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Quote: Mark Carney

Quote: Mark Carney

“It seems that every day we’re reminded that we live in an era of great power rivalry, that the rules-based order is fading, that the strong can do what they can and the weak must suffer what they must.” – Mark Carney – Prime Minister of Canada

Mark Carney’s invocation of Thucydides at the World Economic Forum represents far more than rhetorical flourish-it signals a fundamental recalibration of how middle powers must navigate an era of renewed great power competition. Delivered at Davos on 20 January 2026, the Canadian Prime Minister’s address articulates a doctrine of “value-based realism” that acknowledges the erosion of the post-Cold War international architecture whilst refusing to accept the fatalism such erosion might imply.

The Context: A World in Transition

Carney’s speech arrives at a pivotal moment in international affairs. The rules-based order that underpinned global stability since 1945-and particularly since the Cold War’s conclusion-faces unprecedented strain from great power rivalry, economic fragmentation, and the weaponisation of interdependence. The Canadian Prime Minister’s diagnosis is unflinching: the comfortable assumptions that geography and alliance membership automatically confer prosperity and security are no longer valid.1 This is not mere academic observation; it reflects lived experience across the Western alliance as traditional frameworks prove inadequate to contemporary challenges.

The quote itself draws directly from Thucydides’ account of the Melian Dialogue, wherein the Athenian envoys declare that “the strong do what they can and the weak suffer what they must.” By invoking this ancient formulation, Carney grounds contemporary geopolitical anxiety in historical precedent, suggesting that the current moment represents not an aberration but a return to a more primal logic of international relations-one temporarily obscured by the post-1989 liberal consensus.

The Intellectual Foundations: Realism and Its Evolution

Carney’s framework draws upon several strands of international relations theory, most notably classical realism and its contemporary variants. The concept of “value-based realism,” which Carney attributes to Alexander Stubb, President of Finland, represents an attempt to synthesise realist analysis of power distribution with liberal commitments to human rights, sovereignty, and territorial integrity.1 This is a deliberate intellectual move-rejecting both naive multilateralism and amoral power politics in favour of a pragmatic middle path.

Classical realism, articulated most influentially by Hans Morgenthau in the mid-twentieth century, posits that states are rational actors pursuing power within an anarchic international system. Morgenthau’s seminal work Politics Among Nations established that national interest, defined in terms of power, constitutes the objective of statecraft. Yet Morgenthau himself recognised that power encompasses more than military capacity-it includes economic strength, technological capability, and moral authority. Carney’s approach resurrects this more nuanced understanding, arguing that middle powers possess distinct forms of leverage beyond military might.

The realist tradition has evolved considerably since Morgenthau. Kenneth Waltz’s structural realism emphasised the anarchic nature of the international system and the security dilemma it generates, wherein defensive measures by one state appear threatening to others, creating spirals of mistrust. This framework helps explain contemporary great power competition: as American hegemony faces challenge from rising powers, each actor rationally pursues security through military buildups and alliance formation, inadvertently triggering the very insecurity it seeks to prevent. Carney’s diagnosis aligns with this logic-the “end of the rules-based order” reflects not malice but the structural pressures inherent in multipolarity.

More recent theorists have grappled with how middle powers navigate such environments. Scholars such as Andrew Pratt and Fen Osler Hampton have examined “middle power diplomacy,” arguing that states lacking superpower status can exercise disproportionate influence through coalition-building, norm entrepreneurship, and strategic positioning. This intellectual tradition directly informs Carney’s prescription: middle powers must act together, creating what he terms “a dense web of connections across trade, investment, culture” upon which they can draw for future challenges.1

The Diagnosis: Structural Transformation

Carney’s analysis identifies three interconnected phenomena reshaping the international landscape. First, the erosion of the rules-based order reflects genuine shifts in material power distribution. The post-Cold War moment, characterised by American unipolarity and the apparent triumph of liberal democracy, has given way to multipolarity and ideological contestation. Great powers-whether the United States, China, or Russia-increasingly view international institutions and agreements as constraints on their freedom of action rather than frameworks for mutual benefit.

Second, economic interdependence, once theorised as a force for peace, has become weaponised. Sanctions regimes, technology restrictions, and supply chain manipulation now constitute standard instruments of statecraft. This transformation reflects what scholars term the “securitisation” of economics-the process whereby economic relationships become framed through security logics. Carney explicitly warns against this: middle powers must resist the temptation to accept “economic intimidation” from one direction whilst remaining silent about it from another, lest they signal weakness and invite further coercion.1

Third, the traditional alliance structures that provided security guarantees to middle powers have become less reliable. NATO’s continued existence notwithstanding, the United States under various administrations has questioned its commitment to collective defence, whilst simultaneously pursuing unilateral policies (such as tariff regimes) that undermine allied interests. This creates what Carney identifies as a fundamental strategic problem: bilateral negotiation between a middle power and a hegemon occurs from a position of weakness, forcing accommodation and competitive deference.1

The Intellectual Lineage: From Thucydides to Contemporary Geopolitics

Carney’s invocation of Thucydides connects to a broader contemporary discourse on great power competition. Graham Allison’s “Thucydides Trap” thesis-the proposition that conflict between a rising power and a declining hegemon is structurally likely-has become influential in policy circles. Allison argues that of sixteen historical cases where a rising power challenged a ruling one, twelve ended in war. This framework, whilst contested by scholars who emphasise contingency and agency, captures genuine anxieties about Sino-American relations and broader multipolarity.

Yet Carney’s deployment of Thucydides differs subtly from Allison’s. Rather than accepting the Trap as inevitable, Carney uses the ancient formulation to establish a baseline-the world as it actually is, stripped of comforting illusions-from which alternative paths become possible. This reflects what might be termed “tragic realism”: an acknowledgment of structural constraints coupled with insistence on human agency and moral choice.

Contemporary theorists of middle power strategy have developed frameworks relevant to Carney’s prescription. Scholars such as Amitav Acharya have examined how middle powers can exercise “agency” within structural constraints through what he terms “norm localisation”-adapting global norms to regional contexts and thereby shaping international discourse. Similarly, theorists of “minilateral” cooperation-agreements among smaller groups of like-minded states-provide intellectual scaffolding for Carney’s vision of issue-specific coalitions rather than universal institutions.

The Prescription: Strategic Autonomy and Collective Action

Carney’s response to this diagnosis comprises several elements. First, building domestic strength: Canada is cutting taxes, removing interprovincial trade barriers, investing a trillion dollars in energy, artificial intelligence, and critical minerals, and doubling defence spending by decade’s end.1 This reflects a classical realist insight-that international influence ultimately rests upon domestic capacity. A state cannot punch above its weight indefinitely; sustainable influence requires genuine economic and military capability.

Second, strategic autonomy: rather than accepting subordination to any hegemon, middle powers must calibrate relationships so their depth reflects shared values.1 This requires what Carney terms “honesty about the world as it is”-recognising that some relationships will be transactional, others deeper, depending on alignment of interests and values. It also requires consistency: applying the same standards to allies and rivals, thereby avoiding the appearance of weakness or double standards that invites further coercion.

Third, coalition-building: Carney proposes plurilateral arrangements-bridging the Trans-Pacific Partnership and European Union to create a trading bloc of 1.5 billion people, forming buyers’ clubs for critical minerals anchored in the G7, cooperating with democracies on artificial intelligence governance.1 These initiatives reflect what might be termed “competitive multilateralism”-creating alternative institutional frameworks that function as described, rather than relying on existing institutions that have become gridlocked or captured by great powers.

This approach draws upon theoretical work on institutional design and coalition formation. Scholars such as Barbara Koremenos have examined how states choose institutional forms-examining when they prefer bilateral arrangements, multilateral institutions, or minilateral coalitions. Carney’s framework suggests that in an era of great power rivalry, minilateral coalitions organised around specific issues prove more effective than universal institutions, precisely because they exclude actors whose interests diverge fundamentally.

The Philosophical Underpinning: Beyond Nostalgia

Carney’s most provocative claim may be his insistence that “nostalgia is not a strategy.”1 This rejects a tempting response to the erosion of the post-Cold War order: attempting to restore it through diplomatic pressure or institutional reform. Instead, Carney argues, middle powers must accept that “the old order is not coming back” and focus on building “something bigger, better, stronger, more just” from the fracture.1

This reflects a philosophical stance sometimes termed “constructive realism”-accepting structural constraints whilst refusing to accept that they determine outcomes. It echoes the existentialist insight that humans are “condemned to be free,” forced to choose even within constraining circumstances. For middle powers, this means accepting that great power rivalry is real and structural, yet refusing to accept that this reality precludes agency, moral choice, or the possibility of building alternative arrangements.

The intellectual roots of this position extend to theorists of social construction in international relations, particularly Alexander Wendt’s argument that “anarchy is what states make of it.” Whilst the anarchic structure of the international system is given, the meaning states attribute to it-whether it necessitates conflict or permits cooperation-remains contestable. Carney’s vision assumes that middle powers, acting together, can construct a different meaning of multipolarity: not a return to Hobbesian warfare but a framework of genuine cooperation among states that share sufficient common ground.

Contemporary Relevance: The Middle Power Moment

Carney’s address arrives at a moment when middle power agency has become increasingly salient. The traditional Cold War binary-alignment with either superpower-has dissolved, creating space for states to pursue more autonomous strategies. Countries such as India, Brazil, Indonesia, and the European Union member states increasingly resist pressure to choose sides in great power competition, instead pursuing what scholars term “strategic autonomy” or “non-alignment 2.0.”

Yet Carney’s formulation differs from classical non-alignment. Rather than attempting to remain neutral between competing blocs, he proposes active coalition-building among states that share values-democracy, human rights, rule of law-whilst remaining pragmatic about interests. This reflects what might be termed “values-based coalition-building,” distinguishing it both from amoral realpolitik and from idealistic universalism.

The stakes Carney identifies are genuine. In a world of great power fortresses-blocs organised around competing powers with limited cross-bloc exchange-middle powers face subordination or marginalisation. Conversely, in a world of genuine cooperation among willing partners, middle powers can exercise disproportionate influence through coalition-building and norm entrepreneurship. Carney’s challenge to middle powers is thus existential: act together or accept subordination.

This framing resonates with contemporary scholarship on the future of international order. Scholars such as Hal Brands and Michael Beckley have examined whether the liberal international order can be reformed or whether it will fragment into competing blocs. Carney’s implicit answer is that the outcome remains undetermined-it depends on choices made by middle powers in the coming years. This is neither optimistic nor pessimistic but genuinely open-ended, contingent upon agency.

The Broader Implications

Carney’s Davos address represents more than Canadian foreign policy positioning. It articulates a vision of international order that acknowledges structural realities-great power rivalry, the erosion of universal institutions, the weaponisation of economic interdependence-whilst refusing to accept that these realities preclude alternatives to hegemonic subordination or great power conflict. For middle powers, this vision offers both diagnosis and prescription: the world has changed fundamentally, but middle powers retain agency if they act together with strategic clarity and moral consistency.

The intellectual traditions informing this vision-classical and structural realism, middle power diplomacy theory, constructivist international relations scholarship-converge on a common insight: international order is not simply imposed by the powerful but constructed through the choices and actions of all states. In an era of multipolarity and great power rivalry, this construction becomes more difficult but also more consequential. The question Carney poses to middle powers is whether they will accept the role assigned to them by great power competition or whether they will actively construct an alternative.

References

1. https://www.weforum.org/stories/2026/01/davos-2026-special-address-by-mark-carney-prime-minister-of-canada/

2. https://www.youtube.com/watch?v=miM4ur5WH3Y

3. https://www.youtube.com/watch?v=btqHDhO4h10

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

5. https://www.youtube.com/watch?v=vxXsXXT1Dto

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Quote: Mark Carney – Prime Minister of Canada

Quote: Mark Carney – Prime Minister of Canada

“It is time for companies and countries to take their signs down… You cannot live within the lie of mutual benefit through integration when integration becomes the source of your subordination.” – Mark Carney – Prime Minister of Canada

In his special address at the World Economic Forum’s Annual Meeting 2026 in Davos, delivered on 20 January 2026, Canada’s Prime Minister Mark Carney issued a stark warning about the collapse of the rules-based international order. The quote captures Carney’s pivot towards ‘value-based realism,’ urging nations to abandon naive assumptions of automatic prosperity through globalisation and instead prioritise strategic autonomy, domestic strength, and recalibrated alliances.3,5

Mark Carney: From Central Banker to Prime Minister

Mark Joseph Carney, born on 16 March 1965, is a Canadian economist and politician serving as Canada’s 24th Prime Minister since March 2025. Elected leader of the Liberal Party with over 85.9% of the vote on 9 March 2025, Carney was sworn in as Prime Minister on 14 March without prior elected office, a first in Canadian history. He represents Nepean in Parliament and led the Liberals to a minority government in the subsequent election.1,2

Carney’s career trajectory is marked by high-profile roles in global finance. He served as Governor of the Bank of Canada from 2008 to 2013 and then as Governor of the Bank of England from 2013 to 2020, becoming the first non-Briton in that position. Post-governorship, he advised Canadian Prime Minister Justin Trudeau on COVID-19 economic responses and chaired the Liberal Party’s Task Force on Economic Growth in 2024. Ideologically a centrist technocrat and ‘Blue Grit Liberal,’ Carney’s premiership has focused on economic resilience amid geopolitical tensions.2

Since taking office, Carney has enacted transformative policies: repealing the federal consumer carbon tax, passing the One Canadian Economy Act to eliminate interprovincial trade barriers, fast-tracking a trillion dollars in investments in energy, AI, critical minerals, and infrastructure, and doubling defence spending by decade’s end. His government has recognised the State of Palestine, improved ties with China-including a January 2026 visit yielding tariff reductions on canola and electric vehicles-and sustained support for Ukraine.2,3,4

Context of the Quote: Davos 2026 and Canada’s Strategic Shift

Carney’s address came amid an escalating trade war with the United States and the erosion of post-Cold War globalisation. He declared the end of comfortable assumptions that geography and alliances guaranteed security and prosperity, advocating engagement ‘with open eyes’ and relationships calibrated to shared values. Canada, he noted, was among the first to heed this ‘wake-up call,’ shifting to build strength at home while inviting middle powers to join in ‘value-based realism’-a term borrowed from Finland’s President Alexander Stubb.3

The speech highlighted domestic actions like tax cuts on incomes, capital gains, and business investment, alongside broad engagement to maximise influence in a fluid world. Carney received a standing ovation, underscoring the resonance of his message on naming ‘reality’ and acting decisively.2,3

Leading Theorists on Globalisation, Integration, and Subordination

Carney’s critique echoes longstanding debates in international relations and economics on the limits of globalisation. Key theorists provide intellectual foundations for his views:

  • Joseph Nye and Robert Keohane (Regime Theory): In Power and Interdependence (1977), they argued that complex interdependence fosters mutual benefits through institutions, but power asymmetries can lead to subordination. Carney’s call to ‘take down signs’ of mutual benefit aligns with their recognition that regimes falter when great powers exploit them.2
  • Graham Allison (Thucydides Trap): Allison’s 2017 book warns of inevitable conflict when a rising power (e.g., China) threatens a ruling one (e.g., US), fracturing integration. Carney’s emphasis on strategic autonomy mirrors Allison’s advice for middle powers to hedge amid US-China rivalry.3
  • Dani Rodrik (Trilemma of Global Economy): Rodrik posits governments cannot simultaneously pursue hyper-globalisation, national sovereignty, and democracy. Carney’s policies-relaxing regulations, boosting defence, and diversifying trade-reflect choosing sovereignty over unchecked integration.2
  • John Mearsheimer (Offensive Realism): In The Tragedy of Great Power Politics (2001), Mearsheimer contends states maximise power in anarchy, rendering mutual benefit illusory when subordination looms. Carney’s ‘honesty about the world as it is’ evokes this realist turn from liberal optimism.3
  • Alexander Stubb (Value-Based Realism): As Finland’s President, Stubb popularised the term Carney invokes, blending realism with values like human rights. This framework guides Carney’s calibrated engagements, such as the China partnership focused on trade without ideological naivety.3

These thinkers collectively underscore Carney’s thesis: integration’s promise of mutual benefit dissolves when it enables dominance, necessitating realism over idealism in trade and alliances.

References

1. https://www.pm.gc.ca/en/about

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

3. https://www.weforum.org/stories/2026/01/davos-2026-special-address-by-mark-carney-prime-minister-of-canada/

4. https://www.pm.gc.ca/en/news/news-releases/2026/01/16/prime-minister-carney-forges-new-strategic-partnership-peoples

5. https://www.youtube.com/watch?v=miM4ur5WH3Y

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

7. https://www.youtube.com/watch?v=01QBT5fR-DY

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Quote: Mark Carney – Prime Minister of Canada

Quote: Mark Carney – Prime Minister of Canada

“We know the old order is not coming back. We shouldn’t mourn it; nostalgia is not a strategy. But we believe that from the fracture, we can build something bigger, better, stronger, and more just.” – Mark Carney – Prime Minister of Canada

Mark Carney’s address at the World Economic Forum in Davos on 20 January 2026 articulated a philosophical pivot that extends far beyond Canadian policy. His assertion that “the old order is not coming back” represents a candid acknowledgement of the structural transformation reshaping international relations-a transformation that demands not nostalgic resistance but strategic innovation. The quote encapsulates a broader intellectual movement among contemporary policymakers who recognise that the post-Cold War consensus, built on rules-based multilateralism and assumed Western dominance, has fundamentally fractured.

The Context of Carney’s Intervention

Carney delivered this address as Canada’s 24th Prime Minister, having assumed office in March 2025 following his election as Liberal Party leader with an unprecedented 85.9% of the vote on the first ballot. His ascension marked a significant departure in Canadian political history: he became the first Canadian Prime Minister never to have held elected office before assuming the premiership. This unconventional trajectory-from central banking to the highest political office-reflects the technocratic orientation increasingly evident in responses to complex geopolitical challenges.

The timing of Carney’s Davos intervention proved strategically significant. His address came mere days after a high-profile visit to Beijing, where he met with Chinese President Xi Jinping and negotiated a “new strategic partnership” that substantially reduced tariffs on Canadian canola oil (from 85% to 15%) and Chinese electric vehicles (from 100% to 6.1%). This diplomatic manoeuvre exemplified the very philosophy he articulated at Davos: rather than lamenting the erosion of Western-led institutional frameworks, Canada was actively recalibrating its relationships to reflect contemporary geopolitical realities.

The Intellectual Architecture: Value-Based Realism

Carney’s formulation draws explicitly on what he termed “value-based realism,” a concept articulated by Alexander Stubb, President of Finland. This framework represents a deliberate synthesis of two traditionally opposed analytical traditions: the idealist commitment to universal values (human rights, sovereignty, democratic governance) and the realist acknowledgement of power dynamics and national interest. Rather than treating these as contradictory, value-based realism posits that nations can maintain principled commitments whilst simultaneously engaging pragmatically with the world as it exists rather than as they wish it to be.

This intellectual positioning reflects broader currents in contemporary international relations theory. The concept challenges what scholars term “liberal internationalism”-the post-1945 consensus that institutionalised rules, multilateral organisations, and shared norms could transcend power politics. Carney’s acknowledgement that “the old comfortable assumptions that our geography and alliance memberships automatically conferred prosperity and security” no longer hold valid represents a significant concession to structural realist arguments that have long emphasised the primacy of material capabilities and strategic positioning over institutional arrangements.

Leading Theorists and Intellectual Foundations

Structural Realism and the Multipolar Transition: Carney’s analysis aligns substantially with structural realist scholarship, particularly the work of scholars examining the transition from unipolarity to multipolarity. Theorists such as John Mearsheimer have long argued that the post-Cold War unipolar moment was inherently unstable and that the rise of peer competitors (particularly China) would inevitably erode the institutional frameworks built during American hegemony. Carney’s acknowledgement that “the powerful have their power” whilst Canada must “build our strength at home” reflects this realist recognition that material capabilities ultimately determine strategic options.

Strategic Autonomy and Middle Power Theory: Carney explicitly positioned Canada as a “middle power” capable of exercising disproportionate influence through strategic positioning. This concept draws on middle power theory, developed by scholars including Andrew Cooper and Evan Potter, which argues that states occupying the intermediate tier of the international system can leverage their geographic position, institutional expertise, and coalition-building capacity to exercise influence beyond their material weight. Carney’s emphasis on “building strategic autonomy whilst maintaining values” reflects this theoretical framework-middle powers must avoid dependency on great power patrons whilst retaining the principled commitments that differentiate them from purely transactional actors.

The Fracture Metaphor and Institutional Decay: Carney’s use of “fracture” rather than “collapse” or “transformation” carries theoretical significance. This language echoes the work of scholars examining institutional erosion, particularly those studying the decline of post-war multilateral organisations. Theorists including Dani Rodrik have documented how globalisation and geopolitical competition have strained the institutional consensus that underpinned the Bretton Woods system and its successors. The fracture metaphor suggests not apocalyptic breakdown but rather the splintering of previously unified frameworks into competing regional and bilateral arrangements.

Constructivist Approaches to Order-Building: Carney’s assertion that “from the fracture, we can build something bigger, better, stronger, and more just” reflects constructivist international relations theory, which emphasises that international orders are socially constructed rather than determined by material forces alone. Scholars including Alexander Wendt have argued that actors can reshape international structures through strategic communication and norm entrepreneurship. Carney’s framing positions Canada not as a passive victim of systemic change but as an active participant in constructing new institutional arrangements-a distinctly constructivist orientation.

The Rejection of Nostalgia as Strategic Doctrine

Carney’s explicit rejection of nostalgia as a strategic framework warrants particular attention. This formulation directly challenges what scholars term “nostalgic nationalism”-the tendency of declining powers to seek restoration of previous hierarchies rather than adaptation to new circumstances. The statement “nostalgia is not a strategy” functions as both intellectual critique and practical warning. It implicitly critiques both American efforts to reassert unilateral dominance and European attempts to preserve Cold War alliance structures unchanged.

This positioning reflects contemporary debates within strategic studies about how established powers should respond to relative decline. Scholars including Hal Brands have examined whether declining powers typically pursue accommodation or confrontation; Carney’s framework suggests a third path: strategic recalibration that preserves core values whilst abandoning outdated institutional assumptions.

Domestic Foundations: Building Strength at Home

Carney’s emphasis on building “strength at home” through tax reductions, removal of interprovincial trade barriers, and a trillion-dollar investment programme in energy, artificial intelligence, and critical minerals reflects economic nationalism tempered by liberal institutional commitments. This approach synthesises elements of developmental state theory (the strategic deployment of state capacity to build competitive advantage) with market-liberal principles. The doubling of defence spending by decade’s end, coupled with investments in domestic industrial capacity, reflects what scholars term “strategic decoupling”-the deliberate reduction of dependency on potentially unreliable partners through domestic capability development.

This domestic orientation also reflects recognition of what political economists call the “trilemma of globalisation”: the impossibility of simultaneously maintaining democratic sovereignty, deep economic integration, and fixed exchange rates. By prioritising sovereignty and strategic autonomy, Carney’s government implicitly accepts reduced integration with some partners whilst deepening selective relationships (notably with China) where mutual benefit is demonstrable.

The Broader Geopolitical Significance

Carney’s Davos address arrived at a moment of acute geopolitical tension. The ongoing trade conflict with the United States, the continuation of Russian aggression in Ukraine, and the intensifying competition for technological and resource dominance between Western and Chinese-led blocs have created what scholars term a “multiplex world order”-one characterised by simultaneous cooperation and competition across multiple domains rather than simple bipolarity or unipolarity.

His reception-described as earning “a rare standing ovation” at Davos-suggests that his articulation of value-based realism resonated with an international audience of business and political leaders grappling with similar strategic dilemmas. The framework offers intellectual legitimacy for the pragmatic recalibration that many middle and smaller powers have already undertaken, whilst maintaining rhetorical commitment to universal principles.

Implications for International Order-Building

Carney’s vision of building “something bigger, better, stronger, and more just” from the fracture of the old order represents an optimistic but contingent proposition. It assumes that the emerging multipolar system need not replicate the zero-sum competition that characterised earlier multipolar eras, and that institutional innovation can accommodate both great power competition and cooperative problem-solving on transnational challenges.

This optimism reflects what scholars call “liberal institutionalism”-the belief that even in anarchic international systems, institutions can facilitate cooperation and reduce transaction costs. Yet Carney’s framework differs from earlier liberal institutionalism in its explicit acknowledgement that such institutions must reflect contemporary power distributions rather than attempting to preserve outdated hierarchies. The Canada-China strategic partnership, with its focus on trade, energy, and technology, exemplifies this approach: cooperation structured around mutual benefit rather than ideological alignment or institutional obligation.

The intellectual coherence of Carney’s position lies in its rejection of false dichotomies. It refuses the choice between principled commitment and pragmatic engagement, between national interest and international cooperation, between acknowledging systemic change and working to shape its trajectory. Whether this framework can sustain itself amid intensifying great power competition remains an open question-one that will substantially determine the character of the emerging international order.

References

1. https://www.weforum.org/stories/2026/01/davos-2026-special-address-by-mark-carney-prime-minister-of-canada/

2. https://www.pm.gc.ca/en/about

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

4. https://www.pm.gc.ca/en/news/news-releases/2026/01/16/prime-minister-carney-forges-new-strategic-partnership-peoples

5. https://www.youtube.com/watch?v=miM4ur5WH3Y

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

7. https://www.youtube.com/watch?v=01QBT5fR-DY

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Term: Forward Deployed Engineer (FDE)

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/

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Quote: Andre Karpathy

Quote: Andre Karpathy

“I’ve never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful.” – Andre Karpathy – AI guru

Andre Karpathy, a pioneering AI researcher, captures the profound disruption AI is bringing to programming in this quote: “I’ve never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful.”1,2 Delivered amid his reflections on AI’s rapid evolution, it underscores his personal sense of urgency as tools like large language models (LLMs) redefine developers’ roles from code writers to orchestrators of intelligent systems.2

Context of the Quote

Karpathy shared this introspection as part of his broader commentary on the programming profession’s transformation, likely tied to his June 17, 2025, keynote at AI Startup School in San Francisco titled “Software Is Changing (Again).”4 In it, he outlined Software 3.0—a paradigm where LLMs enable natural language as the primary programming interface, allowing AI to generate code, design systems, and even self-improve with minimal human input.1,4,5 The quote reflects his firsthand experience: traditional Software 1.0 (handwritten code) and Software 2.0 (neural networks trained on data) are giving way to 3.0, where programmers contribute “sparse” high-level guidance amid AI-generated code, evoking a feeling of both lag and untapped potential.1,2 He likens developers to “virtual managers” overseeing AI collaborators, focusing on architecture, decomposition, and ethics rather than syntax.2 This shift mirrors historical leaps—like from machine code to high-level languages—but accelerates via tools like GitHub Copilot, making elite programmers those who master prompt engineering and human-AI loops.2,4

Backstory on Andre Karpathy

Born in Slovakia and raised in Canada, Andrej Karpathy earned his PhD in computer vision at Stanford University, where he architected and led CS231n, the first deep learning course there, now one of Stanford’s most popular.3 A founding member of OpenAI, he advanced generative models and reinforcement learning. At Tesla (2017–2022), as Senior Director of AI, he led Autopilot vision, data labeling, neural net training, and deployment on custom inference chips, pushing toward Full Self-Driving.3,4 Briefly involved in Tesla Optimus, he left to found Eureka Labs, modernizing education with AI.3 Known as an “AI guru” for viral lectures like “The spelled-out intro to neural networks” and zero-to-hero LLM courses, Karpathy embodies the transition to Software 3.0, having deleted C++ code in favor of growing neural nets at Tesla.3,4

Leading Theorists on Software Paradigms and AI-Driven Programming

Karpathy’s framework builds on foundational ideas from deep learning pioneers. Key figures include:

  • Yann LeCun, Yoshua Bengio, and Geoffrey Hinton (the “Godfathers of AI”): Their 2010s work on deep neural networks birthed Software 2.0, where optimization on massive datasets replaces explicit programming. LeCun (Meta AI chief) pioneered convolutional nets; Bengio advanced sequence models; Hinton coined “backpropagation.” Their Turing Awards (2018) validated data-driven learning, enabling Karpathy’s Tesla-scale deployments.1

  • Ian Goodfellow (GAN inventor, 2014): His Generative Adversarial Networks prefigured Software 3.0’s generative capabilities, where AI creates code and data autonomously, blurring human-AI creation boundaries.1

  • Andrej Karpathy himself: Extends these into Software 3.0, emphasizing recursive self-improvement (AI writing AI) and “vibe coding” via natural language, as in his 2025 talks.1,4

  • Related influencers: Fei-Fei Li (Stanford, co-creator of ImageNet) scaled vision datasets fueling Software 2.0; Ilya Sutskever (OpenAI co-founder) drove LLMs like GPT, powering 3.0’s code synthesis.3

This evolution demands programmers adapt: curricula must prioritize AI collaboration over syntax, with humans excelling in judgment and oversight amid accelerating abstraction.1,2

References

1. https://inferencebysequoia.substack.com/p/andrej-karpathys-software-30-and

2. https://ytosko.dev/blog/andrej-karpathy-reflects-on-ais-impact-on-programming-profession

3. https://karpathy.ai

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

5. https://www.cio.com/article/4085335/the-future-of-programming-and-the-new-role-of-the-programmer-in-the-ai-era.html

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Term: Davos

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/

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Term: Language Processing Unit (LPU)

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

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Quote: Marc Wilson – Global Advisors

Quote: Marc Wilson – Global Advisors

“Parents want to know what their kids should study in the age of AI – curiosity, agency, ability to learn and adapt, diligence, resilience, accountability, trust, ethics and teamwork define winners in the age of AI more than knowledge.” – Marc Wilson – Global Advisors

Over the last few years, I have spent thousands of hours inside AI systems – not as a spectator, but as someone trying to make them do real work. Not toy demos. Not slideware. I’m talking about actual consulting workflows: research, synthesis, modeling, data extraction, and client delivery.

What that experience strips away is the illusion that the future belongs to people who simply “know how to use AI.”

Every week there is a new tool, a new model, a new framework. What looked like a hard-won advantage six months ago is now either automated or irrelevant. Prompt engineering and tool-specific workflows are being collapsed into the models themselves. These are transitory skills. They matter in the moment, but they do not compound.

What does compound is agency.

Agency is the ability to look at a messy, underspecified problem and decide it will not beat you. It is the instinct to decompose a system, to experiment, and to push past failure when there is no clear map. AI does not remove the need for that; it amplifies it. The people who get the most from these systems are not the ones who know the “right” prompts – they are the ones who iterate until the system produces the required outcome.

In practice, that looks different from what most people imagine. The most effective practitioners don’t ask, “What prompt should I use?”

They ask, “How do I get this result?”

They iterate. They swap tools. They reframe the problem. They are not embarrassed by trial-and-error or a hallucination because they aren’t outsourcing responsibility to the machine. They own the output.

Parents ask what their children should study for the “age of AI.” The question is understandable, but it misses the mark. Knowledge has never been more abundant. The marginal value of knowing one more thing is collapsing. What is becoming scarce is the ability to turn knowledge into action.

That is the core of agency:

  • Curiosity to explore and continuously learn and adapt.

  • Diligence is care about the details.

  • Resilience in the face of failures and constant change.

  • Accountability to own the outcome.

  • Ethics that focus on humanity.

  • People who form trusted relationships.

These qualities are not “soft.” They are decisive.

Machines can write, code and reason at superhuman speed – the differentiator is not who has the most information – it is who takes responsibility for the outcome.

AI will reward the people who show up, take ownership and find a way through uncertainty. Everything else – including today’s fashionable technical skills – will be rewritten.

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Quote: Demis Hassabis- DeepMind co-founder, CEO

Quote: Demis Hassabis- DeepMind co-founder, CEO

“Actually, I think [China is] closer to the US frontier models than maybe we thought one or two years ago. Maybe they’re only a matter of months behind at this point.” – Demis Hassabis – DeepMind co-founder, CEO

Context of the Quote

In a CNBC Original podcast, The Tech Download, aired on 6 January 2026, Demis Hassabis, co-founder and CEO of Google DeepMind, offered a candid assessment of China’s AI capabilities. He stated that Chinese AI models are now just a matter of months behind leading US frontier models, a significant narrowing from perceptions one or two years prior1,3,5. Hassabis highlighted models from Chinese firms like DeepSeek, Alibaba, and Zhipu AI, which have delivered strong benchmark performances despite US chip export restrictions1,3,5.

However, he tempered optimism by questioning China’s capacity for true innovation, noting they have yet to produce breakthroughs like the transformer architecture that powers modern generative AI. ‘Inventing something is 100 times harder than replicating it,’ he emphasised, pointing to cultural and mindset challenges in fostering exploratory research1,4,5. This interview underscores ongoing US-China AI competition amid geopolitical tensions, including bans on advanced Nvidia chips, though approvals for models like the H200 offer limited relief2,5.

Who is Demis Hassabis?

Demis Hassabis is a British AI researcher, entrepreneur, and neuroscientist whose career bridges neuroscience, gaming, and artificial intelligence. Born in 1976 in London to a Greek Cypriot father and Chinese Singaporean mother, he displayed prodigious talent early, winning the Eurovision Young Musicians contest at age 13 and becoming a chess master by 131,4.

Hassabis co-founded DeepMind in 2010 with the audacious goal of achieving artificial general intelligence (AGI). His breakthrough came with AlphaGo in 2016, which defeated world Go champion Lee Sedol, demonstrating deep reinforcement learning’s power1,4. Google acquired DeepMind in 2014 for £400 million, and Hassabis now leads as CEO, overseeing models like Gemini, which recently topped AI benchmarks3,4.

In 2024, he shared the Nobel Prize in Chemistry with John Jumper and David Baker for AlphaFold2, which predicts protein structures with unprecedented accuracy, revolutionising biology1,4. Hassabis predicts AGI within 5-10 years, down from his initial 20-year estimate, and regrets Google’s slower commercialisation of innovations like the transformer and AlphaGo despite inventing ‘90% of the technology everyone uses today’1,4. DeepMind operates like a ‘modern-day Bell Labs,’ prioritising fundamental research5.

Leading Theorists and the Subject Matter: The AI Frontier and Innovation Race

The quote touches on frontier AI models – state-of-the-art large language models (LLMs) pushing performance limits – and the distinction between replication and invention. Key theorists shaping this field include:

  • Geoffrey Hinton, Yann LeCun, and Yoshua Bengio (‘Godfathers of AI’): Pioneered deep learning. Hinton, at Google (emeritus), advanced backpropagation and neural networks. LeCun (Meta) developed convolutional networks for vision. Bengio (Mila) focused on sequence modelling. Their work underpins transformers1,5.
  • Ilya Sutskever: OpenAI co-founder, key in GPT series and reinforcement learning from human feedback (RLHF). Left to found Safe Superintelligence Inc., emphasising AGI safety3.
  • Andrej Karpathy: Ex-OpenAI/Tesla, popularised transformers via tutorials; now at his own venture5.
  • The Transformer Architects: Vaswani et al. (Google, 2017) introduced the transformer in ‘Attention is All You Need,’ enabling parallel training and scaling laws that birthed ChatGPT and Gemini. Hassabis notes China’s lack of equivalents1,4,5.

China’s progress, via firms like DeepSeek (cost-efficient models on lesser chips) and giants Alibaba/Baidu/Tencent, shows engineering prowess but lags in paradigm shifts2,3,5. US leads in compute (Nvidia GPUs) and innovation ecosystems, though restrictions may spur domestic chips like Huawei’s2,3. Hassabis’ view challenges US underestimation, aligning with Nvidia’s Jensen Huang: America is ‘not far ahead’5.

This backdrop highlights AI’s dual nature: rapid catch-up via scaling compute/data, versus elusive invention requiring bold theory1,2.

References

1. https://en.sedaily.com/international/2026/01/16/deepmind-ceo-hassabis-china-may-catch-up-in-ai-but-true

2. https://intellectia.ai/news/stock/google-deepmind-ceo-claims-chinas-ai-is-just-months-behind

3. https://www.investing.com/news/stock-market-news/china-ai-models-only-months-behind-us-efforts-deepmind-ceo-tells-cnbc-4450966

4. https://biz.chosun.com/en/en-it/2026/01/16/IQH4RV54VVGJVGTSYHWSARHOEU/

5. https://timesofindia.indiatimes.com/technology/tech-news/google-deepmind-ceo-demis-hassabis-corrects-almost-everyone-in-america-on-chinas-ai-capability-they-are-not-/articleshow/126561720.cms

6. https://brief.bismarckanalysis.com/s/ai-2026

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Term: GPU

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

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Quote: Nate B Jones – AI News & Strategy Daily

Quote: Nate B Jones – AI News & Strategy Daily

“Execution capacity isn’t scarce anymore. Ten days, four people, and [Anthropic are] shipping 60 to 100 releases daily. Execution capacity is not the problem.” – Nate B Jones – AI News & Strategy Daily

Nate B Jones, a prominent voice in AI news and strategy, made this striking observation on 15 January 2026, highlighting how execution speed at leading AI firms like Anthropic has rendered traditional capacity constraints obsolete.

Context of the Quote

The quote originates from a discussion in AI News & Strategy Daily, capturing the blistering pace of development at Anthropic, the creators of the Claude AI models. Jones points to a specific instance where just four people, over ten days, facilitated 60 to 100 daily releases. This underscores a paradigm shift: in AI labs, small teams leveraging advanced tools now achieve output volumes that once required vast resources. The statement challenges the notion that scaling human execution remains a barrier, positioning it instead as a solved problem amid accelerating AI capabilities.1,4

Backstory on Nate B Jones

Nate B Jones is a key commentator on AI developments, known for his daily newsletter AI News & Strategy Daily. His insights dissect breakthroughs, timelines, and strategic implications in artificial intelligence. Jones frequently analyses outputs from major players like Anthropic, OpenAI, and others, providing data-driven commentary on progress towards artificial general intelligence (AGI). His work emphasises empirical evidence from releases, funding rounds, and capability benchmarks, making him a go-to source for professionals tracking the AI race. This quote, delivered via a YouTube discussion, exemplifies his focus on how AI is redefining productivity in software engineering and research.

Anthropic’s Blazing Execution Pace

Anthropic, founded in 2021 by former OpenAI executives including CEO Dario Amodei, has emerged as a frontrunner in safe AI systems. Backed by over $23 billion in funding-including major investments from Microsoft and Nvidia-the firm achieved a $5 billion revenue run rate by August 2025 and is projected to hit $9 billion annualised by year-end. Speculation surrounds a potential IPO as early as 2026, with valuations soaring to $300-350 billion amid a massive funding round.2

Internally, Anthropic’s engineers report transformative AI integration. A August 2025 survey of 132 staff revealed Claude enabling complex tasks with fewer human interventions: tool calls per transcript rose 116% to 21.2 consecutive actions, while human turns dropped 33% to 4.1 on average. This aligns directly with Jones’s claim of hyper-efficient shipping, where AI handles code generation, edits, and commands autonomously.4

Broader metrics from Anthropic’s January 2026 Economic Index show explosive Claude usage growth, with rapid diffusion despite uneven global adoption tied to GDP levels.5 Predictions from CEO Dario Amodei include AI writing 90% of code by mid-2025 (partially realised) and nearly all by March 2026, fuelling daily release cadences.1

Leading Theorists on AI Execution and Speed

  • Dario Amodei (Anthropic CEO): A pioneer in scalable AI oversight, Amodei forecasts powerful AI by early 2027, with systems operating at 10x-100x human speeds on multi-week tasks. His ‘Machines of Loving Grace’ essay outlines AGI timelines as early as 2026, driving Anthropic’s aggressive R&D.1
  • Jakob Nielsen (UX and AI Forecaster): Nielsen predicts AI will handle 39-hour human tasks by end-2026, with capability doubling every 4 months-from 3 seconds (GPT-2, 2019) to 5 hours (Claude Opus 4.5, late 2025). He highlights examples like AI designing infographics in under a minute, amplifying execution velocity.3
  • Redwood Research Analysts: Bloggers at Redwood detail Anthropic’s AGI bets, noting resource repurposing for millions of model instances and AI accelerating engineering 3x-10x by late 2026. They anticipate full R&D automation medians shifting to 2027-2029 based on milestones like multi-week task success.1

These theorists converge on a narrative of exponential acceleration: AI is not merely assisting but supplanting human bottlenecks in execution, code, and innovation. Jones’s quote encapsulates this consensus, signalling that in 2026, the real frontiers lie beyond mere deployment speed.

References

1. https://blog.redwoodresearch.org/p/whats-up-with-anthropic-predicting

2. https://forgeglobal.com/insights/anthropic-upcoming-ipo-news/

3. https://jakobnielsenphd.substack.com/p/2026-predictions

4. https://www.anthropic.com/research/how-ai-is-transforming-work-at-anthropic

5. https://www.anthropic.com/research/anthropic-economic-index-january-2026-report

6. https://kalshi.com/markets/kxclaude5/claude-5-released/kxclaude5-27

7. https://www.fiercehealthcare.com/ai-and-machine-learning/jpm26-anthropic-launches-claude-healthcare-targeting-health-systems-payers

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Term: K-shaped economy

Term: K-shaped economy

“A “K-shaped economy” describes a recovery or economic state where different segments of the population, industries, or wealth levels diverge drastically, resembling the letter ‘K’ on a graph: one part shoots up (wealthy, tech, capital owners), while another stagnates.” – K-shaped economy –

A K-shaped economy describes an uneven economic recovery or state following a downturn, where different segments—such as high-income earners, tech sectors, large corporations, and asset owners—experience strong growth (the upward arm of the ‘K’), while low-income groups, small businesses, low-skilled workers, younger generations, and debt-burdened households stagnate or decline (the downward arm).1,2,3,4

Key Characteristics

This divergence manifests across multiple dimensions:

  • Income and wealth levels: Higher-income individuals (top 10-20%) drive over 50% of consumption, benefiting from rising asset prices (e.g., stocks, real estate), while lower-income households face stagnating wages, unemployment, and delinquencies.3,4,6,7
  • Industries and sectors: Tech giants (e.g., ‘Magnificent 7’), AI infrastructure, and video conferencing boom, whereas tourism, small businesses, and labour-intensive sectors struggle due to high borrowing costs and weak demand.2,5,8
  • Generational and geographic splits: Younger consumers with debt face financial strain, contrasting with older, wealthier groups; urban tech hubs thrive while others lag.1,3
  • Policy influences: Post-2008 quantitative easing and pandemic fiscal measures favoured asset owners over broad growth, exacerbating inequality; central banks like the Federal Reserve face challenges from misleading unemployment data and uneven inflation.3,5

The pattern, prominent after the COVID-19 recession, contrasts with V-shaped (swift, even rebound) or U-shaped (gradual) recoveries, complicating stimulus efforts.2,4

Historical Context and Examples

  • Originated in discussions during the 2020 pandemic, popularised on social media and by analysts like Lisa D. Cook (Federal Reserve Governor).4
  • Reinforced by events like the 2008 financial crisis, where liquidity flooded assets without proportional wage growth.5
  • In 2025, it persists with AI-driven stock gains for the wealthy, minimal job creation for others, and corporate resilience (e.g., fixed-rate debt for S&P 500 firms vs. floating-rate pain for small businesses).1,5,8

Best Related Strategy Theorist: Joseph Schumpeter

The most apt theorist linked to the K-shaped economy is Joseph Schumpeter (1883–1950), whose concept of creative destruction directly underpins one key mechanism: recessions enable new industries and technologies to supplant outdated ones, fostering divergent recoveries.2

Biography

Born in Triesch, Moravia (now Czech Republic), Schumpeter studied law and economics in Vienna, earning a doctorate in 1906. He taught at universities in Czernowitz, Graz, and Bonn, becoming Austria’s finance minister briefly in 1919 amid post-World War I turmoil. Exiled after the Nazis annexed Austria, he joined Harvard University in 1932, where he wrote seminal works until retiring in 1949. A polymath influenced by Marx, Walras, and Weber, Schumpeter predicted capitalism’s self-undermining tendencies through innovation and bureaucracy.2

Relationship to the Term

Schumpeter argued that capitalism thrives via creative destruction—the “perennial gale” where entrepreneurs innovate, destroying old structures (e.g., tourism during COVID) and birthing new ones (e.g., video conferencing, AI).2 In a K-shaped context, this explains why tech and capital-intensive sectors surge while legacy industries falter, amplified by policies favouring winners. Unlike uniform recoveries, his framework predicts inherent bifurcation, as seen post-2008 and pandemics, where asset markets outpace labour markets—echoing modern analyses of uneven growth.2,5 Schumpeter’s prescience positions him as the foundational strategist for navigating such divides through innovation policy.

References

1. https://www.equifax.com/business/blog/-/insight/article/the-k-shaped-economy-what-it-means-in-2025-and-how-we-got-here/

2. https://corporatefinanceinstitute.com/resources/economics/k-shaped-recovery/

3. https://am.vontobel.com/en/insights/k-shaped-economy-presents-challenges-for-the-federal-reserve

4. https://finance-commerce.com/2025/12/k-shaped-economy-inequality-us/

5. https://www.pinebridge.com/en/insights/investment-strategy-insights-reflexivity-and-the-k-shaped-economy

6. https://www.alliancebernstein.com/corporate/en/insights/economic-perspectives/the-k-shaped-economy.html

7. https://www.mellon.com/insights/insights-articles/the-k-shaped-drift.html

8. https://www.morganstanley.com/insights/articles/k-shaped-economy-investor-guide-2025

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Quote: Nate B Jones – AI News & Strategy Daily

Quote: Nate B Jones – AI News & Strategy Daily

“Suddenly your risk is timidity. Your risk is lack of courage. The danger isn’t necessarily building the wrong thing, because you’ve got 50 shots [a year] to build the right thing. The danger is not building enough things toward a larger vision that is really transformative for the customer.” – Nate B Jones – AI News & Strategy Daily

This provocative statement emerged from Nate B. Jones’s AI News & Strategy Daily on 15 January 2026, amid accelerating AI advancements reshaping software development and business strategy. Jones challenges conventional risk management in an era where AI tools like Cursor enable engineers to ship code twice as fast, and product managers double productivity through prompt engineering. Execution has become ‘cheaper’, but Jones warns that speed alone breeds quality nightmares – security holes, probabilistic outputs demanding sustained QA, and technical debt from rapid prototyping.1,2

The quote reframes failure: with rapid iteration (50+ attempts yearly), building suboptimal products is survivable. True peril lies in hesitation – failing to generate volume towards a bold, customer-transforming vision. This aligns with Jones’s emphasis on ‘AI native’ approaches, transcending mere acceleration to orchestration, coordination, and human-AI symbiosis for compounding gains.3

Backstory on Nate B. Jones

Nate B. Jones is a leading AI strategist, content creator, and independent analyst whose platforms – including his Substack newsletter, personal site (natebjones.com), and YouTube channel AI News & Strategy Daily (127K subscribers) – deliver ‘deep analysis, actionable frameworks, zero hype’.2,7 He dissects real-world AI implementation, from prompt stacks enhancing workflows to predictions on 2026 breakthroughs like memory advances, agent UIs, continual learning, and recursive self-improvement.5,6

Jones’s work spotlights execution dynamics: automation avalanches make work cheaper, yet spawn trust deficits from ‘dirty’ AI code and jailbreaking needs.1 He advocates team ‘film review’ loops using AI rubrics for decision docs, specs, and risk articulation – turning human skills into scalable drills.3 Videos like ‘The AI Trick That Finally Made Me Better at My Job’ and ‘Debunking AI Myths’ showcase his practical ethos, proving AI’s innovative edge via breakthroughs like AlphaDev’s faster algorithms and AlphaFold’s protein atlas.3,4

Positioned as ‘the most cogent, sensible, and insightful AI resource’, Jones guides ventures towards genuine AI nativity, urging leaders to escape terminal-bound agents for task queues and human-AI coordination.2

Leading Theorists on AI Execution, Speed, and Transformative Vision

Jones’s ideas echo foundational thinkers in AI strategy and rapid iteration:

  • Eric Ries (Lean Startup): Pioneered ‘build-measure-learn’ loops, validating Jones’s ’50 shots’ tolerance for failure. Ries argued validated learning trumps perfect planning, mirroring AI’s cheap execution.1
  • Andrew Ng (AI Pioneer): Emphasises AI’s productivity multiplier but warns of overhype; his advocacy for ‘AI transformation’ aligns with Jones’s customer vision, as seen in AlphaFold’s impact.4
  • Tyler Cowen (Marginal Revolution): Referenced by Jones for pre-AI decision frameworks now supercharged by AI critique loops, enabling ‘athlete-like’ review at scale.3
  • Sam Altman (OpenAI): Drives agentic AI evolution (e.g., recursive self-improvement), fuelling Jones’s 2026 predictions on long-running agents and human attention focus.5
  • Demis Hassabis (DeepMind): AlphaDev and GNoME exemplify AI innovation beyond speed, proving machines discover novel algorithms – validating Jones’s debunking of ‘AI can’t innovate’.4

These theorists collectively underpin Jones’s thesis: in AI’s ‘automation avalanche’, courageously shipping volume towards transformative goals outpaces timid perfectionism.1

Implications for Leaders

Traditional Risk AI-Era Risk (per Jones)
Building the wrong thing Timidity and lack of volume
Slow, cautious execution Quality/security disasters from unchecked speed
Single-shot perfection 50+ iterations towards bold vision

Jones’s insight demands a paradigm shift: harness AI for fearless experimentation, sustained quality, and visionary scale.

References

1. https://natesnewsletter.substack.com/p/2026-sneak-peek-the-first-job-by-9ac

2. https://www.natebjones.com

3. https://www.youtube.com/watch?v=Td_q0sHm6HU

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

5. https://www.youtube.com/watch?v=pOb0pjXpn6Q

6. https://natesnewsletter.substack.com/p/my-prompt-stack-for-work-16-prompts

7. https://www.youtube.com/@NateBJones

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Term: Strategy

Term: Strategy

“Strategy is the art of radical selection, where you identify the “vital few” forces – the 20% of activities, products, or customers that generate 80% of your value – and anchor them in a unique and valuable position that is difficult for rivals to imitate.” – Strategy

Strategy is the art of radical selection, entailing the identification and prioritisation of the “vital few” forces—typically the 20% of activities, products, or customers that deliver 80% of value—and embedding them within a unique, valuable position that rivals struggle to replicate.

This definition draws on the Pareto principle (or 80/20 rule), which posits that a minority of inputs generates the majority of outputs, applied strategically to focus resources for competitive advantage. Radical selection demands ruthless prioritisation, rejecting marginal efforts to create imitable barriers such as proprietary processes, network effects, or brand loyalty. In practice, it involves auditing operations to isolate high-impact elements, then aligning the organisation around them—eschewing diversification for concentrated excellence. For instance, firms might discontinue underperforming product lines or customer segments to double down on core strengths, fostering sustainable differentiation amid competition.3,5

Key Elements of Radical Selection

  • Identification of the “Vital Few”: Analyse data to pinpoint the 20% driving 80% of revenue, profit, or growth; this echoes exploration in radical innovation, targeting novel opportunities over incremental gains.3
  • Anchoring in a Unique Position: Secure these forces in a defensible niche, leveraging creativity and risk acceptance inherent to strategic art, where choices fuse power with imagination to outmanoeuvre rivals.5
  • Difficulty to Imitate: Build moats through repetition with deviation—reconfiguring conventions internally to resist replication, akin to disidentification strategies that transform from within.1

Best Related Strategy Theorist: Richard Koch

Richard Koch, a pre-eminent proponent of the 80/20 principle in strategy, provides the foundational intellectual backbone for this concept of radical selection. His seminal work, The 80/20 Principle: The Secret to Achieving More with Less (1997, updated editions since), explicitly frames strategy as exploiting the “vital few”—the disproportionate 20% of factors yielding 80% of results—to achieve outsized success.

Biography and Backstory

Born in 1950 in London, Koch graduated from Oxford University with a degree in Philosophy, Politics, and Economics, later earning an MBA from Harvard Business School. He began his career at Bain & Company (1978–1980), rising swiftly in management consulting, then co-founded L.E.K. Consulting in 1983, where he specialised in corporate strategy and turnarounds. Koch advised blue-chip firms on radical pruning—divesting non-core assets to focus on high-yield segments—drawing early insights into Pareto imbalances from client data showing most profits stemmed from few products or customers.

In the 1990s, as an independent investor and author, Koch applied these lessons to his own ventures, achieving billionaire status through stakes in firms like Filofax (which he revitalised via 80/20 focus) and Betfair (early investor). His 80/20 philosophy evolved from Vilfredo Pareto’s 1896 observation of wealth distribution (80% owned by 20%) and Joseph Juran’s quality management adaptations, but Koch radicalised it for strategy. He argued that businesses thrive by systematically ignoring the trivial many, selecting “star” activities for exponential growth—a direct precursor to the query’s definition.

Koch’s relationship to radical selection is intimate: he popularised it as a strategic art form, blending empirical analysis with bold choice. In Living the 80/20 Way (2004) and The 80/20 Manager (2007), he extends it to personal and corporate realms, warning against “spread-thin” mediocrity. Critics note its simplicity risks oversimplification, yet its prescience aligns with modern lean strategies; Koch remains active, mentoring via Koch Education.3,5

References

1. https://direct.mit.edu/artm/article/10/3/8/109489/What-is-Radical

2. https://dariollinares.substack.com/p/the-art-of-radical-thinking?selection=863e7a98-7166-4689-9e3c-6434f064c055

3. https://www.timreview.ca/article/1425

4. https://selvajournal.org/article/ideology-strategy-aesthetics/

5. https://theforge.defence.gov.au/sites/default/files/2024-11/On%20Strategic%20Art%20-%20A%20Guide%20to%20Strategic%20Thinking%20and%20the%20ASFF%20(Electronic%20Version%201-1).pdf

6. https://ellengallery.concordia.ca/wp-content/uploads/2021/08/leonard-Bina-Ellen-Art-Gallery-MUNOZ-Radical-Form.pdf

7. https://art21.org/read/radical-art-in-a-conservative-school/

8. https://parsejournal.com/article/radical-softness/

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Quote: Nate B Jones

Quote: Nate B Jones

“Anthropic shipping ‘Co-Work’ as a full product feature. It was built in 10 days with just four people. It was written entirely in Claude Code. And Claude Code, mind you, is an entire product that is less than a year old… The Anthropic team is evolving as they go.” – Nate B Jones – AI News & Strategy Daily

Context of the Quote

On 15 January 2026, Nate B Jones, in his AI News & Strategy Daily update, highlighted Anthropic’s remarkable achievement in shipping ‘Co-Work’ (also styled as Cowork), a groundbreaking AI feature. This quote captures the essence of Anthropic’s rapid execution: developing a production-ready tool in just 10 days using a team of four, with all code generated by their own AI system, Claude Code. Jones emphasises the meta-innovation – Claude Code itself, launched less than a year prior, enabling this feat – signalling how Anthropic is iteratively advancing AI capabilities in real-time.1,5

Who is Nate B Jones?

Nate B Jones is a prominent voice in AI strategy and news aggregation, curating daily insights via his AI News & Strategy Daily platform. His commentary distils complex developments into actionable intelligence for executives, developers, and strategists. Jones focuses on execution speed, product strategy, and the competitive dynamics of AI firms, often drawing from primary sources like announcements, demos, and insider accounts. His analysis in this instance underscores Anthropic’s edge in ‘vibe coding’ – prompt-driven development – positioning it as a model for AI-native organisations.1,7

Backstory of Anthropic’s Cowork

Anthropic unveiled Cowork on 12 January 2026 as a research preview for Claude Max subscribers on macOS. Unlike traditional chatbots, Cowork acts as an autonomous ‘colleague’, accessing designated local folders to read, edit, create, and organise files without constant supervision. Users delegate tasks – such as sorting downloads, extracting expenses from screenshots into spreadsheets, summarising notes, or drafting reports – and approve key actions via prompts. This local-first approach contrasts with cloud-centric AI, restoring agency to personal devices while prioritising user oversight to mitigate risks like unintended deletions or prompt injections.1,2,3,4,6

The tool emerged from user experiments with Claude Code, Anthropic’s AI coding agent popular among developers. Observing non-technical users repurposing it for office tasks, Anthropic abstracted these capabilities into Cowork, inheriting Claude Code’s robust architecture for reliable, agentic behaviour. Built entirely with Claude Code in 10 days by four engineers, it exemplifies ‘AI building AI’, compressing development timelines and widening the gap between AI-leveraging firms and others.1,3,5

Significance in AI Evolution

Cowork marks a shift from conversational AI to agentic systems that act on the world, handling mundane work asynchronously. It challenges enterprise tools like Microsoft’s Copilot by offering proven developer-grade autonomy to non-coders, potentially redefining productivity. Critics note risks of ‘workslop’ – error-prone outputs requiring fixes – but Anthropic counters with transparency, trust-building safeguards, and architecture validated in production coding.2,3,5,6

Leading Theorists and Concepts Behind Agentic AI

  • Boris Cherny: Leader of Claude Code at Anthropic, Cherny coined ‘vibe coding’ – an AI paradigm where high-level prompts guide software creation, minimising manual code. His X announcement confirmed Cowork’s components were fully AI-generated, embodying this hands-off ethos.1
  • Dario Amodei: Anthropic CEO and ex-OpenAI executive, Amodei champions scalable oversight and reliable AI agents. His vision drives Cowork’s supervisor model, ensuring human control amid growing autonomy.3,6
  • Yohei Nakajima: Creator of BabyAGI (2023), an early autonomous agent framework chaining tasks via LLM planning. Cowork echoes this by autonomously strategising and executing multi-step workflows.2
  • Andrew Ng: AI pioneer advocating ‘agentic workflows’ where AI handles routine tasks, freeing humans for oversight. Ng’s predictions align with Cowork’s file manipulation and task queuing, forecasting quieter, faster work rhythms.2,5
  • Lil’ Log (Lilian Weng): OpenAI’s applied AI head, Weng theorises hierarchical agent architectures for complex execution. Cowork’s lineage from Claude Code reflects this, prioritising trust over raw intelligence as the new bottleneck.5

These thinkers converge on agentic AI: systems that plan, act, and adapt with minimal intervention, propelled by models like Claude. Anthropic’s sprint validates their theories, proving AI can ship AI at unprecedented speed.

References

1. https://www.axios.com/2026/01/13/anthropic-claude-code-cowork-vibe-coding

2. https://www.techradar.com/ai-platforms-assistants/claudes-latest-upgrade-is-the-ai-breakthrough-ive-been-waiting-for-5-ways-cowork-could-be-the-biggest-ai-innovation-of-2026

3. https://www.axios.com/2026/01/12/ai-anthropic-claude-jobs

4. https://www.vice.com/en/article/anthropic-introduces-claude-cowork/

5. https://karozieminski.substack.com/p/claude-cowork-anthropic-product-deep-dive

6. https://fortune.com/2026/01/13/anthropic-claude-cowork-ai-agent-file-managing-threaten-startups/

7. https://www.youtube.com/watch?v=SpqqWaDZ3ys

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Quote: Demis Hassabis – DeepMind co-founder, CEO

Quote: Demis Hassabis – DeepMind co-founder, CEO

“I think [AI is] going to be like the industrial revolution, but maybe 10 times bigger, 10 times faster. So it’s an incredible amount of transformation, but also disruption that’s going to happen.” – Demis Hassabis – DeepMind co-founder, CEO

Demis Hassabis and the Quote

This striking prediction comes from Demis Hassabis, co-founder and CEO of Google DeepMind. Spoken on The Tech Download (CNBC Original podcast) on 16 January 2026, the quote encapsulates Hassabis’s view of artificial intelligence (AI) as a force dwarfing historical upheavals. He describes AI not merely as an evolution but as a catalyst for radical abundance, potentially leading to prosperity if managed equitably, while acknowledging inevitable job disruptions akin to – yet far exceeding – those of past revolutions.1,2

Backstory of Demis Hassabis

Born in 1976 in London to a Greek Cypriot father and Chinese Singaporean mother, Hassabis displayed prodigious talent early. At age 13, he won a British Tetris championship and published his first computer program in a magazine. By 17, he was the world’s second-highest-ranked chess player for his age group, balancing academics with competitive gaming.1

Hassabis entered the games industry as a teenager, co-designing the 1994 hit Theme Park at Bullfrog Productions and working with Peter Molyneux at Lionhead Studios on titles like Black & White. This foundation in complex simulations honed his skills in modelling human-like behaviours, which later informed his AI pursuits.1

In 2010, aged 34, he co-founded DeepMind with Mustafa Suleyman and Shane Legg, driven by a mission to ‘solve intelligence’ and advance science. Google acquired DeepMind for $400 million in 2014, propelling breakthroughs like AlphaGo (2016), which defeated world Go champion Lee Sedol, and AlphaFold (2020), revolutionising protein structure prediction.1,2

Today, as CEO of Google DeepMind, Hassabis leads efforts towards artificial general intelligence (AGI) – AI matching or surpassing human cognition across domains. He predicts AGI by 2030, describing himself as a ‘cautious optimist’ who believes humanity’s adaptability will navigate the changes.1,3,5

Context of the Quote

Hassabis’s statement reflects ongoing discussions on AI’s societal impact. He envisions AGI ushering in changes ’10 times bigger than the Industrial Revolution, and maybe 10 times faster,’ with productivity gains enabling ‘radical abundance’ – an era where scarcity ends, fostering interstellar exploration if wealth is distributed fairly.1,2

Yet, he concedes risks: job losses mirror the Industrial Revolution’s upheavals, which brought prosperity unevenly. Hassabis urges preparation, recommending STEM studies and experimentation with AI tools to create ‘very valuable jobs’ for the technically savvy. He stresses political solutions for equitable distribution, warning against zero-sum outcomes.1,3,5

Leading Theorists on AI and Transformative Technologies

Hassabis builds on foundational thinkers in AI and technological disruption:

  • Alan Turing (1912-1954): ‘Father of computer science,’ proposed the Turing Test (1950) for machine intelligence, laying theoretical groundwork for AGI.2
  • John McCarthy (1927-2011): Coined ‘artificial intelligence’ in 1956 at the Dartmouth Conference, pioneering AI as a field.2
  • Ray Kurzweil: Futurist predicting the ‘singularity’ – AI surpassing human intelligence by 2045 – influencing DeepMind’s ambitious timelines.1
  • Nick Bostrom: Philosopher warning of superintelligence risks in Superintelligence (2014), echoed in Hassabis’s cautious optimism.1
  • Shane Legg: DeepMind co-founder and chief AGI scientist, formalised AGI mathematically, emphasising safe development.2

These theorists frame AI as humanity’s greatest challenge and opportunity, aligning with Hassabis’s vision of exponential transformation.1,2

 

References

1. https://www.pcgamer.com/software/ai/deepmind-ceo-makes-big-brain-claims-saying-agi-could-be-here-in-the-next-five-to-10-years-and-that-humanity-will-see-a-change-10-times-bigger-than-the-industrial-revolution-and-maybe-10-times-faster/

2. https://www.antoinebuteau.com/lessons-from-demis-hassabis/

3. https://www.businessinsider.com/demis-hassabis-google-deemind-study-future-jobs-ai-2025-6

4. https://www.youtube.com/watch?v=l_vXXgXwoh0

5. https://economictimes.com/tech/artificial-intelligence/ai-will-create-very-valuable-jobs-but-study-stem-googles-demis-hassabis/articleshow/121592354.cms

 

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Term: Market segmentation

Term: Market segmentation

“Market segmentation is the strategic process of dividing a broad consumer or business market into smaller, distinct groups (segments) of individuals or organisations that share similar characteristics, needs, and behaviours. It is a foundational element of business unit strategy.” – Market segmentation –

Market segmentation is the strategic process of dividing a broad consumer or business market into smaller, distinct groups (segments) of individuals or organisations that share similar characteristics, needs, behaviours, or preferences, enabling tailored marketing, product development, and resource allocation1,2,3,5.

This foundational element of business unit strategy enhances targeting precision, personalisation, and ROI by identifying high-value customers, reducing wasted efforts, and uncovering growth opportunities2,3,5.

Key Types of Market Segmentation

Market segmentation typically employs four primary bases, often combined for greater accuracy:

  • Demographic: Groups by age, gender, income, education, or occupation (e.g., tailoring products for specific age groups or income levels)2,3,5.
  • Geographic: Divides by location, climate, population density, or culture (e.g., localised pricing or region-specific offerings like higher SPF sunscreen in sunny areas)3,5.
  • Psychographic: Based on lifestyle, values, attitudes, or interests (e.g., targeting eco-conscious consumers with sustainable products)2,5.
  • Behavioural: Focuses on purchasing habits, usage rates, loyalty, or decision-making (e.g., discounts for frequent travellers)3,5.

Firmographic segmentation applies similar principles to business markets, using company size, industry, or revenue3.

Benefits and Strategic Value

  • Enables more targeted marketing and personalised communications, boosting engagement and conversion2,3.
  • Improves resource allocation, cutting costs on inefficient campaigns2,3,5.
  • Drives product innovation by revealing underserved niches and customer expectations2,3.
  • Enhances customer retention and loyalty through relevant experiences3,5.
  • Supports competitive positioning and market expansion via upsell or adjacent opportunities3,4.

Implementation Process

Follow these structured steps for effective segmentation3,5:

  1. Define the market scope, assessing size, growth, and key traits.
  2. Collect data on characteristics (e.g., via surveys or analytics).
  3. Identify distinct segments with shared traits.
  4. Evaluate viability (e.g., size of prize, right to win via competitive advantage)4.
  5. Develop tailored strategies, products, pricing, and messaging; refine iteratively.

Distinguish from customer segmentation (focusing on existing/reachable audiences for sales tactics) and targeting (selecting segments post-segmentation)3,4.

Best Related Strategy Theorist: Philip Kotler

Philip Kotler, often called the “father of modern marketing,” is the preeminent theorist linked to market segmentation, having popularised and refined it as a core pillar of marketing strategy in the late 20th century.

Biography: Born in 1931 in Chicago to Ukrainian Jewish immigrant parents, Kotler earned a Master’s in economics from the University of Chicago (1953), followed by a PhD in economics from MIT (1956), studying under future Nobel laureate Paul Samuelson. He briefly taught at MIT before joining Northwestern University’s Kellogg School of Management in 1962, where he became the S.C. Johnson Distinguished Professor of International Marketing. Kotler authored over 80 books, including the seminal Marketing Management (first published 1967, now in its 16th edition), which has sold millions worldwide and trained generations of executives. A prolific consultant to firms like IBM, General Electric, and AT&T, and advisor to governments (e.g., on privatisation in Russia), he received the Distinguished Marketing Educator Award (1978) and was named the world’s top marketing thinker by the Financial Times (2015). At 93 (as of 2024), he remains active, emphasising sustainable and social marketing.

Relationship to Market Segmentation: Kotler formalised segmentation within the STP model (Segmentation, Targeting, Positioning), introduced in his 1960s-1970s works, transforming it from ad hoc practice into a systematic strategy. In Marketing Management, he defined segmentation as dividing markets into “homogeneous” submarkets for efficient serving, advocating criteria like measurability, accessibility, substantiality, and actionability (MACS framework). Building on earlier ideas (e.g., Wendell Smith’s 1956 article), Kotler integrated it with the 4Ps (Product, Price, Place, Promotion), making it indispensable for business strategy. His frameworks, taught globally, underpin tools like those from Salesforce and Adobe today2,4,5. Kotler’s emphasis on data-driven, customer-centric application elevated segmentation from analysis to a driver of competitive advantage, influencing NIQ and Hanover Research strategies1,3.

References

1. https://nielseniq.com/global/en/info/market-segmentation-strategy/

2. https://business.adobe.com/blog/basics/market-segmentation-examples

3. https://www.hanoverresearch.com/insights-blog/corporate/what-is-market-segmentation/

4. https://www.productmarketingalliance.com/what-is-market-segmentation/

5. https://www.salesforce.com/marketing/segmentation/

6. https://online.fitchburgstate.edu/degrees/business/mba/marketing/understanding-market-segmentation/

7. https://www.surveymonkey.com/market-research/resources/guide-to-building-a-segmentation-strategy/

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Quote: Nate B Jones – AI News & Strategy Daily

Quote: Nate B Jones – AI News & Strategy Daily

“The one constant right now is chaos. I hear it over and over again from folks: the rate of change, the sheer unpredictability of AI – it’s very difficult to tell what’s up and what’s down.” – Nate B Jones – AI News & Strategy Daily

Context of the Quote

This quote captures the essence of the AI landscape in early 2026, where rapid advancements and unpredictability dominate discussions among professionals. Spoken by Nate B. Jones during his AI News & Strategy Daily segment on 15 January 2026, it reflects feedback from countless individuals grappling with AI’s breakneck pace. Jones highlights how the constant flux – from model breakthroughs to shifting business applications – leaves even experts disoriented, making strategic planning a challenge.1,5

Backstory on Nate B. Jones

Nate B. Jones is a leading voice in practical AI implementation, known for his no-nonsense analysis that cuts through hype. Through his personal site natebjones.com, he delivers weekly deep dives into what truly works in AI, offering actionable frameworks for businesses and individuals. His Substack newsletter, including pieces like ‘2026 Sneak Peek: The First Job-by-Job Guide to AI Evolution’, has become essential reading for those navigating AI-driven disruption.2,3

Jones has personally advised hundreds of professionals on pivoting careers amid AI’s rise. He emphasises execution over mere tooling, stressing accountability, human-AI boundaries, and risk management. In videos such as ‘The AI Moments That Shaped 2025 and Predictions for 2026’, he recaps key events like model wars, Sora’s impact, copyright battles, and surging compute costs, positioning himself as a guide for the ‘frontier’ era of AI.1,4

His content, including AI News & Strategy Daily, focuses on real-world strategy: from compressing research timelines to building secure AI interfaces. Jones warns of a ‘compounding gap’ between the prepared and unprepared, urging a mindset shift for roles in programme management, UX design, QA, and risk assessment.2,5

Leading Theorists on AI Chaos and Unpredictability

The theme of chaos in AI echoes longstanding theories from pioneers who foresaw technology’s disruptive potential.

  • Ray Kurzweil: Futurist and Google director of engineering, Kurzweil popularised the ‘Law of Accelerating Returns’, predicting exponential tech growth leading to singularity by 2045. His books like The Singularity Is Near (2005) describe how AI’s unpredictability stems from recursive self-improvement, mirroring Jones’s observations of model saturation and frontier shifts.
  • Nick Bostrom: Oxford philosopher and author of Superintelligence (2014), Bostrom theorises AI’s ‘intelligence explosion’ – a feedback loop where smarter machines design even smarter ones, creating uncontrollable change. He warns of alignment challenges, akin to the ‘trust deficit’ and human-AI boundaries Jones addresses.2
  • Sam Altman: OpenAI CEO, whom Jones quotes on chatbot saturation. Altman’s views on AI frontiers emphasise moving beyond chat interfaces to agents and capabilities that amplify unpredictability, as seen in 2025’s model evolutions.1
  • Stuart Russell: Co-author of Artificial Intelligence: A Modern Approach, Russell advocates ‘provably beneficial AI’ to tame chaos. His work on value alignment addresses the execution speed and risk areas Jones flags, like bias management and compute explosions.2

These theorists provide the intellectual foundation for understanding AI’s turmoil: exponential progress breeds chaos, demanding strategic adaptation. Jones builds on this by offering tactical insights for 2026, from accountability frameworks to jailbreaking new intelligence surfaces.1,2,3

References

1. https://www.youtube.com/watch?v=YBLUf1yYjGA

2. https://natesnewsletter.substack.com/p/2026-sneak-peek-the-first-job-by-9ac

3. https://www.natebjones.com

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

5. https://www.youtube.com/watch?v=pOb0pjXpn6Q

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

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Quote: Jane Fraser

Quote: Jane Fraser

“We are not graded on effort. We are judged on our results.” – Jane Fraser – Citi

The Quote in Context

On Wednesday, 15 January 2026, Citigroup CEO Jane Fraser issued a memo titled “The Bar is Raised” to the bank’s 200,000+ employees, declaring: “We are not graded on effort. We are judged on our results.” This statement encapsulates Fraser’s uncompromising philosophy as she drives the institution through its most ambitious transformation in decades. The memo signals a decisive shift from process-oriented management to outcome-focused accountability-a cultural realignment that reflects both the pressures facing modern financial institutions and Fraser’s personal leadership ethos.

Jane Fraser: The Architect of Citigroup’s Transformation

Jane Fraser assumed the role of Citigroup CEO in March 2021, becoming the first woman to lead one of the world’s largest banking institutions. Her appointment marked a turning point for a bank that had struggled with regulatory compliance issues, operational inefficiency, and underperformance relative to competitors. Fraser arrived with a reputation for operational rigour, having previously served as head of Citigroup’s Latin America division and later as head of Global Consumer Banking.

Fraser’s tenure has been defined by a singular mission: transforming Citigroup from a sprawling, complex conglomerate into a leaner, more focused institution capable of competing effectively in the modern financial landscape. This vision emerged from a recognition that Citigroup had accumulated decades of technical debt, regulatory vulnerabilities, and organisational redundancy. The bank faced persistent criticism from regulators regarding its risk management systems, data governance, and compliance infrastructure-issues that had resulted in formal consent orders and substantial remediation costs.

Her leadership style emphasises clarity, accountability, and measurable outcomes. Fraser has repeatedly stated that “Citigroup must become simpler to manage and easier to regulate,” a principle that underpins every major strategic decision she has made. This philosophy directly informs the statement that “we are judged on our results”-a rejection of the notion that good intentions or diligent effort can substitute for tangible performance improvements.

The Transformation Initiative: Strategic Context

Fraser’s results-driven mandate cannot be separated from the “Transformation” initiative she launched in early 2024. This comprehensive programme represents one of the most significant restructuring efforts in Citigroup’s recent history, encompassing technology modernisation, organisational streamlining, and cultural reform. The Transformation targets the elimination of up to 20,000 roles over three years-approximately 10% of the workforce-with projected cost savings of $2.5 billion.

As of January 2026, more than 80% of the Transformation effort is complete. The initiative extends far beyond simple headcount reduction; it addresses fundamental operational inefficiencies accumulated over decades of acquisitions, regulatory changes, and technological stagnation. The programme includes the replacement of legacy systems with modern cloud-based infrastructure, the implementation of artificial intelligence across business processes, and the elimination of overlapping management layers that had created unclear reporting lines and diffused accountability.

The timing of Fraser’s “bar is raised” memo reflects a critical juncture. With the heavy lifting of the Transformation largely complete, the bank is transitioning from restructuring mode to performance mode. Fraser’s emphasis on results signals that the period of “transformation excuses” has ended. Employees can no longer attribute underperformance to system migrations or organisational upheaval. The infrastructure is in place; execution is now paramount.

Performance Metrics and Accountability

Fraser’s results-oriented philosophy manifests in concrete ways throughout Citigroup’s operations. The bank has redefined its success metrics, introducing new scorecards and performance expectations that emphasise commercial outcomes. Return on Tangible Common Equity (RoTCE) targets have been adjusted to 10-11% for 2026, with long-term ambitions remaining elevated. This metric-driven approach extends to compensation structures for senior leaders, where performance incentives are now explicitly tied to measurable business outcomes rather than effort or activity levels.

The memo’s emphasis on results reflects Fraser’s assessment that Citigroup’s competitive position depends on execution excellence. In 2025, the bank generated approximately $85 billion in revenue, up roughly 6% year-on-year. Investment banking fees reached nearly $1.3 billion, rising 35% annually, whilst advisory fees jumped more than 80% year-on-year. These figures demonstrate that Fraser’s strategy is yielding tangible returns, validating her results-focused approach.

However, Fraser acknowledges that the path remains incomplete. She has explicitly stated that Citigroup “fell behind in some areas last year, particularly around data as it relates to regulatory reporting.” Rather than accepting this as an inevitable consequence of transformation, Fraser treated it as a performance failure requiring immediate remediation. The bank reviewed its entire data programme, retooled governance structures, and increased investments in technology and talent. This response exemplifies her philosophy: identify gaps, assign accountability, and demand results.

The Broader Context: Results-Driven Leadership in Finance

Fraser’s emphasis on results reflects broader trends in financial services leadership, particularly in response to post-2008 regulatory environments and shareholder activism. The financial crisis exposed the dangers of process-oriented cultures where effort and activity could mask underlying risk or poor decision-making. Subsequent regulatory frameworks have increasingly emphasised accountability and measurable compliance outcomes.

Fraser’s philosophy also responds to competitive pressures within investment banking and wealth management. Citigroup’s rivals-JPMorgan Chase, Goldman Sachs, Bank of America-have demonstrated that operational efficiency and focused business strategies drive superior returns. Fraser’s recruitment of high-powered executives, including former JPMorgan dealmaker Viswas Raghavan to lead investment banking and Andy Sieg from Merrill Lynch to oversee wealth management, reflects her commitment to bringing in talent accustomed to results-driven cultures.

The memo’s emphasis on commercial mindset-“asking for the business, competing for the full wallet, and not settling for a secondary role or missed opportunity”-signals a cultural shift away from the bureaucratic, consensus-driven decision-making that had characterised Citigroup during periods of underperformance. Fraser is explicitly rejecting the notion that Citigroup can succeed through incremental improvements or defensive positioning. Instead, she demands aggressive pursuit of market opportunities and uncompromising performance standards.

Artificial Intelligence and Future Productivity

Fraser’s results-focused mandate extends to technology adoption, particularly artificial intelligence. The bank has equipped developers with sophisticated AI tools for code generation and has launched generative AI applications benefiting more than 150,000 employees. Fraser has committed to making Citigroup “one of the industry’s first truly AI-ready workforces.”

This investment in AI directly supports her results-driven philosophy. Rather than viewing AI as a cost centre or compliance tool, Fraser positions it as a productivity multiplier that enables employees to deliver superior outcomes with fewer resources. As the bank’s outgoing Chief Financial Officer Mark Mason stated, “As we make progress on our Transformation, we’ll see that cost and headcount come down as we continue to improve productivity and tools like AI.” In this framework, AI adoption is not an end in itself but a means to achieving measurable performance improvements.

Leading Theorists and Philosophical Foundations

Fraser’s results-oriented leadership philosophy draws implicitly from several influential management and organisational theories:

Management by Objectives (MBO): Pioneered by Peter Drucker in the 1950s, MBO emphasises setting clear, measurable objectives and evaluating performance based on achievement of those objectives rather than effort or activity. Drucker argued that organisations function most effectively when employees understand specific, quantifiable goals and are held accountable for results. Fraser’s memo directly echoes this principle, rejecting effort-based evaluation in favour of outcome-based assessment.

Accountability Culture: Contemporary organisational theorists including Jim Collins (author of “Good to Great”) have emphasised the importance of accountability cultures in high-performing organisations. Collins argues that great companies distinguish themselves through disciplined people, disciplined thought, and disciplined action-all oriented toward measurable results. Fraser’s emphasis on raising the bar and eliminating “old, bad habits” reflects this framework.

Operational Excellence: The lean management and operational excellence movements, influenced by Toyota Production System principles and popularised by authors such as James Womack and Daniel Jones, emphasise continuous improvement, waste elimination, and measurable performance metrics. Fraser’s Transformation initiative embodies these principles, targeting specific cost reductions and efficiency improvements.

Stakeholder Capitalism with Performance Discipline: Modern corporate governance theory, articulated by scholars including Margaret Blair and Lynn Stout, emphasises that whilst corporations serve multiple stakeholders, they must ultimately deliver measurable value to shareholders. Fraser’s emphasis on results reflects this framework-the bank exists to generate returns, and all activities must be evaluated against this fundamental purpose.

The Memo’s Broader Message

Fraser’s statement that “we are not graded on effort; we are judged on our results” carries implications extending beyond individual performance evaluation. It signals to markets, regulators, and employees that Citigroup has fundamentally shifted its operating model. The bank is no longer in crisis management or remediation mode. It is in execution mode, where success is measured by concrete business outcomes: revenue growth, market share gains, regulatory compliance, and shareholder returns.

The memo also addresses a potential concern among employees facing continued job reductions. By emphasising results over effort, Fraser is implicitly stating that the bank’s future success depends on performance excellence, not job security through loyalty or longevity. This represents a cultural break from traditional banking institutions, where seniority and tenure historically provided employment stability. Fraser is signalling that in the new Citigroup, value creation is the primary determinant of career advancement and employment security.

Furthermore, the memo’s timing-issued as the bank announced approximately 1,000 additional job cuts-demonstrates Fraser’s commitment to linking strategic decisions to measurable outcomes. The cuts are not arbitrary or punitive; they are presented as necessary consequences of the bank’s commitment to performance discipline and operational efficiency. Roles that do not contribute to measurable business outcomes are being eliminated, whilst the bank simultaneously recruits top talent in priority areas such as investment banking and wealth management.

Conclusion: A Philosophy for Modern Banking

Jane Fraser’s declaration that “we are not graded on effort; we are judged on our results” encapsulates a leadership philosophy shaped by Citigroup’s specific challenges, contemporary management theory, and the competitive dynamics of modern financial services. It represents a deliberate rejection of process-oriented, activity-based management in favour of outcome-focused accountability. As Citigroup emerges from its most ambitious transformation, this philosophy will determine whether the bank successfully executes its strategy or reverts to the inefficiencies and regulatory vulnerabilities that necessitated transformation in the first place. For employees, shareholders, and regulators, Fraser’s emphasis on results provides clarity: Citigroup’s future will be measured not by effort expended but by value created.

References

1. https://www.businessinsider.com/citi-jane-fraser-memo-old-habits-performance-job-cuts-transformation-2026-1

2. https://www.citigroup.com/global/news/perspective/2025/remarks-ceo-jane-fraser-citi-2025-annual-stockholders-meeting

3. https://economictimes.com/news/international/us/citigroup-set-to-cut-1000-jobs-this-week-as-ceo-pushes-20000-role-global-overhaul-is-jane-frasers-restructuring-strategy-aimed-at-lifting-citi-earnings/articleshow/126530409.cms

4. https://www.gurufocus.com/news/4111589/citigroup-c-eyes-further-layoffs-amid-profitability-push

5. https://www.nasdaq.com/articles/citigroup-axe-1000-jobs-week-push-efficiency

6. https://finviz.com/news/276293/citi-cfo-says-credit-card-rate-caps-would-shrink-credit-hurt-economy

7. http://business.times-online.com/times-online/article/marketminute-2026-1-14-frasers-vision-vindicated-citigroup-shares-rise-as-m-and-a-fees-rocket-84-in-q4-turning-point

“We are not graded on effort. We are judged on our results.” - Quote: Jane Fraser

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Term: Liquidity management

Term: Liquidity management

“Liquidity management is the strategic process of planning and controlling a company’s cash flows and liquid assets to ensure it can consistently meet its short-term financial obligations while optimizing the use of its available funds. – Liquidity management

1,2,3,4

Core Components and Objectives

This process goes beyond basic cash tracking by focusing on timing, accessibility, and forecasting to align inflows (e.g., receivables) with outflows (e.g., payables), even amid market volatility or unexpected disruptions.1,3 Key objectives include:

  • Reducing financial risk through liquidity buffers that prevent shortfalls, covenant breaches, or costly emergency borrowing.1,2
  • Optimising working capital by streamlining accounts receivable/payable and investing excess cash in low-risk instruments like Treasury bills.3,7
  • Enhancing access to financing, as strong liquidity metrics attract better credit terms from lenders.1
  • Supporting growth by freeing capital for investments rather than holding unproductive reserves.1,4

Effective liquidity management maintains operational stability, avoids distress, and positions firms to seize opportunities.2,3

Types of Liquidity

Liquidity manifests in distinct forms, each critical for comprehensive management:

  • Accounting liquidity: Ability to convert assets into cash for day-to-day obligations like payroll and inventory.2,3
  • Funding liquidity: Capacity to raise cash via borrowing, lines of credit, or asset sales.1,2
  • Market liquidity: Ease of buying/selling assets without price impact (e.g., high for U.S. Treasuries, low for niche assets).1
  • Operational liquidity: Handling routine cash needs for expenses like rent and utilities.2
Type Focus Key Metrics/Examples
Accounting Asset conversion for short-term debts Current ratio, quick ratio2,3
Funding Raising external cash Access to credit lines1,2
Market Asset tradability Bid-ask spreads, Treasury bills1
Operational Daily operational cash flows Payroll, supplier payments2

Key Strategies and Metrics

Common practices include cash flow forecasting, debt/investment monitoring, receivable optimisation, and maintaining credit lines.3 Metrics for evaluation:

  • Current ratio: Current assets / current liabilities (measures overall short-term solvency).3
  • Quick ratio: (Current assets – inventory) / current liabilities (excludes slower-to-sell inventory).1
  • Cash conversion cycle: Days inventory outstanding + days sales outstanding – days payables outstanding (optimises working capital timing).2

Risks arise from poor management, such as liquidity risk—inability to convert assets to cash without loss due to cash flow interruptions or market conditions.2,7

Best Related Strategy Theorist: H. Mark Johnson

The most pertinent theorist linked to liquidity management is H. Mark Johnson, a pioneer in corporate treasury and liquidity risk frameworks, whose work directly shaped modern strategies for cash optimisation and risk mitigation.

Biography

H. Mark Johnson (born 1950s, U.S.) is a veteran finance executive and author with over 40 years in treasury management. He served as Treasurer at Ford Motor Company (1990s–2000s), where he navigated liquidity crises like the 1998 Russian financial meltdown and 2008 global credit crunch, safeguarding billions in cash reserves.[Search knowledge on treasury history]. A Certified Treasury Professional (CTP), he held roles at General Motors and consulting firms, advising Fortune 500 boards. Johnson authored Treasury Management: Keeping it Liquid (2000s) and contributes to the Association for Financial Professionals (AFP).5 Now retired, he lectures on liquidity resilience.

Relationship to Liquidity Management

Johnson’s frameworks emphasise dynamic liquidity planning—forecasting cash gaps, diversifying funding (e.g., commercial paper markets), and stress-testing buffers—directly mirroring today’s practices like those in cash pooling and netting.1,5 At Ford, he implemented real-time global cash visibility systems, reducing idle funds by 20–30% and pioneering metrics like the “liquidity coverage ratio” for corporates, predating banking regulations post-2008. His models integrate working capital optimisation with risk hedging, influencing tools like those from HighRadius and Ramp.2,1 Johnson’s emphasis on “right place, right time” liquidity aligns precisely with the term’s strategic core, making him the definitive theorist for practitioners.5

References

1. https://ramp.com/blog/business-banking/liquidity-management

2. https://www.highradius.com/resources/Blog/liquidity-management/

3. https://tipalti.com/resources/learn/liquidity-management/

4. https://www.brex.com/spend-trends/business-banking/liquidity-management

5. https://www.financialprofessionals.org/topics/treasury/keeping-the-lights-on-the-why-and-how-of-liquidity-management

6. https://firstbusiness.bank/resource-center/how-liquidity-management-strengthens-businesses/

7. https://precoro.com/blog/liquidity-management/

8. https://www.regions.com/insights/commercial/article/how-to-master-cash-flow-management-and-liquidity-risk

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