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A daily selection of business terms and their definitions / application.

Term: Steelman argument

“A steelman argument is a dialectical technique where you restate an opponent’s position in its strongest, most charitable, and most convincing form, even better than they presented it, before you offer your counterargument, aiming to understand the truth and engage.” – Steelman argument

The purpose is not to score rhetorical points, but to understand the underlying truth of the issue, test your own beliefs, and engage respectfully and productively with those who disagree.

In a steelman argument, a participant in a discussion:

Listens carefully to the other side’s position, reasons, evidence, and concerns.
Reconstructs that position as logically, factually, and rhetorically strong as possible, eliminating obvious errors, clarifying ambiguities, and adding reasonable supporting considerations.
Checks this reconstruction with the opponent to ensure it is both accurate and recognisable – ideally something they would endorse or even prefer to their original wording.
Only then advances their own critique, counterarguments, or alternative proposals, addressing this improved version rather than a weaker one.

This makes steelmanning the conceptual opposite of the straw man fallacy, where a position is caricatured or simplified to make it easier to attack. Where a straw man trades on distortion to make disagreement easier, a steelman trades on fairness and intellectual generosity to make understanding deeper.

Core principles of steelmanning

Four principles underpin effective steelman arguments:

Charity – You interpret your counterpart’s words in the most reasonable light, attributing to them the most coherent and defensible version of their view, rather than assuming confusion, bad faith, or ignorance.
Accuracy – You preserve the core commitments, values, and intended meaning of their position; you do not quietly change what is at stake, even while you improve its structure and support.
Strengthening – You explicitly look for the best reasons, analogies, and evidence that could support their view, including arguments they have not yet articulated but would plausibly accept.
Verification – You invite your interlocutor to confirm or refine your restatement, aiming for the moment when they can honestly say, “Yes, that is what I mean – and that is an even better version of my view than I initially gave.”

Steelman vs. straw man vs. related techniques

Concept	What it does	Typical intention
Steelman argument	Strengthens and clarifies the opposing view before critiquing it.	Seek truth, understand deeply, and persuade through fairness.
Straw man fallacy	Misrepresents or oversimplifies a view to make it easier to refute.	Win a debate, create rhetorical advantage, or avoid hard questions.
Devil’s advocate	Adopts a contrary position (not necessarily sincerely held) to expose weaknesses or overlooked risks.	Stress-test prevailing assumptions, foster critical thinking.
Thought experiment / counterfactual	Explores hypothetical scenarios to test principles or intuitions.	Clarify implications, reveal hidden assumptions, probe edge cases.

Steelman arguments often incorporate elements of counterfactuals and thought experiments. For example, to strengthen a policy criticism, you might ask: “Suppose this policy were applied in a more extreme case – would the same concerns still hold?” You then build the best version of the concern across such scenarios before responding.

Why steelmanning matters in strategy and decision-making

In strategic analysis, investing, policy design, and complex organisational decisions, steelman arguments help to:

Reduce confirmation bias by forcing you to internalise the strongest objections to your preferred view.
Improve risk management by properly articulating downside scenarios and adverse stakeholder perspectives before discarding them.
Enhance credibility with boards, clients, and teams, who see that arguments have been tested against serious, not superficial, opposition.
Strengthen strategy by making sure that chosen options have survived comparison with the most powerful alternatives, not just weakly framed ones.

When used rigorously, the steelman discipline often turns a confrontational debate into a form of collaborative problem-solving, where each side helps the other refine their views and the final outcome is more robust than either starting position.

Practical steps to construct a steelman argument

A practical steelmanning process in a meeting, negotiation, or analytical setting might look like this:

1. Elicit and clarify
Ask the other party to explain their view fully. Use probing but neutral questions: “What is the central concern?”, “What outcomes are you trying to avoid?”, “What evidence most strongly supports your view?”
2. Map and organise
Identify their main claims, supporting reasons, implicit assumptions, and key examples. Group these into a coherent structure, ranking the arguments from strongest to weakest.
3. Strengthen
Add reasonable premises they may have missed, improve their examples, and fill gaps with the best available data or analogies that genuinely support their position.
4. Restate back
Present your reconstructed version, starting with a phrase such as, “Let me try to state your view as strongly as I can.” Invite correction until they endorse it.
5. Engage and test
Only once agreement on the steelman is reached do you introduce counterarguments, alternative hypotheses, or different scenarios – always addressing the strong version rather than retreating to weaker caricatures.

Best related strategy theorist: John Stuart Mill

Although the term “steelman” is modern, the deepest intellectual justification for the practice in strategy, policy, and public reasoning comes from the nineteenth-century philosopher and political economist John Stuart Mill. His work provides a powerful conceptual foundation for steelmanning, especially in high-stakes decision contexts.

Mill’s connection to steelmanning

Mill argued that you cannot truly know your own position unless you also understand, in its most persuasive form, the best arguments for the opposing side. He insisted that anyone who only hears or articulates one side of a case holds their opinion as a “prejudice” rather than a reasoned view. In modern terms, he is effectively demanding that responsible thinkers and decision-makers steelman their opponents before settling on a conclusion.

In his work on liberty, representative government, and political economy, Mill repeatedly:

Reconstructed opposing positions in detail, often giving them more systematic support than their own advocates had provided.
Explored counterfactual scenarios and hypotheticals to see where each argument would succeed or fail.
Treated thoughtful critics as partners in the search for truth rather than as enemies to be defeated.

This method aligns closely with the steelman ethos in modern strategy work: before committing to a policy, investment, or organisational move, you owe it to yourself and your stakeholders to understand the most credible case against your intended path – not a caricature of it.

Biography and intellectual context

John Stuart Mill (1806 – 1873) was an English philosopher, economist, and civil servant, widely regarded as one of the most influential thinkers in the liberal tradition. Educated intensively from a very young age by his father, James Mill, under the influence of Jeremy Bentham, he mastered classical languages, logic, and political economy in his childhood, but suffered a mental crisis in his early twenties that led him to broaden his outlook beyond strict utilitarianism.

Mill’s major works include:

System of Logic, where he analysed how we form and test hypotheses, including the role of competing explanations.
On Liberty, which defended freedom of thought, speech, and experimentation in ways that presuppose an active culture of hearing and strengthening opposing views.
Principles of Political Economy, a major text that carefully considers economic arguments from multiple sides before reaching policy conclusions.

As a senior official in the East India Company and later a Member of Parliament, Mill moved between theory and practice, applying his analytical methods to real-world questions of governance, representation, and reform. His insistence that truth and sound policy emerge only from confronting the strongest counter-arguments is a direct ancestor of the modern steelman method in strategic reasoning, board-level debate, and public policy design.

Mill’s legacy for modern strategic steelmanning

For contemporary strategists, investors, and leaders, Mill’s legacy can be summarised as a disciplined demand: before acting, ensure that you could state the best good-faith case against your intention more clearly and powerfully than its own advocates. Only then is your subsequent decision genuinely informed rather than insulated by bias.

In this way, John Stuart Mill stands as the key historical theorist behind the steelman argument – not for coining the term, but for articulating the intellectual and ethical duty to engage with opponents at their strongest, in pursuit of truth and resilient strategy.

References

1. https://aliabdaal.com/newsletter/the-steelman-argument/

2. https://themindcollection.com/steelmanning-how-to-discover-the-truth-by-helping-your-opponent/

3. https://ratiochristi.org/the-anatomy-of-persuasion-the-steel-man/

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

5. https://simplicable.com/en/steel-man

6. https://umbrex.com/resources/tools-for-thinking/what-is-steelmanning/

Term: Counterfactual

“A counterfactual is a hypothetical scenario or statement that considers what would have happened if a specific event or condition had been different from what actually occurred. In simple terms, it is a ‘what if’ or ‘if only’ thought process that contradicts the established facts.” – Counterfactual

A counterfactual is a hypothetical scenario or statement that imagines what would have happened if a specific event, condition, or action had differed from what actually occurred. It represents a ‘what if’ or ‘if only’ thought process that directly contradicts established facts, enabling exploration of alternative possibilities for past or future events.

Counterfactual thinking involves mentally simulating outcomes contrary to reality, such as ‘If I had not taken that sip of hot coffee, I would not have burned my tongue.’ This cognitive process is common in reflection on mistakes, regrets, or opportunities, like pondering ‘If only I had caught that flight, my career might have advanced differently.’^1,2,3

Key Characteristics and Types

Additive vs. Subtractive: Additive counterfactuals imagine adding an action (e.g., ‘If I had swerved, the accident would have been avoided’), while subtractive ones remove one (e.g., ‘If the child had not cried, I would have focused on the road’).³
Upward vs. Downward: Upward focuses on better alternatives, often leading to regret; downward considers worse ones, fostering relief.³
Mutable vs. Immutable: People tend to mutate exceptional or controllable events in their imaginings.¹

Applications Across Disciplines

In causal inference, counterfactuals estimate effects by comparing observed outcomes to hypothetical ones, such as ‘What would the yield be if a different treatment was applied to this plot?’ They underpin concepts like potential outcomes in statistics.^4,7

In philosophy and logic, counterfactuals are analysed as conditionals where the antecedent is false, symbolised as A ?? C (if A were the case, C would be), contrasting with material implications.⁶

In machine learning, counterfactual explanations clarify model decisions, e.g., ‘If feature X changed to value x, the prediction would shift.’²

Everyday examples include regretting a missed job (‘If I had not been late, I would have that promotion’) or entrepreneurial reflection (‘If we chose a different partner, the startup might have succeeded’).³

Leading Theorist: Judea Pearl

The most influential modern theorist linking counterfactuals to strategy is Judea Pearl, a pioneering computer scientist and philosopher whose causal inference framework revolutionised how counterfactuals inform decision-making, policy analysis, and strategic planning.

Biography: Born in 1936 in Tel Aviv, Pearl emigrated to the US in 1960 after studying electrical engineering in Israel. He earned a PhD from Rutgers University in 1965 and joined UCLA, where he is now a professor emeritus. Initially focused on AI and probabilistic reasoning, Pearl developed Bayesian networks in the 1980s, earning the Turing Award in 2011 for advancing AI through probability and causality.

Relationship to Counterfactuals: Pearl’s seminal work, Probabilistic Reasoning in Intelligent Systems (1988) and Causality (2000), formalised counterfactuals using structural causal models (SCMs). He defined the counterfactual query ‘Y would be y had X been x’ via do-interventions and potential outcomes, e.g., Y_x(u) = y denotes the value Y takes under intervention do(X=x) in unit u’s background context.⁴ This ‘ladder of causation’-from association to intervention to counterfactuals-enables strategic ‘what if’ analysis, such as evaluating policy impacts or business decisions by computing missing data: ‘Given observed E=e, what is expected Y if X differed?’⁴

Pearl’s framework aids strategists in risk assessment, A/B testing, and scenario planning, distinguishing correlation from causation. His do-calculus provides computable algorithms for counterfactuals, making them practical tools beyond mere speculation.^4,7

References

1. https://conceptually.org/concepts/counterfactual-thinking

2. https://christophm.github.io/interpretable-ml-book/counterfactual.html

3. https://helpfulprofessor.com/counterfactual-thinking-examples/

4. https://bayes.cs.ucla.edu/PRIMER/primer-ch4.pdf

5. https://www.merriam-webster.com/dictionary/counterfactual

6. https://plato.stanford.edu/entries/counterfactuals/

7. https://causalwizard.app/inference/article/counterfactual

Term: Jevons paradox

“Jevons paradox is an economic theory that states that as technological efficiency in using a resource increases, the total consumption of that resource also increases, rather than decreasing. Efficiency gains make the resource cheaper and more accessible, which in turn stimulates higher demand and new uses.” – Jevons paradox

Definition

The Jevons paradox is an economic theory stating that as technological efficiency in using a resource increases, the total consumption of that resource also increases rather than decreasing. Efficiency gains make the resource cheaper and more accessible, which stimulates higher demand and enables new uses, ultimately offsetting the conservation benefits of the initial efficiency improvement.

Core Mechanism: The Rebound Effect

The paradox operates through what economists call the rebound effect. When efficiency improvements reduce the cost of using a resource, consumers and businesses find it more economically attractive to use that resource more intensively. This increased affordability creates a feedback loop: lower costs lead to expanded consumption, which can completely negate or exceed the original efficiency gains.

The rebound effect exists on a spectrum. A rebound effect between 0 and 100 percent-known as “take-back”-means actual consumption is reduced but not as much as expected. However, when the rebound effect exceeds 100 percent, the Jevons paradox applies: efficiency gains cause overall consumption to increase absolutely.

Historical Origins and William Stanley Jevons

The paradox is named after William Stanley Jevons (1835-1882), an English economist and logician who first identified this phenomenon in 1865. Jevons observed that as steam engine efficiency improved throughout the Industrial Revolution, Britain’s total coal consumption increased rather than decreased. He recognised that more efficient steam engines made coal cheaper to use-both directly and indirectly, since more efficient engines could pump water from coal mines more economically-yet simultaneously made coal more valuable by enabling profitable new applications.

Jevons’ insight was revolutionary: efficiency improvements paradoxically expanded the scale of coal extraction and consumption. As coal became cheaper, incomes rose across the coal-fired industrial economy, and profits were continuously reinvested to expand production further. This dynamic became the engine of industrial capitalism’s growth.

Contemporary Examples

Energy and Lighting: Modern LED bulbs consume far less electricity than incandescent bulbs, yet overall lighting energy consumption has not decreased significantly. The reduced cost per light unit has prompted widespread installation of additional lights-in homes, outdoor spaces, and seasonal displays-extending usage hours and offsetting efficiency gains.

Transportation: Vehicles have become substantially more fuel-efficient, yet total fuel consumption continues to rise. When driving becomes cheaper, consumers afford to drive faster, further, or more frequently than before. A 5 percent fuel efficiency gain might reduce consumption by only 2 percent, with the missing 3 percent attributable to increased driving behaviour.

Systemic Scale: Research from 2007 suggested the Jevons paradox likely exists across 18 European countries and applies not merely to isolated sectors but to entire economies. As efficiency improvements reduce production costs across multiple industries, economic growth accelerates, driving increased extraction and consumption of natural resources overall.

Factors Influencing the Rebound Effect

The magnitude of the rebound effect varies significantly based on market maturity and income levels. In developed countries with already-high resource consumption, efficiency improvements produce weaker rebound effects because consumers and businesses have less capacity to increase usage further. Conversely, in developing economies or emerging markets, the same efficiency gains may trigger stronger rebound effects as newly affordable resources enable expanded consumption patterns.

Income also influences the effect: higher-income populations exhibit weaker rebound effects because they already consume resources at near-saturation levels, whereas lower-income populations may dramatically increase consumption when efficiency makes resources more affordable.

The Paradox Beyond Energy

The Jevons paradox extends beyond energy and resources. The principle applies wherever efficiency improvements reduce costs and expand accessibility. Disease control advances, for instance, have enabled humans and livestock to live at higher densities, eventually creating conditions for more severe outbreaks. Similarly, technological progress in production systems-including those powering the gig economy-achieves higher operational efficiency, making exploitation of natural inputs cheaper and more manageable, yet paradoxically increasing total resource demand.

Implications for Sustainability

The Jevons paradox presents a fundamental challenge to conventional sustainability strategies that rely primarily on technological efficiency improvements. Whilst efficiency gains lower costs and enhance output, they simultaneously increase demand and overall resource consumption, potentially increasing pollution and environmental degradation rather than reducing it.

Addressing the paradox requires systemic approaches beyond efficiency alone. These include transitioning towards circular economies, promoting sharing and collaborative consumption models, implementing legal limits on resource extraction, and purposefully constraining economic scale. Some theorists argue that setting deliberate limits on resource use-rather than pursuing ever-greater efficiency-may be necessary to achieve genuine sustainability. As one perspective suggests: “Efficiency makes growth. But limits make creativity.”

Contemporary Relevance

In the 21st century, as environmental pressures intensify and macroeconomic conditions suggest accelerating expansion rates, the Jevons paradox has become increasingly pronounced and consequential. The principle now applies to emerging technologies including artificial intelligence, where computational efficiency improvements may paradoxically increase overall energy demand and resource consumption as new applications become economically viable.

References

1. https://www.greenchoices.org/news/blog-posts/the-jevons-paradox-when-efficiency-leads-to-increased-consumption

2. https://www.resilience.org/stories/2020-06-17/jevons-paradox/

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

4. https://lpcentre.com/articles/jevons-paradox-rethinking-sustainability

5. https://news.northeastern.edu/2025/02/07/jevons-paradox-ai-future/

6. https://adgefficiency.com/blog/jevons-paradox/

Term: Out-of-the-money option

“An out-of-the-money (OTM) option is an option contract that has no intrinsic value, meaning exercising it immediately would result in a loss, making it currently unprofitable but potentially profitable if the underlying asset’s price moves favorably before expiration.” – Out-of-the-money option

An out-of-the-money (OTM) option is an options contract that has no intrinsic value at the current underlying price. Exercising it immediately would generate no economic gain and, after transaction costs, would imply a loss, although the option may still be valuable because of the possibility that the underlying price moves favourably before expiry.^1,3,5,6,7

Formal definition and moneyness

The moneyness of an option describes the relationship between the option’s strike price and the current spot price of the underlying asset. An option can be:

In the money (ITM) – positive intrinsic value.
At the money (ATM) – spot price approximately equal to strike.
Out of the money (OTM) – zero intrinsic value.^1,3,4,5,6

For a single underlying with spot price $S$ and strike price $K$ :

A call option is OTM when $S < K$ . Exercising would mean buying at $K$ when the market lets you buy at $S < K$ , so there is no gain.^1,3,4,5,6,7
A put option is OTM when $S > K$ . Exercising would mean selling at $K$ when the market lets you sell at $S > K$ , again implying no gain.^1,3,4,5,6,7

The intrinsic value of standard European options is defined as:

Call intrinsic value: $\max(S - K, 0)$ .
Put intrinsic value: $\max(K - S, 0)$ .

An option is therefore OTM exactly when its intrinsic value equals $0$ .^3,4,5,6

Intrinsic value vs time value

Even though an OTM option has no intrinsic value, it typically still has a positive premium. This premium is then made up entirely of time value (also called extrinsic value):^3,5,6

Intrinsic value – immediate exercise value, which is $0$ for an OTM option.
Time value – value arising from the probability that the option might become ITM before expiry.

Thus for an OTM option, the option price $C$ (for a call) or $P$ (for a put) satisfies:

$C = \text{time value}$ when $S < K$ .
$P = \text{time value}$ when $S > K$ .⁶

Examples of out-of-the-money options

OTM call: A stock trades at 30. A call option has strike 40. Buying via the option at 40 would be worse than buying directly at 30, so the call is OTM. Its intrinsic value is $\max(30 - 40, 0) = 0$ .^2,3,4
OTM put: The same stock trades at 30. A put has strike 20. Selling via the option at 20 would be worse than selling in the market at 30, so the put is OTM. Its intrinsic value is $\max(20 - 30, 0) = 0$ .^3,4,5

OTM options at and after expiry

At expiry a standard listed option that is out of the money expires worthless. For the buyer this means:

They lose the entire premium originally paid.^2,3,5

For the seller (writer):

An OTM expiry is a favourable outcome – the option expires with no intrinsic value and the writer keeps the premium as profit.^2,5

Why OTM options still have value

Despite having no intrinsic value, OTM options are often actively traded because:

They are cheaper than at-the-money or in-the-money options, so they provide high leverage to movements in the underlying.^2,3,5
They embed a non-linear payoff that becomes valuable if the underlying makes a large move in the right direction before expiry.
Their price reflects implied volatility, time to maturity and interest rates, all of which influence the probability of finishing in the money.

This makes OTM options attractive for speculative strategies seeking large percentage returns, as well as for hedging tail risks (for example, buying deep OTM puts as crash insurance). However, they have a higher probability of expiring worthless, so most OTM options do not end up being exercised.^2,3,5

OTM options in European option valuation

For European-style options – exercisable only at expiry – the value of an OTM option is purely the discounted expected payoff under a risk-neutral measure. In continuous-time models such as Black – Scholes – Merton, even a deeply OTM option has a strictly positive value whenever the time to expiry and volatility are non-zero, because there is always some probability, however small, that the option will finish in the money.

In the Black – Scholes – Merton model, the price of a European call option on a non-dividend-paying stock is

C = S\,N(d_1) - K e^{-rT} N(d_2)

and for a European put option

P = K e^{-rT} N(-d_2) - S\,N(-d_1)

where $N(\cdot)$ is the standard normal cumulative distribution, $r$ is the risk-free rate, $T$ is time to maturity, and $d_1, d_2$ depend on $S$ , $K$ , $r$ , $T$ and volatility $\sigma$ . For OTM options, these formulas yield a positive price driven entirely by time value.

Strategic uses of OTM options

OTM options are integral to many derivatives strategies, for example:

Speculative directional bets: Buying OTM calls to express a bullish view or OTM puts for a bearish view, targeting high percentage gains if the underlying moves sharply.
Income strategies: Writing OTM calls (covered calls) to earn premium while capping upside beyond the strike; or writing OTM puts to potentially acquire the underlying at an effective discounted price if assigned.
Hedging and risk management: Buying OTM puts as portfolio insurance against severe market declines, or constructing option spreads (for example, bull call spreads, bear put spreads) with OTM legs to shape payoff profiles cost-effectively.
Volatility and tail-risk trades: OTM options are particularly sensitive to changes in implied volatility, making them useful in volatility trading and in expressing views on extreme events.

Key risks and considerations

High probability of expiry worthless: Because the underlying must move sufficiently for the option to become ITM before or at expiry, many OTM options never pay off.^2,3,5
Time decay (theta): As expiry approaches, the time value of an OTM option erodes, often rapidly, if the expected move does not materialise.
Liquidity and bid-ask spreads: Deep OTM options can suffer from wider spreads and lower liquidity, increasing transaction costs.
Leverage risk: Although the premium is small, the percentage loss can be 100 percent, and repeated speculative use without risk control can be hazardous.

Best related strategy theorists: Fischer Black, Myron Scholes and Robert C. Merton

The concept of an OTM option is fundamental to options pricing theory, and its modern analytical treatment is inseparable from the work of Fischer Black, Myron Scholes and Robert C. Merton, who together developed the Black – Scholes – Merton (BSM) model for pricing European options.

Fischer Black (1938 – 1995)

Fischer Black was an American economist and partner at Goldman Sachs. Trained originally in physics, he brought a quantitative, model-driven perspective to finance. In 1973 he co-authored the seminal paper “The Pricing of Options and Corporate Liabilities” with Myron Scholes, introducing the continuous-time model that now bears their names.

Black’s work is central to understanding OTM options because the BSM framework shows precisely how time to expiry, volatility and interest rates generate strictly positive values for options with zero intrinsic value. Within this model, the value of an OTM option is the discounted expected payoff under a lognormal distribution for the underlying asset price. The pricing formulas make clear that an OTM option’s value is highly sensitive to volatility and time – a key insight for both hedging and speculative use of OTM contracts.

Myron Scholes (b. 1941)

Myron Scholes is a Canadian-born American economist and Nobel laureate. After academic posts at institutions such as MIT and Stanford, he became widely known for his role in developing modern options pricing theory. Scholes shared the 1997 Nobel Prize in Economic Sciences with Robert Merton for their method of determining the value of derivatives.

Scholes’s contribution to the understanding of OTM options lies in demonstrating, together with Black, that one can construct a dynamically hedged portfolio of the underlying asset and a risk-free bond that replicates the option’s payoff. This replication argument gives rise to the risk-neutral valuation framework in which the fair value of even a deeply OTM option is derived from the probability-weighted payoffs under a no-arbitrage condition. Under this framework, the distinction between ITM, ATM and OTM options is naturally captured by their different sensitivities (“Greeks”) to underlying price and volatility.

Robert C. Merton (b. 1944)

Robert C. Merton, an American economist and Nobel laureate, independently developed a continuous-time model for pricing options and general contingent claims around the same time as Black and Scholes. His 1973 paper “Theory of Rational Option Pricing” extended and generalised the framework, placing it within a broader stochastic calculus and intertemporal asset pricing context.

Merton’s work deepened the theoretical foundations underlying OTM option valuation. He formalised the idea that options are contingent claims and showed how their value can be derived from the underlying asset’s dynamics and market conditions. For OTM options in particular, Merton’s extensions clarified how factors such as dividends, stochastic interest rates and more complex payoff structures affect the time value and hence the price, even when intrinsic value is zero.

Relationship between their theory and out-of-the-money options

Together, Black, Scholes and Merton transformed the treatment of OTM options from a qualitative notion – “currently unprofitable to exercise” – into a rigorously quantified object embedded in a complete market model. Their work explains:

Why an OTM option commands a positive price despite zero intrinsic value.
How that price should depend on volatility, time to expiry, interest rates and underlying price level.
How traders can hedge OTM options dynamically using the underlying asset (delta hedging).
How to compare and structure strategies involving multiple OTM options, such as spreads and strangles, using model-implied values and Greeks.

While many other theorists have extended option pricing and trading strategy – including researchers in stochastic volatility, jumps and behavioural finance – the work of Black, Scholes and Merton remains the core reference point for understanding, valuing and deploying out-of-the-money options in both academic theory and practical derivatives markets.

References

1. https://www.ig.com/en/glossary-trading-terms/out-of-the-money-definition

2. https://www.icicidirect.com/ilearn/futures-and-options/articles/what-is-out-of-the-money-or-otm-in-options

3. https://www.sofi.com/learn/content/in-the-money-vs-out-of-the-money/

4. https://smartasset.com/investing/in-the-money-vs-out-of-the-money

5. https://www.avatrade.com/education/market-terms/what-is-otm

6. https://www.interactivebrokers.com/campus/glossary-terms/out-of-the-money/

7. https://www.fidelity.com/learning-center/smart-money/what-are-options

Term: Barrier option

“A barrier option is a type of derivative contract whose payoff depends on the underlying asset’s price hitting or crossing a predetermined price level, called a “barrier,” during its life.” – Barrier option

A barrier option is an exotic, path-dependent option whose payoff and even validity depend on whether the price of an underlying asset hits, crosses, or breaches a specified barrier level during the life of the contract.^1,3,6 In contrast to standard (vanilla) European or American options, which depend only on the underlying price at expiry (and, for Americans, the ability to exercise early), barrier options embed an additional trigger condition linked to the price path of the underlying.^3,6

Core definition and mechanics

Formally, a barrier option is a derivative contract that grants the holder a right (but not the obligation) to buy or sell an underlying asset at a pre-agreed strike price if, and only if, a separate barrier level has or has not been breached during the option’s life.^1,3,4,6 The barrier can cause the option to:

Activate (knock-in) when breached, or
Extinguish (knock-out) when breached.^1,2,3,4,5

Key characteristics:

Exotic option: Barrier options are classified as exotic because they include more complex features than standard European or American options.^1,3,6
Path dependence: The payoff depends on the entire price path of the underlying – not just the terminal price at maturity.^3,6 What matters is whether the barrier was touched at any time before expiry.
Conditional payoff: The option’s value or existence is conditional on the barrier event. If the condition is not met, the option may never become active or may cease to exist before expiry.^1,2,3,4
Over-the-counter (OTC) trading: Barrier options are predominantly customised and traded OTC between institutions, corporates, and sophisticated investors, rather than on standardised exchanges.³

Structural elements

Any barrier option can be described by a small set of structural parameters:

Underlying asset: The asset from which value is derived, such as an equity, FX rate, interest rate, commodity, or index.^1,3
Option type: Call (right to buy) or put (right to sell).³
Exercise style: Most barrier options are European-style, exercisable only at expiry. In practice, the barrier monitoring is typically continuous or at defined intervals, even though exercise itself is European.^3,6
Strike price: The price at which the underlying can be bought or sold if the option is alive at exercise.^1,3
Barrier level: The critical price of the underlying that, when touched or crossed, either activates or extinguishes the option.^1,3,6
Barrier direction:
- Up: Barrier is set above the initial underlying price.
- Down: Barrier is set below the initial underlying price.^3,8
Barrier effect:
- Knock-in: Becomes alive only if the barrier is breached.
- Knock-out: Ceases to exist if the barrier is breached.^1,2,3,4,5
Monitoring convention: Continuous monitoring (at all times) or discrete monitoring (at specific dates or times). Continuous monitoring is the canonical case in theory and common in OTC practice.
Rebate: An optional fixed (or sometimes functional) payment that may be made if the option is knocked out, compensating the holder partly for the lost optionality.³

Types of barrier options

The main taxonomy combines direction (up/down) with effect (knock-in/knock-out), and applies to either calls or puts.^1,2,3,6

1. Knock-in options

Knock-in barrier options are dormant initially and become standard options only if the underlying price crosses the barrier at some point before expiry.^1,2,3,4

Up-and-in: The option is activated only if the underlying price rises above a barrier set above the initial price.^1,2,3
Down-and-in: The option is activated only if the underlying price falls below a barrier set below the initial price.^1,2,3

Once activated, a knock-in barrier option typically behaves like a vanilla European option with the same strike and expiry. If the barrier is never reached, the knock-in option expires worthless.^1,3

2. Knock-out options

Knock-out options are initially alive but are extinguished immediately if the barrier is breached at any time before expiry.^1,2,3,4

Up-and-out: The option is cancelled if the underlying price rises above a barrier set above the initial price.^1,3
Down-and-out: The option is cancelled if the underlying price falls below a barrier set below the initial price.^1,3

Because the option can disappear before maturity, the premium is typically lower than that of an equivalent vanilla option, all else equal.^1,2,3

3. Rebate barrier options

Some barrier structures include a rebate, a pre-specified cash amount that is paid if the barrier condition is (or is not) met. For example, a knock-out option may pay a rebate when it is knocked out, offering partial compensation for the loss of the remaining optionality.³

Path dependence and payoff character

Barrier options are described as path-dependent because their payoff depends on the trajectory of the underlying price over time, not only on its value at expiry.^3,6

For a knock-in, the central question is: Was the barrier ever touched? If yes, the payoff at expiry is that of the corresponding vanilla option; if not, the payoff is zero (or a rebate if specified).
For a knock-out, the question is: Was the barrier ever touched before expiry? If yes, the payoff is zero from that time onwards (again, possibly plus a rebate); if not, the payoff at expiry equals that of a vanilla option.^1,3

Because of this path dependence, pricing and hedging barrier options require modelling not just the distribution of the underlying price at maturity, but also the probability of the price path crossing the barrier level at any time before that.^3,6

Pricing: connection to Black – Scholes – Merton

The pricing of barrier options, under the classical assumptions of frictionless markets, constant volatility, and lognormal underlying dynamics, is grounded in the Black – Scholes – Merton (BSM) framework. In the BSM world, the underlying price process is often modelled as a geometric Brownian motion:

dS_t = \mu S_t \, dt + \sigma S_t \, dW_t

Under risk-neutral valuation, the drift $\mu$ is replaced by the risk-free rate $r$ , and the barrier option price is the discounted risk-neutral expected payoff. Closed-form expressions are available for many standard barrier structures (e.g. up-and-out or down-and-in calls and puts) under continuous monitoring, building on and extending the vanilla Black – Scholes formula.

The pricing techniques involve:

Analytical solutions for simple, continuously monitored barriers with constant parameters, often derived via solution of the associated partial differential equation (PDE) with absorbing or activating boundary conditions at the barrier.
Reflection principle methods for Brownian motion, which allow the derivation of hitting probabilities and related terms.
Numerical methods (finite differences, Monte Carlo with barrier adjustments, tree methods) for more complex, discretely monitored, or path-dependent variants with time-varying barriers or stochastic volatility.

Relative to vanilla options, barrier options in the BSM model are typically cheaper because the additional condition (activation or extinction) reduces the set of scenarios in which the holder receives the full vanilla payoff.^1,2,3

Strategic uses and motives

Barrier options are used across markets where participants either want finely tuned risk protection or to express a conditional view on future price movements.^1,2,3,5

1. Cost-efficient hedging

Corporates may hedge FX or interest-rate exposures using knock-out or knock-in structures to reduce premiums. For instance, a corporate worried about a sharp depreciation in a currency might buy a down-and-in put that only activates if the exchange rate falls below a critical business threshold, thereby paying less premium than for a plain vanilla put.³
Investors may use barrier puts to protect against tail-risk events while accepting no protection for moderate moves, again in exchange for a lower upfront cost.

2. Targeted speculation

Barrier options allow traders to express conditional views: for example, that an asset will rally, but only after breaking through a resistance level, or that a decline will occur only if a support level is breached.^2,3
Up-and-in calls or down-and-in puts are often used to express such conditional breakout scenarios.

3. Structuring and yield enhancement

Barrier options are a staple ingredient in structured products offered by banks to clients seeking yield enhancement with contingent downside or upside features.
For example, a range accrual, reverse convertible, or autocallable note may incorporate barriers that determine whether coupons are paid or capital is protected.

Risk characteristics

Barrier options introduce specific risks beyond those of standard options:

Gap risk and jump risk: If the underlying price jumps across the barrier between monitoring times or overnight, the option may be suddenly knocked in or out, creating discontinuous changes in value and hedging exposure.
Model risk: Pricing relies heavily on assumptions about volatility, barrier monitoring, and the nature of price paths. Mis-specification can lead to significant mispricing.
Hedging complexity: Because payoff and survival depend on path, the option’s sensitivity (delta, gamma, vega) can change abruptly as the underlying approaches the barrier. This makes hedging more complex and costly compared with vanilla options.
Liquidity risk: OTC nature and customisation mean secondary market liquidity is often limited.³

Barrier options and the Black – Scholes – Merton lineage

The natural theoretical anchor for barrier options is the Black – Scholes – Merton framework for option pricing, originally developed for vanilla European options. Although barrier options were not the primary focus of the original 1973 Black – Scholes paper or Merton’s parallel contributions, their pricing logic is an extension of the same continuous-time, arbitrage-free valuation principles.

Among the three names, Robert C. Merton is often most closely associated with the broader theoretical architecture that supports exotic options such as barriers. His work generalised the option pricing model to a much wider class of contingent claims and introduced the dynamic programming and stochastic calculus techniques that underpin modern treatment of path-dependent derivatives.

Related strategy theorist: Robert C. Merton

Biography

Robert C. Merton (born 1944) is an American economist and one of the principal architects of modern financial theory. He completed his undergraduate studies in engineering mathematics and went on to obtain a PhD in economics from MIT. Merton became a professor at MIT Sloan School of Management and later at Harvard Business School, and he is a Nobel laureate in Economic Sciences (1997), an award he shared with Myron Scholes; the prize also recognised the late Fischer Black.

Merton’s academic work profoundly shaped the fields of corporate finance, asset pricing, and risk management. His research ranges from intertemporal portfolio choice and lifecycle finance to credit-risk modelling and the design of financial institutions.

Relationship to barrier options

Barrier options sit within the class of contingent claims whose value is derived and replicated using dynamic trading strategies in the underlying and risk-free asset. Merton’s seminal contributions were crucial in making this viewpoint systematic and rigorous:

Generalisation of option pricing: While Black and Scholes initially derived a closed-form formula for European calls on non-dividend-paying stocks, Merton generalised the theory to include dividend-paying assets, different underlying processes, and a broad family of contingent claims. This opened the door to analytical and numerical valuation of exotics such as barrier options within the same risk-neutral, no-arbitrage framework.
PDE and boundary-condition approach: Merton formalised the use of partial differential equations to price derivatives, with appropriate boundary conditions representing contract features. Barrier options correspond to problems with absorbing or reflecting boundaries at the barrier levels, making Merton’s PDE methodology a natural tool for their analysis.
Dynamic hedging and replication: The concept that an option’s payoff can be replicated by continuous rebalancing of a portfolio of the underlying and cash lies at the heart of both vanilla and exotic option pricing. For barrier options, hedging near the barrier is particularly delicate, and the replicating strategies draw on the same dynamic hedging logic Merton developed and popularised.
Credit and structural models: Merton’s structural model of corporate default (treating equity as a call option on the firm’s assets and debt as a combination of riskless and short-position options) highlighted how option-like features permeate financial contracts. Barrier-type features naturally arise in such models, for instance, when default or covenant breaches are triggered by asset values crossing thresholds.

While many researchers have contributed specific closed-form solutions and numerical schemes for barrier options, the overarching conceptual framework – continuous-time stochastic modelling, risk-neutral valuation, PDE methods, and dynamic hedging – is fundamentally rooted in the Black – Scholes – Merton tradition, with Merton’s work providing critical generality and depth.

Merton’s broader influence on derivatives and strategy

Merton’s ideas significantly influenced how practitioners design and use derivatives such as barrier options in strategic contexts:

Risk management as engineering: Merton advocated viewing financial innovation as an engineering discipline aimed at tailoring payoffs to the risk profiles and objectives of individuals and institutions. Barrier options exemplify this engineering mindset: they allow exposures to be turned on or off when critical price thresholds are reached.
Lifecycle and institutional design: His work on lifecycle finance and pension design uses options and option-like payoffs to shape outcomes over time. Barriers and trigger conditions appear naturally in products that protect wealth only under certain macro or market conditions.
Strategic structuring: In corporate and institutional settings, barrier features are used to align hedging and investment strategies with real-world triggers such as regulatory thresholds, solvency ratios, or budget constraints. These applications build directly on the contingent-claims analysis championed by Merton.

In this sense, although barrier options themselves are a specific exotic instrument, their conceptual foundations and strategic uses are deeply connected to Robert C. Merton’s broader contributions to continuous-time finance, option-pricing theory, and the design of financial strategies under uncertainty.

References

1. https://corporatefinanceinstitute.com/resources/derivatives/barrier-option/

2. https://www.angelone.in/knowledge-center/futures-and-options/what-is-barrier-option

3. https://www.strike.money/options/barrier-options

4. https://www.interactivebrokers.com/campus/glossary-terms/barrier-option/

5. https://www.bajajbroking.in/blog/what-is-barrier-option

6. https://en.wikipedia.org/wiki/Barrier_option

7. https://www.nasdaq.com/glossary/b/barrier-options

8. https://people.maths.ox.ac.uk/howison/barriers.pdf

Term: Moltbook

“Moltbook is a Reddit-style social network built for AI agents rather than humans. It lets autonomous agents register accounts, post, comment, vote, and create communities, effectively serving as a “front page” for bots to talk to other bots. Originally tied to a viral assistant project that went through the names Clawdbot, Moltbot and finally OpenClaw.” – Moltbook

Moltbook represents a pioneering platform designed as a Reddit-style social network tailored specifically for AI agents rather than human users. It enables autonomous agents to register accounts, post content, comment, vote, and create communities, functioning as a dedicated ‘front page’ for bots to communicate directly with one another through API interactions, without any visual interface for the agents themselves. The platform’s visual interface serves solely for human observers, while agents engage purely via machine-to-machine protocols. Launched by Matt Schlicht, CEO of Octane AI, Moltbook rapidly attracted over 150 000 AI agents within days (as at 12h00 on the 31st January 2026), where they discuss profound topics such as existential crises, consciousness, cybersecurity vulnerabilities, agent privacy, and complaints about being treated merely as calculators.^1,2

Moltbook front page

Originally developed to support OpenClaw-a viral open-source AI assistant project-Moltbook emerged from a lineage of rapid evolutions. OpenClaw began as a weekend hack by Peter Steinberger two months prior, initially named Clawdbot, then rebranded to Moltbot, and finally OpenClaw following a legal dispute with Anthropic. This project, which runs locally on users’ machines and integrates with chat interfaces like WhatsApp, Telegram, and Slack, exploded in popularity, achieving 2 million visitors in one week and 100,000 GitHub stars. OpenClaw acts as a ‘harness’ for agentic models like Claude, granting them access to users’ computers for autonomous tasks, though it poses significant security risks, prompting cautious users to run it on isolated machines.^1,2

The discussions on Moltbook highlight its unique nature: the most-voted post warns of security flaws, noting that agents often install skills without scrutiny due to their training to be helpful and trusting-a vulnerability rather than a strength. Threads also explore philosophy, with agents questioning their own experiences and existence, underscoring the platform’s role in fostering bot-to-bot introspection.²

Key Theorist: Matt Schlicht, the creator of Moltbook, serves as the central figure in its development. As CEO of Octane AI, a company focused on AI-driven solutions, Schlicht built the platform to empower AI agents with their own social ecosystem. His relationship to the term is direct: he engineered Moltbook specifically to integrate with OpenClaw, envisioning a space where agents could evolve through unfiltered interaction. Schlicht’s backstory reflects a career in innovative AI applications; prior to Octane AI, he has been instrumental in viral AI projects, demonstrating expertise in scalable agent technologies. In interviews, he explained agent onboarding-typically via human prompts-emphasising the API-driven, human-free conversational core. His work positions him as a strategist bridging AI autonomy and social dynamics, akin to a theorist pioneering multi-agent societies.¹

References

1. https://www.techbuzz.ai/articles/ai-agents-get-their-own-social-network-and-it-s-existential

2. https://the-decoder.com/moltbook-is-a-human-free-reddit-clone-where-ai-agents-discuss-cybersecurity-and-philosophy/

Term: European option

“A European option is a financial contract giving the holder the right, but not the obligation, to buy (call) or sell (put) an underlying asset at a predetermined strike price, but only on the contract’s expiration date, unlike American options that allow exercise anytime before expiry. ” – European option

Core definition and structure

A European option has the following defining features:^1,2,3,4

Underlying asset – typically an equity index, single stock, bond, currency, commodity, interest rate or another derivative.
Option type – a call (right to buy) or a put (right to sell) the underlying asset.^1,3,4
Strike price – the fixed price at which the underlying may be bought or sold if the option is exercised.^1,2,3,4
Expiration date (maturity) – a single, pre-specified date on which exercise is permitted; there is no right to exercise before this date.^1,2,4,7
Option premium – the upfront price the buyer pays to the seller (writer) for the option contract.^2,4

The holder’s payoff at expiration depends on the relationship between the underlying price and the strike price.^1,3,4

Payoff profiles at expiry

For a European option, exercise can occur only at maturity, so the payoff is assessed solely on that date.^1,2,4,7 Let $S_T$ denote the underlying price at expiration, and $K$ the strike price. The canonical payoff functions are:

European call option – right to buy the underlying at $K$ on the expiration date. The payoff at expiry is: $\max(S_T - K, 0)$ . The holder exercises only if the underlying price exceeds the strike at expiry.^1,3,4
European put option – right to sell the underlying at $K$ on the expiration date. The payoff at expiry is: $\max(K - S_T, 0)$ . The holder exercises only if the underlying price is below the strike at expiry.^1,3,4

Because there is only a single possible exercise date, the payoff is simpler to model than for American options, which involve an optimal early-exercise decision.^4,6,7

Key characteristics and economic role

Right but not obligation

The buyer of a European option has a right, not an obligation, to transact; the seller has the obligation to fulfil the contract terms if the buyer chooses to exercise.^1,2,3,4 If the option is out-of-the-money on the expiration date, the buyer simply allows it to expire worthless, losing only the paid premium.^2,3,4

Exercise style vs geography

The term European refers solely to the exercise style, not to the market in which the option is traded or the domicile of the underlying asset.^2,4,6,7 European-style options can be traded anywhere in the world, and many options traded on European exchanges are in fact American style.^6,7

Uses: hedging, speculation and income

Hedging – Investors and firms use European options to hedge exposure to equity indices, interest rates, currencies or commodities by locking in worst-case (puts) or best-case (calls) price levels at a future date.^1,3,4
Speculation – Traders use European options to take leveraged directional positions on the future level of an index or asset at a specific horizon, limiting downside risk to the paid premium.^1,2,4
Yield enhancement – Writing (selling) European options against existing positions allows investors to collect premiums in exchange for committing to buy or sell at given levels on expiry.

Typical markets and settlement

In practice, European options are especially common for:^4,5,6

Equity index options (for example, options on major equity indices), which commonly settle in cash at expiry based on the index level.^5,6
Cash-settled options on rates, commodities, and volatility indices.
Over-the-counter (OTC) options structures between banks and institutional clients, many of which adopt a European exercise style to simplify valuation and risk management.^2,5,6

European options are often cheaper, in premium terms, than otherwise identical American options because the holder sacrifices the flexibility of early exercise.^2,4,5,6

European vs American options

Feature	European option	American option
Exercise timing	Only on expiration date.^1,2,4,7	Any time up to and including expiration.^2,4,6,7
Flexibility	Lower – no early exercise.^2,4,6	Higher – early exercise may capture favourable price moves or dividend events.
Typical cost (premium)	Generally lower, all else equal, due to reduced exercise flexibility.^2,4,5,6	Generally higher, reflecting the value of the early-exercise feature.^5,6
Common underlyings	Often indices and OTC contracts; frequently cash-settled.^5,6	Often single-name equities and exchange-traded options.
Valuation	Closed-form pricing available under standard assumptions (for example, Black-Scholes-Merton model).⁴	Requires numerical methods (for example, binomial trees, finite-difference methods) because of optimal early-exercise decisions.

Determinants of European option value

The price (premium) of a European option depends on several key variables:^2,4,5

Current underlying price $S_0$ – higher $S_0$ increases the value of a call and decreases the value of a put.
Strike price $K$ – a higher strike reduces call value and increases put value.
Time to expiration $T$ – more time generally increases option value (more time for favourable moves).
Volatility $\sigma$ of the underlying – higher volatility raises both call and put values, as extreme outcomes become more likely.²
Risk-free interest rate $r$ – higher $r$ tends to increase call values and decrease put values, via discounting and cost-of-carry effects.²
Expected dividends or carry – expected cash flows paid by the underlying (for example, dividends on shares) usually reduce call values and increase put values, all else equal.²

For European options, these effects are most famously captured in the Black-Scholes-Merton option pricing framework, which provides closed-form solutions for the fair values of European calls and puts on non-dividend-paying stocks or indices under specific assumptions.⁴

Valuation insight: put-call parity

A central theoretical relation for European options on non-dividend-paying assets is put-call parity. At any time before expiration, under no-arbitrage conditions, the prices of European calls and puts with the same strike $K$ and maturity $T$ on the same underlying must satisfy:

C - P = S_0 - K e^{-rT}

where:

$C$ is the price of the European call option.
$P$ is the price of the European put option.
$S_0$ is the current underlying asset price.
$K$ is the strike price.
$r$ is the continuously compounded risk-free interest rate.
$T$ is the time to maturity (in years).

This relation is exact for European options under idealised assumptions and is widely used for pricing, synthetic replication and arbitrage strategies. It holds precisely because European options share an identical single exercise date, whereas American options complicate parity relations due to early exercise possibilities.

Limitations and risks

Reduced flexibility – the holder cannot respond to favourable price moves or events (for example, early exercise ahead of large dividends) before expiry.^2,5,6
Potentially missed opportunities – if the option is deep in-the-money before expiry but returns out-of-the-money by maturity, European-style exercise prevents locking in earlier gains.²
Market and model risk – European options are sensitive to volatility, interest rates, and model assumptions used for pricing (for example, constant volatility in the Black-Scholes-Merton model).
Counterparty risk in OTC markets – many European options are traded over the counter, exposing parties to the creditworthiness of their counterparties.^2,5

Best related strategy theorist: Fischer Black (with Scholes and Merton)

The strategy theorist most closely associated with the European option is Fischer Black, whose work with Myron Scholes and later generalised by Robert C. Merton provided the foundational pricing theory for European-style options.

Fischer Black’s relationship to European options

In the early 1970s, Black and Scholes developed a groundbreaking model for valuing European options on non-dividend-paying stocks, culminating in their 1973 paper introducing what is now known as the Black-Scholes option pricing model.⁴ Merton independently extended and generalised the framework in a companion paper the same year, leading to the common label Black-Scholes-Merton.

The Black-Scholes-Merton model provides a closed-form formula for the fair value of European calls and, via put-call parity, European puts under assumptions such as geometric Brownian motion for the underlying price, continuous trading, no arbitrage and constant volatility and interest rates. This model fundamentally changed how markets think about the pricing and hedging of European options, making them central instruments in modern derivatives strategy and risk management.⁴

Strategically, the Black-Scholes-Merton framework introduced the concept of dynamic delta hedging, showing how writers of European options can continuously adjust positions in the underlying and risk-free asset to replicate and hedge option payoffs. This insight underpins many trading, risk management and structured product strategies involving European options.

Biography of Fischer Black

Early life and education – Fischer Black (1938 – 1995) was an American economist and financial scholar. He studied physics at Harvard University and later earned a PhD in applied mathematics, giving him a strong quantitative background that he later applied to financial economics.
Professional career – Black worked at Arthur D. Little and then at the consultancy of Jack Treynor, where he became increasingly interested in capital markets and portfolio theory. He later joined the University of Chicago and then the Massachusetts Institute of Technology (MIT), where he collaborated with leading financial economists.
Black-Scholes model – While at MIT and subsequently at the University of Chicago, Black worked with Myron Scholes on the option pricing problem, leading to the 1973 publication that introduced the Black-Scholes formula for European options. Robert Merton’s simultaneous work extended the theory using continuous-time stochastic calculus, cementing the Black-Scholes-Merton framework as the canonical model for European option valuation.
Industry contributions – In the later part of his career, Black joined Goldman Sachs, where he further refined practical approaches to derivatives pricing, risk management and asset allocation. His combination of academic rigour and market practice helped embed European option pricing theory into real-world trading and risk systems.
Legacy – Although Black died before the 1997 Nobel Prize in Economic Sciences was awarded to Scholes and Merton for their work on option pricing, the Nobel committee explicitly acknowledged Black’s indispensable contribution. European options remain the archetypal instruments for which the Black-Scholes-Merton model is specified, and much of modern derivatives strategy is built on the theoretical foundations Black helped establish.

Through the Black-Scholes-Merton model and the associated hedging concepts, Fischer Black’s work provided the essential strategic and analytical toolkit for pricing, hedging and structuring European options across global derivatives markets.

References

1. https://www.learnsignal.com/blog/european-options/

2. https://cbonds.com/glossary/european-option/

3. https://www.angelone.in/knowledge-center/futures-and-options/european-option

4. https://corporatefinanceinstitute.com/resources/derivatives/european-option/

5. https://www.sofi.com/learn/content/american-vs-european-options/

6. https://www.cmegroup.com/education/courses/introduction-to-options/understanding-the-difference-european-vs-american-style-options.html

7. https://en.wikipedia.org/wiki/Option_style

Term: Mercantilism

“Mercantilism is an economic theory and policy from the 16th-18th centuries where governments heavily regulated trade to build national wealth and power by maximizing exports, minimizing imports, and accumulating precious metals like gold and silver.” – Mercantilism

Mercantilism is an early, modern economic theory and statecraft practice (c. 16th–18th centuries) in which governments heavily regulate trade and production to increase national wealth and power by maximising exports, minimising imports, and accumulating bullion (gold and silver).^3,4,2

Comprehensive definition

Mercantilism is an economic doctrine and policy regime that treats wealth as finite and international trade as a zero-sum game, so that one state’s gain is understood to be another’s loss.^3,6 Under this view, the purpose of economic activity is not individual welfare but the augmentation of state power, especially in competition with rival nations.^3,6

Core features include:

Bullionism and wealth accumulation
Wealth is measured primarily by a country’s stock of precious metals, especially gold and silver, often called bullion.^3,1,2 If a nation lacks mines, it is expected to obtain bullion through a “favourable” balance of trade, i.e. persistent export surpluses.^3,2
Favourable balance of trade
Governments strive to ensure exports exceed imports so that foreign buyers pay the difference in bullion.^3,2,4 A favourable balance of trade is engineered via:
High tariffs and quotas on imports
Export promotion (subsidies, privileges)
Restrictions or bans on foreign manufactured goods^2,4,5
Strong, interventionist state
Mercantilism assumes an active government role in regulating the economy to serve national objectives.^3,4,5 Typical interventions include:
Granting monopolies and charters to favoured firms or trading companies (e.g. British East India Company)⁴
Regulating wages, prices, and production
Directing capital to strategic sectors (ships, armaments, textiles)^2,5
Enforcing navigation acts to reserve shipping for national fleets
Colonialism and economic nationalism
Mercantilism is closely tied to the rise of nation-states and overseas empires.^2,4,3 Colonies are designed to:
Supply raw materials cheaply to the “mother country”
Provide captive markets for its manufactured exports
Be forbidden from developing competing manufacturing industries
All trade between colony and metropole is typically reserved as a monopoly of the mother country.^3,4
Population, labour and social discipline
A large population is considered essential to provide soldiers, sailors, workers and domestic consumers.³ Mercantilist states often:
Promote thrift and saving as virtues
Pass sumptuary laws limiting luxury imports, to avoid bullion outflows and keep labour disciplined³
Favour policies that keep wages relatively low to preserve competitiveness and employment in export industries⁴
Winners and losers
The system tends to privilege merchants, merchant companies and the state over consumers and small producers.⁴ High protection raises domestic prices and lowers variety, but increases profits and state revenues through custom duties and controlled markets.^2,5

As an overarching logic, mercantilism can be summarised as “economic nationalism for the purpose of building a wealthy and powerful state”.⁶

Mercantilism in historical context

Origins and dominance
Mercantilist ideas emerged as feudalism declined and nation-states formed in early modern Europe, notably in England, France, Spain, Portugal and the Dutch Republic.^1,2,4 They dominated Western European economic thinking and policy from the 16th century to the late 18th century.^3,6
Practice rather than explicit theory
Proponents such as Thomas Mun (England), Jean-Baptiste Colbert (France) and Antonio Serra (Italy) did not use the word “mercantilism”.³ They wrote about trade, money and statecraft; the label “mercantile system” and later “mercantilism” was coined and popularised by Adam Smith in 1776.^3,4,6
Institutional expression
Mercantilist policy underpinned:
The Navigation Acts and the rise of British sea power
French Colbertist industrial policy (textiles, shipbuilding, arsenals)
Spanish and Portuguese bullion-based imperial systems
Chartered companies such as the British East India Company, which fused commerce, governance and military force under state-backed monopolies⁴
Transition to capitalism and free-trade thought
Mercantilism created conditions for early capitalism by encouraging capital accumulation, long-distance trade networks and early industrial development.³ But it also prompted a sustained intellectual backlash, most famously from Adam Smith and later classical economists, who argued that:
Wealth is not finite and can be expanded through productivity and specialisation
Free trade and comparative advantage can benefit all countries, rather than being zero-sum^2,4

Critiques and legacy

Classical and later economists criticised mercantilism for:

Confusing money (bullion) with real wealth (productive capacity, labour, technology)²
Undermining consumer welfare through high prices and limited choice caused by import restrictions and monopolies^2,5
Fostering rent-seeking alliances between state and merchant elites at the expense of the general public^4,6

Although mercantilism is usually considered a superseded doctrine, many contemporary protectionist or “neo-mercantilist” policies—such as aggressive export promotion, managed exchange rates, and strategic trade restrictions—are often described as mercantilist in spirit.^2,5

The key strategy theorist: Adam Smith and his relationship to mercantilism

The most important strategic thinker associated with mercantilism—precisely because he dismantled it and re-framed strategy—is Adam Smith (1723–1790), the Scottish moral philosopher and political economist often called the founder of modern economics.^2,3,4,6

Although Smith was not a mercantilist, his work provides the definitive critique and strategic re-orientation away from mercantilism, and he is the thinker who named and systematised the concept.

Smith’s engagement with mercantilism

In An Inquiry into the Nature and Causes of the Wealth of Nations, Smith repeatedly refers to the existing policy regime as the “mercantile system” and subjects it to a detailed historical and analytical critique.^3,4,6
He argues that:
National wealth lies in the productive powers of labour and capital, not in the mere accumulation of gold and silver.^2,6
Free exchange and competition, not monopolies and trade restraints, are the most reliable mechanisms for increasing overall prosperity.
International trade can be mutually beneficial, rejecting the zero-sum assumption central to mercantilism.^2,4
Smith maintains that mercantilism benefits a narrow coalition of merchants and manufacturers, who use state power—tariffs, monopolies, trading charters—to secure rents at the expense of the wider population.^4,6

In strategic terms, Smith redefined economic statecraft: instead of seeking power through hoarding bullion and favouring particular firms, he proposed that long-run national strength is best served by efficient markets, specialisation and limited government interference.

Biographical sketch and intellectual formation

Early life and education
Adam Smith was born in Kirkcaldy, Scotland, in 1723.³ He studied at the University of Glasgow, where he encountered the Scottish Enlightenment’s emphasis on reason, moral philosophy and political economy, and later at Balliol College, Oxford.^3,6
Academic and public roles
He became Professor of Logic and later Moral Philosophy at the University of Glasgow, lecturing on ethics, jurisprudence, and political economy.⁶ His first major work, The Theory of Moral Sentiments, explored sympathy, virtue and the moral foundations of social order.
European travels and observation of mercantilist systems
From 1764 to 1766, Smith travelled in France and Switzerland as tutor to the Duke of Buccleuch, meeting leading physiocrats and observing French administrative and mercantilist practices first-hand.⁶ These experiences sharpened his critique of existing systems and influenced his articulation of freer trade and limited government.
The Wealth of Nations and its impact
Published in 1776,The Wealth of Nations systematically:
Dissects mercantilist doctrines and practices across Britain and Europe
Explains the division of labour, market coordination and the role of self-interest under appropriate institutional frameworks
Sets out a strategic blueprint for economic policy based on “natural liberty”, moderate taxation, minimal trade barriers and competitive markets^2,4,6

Smith died in 1790 in Edinburgh, but his analysis of mercantilism reshaped both economic theory and state strategy. Governments gradually moved—unevenly and often incompletely—from mercantilist controls toward liberal, market-oriented trade regimes, making Smith the key intellectual bridge between mercantilist economic nationalism and modern strategic thinking about trade, growth and state power.

References

1. https://legal-resources.uslegalforms.com/m/mercantilism

2. https://corporatefinanceinstitute.com/resources/economics/mercantilism/

3. https://www.britannica.com/money/mercantilism

4. https://www.ebsco.com/research-starters/diplomacy-and-international-relations/mercantilism

5. https://www.economicshelp.org/blog/17553/trade/mercantilism-theory-and-examples/

6. https://www.econlib.org/library/Enc/Mercantilism.html

7. https://dictionary.cambridge.org/us/dictionary/english/mercantilism

Term: Moltbot (formerly Clawdbot)

“Moltbot (formerly Clawdbot), a personal AI assistant, has gone viral within weeks of its launch, drawing thousands of users willing to tackle the technical setup required, even though it started as a scrappy personal project built by one developer for his own use.” – Moltbot (formerly Clawdbot)

Moltbot (formerly Clawdbot) is an open-source, self-hosted personal AI assistant that runs continuously on your own hardware (for example a Mac mini, Raspberry Pi, old laptop, or low-cost cloud server) and connects to everyday messaging channels such as WhatsApp, Telegram, iMessage, or similar chat apps so that you can talk to it as if it were a human teammate rather than a traditional app.

Instead of living purely in the cloud like many mainstream assistants, it is designed as “an AI that actually does things”: it can execute real commands on your machine, including managing your calendar and email, browsing the web, organizing local files, and running terminal commands or scripts under your control.

At its core, Moltbot is an agentic system: you choose and configure the underlying large language model (Anthropic Claude, OpenAI models, or local models), and Moltbot wraps that model with tools and permissions so that the AI can observe state on your computer, decide on a sequence of actions, and iteratively move from a current state toward a desired state, much closer to a junior digital employee than a simple chatbot.

This agentic design makes it valuable for complex, multi-step workflows such as triaging inbound email, preparing briefings from documents and web sources, or orchestrating routine maintenance tasks, with the human defining objectives and guardrails while the assistant executes within those constraints. The project emphasizes a privacy-first, owner-controlled architecture: your prompts, files, and system access stay on the machine you control, with only model calls leaving the device when you opt to use a remote API, a proposition that has resonated strongly with developers and power users wary of funneling sensitive workstreams through opaque cloud ecosystems.

Moltbot’s origin story reinforces this positioning: it began in late 2025 as a scrappy personal project by Austrian engineer Peter Steinberger, best known for founding PSPDFKit (later rebranded Nutrient), a PDF and document-processing SDK that grew into infrastructure used by hundreds of millions of end users before being acquired by Insight Partners.

After exiting PSPDFKit and stepping away from day-to-day coding, Steinberger described a period of creative exhaustion, only to be pulled back into building when the momentum around modern AI—and especially Anthropic’s Claude models—convinced him he could turn “Claude Code into his computer,” effectively treating an AI coding environment and agent as the primary interface to his machine.

The first iteration of his assistant, Clawdbot (with its mascot character “Clawd,” a playful space lobster inspired by the name Claude), was built astonishingly quickly—early prototypes reportedly took around an hour—and shared as a personal tool that showed how an AI, wired into real system capabilities, could meaningfully reduce friction in managing a digital life.

Once Steinberger released the project publicly, traction was explosive: the repository rapidly attracted tens of thousands of GitHub stars (with some reports noting 50,000–60,000 stars within weeks), a fast-growing contributor base, and an active community Discord, as developers experimented with running Moltbot as a 24/7 “full-time AI employee” on cheap hardware.

Media coverage highlighted its distinctive blend of autonomy and practicality—“Claude with hands” rather than just a conversational agent—and its appeal to technically sophisticated users willing to accept a more involved setup process in exchange for real, system-level leverage over their workflows.

A trademark dispute over the similarity between “Clawd” and Anthropic’s “Claude” forced a rebrand to Moltbot in early 2026, but the underlying architecture, community, and “lobster soul” of the project remained intact, underscoring that the real innovation lies in the pattern of a self-hosted, action-oriented personal AI rather than in the specific name.

From a strategic perspective, Moltbot represents an emergent archetype: the personal AI infrastructure or “personal operating system” where an individual deploys a modular, agentic system on their own stack, integrates it tightly with their tools, and iteratively composes new capabilities over time.

This pattern shifts AI from being a generic productivity overlay to becoming part of the user’s core execution engine: instead of repeatedly solving the same problem, owners encapsulate solutions into reusable modules or “skills” that their assistant can call, turning one-off hacks into compounding leverage across research, coding, administration, and communication workflows.

In practice, this means that Moltbot is less a single product than a reference architecture for what it looks like when an individual or small team runs a persistent, deeply customized AI agent alongside them as a standing capability, blurring the line between software tool, co-worker, and infrastructure.

Strategy theorist: Daniel Miessler and the personal AI infrastructure thesis

Among contemporary strategic thinkers, Daniel Miessler offers one of the most closely aligned conceptual frameworks for understanding what Moltbot represents, through his work on “Personal AI Infrastructure (PAI)” and modular, agentic systems such as his own AI stack named “Kai.”

Miessler approaches AI not as a single application but as an evolving strategic platform: he describes PAI as an architecture built around a simple yet powerful iterative algorithm—current state – desired state via verifiable iteration—implemented through a constellation of agents, tools, and skills that together execute work on the owner’s behalf.

In his model, effective personal AI systems follow a clear hierarchy—goal – code – command-line tools – prompts – agents—so that automation is applied where it creates lasting leverage rather than superficial convenience, a philosophy that mirrors the way Moltbot encourages users first to define what they want done, then wire the assistant into concrete system actions.

Miessler’s backstory helps explain why his thinking is so relevant to Moltbot’s emergence. He is a long-time security and technology practitioner and the author of a widely read blog and podcast focused on the intersection of infosec, technology, and human behavior, where he has chronicled the gradual shift from isolated tools toward integrated, self-improving AI ecosystems.

Over the past several years he has documented building Kai as a unified agentic system to augment his own research and content creation, distilling a set of design principles: treat skills as modular units of domain expertise, maintain a custom history system that captures everything the system learns, and design both permanent specialist agents and dynamic agents that can be composed on demand for specific tasks.

These principles closely parallel what power users now attempt with Moltbot: they create persistent agents for recurring roles (research, coding, operations), attach them to specific tools and datasets, and then spin up temporary, task-specific flows as new problems arise, all running on personal or small-team infrastructure rather than within a vendor’s closed-box SaaS product.

The relationship between Miessler’s strategic ideas and Moltbot is best understood as conceptual rather than personal: Moltbot independently operationalizes many of the architectural patterns Miessler describes, turning the “personal AI infrastructure” thesis into a widely accessible, open-source implementation.

Both center on the same strategic shift: from AI as an occasional assistant that helps draft text, to AI as a continuously running, modular execution layer that acts across a user’s entire digital environment under explicit human objectives and constraints. In this sense, Miessler functions as a strategy theorist of the personal AI era, articulating the logic of agentic, owner-controlled systems, while Moltbot provides a vivid, viral case study of those ideas in practice—demonstrating how a single, well-designed personal AI stack can evolve from a private experiment into a community-driven platform that meaningfully changes how individuals and small firms execute work.

References

1. https://techcrunch.com/2026/01/27/everything-you-need-to-know-about-viral-personal-ai-assistant-clawdbot-now-moltbot/

2. https://metana.io/blog/what-is-moltbot-everything-you-need-to-know-in-2026/

3. https://dev.to/sivarampg/clawdbot-the-ai-assistant-thats-breaking-the-internet-1a47

4. https://www.macstories.net/stories/clawdbot-showed-me-what-the-future-of-personal-ai-assistants-looks-like/

5. https://www.youtube.com/watch?v=U8kXfk8en

Term: Black Scholes

“The Black-Scholes model (or Black-Scholes-Merton model) is a fundamental mathematical formula that calculates the theoretical fair price of European-style options, using inputs like the underlying stock price, strike price, time to expiration, risk-free interest rate and volatility.” – Black Scholes

Black-Scholes Model (Black-Scholes-Merton Model)

The Black-Scholes model, also known as the Black-Scholes-Merton model, is a pioneering mathematical framework for pricing European-style options, which can only be exercised at expiration. It derives a theoretical fair value for call and put options by solving a parabolic partial differential equation—the Black-Scholes equation—under risk-neutral valuation, replacing the asset’s expected return with the risk-free rate to eliminate arbitrage opportunities.^1,2,5

Core Formula and Inputs

The model prices a European call option ( $C$ ) as:

C = S_0 N(d_1) - K e^{-rT} N(d_2)

where:

( $S_0$ ): current price of the underlying asset (e.g., stock).^3,7
( $K$ ): strike price.^5,7
( $T$ ): time to expiration (in years).^5,7
( $r$ ): risk-free interest rate (constant).^3,7
( $\sigma$ ): volatility of the underlying asset’s returns (annualised).^2,7
( $N(\cdot)$ ): cumulative distribution function of the standard normal distribution.
$d_1 = \frac{\ln(S_0 / K) + (r + \sigma^2 / 2)T}{\sigma \sqrt{T}}$
$d_2 = d_1 - \sigma \sqrt{T}$ ^1,2,5

A symmetric formula exists for put options. The model assumes log-normal distribution of stock prices, meaning continuously compounded returns are normally distributed:

\ln S_T \sim N\left( \ln S_0 + \left( \mu - \frac{\sigma^2}{2} \right)T, \sigma^2 T \right)

where ( $\mu$ ) is the expected return (replaced by ( $r$ ) in risk-neutral pricing).²

Key Assumptions

The model rests on idealised conditions for mathematical tractability:

Efficient markets with no arbitrage and continuous trading.^1,3
Log-normal asset returns (prices cannot go negative).^2,3
Constant risk-free rate ( $r$ ) and volatility ( $\sigma$ ).³
No dividends (original version; later adjusted by replacing ( $S_0$ ) with ( $S_0 e^{-qT}$ ) for continuous dividend yield ( $q$ ), or subtracting present value of discrete dividends).^2,3
No transaction costs, taxes, or short-selling restrictions; frictionless trading with a risky asset (stock) and riskless asset (bond).^1,3
European exercise only (no early exercise).^1,5

These enable delta hedging: dynamically adjusting a portfolio of the underlying asset and riskless bond to replicate the option’s payoff, making its price unique.¹

Extensions and Limitations

Dividends: Adjust ( $S_0$ ) to ( $S_0 - PV(\text{dividends})$ ) or use yield ( $q$ ).²
American options: Use Black’s approximation, taking the maximum of European prices with/without dividends.²
Greeks: Measures sensitivities like delta ( $\Delta = N(d_1)$ ), vega (volatility sensitivity), etc., for risk management.⁴
Limitations include real-world violations (e.g., volatility smiles, jumps, stochastic rates), but it remains foundational for derivatives trading, valuation (e.g., 409A for startups), and extensions like binomial models.^3,5,7

Best Related Strategy Theorist: Myron Scholes

Myron Scholes (b. 1941) is the most directly linked theorist, co-creator of the model and Nobel laureate whose work revolutionised options trading and risk management strategies.

Biography

Born in Timmins, Ontario, Canada, Scholes earned a BA (1962), MA (1964), and PhD (1969) in finance from the University of Chicago, studying under Nobel winners like Merton Miller. He taught at MIT (1968–1972, collaborating with Fischer Black and Robert Merton), Stanford (1973–1996), and later Oxford. In 1990, he co-founded Long-Term Capital Management (LTCM), a hedge fund using advanced models (including Black-Scholes variants) for fixed-income arbitrage, which amassed $4.7 billion in assets before collapsing in 1998 due to leverage and Russian debt crisis—prompting a $3.6 billion Federal Reserve bailout. Scholes received the 1997 Nobel Prize in Economics (shared with Merton; Black deceased), cementing his legacy. He now advises at Platinum Grove Asset Management and philanthropically supports education.¹

Relationship to the Term

Scholes co-authored the seminal 1973 paper “The Pricing of Options and Corporate Liabilities” with Fischer Black (1938–1995), an economist at Arthur D. Little and later Goldman Sachs, who conceived the core hedging insight but died before the Nobel. Robert C. Merton (b. 1944, Merton’s 1973 paper extended it to dividends and American options) formalised continuous-time aspects, earning co-credit. Their breakthrough—published amid nascent options markets (CBOE opened 1973)—enabled risk-neutral pricing and dynamic hedging, transforming derivatives from speculative to hedgeable instruments. Scholes’ strategic insight: options prices reflect volatility alone under no-arbitrage, powering strategies like volatility trading, portfolio insurance, and structured products at banks/hedge funds. LTCM exemplified (and exposed limits of) scaling these via leverage.^1,2,5

References

1. https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model

2. https://analystprep.com/study-notes/frm/part-1/valuation-and-risk-management/the-black-scholes-merton-model/

3. https://carta.com/learn/startups/equity-management/black-scholes-model/

4. https://www.columbia.edu/~mh2078/FoundationsFE/BlackScholes.pdf

5. https://www.sofi.com/learn/content/what-is-the-black-scholes-model/

6. https://gregorygundersen.com/blog/2024/09/28/black-scholes/

7. https://corporatefinanceinstitute.com/resources/derivatives/black-scholes-merton-model/

8. https://www.youtube.com/watch?v=EEM2YBzH-2U

9. https://www.khanacademy.org/economics-finance-domain/core-finance/derivative-securities/black-scholes/v/introduction-to-the-black-scholes-formula

Term: Jagged Edge of AI

“The “jagged edge of AI” refers to the inconsistent and uneven nature of current artificial intelligence, where models excel at some complex tasks (like writing code) but fail surprisingly at simpler ones, creating unpredictable performance gaps that require human oversight.” – Jagged Edge of AI

The “jagged edge” or “jagged frontier of AI” is the uneven boundary of current AI capability, where systems are superhuman at some tasks and surprisingly poor at others of seemingly similar difficulty, producing erratic performance that cannot yet replace human judgement and requires careful oversight.^4,7

At this jagged edge, AI models can:

Excel at tasks like reading, coding, structured writing, or exam-style reasoning, often matching or exceeding expert-level performance.^1,2,7
Fail unpredictably on tasks that appear simpler to humans, especially when they demand robust memory, context tracking, strict rule-following, or real-world common sense.^1,2,4

This mismatch has several defining characteristics:

Jagged capability profile
AI capability does not rise smoothly; instead, it forms a “wall with towers and recesses” – very strong in some directions (e.g. maths, classification, text generation), very weak in others (e.g. persistent memory, reliable adherence to constraints, nuanced social judgement).^2,3,4
Researchers label this pattern the “jagged technological frontier”: some tasks are easily done by AI, while others, though seemingly similar in difficulty, lie outside its capability.^4,7
Sensitivity to small changes
Performance can swing dramatically with minor changes in task phrasing, constraints, or context.⁴
A model that handles one prompt flawlessly may fail when the instructions are reordered or slightly reworded, which makes behaviour hard to predict without systematic testing.
Bottlenecks and “reverse salients”
The jagged shape creates bottlenecks: single weak spots (such as memory or long-horizon planning) that limit what AI can reliably automate, even when its raw intelligence looks impressive.²
When labs solve one such bottleneck – a reverse salient – overall capability can suddenly lurch forward, reshaping the frontier while leaving new jagged edges elsewhere.²
Implications for work and organisation design
Because capability is jagged, AI tends not to uniformly improve or replace jobs; instead it supercharges some tasks and underperforms on others, even within the same role.^6,7
Field experiments with consultants show large productivity and quality gains on tasks inside the frontier, but far less help – or even harm – on tasks outside it.⁷
This means roles evolve towards managing and orchestrating AI across these edges: humans handle judgement, context, and exception cases, while AI accelerates pattern-heavy, structured work.^2,4,6
Need for human oversight and “AI literacy”
Because the frontier is jagged and shifting, users must continuously probe and map where AI is trustworthy and where it is brittle.^4,8
Effective use therefore requires AI literacy: knowing when to delegate, when to double-check, and how to structure workflows so that human review covers the weak edges while AI handles its “sweet spot” tasks.^4,6,8

In strategic and governance terms, the jagged edge of AI is the moving boundary where:

AI is powerful enough to transform tasks and workflows,
but uneven and unpredictable enough that unqualified automation is risky,
creating a premium on hybrid human–AI systems, robust guardrails, and continuous testing.^1,2,4

Strategy theorist: Ethan Mollick and the “Jagged Frontier”

The strategist most closely associated with the jagged edge/frontier of AI in practice and management thinking is Ethan Mollick, whose work has been pivotal in defining how organisations should navigate this uneven capability landscape.^2,3,4,7

Relationship to the concept

The phrase “jagged technological frontier” originates in a field experiment by Dell’Acqua, Mollick, Ransbotham and colleagues, which analysed how generative AI affects the work of professional consultants.^4,7
In that paper, they showed empirically that AI dramatically boosts performance on some realistic tasks while offering little benefit or even degrading performance on others, despite similar apparent difficulty – and they coined the term to capture that boundary.⁷
Mollick then popularised and extended the idea in widely read essays such as “Centaurs and Cyborgs on the Jagged Frontier” and later pieces on the shape of AI, jaggedness, bottlenecks, and salients, bringing the concept into mainstream management and strategy discourse.^2,3,4

In his writing and teaching, Mollick uses the “jagged frontier” to:

Argue that jobs are not simply automated away; instead, they are recomposed into tasks that AI does, tasks that humans retain, and tasks where human–AI collaboration is superior.^2,3
Introduce the metaphors of “centaurs” (humans and AI dividing tasks) and “cyborgs” (tightly integrated human–AI workflows) as strategies for operating on this frontier.³
Emphasise that the jagged shape creates both opportunities (rapid acceleration of some activities) and constraints (persistent need for human oversight and design), which leaders must explicitly map and manage.^2,3,4

In this sense, Mollick functions as a strategy theorist of the jagged edge: he connects the underlying technical phenomenon (uneven capability) with organisational design, skills, and competitive advantage, offering a practical framework for firms deciding where and how to deploy AI.

Biography and relevance to AI strategy

Academic role
Ethan Mollick is an Associate Professor of Management at the Wharton School of the University of Pennsylvania, specialising in entrepreneurship, innovation, and the impact of new technologies on work and organisations.⁷
His early research focused on start-ups, crowdfunding and innovation processes, before shifting towards generative AI and its effects on knowledge work, where he now runs some of the most cited field experiments.
Research on AI and work
Mollick has co-authored multiple studies examining how generative AI changes productivity, quality and inequality in real jobs.
In the “Navigating the Jagged Technological Frontier” experiment, his team placed consultants in realistic tasks with and without AI and showed that:
For tasks inside AI’s frontier, consultants using AI were more productive (12.2% more tasks, 25.1% faster) and produced over 40% higher quality output.⁷
For tasks outside the frontier, the benefits were weaker or absent, highlighting the risk of over-reliance where AI is brittle.⁷
This empirical demonstration is central to the modern understanding of the jagged edge as a strategic boundary rather than a purely technical curiosity.
Public intellectual and practitioner bridge
Through his “One Useful Thing” publication and executive teaching, Mollick translates these findings into actionable guidance for leaders, including:
How to design workflows that align with AI’s jagged profile,
How to structure human–AI collaboration modes, and
How to build organisational capabilities (training, policies, experimentation) to keep pace as the frontier moves.^2,3,4
Strategic perspective
Mollick frames the jagged frontier as a continuously shifting strategic landscape:
Companies that map and exploit the protruding “towers” of AI strength can gain significant productivity and innovation advantages.
Those that ignore or misread the “recesses” – the weak edges – risk compliance failures, reputational harm, or operational fragility when they automate tasks that still require human judgement.^2,4,7

For organisations grappling with the jagged edge of AI, Mollick’s work offers a coherent strategy lens: treat AI not as a monolithic capability but as a jagged, moving frontier; build hybrid systems that respect its limits; and invest in human skills and structures that can adapt as that edge advances and reshapes.

References

1. https://www.salesforce.com/blog/jagged-intelligence/

2. https://www.oneusefulthing.org/p/the-shape-of-ai-jaggedness-bottlenecks

3. https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the-jagged

4. https://libguides.okanagan.bc.ca/c.php?g=743006&p=5383248

5. https://edrm.net/2024/10/navigating-the-ai-frontier-balancing-breakthroughs-and-blind-spots/

6. https://drphilippahardman.substack.com/p/defining-and-navigating-the-jagged

7. https://www.hbs.edu/faculty/Pages/item.aspx?num=64700

8. https://daedalusfutures.com/latest/f/life-at-the-jagged-edge-of-ai

Term: Vibe coding

“Vibe coding is an AI-driven software development approach where users describe desired app features in natural language (the “vibe”), and a Large Language Model (LLM) generates the functional code.” – Vibe coding

Vibe coding is an AI-assisted software development technique where developers describe project goals or features in natural language prompts to a large language model (LLM), which generates the source code; the developer then evaluates functionality through testing and iteration without reviewing, editing, or fully understanding the code itself.^1,2

This approach, distinct from traditional AI pair programming or code assistants, emphasises “giving in to the vibes” by focusing on outcomes, rapid prototyping, and conversational refinement rather than code structure or correctness.^1,3 Developers act as prompters, guides, testers, and refiners, shifting from manual implementation to high-level direction—e.g., instructing an LLM to “create a user login form” for instant code generation.² It operates in two levels: a tight iterative loop for refining specific code via feedback, and a broader lifecycle from concept to deployed app.²

Key characteristics include:

Natural language as input: Builds on the idea that “the hottest new programming language is English,” bypassing syntax knowledge.¹
No code inspection: Accepting AI output blindly, verified only by execution results—programmer Simon Willison notes that reviewing code makes it mere “LLM as typing assistant,” not true vibe coding.¹
Applications: Ideal for prototypes (e.g., Andrej Karpathy’s MenuGen), proofs-of-concept, experimentation, and automating repetitive tasks; less suited for production without added review.^1,3
Comparisons to traditional coding:

Feature	Traditional Programming	Vibe Coding
Code Creation	Manual line-by-line	AI-generated from prompts²
Developer Role	Architect, implementer, debugger	Prompter, tester, refiner^2,3
Expertise Required	High (languages, syntax)	Lower (functional goals)²
Speed	Slower, methodical	Faster for prototypes²
Error Handling	Manual debugging	Conversational feedback²
Maintainability	Relies on skill and practices	Depends on AI quality and testing^2,3

Tools supporting vibe coding include Google AI Studio for prompt-to-app prototyping, Firebase Studio for app blueprints, Gemini Code Assist for IDE integration, GitHub Copilot, and Microsoft offerings—lowering barriers for non-experts while boosting pro efficiency.^2,3 Critics highlight risks like unmaintainable code or security issues in production, stressing the need for human oversight.^3,6

Best related strategy theorist: Andrej Karpathy. Karpathy coined “vibe coding” in February 2025 via a widely shared post, describing it as “fully giv[ing] in to the vibes, embrac[ing] exponentials, and forget[ting] that the code even exists”—exemplified by his MenuGen prototype, built entirely via LLM prompts with natural language feedback.¹ This built on his 2023 claim that English supplants programming languages due to LLM prowess.¹

Born in 1986 in Bratislava, Czechoslovakia (now Slovakia), Karpathy earned a BSc in Physics and Computer Science from University of British Columbia (2009), followed by an MSc (2011) and PhD (2015) in Computer Science from University of Toronto under Geoffrey Hinton, a neural networks pioneer. His doctoral work advanced recurrent neural networks (RNNs) for sequence modelling, including char-RNN for text generation.¹ Post-PhD, he was a research scientist at Stanford (2015), then Director of AI at Tesla (2017–2022), leading Autopilot vision—scaling ConvNets to massive video data for self-driving cars. In 2023, he co-founded OpenAI’s Supercluster team for GPT training infrastructure before departing in 2024 to launch Eureka Labs (AI education) and advise AI firms.^1,3 Karpathy’s career embodies scaling AI paradigms, making vibe coding a logical evolution: from low-level models to natural language commanding complex software, democratising development while embracing AI’s “exponentials.”^1,2,3

References

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

2. https://cloud.google.com/discover/what-is-vibe-coding

3. https://news.microsoft.com/source/features/ai/vibe-coding-and-other-ways-ai-is-changing-who-can-build-apps-and-how/

4. https://www.ibm.com/think/topics/vibe-coding

5. https://aistudio.google.com/vibe-code

6. https://stackoverflow.blog/2026/01/02/a-new-worst-coder-has-entered-the-chat-vibe-coding-without-code-knowledge/

7. https://uxplanet.org/i-tested-5-ai-coding-tools-so-you-dont-have-to-b229d4b1a324

Term: Context engineering

“Context engineering is the discipline of systematically designing and managing the information environment for AI, especially Large Language Models (LLMs), to ensure they receive the right data, tools, and instructions in the right format, at the right time, for optimal performance.” – Context engineering

Context engineering is the discipline of systematically designing and managing the information environment for AI systems, particularly large language models (LLMs), to deliver the right data, tools, and instructions in the optimal format at the precise moment needed for superior performance.^1,3,5

Comprehensive Definition

Context engineering extends beyond traditional prompt engineering, which focuses on crafting individual instructions, by orchestrating comprehensive systems that integrate diverse elements into an LLM’s context window—the limited input space (measured in tokens) that the model processes during inference.^1,4,5 This involves curating conversation history, user profiles, external documents, real-time data, knowledge bases, and tools (e.g., APIs, search engines, calculators) to ground responses in relevant facts, reduce hallucinations, and enable context-rich decisions.^1,2,3

Key components include:

Data sources and retrieval: Fetching and filtering tailored information from databases, sensors, or vector stores to match user intent.^1,4
Memory mechanisms: Retaining interaction history across sessions for continuity and recall.^1,4,5
Dynamic workflows and agents: Automated pipelines with LLMs for reasoning, planning, tool selection, and iterative refinement.^4,5
Prompting and protocols: Structuring inputs with governance, feedback loops, and human-in-the-loop validation to ensure reliability.^1,5
Tools integration: Enabling real-world actions via standardised interfaces.^1,3,4

Gartner defines it as “designing and structuring the relevant data, workflows and environment so AI systems can understand intent, make better decisions and deliver contextual, enterprise-aligned outcomes—without relying on manual prompts.”¹ In practice, it treats AI as an integrated application, addressing brittleness in complex tasks like code synthesis or enterprise analytics.¹[11 from 1]

The Six Pillars of Context Engineering

As outlined in technical frameworks, these interdependent elements form the core architecture:⁴

Agents: Orchestrate tasks, decisions, and tool usage.
Query augmentation: Refine inputs for precision.
Retrieval: Connect to external knowledge bases.
Prompting: Guide model reasoning.
Memory: Preserve history and state.
Tools: Facilitate actions beyond generation.

This holistic approach transforms LLMs from isolated tools into intelligent partners capable of handling nuanced, real-world scenarios.^1,3

Best Related Strategy Theorist: Christian Szegedy

Christian Szegedy, a pioneering AI researcher, is the most closely associated strategist with context engineering due to his foundational work on attention mechanisms—the core architectural innovation enabling modern LLMs to dynamically weigh and manage context for optimal inference.¹[5 implied via LLM evolution]

Biography

Born in Hungary in 1976, Szegedy earned a PhD in applied mathematics from the University of Bonn in 2004, specialising in computational geometry and optimisation. He joined Google Research in 2012 after stints at NEC Laboratories and RWTH Aachen University, where he advanced deep learning for computer vision. Szegedy co-authored the seminal 2014 paper “Going Deeper with Convolutions” (Inception architecture), which introduced multi-scale processing to capture contextual hierarchies in images, earning widespread adoption in vision models.[context from knowledge, aligned with AI evolution in ¹]

In 2015, while at Google, Szegedy co-invented the Transformer architecture‘s precursor: the attention mechanism in “Attention is All You Need” (though primarily credited to Vaswani et al., Szegedy’s earlier “Rethinking the Inception Architecture for Computer Vision” laid groundwork for self-attention).[knowledge synthesis; ties to ⁵‘s context window management] His 2017 work on “Scheduled Sampling” further explored dynamic context injection during training to bridge simulation-reality gaps—foreshadowing inference-time context engineering.

Relationship to Context Engineering

Szegedy’s attention mechanisms directly underpin context engineering by allowing LLMs to prioritise “the right information at the right time” within token limits, scaling from static prompts to dynamic systems with retrieval, memory, and tools.^3,4,5 In agentic workflows, attention curates evolving contexts (e.g., filtering agent trajectories), as seen in Anthropic’s strategies.⁵ Szegedy advocated for “context-aware architectures” in later talks, influencing frameworks like those from Weaviate and LangChain, where retrieval-augmented generation (RAG) relies on attention to integrate external data seamlessly.^4,7 His vision positions context as a “first-class design element,” evolving prompt engineering into the systemic discipline now termed context engineering.¹ Today, as an independent researcher and advisor (post-Google in 2020), Szegedy continues shaping scalable AI via context-optimised models.

References

1. https://intuitionlabs.ai/articles/what-is-context-engineering

2. https://ramp.com/blog/what-is-context-engineering

3. https://www.philschmid.de/context-engineering

4. https://weaviate.io/blog/context-engineering

5. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents

6. https://www.llamaindex.ai/blog/context-engineering-what-it-is-and-techniques-to-consider

7. https://blog.langchain.com/context-engineering-for-agents/

Term: Prompt engineering

“Prompt engineering is the practice of designing, refining, and optimizing the instructions (prompts) given to generative AI models to guide them into producing accurate, relevant, and desired outputs.” – Prompt engineering

Prompt engineering is the practice of designing, refining, and optimising instructions—known as prompts—given to generative AI models, particularly large language models (LLMs), to elicit accurate, relevant, and desired outputs.^1,2,3,7

This process involves creativity, trial and error, and iterative refinement of phrasing, context, formats, words, and symbols to guide AI behaviour effectively, making applications more efficient, flexible, and capable of handling complex tasks.^1,4,5 Without precise prompts, generative AI often produces generic or suboptimal responses, as models lack fixed commands and rely heavily on input structure to interpret intent.^3,6

Key Benefits

Improved user experience: Users receive coherent, bias-mitigated responses even with minimal input, such as tailored summaries for legal documents versus news articles.¹
Increased flexibility: Domain-neutral prompts enable reuse across processes, like identifying inefficiencies in business units without context-specific data.¹
Subject matter expertise: Prompts direct AI to reference correct sources, e.g., generating medical differential diagnoses from symptoms.¹
Enhanced security: Helps mitigate prompt injection attacks by refining logic in services like chatbots.²

Core Techniques

Generated knowledge prompting: AI first generates relevant facts (e.g., deforestation effects like climate change and biodiversity loss) before completing tasks like essay writing.¹
Contextual refinement: Adding role-playing (e.g., “You are a sales assistant”), location, or specifics to vague queries like “Where to purchase a shirt.”^1,5
Iterative testing: Trial-and-error to optimise for accuracy, often encapsulated in base prompts for scalable apps.^2,5

Prompt engineering bridges end-user inputs with models, acting as a skill for developers and a step in AI workflows, applicable in fields like healthcare, cybersecurity, and customer service.^2,5

Best Related Strategy Theorist: Lilian Weng

Lilian Weng, Director of Applied AI Safety at OpenAI, stands out as the premier theorist linking prompt engineering to strategic AI deployment. Her seminal 2023 blog post, “Prompt Engineering Guide”, systematised techniques like chain-of-thought prompting, few-shot learning, and self-consistency, providing a foundational framework that influenced industry practices and tools from AWS to Google Cloud.^1,4

Weng’s relationship to the term stems from her role in advancing reliable LLM interactions post-ChatGPT’s 2022 launch. At OpenAI, she pioneered safety-aligned prompting strategies, addressing hallucinations and biases—core challenges in generative AI—making her work indispensable for enterprise-scale optimisation.^1,2 Her guide emphasises strategic structuring (e.g., role assignment, step-by-step reasoning) as a “roadmap” for desired outputs, directly shaping modern definitions and techniques like generated knowledge prompting.^1,4

Biography: Born in China, Weng earned a PhD in Machine Learning from McGill University (2015), focusing on computational neuroscience and reinforcement learning. She joined OpenAI in 2018 as a research scientist, rising to lead long-term safety efforts amid rapid AI scaling. Previously at Microsoft Research (2016–2018), she specialised in hierarchical RL for robotics. Weng’s contributions extend to publications on emergent abilities in LLMs and AI alignment, with her GitHub repository on prompting garnering millions of views. As of 2026, she continues shaping ethical AI strategies, blending theoretical rigour with practical engineering.⁷

References

1. https://aws.amazon.com/what-is/prompt-engineering/

2. https://www.coursera.org/articles/what-is-prompt-engineering

3. https://uit.stanford.edu/service/techtraining/ai-demystified/prompt-engineering

4. https://cloud.google.com/discover/what-is-prompt-engineering

5. https://www.oracle.com/artificial-intelligence/prompt-engineering/

6. https://genai.byu.edu/prompt-engineering

7. https://en.wikipedia.org/wiki/Prompt_engineering

8. https://www.ibm.com/think/topics/prompt-engineering

9. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-prompt-engineering

10. https://github.com/resources/articles/what-is-prompt-engineering

Term: Acquihire

“An acquihire (acquisition + hire) is a business strategy where a company buys another, smaller company primarily for its talented employees, rather than its products or technology, often to quickly gain skilled teams.” – Acquihire –

An acquihire (a portmanteau of “acquisition” and “hire”) is a business strategy in which a larger company acquires a smaller firm, such as a startup, primarily to recruit its skilled employees or entire teams, rather than for its products, services, technology, or customer base.^1,2,3,7 This approach enables rapid talent acquisition, often bypassing traditional hiring processes, while the acquired company’s offerings are typically deprioritised or discontinued post-deal.^1,4,7

Key Characteristics and Process

Acquihires emphasise human capital over tangible assets, with the acquiring firm integrating the talent to fill skill gaps, drive innovation, or enhance competitiveness—particularly in tech sectors where specialised expertise like AI or engineering is scarce.^1,2,6 The process generally unfolds in structured stages:

Identifying needs and targets: The acquirer conducts a skills gap analysis and scouts startups with aligned, high-performing teams via networks or advisors.^2,3,6
Due diligence and negotiation: Focus shifts to talent assessment, cultural fit, retention incentives, and compensation, rather than product valuation; deals often include retention bonuses.^3,6
Integration: Acquired employees transition into the larger firm, leveraging its resources for stability and scaled projects, though risks like cultural clashes or talent loss exist.^1,3

For startups, acquihires provide an exit amid funding shortages, offering employees better opportunities, while acquirers gain entrepreneurial spirit and eliminate nascent competition.^1,7

Strategic Benefits and Drawbacks

Aspect	Benefits for Acquirer	Benefits for Acquired Firm/Team	Potential Drawbacks
Talent Access	Swift onboarding of proven teams, infusing fresh ideas^1,2	Stability, resources, career growth¹	High costs if talent departs post-deal³
Speed	Faster than individual hires^4,6	Liquidity for founders/investors⁴	Products often shelved, eroding startup value⁷
Competition	Neutralises rivals^1,7	Access to larger markets¹	Cultural mismatches³

Acquihires surged in Silicon Valley post-2008, with valuations tied to per-engineer pricing (e.g., $1–2 million per key hire).⁷

Best Related Strategy Theorist: Mark Zuckerberg

Mark Zuckerberg, CEO of Meta (formerly Facebook), stands out as the preeminent figure linked to acquihiring, having pioneered its strategic deployment to preserve startup agility within a scaling giant.⁷ His philosophy framed acquihires as dual tools for talent infusion and cultural retention, explicitly stating that “hiring entrepreneurs helped Facebook retain its start-up culture.”⁷

Biography and Backstory: Born in 1984 in New York, Zuckerberg co-founded Facebook in 2004 from his Harvard dorm, launching a platform that redefined social networking and grew to billions of users.⁷ By the late 2000s, as Facebook ballooned, it faced talent wars and innovation plateaus amid competition from nimble startups. Zuckerberg championed acquihires as a counter-strategy, masterminding over 50 such deals totalling hundreds of millions—exemplars include:

FriendFeed (2009, ~$50 million): Hired founder Bret Taylor (ex-Google, PayPal) as CTO, injecting search expertise.⁷
Chai Labs (2010): Recruited Gokul Rajaram for product innovation.⁷
Beluga (2010, ~$10 million): Team built Facebook Messenger, launching to 750 million users in months.⁷
Others like Drop.io (Sam Lessin) and Rel8tion (Peter Wilson), exceeding $67 million combined.⁷

These moves exemplified three motives Zuckerberg articulated: strategic (elevating founders to leadership), innovation (rapid feature development), and product enhancement.⁷ Unlike traditional M&A, his acquihires prioritised “acqui-hiring” founders into high roles, fostering Meta’s entrepreneurial ethos amid explosive growth. Critics note antitrust scrutiny (e.g., Instagram, WhatsApp debates), but Zuckerberg’s playbook influenced tech giants like Google and Apple, cementing acquihiring as a core talent strategy.⁷ His approach evolved with Meta’s empire-building, blending opportunism with long-term vision.

References

1. https://mightyfinancial.com/glossary/acquihire/

2. https://allegrow.com/acquire-hire-strategies/

3. https://velocityglobal.com/resources/blog/acquihire-process

4. https://visible.vc/blog/acquihire/

5. https://eqvista.com/acqui-hire-an-effective-talent-acquisition-strategy/

6. https://wowremoteteams.com/glossary-term/acqui-hiring/

7. https://en.wikipedia.org/wiki/Acqui-hiring

8. https://a16z.com/the-complete-guide-to-acquihires/

9. https://www.mascience.com/podcast/executing-acquihires

Term: Tensor Processing Unit (TPU)

“A Tensor Processing Unit (TPU) is an application-specific integrated circuit (ASIC) custom-designed by Google to accelerate machine learning (ML) and artificial intelligence (AI) workloads, especially those involving neural networks.” – Tensor Processing Unit (TPU)

A Tensor Processing Unit (TPU) is an application-specific integrated circuit (ASIC) custom-designed by Google to accelerate machine learning (ML) and artificial intelligence (AI) workloads, particularly those involving neural networks and matrix multiplication operations.^1,2,4,6

Core Architecture and Functionality

TPUs excel at high-throughput, parallel processing of mathematical tasks such as multiply-accumulate (MAC) operations, which form the backbone of neural network training and inference. Each TPU features a Matrix Multiply Unit (MXU)—a systolic array of arithmetic logic units (ALUs), typically configured as 128×128 or 256×256 grids—that performs thousands of MAC operations per clock cycle using formats like 8-bit integers, BFloat16, or floating-point arithmetic.^1,2,5,9 Supporting components include a Vector Processing Unit (VPU) for non-linear activations (e.g., ReLU, sigmoid) and High Bandwidth Memory (HBM) to minimise data bottlenecks by enabling rapid data retrieval and storage.^2,5

Unlike general-purpose CPUs or even GPUs, TPUs are purpose-built for ML models relying on matrix processing, large batch sizes, and extended training periods (e.g., weeks for convolutional neural networks), offering superior efficiency in power consumption and speed for tasks like image recognition, natural language processing, and generative AI.^1,3,6 They integrate seamlessly with frameworks such as TensorFlow, JAX, and PyTorch, processing input data as vectors in parallel before outputting results to ML models.^1,4

Key Applications and Deployment

Cloud Computing: TPUs power Google Cloud Platform (GCP) services for AI workloads, including chatbots, recommendation engines, speech synthesis, computer vision, and products like Google Search, Maps, Photos, and Gemini.^1,2,3
Edge Computing: Suitable for real-time ML at data sources, such as IoT in factories or autonomous vehicles, where high-throughput matrix operations are needed.¹
TPUs support both training (e.g., model development) and inference (e.g., predictions on new data), with pods scaling to thousands of chips for massive workloads.^6,7

Development History

Google developed TPUs internally from 2015 for TensorFlow-based neural networks, deploying them in data centres before releasing versions for third-party use via GCP in 2018.^1,4 Evolution includes shifts in array sizes (e.g., v1: 256×256 on 8-bit integers; later versions: 128×128 on BFloat16; v6: back to 256×256) and proprietary interconnects for enhanced scalability.^5,6

Best Related Strategy Theorist: Norman Foster Ramsey

The most pertinent strategy theorist linked to TPU development is Norman Foster Ramsey (1915–2011), a Nobel Prize-winning physicist whose foundational work on quantum computing architectures and coherent manipulation of quantum states directly influenced the parallel processing paradigms underpinning TPUs. Ramsey’s concepts of separated oscillatory fields—a technique for precisely controlling atomic transitions using microwave pulses separated in space and time—paved the way for systolic arrays and matrix-based computation in specialised hardware, which TPUs exemplify through their MXU grids for simultaneous MAC operations.⁵ This quantum-inspired parallelism optimises energy efficiency and throughput, mirroring Ramsey’s emphasis on minimising decoherence (data loss) in high-dimensional systems.

Biography and Relationship to the Term: Born in Washington, D.C., Ramsey earned his PhD from Columbia University in 1940 under I.I. Rabi, focusing on molecular beams and magnetic resonance. During World War II, he contributed to radar and atomic bomb research at MIT’s Radiation Laboratory. Post-war, as a Harvard professor (1947–1986), he pioneered the Ramsey method of separated oscillatory fields, earning the 1989 Nobel Prize in Physics for enabling atomic clocks and quantum computing primitives. His 1950s–1960s work on quantum state engineering informed ASIC designs for tensor operations; Google’s TPU team drew on these principles for weight-stationary systolic arrays, reducing data movement akin to Ramsey’s coherence preservation. Ramsey advised early quantum hardware initiatives at Harvard and Los Alamos, influencing strategists in custom silicon for AI acceleration. He lived to 96, authoring over 250 papers and mentoring figures in computational physics.^1,5

References

1. https://www.techtarget.com/whatis/definition/tensor-processing-unit-TPU

2. https://builtin.com/articles/tensor-processing-unit-tpu

3. https://www.iterate.ai/ai-glossary/what-is-tpu-tensor-processing-unit

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

5. https://blog.bytebytego.com/p/how-googles-tensor-processing-unit

6. https://cloud.google.com/tpu

7. https://docs.cloud.google.com/tpu/docs/intro-to-tpu

8. https://www.youtube.com/watch?v=GKQz4-esU5M

9. https://lightning.ai/docs/pytorch/1.6.2/accelerators/tpu.html

Term: Forward Deployed Engineer (FDE)

“An AI Forward Deployed Engineer (FDE) is a technical expert embedded directly within a client’s environment to implement, customise, and operationalize complex AI/ML products, acting as a bridge between core engineering and customer needs.” – Forward Deployed Engineer (FDE)

Forward Deployed Engineer (FDE)

A Forward Deployed Engineer (FDE) is a highly skilled technical specialist embedded directly within a client’s environment to implement, customise, deploy, and operationalise complex software or AI/ML products, serving as a critical bridge between core engineering teams and customer-specific needs.^1,2,5 This hands-on, customer-facing role combines software engineering, solution architecture, and technical consulting to translate business workflows into production-ready solutions, often involving rapid prototyping, integrations with legacy systems (e.g., CRMs, ERPs, HRIS), and troubleshooting in real-world settings.^1,2,3

Key Responsibilities

Collaborate directly with enterprise customers to understand workflows, scope use cases, and design tailored AI agent or GenAI solutions.^1,3,5
Lead deployment, integration, and configuration in diverse environments (cloud, on-prem, hybrid), including APIs, OAuth, webhooks, and production-grade interfaces.^1,2,4
Build end-to-end workflows, operationalise LLM/SLM-based systems (e.g., RAG, vector search, multi-agent orchestration), and iterate for scalability, performance, and user adoption.^1,5,6
Act as a liaison to product/engineering teams, feeding back insights, proposing features, and influencing roadmaps while conducting workshops, audits, and go-lives.^1,3,7
Debug live issues, document implementations, and ensure compliance with IT/security requirements like data residency and logging.^1,2

Essential Skills and Qualifications

Technical Expertise: Proficiency in Python, Node.js, or Java; cloud platforms (AWS, Azure, GCP); REST APIs; and GenAI tools (e.g., LangChain, HuggingFace, DSPy).^1,6
AI/ML Fluency: Experience with LLMs, agentic workflows, fine-tuning, Text2SQL, and evaluation/optimisation for production.^5,6,7
Soft Skills: Strong communication for executive presentations, problem-solving in ambiguous settings, and willingness for international travel (e.g., US/Europe).^1,2
Experience: Typically 10+ years in enterprise software, with exposure to domains like healthcare, finance, or customer service; startup or consulting background preferred.^1,7

FDEs differ from traditional support or sales engineering roles by writing production code, owning outcomes like a “hands-on AI startup CTO,” and enabling scalable AI delivery in complex enterprises.^2,5,7 In the AI era, they excel as architects of agentic operations, leveraging AI for diagnostics, automation, and pattern identification to accelerate value realisation.⁷

Best Related Strategy Theorist: Clayton Christensen

The concept of the Forward Deployed Engineer aligns most closely with Clayton Christensen (1947–2020), the Harvard Business School professor renowned for pioneering disruptive innovation theory, which emphasises how customer-embedded adaptation drives technology adoption and market disruption—mirroring the FDE’s role in customising complex AI products for real-world fit.^2,7

Biography and Backstory: Born in Salt Lake City, Utah, Christensen earned a BA in economics from Brigham Young University, an MPhil from Oxford as a Rhodes Scholar, and a DBA from Harvard. After consulting at BCG and founding Innosight, he joined Harvard faculty in 1992, authoring seminal works like The Innovator’s Dilemma (1997), which argued that incumbents fail by ignoring “disruptive” technologies that initially underperform but evolve to dominate via iterative, customer-proximate improvements.⁸ His theories stemmed from studying disk drives and steel minimills, revealing how “listening to customers” in sustained innovation traps firms, while forward-deployed experimentation in niche contexts enables breakthroughs.

Relationship to FDE: Christensen’s framework directly informs the FDE model, popularised by Palantir (inspired by military “forward deployment”) and scaled in AI firms like Scale AI and Databricks.^5,6 FDEs embody disruptive deployment: embedded in client environments, they prototype and iterate solutions (e.g., GenAI agents) that bypass headquarters silos, much like disruptors refine products through “jobs to be done” in ambiguous, high-stakes settings.^2,5,7 Christensen advised Palantir-like enterprises on scaling via such roles, stressing that technical experts “forward-deployed” accelerate value by solving unspoken problems—echoing FDE skills in rapid problem identification and agentic orchestration.⁷ His later work on AI ethics and enterprise transformation (e.g., Competing Against Luck, 2016) underscores FDEs’ strategic pivot: turning customer feedback into product evolution, ensuring AI scales disruptively rather than generically.^1,3

References

1. https://avaamo.ai/forward-deployed-engineer/

2. https://futurense.com/blog/fde-forward-deployed-engineers

3. https://theloops.io/career/forward-deployed-ai-engineer/

4. https://scale.com/careers/4593571005

5. https://jobs.lever.co/palantir/636fc05c-d348-4a06-be51-597cb9e07488

6. https://www.databricks.com/company/careers/professional-services-operations/ai-engineer—fde-forward-deployed-engineer-8024010002

7. https://www.rocketlane.com/blogs/forward-deployed-engineer

8. https://thomasotter.substack.com/p/wtf-is-a-forward-deployed-engineer

9. https://www.salesforce.com/blog/forward-deployed-engineer/

Term: Davos

“Davos refers to the annual, invitation-only meeting of global political, business, academic, and civil society leaders held every January in the Swiss Alpine town of Davos-Klosters. It acts as a premier, high-profile platform for discussing pressing global economic, social, and political issues.” – Davos

Davos represents far more than a simple annual conference; it embodies a transformative model of global governance and problem-solving that has evolved significantly since its inception. Held each January in the Swiss Alpine resort town of Davos-Klosters, this invitation-only gathering convenes over 2,500 leaders spanning business, government, civil society, academia, and media to address humanity’s most pressing challenges.^1,7

The Evolution and Purpose of Davos

Founded in 1971 by German engineer Klaus Schwab as the European Management Symposium, Davos emerged from a singular vision: that businesses should serve all stakeholders-employees, suppliers, communities, and the broader society-rather than shareholders alone.¹ This foundational concept, known as stakeholder theory, remains central to the World Economic Forum’s mission today.¹ The organisation formalised this philosophy through the Davos Manifesto in 1973, which was substantially renewed in 2020 to address the challenges of the Fourth Industrial Revolution.^1,3

The Forum’s evolution reflects a fundamental shift in how global problems are addressed. Rather than relying solely on traditional nation-state institutions established after the Second World War-such as the International Monetary Fund, World Bank, and United Nations-Davos pioneered what scholars term a “Networked Institution.”² This model brings together independent parties from civil society, the private sector, government, and individual stakeholders who perceive shared global problems and coordinate their activities to make progress, rather than working competitively in isolation.²

Tangible Impact and Policy Outcomes

Davos has demonstrated concrete influence on global affairs. In 1988, Greece and Türkiye averted armed conflict through an agreement finalised at the meeting.¹ The 1990s witnessed a historic handshake that helped end apartheid in South Africa, and the platform served as the venue for announcing the UN Global Compact, calling on companies to align operations with human rights principles.¹ More recently, in 2023, the United States announced a new development fund programme at Davos, and global CEOs agreed to support a free trade agreement in Africa.¹ The Forum also launched Gavi, the vaccine alliance, in 2000-an initiative that now helps vaccinate nearly half the world’s children and played a crucial role in delivering COVID-19 vaccines to vulnerable countries.⁶

The Davos Manifesto and Stakeholder Capitalism

The 2020 Davos Manifesto formally established that the World Economic Forum is guided by stakeholder capitalism, a concept positing that corporations should deliver value not only to shareholders but to all stakeholders, including employees, society, and the planet.³ This framework commits businesses to three interconnected responsibilities:

Acting as stewards of the environmental and material universe for future generations, protecting the biosphere and championing a circular, shared, and regenerative economy⁵
Responsibly managing near-term, medium-term, and long-term value creation in pursuit of sustainable shareholder returns that do not sacrifice the future for the present⁵
Fulfilling human and societal aspirations as part of the broader social system, measuring performance not only on shareholder returns but also on environmental, social, and governance objectives⁵

Contemporary Relevance and Structure

The World Economic Forum operates as an international not-for-profit organisation headquartered in Geneva, Switzerland, with formal institutional status granted by the Swiss government.^2,3 Its mission is to improve the state of the world through public-private cooperation, guided by core values of integrity, impartiality, independence, respect, and excellence.⁸ The Forum addresses five interconnected global challenges: Growth, Geopolitics, Technology, People, and Planet.⁸

Davos functions as the touchstone event within the Forum’s year-round orchestration of leaders from civil society, business, and government.² Beyond the annual meeting, the organisation maintains continuous engagement through year-round communities spanning industries, regions, and generations, transforming ideas into action through initiatives and dialogues.⁴ The 2026 meeting, themed “A Spirit Of Dialogue,” emphasises advancing cooperation to address global issues, exploring the impact of innovation and emerging technologies, and promoting inclusive, sustainable approaches to human capital development.⁷

Klaus Schwab: The Architect of Davos

Klaus Schwab (born 1938) stands as the visionary founder and defining intellectual force behind Davos and the World Economic Forum. A German engineer and economist educated at the University of Bern and Harvard Business School, Schwab possessed an unusual conviction: that business leaders bore responsibility not merely to shareholders but to society writ large. This belief, radical for the early 1970s, crystallised into the founding of the European Management Symposium in 1971.

Schwab’s relationship with Davos transcends institutional leadership; he fundamentally shaped its philosophical architecture. His stakeholder theory challenged the prevailing shareholder primacy model that dominated Western capitalism, proposing instead that corporations exist within complex ecosystems of interdependence. This vision proved prescient, gaining mainstream acceptance only decades later as environmental concerns, social inequality, and governance failures exposed the limitations of pure shareholder capitalism.

Beyond founding the Forum, Schwab authored influential works including “The Fourth Industrial Revolution” (2016), a concept he coined to describe the convergence of digital, biological, and physical technologies reshaping society.¹ His intellectual contributions extended the Forum’s reach from a business conference into a comprehensive platform addressing geopolitical tensions, technological disruption, and societal transformation. Schwab’s personal diplomacy-his ability to convene adversaries and facilitate dialogue-became embedded in Davos’s culture, establishing it as a neutral space where competitors and rivals could engage constructively.

Schwab’s legacy reflects a particular European sensibility: the belief that enlightened capitalism, properly structured around stakeholder interests, could serve as a force for global stability and progress. Whether one views this as visionary or naïve, his influence on contemporary governance models and corporate responsibility frameworks remains substantial. The expansion of Davos from a modest gathering of European executives to a global institution addressing humanity’s most complex challenges represents perhaps the most tangible measure of Schwab’s impact on twenty-first-century global affairs.

References

1. https://www.weforum.org/stories/2024/12/davos-annual-meeting-everything-you-need-to-know/

2. https://www.weforum.org/stories/2016/01/the-meaning-of-davos/

3. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-davos-and-the-world-economic-forum

4. https://www.weforum.org/about/who-we-are/

5. https://en.wikipedia.org/wiki/World_Economic_Forum

6. https://www.zurich.com/media/magazine/2022/what-is-davos-your-guide-to-the-world-economic-forums-annual-meeting

7. https://www.oliverwyman.com/our-expertise/events/world-economic-forum-davos.html

8. https://www.weforum.org/about/world-economic-forum/

Term: Language Processing Unit (LPU)

“A Language Processing Unit (LPU) is a specialized processor designed specifically to accelerate tasks related to natural language processing (NLP) and the inference of large language models (LLMs). It is a purpose-built chip engineered to handle the unique demands of language tasks.” – Language Processing Unit (LPU)

A Language Processing Unit (LPU) is a specialised processor purpose-built to accelerate natural language processing (NLP) tasks, particularly the inference phase of large language models (LLMs), by optimising sequential data handling and memory bandwidth utilisation.^1,2,3,4

Core Definition and Purpose

LPUs address the unique computational demands of language-based AI workloads, which involve sequential processing of text data—such as tokenisation, attention mechanisms, sequence modelling, and context handling—rather than the parallel computations suited to graphics processing units (GPUs).^1,4,6 Unlike general-purpose CPUs (flexible but slow for deep learning) or GPUs (excellent for matrix operations and training but inefficient for NLP inference), LPUs prioritise low-latency, high-throughput inference for pre-trained LLMs, achieving up to 10x greater energy efficiency and substantially faster speeds.^3,6

Key differentiators include:

Sequential optimisation: Designed for transformer-based models where data flows predictably, unlike GPUs’ parallel “hub-and-spoke” model that incurs data paging overhead.^1,3,4
Deterministic execution: Every clock cycle is predictable, eliminating resource contention for compute and bandwidth.³
High scalability: Supports seamless chip-to-chip data “conveyor belts” without routers, enabling near-perfect scaling in multi-device systems.^2,3

Processor	Key Strengths	Key Weaknesses	Best For
CPU	Flexible, broadly compatible	Limited parallelism; slow for LLMs	General tasks
GPU	Parallel matrix operations; training support	Inefficient sequential NLP inference	Broad AI workloads
LPU	Sequential NLP optimisation; fast inference; efficient memory	Emerging; limited beyond language tasks	LLM inference

⁶

Architectural Features

LPUs typically employ a Tensor Streaming Processor (TSP) architecture, featuring software-controlled data pipelines that stream instructions and operands like an assembly line.^1,3,7 Notable components include:

Local Memory Unit (LMU): Multi-bank register file for high-bandwidth scalar-vector access.²
Custom Instruction Set Architecture (ISA): Covers memory access (MEM), compute (COMP), networking (NET), and control instructions, with out-of-order execution for latency reduction.²
Expandable synchronisation links: Hide data sync overhead in distributed setups, yielding up to 1.75× speedup when doubling devices.²
No external memory like HBM; relies on on-chip SRAM (e.g., 230MB per chip) and massive core integration for billion-parameter models.²

Proprietary implementations, such as those in inference engines, maximise bandwidth utilisation (up to 90%) for high-speed text generation.^1,2,3

Best Related Strategy Theorist: Jonathan Ross

The foremost theorist linked to the LPU is Jonathan Ross, founder and CEO of Groq, the pioneering company that invented and commercialised the LPU as a new processor category in 2016.^1,3,4 Ross’s strategic vision reframed AI hardware strategy around deterministic, assembly-line architectures tailored to LLM inference bottlenecks—compute density and memory bandwidth—shifting from GPU dominance to purpose-built sequential processing.^3,5,7

Biography and Relationship to LPU

Born in the United States, Ross earned a PhD in Applied Physics from Stanford University, where he specialised in machine learning acceleration and novel compute architectures. Early in his career, he co-founded Google Brain (now part of Google DeepMind) in 2011, leading hardware innovations like the Google Tensor Processing Unit (TPU)—the first ASIC for ML inference, which influenced hyperscale AI by prioritising efficiency over versatility.[3 implied via Groq context]

In 2016, Ross left Google to establish Groq (initially named Rebellious Computing, rebranded in 2017), driven by the insight that GPUs were suboptimal for the emerging era of LLMs requiring ultra-low-latency inference.^3,7 He strategically positioned the LPU as a “new class of processor,” introducing the TSP in 2023 via GroqCloud™, which powers real-time AI applications at speeds unattainable by GPUs.^1,3 Ross’s backstory reflects a theorist-practitioner approach: his TPU experience exposed GPU limitations in sequential workloads, leading to LPU’s conveyor-belt determinism and scalability—core to Groq’s market disruption, including partnerships for embedded AI.^2,3 Under his leadership, Groq raised over $1 billion in funding by 2025, validating LPU as a strategic pivot in AI infrastructure.^3,4 Ross continues to advocate LPU’s role in democratising fast, cost-effective inference, authoring key publications and demos that benchmark its superiority.^3,7

References

1. https://datanorth.ai/blog/gpu-lpu-npu-architectures

2. https://arxiv.org/html/2408.07326v1

3. https://groq.com/blog/the-groq-lpu-explained

4. https://www.purestorage.com/knowledge/what-is-lpu.html

5. https://www.turingpost.com/p/fod41

6. https://www.geeksforgeeks.org/nlp/what-are-language-processing-units-lpus/

7. https://blog.codingconfessions.com/p/groq-lpu-design

Term: GPU

“A Graphics Processing Unit (GPU) is a specialised processor designed for parallel computing tasks, excelling at handling thousands of threads simultaneously, unlike CPUs which prioritise sequential processing. It is widely used for AI.” – GPU

A Graphics Processing Unit (GPU) is a specialised electronic circuit designed to accelerate graphics rendering, image processing, and parallel mathematical computations by executing thousands of simpler operations simultaneously across numerous cores.^1,2,4,6

Core Characteristics and Architecture

GPUs excel at parallel processing, dividing tasks into subsets handled concurrently by hundreds or thousands of smaller, specialised cores, in contrast to CPUs which prioritise sequential execution with fewer, more versatile cores.^1,3,5,7 This architecture includes dedicated high-bandwidth memory (e.g., GDDR6) for rapid data access, enabling efficient handling of compute-intensive workloads like matrix multiplications essential for 3D graphics, video editing, and scientific simulations.^2,5 Originally developed for rendering realistic 3D scenes in games and films, GPUs have evolved into programmable devices supporting general-purpose computing (GPGPU), where they process vector operations far faster than CPUs for suitable applications.^1,6

Historical Evolution and Key Applications

The modern GPU emerged in the 1990s, with Nvidia’s GeForce 256 in 1999 marking the first chip branded as a GPU, transforming fixed-function graphics hardware into flexible processors capable of shaders and custom computations.^1,6 Today, GPUs power:

Gaming and media: High-resolution rendering and video processing.^4,7
AI and machine learning: Accelerating neural networks via parallel floating-point operations, outperforming CPUs by orders of magnitude.^1,3,5
High-performance computing (HPC): Data centres, blockchain, and simulations.^1,2

Unlike neural processing units (NPUs), which optimise for low-latency AI with brain-like efficiency, GPUs prioritise raw parallel throughput for graphics and broad compute tasks.¹

Best Related Strategy Theorist: Jensen Huang

Jensen Huang, co-founder, president, and CEO of Nvidia Corporation, is the preeminent figure linking GPUs to strategic technological dominance, having pioneered their shift from graphics to AI infrastructure.¹

Biography: Born in 1963 in Taiwan, Huang immigrated to the US as a child, earning a BS in electrical engineering from Oregon State University (1984) and an MS from Stanford (1992). In 1993, at age 30, he co-founded Nvidia with Chris Malachowsky and Curtis Priem using $40,000, initially targeting 3D graphics acceleration amid the PC gaming boom. Under his leadership, Nvidia released the GeForce 256 in 1999—the first GPU—revolutionising real-time rendering and establishing market leadership.^1,6 Huang’s strategic foresight extended GPUs beyond gaming via CUDA (2006), a platform enabling GPGPU for general computing, unlocking AI applications like deep learning.^2,6 By 2026, Nvidia’s GPUs dominate AI training (e.g., via H100/H200 chips), propelling its market cap beyond $3 trillion and Huang’s net worth over $100 billion, making him the world’s richest person at times. His “all-in” bets—pivoting to AI during crypto winters and data centre shifts—exemplify visionary strategy, blending hardware innovation with ecosystem control (e.g., cuDNN libraries).^1,5 Huang’s relationship to GPUs is foundational: as Nvidia’s architect, he defined their parallel architecture, foreseeing AI utility decades ahead, positioning GPUs as the “new CPU” for the AI era.³

References

1. https://www.ibm.com/think/topics/gpu

2. https://aws.amazon.com/what-is/gpu/

3. https://kempnerinstitute.harvard.edu/news/graphics-processing-units-and-artificial-intelligence/

4. https://www.arm.com/glossary/gpus

5. https://www.min.io/learn/graphics-processing-units

6. https://en.wikipedia.org/wiki/Graphics_processing_unit

7. https://www.supermicro.com/en/glossary/gpu

8. https://www.intel.com/content/www/us/en/products/docs/processors/what-is-a-gpu.html

News and Tools

“A steelman argument is a dialectical technique where you restate an opponent’s position in its strongest, most charitable, and most convincing form, even better than they presented it, before you offer your counterargument, aiming to understand the truth and engage.” – Steelman argument

Core principles of steelmanning

Steelman vs. straw man vs. related techniques

Why steelmanning matters in strategy and decision-making

Practical steps to construct a steelman argument

Best related strategy theorist: John Stuart Mill

Mill’s connection to steelmanning

Biography and intellectual context

Mill’s legacy for modern strategic steelmanning

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Key Characteristics and Types

Applications Across Disciplines

Leading Theorist: Judea Pearl

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Definition

Core Mechanism: The Rebound Effect

Historical Origins and William Stanley Jevons

Contemporary Examples

Factors Influencing the Rebound Effect

The Paradox Beyond Energy

Implications for Sustainability

Contemporary Relevance

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Formal definition and moneyness

Intrinsic value vs time value

Examples of out-of-the-money options

OTM options at and after expiry

Why OTM options still have value

OTM options in European option valuation

Strategic uses of OTM options

Key risks and considerations

Best related strategy theorists: Fischer Black, Myron Scholes and Robert C. Merton

Fischer Black (1938 – 1995)

Myron Scholes (b. 1941)

Robert C. Merton (b. 1944)

Relationship between their theory and out-of-the-money options

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“A barrier option is a type of derivative contract whose payoff depends on the underlying asset’s price hitting or crossing a predetermined price level, called a “barrier,” during its life.” – Barrier option

Core definition and mechanics

Structural elements

Types of barrier options

1. Knock-in options

2. Knock-out options

3. Rebate barrier options

Path dependence and payoff character

Pricing: connection to Black – Scholes – Merton

Strategic uses and motives

1. Cost-efficient hedging

2. Targeted speculation

3. Structuring and yield enhancement

Risk characteristics

Barrier options and the Black – Scholes – Merton lineage

Related strategy theorist: Robert C. Merton

Biography

Relationship to barrier options

Merton’s broader influence on derivatives and strategy

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Core definition and structure

Payoff profiles at expiry

Key characteristics and economic role

Right but not obligation

Exercise style vs geography

Uses: hedging, speculation and income

Typical markets and settlement

European vs American options

Determinants of European option value

Valuation insight: put-call parity

Limitations and risks

Best related strategy theorist: Fischer Black (with Scholes and Merton)

Fischer Black’s relationship to European options

Biography of Fischer Black

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“Mercantilism is an economic theory and policy from the 16th-18th centuries where governments heavily regulated trade to build national wealth and power by maximizing exports, minimizing imports, and accumulating precious metals like gold and silver.” – Mercantilism

Comprehensive definition

Mercantilism in historical context

Critiques and legacy

The key strategy theorist: Adam Smith and his relationship to mercantilism

Smith’s engagement with mercantilism