Autonomy Economics

Autonomy Economics is a reference discipline that studies how value capture, commercial logic, and defensibility change as autonomous capability makes it possible to carry activity forward, mediate interaction, and access capability with less dependence on the conditions that previously structured how value was created and captured.^[4] It defines the paradoxes, exposure vectors, and redesign imperatives that emerge as autonomous capability becomes economically meaningful across software, services, financial institutions, and interface-based models.

The discipline was founded by Elemi Atigolo, Managing Partner at Consult Venture Partners, who introduced its central concept, the Agentic Profit Paradox, in FT The Banker in February 2026.^[1] The foundation paper, The Agentic Profit Paradox and the Reorganisation of Value Capture, was published in April 2026 and formalises the field's analytical framework. The framework has since been applied in institutional engagements across financial services and has been cited in sectoral analysis of AI's economic impact in banking.^[2]

The Discipline

Autonomy Economics studies how value capture, commercial logic, and defensibility change wherever autonomous capability weakens the structural conditions on which a commercial unit was built. The field examines three distinct axes of exposure: human-bounded progression (HBP), where value depends on activity being carried forward through human effort or sequencing; human-bounded interaction (HBI), where value depends on interaction, interface, or attention through which value is captured; and capability scarcity dependence (CSD), where value depends on the limited availability of, or controlled access to, underlying capability.

The discipline applies wherever autonomous systems change how activity is carried forward, how interaction takes place, or how access to capability is structured. It focuses on revenue durability, margin structures, and the redistribution of value as autonomous capability alters the conditions that previously made commercial units scarce and defensible. The central claim is that economic advantage from autonomous capability depends on whether the structure of value capture remains aligned with the conditions on which the commercial unit depends.

The Agentic Profit Paradox

The Agentic Profit Paradox is the central mechanism of Autonomy Economics. It describes a condition in which increasing autonomous capability can weaken the commercial structures that previously sustained profit by creating a misalignment between the conditions under which activity is carried forward, interaction takes place, and capability remains scarce. As that misalignment grows, the basis on which value is justified, retained, and monetised can begin to weaken.^[1]

More specifically, the paradox describes a structural tension in which firms may need to adopt autonomous capability to remain competitive and access new sources of value, even when that same capability can weaken the revenue structures, operating assumptions, and value-capture logic that previously supported their profits. The pressure does not depend on where capability originates; it may arise within the firm or from external actors that alter those conditions. What matters is whether value capture still fits the conditions under which activity, interaction, and capability access are now structured.

The concept was first published by Elemi Atigolo in FT The Banker in February 2026^[1] and has since been recognised as a key framework for understanding AI's economic impact across financial services and enterprise models.^[2] The Agentic Profit Paradox is the subject of a dedicated flagship research and reference site at theagenticprofitparadox.com.^[3]

Foundation Paper

The foundation paper for Autonomy Economics, titled The Agentic Profit Paradox and the Reorganisation of Value Capture, establishes the economic logic behind the Agentic Profit Paradox and defines the core constructs of the discipline. Published in April 2026 and available via SSRN, it provides the formal basis for understanding how value reorganises under autonomous progression and outlines the central claims regarding revenue durability, margin structures, and commercial defensibility.

The full 145-page paper is available directly as a PDF (1.9 MB) if the SSRN security check does not clear.

The paper develops the Three Laws of Autonomy Economics, introduces the Three Axes of Exposure (human-bounded progression, human-bounded interaction, and capability scarcity dependence), and proposes the Revenue Durability and Capability Durability Frameworks. It draws on historical precedents across more than two centuries to show that the migration of value away from established commercial units is a recurring structural condition, not a conjecture about the future.

JEL Classification: B41, B59, O33, L20, M21, D21. Keywords: Autonomy Economics, Agentic Profit Paradox, agentic AI, value capture, revenue durability, autonomy-driven business models, economic restructuring, business model redesign, capital-structured autonomy ecosystems.

The Three Laws of Autonomy Economics

The Three Laws of Autonomy Economics are the foundational principles that describe how value reorganises once autonomous capability becomes commercially meaningful in how activity is carried forward, how interaction takes place, or how access to capability changes as scarcity weakens. The term laws describes directional regularities observed under those conditions, not universally binding relationships. Together they identify the common economic direction of change as activity, interaction, and capability access become less dependent on human-bounded conditions.

Law 1: Value moves from production to orchestration

Production tends to lose economic weight once outputs can be generated at much lower marginal cost. Scarcity no longer lies primarily in producing the output itself, but in how outputs are sequenced, checked, combined, governed, and applied. In interaction-based models, a similar pattern appears as value moves away from interface-bound participation toward the orchestration and routing of demand. In capability-scarcity-dependent settings, value moves away from access to the underlying capability itself and toward how that capability is applied, combined, and directed within a broader commercial structure.

Autonomous systems increase the volume of activity that can be generated, but volume alone does not create durable advantage. Value lies in how that activity is organised, directed, verified, and applied within a broader commercial structure.

Law 2: Value moves from access to outcomes

Many established commercial models were built around access, charging for seats, hours, review stages, attention, and other units tied to human-bounded conditions. That logic can weaken once autonomous capability changes how activity is carried forward, how interaction takes place, or how access to capability is structured. Access becomes a less reliable basis for explaining value. The focus moves from participation in the process to the result produced.

Accuracy, speed, coverage, compliance, decision quality, and financial performance tend to carry more weight once the underlying activity can be progressed differently. Regulatory frameworks such as the FCA's Consumer Duty and SM&CR require firms to evidence consumer outcomes and accountability directly, reinforcing the commercial shift. Human judgement, fiduciary responsibility, and accountability do not diminish in this transition; they become more concentrated and more valuable as autonomous capability expands beneath them.

Law 3: Value concentrates where scarce judgement governs autonomous throughput

Judgement, governance, and control remain central to the extent that they determine what is done, whether it is acceptable, and how it is applied. What changes is the economic base beneath them. As autonomous systems carry forward larger volumes of activity, mediate more interaction, and broaden access to capability, scarcity moves toward the layer that directs, constrains, authorises, and accepts that activity, whether that sits in human decision-making, governed systems, or a combination of both.

The strongest positions are less likely to sit where effort remains distributed across each stage of progression. They are more likely to sit where scarce judgement governs greater throughput without recreating earlier layers of manual progression. In financial institutions and other regulated settings, accountability, fiduciary duty, risk ownership, and supervisory responsibility do not disappear as autonomous capability expands. Their role becomes clearer because they sit above a larger base of activity that no longer requires continuous human direction. The premium becomes more concentrated where judgement, governance, and control direct, authorise, and govern autonomous capacity.

Human-Bounded Models & Axes of Exposure

Autonomy Economics identifies three axes of structural exposure through which autonomous capability puts pressure on established commercial models. These axes describe the distinct ways in which value is structured and exposed as autonomous capability advances.

Human-Bounded Progression

Human-bounded progression (HBP) is a core form of exposure in Autonomy Economics. It refers to activity whose movement or completion depends materially on human effort, review, judgement, coordination, or intervention in ways that shape how value is created, justified, or retained. Where a commercial model depends on this structure, autonomous capability makes it possible to carry activity forward with less dependence on continuous human direction.

Exposure is most visible where value capture is anchored directly to human-bounded progression, particularly in consulting, SaaS, legal review, wealth management, audit, and compliance. As that condition weakens, the layer through which value was defined, justified, and defended weakens with it.

Human-Bounded Interaction

Human-bounded interaction (HBI) refers to interaction whose commercial value depends materially on direct human attention, engagement, discovery, navigation, or interface use in ways that shape how demand is formed, routed, or captured. Where a commercial model depends on this structure, autonomous capability can reduce or displace the need for direct human interaction by acting on the user's behalf.

Exposure is most visible in consumer, media, platform, marketplace, and interface-based settings. As agents take over discovery and execution on behalf of users, the traditional engagement-based revenue models face significant structural pressure.

Capability Scarcity Dependence

Capability scarcity dependence (CSD) is the third axis of exposure in Autonomy Economics. It refers to conditions in which value capture depends materially on the limited availability, control, or constrained access to underlying capability in ways that shape how value is created, justified, or retained. Where a commercial model depends on this structure, autonomous capability can expand access, replicate capability, or weaken the scarcity on which that model depends.

Exposure is most visible in models where value depends on control over technical, cognitive, or operational capability, including systems in which access to that capability is priced, bundled, or embedded within a product or service. As autonomous capability improves and diffuses, the scarcity on which those models depend may erode even while underlying capability continues to advance.

The three axes can overlap. Multiple forms of exposure can exist within the same business, with different revenue lines affected through different mechanisms and on different timelines.

Value Reorganisation

Value reorganisation in Autonomy Economics defines how value moves as autonomous capability becomes economically meaningful across the three axes of exposure. As production becomes less scarce, as interaction is compressed, and as capability access broadens, value begins to reorganise around the structures that determine how autonomy is directed, verified, absorbed, and governed, both inside firms and across coordinated networks.

Value does not vanish from the system. It accumulates in different layers. As autonomous capability becomes commercially meaningful, value tends to concentrate where autonomous capacity is organised, constrained, verified, and put to use, in the layers that remain scarce once production, progression, interaction, and access are no longer the main constraints.

From Production to Orchestration

As autonomous systems reduce the marginal cost of production, value shifts toward orchestration. In this context, orchestration refers to the commercial and operational layer through which a firm decides what its autonomous capability does, in what order, to what standard, and toward what purpose. It is not merely the technical coordination of APIs or software systems.

Without something directing what gets done, in what sequence, to what standard, and how it connects to the wider process, additional capacity creates noise, duplication, and inconsistency. Firms that control the orchestration layer are positioned to capture the value that production-heavy models are losing.

From Access to Outcomes

Access-based pricing weakens when autonomous progression changes how activity is carried forward, how interaction takes place, and how access to capability is structured. Value shifts toward outcomes: accuracy, completeness, compliance, and speed. Regulatory frameworks such as the Consumer Duty and SM&CR require firms to evidence outcomes and accountability directly, reinforcing the commercial shift toward models that demonstrate suitability and measurable results.

In regulated settings, verification, supervision, and evidencing do not disappear; they become the basis of defensibility. Human accountability, fiduciary duty, and supervisory judgement reorganise toward higher-value functions as autonomous capability expands beneath them.

To Governed Throughput Structures

Autonomous systems increase throughput, and as judgement, governance, and control govern larger volumes of autonomous activity, scarcity moves toward the layer that directs, constrains, authorises, and accepts that activity. Firms that can absorb machine-speed throughput through strong orchestration, verification, and governance gain a structural advantage in margin durability and competitive position.

The ability to govern throughput, not merely absorb it, is the key differentiator. Value concentrates where scarce judgement directs and approves autonomous activity at scale.

Where Value Accumulates Next

As production loses economic weight, value begins to accumulate in the layers that organise, constrain, and direct autonomous progression. These layers represent the new scarcity in an economy of abundant production. Value holds in the following critical functions:

• Orchestration: sequencing tasks and combining outputs; gains weight when production is no longer the constraint and converts autonomous throughput into something a firm can deploy, manage, and use commercially
• Verification: evidencing reliability and tracing decisions; trust depends on checking, evidencing, validating, and tracing what has been done, and provenance becomes as important as quality
• Governance: enforcing policy and managing exceptions; defines what actions are allowed, where escalation is required, and who carries responsibility, moving from overhead into a core economic layer
• Absorption: converting throughput into usable commercial value; firms with strong absorption convert throughput into broader coverage, faster decision-making, and improved outcomes
• Judgement: directing edge cases and approving outcomes; becomes more concentrated as it governs a larger base of activity, with the premium lying in directing and constraining larger volumes without recreating earlier layers of manual progression

Capital Structured Autonomy Ecosystems

Autonomy Economics applies beyond individual firms to coordinated groups of entities. Capital Structured Autonomy Ecosystems emerge where ownership or control structures shape how autonomy is introduced, scaled, and standardised across a network. These environments include private equity portfolio groups, corporate conglomerates with central governance, franchise networks with standardised operating models, large enterprises with shared services, and holding companies with cross-company playbooks.

In these ecosystems, capital acts as the organising force, determining where capability is deployed, how adoption priorities are set, and how operating gains are captured. Value often migrates upward from firm-level execution to ecosystem-level orchestration. The most defensible position may sit with the actor that coordinates standards, learning loops, operating models, and value capture across a wider group, not only with individual operating companies that deploy autonomous capability within their own boundaries.

The Orchestration Layer

The orchestration layer determines how autonomous outputs are sequenced, verified, governed, and applied. It becomes the scarce layer once production can be generated at low marginal cost, providing a new basis for commercial defensibility.

Firms that build robust orchestration layers hold their economic position more securely as production becomes commoditised.

Outcome-Based Pricing

Outcome-based pricing replaces access-based models when autonomous progression makes access less scarce. Clients shift from paying for seats or hours to paying for accuracy, speed, compliance, and completeness.

Across financial services, healthcare, legal services, and other heavily supervised sectors, regulators in multiple jurisdictions are independently requiring firms to demonstrate that fees and charges are justified by outcomes, not by the cost of the process that produced them. Firms in these markets may face the requirement to move toward outcome-based justification before autonomous capability has fully matured, because regulators are already demanding it. Human accountability, fiduciary duty, and supervisory judgement do not disappear in this transition; they reorganise toward higher-value functions as autonomous capability expands beneath them.

Sectoral Exposure Maps

Autonomy Economics applies across all sectors, though exposure levels vary significantly. The primary determinant of exposure is how closely a revenue line depends on human-bounded progression or interaction. Sectors with structured, decomposable workflows face the earliest and most intense pressure from autonomous capability.

Consulting

Consulting is exposed where value is tied to human-bounded progression across structured stages. Autonomous capability compresses analysis, synthesis, and documentation layers, weakening traditional time-based and stage-based pricing models. Firms must shift toward orchestration and outcome-based value to maintain margin durability.

SaaS

SaaS models built on seats, usage, or access face exposure as autonomous systems reduce the scarcity of access and compress interface-based monetisation. The traditional per-seat model weakens when agents can perform tasks without human-led interface interaction. Value reorganisation in SaaS requires a shift toward outcome-based and throughput-absorbing structures.

Legal

Legal workflows with structured review, drafting, and analysis face early exposure to autonomous progression. Outcome-based models gain relevance as autonomous capability expands, displacing traditional hourly billing structures. Defensibility in legal services shifts toward judgement, governance, and complex orchestration.

Wealth Management

Wealth management models tied to human-bounded progression, including planning, review, and rebalancing, face compression as autonomous systems increase throughput.^[2][5] The value of human-led administration diminishes as agents take over routine execution. Strategic advice and complex relationship management remain the primary human-bounded value layers.

Audit

Audit is exposed where structured verification and reconciliation can be automated. Orchestration and judgement layers become the primary sources of defensibility as autonomous systems handle the bulk of data-heavy verification. The role of the auditor shifts from production to high-level governance and verification of autonomous outputs.

Compliance

Compliance functions with rule-based workflows face early exposure to autonomous systems. Value shifts from routine monitoring to governance, oversight, and exception handling as agents manage standard compliance tasks. Margin durability in compliance depends on the ability to absorb machine-speed throughput.

Consumer Apps

Apps dependent on engagement or discovery face compression as agentic systems reroute demand and reduce interface surface. When agents act on the user's behalf, the traditional monetisation of attention and discovery is fundamentally altered. Consumer platforms must redesign for agentic interaction to remain commercially viable.

Marketplaces

Marketplaces are exposed where discovery and matching are automated by agentic systems. Value shifts from the matching process to trust, verification, and governance layers that ensure the quality of autonomous transactions. Defensibility in marketplaces depends on the integrity of the ecosystem orchestration.

Autonomy vs Capability Capture

Capability capture refers to technical adoption, while economic defensibility refers to whether the business model remains secure. A firm can adopt autonomous capability and still weaken its position, improving speed, lowering cost, and increasing throughput while leaving the commercial structure largely intact. Firms can become more capable while simultaneously becoming less economically secure if they fail to redesign their revenue models.

The distinction is not between firms that adopt autonomous capability and those that do not. It is between firms that adopt within inherited commercial structures and firms that redesign those structures around the new distribution of value. Redesign is required to convert technical capability into durable commercial advantage. When efficiency becomes transparent and widely understood, firms cannot rely on operational excellence alone. Defensibility moves toward structural positions across the following dimensions:

Data moats

Proprietary data can create learning advantages that competitors cannot reproduce. Adaptive systems trained on richer data produce better outcomes, and that advantage compounds over time.

Optimisation quality

The way a firm orchestrates and governs adaptive capability can become a source of strength. The routines that surround the models determine how well autonomy performs in practice. Execution quality remains a differentiator even when the underlying capability is widely available.

Ecosystem control

Owning the operating surface, distribution path, or integration layer shapes the competitive terrain. Adaptive systems run inside these environments. Control over that infrastructure influences how value is captured across the ecosystem.

Regulatory and structural moats

Licences, switching costs, trust, and incumbent relationships provide protection that automation cannot erode. These moats may remain human-bounded even as execution becomes autonomous, and they tend to hold longer than operational advantages.

Judgement and accountability

Human judgement, accountability, and interpretive capacity remain central where consequences are high. Autonomy can support execution in these settings, but responsible decision-making still sits with people. That layer does not weaken under autonomous progression. It becomes more concentrated and more valuable.

Dispersion and Early Pressure

Autonomy does not require full deployment to trigger repricing; markets respond to directionality. Autonomous capability does not need to be fully deployed for its economic implications to matter; it only needs to become credible in commercially meaningful activities. Exposure appears first in structured, decomposable work tied to HBP, in interaction-dependent models tied to HBI, and in models where capability scarcity is beginning to weaken through CSD.

Margin pressure often precedes demand erosion as the perceived value of human-bounded tasks declines. Commercial terms often adjust slowly, but the basis of value can weaken earlier. In that setting, markets may respond to direction rather than completeness.

Intelligence becomes cheaper, usage expands

The cost of model inference falls. Token prices decline. Context windows widen. Open-weight models reduce dependency on proprietary APIs. At the same time, usage rises. Firms automate more routines, run agents continuously, and embed decision logic across surfaces. This mirrors the pattern seen in cloud computing: unit cost falls while aggregate spend grows. The key change is that execution cost, interaction efficiency, and access to capability become more measurable and, in some settings, easier to compare across firms and models.

Efficiency becomes visible and therefore negotiable

Clients observe faster turnaround, reduced staffing, and automated progression across stages of activity. They begin to question fee structures built on human progression, human-bounded interaction, or scarce capability access. An early signal appeared when KPMG pressed its auditor Grant Thornton UK for a reduction in audit fees, arguing that AI-enabled efficiency gains should be reflected in lower prices, resulting in a fee reduction from $416,000 to $357,000 for the 2025 audit. A change in how part of the underlying activity could be carried forward was enough to affect the commercial basis of the engagement.

Competitors respond to visible efficiency

Once efficiency becomes measurable and sufficiently comparable across firms, it enters competition more directly. Commercial structures adjust, positioning changes, and automation becomes more expected. In many settings, operational advantage becomes harder to sustain on process alone, even though integration quality, workflow design, and human-system coordination remain important differentiators. As more firms reach similar levels of execution capability, differentiation moves away from how activity is carried forward toward how that capability is organised and applied.

Frameworks

Autonomy Economics includes two proprietary analytical frameworks applied in institutional engagements to identify strategic exposure and redesign imperatives.

Revenue Durability Framework

The Revenue Durability Framework evaluates whether a business model can withstand, absorb, or reorganise under autonomous progression. It examines the dependency of revenue lines on human-bounded progression (HBP), human-bounded interaction (HBI), and capability scarcity dependence (CSD), and determines whether economic position can be maintained as autonomy becomes structurally meaningful across those axes.

The framework identifies where pressure appears first, where value is beginning to accumulate, and what redesign is required to align value capture with the new activity structure. It is applied in institutional engagements to identify strategic exposure and redesign imperatives.

Capability Durability Framework

The Capability Durability Framework addresses a related but distinct question: whether the organisation can absorb autonomous capacity and convert it into stronger capability as the activity structure changes. It distinguishes between technical adoption and commercial defensibility, assessing whether increased autonomous capability strengthens or weakens the underlying economic position of the firm.

A firm may recognise exposure without building the structure needed to capture value. Exposure and capture move separately. The Capability Durability Framework is applied in settings where autonomy is being considered as a structural, rather than tactical, change to the commercial model.

Autonomy-Driven Business Models

An Autonomy-Driven Business Model is one in which autonomous capability is embedded in the commercial logic through which the firm defines, delivers, and captures value. It shapes how activity is carried forward, how interaction takes place, how access to capability is structured, how value is delivered, how revenue is generated, and how advantage is defended. These models are distinguished from those that merely layer autonomous capability onto existing delivery structures without changing the commercial basis on which value is created and defended.

When redesign occurs, commercial structures move away from units tied to human-bounded progression, human-bounded interaction, and constrained capability access, and toward the outcomes, results, and capabilities that autonomous systems make possible. Human contribution moves from carrying activity across stages to directing, evaluating, and governing larger volumes of activity and interaction, and to determining how capability is used once access broadens. The operating model no longer treats autonomy as a tool, but as part of the structure through which value is created and captured.

There is no single standard form for these models. They vary by sector, regulatory setting, client expectation, and the nature of the activity involved. In sectors where autonomous capability is becoming commercially viable, models that remain tied to earlier assumptions tend to come under pressure over time.

Elemi Atigolo

Add elemi-atigolo.jpg to /public

Elemi Atigolo is the founder of Autonomy Economics and Managing Partner at Consult Venture Partners. His work examines how agentic AI is changing business models and the economics of value creation across financial services and other complex industries.^[4]

He first introduced the Agentic Profit Paradox in FT The Banker in February 2026.^[1] The foundation paper, The Agentic Profit Paradox and the Reorganisation of Value Capture, was published in April 2026 and formally establishes the field's analytical framework, including the Three Laws of Autonomy Economics, the Three Axes of Exposure, and the Revenue Durability and Capability Durability Frameworks. His perspective is shaped by earlier work in banking and private wealth management at HSBC and St. James's Place, where he focused on investment strategy and capital allocation in regulated settings.

Since 2020, he has built AI systems in production environments and has contributed to discussions with the Financial Conduct Authority on the responsible use of AI in financial services. Elemi holds a BSc in Computer Science and an MA in Accounting and Finance.

Frequently asked questions

What is Autonomy Economics?

Autonomy Economics is a reference discipline that studies how value capture, commercial logic, and defensibility change when autonomous capability reduces the dependence of an activity on continuous human direction. It analyses three axes of exposure (human-bounded progression, human-bounded interaction, and capability scarcity dependence) and sets out how firms should redesign pricing and business models when the conditions that made a commercial unit scarce begin to weaken.^[4]

How does Autonomy Economics differ from autonomous consumption or autonomous expenditure?

They are unrelated concepts that share a word. Autonomous consumption and autonomous expenditure are long-established terms in macroeconomics describing spending that does not vary with income. Autonomy Economics, by contrast, is a discipline concerned with autonomous capability (machine systems that carry work forward without continuous human direction) and with what that does to pricing, margin, and commercial defensibility. The two fields share no analytical framework.

Who founded Autonomy Economics?

Autonomy Economics was founded by Elemi Atigolo, Managing Partner at Consult Venture Partners. He introduced the field’s central concept, the Agentic Profit Paradox, in FT The Banker in February 2026,^[1] and formalised the discipline in the foundation paper The Agentic Profit Paradox and the Reorganisation of Value Capture, published in April 2026.

What is the Agentic Profit Paradox?

The Agentic Profit Paradox describes a structural tension in which a firm becomes more capable while the commercial basis of its business becomes less secure. It arises when autonomous capability weakens the conditions that made a commercial unit scarce and defensible, while value capture remains anchored to those earlier conditions.^[1]

What are the Three Laws of Autonomy Economics?

The Three Laws describe how value migrates as autonomous capability expands. First, value moves from production to orchestration. Second, value moves from access to outcomes. Third, value concentrates where scarce judgement governs autonomous throughput.

Which sectors are most exposed to the Agentic Profit Paradox?

Exposure is highest where revenue is tied to human-bounded progression or to controlled access rather than to outcomes. The discipline maps sectoral exposure across consulting, SaaS, legal services, wealth management, audit, compliance, consumer applications, and marketplaces.

Where can I read the foundation paper?

The foundation paper, The Agentic Profit Paradox and the Reorganisation of Value Capture, is available on SSRN. A companion reference site is published at theagenticprofitparadox.com.^[3]

See also

• The Agentic Profit Paradox
• The Three Laws of Autonomy Economics
• The Orchestration Layer
• Sectoral Exposure Maps
• Revenue and Capability Durability Frameworks
• Autonomy-Driven Business Models

References

1. ^Atigolo, Elemi (February 2026). "The Agentic Profit Paradox". FT The Banker. Financial Times Group. thebanker.com
2. ^"Agentic AI Profit Paradox in Banking". Finance AI Insiders. 2026. financeaiinsiders.com
3. ^The Agentic Profit Paradox, flagship reference and research site. Autonomy Economics. theagenticprofitparadox.com
4. ^Elemi Atigolo, author and founder of Autonomy Economics. Personal site. elemiatigolo.com
5. ^"The Agentic AI Profit Paradox: Implications for Swiss Private Banking Clients". SKN CBBA. 2026. skncbba.com
6. ^Financial Conduct Authority. "Artificial Intelligence in retail financial services: Engagement Paper." January 2026. fca.org.uk
7. ^European Union. "Regulation (EU) 2024/1689 on artificial intelligence." Official Journal. August 2024. eur-lex.europa.eu
8. ^Office of the Superintendent of Financial Institutions Canada. "Guideline E-23: Enterprise Risk Management (Model Risk Management and Artificial Intelligence)." Effective May 2027. osfi-bsif.gc.ca
9. ^Australian Securities and Investments Commission. "Report 798: Digital Financial Services and Cyber Resilience Vulnerabilities." October 2024. asic.gov.au
10. ^American Bar Association. "Formal Opinion 512: The Paradigm for Generative AI in Legal Practice." 29 July 2024. americanbar.org
11. ^Health Canada. "Pre-market Guidance for Machine-Learning-Enabled Medical Devices." February 2025. canada.ca
12. ^Food and Drug Administration. "Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management." January 2025. fda.gov
13. ^World Health Organization. "Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models." 18 January 2024. who.int
14. ^Bohnsack, R. & de Wet, M. (2025). "AI is the Strategy: From Agentic AI to Autonomous Business Models onto Strategy in the Age of AI." arXiv:2506.17339. arxiv.org
15. ^Marks, Howard (February 2026). "AI Hurtles Ahead." Oaktree Capital. oaktreecapital.com
16. ^Marks, Howard (December 2025). "Is It a Bubble?" Oaktree Capital. oaktreecapital.com
17. ^Reuters (March 2026). "HSBC appoints first chief AI officer as it seeks cost cuts." reuters.com
18. ^Reuters (March 2026). "HSBC weighs deep job cuts as AI overhaul unfolds." reuters.com
19. ^Financial Times (February 2026). "Consultancies set for fastest growth in years on back of AI boom." ft.com
20. ^Freshfields Bruckhaus Deringer (February 2026). "The FCA looks to 2030: Key takeaways from the Mills Review on AI in retail financial services." freshfields.com

External links

Primary sources

• Autonomy Economics , flagship site for the field
• The Agentic Profit Paradox , central concept: dedicated research and reference site
• The Agentic Profit Paradox and the Reorganisation of Value Capture , foundation paper, SSRN, April 2026
• Elemi Atigolo , author and founder of Autonomy Economics
• FT The Banker: The Agentic Profit Paradox , original publication, February 2026
• Agentic AI Profit Paradox in Banking , Finance AI Insiders
• The Agentic AI Profit Paradox: Implications for Swiss Private Banking Clients, SKN CBBA

Academic and research alignment

• AI is the Strategy: From Agentic AI to Autonomous Business Models onto Strategy in the Age of AI (Bohnsack & de Wet, 2025), peer-reviewed framework alignment

Market commentary and analysis

• AI Hurtles Ahead (Howard Marks, Oaktree Capital, February 2026)
• Is It a Bubble? (Howard Marks, Oaktree Capital, December 2025)
• Consultancies set for fastest growth in years on back of AI boom (Financial Times, February 2026)
• HSBC appoints first chief AI officer as it seeks cost cuts (Reuters, March 2026)
• HSBC weighs deep job cuts as AI overhaul unfolds (Reuters, March 2026)

Regulatory analysis

• The FCA looks to 2030: Key takeaways from the Mills Review on AI in retail financial services (Freshfields Bruckhaus Deringer, February 2026)

Regulatory frameworks

• Financial services: FCA Mills Review on AI in retail financial services, OSFI Guideline E-23, ASIC Report 798, EU AI Act
• Legal services: ABA Formal Opinion 512 on generative AI in legal practice, Bar Standards Board and Solicitors Regulation Authority guidance
• Healthcare: FDA guidance on AI-enabled medical device software, Health Canada pre-market guidance, WHO ethics and governance framework