Research as a public test
The Imagination Economy is a proposition to investigate—not a conclusion looking for supporting quotes.
Our central hypothesis is structural: when the marginal cost of producing an artefact falls, value relocates toward deciding what should exist, specifying it precisely, comparing genuinely different possibilities, and taking responsibility for the choice. The work here asks whether that claim is measurable, where it fails, and what evidence would change our minds.
1. The Constraint Hypothesis
If execution becomes abundant, does the quality of specification become a limiting factor in the value an organisation can create?
The available evidence is consistent with the claim, but does not yet prove it. Our task is to build a measure capable of being wrong.
Generation becomes faster, cheaper, and more widely available.
Producing more answers no longer guarantees a better search.
Framing, divergence, judgment, and responsibility determine the result.
Signals that justify the question
of skills in a typical US job posting were classified by Indeed as candidates for “hybrid transformation”: AI performs substantial routine work while people oversee, evaluate, and intervene.
Indeed Hiring Lab, 2025 ↗McKinsey found 88% reporting regular AI use in at least one function, while 39% reported enterprise-level EBIT impact. Workflow redesign is a distinguishing practice among high performers.
McKinsey State of AI, 2025 ↗year-over-year growth in critical-thinking enrolments among Coursera learners studying generative AI—a behavioural signal that validation is becoming more important alongside generation.
Coursera Job Skills Report, 2026 ↗Research on a global freelancing platform found declining demand in categories exposed to generative AI, establishing substitution pressure without implying that every affected occupation disappears.
HBS AI Institute, 2024 ↗Proof of concept: the evidence ledger
These figures do not form one causal chain. They are different kinds of evidence—an econometric working paper, model-based exposure estimates, organisational surveys, learning behaviour, and a market forecast. Their value lies in the pattern they make visible when their limits remain attached.
Automation-prone writing and coding job posts
Estimated decrease relative to manual-intensive jobs within eight months of ChatGPT’s introduction.
Image-creation job posts
Estimated decrease following the introduction of image-generating AI in the same freelance-market study.
Skills classified as hybrid transformation
AI can perform substantial routine work, while human oversight, judgment, and exception handling remain essential.
AI use versus enterprise EBIT impact
Regular use in at least one function versus respondents reporting enterprise-level EBIT impact; n=1,993 across 105 nations.
AI high performers
Respondents attributing at least 5% EBIT impact to AI and reporting significant value; workflow redesign is more common in this group.
Worldwide AI spending in 2026
Gartner’s forecast, up 47% year over year and dominated by infrastructure, vendors, and hyperscalers—not evidence of realised value.
Critical-thinking enrolments among GenAI learners
Year-over-year growth on Coursera’s enterprise-learning platform; strong directional evidence, but not a labour-market prevalence estimate.
Version-control note: the freelance working paper’s headline estimates changed across revisions. The strategy plate used −30.4% and −18.5%; the latest public abstract reports −21% and −17%. This page uses the current public figures. The reported increase in complexity and pay among surviving jobs remains qualitative here until the exact estimand and table are attached.
Self-reported organisational survey data. The bars describe different measures and should not be read as a conversion funnel.
BCG’s 10–20–70 rule is a transformation heuristic derived from consulting experience, not an experimentally established constant. It supports testing organisational capability rather than assuming another tool will close the value gap.
BCG methodology ↗What the instrument must measure
Brief quality
Is the problem framed with context, boundaries, tensions, and a meaningful definition of success?
Option-set diversity
Were genuinely distinct directions explored, or were many cosmetic variations mistaken for choice?
Decision traceability
Can the organisation explain why one direction was selected and what evidence displaced the alternatives?
Outcome relevance
Do stronger scores predict consequences that matter: adoption, differentiation, reduced rework, or decision confidence?
Method before movement
- 01Write the construct. Define each dimension, its scale, its evidence, and its failure cases before collecting scores.
- 02Score by hand. Use multiple trained raters on real briefs and option sets; disagreement is diagnostic evidence, not noise to hide.
- 03Test across contexts. Pilot inside and outside technology, and separate task type, organisation size, and industry effects.
- 04Validate against outcomes. Establish construct and predictive validity with an independent research partner before making benchmark claims.
- 05Publish the method. Release the rubric, limitations, and aggregated distributions only when the comparison base is defensible.
If specification quality and option-set diversity do not reliably predict outcomes people or organisations care about, the instrument must be revised—or the claim rejected. A benchmark that cannot survive attack is branding, not research.
2. Theoretical Foundations
Fictional Expectations in the Economy
Beckert developed the foundational theory of “fictional expectations” in economic action. His seminal 2013 paper in Theory and Society argues that economic decision-making under fundamental uncertainty is anchored in fictions—imagined future states and beliefs in causal mechanisms leading to those states.
Key Publication: Beckert, J. (2016). Imagined Futures: Fictional Expectations and Capitalist Dynamics. Harvard University Press.
The Romantic Economist
Richard Bronk explores why economists need imagination in his work The Romantic Economist: Imagination in Economics. His research examines three key dimensions:
- Imaginative empathy with human subjects of study
- Creative formation of new systems of thought and metaphors
- The imaginative use of different perspectives in economic analysis
Recent collaboration: Beckert, J. & Bronk, R. (2018). Uncertain Futures: Imaginaries, Narratives, and Calculation in the Economy. Oxford University Press.
3. Economic Evolution Framework
From Experience Economy to Transformation Economy
Pine and Gilmore mapped the progression of economic value through distinct stages:
Their 1998 Harvard Business Review article “Welcome to the Experience Economy” established this framework.
4. The Creative Class & Creative Economy
Florida introduced the creative class concept, arguing it is a key driving force for post-industrial economic development. The creative class comprises approximately 40 million U.S. workers (30% of workforce).
The 3Ts of Economic Development
Florida proposes that growth and prosperity in creative capitalism turn on three factors:
- Talent — Human capital and skilled workers
- Technology — Innovation and R&D capacity
- Tolerance — Openness to diversity and new ideas
The Global Creativity Index annually ranks 139 nations on these dimensions.
Key Publications
- The Rise of the Creative Class (2002)
- Who's Your City? (2009)
- The New Urban Crisis (2018)
6. Entrepreneurial Imagination Research
Recent academic work has established entrepreneurial imagination (EI) as foundational to entrepreneurship studies. A 2025 pluralistic scoping review published in the Journal of Business Venturing identified eight distinct theoretical perspectives on entrepreneurial imagination: cognitive, linguistic, moral, aesthetic, and others.
Recent Publications
- Special issue on “Fiction and the Entrepreneurial Imagination” (2025) — 10 published papers
- “The Imagination Advantage” (Strategy Science, 2024) — on thought experiments and counterfactuals in strategy
7. World Economic Forum: Future of Work
The WEF identifies imagination-related skills as critical for the 2025-2030 workforce:
Four Futures Framework
The WEF outlines four potential scenarios:
- Supercharged ProgressAI boosts productivity with quick role transitions
- Age of DisplacementTech outpaces reskilling capabilities
- Co-pilot EconomyAI enhances human expertise collaboratively
- Stalled ProgressLagging workforce readiness limits adoption
8. Creative Economy: Global Statistics
Generative AI in Creative Industries
29.6% CAGR
9. Key References & Sources
Academic Papers
- Beckert, J. (2013). Imagined futures: fictional expectations in the economy. Theory and Society, 42, 219-240.
- Salis, F. & Leng, M. (2023). Inseparable Bedfellows: Imagination and Mathematics in Economic Modeling. Philosophical Studies.
- McGrath, R.G., van Putten, A.B., & Pierantozzi, R. (2021). The Imagination Premium: an anticipative performance metric. Strategy & Leadership.
- Florida, R. (2014). The Creative Class and Economic Development. Economic Development Quarterly.
Books
- Beckert, J. (2016). Imagined Futures: Fictional Expectations and Capitalist Dynamics. Harvard University Press.
- Bronk, R. (2009). The Romantic Economist: Imagination in Economics. Cambridge University Press.
- Pine, B.J. & Gilmore, J.H. (2011). The Experience Economy (Updated Edition). Harvard Business Review Press.
- Florida, R. (2002). The Rise of the Creative Class. Basic Books.
Reports
- Demirci, Ö., Hannane, J., & Zhu, X. (2024). Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms. CESifo Working Paper 11276.
- Indeed Hiring Lab (2025). AI at Work Report 2025: How GenAI Is Rewiring the DNA of Jobs.
- McKinsey & Company (2025). The State of AI: Agents, Innovation, and Transformation. Global survey, n=1,993.
- Coursera (2026). Job Skills Report 2026.
- Gartner (2026). Worldwide AI Spending Forecast, May 2026.
- World Economic Forum (2025). The Future of Jobs Report 2025.
- UNCTAD. Creative Economy Programme Reports.
- Martin Prosperity Institute. Global Creativity Index (annual).
Explore Further
This research compilation provides the academic foundation for understanding the Imagination Economy. To explore how these concepts apply in practice, visit our FAQ for practical explanations or read our latest articles on emerging developments.