JERRY YANG / BRAIN AS A SERVICE
From AI capability to operating advantage.
I am a former CEO and a hands-on AI builder. I work where executive judgment, enterprise reality, and frontier technology meet—turning consequential business questions into systems that can be used, trusted, and scaled.
Open to AI leadership roles in San Francisco and Singapore
OPERATING RECORD
THE WORKING THESIS
01AI transformation is not a technology rollout.
It is a redesign of how a company senses, decides, builds, and learns. The durable advantage comes from connecting models to customer value, workflow, accountability, and feedback—not from adding another tool.
Brain as a Service is the name I give to that practice: making intelligence an operating capability.
AREAS OF PRACTICE
One problem, seen from four levels.
The most important AI problems rarely belong to one function. I move between the CEO question and the working system.
Executive transformation
Redesign decisions, workflows, operating cadence, and accountability around AI.
AI-native products
Turn an ambiguous market signal into a product thesis, working prototype, and evidence loop.
Decision systems
Connect data, models, human judgment, and traceable evidence to improve consequential decisions.
Capability building
Translate frontier capability into executive understanding, team practice, governance, and adoption.
FLAGSHIP SYSTEM / FSTEA
A new way for enterprises to develop products.
FSTEA converts fragmented market and enterprise data into traceable trends, opportunity hypotheses, product concepts, and executive-ready decisions.
The business problem
Product teams have more signals than ever, but no dependable mechanism for deciding what matters, where an opportunity exists, or why a concept deserves investment.My role
I framed the product thesis, designed the operating workflow and architecture, built the system end to end, worked directly with the client, and extracted reusable software foundations after delivery.01Multi-source inputs
02Signal extraction
03Trend synthesis
04Opportunity scoring
05Concept development
06Evidence & reports
What it proved
The moat is not the crawler or the model. It is the closed loop that turns evidence into decisions—and improves through use.
SELECTED SYSTEMS
Built to change a decision, not to stage a demo.
A selection of products, internal tools, and client systems from the past year.
Mobile Data Crawler
A read-only mobile collection system using ADB and multimodal AI to turn visible app and mini-program screens into structured, reviewable product data.
Evidence capture / normalization / review queueGitHub ↗Financial Intelligence Agents
Agentic research workflows built around source traceability, structured analysis, human confirmation, and decision-oriented synthesis.
Agents / tools / citations / human-in-the-loopLong-form Audio Systems
Pipelines that turn financial audio, books, and long-form text into structured evidence or production-ready voice assets.
Transcription / synthesis / orchestration / QABusiness Rule & Recommendation Engines
A royalty billing system for a generative-music company and a new-product recommendation system for a consumer brand.
Rules / reconciliation / scoring / auditabilityEnterprise Diagnostic
A CEO-level engagement separating symptoms from root causes and translating the diagnosis into prioritized operating interventions.
Interviews / root causes / intervention roadmapOPERATING EXPERIENCE
Twenty years from engineering to the CEO seat.
My AI work is grounded in lived responsibility for customers, P&L, product, frontline operations, organization, and risk.
StarRides
Chief Executive Officer
Owned the company-wide P&L, operating model, product experience, growth, and organization after serving as COO from 2019 to 2021.
DiDi Chuxing
Regional General Manager
Led multi-province growth and 24/7 operations across product lines and a 200,000+ driver network.
Jaguar Land Rover
Senior Manager, Market & Service Strategy
Connected market research, customer insight, competitive analysis, and service strategy across China, the UK, and India.
Ford Motor Company
Powertrain R&D Engineer
Ford Six Sigma Green Belt and co-inventor on a transmission patent.
FIELD NOTES
Learning in public.
Research notes on models, agents, multimodal systems, product design, enterprise adoption, and the operating consequences of frontier AI.
Read the notes ↗PUBLIC RECORD
Ideas tested in the real world.
EDUCATION
Stanford Engineering
Artificial Intelligence Graduate Program
Harvard Business School
Senior Executive Leadership Program
University of Michigan
Master of Data Science
CEIBS
MBA
THE NEXT PROBLEM
The work I want to do is unusually consequential.
I am interested in leadership roles and selected collaborations where AI, customer value, executive judgment, and organizational execution genuinely collide.