The Hidden Signals in a Startup’s Tech Stack That Drive VC Valuation: What Most Investors Miss

I’ve spent years evaluating startups, and one thing never changes: the stack tells a story. Not just about what a team builds, but *how* and *why*. That story? It’s often the missing piece in valuation. Let’s look at what most VCs glance over—and what actually separates the winners.

The Myth of “Undervalued” Assets: Why Scarcity and Demand Matter More Than Price

People talk about “undervalued” startups like they’re mispriced art. But here’s what I’ve learned from watching markets—tech and otherwise: price is noise. Scarcity, demand, and narrative? That’s the signal.

Take collectibles. A $10,000 coin can be a steal if it’s one of five known. A $500,000 coin can be a trap if it’s mass-produced and trending on hype alone. Same rules apply in tech. I care less about your cap table and more about this: is your team building something rare—something others can’t just spin up in a weekend? Because scarcity in tech isn’t about headcount. It’s about *what* you’ve built and *who* can replicate it.

Scarcity in Tech: When “Population” Isn’t Just Headcount

In coin collecting, “population” means how many exist. In startups? It’s how many people can actually do what you do. Let’s compare:

• Startup A: 10 engineers on a standard stack—React, Node, PostgreSQL. Solid, but common.
• Startup B: 5 engineers, but their stack? Rust for ultra-low latency, custom on-device ML, and a distributed system they built from scratch.

Startup A has more bodies. But Startup B has technical scarcity. And that’s what gets me excited. Not because it’s fancy, but because it’s defensible. It’s not just code—it’s the years of iteration, the tribal knowledge, the infrastructure that took months to tune. That’s a moat.

Demand Signals: The “Substitution Effect” in Tech

I once saw a collector swap gold for silver when prices spiked. Functionally similar. More accessible. That’s the substitution effect. And it’s happening in tech right now:

• Cloud costs go up? Founders pivot to WASM, edge computing, or lightweight runtimes.
• AI inference gets expensive? Demand for on-device models, pruning, and quantization surges.

The best startups don’t just solve the problem. They solve it in a way that holds up when the market shifts. They’re the affordable alternative that doesn’t sacrifice quality—the tech equivalent of a vintage dollar coin with a cult following.

Technical Due Diligence: What’s in the Stack Tells Me Everything

At seed and Series A, I spend nearly a third of my time here. Not just *what* tools they use, but the reasoning behind them. How they maintain it. Where the technical debt hides. Here’s what I’m really looking for:

1. The “Why” Behind the Stack

I ask founders: “Why this database? Why build auth from scratch instead of using Auth0?”

The right answer? “We tested PostgreSQL against DynamoDB for three weeks. We needed ACID for financial data. This was the only fit.” That’s clarity. The wrong answer? “We used Supabase because everyone was talking about it.” That’s a red flag.

One fintech startup I backed chose PostgreSQL with TimescaleDB—not because it was easy, but because it handled their needs:

• Complex queries for audit trails.
• Self-hosting for compliance.
• Seamless integration with their analytics stack.

2. Infrastructure as a Signal of Maturity

At Series A, I expect to see more than just working code. I want to see systems that scale and survive:

• Observability: Not just logs. Think Prometheus metrics, Grafana dashboards, OpenTelemetry traces.
• CI/CD: Automated testing, canary rollouts, rollback plans.
• Security: Regular pen tests, IAM policies, secrets in Vault.

Here’s a snippet from a team I funded:

// GitHub Actions config for canary deployments
name: Deploy Canary
on:
push:
branches: [ main ]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- name: Deploy to 10% of users
run: |
kubectl apply -f canary.yaml
kubectl rollout status deployment/canary-service --timeout=60s


This isn’t just about uptime. It’s about how the team thinks. Automating canaries at Series A? That’s a team already planning for 100x growth.

3. The “Shipwreck Hoard” Factor: Hidden Technical Capital

Remember the coins pulled from the ocean floor? Suddenly, common pieces became priceless—because they were rare *and* had a story. In tech, that’s hidden technical capital:

• 10 years of anonymized user data no one else has.
• A custom compiler that makes their ML model 10x faster.
• A taxonomy built by parsing a million legal contracts.

One team had a homegrown distributed lock because off-the-shelf tools couldn’t handle their load. When they open-sourced it, it became a standard in their niche. That lock? It turned from a necessity into a moat.

Valuation Multipliers: When Technical Excellence Becomes Narrative

Here’s the truth: great tech doesn’t just reduce risk—it boosts valuation. At Series A, a team with a rare stack (WebAssembly, homomorphic encryption, federated learning) can command 2-3x the P/S of a “me-too” competitor because:

• It’s harder to copy.
• It’s harder to compete with.
• It pulls in elite talent—PhDs, ex-FAANG, domain specialists.

The “1804 Dollar” Effect: Crossing Over to New Buyer Pools

The 1804 silver dollar isn’t just a coin. It’s a museum piece, a hedge, a corporate trophy. Similarly, the best startups outgrow their category:

• A health tech startup with a new compression algorithm? Suddenly, telecom investors care.
• A SaaS tool with a custom observability engine? Now it’s an infra play.

One climate startup I backed built a carbon accounting platform. But its real edge? A scalable methodology for measuring emissions. When new ESG rules dropped, consulting firms started licensing it. The TAM? It exploded overnight.

Actionable Takeaways for Founders and Investors

For founders:

• Build scarcity, not just features. Ask: “What can we do that 90% of teams can’t?”
• Write it down. A one-page “stack rationale” in your deck? That’s worth hours of pitch time.
• Turn your edge into a story. Using WebAssembly for edge compute? Say: “We’re the first to run X at the edge without losing Y.”

For investors:

• Dig into the “why”. Choosing Rails for speed isn’t the same as choosing Go for performance.
• Look for population asymmetry. A tiny team with deep niche expertise? Often more valuable than a large generalist crew.
• Listen to the buzz. If a blog post on latency optimization is trending, they’re attracting the right eyes.

Conclusion: The Real “Undervalued” Startup

There’s no such thing as an “undervalued” startup. Only ones where the technical scarcity, operational muscle, and narrative power haven’t clicked yet. I’m not looking for the cheapest deal. I’m looking for the team whose stack says:

• Defensibility: Custom infra, proprietary data, irreplicable systems.
• Efficiency: Low burn, high velocity, clean architecture.
• Scalability: Ready for 100x growth, no rewrites needed.

The most valuable startups? They’re not just solving problems. They’re redefining what’s possible with the tools they’ve built. And that’s the signal worth betting on.

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