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Thanks to reader Fady who requested this topic!
As a former engineer who traded code sprints for cap tables, I often get asked how VCs really figure out if a tiny pre-seed startup is feasible.
In Big Tech, “feasible” meant can we build it?
In VC-land, it means should we build it, will anyone care, and can this scrappy team pull it off?
Evaluating an early-stage startup’s feasibility is a bit like debugging an empty code file – there’s not much to go on, so you rely on patterns, intuition, and a healthy dose of faith. Let’s break it down.
👥 Team: Betting on the Jockeys
VCs often say they bet on the jockey, not the horse – meaning the founding team is usually the most critical factor at pre-seed.
What we look for in teams:
Founder-Market Fit
Do they have deep experience or a personal connection to the problem?
Execution Track Record
Have they built anything before, even side projects or open-source tools?
Complementary Skills
Tech + go-to-market coverage is ideal. Bonus points if they’re not all “CEO” types.
Resilience & Coachability
Pivoting is common at this stage. Can they adapt?
Talent Magnetism
Can they attract early believers (teammates, advisors, first customers)?
🧪 Example:
DripScript, a solo founder project that auto-wrote release notes using AI, had a janky demo — but the founder had real technical chops and user love. ✅
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💻 Tech & Product: Rocket Science or Hot Air?
This is where your friendly neighborhood ex-engineer-turned-VC squints at architecture diagrams and asks: “Wait, how does this actually work?”
What we check:
Prototype or MVP
Even a rough demo shows capability.
Feasibility vs. Fantasy
Are they using “AI” or just calling Google Translate?
Differentiation
Is it just another clone or solving a niche pain?
Scalability & Technical Risk
We like ambition, but not “requires a Nobel Prize” levels of technical risk.
IP or Unique Know-How
Proprietary tech or deep domain knowledge both help.
🔍 Watch out for: slide decks full of buzzwords like “quantum-blockchain-powered NLP.” Seen it. Didn’t fund it.
📈 Market Signals: Traction, Tea Leaves, and Proof in the Pudding
Startups at this stage rarely have revenue, so we look for indicators.
Signals we love:
Early Traction
Waitlists
Pilot users
LOIs from potential customers
Engagement Over Vanity
100 daily actives > 1,000 signups with no retention
Customer Feedback
Screenshots of Slack messages saying “where has this been all my life?” are 💯
Competitive Context
Some competition is a good sign. No competition? Red flag.
📊 Example:
GlitterPay had only 200 users, but parents & teens loved it, and the product had real stickiness. That made all the difference.
⏳ Timing: Too Early, Too Late, or Just Right?
Timing might be the most underestimated factor - and one of the most decisive.
How we assess it:
Trends & Enablers
Is this riding a wave? (e.g. AI in 2025, yes. VR dating apps, maybe not yet.)
Market Readiness
Do customers understand the product and want it now?
Goldilocks Zone
First = risky (you may need to create the market)
Tenth = tough (crowded field)
Early but not alone = just right
📉 Example:
SmartFridgeCo in 2018 made connected blenders. Market wasn’t ready. Similar ideas now? Much more feasible.
🧩 Putting It All Together
Feasibility =
✅ Team
✅ Tech
✅ Market Signals
✅ Timing
Even if one is weak, others can make up for it.
📚 Final Anecdote:
We backed a startup (let’s call it Project Apollo) doing AI-powered code review tools. Small user base, but fanatical engagement, killer team, perfect timing. It worked.
🎤 Parting Words
Pre-seed investing is half science, half gut instinct - and all about potential.
I’ve learned feasibility isn’t just about what can be built - it’s about what should be built, by whom, and when.
If you’re a founder, know that we’re reading your signals (even the tiny ones) and hoping they spell something big.
Until next time,
💻 SWEdonym
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