Mrinal Ahlawat
Mrinal Ahlawat is an engineering leader at Google One, leading AI monetization for consumers. His areas of expertise include digital monetization, large-scale distributed systems, data infrastructure, and AI commercialization. He leads efforts that define the infrastructure and monetization strategy of a platform with over 150 million paying subscribers, and 200+ engineers. Before his current role, Mrinal served as a founding engineer for Google Pay India (Tez), where he pioneered the patented "Cash Mode" framework enabling secure peer-to-peer payments via ultrasonic audio.
Mrinal is a Fellow of the Institution of Engineering and Technology (FIET), Fellow of the British Computer Society (FBCS), and a Distinguished Fellow of the Soft Computing Research Society (DFSCRS). He serves on the Program Committees for premier conferences like ECIR and ACM IUI, and as a reviewer for prestigious peer-reviewed journals such as the International Journal of Data Science and Analytics, IEEE Computing. Furthermore, he lends his expertise to judging committees for top industry awards, including the CES Innovation Awards.
Mrinal is a Fellow of the Institution of Engineering and Technology (FIET), Fellow of the British Computer Society (FBCS), and a Distinguished Fellow of the Soft Computing Research Society (DFSCRS). He serves on the Program Committees for premier conferences like ECIR and ACM IUI, and as a reviewer for prestigious peer-reviewed journals such as the International Journal of Data Science and Analytics, IEEE Computing. Furthermore, he lends his expertise to judging committees for top industry awards, including the CES Innovation Awards.
Authored Publications
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Context: The cost of frontier large language model inference has fallen by two orders of magnitude since 2023, yet the techno-economic forces governing AI value capture remain poorly understood. No existing work provides a unified, multi-layer framework connecting hardware physics to commercial pricing to actuarial constraints.
Objectives: This survey aims to establish that Generative AI (GenAI) monetization is structurally bound by five interdependent techno-economic layers: (1) the physical constraints of
memory bandwidth and compute, (2) deflationary architectural innovations, (3) the algorithmic
economics of inference-time compute, (4) the verification economics governing outcome-based
pricing, and (5) the macro-legal realities of enterprise liability.
Methods: We conduct a Multivocal Literature Review (MLR) adapting the PRISMA protocol,
synthesizing peer-reviewed and grey literature sources—vendor documentation, SLAs, and API
pricing data (2022–2026). Two reviewers independently screened all records (Cohen’s κ ≥ 0.81
across all decision stages).
Results: We contribute four primary artifacts.
First, the Viability Inequality, an analytical
model formalizing the conditions under which outcome-based AI pricing is economically sustainable.
Second, the Billing Fallacy: aggregate cost growth is driven by Agentic Recursion,
not quadratic attention complexity.
Third, the Verifiability Bifurcation: objective task domains
enable outcome pricing, while subjective domains depend on proxy-based models.
Fourth, the
Multi-Layer Techno-Economic Taxonomy (M-TET), a unified five-layer framework mapping the
full monetization stack from silicon-anchored token pricing through actuarial risk ceilings.
Conclusion: GenAI monetization is not a commercial pricing exercise but a dynamic negotiation across hardware, algorithmic, economic, and actuarial layers. In subjective and hybrid
task domains, the binding constraint on outcome-based pricing is the cost of verification, not
generation. AI value capture depends on engineering low-cost, high-fidelity Verification Engines.
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Preview abstract
Config Driven User Interface (CDUI) frameworks, often referred to interchangeably as
Server Driven UI (SDUI) have fundamentally altered mobile and web application development. By shifting layout and orchestration logic from compiled client binaries to dynamic
server responses, organizations bypass slow app store review cycles and ensure cross-platform
consistency. Despite its widespread adoption by industry leaders, CDUI remains underexplored in formal software engineering literature. This paper investigates the evolution of
CDUI through a hybrid methodology, combining a Multivocal Literature Review (MLR) of
grey literature from organizations including Uber, Airbnb, Lyft, Spotify, Faire, and Zalando,
with a structural artifact analysis of open-source production frameworks (Yandex DivKit,
Zup Beagle, and Spotify Hub). We introduce a taxonomy grounded in established foundational UI components and evaluate systems across three axes: Modularity, Centralization,
and Strictness. By tracing the architectural evolution from simple remote configurations to
strongly typed, Protobuf-driven contracts, our empirical artifact analysis (evaluating 570
schema commits across three repositories) demonstrates the critical magnitude of schema
governance and the complex challenge of managing “contract fragility.” We further analyze
cross-cutting concerns such as security sandboxing, “BFF Bloat,” and native accessibility
mapping, culminating in a future research agenda focused on the formal verification of UI
configurations.
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