My contrarian investment philosophy and the framework that’s driven portfolio success from Y Combinator startups to billion-dollar companies
Many venture capitalists seen to work backwards. They see impressive technology—AI models, robotics platforms, quantum computers—and then ask, “What problems could this solve?”
I do the opposite.
I start with problems so compelling that I can’t stop thinking about them, then find the founders crazy enough to spend years building solutions.
The Romanos Vetridis Approach
Let me give you a concrete example. In 2019, I was evaluating what many VCs would have categorized as “another robotic process automation platform.” ElectroNeek had solid technology, but dozens of RPA companies were competing for similar enterprise accounts.
The founders, however, weren’t selling RPA, they were obsessed with a specific problem: businesses were losing over 75% of their productive capacity to mind-numbing repetitive tasks.
While other investors analyzed ElectroNeek’s features against competitors, I investigated the underlying problem. Today, ElectroNeek has evolved into an intelligent automation platform combining RPA, document processing, and AI-driven orchestration. The solution changed dramatically, but the core problem remained the same.
My investment approach recognizes a fundamental truth: problems persist while solutions evolve. When you understand the problem deeply enough, you can support founders through inevitable pivots and technology shifts that destroy solution-focused companies.
This framework has driven portfolio success from Y Combinator startups to billion-dollar companies like Solugen. I’ll walk you through exactly how it works, share real examples from aerospace to construction, and highlight common pitfalls that can turn this approach into expensive mistakes.
Most VCs Think About Opportunities
Walk into any Silicon Valley conference room during pitch season, and you’ll hear the same pattern: founders present technology, investors immediately start pattern matching. “This reminds me of Salesforce,” or “It’s like Uber for industrial equipment.”
This approach works for incremental innovations. But it fails catastrophically with breakthrough technologies. I recently watched a prominent firm pass on a quantum computing startup because they couldn’t find “typical SaaS growth metrics.” The partner asked, “What’s the quantum equivalent of monthly recurring revenue?”
The fundamental flaw is market sizing based on existing categories. When evaluating UpCodes, a building code compliance platform, one firm analyzed “construction software market size.”
They completely missed the real problem: teams waste thousands of hours navigating 1,400+ jurisdiction codes, creating compliance risks that can halt billion-dollar projects.
By focusing on solution categories, they underestimated opportunity size by orders of magnitude.
Why This Approach Fails in Deep Tech
Solution-first thinking becomes destructive when applied to complex technologies requiring years of development. The VC industry’s obsession with rapid scaling creates systematic bias against the patient capital deep tech requires.
Here’s what investors miss: solutions evolve rapidly, but fundamental problems remain stable. My portfolio company Akash Systems exemplifies this. They’ve pivoted their semiconductor approach multiple times—from satellite systems to GPU cooling—but the core problem of thermal management limitations has remained constant and grown more valuable.
According to McKinsey research, successful deep tech companies typically require 7-10 years to reach maturity and change solution approaches 3-5 times during development. Traditional VC methods systematically undervalue companies navigating this normal development process.
My portfolio data supports this:
Companies backed on problem validation achieve 200-400% annual revenue growth, compared to 80-120% for investments prioritizing initial product-market fit within established categories.
My Problem-First Investment Framework
After watching traditional approaches fail repeatedly, I’ve developed a four-step framework that flips conventional due diligence on its head.
Step 1: Problem Identification and Validation
I look for problems that meet four criteria: they affect large numbers of people or businesses, current solutions are inadequate, the problem is getting worse, and economic impact is measurable.
Point One Navigation exemplifies this perfectly. Standard GPS accuracy wasn’t sufficient for autonomous vehicles or industrial applications requiring centimeter precision. The problem was well-defined, growing with autonomous vehicle adoption, and had clear economic value—navigation errors cost industries billions annually.
My validation process involves direct customer interviews focused on pain points rather than product satisfaction. For Point One, I spoke with automotive engineers who described GPS limitations as a fundamental barrier to autonomous deployment. The problem was real, urgent, and expensive.
Step 2: Market Timing Assessment
Great problems can have terrible timing. I evaluate four factors: problem maturity (has it reached critical mass?), enabling technology convergence, regulatory drivers, and global manifestation patterns.
The robotics labor shortage problem illustrates perfect timing convergence. Manufacturing companies now face acute worker shortages, sensor costs have dropped 40% since 2020, and 5G networks enable reliable edge computing. Five years ago, these conditions didn’t exist.
My global perspective across Silicon Valley, Europe, and Asia helps identify when problems are reaching simultaneous urgency across markets—usually signaling commercial readiness.
Step 3: Solution Evaluation (Secondary)
Only after validating the problem do I evaluate solutions. I focus on technical feasibility, team capability to evolve approaches, and competitive differentiation based on problem understanding rather than feature comparison.
When evaluating Alcatraz AI’s facial authentication systems, I didn’t compare features against existing access control systems. Instead, I assessed whether their approach could solve the fundamental problem of secure, touchless entry for high-security environments. Their deep understanding of security challenges, not their current product capabilities, drove my investment decision.
Step 4: Long-term Problem Evolution
The best investments solve problems that expand over time. I map how problems might evolve over 5-10 years, identify adjacent problems the solution could address, and assess platform potential.
Solugen started focused on sustainable chemical manufacturing but their enzyme engineering platform addresses the broader problem of carbon-intensive industrial processes. Understanding this expansion potential helped justify their eventual $1.8+ billion valuation—they’re not just solving one chemical problem, they’re building infrastructure for sustainable industry transformation.
Common Pitfalls and How to Avoid Them
Pitfall 1: Problem Without Solution Path
Warning sign: Real problems with no technological or economic path to solutions.
Example: Many climate problems are urgent but lack feasible solution approaches with current technology.
Solution: Technology readiness assessment and economic viability analysis before investment.
Pitfall 2: Problem Too Abstract
Warning sign: Can’t quantify impact or identify specific customer segments.
Example: “Inefficiency in general” versus “23% energy waste in industrial heating systems.”
Solution: Concrete problem definition with measurable impact metrics.
Pitfall 3: Mistaking Symptoms for Root Problems
Warning sign: Solutions address visible issues but not underlying causes.
Example: Apps treating productivity symptoms versus understanding fundamental workflow problems.
Solution: Five-whys analysis to identify root causes rather than surface manifestations.
Pitfall 4: Single-Problem Thinking
Warning sign: Solutions only address narrow problems with no expansion potential.
Example: Point solutions versus platform opportunities like ElectroNeek’s evolution from RPA to comprehensive automation.
Solution: Problem adjacency mapping and platform potential assessment during evaluation.
Portfolio Results and Lessons Learned
This framework has generated companies spanning Y Combinator startups to billion-dollar enterprises. Solugen’s $1.8+ billion valuation, Boom Supersonic’s supersonic aviation breakthrough, and Akash Systems’ $68 million CHIPS Act recognition all started with deep problem understanding rather than impressive initial solutions.
The common thread: these companies built sustainable competitive advantages by solving fundamental problems that traditional VCs might have considered “too complex” or “too slow.” These constraints often indicate problems worth solving rather than optimizing existing solutions.
SWAT Mobility demonstrates global problem validation—their urban transportation efficiency solutions work across Singapore, Australia, and Vietnam because the underlying problem of inefficient public transit manifests consistently across different markets and cultures.
My investment philosophy increasingly favors companies that others might pass on for taking “too long” or having “unclear market timing.” When everyone else demands rapid progress, patient capital supporting difficult problems often generates the highest returns.
If this sounds like an approach you can stomach, please feel free to reach out to me at rv@romanosvetridis.com and let’s chat.
