The Entrepreneur First Experience

In 2019, I joined Entrepreneur First (EF) in Hong Kong — one of the world’s leading talent investors. Unlike Y Combinator, Techstars, or Antler, EF doesn’t require you to have a co-founder or an idea before you join. You show up with your skills, and you find both inside the program.

That model is powerful. It forces you to think about what you actually bring to a partnership, not just what idea sounds exciting over drinks.

What I Worked On

At EF, I explored two AI SaaS startup ideas:

Audio advertising optimization — using machine learning to match audio ad content to listener context. We built early models, ran customer development interviews with ad agencies, and explored product-market fit. The idea had technical merit but the market timing wasn’t right.

Supply chain automation SaaS — applying NLP and predictive analytics to reduce friction in cross-border logistics. We validated the pain point through interviews with logistics operators across Asia, but the sales cycle for enterprise logistics turned out to be too long for a pre-seed startup.

Neither idea made it to launch. And that’s the point.

What I Actually Learned

The technical work was the easy part. What EF taught me was everything around it:

1. Co-founder fit matters more than the idea

The best SaaS idea dies with the wrong partner. I learned to evaluate co-founders not on their resume but on how we work through disagreement, how we split ambiguous decisions, and whether we make each other faster or slower.

2. Customer development is a skill, not a formality

Talking to customers isn’t just “market research.” It’s the core loop of a startup. I learned to structure interviews, spot false positives (people saying they’d pay when they wouldn’t), and extract the real pain points that drive SaaS purchasing decisions and annual recurring revenue (ARR).

3. The zero-to-one gap is real

Going from an idea to a working SaaS prototype that someone will pay for is a different skill than building software at a company. There’s no spec, no product manager, no roadmap. You have to hold ambiguity, move fast, and make irreversible decisions with incomplete information.

4. Speed over perfection

In consulting, you optimize for correctness. In startups, you optimize for learning speed. I had to unlearn the habit of building complete solutions and instead build the smallest thing that tests the riskiest assumption — the true MVP.

5. Asia-Pacific is an underserved market for data SaaS

Most data infrastructure and B2B SaaS companies are built for the US market. But businesses in Japan, China, Taiwan, and Southeast Asia have unique data challenges — different regulatory environments (APPI, PIPL), multilingual data, cross-border complexity. Being trilingual in English, Japanese, and Mandarin Chinese is a genuine technical moat for building data SaaS products in this region.

What I’ve Done Since

After EF, I went deeper into enterprise data consulting. Over the last several years, I’ve:

  • Built Microsoft Fabric ETL pipelines and Power BI reporting systems at a major consulting firm
  • Engineered automated ETL pipelines using GCP Cloud Functions for e-commerce KPI automation
  • Implemented data governance frameworks with Collibra, Informatica, and Alteryx for insurance companies
  • Executed SAP-to-Dynamics 365 data migrations as a freelance consultant
  • Worked across all 11 DMBOK 2 knowledge areas — from data architecture to data quality

This consulting work gave me something I didn’t have at EF: deep domain expertise across regulated industries and a clear understanding of what data problems are actually worth solving — and which ones could become scalable SaaS products.

Why I’m Looking for a Co-Founder Again

I’m ready to go from zero to one again. But this time with:

  • 10+ years of battle-tested technical skills across data engineering, data science, ML, and data governance
  • Real enterprise domain knowledge in banking, healthcare, insurance, and e-commerce — understanding the buyer journey for B2B SaaS in these verticals
  • Trilingual go-to-market capability across Japan, China, Taiwan, and Southeast Asia
  • DMBOK 2 expertise across all 11 knowledge areas — critical for building data SaaS products that enterprises will actually buy
  • Full-stack SaaS building capability — from data infrastructure and ML backend to API design and analytics pipelines

What I’m looking for in a co-founder

I work best with someone who complements my technical depth with commercial instinct:

  • A domain expert or GTM-focused co-founder who knows how to sell SaaS, build partnerships, and find product-market fit
  • Someone who is passionate about solving real problems with data and AI
  • Comfortable with ambiguity and the zero-to-one journey
  • Open to applying together to Y Combinator, Techstars, Antler, 500 Global, or similar programs
  • Ideally has experience with SaaS metrics: ARR, churn, LTV, CAC, net revenue retention

SaaS verticals I’m excited about

  • Data governance SaaS for mid-market companies — DMBOK-aligned but lighter than enterprise tools like Collibra
  • AI-powered BI SaaS that auto-generates dashboards and insights from raw data — self-service analytics for non-technical teams
  • Cross-border analytics SaaS for Asia-Pacific e-commerce sellers — multilingual, multi-marketplace
  • Data quality monitoring SaaS — like Datadog but for data pipelines, with automated profiling, alerting, and lineage
  • Multilingual AI agent SaaS for customer support across Japanese, Chinese, and English markets
  • Vertical AI SaaS for insurance — underwriting automation, claims processing, actuarial analysis
  • MLOps platform — model deployment, monitoring, and retraining for enterprise ML teams
  • Data catalog SaaS — lightweight metadata management and data discovery for growing companies

Let’s Talk

If you’re a co-founder looking for a technical partner who can build the data and ML infrastructure for a SaaS product from day one — and who can help you navigate Asia-Pacific markets in three languages — I’d love to hear from you.

Email: [email protected] (put “co-founder” in the subject line) LinkedIn: linkedin.com/in/hushinghai

The best SaaS startups come from complementary founders who share conviction on a problem. If you’re reading this and thinking “this is the technical co-founder I’ve been looking for” — reach out. Let’s see if there’s a fit.

Simba Hu helps companies make better decisions with data and AI — from strategy to implementation. Based in Tokyo, serving clients globally. Book a strategy call or visit simbahu.com.