Why VCs Should Care About the 1946 Jefferson Nickel: A Startup Valuation Perspective on Technical Due Diligence

As a VC, I look for signals of technical excellence and efficiency in a startup’s DNA. I want to share why a team’s approach to technical due diligence matters – and how it can signal future success and higher valuation. Think of it like spotting a rare coin in a stack of ordinary ones. The details matter more than the hype.

The Importance of Technical Due Diligence

When I evaluate startups at seed or Series A, technical due diligence isn’t optional. It’s the foundation. Like identifying a rare 1946 Jefferson nickel among common coins, assessing a startup’s tech stack requires patience, precision, and a keen eye for what’s *really* under the hood. The parallels are uncanny: both processes demand methodical scrutiny to separate the valuable from the ordinary.

Why Technical Excellence Matters

Technical excellence isn’t about shiny buzzwords or the “latest” framework. It’s about solving problems efficiently. A coin collector doesn’t care about flashy claims – they care if the coin is real. Similarly, I look for startups where the tech stack fits the problem, where code is clean and scalable, and where the team has a lean, focused approach. This isn’t just about features; it’s about execution.

Analyzing the Tech Stack

When I’m evaluating a tech stack, these are my go-to questions:

• Technology Choice: Does the stack make sense for their problem? Are they using tools that are outdated, or choosing wisely?
• Code Quality: Is the code clean, documented, and maintainable? Do they follow best practices?
• Scalability: Can their system handle growth without cracking?
• Efficiency: Are they using time, money, and compute resources wisely?
• Innovation: Are they using smart, novel solutions – or just rehashing old ideas?

Lessons from the 1946 Jefferson Nickel

The 1946 Jefferson nickel story offers a perfect analogy for startup evaluation. Someone thought it might be a rare transitional error. They tested it with a magnet, based on a hypothesis. But the method was flawed. Sound familiar? In tech due diligence, we start with hypotheses about a startup’s potential. But like the coin, we need to test those assumptions rigorously – not just accept the surface story.

Misguided Hypotheses

Here’s the first lesson: don’t trust untested claims. The 1946 nickel’s magnetism test was based on bad info from an AI. Similarly, startup teams (and AI tools) can spin compelling narratives. But without real data, benchmarks, or code to back it up? It’s just storytelling. Always demand proof. Ask: *”Show me the numbers, the architecture, the tests.”*

Weight and Other Physical Properties

Just as a coin’s weight and composition reveal authenticity, startups need to prove their worth with tangible results. I look for:

• Performance Benchmarks: How does their product *actually* compare to competitors in the real world?
• Customer Feedback: What do users *really* say? Are there concrete testimonials or case studies?
• Technical Documentation: Can they show detailed architecture diagrams, APIs, and code? Transparency is key.

Real-World Application: A Case Study

Imagine a fintech startup claiming their payment system is “10x faster.” Sounds great. But how do we verify it? Here’s my approach:

1. Get their architecture diagrams and code. Understand their stack and implementation.

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2. Run performance tests in realistic conditions. Compare against established competitors.

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3. Evaluate their engineering practices: code reviews, testing, deployment processes. Are they disciplined?

Code Quality and Best Practices

High-quality code isn’t magic. It’s about fundamentals. Here’s what I look for in a fintech app:


// Pseudocode for a high-performance payment processor
function processPayment(transaction) {
try {
// Validate transaction
if (!validateTransaction(transaction)) {
throw new Error('Invalid transaction');
}

// Process payment using optimized algorithm
const result = optimizedPaymentProcessing(transaction);

// Log and monitor performance
logPerformanceMetrics(transaction, result);

// Return result
return result;
} catch (error) {
// Handle errors gracefully
handleError(error);
}
}


See the hallmarks? Input validation, error handling, performance optimization, and logging. These are signs of a system built to last – not just to impress in a demo.

Common Pitfalls in Technical Evaluation

Like the coin collector who learned magnetism wasn’t a reliable test, VCs face similar traps. Here’s what to watch for:

Over-reliance on AI and Hype

AI tools are helpful, but they’re not infallible. The 1946 nickel example shows how easily bad AI info can lead us astray. Similarly, startups love to tout “AI” or “machine learning” as differentiators. But is it just a buzzword? Or is it core to their value? Always dig into the actual implementation. Ask: *”How does this tech actually solve the problem?”*

Misleading Metrics

“10x faster” sounds impressive. But in what context? A lab test? A real-world load? I always ask for benchmarks under realistic conditions. Compare their metrics to industry standards. Context is everything.

Lack of Transparency

Just as a coin collector should avoid wasting time on unnecessary PCGS submissions, I avoid startups that dodge technical scrutiny. If they won’t share code, documentation, or architecture, it’s a red flag. A strong team welcomes technical questions and can defend their choices with data.

Actionable Takeaways for VCs

From the 1946 nickel story, here are clear steps to improve your technical due diligence:

Verify, Don’t Assume

• Demand real-world data and benchmarks. Don’t accept claims at face value.
• Run independent tests. Hire third-party experts if needed.
• Scrutinize AI and emerging tech claims. Are they substance or just buzz?

Prioritize Transparency

• Require detailed technical documentation: architecture, code samples, performance data.
• Foster open technical discussions. Assess the team’s depth of knowledge.
• Look for teams comfortable in technical debates. They should defend choices with evidence.

Evaluate Team Expertise

• Assess engineering background and past experience. Look for a track record of building reliable systems.
• Evaluate their problem-solving. Do they use a systematic, hypothesis-driven approach?
• Look for a learning culture: research, open-source contributions, publications.

Use a Multi-Faceted Approach

• Combine technical due diligence with customer validation, market analysis, and financials.
• Evaluate scalability, security, and compliance as part of the tech assessment.
• Assess adaptability and innovation. These are critical for long-term success.

Conclusion

The 1946 Jefferson nickel teaches us a simple truth: value isn’t in the surface. It’s in the details. For VCs, this means going beyond the pitch deck and the “10x faster” claims. It means digging into the code, the architecture, the team’s approach. Technical due diligence isn’t just about finding flaws – it’s about finding signals of excellence. By verifying, prioritizing transparency, evaluating expertise, and using a multi-faceted approach, you can spot the startups with real potential.

At the end of the day, that 1946 nickel might just be ordinary. But the *process* of verifying it? That’s where the real value lies. When you’re evaluating your next startup, remember: the best investments aren’t the loudest. They’re the ones with the strongest foundation. The ones where the tech speaks for itself.

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