The Physics of Finance: How Yaniv Bertele Applies Scientific Thinking to Investment Strategy
November 21, 2025
What does theoretical physics have in common with venture capital investing? On the surface, very little.
One deals with abstract mathematical models of fundamental forces; the other with market dynamics, competitive positioning, and business execution. Yet the methodologies underlying both disciplines share surprising similarities: systematic observation, hypothesis formation, rigorous testing, and iterative refinement based on empirical results.
Yaniv Bertele embodies this intersection. With dual Master’s degrees in Physics and Mathematics from the University of Gothenburg, his academic training instilled a preference for measurable frameworks over intuitive hunches. Rather than abandoning this analytical rigor when he transitioned from academia to entrepreneurship, Bertele adapted scientific methodology to evaluate business opportunities across industries ranging from diving equipment to water technology to insurance‑linked assets (life settlements/longevity risk).
The result is a distinctive approach to investment strategy that prioritizes problem definition over solution sophistication, empirical validation over theoretical elegance, and systematic thinking over opportunistic pattern recognition. For entrepreneurs and investors operating in an era of technological abundance — where exciting solutions search frantically for problems to solve — Bertele’s framework offers a counterintuitive insight: the best innovations start not with breakthrough technology but with clearly defined market needs.
The Scientific Framework for Evaluating Opportunities
Most entrepreneurs approach opportunity evaluation backward. They fall in love with a technology — artificial intelligence, blockchain, quantum computing — and then search for applications. The technology becomes the hammer seeking nails, often forcing fit between impressive capabilities and marginal market needs. This approach occasionally yields breakthrough companies, but more often produces well-engineered solutions to problems that don’t meaningfully exist.
Yaniv Bertele’s framework inverts this sequence. “The first thing that I do is ask myself what the use case is, what the market potential is, and how do you productize the unique selling point of the solution that you bring to the table regardless of the technology,” he explains. This problem-first orientation reflects scientific training: physicists don’t start with mathematical tools and then look for phenomena to model; they observe phenomena and develop appropriate mathematical frameworks to explain them.
The methodology proceeds systematically through distinct stages. First comes problem identification and validation. Does a genuine market inefficiency or unmet need exist? Can it be quantified? Who experiences this problem, and how acute is their pain? This stage demands skepticism — many apparent problems dissolve under scrutiny, revealing themselves as minor inconveniences or already-addressed issues repackaged as urgent needs.
Second, solution viability receives examination. Can the proposed approach actually solve the identified problem? Is it demonstrably better than existing alternatives, or merely different? What evidence supports the claim of superiority? This stage particularly benefits from scientific thinking, which demands reproducible results and measurable improvement rather than anecdotal success stories.
Third comes financial feasibility analysis. Even if a solution effectively addresses a real problem, does the economics work? Will customers pay enough to support a sustainable business model? What are the unit economics, customer acquisition costs, and lifetime value relationships? Bertele’s mathematical background proves valuable here — financial modeling becomes an exercise in parameter estimation and sensitivity analysis rather than hopeful projection.
Fourth, market quantification provides reality checks on ambition. Total addressable market calculations are notoriously optimistic in startup pitches, but rigorous analysis demands conservative assumptions. How many potential customers actually exist? What realistic penetration rates could be achieved? What competitive dynamics will constrain market share? These questions separate billion-dollar opportunities from niche products regardless of technological sophistication.
Finally comes go-to-market strategy evaluation. “Understanding that there is a problem, that we have a solution, understanding who the customer is, understanding the market, the market size, and how you go to market.” completes Bertele’s framework. Distribution channels, partnership requirements, sales cycle characteristics, and scaling dynamics all receive analytical attention before capital deployment decisions get made.
“The technological item is an enabler, it’s a differentiator. It creates a barrier to entry, first-mover advantage. But first and foremost, what is the problem that we’re trying to solve?” This hierarchy — problem before technology — distinguishes Bertele’s approach from typical Silicon Valley enthusiasm for innovation without immediate application.
Data-Driven Decision Making in Practice
Abstract frameworks matter less than practical application. Yaniv Bertele’scareer demonstrates this methodology across diverse industries, suggesting its robustness beyond any single market context.first‑mover advantage
At Poseidon Diving Systems in Sweden, where he worked as a physicist developing high-flow regulators, the framework emerged organically. Engineers from the former Eastern Bloc approached Poseidon with technologies seeking applications — scrubbers from large vessels they believed could be repurposed, intellectual property without clear commercialization paths. The established company had no systematic approach for evaluating these opportunities.
Bertele created one. Rather than evaluating technologies in isolation, he developed processes for identifying market needs within diving and related industries, then matching available intellectual property to those needs. Some technologies were acquired, others sold, and partnerships formed around commercialization opportunities that cleared the systematic evaluation hurdles. This entrepreneurial center helped Poseidon monetize innovation that would otherwise have remained in file drawers.
The methodology scaled to venture capital. As Head of Corporate Venture Capital at Mekorot, Israel’s National Water Company, Bertele managed 16 portfolio investments in water technology and cleantech startups. The success metrics were clear: Sun Corporation acquired a controlling interest in Bacsoft in 2015; Bacsoft became a subsidiary, demonstrating that systematic opportunity evaluation could identify winners in emerging technology sectors. The venture capital context provided thousands of data points — startups with compelling technologies but unclear markets, genuine market needs without adequate solutions, and the rare alignment between both.
At EverOak Innovations, the same framework guided strategy development in life settlements. The problem was well-defined: a small but growing flow today (~$4.67B face purchased in 2023) against a very large latent pool (>9M policies/~$725B face lapse or surrender annually) with persistent inefficiencies in underwriting, valuation, and institutional access. The existing solutions were inadequate: opaque methodologies, inconsistent processes, and expertise barriers preventing capital markets participation. The financial viability was demonstrable: uncorrelated returns, actuarial predictability, and regulatory maturity supporting institutional adoption.
Rather than immediately building technology, EverOak first ran a pilot program starting in early 2022, actually acquiring policies to gain an empirical understanding of market mechanics. This experimental phase — essentially hypothesis testing through direct market participation — generated data informing both technology development priorities and strategic positioning decisions. Only after validating the opportunity through direct experience did full-scale company formation proceed.
This patient, evidence-based approach reflects scientific training more than typical entrepreneurial impulsiveness. Physicists don’t publish theories without experimental validation. Mathematicians don’t declare theorems proven without rigorous proof. Bertele applies the same standards to business strategy: test assumptions, gather data, and validate hypotheses before scaling investment.
The analytical rigor extends to technology development itself. EverOak’s AI systems for life expectancy prediction emerged from systematic problem decomposition. What specifically makes traditional underwriting inadequate? Which variables most influence prediction accuracy? What data sources can be accessed and processed systematically? How do you validate model outputs against actual mortality outcomes? These questions drive technology architecture rather than using whatever machine learning tools are currently fashionable.
From Methodology to Market Impact
Scientific thinking provides competitive advantages in investment strategy that extend beyond individual opportunity evaluation. The systematic approach creates organizational capabilities for repeating success rather than depending on occasional insight or lucky timing.
Consider pattern recognition. Investors often claim pattern recognition abilities developed through experience — they “just know” which opportunities will succeed based on gut feeling honed over decades. This intuitive approach works for some individuals but doesn’t transfer or scale. Scientific methodology, by contrast, makes pattern recognition explicit and reproducible. What characteristics distinguish successful opportunities from failures? Can these be quantified and systematically evaluated?
Yaniv Bertele’s framework creates this transferability. Team members can apply the methodology to new opportunities without requiring decades of personal experience. The systematic questions — What problem? What solution? What is economics? What market? What distribution? — guide analysis regardless of individual intuition levels. This democratization of opportunity evaluation allows organizations to scale beyond founder capabilities.
The approach also provides decision discipline during market euphoria. When capital floods specific sectors — as happened with crypto, as happens periodically with various “next big things” — systematic frameworks prevent allocation to opportunities that fail fundamental criteria despite market enthusiasm. If the problem isn’t clearly defined or the economics don’t work without assuming perpetually rising valuations, the framework flags concerns regardless of surrounding hype.
Perhaps most importantly, scientific thinking about investment strategy enables iterative improvement. Each investment becomes an experiment generating data. What hypotheses were validated? Which assumptions proved incorrect? What unexpected variables influenced outcomes? This continuous learning cycle, familiar to any research scientist, allows investment approaches to evolve based on evidence rather than doubling down on initial assumptions.
For Bertele, the synthesis of scientific training and financial innovation creates a distinctive market position. In industries where technological enthusiasm often overwhelms business fundamentals, systematic evaluation frameworks identify opportunities that survive scrutiny. In markets where opacity and expertise barriers limit institutional participation, analytical rigor and transparent methodology open access to previously closed opportunities.
The lesson for both entrepreneurs and investors is clear: breakthrough innovations rarely come from technology alone. They emerge when systematic thinking identifies genuine problems, rigorous analysis validates potential solutions, and disciplined execution translates opportunity into reality. The physics of finance, it turns out, requires the same qualities that advance scientific understanding — curiosity about how systems actually work, skepticism toward unvalidated claims, and commitment to letting evidence guide conclusions.
