A prominent PE firm was a week away from closing a $10 million Series B investment. The target? A “revolutionary” AI-powered imagery analytics platform that promised to “transform how enterprises understand the world.”

The signs looked perfect. The founders were academics with impressive backgrounds. Portions of the core technology were licensed from a National Lab. Early customers provided glowing testimonials. The demo was flawless—the AI seemed to predict outcomes with high accuracy. While early, revenue growth was accelerating year-over-year. Office space was modern in a quirky way.

Everything confirmed what the partners desperately wanted to believe: they’d found the next big thing.

Then they called Osparna for technical due diligence.

The Unraveling

Within 48 hours, our technical evaluation revealed a different reality. The “revolutionary AI” was largely rules-based automation with minimal machine learning—sophisticated decision trees masquerading as artificial intelligence. The impressive demo ran exclusively on carefully curated datasets that would never reflect the messy, incomplete data of real-world collection available to enterprises.

Most damning: during our technical interviews, the founding team couldn’t explain their own algorithms beyond marketing buzzwords. When pressed on scalability, they referenced “proprietary approaches” that, upon inspection, were basic models available in any statistics textbook.

The PE firm walked away. Later, the company’s platform collapsed under real-world data loads, customer churn spiked, and the founders quietly admitted they needed to “rebuild the entire architecture from scratch.”

This $10 million near-miss illustrates a phenomenon we see repeatedly: confirmation bias derailing otherwise sophisticated investors.

The Psychology of Investment Blindness

Daniel Kahneman’s research shows that confirmation bias—our tendency to search for, interpret, and recall information that confirms pre-existing beliefs while ignoring contradictory evidence—becomes amplified under pressure. Technology investments create perfect conditions for this cognitive trap:

• Complexity obscures truth: The technical complexity of modern platforms creates information asymmetries that non-technical investors can struggle to navigate
• FOMO distorts judgment: The fear of missing the next trend makes investors more likely to see validation where none exists
• Pattern matching misleads: Success with previous investments creates mental models that don’t always apply to fundamentally different technologies

The Three Confirmation Traps We See Most

1. The Demo Deception

Investors routinely mistake polished demonstrations for production-ready technology. We recently evaluated a “real-time” system that processed transactions in under 100 milliseconds during demos—impressive until we discovered it ran on pre-processed data with all edge cases removed. In production, response times exceeded 10 seconds, making it unusable for actual detection and prevention.

Red flag: When demos work perfectly but the company can’t explain their testing methodology on realistic data.

2. The Pedigree Paradox

Impressive founder backgrounds create a halo effect that obscures technical realities. We’ve seen brilliant consultants, successful serial entrepreneurs, and PhD researchers build technically unsound products because domain expertise doesn’t automatically translate to buildable architecture.

Red flag: When founders deflect technical questions by referencing their credentials rather than their architecture.

3. The Growth Illusion

Rapid user acquisition often masks underlying technical fragility. High-growth consumer apps frequently accumulate massive technical debt that becomes apparent only at scale. Enterprise platforms can appear successful while running on architectures that cannot support their own growth trajectories.

Red flag: When companies celebrate user growth but can’t articulate their scaling strategy or current system limitations.

Our Decision Intelligence Framework

After seeing dozens of near misses, we developed a systematic approach to combat confirmation bias in technical due diligence:

Pre-Mortem Analysis: Before any evaluation, we ask: “If this investment failed catastrophically in 18 months, what would be the most likely technical cause?” We then design our due diligence to specifically investigate those failure modes.

Adversarial Questioning: We actively seek evidence that contradicts the investment thesis. Instead of asking “How does your AI work?” we ask “What scenarios would break your AI?” Instead of “How do you scale?” we ask “What’s the first bottleneck you’ll hit at 10x current usage?”

Technical Archaeology: We dig into the actual code architecture, not just the product interface. We examine database schemas, API designs, error handling, and monitoring systems. We’ve found that companies with fundamentally sound technology are eager to discuss these details, while those with technical issues deflect or provide superficial answers.

Stress Testing Scenarios: We simulate realistic production conditions rather than accepting controlled demonstrations. This includes testing with incomplete data, concurrent user loads, and integration challenges that mirror real enterprise environments.

The Builder’s Advantage

Our background as builders provides crucial context that pure auditors lack. We’ve lived through the gap between PowerPoint architecture and production reality. We know what maintainable code looks like, how technical debt accumulates, and why brilliant engineers sometimes build unbuildable products.

This perspective helps us distinguish between companies that have solved hard technical problems and those that have simply delayed encountering them. We can spot the difference between scalable architecture and clever workarounds that will collapse under growth.

Beyond Bias: Systematic Skepticism

The most successful technology investors aren’t the ones who never make mistakes—they’re the ones who systematically challenge their assumptions before writing checks. Confirmation bias is expensive, but it’s also predictable and therefore manageable.

At Osparna, we believe decision intelligence isn’t about being smarter than the market. It’s about being more systematic in how we evaluate what we think we know. Because in technology investing, the most dangerous words are “we’re confident that…”

The best technical due diligence combines healthy skepticism with deep technical expertise. It’s the difference between buying what you want to see and understanding what you’re actually seeing.

After all, $10 million mistakes are entirely avoidable—if you know where to look.