Esther Suebsubanant
AI products, compute and capital. Stanford MBA and MS candidate.
M&A advisory across more than 30 cross-border deals, then the first corporate venture hire at Thai Union, building a $30M fund from scratch. Now at Stanford, studying how AI really gets adopted inside companies and what it costs to run.
Looking for AI product and forward deployed rolesWork
Tools and research, mostly about how AI gets bought, deployed and paid for.
A weekly board on the three things that constrain AI right now. Compute, capital and rules. Every price, deal and regulation is tagged by which one it tightens or loosens, with what it means for investors, big tech and builders. GPU prices refresh every week by scraping each provider's pricing page.
About 40 interviews with people who build and deploy AI inside companies, from Databricks to a16z-backed startups. I ran outreach, scheduling and summaries through agents I built on a VPS. The finding was that AI arrives top-down and stalls with the people who have to use it.
A WhatsApp-native AI companion for people caring for someone with Alzheimer's. Built on the channel caregivers already use every day, so there is nothing new to install or learn.
An operations and brand-intelligence dashboard prototype for a K-beauty logistics operator whose freight and warehousing data spans about 350 brands, with category benchmarks for each brand against its peers.
AI due diligence over M&A data rooms, built with Kenaj Washington for Stanford's CS 224V. The tool I wanted on every deal at The Quant Group.
A card game for naming unacceptable behavior at work, made for the women-in-business group I founded in Bangkok. It has since spread to other companies and industries.
About
I ask a lot of questions until I can see where a business is constrained, then I build the structure around it. In M&A that meant finding the one asset a deal really turned on. In corporate venture it meant building Thai Union's fund from nothing, under a $30M mandate.
At Stanford I'm adding an MS in AI, compute and energy to the MBA, because most AI products fail on the business model, not on what the technology can do. My research with Stanford RDI found the same thing inside companies. AI arrives from the top and stalls with the people who have to use it. I'm looking for AI product and forward deployed roles where closing that gap is the job.
MBA and MS candidate
Graduate School of Business, with an MS through EIPER in AI, compute and energy. Independent research on agentic AI SaaS with Stanford RDI. Leads professional development for Women in Management.
First CVC manager
Built the corporate venture function from scratch under a $30M mandate, and led corporate strategy work with McKinsey and Bain as the client.
Advisory
More than 30 cross-border transactions.
Management trainee
Rotations across finance, commercial and supply chain.
Valedictorian
King Scholarship.
Most recent first. Scroll sideways for earlier roles.
Skills
An investor's read on a business, with enough technical depth to see where the bottleneck is.
Hiring for AI product or forward deployed work, investing in the stack, or building on it. I'm happy to compare notes.
