Google Research
Susmita Singh

Susmita Singh

Susmita is a Tech Lead at Google Store, where she drives core AI infra, system architecture, and technical strategy for the Google Store AI Infrastructure team. Operating at the intersection of applied AI and core software engineering, she leads cross-functional initiatives spanning high-throughput AI infrastructure, scalable model serving, server-driven architectures, and large-scale distributed systems. Prior to her work on Google Store, Susmita built a strong foundation in software engineering, developing highly scalable distributed architectures and resilient consumer-facing applications.

Susmita is a Fellow of the Institution of Engineering and Technology (FIET), a Distinguished Fellow of the Soft Computing Research Society (DFSCRS), and an IEEE Senior Member. She actively serves on program committees and judging panels for premier international technology conferences, including SIGCSE, CAV, ASE, and ACSOS. Her academic contributions include reviewing for peer-reviewed journals such as the Journal of Open Research Software and Engineering Applications of Artificial Intelligence.

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From Silicon to Boardroom: A Multivocal Survey of the Techno-Economic Layers Governing Generative AI Monetization
Vipin Singh
Susmita Singh
Mrinal Ahlawat
SSRN (2026)
Preview abstract 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. View details
The Evolution of Config Driven UI Frameworks: A Hybrid MLR and Structural Artifact Analysis of Industry Practices
Vipin Singh
Pratik Mishra
Mrinal Ahlawat
Susmita Singh
Anamika Modi
2026
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. View details
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