metaphor: commercial large-language-model services are literally billed per token - a standardised unit of machine cognition - at rates per million tokens (Anthropic, 2026). A "Token Economy" is therefore emerging, in which intelligence (here a computational unit, not a cryptocurrency) becomes modular, measurable, priced and consumed on demand, much as electricity moved from a specialist input to a metered utility. Agrawal, Gans and Goldfarb (2018) recast Al as a collapse in the cost of prediction; Acemoglu and Restrepo (2019) show automation reallocates the task content of production between capital and labour; lansiti and Lakhani (2020) show Al-centred firms scale without proportional headcount. Yet business research still treats Al mainly as a tool that improves existing models, not as a force that redefines the unit of economic exchange. We therefore understand little about how firms should create, capture and price value once intelligence itself is a metered, tradable resource.
The overall aims of the project are to:
- Theorise value creation and capture under metered intelligence consumption
- Identify the capabilities, cost structures and pricing logics that distinguish successful adopters
- Produce validated, practice-ready tools that help organisations make the transition
The projects expects, academically, to generate a new framework for value creation in a metered-intelligence economy, targeted at leading operations, strategy and information-systems journals. For practice and commercialisation, the project intends to deliver an Intelligence-as-a Utility Readiness Assessment, an AI-business-model-innovation toolkit, and a methodology for pricing intelligence-based services - each a foundation for consultancy, executive education and diagnostic-software spin-outs across sectors. This dual contribution gives the project the clear commercial pathway expected of a practice-focused doctorate.
Research Questions
- How does the shift from labour-time pricing to metered intelligence pricing reshape firms' business models and cost structures?
- How does competitive advantage migrate from human capital to computational capital - algorithms, compute and data - and what does this imply for the resource-based view (Barney, 1991)?
- How can firms commercially price, package and capture value from tokenised 'intelligence-as-a-service', and which capabilities enable the transition?
Proposed Methodology
Phase 1: systematic literature review and conceptual framework Phase 2: qualitative multiple-case studies and ~30-40 expert interviews across firms buying and selling AI services (consulting, professional services, software, manufacturing, healthcare, creative industries), analysed abductively to build constructs Phase 3: quantitative validation through a survey instrument and analysis of AI Pricing, adoption and firm-performance data. The design follows established business-model-innovation research traditions (Zott, Amit and Massa, 2011)
Funding Notes
Bursary available (subject to satisfactory performance):
Year 1: £23,805 (UKRI 2026-27 standard rate including London Weighting)
Years 2 & 3: In line with relevant UKRI rate
In addition, the successful candidate will receive a contribution to tuition fees equivalent to the university's Home rate for the duration of their scholarship. International applicants will need to pay the remainder tuition fee for the duration of their scholarship. The fee is subject to an annual increase.
