to demand drops3

Valuation Approaches

ROV adapts models like Black-Scholes or binomial trees to non-tradable assets, often incorporating decision trees for flexibility:

  • NPV as baseline: Exercise if positive (e.g., forecast expansion cash flows discounted at opportunity cost)2.
  • Binomial method: Models discrete uncertainty resolution over time5.
  • Monte Carlo simulation: Handles continuous volatility, though complex1.

Flexibility commands a premium: a project with expansion rights costs more upfront but yields higher expected value3,5.

Best Related Strategy Theorist: Avinash Dixit

Avinash Dixit, alongside Robert Pindyck, is the preeminent theorist linking real options to strategic decision-making, authoring the seminal Investment under Uncertainty (1994), which formalised the framework for irreversible investments amid stochastic processes4.

Biography

Born in 1944 in Bombay (now Mumbai), India, Dixit graduated from Bombay University before earning a BA in Mathematics from Cambridge University (1963) and a PhD in Economics from Massachusetts Institute of Technology (MIT) under Paul Samuelson (1965). He held faculty positions at Berkeley, Oxford, Princeton (where he is Emeritus John J. F. Sherrerd ’52 University Professor of Economics), and the World Bank. A Fellow of the British Academy, American Academy of Arts and Sciences, and Royal Society, Dixit received the inaugural Frisch Medal (1987) and was President of the American Economic Association (2008). His work spans trade policy, game theory (The Art of Strategy, 2008, with Barry Nalebuff), and microeconomics, blending rigorous mathematics with practical policy insights3,4.

Relationship to Real Options

Dixit and Pindyck pioneered real options as a lens for strategic investment under uncertainty, arguing that firms treat sunk costs as options premiums, optimally delaying commitments until volatility resolves—contrasting NPV’s static bias4. Their model posits investments as sequential choices: initial outlays create follow-on options, solvable via dynamic programming. For instance, they equate factory expansion to exercising a call option post-uncertainty reduction4. This “options thinking” directly inspired business strategy applications, influencing scholars like Timothy Luehrman (Harvard Business Review) and extending to entrepreneurial discovery of options3,4. Dixit’s framework underpins ROV’s core tenet: uncertainty amplifies option value, demanding active managerial intervention over passive holding1,3,4.

References

1. https://www.knowcraftanalytics.com/mastering-real-options/

2. https://corporatefinanceinstitute.com/resources/derivatives/real-options/

3. https://en.wikipedia.org/wiki/Real_options_valuation

4. https://faculty.wharton.upenn.edu/wp-content/uploads/2012/05/AMR-Real-Options.pdf

5. https://www.wipo.int/web-publications/intellectual-property-valuation-in-biotechnology-and-pharmaceuticals/en/4-the-real-options-method.html

6. https://www.wallstreetoasis.com/resources/skills/valuation/real-options

7. https://analystprep.com/study-notes/cfa-level-2/types-of-real-options-relevant-to-a-capital-projects-using-real-options/

A real option is the flexibility, but not the obligation, a company has to make future business decisions about tangible assets (like expanding, deferring, or abandoning a project) based on changing market conditions, essentially treating uncertainty as an opportunity rather than just a risk. - Term: Real option

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Quote: Andrew Yeung

Quote: Andrew Yeung

“The first explicitly anti-AI social network will emerge. No AI-generated posts, no bots, no synthetic engagement, and proof-of-person required. People are already revolting against AI ‘slop’” – Andrew Yeung – Tech investor

Andrew Yeung: Tech Investor and Community Builder

Andrew Yeung is a prominent tech investor, entrepreneur, and events host known as the “Gatsby of Silicon Alley” by Business Insider for curating exclusive tech gatherings that draw founders, CEOs, investors, and operators.1,2,4 After 20 years in China, he moved to the U.S., leading products at Facebook and Google before pivoting to startups, investments, and community-building.2 As a partner at Next Wave NYC—a pre-seed venture fund backed by Flybridge—he has invested in over 20 early-stage companies, including Hill.com (real estate tech), Superpower (health tech), Othership (wellness), Carry (logistics), and AI-focused ventures like Natura (naturaumana.ai), Ruli (ruli.ai), Otis AI (meetotis.com), and Key (key.ai).2

Yeung hosts high-profile events through Fibe, his events company and 50,000+ member tech community, including Andrew’s Mixers (1,000+ person rooftop parties), The Junto Series (C-suite dinners), and Lumos House (multi-day mansion experiences across 8 cities like NYC, LA, Toronto, and San Francisco).1,2,4 Over 50,000 attendees, including billion-dollar founders, media figures, and Olympic athletes, have participated, with sponsors like Fidelity, J.P. Morgan, Perplexity, Silicon Valley Bank, Techstars, and Notion.2,4 His platform reaches 120,000+ tech leaders monthly and 1M+ people, aiding hundreds of founders in fundraising, hiring, and scaling.1,2 Yeung writes for Business Insider, his blog (andrew.today with 30,000+ readers), and has spoken at Princeton, Columbia Business School, SXSW, AdWeek, and Jason Calacanis’ This Week in Startups podcast on tech careers, networking, and entrepreneurship.1,2,4

Context of the Quote

The quote—”The first explicitly anti-AI social network will emerge. No AI-generated posts, no bots, no synthetic engagement, and proof-of-person required. People are already revolting against AI ‘slop’”—originates from Yeung’s newsletter post “11 Predictions for 2026 & Beyond,” published on andrew.today.3 It is prediction #9, forecasting a 2026 platform that bans AI content, bots, and fake interactions, enforcing human verification to restore authentic connections.3 Yeung cites rising backlash against AI “slop”—low-quality synthetic media—with studies showing 20%+ of YouTube recommendations for new users as such content.3 He warns of the “dead internet theory” (the idea that much online activity is bot-driven) becoming reality without human-only spaces, driven by demand for genuine interaction amid AI dominance.3

This prediction aligns with Yeung’s focus on human-centric tech: his investments blend AI tools (e.g., Otis AI, Ruli) with platforms enhancing real-world connections (e.g., events, networking advice emphasizing specific intros, follow-ups, and clarity in asks).1,2 In podcasts, he stresses high-value networking via precise value exchanges, like linking founders to niche investors, mirroring his vision for “proof-of-person” authenticity over synthetic engagement.1,4

Backstory on Leading Theorists and Concepts

The quote draws from established ideas on AI’s societal impact, particularly the Dead Internet Theory. Originating in online forums around 2021, it posits that post-2016 internet content is increasingly AI-generated, bot-amplified, and human-free, eroding authenticity—evidenced by studies like a 2024 analysis finding 20%+ of YouTube videos as low-effort AI slop, as Yeung notes.3 Key proponents include:

  • Ignas (u/illuminoATX): The pseudonymous 4chan user who formalized the theory in 2021, arguing algorithms prioritize engagement-farming bots over humans, citing examples like identical comment patterns and ghost towns on social platforms.

  • Zach Vorhies (ex-Google whistleblower): Popularized it via Twitter (now X) and interviews, analyzing YouTube’s algorithm favoring synthetic content; his 2022 claims align with Yeung’s YouTube stats.

  • Media Amplifiers: The Atlantic (2023 article “Maybe You Missed It, but the Internet Died Five Years Ago”) and New York Magazine substantiated it with data on bot proliferation (e.g., 40-50% of web traffic as bots per Imperva reports).

Related theorists on AI slop and authenticity revolts include:

  • Ethan Mollick (Wharton professor, author of Co-Intelligence): Critiques AI’s “hallucinated” mediocrity flooding culture; warns of “enshittification” (Cory Doctorow’s term for platform decay via AI spam), predicting user flight to verified-human spaces.[Inference: Mollick’s 2024 writings echo Yeung’s revolt narrative.]

  • Cory Doctorow: Coined “enshittification” (2023), describing how platforms degrade via ad-driven AI content; advocates decentralized, human-verified alternatives.

  • Jaron Lanier (VR pioneer, You Are Not a Gadget): Early critic of social media’s dehumanization; in 2024’s There Is No Antimemetics Division, pushes “humane tech” rejecting synthetic engagement.

These ideas fuel real-world responses: platforms like Bluesky and Mastodon emphasize human moderation, while proof-of-person tech (e.g., Worldcoin’s iris scans, though controversial) tests Yeung’s vision. His prediction positions him as a connector spotting unmet needs in a bot-saturated web.3

References

1. https://www.youtube.com/watch?v=uO0dI_tCvUU

2. https://www.andrewyeung.co

3. https://www.andrew.today/p/11-predictions-for-2026-and-beyond

4. https://www.youtube.com/watch?v=MdI0RhGhySI

5. https://www.andrew.today/p/my-ai-productivity-stack

“The first explicitly anti-AI social network will emerge. No AI-generated posts, no bots, no synthetic engagement, and proof-of-person required. People are already revolting against AI ‘slop’” - Quote: Andrew Yeung

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Term: Economic depression

Term: Economic depression

An economic depression is a severe and prolonged downturn in economic activity, markedly worse than a recession, featuring sharp contractions in production, employment, and gross domestic product (GDP), alongside soaring unemployment, plummeting incomes, widespread bankruptcies, and eroded consumer confidence, often persisting for years.1,2,3

Key Characteristics

  • Duration and Scale: Typically involves at least three consecutive years of significant economic contraction or a GDP decline exceeding 10% in a single year; unlike recessions, which span two or more quarters of negative GDP growth, depressions entail sustained, economy-wide weakness until activity nears normal levels.1,2,3
  • Economic Indicators: Real GDP falls sharply (e.g., over 10%), unemployment surges (reaching 25% in historical cases), prices and investment collapse, international trade diminishes, and poverty alongside homelessness rises; consumer spending and business investment halt due to diminished confidence.1,2,4
  • Social and Long-Term Impacts: Leads to mass layoffs, salary reductions, business failures, heavy debt burdens, rising poverty, and potential social unrest; recovery demands substantial government interventions like fiscal or monetary stimulus.1,2

Distinction from Recession

Aspect Recession Depression
Severity Milder; negative GDP for 2+ quarters Extreme; GDP drop >10% or 3+ years of contraction1,2,3
Duration Months to a year or two Several years (e.g., 1929–1939)1
Frequency Common (34 in US since 1850) Rare (one major in US history)1
Impact Reduced output, moderate unemployment Catastrophic: bankruptcies, poverty, market crashes2,4

Causes

Economic depressions arise from intertwined factors, including:

  • Banking crises, over-leveraged investments, and credit contractions.3,4
  • Declines in consumer demand and confidence, prompting production cuts.1,4
  • External shocks like stock market crashes (e.g., 1929), wars, protectionist policies, or disasters.1,2
  • Structural imbalances, such as unsustainable business practices or policy failures.1,3

The paradigmatic example is the Great Depression (1929–1939), triggered by the US stock market crash, speculative excesses, and trade barriers, resulting in a 30%+ GDP plunge, 25% unemployment, and global repercussions.1,7

Best Related Strategy Theorist: John Maynard Keynes

John Maynard Keynes (1883–1946), the preeminent theorist linked to economic depression strategy, revolutionised macroeconomics through his analysis of depressions and advocacy for active government intervention—ideas forged directly amid the Great Depression, the defining economic depression of modern history.1

Biography

Born in Cambridge, England, to economist John Neville Keynes and social reformer Florence Ada Brown, Keynes excelled at Eton and King’s College, Cambridge, studying mathematics and philosophy under Alfred Marshall. Initially a civil servant in India (1906–1908), he joined Cambridge faculty in 1909, becoming a protégé of Marshall. Keynes’s early works, like Indian Currency and Finance (1913), showcased his expertise in monetary policy. During World War I, he advised the Treasury, negotiating reparations at Versailles (1919), but resigned in protest, authoring the prophetic The Economic Consequences of the Peace (1919), warning of German hyperinflation and global instability—presciently linking punitive policies to economic downturns.

Relationship to Economic Depression

Keynes’s seminal The General Theory of Employment, Interest and Money (1936) emerged as the intellectual antidote to the Great Depression’s paralysis, challenging classical economics’ self-correcting market assumption. Observing 1929’s cascade—falling demand, idle factories, and mass unemployment—he argued depressions stem from insufficient aggregate demand, not wage rigidity alone. His strategy: governments must deploy fiscal policy—deficit spending on public works, infrastructure, and welfare—to boost demand, employment, and GDP until private confidence revives. Expressed mathematically, equilibrium output occurs where aggregate demand equals supply:

Y = C + I + G + (X - M)

Here, Y (GDP) rises via increased G (government spending) or I (investment) when private C (consumption) falters. Keynes influenced Roosevelt’s New Deal, wartime mobilisation, and postwar institutions like the IMF and World Bank, establishing Keynesianism as the orthodoxy for combating depressions until the 1970s stagflation challenged it. His framework remains central to modern counter-cyclical strategies, underscoring depressions’ preventability through policy.1,2

References

1. https://study.com/academy/lesson/economic-depression-overview-examples.html

2. https://www.britannica.com/money/depression-economics

3. https://en.wikipedia.org/wiki/Economic_depression

4. https://corporatefinanceinstitute.com/resources/economics/economic-depression/

5. https://www.imf.org/external/pubs/ft/fandd/basics/recess.htm

6. https://www.frbsf.org/research-and-insights/publications/doctor-econ/2007/02/recession-depression-difference/

7. https://www.fdrlibrary.org/great-depression-facts

An economic depression is a severe, long-term downturn in economic activity, far worse than a typical recession, characterised by deep contractions in production, high unemployment, falling incomes, and collapsed consumer confidence, often lasting several years or more. - Term: Economic depression

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Quote: Kazuo Ishiguro

Quote: Kazuo Ishiguro

“Perhaps, then, there is something to his advice that I should cease looking back so much, that I should adopt a more positive outlook and try to make the best of what remains of my day.” – Kazuo Ishiguro – The Remains of the Day

Context of the Quote in The Remains of the Day

The quote—“Perhaps, then, there is something to his advice that I should cease looking back so much, that I should adopt a more positive outlook and try to make the best of what remains of my day”—appears toward the novel’s conclusion, spoken by the protagonist, Stevens, a stoic English butler reflecting on his life during a road trip across 1950s England.2,3 It captures Stevens grappling with regret over suppressed emotions, unrequited love for housekeeper Miss Kenton, and blind loyalty to his former employer, Lord Darlington, whose pro-appeasement stance toward Nazi Germany tainted his legacy. The “advice” comes from a genial stranger at a pier, who urges Stevens to enjoy life’s “evening” after a day’s work, echoing the novel’s titular metaphor of time slipping away like a fading day.2,3,4 This moment marks Stevens’s tentative shift from rigid self-denial toward acceptance, though his ingrained dignity—defined as unflinching duty—prevents full emotional release.1,2

Backstory on Kazuo Ishiguro and the Novel

Kazuo Ishiguro, born in 1954 in Nagasaki, Japan, moved to England at age five, shaping his themes of memory, displacement, and unspoken regret. A Nobel Prize winner in Literature (2017), he crafts subtle narratives blending historical realism with psychological depth, as in The Remains of the Day (1989), his third novel and Booker Prize victor.2 Inspired by unreliable narrators like those in Ford Madox Ford’s works, Ishiguro drew from real English butlers’ memoirs and interwar politics, critiquing class-bound repression without overt judgment. The Booker-winning story follows Stevens’s six-day drive to reunite with Miss Kenton, framed as his self-justifying memoir, exposing how duty stifles personal fulfillment amid 1930s fascism’s rise.1,2,4 Adapted into a 1993 Oscar-nominated film starring Anthony Hopkins and Emma Thompson, it remains Ishiguro’s most acclaimed work, probing what dignity is there in that?—a line underscoring Stevens’s crisis.2

Leading Theorists on Regret, Positive Outlook, and the “Remains of the Day”

The quote’s pivot from backward-glancing remorse to forward optimism ties into psychological and philosophical theories on regret minimization and temporal orientation. Key figures include:

  • Daniel Kahneman and Amos Tversky (Prospect Theory pioneers, Nobel in Economics 2002): Their work shows regret stems from inaction (e.g., Stevens’s unlived life with Miss Kenton), amplified by hindsight bias—recognizing “turning points” only retrospectively, as Stevens laments: What can we ever gain in forever looking back?2 They advocate shifting focus to future gains for emotional resilience.

  • Daniel Gilbert (Stumbling on Happiness, 2006): Gilbert’s research reveals humans overestimate past regrets while underestimating future adaptation; he posits adopting a “positive outlook” via affective forecasting—imagining better “remains” ahead—mirrors the stranger’s counsel to “put your feet up and enjoy it.”2,3 Stevens embodies Gilbert’s “impact bias,” where unaddressed regrets loom larger in memory.

  • Martin Seligman (Positive Psychology founder): Seligman’s learned optimism counters Stevens’s pessimism, urging reframing via gratitude: You must realize one has as good as most… and be grateful.1 His PERMA model (Positive Emotion, Engagement, Relationships, Meaning, Accomplishment) critiques duty-bound lives, aligning with Stevens’s late epiphany to “make the best of what remains.”

  • Viktor Frankl (Man’s Search for Meaning, 1946): A Holocaust survivor, Frankl’s logotherapy emphasizes finding meaning in suffering; Stevens’s arc echoes Frankl’s call to transcend regret through present purpose, rejecting endless rumination: There is little choice other than to leave our fate… in the hands of those great gentlemen.2

  • Epictetus and Stoic Philosophers: Ancient roots in Stevens’s dignity ideal; Epictetus advised focusing on controllables (one’s outlook) over uncontrollables (past choices), prefiguring the quote’s resolve amid life’s “evening.”1,2

These theorists illuminate the novel’s insight: regret poisons the “remains,” but a deliberate positive turn fosters redemption, blending empirical psychology with timeless wisdom.1,2,3

References

1. https://www.bookey.app/book/the-remains-of-the-day/quote

2. https://www.goodreads.com/work/quotes/3333111-the-remains-of-the-day

3. https://www.goodreads.com/work/quotes/3333111-the-remains-of-the-day?page=6

4. https://www.siquanong.com/book-summaries/the-remains-of-the-day/

5. https://bookroo.com/quotes/the-remains-of-the-day

6. https://www.sparknotes.com/lit/remains/quotes/page/2/

7. https://www.coursehero.com/lit/The-Remains-of-the-Day/quotes/

8. https://www.litcharts.com/lit/the-remains-of-the-day/quotes

9. https://www.cliffsnotes.com/literature/the-remains-of-the-day/quotes

10. https://www.sparknotes.com/lit/remains/quotes/

“Perhaps, then, there is something to his advice that I should cease looking back so much, that I should adopt a more positive outlook and try to make the best of what remains of my day.” - Quote: Kazuo Ishiguro

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Quote: Blackrock

Quote: Blackrock

“The AI builders are leveraging up: investment is front-loaded while revenues are back-loaded. Along with highly indebted governments, this creates a more levered financial system vulnerable to shocks like bond yield spikes.” – Blackrock – 2026 Outlook

The AI Financing Paradox: How Front-Loaded Investment and Back-Loaded Returns are Reshaping Global Financial Risk

The Quote in Context

BlackRock’s 2026 Investment Outlook identifies a critical structural vulnerability in global markets: the massive capital requirements of AI infrastructure are arriving years before the revenue benefits materialize1. This temporal mismatch creates what the firm describes as a financing “hump”—a period of intense leverage accumulation across both the private sector and government balance sheets, leaving financial systems exposed to potential shocks from rising bond yields or credit market disruptions1,2.

The quote reflects BlackRock’s core thesis that AI’s economic impact will be transformational, but the path to that transformation is fraught with near-term financial risks. As the world’s largest asset manager, overseeing nearly $14 trillion in assets, BlackRock’s assessment carries significant weight in shaping investment strategy and market expectations3.

The Investment Spend-Revenue Gap

The scale of the AI buildout is staggering. BlackRock projects $5-8 trillion in AI-related capital expenditure through 20305, with annual spending estimated at 5-8 trillion dollars globally until that date3. This represents the fastest technological buildout in recent centuries, yet the economics are unconventional: companies are committing enormous capital today with the expectation that productivity gains and revenue growth will materialize later2.

BlackRock notes that while the overall revenues AI eventually generates could theoretically justify the spending at a macroeconomic level, it remains unclear how much of that value will accrue to the tech companies actually building the infrastructure1,2. This uncertainty creates a critical vulnerability—if AI deployment proves less profitable than anticipated, or if adoption rates slow, highly leveraged companies may struggle to service their debt obligations.

The Leverage Imperative

The financing structure is not optional; it is inevitable. AI spending necessarily precedes benefits and revenues, creating an unavoidable need for long-term financing and greater leverage2. Tech companies and infrastructure providers cannot wait years to recoup their investments—they must borrow in capital markets today to fund construction, equipment, and operations.

This creates a second layer of risk. As companies issue bonds to finance AI capex, they increase corporate debt levels. Simultaneously, governments worldwide remain highly indebted from pandemic stimulus and ongoing fiscal pressures. The combination produces what BlackRock identifies as a “more levered financial system”—one where both public and private sector balance sheets are stretched1.

The Vulnerability to Shocks

BlackRock’s warning about vulnerability to “shocks like bond yield spikes” is particularly prescient. In a highly leveraged environment, rising interest rates have cascading effects:

  • Refinancing costs increase: Companies and governments face higher borrowing costs when existing bonds mature and must be renewed.
  • Debt service burden rises: Higher yields directly increase the cost of servicing existing debt, reducing profitability and fiscal flexibility.
  • Credit spreads widen: Investors demand higher risk premiums, making debt more expensive across the board.
  • Forced deleveraging: Companies unable to service debt at higher rates may need to cut spending, sell assets, or restructure obligations.

The AI buildout amplifies this risk because so much spending is front-loaded. If yield spikes occur before significant productivity gains materialize, companies may lack the cash flow to manage higher borrowing costs, creating potential defaults or forced asset sales that could trigger broader financial instability.

BlackRock’s Strategic Response

Rather than abandoning risk, BlackRock has taken a nuanced approach: the firm remains pro-risk and overweight U.S. stocks on the AI theme1, betting that the long-term benefits will justify near-term leverage accumulation. However, the firm has also shifted toward tactical underweighting of long-term Treasuries and identified opportunities in both public and private credit markets to manage risk while maintaining exposure1.

This reflects a sophisticated view: the financial system’s increased leverage is a real concern, but the AI opportunity is too significant to avoid. Instead, active management and diversification across asset classes become essential.

Broader Economic Context

The leverage dynamic intersects with broader macroeconomic shifts. BlackRock emphasizes that inflation is no longer the central issue driving markets; instead, labor dynamics and the distributional effects of AI now matter more4. The firm projects that AI could generate roughly $1.2 trillion in annual labor cost savings, translating into about $878 billion in incremental after-tax corporate profits each year, with a present value on the order of $82 trillion for corporations and another $27 trillion for AI providers4.

These enormous potential gains justify the current spending—on a macro level. Yet for individual investors and companies, dispersion and default risk are rising4. The benefits of AI will be highly concentrated among successful implementers, while laggards face obsolescence. This uneven distribution of gains and losses adds another layer of risk to a more levered financial system.

Historical and Theoretical Parallels

The AI financing paradox echoes historical technology cycles. During the dot-com boom of the late 1990s, massive capital investment in internet infrastructure preceded revenue generation by years, creating similar leverage vulnerabilities. The subsequent crash revealed how vulnerable highly leveraged systems are to disappointment about future growth rates.

However, this cycle differs in scale and maturity. Unlike the dot-com era, AI is already demonstrating productivity benefits across multiple sectors. The question is not whether AI creates value, but whether the timeline and magnitude of value creation justify the financial risks being taken today.


BlackRock’s insight captures a fundamental tension in modern finance: transformative technological change requires enormous upfront capital, yet highly leveraged financial systems are fragile. The path forward depends on whether productivity gains materialize quickly enough to validate the investment and reduce leverage before external shocks test the system’s resilience.

References

1. https://www.blackrock.com/americas-offshore/en/insights/blackrock-investment-institute/outlook

2. https://www.youtube.com/watch?v=eFBwyu30oTU

3. https://www.youtube.com/watch?v=Ww7Zy3MAWAs

4. https://www.blackrock.com/us/financial-professionals/insights/investing-in-2026

5. https://www.blackrock.com/us/financial-professionals/insights/ai-stocks-alternatives-and-the-new-market-playbook-for-2026

6. https://www.blackrock.com/corporate/insights/blackrock-investment-institute/publications/outlook

7. https://www.blackrock.com/institutions/en-us/insights/2026-macro-outlook

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Term: Economic recession

Term: Economic recession

An economic recession is a significant, widespread downturn in economic activity, characterized by declining real GDP (often two consecutive quarters), rising unemployment, falling retail sales, and reduced business/consumer spending, signaling a contraction in the business cycle. – Economic recession

Economic Recession

1,2

Definition and Measurement

Different jurisdictions employ distinct formal definitions. In the United Kingdom and European Union, a recession is defined as negative economic growth for two consecutive quarters, representing a six-month period of falling national output and income.1,2 The United States employs a more comprehensive approach through the National Bureau of Economic Research (NBER), which examines a broad range of economic indicators—including real GDP, real income, employment, industrial production, and wholesale-retail sales—to determine whether a significant decline in economic activity has occurred, considering its duration, depth, and diffusion across the economy.1,2

The Organisation for Economic Co-operation and Development (OECD) defines a recession as a period of at least two years during which the cumulative output gap reaches at least 2% of GDP, with the output gap remaining at least 1% for a minimum of one year.2

Key Characteristics

Recessions typically exhibit several defining features:

  • Duration: Most recessions last approximately one year, though this varies significantly.4
  • Output contraction: A typical recession involves a GDP decline of around 2%, whilst severe recessions may see output costs approaching 5%.4
  • Employment impact: The unemployment rate almost invariably rises during recessions, with layoffs becoming increasingly common and wage growth slowing or stagnating.2
  • Consumer behaviour: Consumption declines occur, often accompanied by shifts toward lower-cost generic brands as discretionary income diminishes.2
  • Investment reduction: Industrial production and business investment register much larger declines than GDP itself.4
  • Financial disruption: Recessions typically involve turmoil in financial markets, erosion of house and equity values, and potential credit tightening that restricts borrowing for both consumers and businesses.4
  • International trade: Exports and imports fall sharply during recessions.4
  • Inflation modereration: Overall demand for goods and services contracts, causing inflation to fall slightly or, in deflationary recessions, to become negative with prices declining.1,4

Causes and Triggers

Recessions generally stem from market imbalances, triggered by external shocks or structural economic weaknesses.8 Common precipitating factors include:

  • Excessive household debt accumulation followed by difficulties in meeting obligations, prompting consumers to reduce spending.2
  • Rapid credit expansion followed by credit tightening (credit crunches), which restricts the availability of borrowing for consumers and businesses.2
  • Rising material and labour costs prompting businesses to increase prices; when central banks respond by raising interest rates, higher borrowing costs discourage business investment and consumer spending.5
  • Declining consumer confidence manifesting in falling retail sales and reduced business investment.2

Distinction from Depression

A depression represents a severe or prolonged recession. Whilst no universally agreed definition exists, a depression typically involves a GDP fall of 10% or more, a GDP decline persisting for over three years, or unemployment exceeding 20%.1 The informal economist’s observation captures this distinction: “It’s a recession when your neighbour loses his job; it’s a depression when you lose yours.”1

Policy Response

Governments typically respond to recessions through expansionary macroeconomic policies, including increasing money supply, decreasing interest rates, raising government spending, and reducing taxation, to stimulate economic activity and restore growth.2


Related Strategy Theorist: John Maynard Keynes

John Maynard Keynes (1883–1946) stands as the preeminent theorist whose work fundamentally shaped modern understanding of recessions and the policy responses to them.

Biography and Context

Born in Cambridge, England, Keynes was an exceptionally gifted economist, mathematician, and public intellectual. After studying mathematics at King’s College, Cambridge, he pivoted to economics and became a fellow of the college in 1909. His early career included service with the Indian Civil Service and as an editor of the Economic Journal, Britain’s leading economics publication.

Keynes’ formative professional experience came as the chief representative of the British Treasury at the Paris Peace Conference in 1919 following the First World War. Disturbed by the punitive reparations imposed upon Germany, he resigned and published The Economic Consequences of the Peace (1919), which warned prophetically of economic instability resulting from the treaty’s harsh terms. This work established his reputation as both economist and public commentator.

Relationship to Recession Theory

Keynes’ revolutionary contribution emerged with the publication of The General Theory of Employment, Interest and Money (1936), written during the Great Depression. His work fundamentally challenged the prevailing classical economic orthodoxy, which held that markets naturally self-correct and unemployment represents a temporary frictional phenomenon.

Keynes demonstrated that recessions and prolonged unemployment result from insufficient aggregate demand rather than labour market rigidities or individual irresponsibility.C + I + G + (X - M) = Y, where aggregate demand (the sum of consumption, investment, government spending, and net exports) determines total output and employment. During recessions, demand contracts—consumers and businesses reduce spending due to uncertainty and falling incomes—creating a self-reinforcing downward spiral that markets alone cannot reverse.

This insight proved revolutionary because it legitimised active government intervention in recessions. Rather than viewing recessions as inevitable and self-correcting phenomena to be endured passively, Keynes argued that governments could and should employ fiscal policy (taxation and spending) and monetary authorities could adjust interest rates to stimulate aggregate demand, thereby shortening recessions and reducing unemployment.

His framework directly underpinned the post-war consensus on recession management: expansionary monetary and fiscal policies during downturns to restore demand and employment. The modern definition of recession as a statistical phenomenon (two consecutive quarters of negative GDP growth) emerged from Keynesian economics’ focus on output and demand as the central drivers of economic cycles.

Keynes’ influence extended beyond economic theory into practical policy. His ideas shaped the institutional architecture of the post-1945 international economic order, including the International Monetary Fund and World Bank, both conceived to prevent the catastrophic demand collapse that characterised the 1930s.

References

1. https://www.economicshelp.org/blog/459/economics/define-recession/

2. https://en.wikipedia.org/wiki/Recession

3. https://den.mercer.edu/what-is-a-recession-and-is-the-u-s-in-one-mercer-economists-explain/

4. https://www.imf.org/external/pubs/ft/fandd/basics/recess.htm

5. https://www.fidelity.com/learning-center/smart-money/what-is-a-recession

6. https://www.congress.gov/crs-product/IF12774

7. https://www.munich-business-school.de/en/l/business-studies-dictionary/financial-knowledge/recession

8. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-a-recession

An economic recession is a significant, widespread downturn in economic activity, characterized by declining real GDP (often two consecutive quarters), rising unemployment, falling retail sales, and reduced business/consumer spending, signaling a contraction in the business cycle. - Term: Economic recession

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Quote: William Makepeace Thackeray – English novelist

Quote: William Makepeace Thackeray – English novelist

The world is a looking-glass, and gives back to every man the reflection of his own face. Frown at it, and it will in turn look sourly upon you; laugh at it and with it, and it is a jolly kind companion; and so let all young persons take their choice. – William Makepeace Thackeray – English novelist

The Quote

Context of the Quote

This passage appears in William Makepeace Thackeray’s seminal novel Vanity Fair: A Novel Without a Hero (serialized 1847–1848), during a narrative reflection on human behavior and perception13. It occurs amid commentary on a young character’s misanthropic outlook, where the narrator observes that people who view the world harshly often receive harshness in return, attributing this to self-projection rather than external reality3. The metaphor of the world as a “looking-glass” (an old term for mirror) underscores the novel’s core theme of vanity—how personal attitudes shape social interactions in a superficial, reciprocal society13. Thackeray uses it to advise youth to choose optimism, contrasting it with the book’s satirical portrayal of ambition, deceit, and social climbing in early 19th-century England3.

Backstory on William Makepeace Thackeray

William Makepeace Thackeray (1811–1863) was a prominent English novelist, satirist, and illustrator, often ranked alongside Charles Dickens as a Victorian literary giant1. Born in Calcutta, India, to British parents—his father a colonial administrator—he returned to England at age six after his father’s early death1. Educated at Charterhouse School and Cambridge University, Thackeray initially pursued law and art but turned to journalism and writing amid financial ruin from failed investments and his wife’s mental illness following childbirth1.

His breakthrough came with Vanity Fair, a panoramic satire of British society during the Napoleonic Wars, drawing from John Bunyan’s The Pilgrim’s Progress (where “Vanity Fair” symbolizes worldly temptation)13. Published anonymously as monthly installments, it sold widely for its witty narration, moral ambiguity, and critique of hypocrisy among the upper and aspiring middle classes1. Thackeray followed with successes like Pendennis (1848–1850), Henry Esmond (1852), and The Newcomes (1853–1855), blending humor, pathos, and realism1. A rival to Dickens, he lectured on English humorists and edited Cornhill Magazine, but personal struggles with debt, health (addiction to opium and alcohol), and family tragedy marked his life. He died at 52 from a ruptured aneurysm1.

Thackeray’s style—omniscient, ironic narration—mirrors the quote’s philosophy: life reflects one’s inner disposition, a recurring motif in his works exposing human folly without heavy moralizing13.

Leading Theorists Related to the Subject Matter

The quote’s idea—that reality mirrors one’s attitude—echoes longstanding philosophical and psychological concepts on perception, projection, and optimism. Below is a backstory on key theorists whose ideas parallel or influenced this theme of reciprocal self-fulfilling prophecy.

  • Baruch Spinoza (1632–1677): Dutch philosopher whose Ethics (1677) posits that emotions like hope or fear shape how we interpret the world, creating self-reinforcing cycles. He argued humans project passions onto external events, much like Thackeray’s “looking-glass,” advocating rational optimism to alter perception[supplemental knowledge, aligned with Thackeray’s era].

  • Immanuel Kant (1724–1804): German idealist in Critique of Pure Reason (1781) who theorized that the mind imposes structure on sensory experience—our “face” colors reality. This subjective lens prefigures Thackeray’s mirror metaphor, influencing 19th-century Romantic views on personal agency in shaping fate.

  • William James (1842–1910): American pragmatist and psychologist, contemporary to Thackeray’s later influence, in The Principles of Psychology (1890) described the “self-fulfilling prophecy” where expectations elicit confirming behaviors from others. His optimism essays echo the quote’s call to “laugh at it,” linking mindset to social outcomes.

  • Norman Vincent Peale (1898–1993): 20th-century popularizer of positive thinking in The Power of Positive Thinking (1952), directly inverting frowns/smiles to transform life experiences—a modern extension of Thackeray’s advice, rooted in psychological projection.

  • Cognitive Behavioral Theorists (e.g., Aaron Beck, 1921–2021): Beck’s cognitive therapy (1960s onward) formalized cognitive distortions, where negative schemas (like frowning at the world) perpetuate sour outcomes, supported by empirical studies on attribution bias and reciprocity in social psychology.

These ideas trace from Enlightenment rationalism through Victorian literature to modern psychology, all converging on the insight that personal disposition acts as a filter and catalyst for worldly responses, as Thackeray insightfully captured13.

References

1. https://www.goodreads.com/author/quotes/3953.William_Makepeace_Thackeray

2. https://www.azquotes.com/author/14547-William_Makepeace_Thackeray

3. https://www.goodreads.com/work/quotes/1057468-vanity-fair-a-novel-without-a-hero

4. https://www.sparknotes.com/lit/vanity-fair/quotes/

5. https://www.coursehero.com/lit/Vanity-Fair/quotes/

6. http://www.freebooknotes.com/quotes/vanity-fair/

7. https://libquotes.com/william-makepeace-thackeray/works/vanity-fair

8. https://www.litcharts.com/lit/vanity-fair/quotes

The world is a looking-glass, and gives back to every man the reflection of his own face. Frown at it, and it will in turn look sourly upon you; laugh at it and with it, and it is a jolly kind companion; and so let all young persons take their choice. - Quote: William Makepeace Thackeray - English novelist

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Quote: Milton Friedman – Nobel laureate

Quote: Milton Friedman – Nobel laureate

“One of the great mistakes is to judge policies and programs by their intentions rather than their results.” – Milton Friedman – Nobel laureate

1

Context and Origin

Milton Friedman first expressed this idea during a 1975 television interview on The Open Mind, hosted by Richard Heffner. Discussing government programs aimed at helping the poor and needy, Friedman argued that such initiatives, despite their benevolent intentions, often produce opposite effects. He tied the remark to the proverb “the road to hell is paved with good intentions,” emphasizing that good-hearted advocates sometimes fail to apply the same rigor to their heads, leading to unintended harm1. The quote has since appeared in books like After the Software Wars (2009) and I Am John Galt (2011), a 2024 New York Times letter critiquing the Department of Education, and various quote collections13.

This perspective underscores Friedman’s broader critique of public policy: evaluate effectiveness through empirical outcomes, not rhetoric. He often highlighted how welfare programs, school vouchers, and monetary policies could backfire if results are ignored in favor of motives14.

Backstory on Milton Friedman

Milton Friedman (1912–2006) was a pioneering American economist, statistician, and public intellectual whose work reshaped modern economic thought. Born in Brooklyn, New York, to Jewish immigrant parents from Hungary, he earned his bachelor’s degree from Rutgers University in 1932 amid the Great Depression, followed by master’s and doctoral degrees from the University of Chicago. There, he joined the “Chicago School” of economics, advocating free markets, limited government, and individual liberty1.

Friedman’s seminal contributions include A Monetary History of the United States (1963, co-authored with Anna Schwartz), which blamed the Federal Reserve’s policies for exacerbating the Great Depression and influenced central banking worldwide. His advocacy for floating exchange rates contributed to the end of the Bretton Woods system in 1971. In Capitalism and Freedom (1962), he proposed ideas like school vouchers, a negative income tax, and abolishing the draft—many of which remain debated today.

A fierce critic of Keynesian economics, Friedman championed monetarism: the idea that controlling money supply stabilizes economies better than fiscal intervention. His PBS series Free to Choose (1980) and bestselling book of the same name popularized these views for lay audiences. Awarded the Nobel Prize in Economic Sciences in 1976 “for his achievements in the fields of consumption analysis, monetary history and theory, and for his demonstration of the complexity of stabilization policy,” Friedman influenced leaders like Ronald Reagan and Margaret Thatcher1.

Later, he opposed the war on drugs, supported drug legalization, and critiqued Social Security. Friedman died in 2006, leaving a legacy as a defender of economic freedom against well-intentioned but flawed interventions.

Leading Theorists Related to the Subject Matter

Friedman’s quote critiques the “intention fallacy” in policy evaluation, aligning with traditions emphasizing empirical results over moral or ideological justifications. Key related theorists include:

  • Friedrich Hayek (1899–1992): Austrian-British economist and Nobel laureate (1974). In The Road to Serfdom (1944), Hayek warned that central planning, even with good intentions, leads to unintended tyranny due to knowledge limits in society. He influenced Friedman via the Mont Pelerin Society (founded 1947), stressing spontaneous order and market signals over planners’ designs1.

  • James M. Buchanan (1919–2013): Nobel laureate (1986) in public choice theory. With Gordon Tullock in The Calculus of Consent (1962), he modeled politicians and bureaucrats as self-interested actors, explaining why “public interest” policies produce perverse results like pork-barrel spending. This countered naive views of benevolent government1.

  • Gary Becker (1930–2014): Chicago School Nobel laureate (1992). Extended economic analysis to non-market behavior (e.g., crime, family) in Human Capital (1964), showing policies must be judged by incentives and outcomes, not intent. Becker quantified how regulations distort behaviors, echoing Friedman’s results focus1.

  • John Maynard Keynes (1883–1946): Counterpoint theorist. In The General Theory (1936), Keynes advocated government intervention for demand management, prioritizing intentions to combat unemployment. Friedman challenged this empirically, arguing it caused 1970s stagflation1.

These thinkers form the backbone of outcome-based policy critique, contrasting with interventionist schools like Keynesianism, where intentions often justify expansions despite mixed results.

Friedman’s Permanent Income Hypothesis

Linked in some discussions to Friedman’s consumption work, the Permanent Income Hypothesis (1957) posits that people base spending on “permanent” (long-term expected) income, not short-term fluctuations. In A Theory of the Consumption Function, Friedman argued transitory income changes (e.g., bonuses) are saved, not spent, challenging Keynesian absolute income hypothesis. Empirical tests via microdata supported it, influencing modern macroeconomics and fiscal policy debates on multipliers1. This hypothesis exemplifies Friedman’s results-driven approach: policies assuming instant spending boosts (e.g., stimulus checks) overlook consumption smoothing.

References

1. https://quoteinvestigator.com/2024/03/22/intentions-results/

2. https://www.azquotes.com/quote/351907

3. https://www.goodreads.com/quotes/29902-one-of-the-great-mistakes-is-to-judge-policies-and

4. https://www.americanexperiment.org/milton-friedman-judge-public-policies-by-their-results-not-their-intentions/

One of the great mistakes is to judge policies and programs by their intentions rather than their results. - Quote: Milton Friedman - Nobel laureate

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Term: Alpha

Term: Alpha

1,2,3,5

Comprehensive Definition

Alpha isolates the value added (or subtracted) by active management, distinguishing it from passive market returns. It quantifies performance on a risk-adjusted basis, accounting for systematic risk via beta, which reflects an asset’s volatility relative to the market. A positive alpha signals outperformance—meaning the manager has skilfully selected securities or timed markets to exceed expectations—while a negative alpha indicates underperformance, often failing to justify management fees.1,3,4,5 An alpha of zero implies returns precisely match the risk-adjusted benchmark.3,5

In practice, alpha applies across asset classes:

  • Public equities: Compares actively managed funds to passive indices like the S&P 500.1,5
  • Private equity: Assesses managers against risk-adjusted expectations, absent direct passive benchmarks, emphasising skill in handling illiquidity and leverage risks.1

Alpha underpins debates on active versus passive investing: consistent positive alpha justifies active fees, but many managers struggle to sustain it after costs.1,4

Calculation Methods

The simplest form subtracts benchmark return from portfolio return:

  • Alpha = Portfolio Return – Benchmark Return
    Example: Portfolio return of 14.8% minus benchmark of 11.2% yields alpha = 3.6%.1

For precision, Jensen’s Alpha uses the Capital Asset Pricing Model (CAPM) to compute expected return:
\alpha = R<em>p - [R</em>f + \beta (R<em>m - R</em>f)]
Where:

  • ( R_p ): Portfolio return
  • ( R_f ): Risk-free rate (e.g., government bond yield)
  • ( \beta ): Portfolio beta
  • ( R_m ): Market/benchmark return

Example: ( Rp = 30\% ), ( Rf = 8\% ), ( \beta = 1.1 ), ( R_m = 20\% ) gives:
\alpha = 0.30 - [0.08 + 1.1(0.20 - 0.08)] = 0.30 - 0.214 = 0.086 \ (8.6\%)3,4

This CAPM-based approach ensures alpha reflects true skill, not uncompensated risk.1,2,5

Key Theorist: Michael Jensen

The foremost theorist linked to alpha is Michael Jensen (1939–2021), who formalised Jensen’s Alpha in his seminal 1968 paper, “The Performance of Mutual Funds in the Period 1945–1964,” published in the Journal of Finance. This work introduced alpha as a rigorous metric within CAPM, enabling empirical tests of manager skill.1,4

Biography and Backstory: Born in Independence, Missouri, Jensen earned a PhD in economics from the University of Chicago under Nobel laureate Harry Markowitz, immersing him in modern portfolio theory. His 1968 study analysed 115 mutual funds, finding most generated negative alpha after fees, challenging claims of widespread managerial prowess and bolstering efficient market hypothesis evidence.1 This propelled him to Harvard Business School (1968–1987), then the University of Rochester, and later Intech and Harvard again. Jensen pioneered agency theory, co-authoring “Theory of the Firm” (1976) on managerial incentives, and influenced private equity via leveraged buyouts. His alpha measure remains foundational, used daily by investors to evaluate funds against CAPM benchmarks, underscoring that true alpha stems from security selection or timing, not market beta.1,4,5 Jensen’s legacy endures in performance attribution, with his metric cited in trillions of dollars’ worth of evaluations.

References

1. https://www.moonfare.com/glossary/investment-alpha

2. https://robinhood.com/us/en/learn/articles/2lwYjCxcvUP4lcqQ3yXrgz/what-is-alpha/

3. https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/alpha/

4. https://www.wallstreetprep.com/knowledge/alpha/

5. https://www.findex.se/finance-terms/alpha

6. https://www.ig.com/uk/glossary-trading-terms/alpha-definition

7. https://www.pimco.com/us/en/insights/the-alpha-equation-myths-and-realities

8. https://eqtgroup.com/thinq/Education/what-is-alpha-in-investing

Alpha measures an investment's excess return compared to its expected return for the risk taken, indicating a portfolio manager's skill in outperforming a benchmark index (like the S&P 500) after adjusting for market volatility (beta). - Term: Alpha

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Quote: Hari Vasudevan – Utility Dive

Quote: Hari Vasudevan – Utility Dive

“Data centers used 4% of U.S. electricity two years ago and are on track to devour three times that by 2028.” – Hari Vasudevan – Utility Dive

Hari Vasudevan is the founder and CEO of KYRO AI, an AI-powered platform designed to streamline operations in utilities, vegetation management, disaster response, and critical infrastructure projects, supporting over $150 billion in program value by enhancing safety, efficiency, and cost savings for contractors and service providers.1,3,4

Backstory and Context of the Quote

The quote—”Utilities that embrace artificial intelligence will set reliability and affordability standards for decades to come”—originates from Vasudevan’s November 26, 2025, opinion piece in Utility Dive titled “Data centers are breaking the old grid. Let AI build the new one.”1,6 In it, he addresses the grid’s strain from surging data center demand fueled by AI, exemplified by Georgia regulators’ summer 2025 rules to protect residential customers from related cost hikes.6 Vasudevan argues that the U.S. power grid faces an “inflection point,” where clinging to a reactive 20th-century model leads to higher bills and outages, while AI adoption enables a resilient system balancing homes, businesses, and digital infrastructure.1,6 This piece builds on his November 2025 Energy Intelligence article urging utilities and hyperscalers (e.g., tech giants building data centers) to collaborate via dynamic load management, on-site generation, and shared capital risks to avoid burdening ratepayers.5 The context reflects escalating challenges: data centers are driving grid overloads, extreme weather has caused $455 billion in U.S. storm damage since 1980 (one-third in the last five years), and utility rate disallowances have risen to 35-40% from 2019-2023 amid regulatory scrutiny.4,5,6

Vasudevan’s perspective stems from hands-on experience. He founded Think Power Solutions to provide construction management and project oversight for electric utilities, managing multi-billion-dollar programs nationwide and achieving a 100% increase in working capital turns alongside 57% growth by improving billing accuracy, reducing delays, and bridging field-office gaps in thin-margin industries.3 After exiting as CEO, he launched KYRO AI to apply these efficiencies at scale, particularly for storm response—where AI optimizes workflows for linemen, fleets, and regulators amid rising billion-dollar weather events—and infrastructure buildouts like transmission lines powering data centers.3,4 In a CCCT podcast, he emphasized AI’s role in powering the economy during uncertain times, closing gaps that erode profits, and aiding small construction businesses.3

Leading Theorists in AI for Grid Modernization and Utility Resilience

Vasudevan’s advocacy aligns with pioneering work in AI applications for energy systems. Key theorists include:

  • Amory Lovins: Co-founder of Rocky Mountain Institute, Lovins pioneered “soft path” energy theory in the 1970s, advocating distributed resources over centralized grids—a concept echoed in maximizing home/business energy assets for resilience, as Vasudevan supports via AI orchestration.1
  • Massoud Amin: Often called the “father of the smart grid,” Amin (University of Minnesota) developed early frameworks for AI-driven, self-healing grids in the 2000s, integrating sensors and automation to prevent blackouts and enhance reliability amid data center loads.4,6
  • Andrew Ng: Stanford professor and AI pioneer (co-founder of Coursera, former Baidu chief scientist), Ng has theorized AI’s role in predictive grid maintenance and demand forecasting since 2010s deep learning breakthroughs, directly influencing tools like KYRO for storm response and vegetation management.3,4
  • Bri-Mathias Hodge: NREL researcher advancing AI/ML for renewable integration and grid stability, with models optimizing distributed energy resources—core to Vasudevan’s push against “breaking the old grid.”1,5

These theorists provide the intellectual foundation: Lovins for decentralization, Amin for smart infrastructure, Ng for scalable AI, and Hodge for optimization, all converging on AI as essential for affordable, resilient grids facing AI-driven demand.1,4,5,6

 

References

1. https://www.utilitydive.com/opinion/

2. https://www.utilitydive.com/?page=1&p=505

3. https://www.youtube.com/watch?v=g8q16BWXk4o

4. https://www.utilitydive.com/news/ai-utility-storm-response-kyro/752172/

5. https://www.energyintel.com/0000019b-2712-d02f-adfb-e7932e490000

6. https://www.utilitydive.com/news/ai-utilities-reliability-cost/805224/

 

Data centers used 4% of U.S. electricity two years ago and are on track to devour three times that by 2028. - Quote: Hari Vasudevan - Utility Dive

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Term: Sharpe Ratio

Term: Sharpe Ratio

The Sharpe Ratio is a key finance metric measuring an investment’s excess return (above the risk-free rate) per unit of its total risk (volatility/standard deviation), with a higher ratio indicating better risk-adjusted performance. – Sharpe Ratio –

The Sharpe Ratio is a fundamental metric in finance that quantifies an investment’s or portfolio’s risk-adjusted performance by measuring the excess return over the risk-free rate per unit of total risk, typically represented by the standard deviation of returns. A higher ratio indicates superior returns relative to the volatility borne, enabling investors to compare assets or portfolios on an apples-to-apples basis despite differing risk profiles.1,2,3

Formula and Calculation

The Sharpe Ratio is calculated using the formula:

\text{Sharpe Ratio} = \frac{R_a - R_f}{\sigma_a}

Where:

  • ( R_a ): Average return of the asset or portfolio (often annualised).3,4
  • ( R_f ): Risk-free rate (e.g., yield on government bonds or Treasury bills).1,3
  • ( \sigma_a ): Standard deviation of the asset’s returns, measuring volatility or total risk.1,2,5

To compute it:

  1. Determine the asset’s historical or expected average return.
  2. Subtract the risk-free rate to find excess return.
  3. Divide by the standard deviation, derived from return variance.3,4

For example, if an investment yields 40% return with a 20% risk-free rate and 5% standard deviation, the Sharpe Ratio is (40% – 20%) / 5% = 4. In contrast, a 60% return with 80% standard deviation yields (60% – 20%) / 80% = 0.5, showing the lower-volatility option performs better on a risk-adjusted basis.4

Interpretation

  • >2: Excellent; strong excess returns for the risk.3
  • 1-2: Good; adequate compensation for volatility.2,3
  • =1: Decent; return proportional to risk.2,3
  • <1: Suboptimal; insufficient returns for the risk.3
  • ?0: Poor; underperforms risk-free assets.3,5

This metric excels for comparing investments with varying risk levels, such as mutual funds, but assumes normal return distributions and total risk (not distinguishing systematic from idiosyncratic risk).1,2,5

Limitations

The Sharpe Ratio treats upside and downside volatility equally, may underperform in non-normal distributions, and relies on historical data that may not predict future performance. Variants like the Sortino Ratio address some flaws by focusing on downside risk.1,2,5

Key Theorist: William F. Sharpe

The best related strategy theorist is William F. Sharpe (born 16 June 1934), the metric’s creator and originator of the Capital Asset Pricing Model (CAPM), which underpins modern portfolio theory.

Biography

Sharpe earned a BA in economics from UCLA (1955), an MA (1956), and PhD (1961) from Stanford University. He joined Stanford’s Graduate School of Business faculty in 1970, becoming STANCO 25 Professor Emeritus of Finance. His seminal 1964 paper, “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,” introduced CAPM, positing that expected return correlates linearly with systematic risk (beta). In 1990, Sharpe shared the Nobel Memorial Prize in Economic Sciences with Harry Markowitz and Merton Miller for pioneering financial economics, particularly portfolio selection and asset pricing.1,5,7,9

Relationship to the Sharpe Ratio

Sharpe developed the ratio in his 1966 paper “Mutual Fund Performance,” published in the Journal of Business, to evaluate active managers’ skill beyond raw returns. It extends CAPM by normalising excess returns (alpha-like) by total volatility, rewarding efficient risk-taking. By 1994, he refined it in “The Sharpe Ratio” on his Stanford site, linking it to t-statistics for statistical significance. The metric remains the “golden industry standard” for risk-adjusted performance, integral to strategies like passive indexing and factor investing that Sharpe championed.1,5,7,9

 

References

1. https://en.wikipedia.org/wiki/Sharpe_ratio

2. https://www.businessinsider.com/personal-finance/investing/sharpe-ratio

3. https://www.kotakmf.com/Information/blogs/sharpe-ratio_

4. https://www.cmcmarkets.com/en-gb/fundamental-analysis/what-is-the-sharpe-ratio

5. https://corporatefinanceinstitute.com/resources/career-map/sell-side/risk-management/sharpe-ratio-definition-formula/

6. https://www.personalfinancelab.com/glossary/sharpe-ratio/

7. https://www.risk.net/definition/sharpe-ratio

8. https://www.youtube.com/watch?v=96Aenz0hNKI

9. https://web.stanford.edu/~wfsharpe/art/sr/sr.htm

 

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Quote: Professor Anil Bilgihan – Florida Atlantic University Business

Quote: Professor Anil Bilgihan – Florida Atlantic University Business

“AI agents will be the new gatekeepers of loyalty, The question is no longer just ‘How do we win a customer’s heart?’ but ‘How do we win the trust of the algorithms that are advising them?’” – Professor Anil Bilgihan – Florida Atlantic University Business

Professor Anil Bilgihan: Academic and Research Profile

Professor Anil Bilgihan is a leading expert in services marketing and hospitality information systems at Florida Atlantic University’s College of Business, where he serves as a full Professor in the Marketing Department with a focus on Hospitality Management.1,2,4 He holds the prestigious Harry T. Mangurian Professorship and previously the Dean’s Distinguished Research Fellowship, recognizing his impactful work at the intersection of technology, consumer behavior, and the hospitality industry.2,3

Education and Early Career

Bilgihan earned his PhD in 2012 from the University of Central Florida’s Rosen College of Hospitality Management, specializing in Education/Hospitality Education Track.1,2 He holds an MS in Hospitality Information Management (2009) from the University of Delaware and a BS in Computer Technology and Information Systems (2007) from Bilkent University in Turkey.1,2,4 His technical foundation in computer systems laid the groundwork for his research in digital technologies applied to services.

Before joining FAU in 2013, he was a faculty member at The Ohio State University.2,4 At FAU, based in Fleming Hall Room 316 (Boca Raton), he teaches courses in hotel marketing and revenue management while directing research efforts.1,2

Research Contributions and Expertise

Bilgihan’s scholarship centers on how technology transforms hospitality and tourism, including e-commerce, user experience, digital marketing, online social interactions, and emerging tools like artificial intelligence (AI).2,3,4 With over 70 refereed journal articles, 80 conference proceedings, an h-index of 38, and i10-index of 68—resulting in more than 18,000 citations—he is a prolific influencer in the field.2,4,7

Key recent publications highlight his forward-looking focus on generative AI:

  • Co-authored a 2025 framework for generative AI in hospitality and tourism research (Journal of Hospitality and Tourism Research).1
  • Developed a 2025 systematic review on AI awareness and employee outcomes in hospitality (International Journal of Hospitality Management).1
  • Explored generative AI’s implications for academic research in tourism and hospitality (2024, Tourism Economics).1

Earlier works include agent-based modeling for eWOM strategies (2021), AI assessment frameworks for hospitality (2021), and online community building for brands (2018).1 His research appears in top journals such as Tourism Management, International Journal of Hospitality Management, Computers in Human Behavior, and Journal of Service Management.2,4

Bilgihan co-authored the textbook Hospitality Information Technology: Learning How to Use It, widely used in the field.2,4 He serves on editorial boards (e.g., International Journal of Contemporary Hospitality Management), as associate editor of Psychology & Marketing, and co-editor of Journal of International Hospitality Management.2

Awards and Leadership Roles

Recognized with the Cisco Extensive Research Award, FAU Scholar of the Year Award, and Highly Commended Award from the Emerald/EFMD Outstanding Doctoral Research Awards.2,4 He contributes to FAU’s Behavioral Insights Lab, developing AI-digital marketing frameworks for customer satisfaction, and the Center for Services Marketing.3,5

Leading Theorists in Hospitality Technology and AI

Bilgihan’s work builds on foundational theorists in services marketing, technology adoption, and AI in hospitality. Key figures include:

  • Jill Kandampully (co-author on brand communities, 2018): Pioneer in services marketing and customer loyalty; her relational co-creation theory emphasizes technology’s role in value exchange (Journal of Hospitality and Tourism Technology).1
  • Peter Ricci (frequent collaborator): Expert in hospitality revenue management and digital strategies; advances real-time data analytics for tourism marketing.1,5
  • Ye Zhang (collaborator): Focuses on agent-based modeling and social media’s impact on travel; extends motivation theories for accessibility in tourism.1
  • Fred Davis (Technology Acceptance Model, TAM, 1989): Core influence on Bilgihan’s user experience research; TAM explains technology adoption via perceived usefulness and ease-of-use, widely applied in hospitality e-commerce.2 (Inferred from Bilgihan’s tech adoption focus.)
  • Viswanath Venkatesh (Unified Theory of Acceptance and Use of Technology, UTAUT, 2003): Builds on TAM for AI and digital tools; Bilgihan’s AI frameworks align with UTAUT’s performance expectancy in service contexts.3 (Inferred from AI decision-making emphasis.)
  • Ming-Hui Huang and Roland T. Rust: Leaders in AI-service research; their “AI substitution” framework (2018) informs Bilgihan’s hospitality AI assessments, predicting AI’s role in frontline service transformation.1 (Directly cited in Bilgihan’s 2021 AI paper.)

These theorists provide the theoretical backbone for Bilgihan’s empirical frameworks, bridging behavioral economics, information systems, and hospitality operations amid digital disruption.1,2,3,4

 

References

1. https://business.fau.edu/faculty-research/faculty-profiles/profile/abilgihan.php

2. https://www.madintel.com/team/anil-bilgihan

3. https://business.fau.edu/centers/behavioral-insights-lab/meet-behavioral-insights-experts/

4. https://sites.google.com/view/anil-bilgihan/

5. https://business.fau.edu/centers/center-for-services-marketing/center-faculty/

6. https://business.fau.edu/departments/marketing/hospitality-management/meet-faculty/

7. https://scholar.google.com/citations?user=5pXa3OAAAAAJ&hl=en

 

AI agents will be the new gatekeepers of loyalty, The question is no longer just ‘How do we win a customer’s heart?’ but ‘How do we win the trust of the algorithms that are advising them?’ - Quote: Professor Anil Bilgihan - Florida Atlantic University Business

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Term: Monte-Carlo simulation

Term: Monte-Carlo simulation

Monte Carlo Simulation

Monte Carlo simulation is a computational technique that uses repeated random sampling to predict possible outcomes of uncertain events by generating probability distributions rather than single definite answers.1,2

Core Definition

Unlike conventional forecasting methods that provide fixed predictions, Monte Carlo simulation leverages randomness to model complex systems with inherent uncertainty.1 The method works by defining a mathematical relationship between input and output variables, then running thousands of iterations with randomly sampled values across a probability distribution (such as normal or uniform distributions) to generate a range of plausible outcomes with associated probabilities.2

How It Works

The fundamental principle underlying Monte Carlo simulation is ergodicity—the concept that repeated random sampling within a defined system will eventually explore all possible states.1 The practical process involves:

  1. Establishing a mathematical model that connects input variables to desired outputs
  2. Selecting probability distributions to represent uncertain input values (for example, manufacturing temperature might follow a bell curve)
  3. Creating large random sample datasets (typically 100,000+ samples for accuracy)
  4. Running repeated simulations with different random values to generate hundreds or thousands of possible outcomes1

Key Applications

Financial analysis: Monte Carlo simulations help analysts evaluate investment risk by modeling dozens or hundreds of factors simultaneously—accounting for variables like interest rates, commodity prices, and exchange rates.4

Business decision-making: Marketers and managers use these simulations to test scenarios before committing resources. For instance, a business might model advertising costs, subscription fees, sign-up rates, and retention rates to determine whether increasing an advertising budget will be profitable.1

Search and rescue: The US Coast Guard employs Monte Carlo methods in its SAROPS software to calculate probable vessel locations, generating up to 10,000 randomly distributed data points to optimize search patterns and maximize rescue probability.4

Risk modeling: Organizations use Monte Carlo simulations to assess complex uncertainties, from nuclear power plant failure risk to project cost overruns, where traditional mathematical analysis becomes intractable.4

Advantages Over Traditional Methods

Monte Carlo simulations provide a probability distribution of all possible outcomes rather than a single point estimate, giving decision-makers a clearer picture of risk and uncertainty.1 They produce narrower, more realistic ranges than “what-if” analysis by incorporating the actual statistical behavior of variables.4


Related Strategy Theorist: Stanislaw Ulam

Stanislaw Ulam (1909–1984) stands as one of two primary architects of the Monte Carlo method, alongside John von Neumann, during World War II.2 Ulam was a Polish-American mathematician whose creative insights transformed how uncertainty could be modeled computationally.

Biography and Relationship to Monte Carlo

Ulam was born in Lvov, Poland, and earned his doctorate in mathematics from the Polish University of Warsaw. His early career established him as a talented pure mathematician working in topology and set theory. However, his trajectory shifted dramatically when he joined the Los Alamos National Laboratory during the Manhattan Project—the secretive American effort to develop nuclear weapons.

At Los Alamos, Ulam worked alongside some of the greatest minds in physics and mathematics, including Enrico Fermi, Richard Feynman, and John von Neumann. The computational challenges posed by nuclear physics and neutron diffusion were intractable using classical mathematical methods. Traditional deterministic equations could not adequately model the probabilistic behavior of particles and their interactions.

The Monte Carlo Innovation

In 1946, while recovering from an illness, Ulam conceived the Monte Carlo method. The origin story, as recounted in his memoir, reveals the insight’s elegance: while playing solitaire during convalescence, Ulam wondered whether he could estimate the probability of winning by simply playing out many hands rather than solving the mathematical problem directly. This simple observation—that repeated random sampling could solve problems resistant to analytical approaches—became the conceptual foundation for Monte Carlo simulation.

Ulam collaborated with von Neumann to formalize the method and implement it on ENIAC, one of the world’s first electronic computers. They named it “Monte Carlo” because of the method’s reliance on randomness and chance, evoking the famous casino in Monaco.2 This naming choice reflected both humor and insight: just as casino outcomes depend on probability distributions, their simulation method would use random sampling to explore probability distributions of complex systems.

Legacy and Impact

Ulam’s contribution extended far beyond the initial nuclear physics application. He recognized that Monte Carlo methods could solve a vast range of problems—optimization, numerical integration, and sampling from probability distributions.4 His work established a computational paradigm that became indispensable across fields from finance to climate modeling.

Ulam remained at Los Alamos for most of his career, continuing to develop mathematical theory and mentor younger scientists. He published over 150 scientific papers and authored the memoir Adventures of a Mathematician, which provides invaluable insight into the intellectual culture of mid-20th-century mathematical physics. His ability to see practical computational solutions where others saw only mathematical intractability exemplified the creative problem-solving that defines strategic innovation in quantitative fields.

The Monte Carlo method remains one of the most widely-used computational techniques in modern science and finance, a testament to Ulam’s insight that sometimes the most powerful way to understand complex systems is not through elegant equations, but through the systematic exploration of possibility spaces via randomness and repeated sampling.

References

1. https://aws.amazon.com/what-is/monte-carlo-simulation/

2. https://www.ibm.com/think/topics/monte-carlo-simulation

3. https://www.youtube.com/watch?v=7ESK5SaP-bc

4. https://en.wikipedia.org/wiki/Monte_Carlo_method

Monte-Carlo simulation - Term: Monte-Carlo simulation

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Quote: Grocery Dive

Quote: Grocery Dive

“Households with users of GLP-1 medications for weight loss are set to account for more than a third of food and beverage sales over the next five years, and stand to reshape consumer preferences and purchasing patterns.” – Grocery Dive

GLP-1 receptor agonists—such as semaglutide (Ozempic®, Wegovy®) and tirzepatide (Zepbound®, Mounjaro®)—mimic the glucagon-like peptide-1 hormone, regulating blood sugar, curbing appetite, and promoting satiety to drive significant weight loss of 10–20% body weight in responsive patients.1,3 Initially approved for type 2 diabetes management, these drugs exploded in popularity for obesity treatment after regulatory approvals in 2021, with US adult usage surging from 5.8% in early 2024 to 12.4% by late 2025, correlating with a national obesity rate decline from 39.9% to 37%.2

Market Evolution and Accessibility Breakthroughs

High costs—exceeding $1,000 monthly out-of-pocket—limited early adoption to affluent users, but a landmark 2026 federal agreement brokered with Eli Lilly and Novo Nordisk slashes prices by 60–70% to $300–$400 for cash-pay patients and as low as $50 via expanded Medicare/Medicaid coverage for weight loss (previously diabetes-only).1,4 This shift, via the TrumpRx platform launching early 2026, democratises access, enabling consistent therapy and reducing the 15–20% non-responder dropout rate through integrated lifestyle support.1 Employer coverage rose to 44% among firms with 500+ employees in 2024, though cost pressures may temper growth; generics remain over five years away, with oral formulations in late-stage trials.3

Profound Business Impacts on Food and Beverage

Households using GLP-1s for weight loss—now 78% of prescriptions, up 41 points since 2021—over-index on food and beverage spending pre- and post-treatment, poised to represent over one-third of sector sales within five years.2 While initial fears of 1,000-calorie daily cuts devastating packaged goods have eased, users prioritise protein-rich, nutrient-dense products, high-volume items, and satiating formats like soups, reshaping CPG portfolios toward health-focused innovation.2 Affluent “motivated” weight-loss users contrast with larger-household disease-management cohorts from middle/lower incomes, both retaining high lifetime value for manufacturers and retailers adapting to journey-stage needs: initiation, cycling off, or maintenance.2

Scientific Foundations and Key Theorists

GLP-1 research traces to the 1980s discovery of glucagon-like peptide-1 as an incretin hormone enhancing insulin secretion post-meal. Pioneering Danish endocrinologist Jens Juul Holst elucidated its gut-derived physiology and degradation by DPP-4 enzymes, laying groundwork for stabilised analogues; his lab at the University of Copenhagen advanced semaglutide development.1,3 Daniel Drucker, at Mount Sinai, expanded understanding of GLP-1’s broader receptor actions on appetite suppression via hypothalamic pathways, authoring seminal reviews on therapeutic potential beyond diabetes.3 Clinical validation came through Novo Nordisk’s STEP trials (led by researchers like Wadden et al.), demonstrating superior efficacy over lifestyle interventions alone, while Eli Lilly’s SURMOUNT studies confirmed tirzepatide’s dual GLP-1/GIP agonism for enhanced outcomes.1,2,3 These insights propelled GLP-1s from niche diabetes tools to transformative obesity therapies, now expanding to cardiovascular risk, sleep apnoea, kidney disease, and investigational roles in addiction and neurodegeneration.3

Challenges persist: side effects prompt discontinuation among some older users, and optimal results demand multidisciplinary integration of pharmacology with nutrition and behaviour.1,5 For businesses, this signals a pivotal realignment—prioritising GLP-1-aligned products to capture evolving preferences in a market where obesity treatment transitions from elite to mainstream.

References:

1
https://grandhealthpartners.com/glp-1-weight-loss-announcement/

2
https://www.foodnavigator-usa.com/Article/2025/12/15/soup-to-nuts-podcast-how-will-glp-1s-reshape-food-in-2026/

3
https://www.mercer.com/en-us/insights/us-health-news/glp-1-considerations-for-2026-your-questions-answered/

4
https://www.aarp.org/health/drugs-supplements/weight-loss-drugs-price-drop/

5
https://www.foxnews.com/health/older-americans-quitting-glp-1-weight-loss-drugs-4-key-reasons

6 https://www.grocerydive.com/news/glp1s-weight-loss-food-beverage-sales-2030/806424/

“Households with users of GLP-1 medications for weight loss are set to account for more than a third of food and beverage sales over the next five years, and stand to reshape consumer preferences and purchasing patterns.” - Quote: Grocery Dive

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