Ray Dalio, Bridgewater’s Ray Dalio, is a quiet pioneer of Big Data, Machine Learning and Fintech.

Ray Dalio, an investing legend, is a man whose success has been attributed primarily to his investment knowledge. This article argues his success was due in part to his vision as a quiet but early adopter of big data, computational systems, and artificial intelligence. Bridgewater was among the first fintech companies in the world.

Dream Big. Start Small.

In 1975, Ray Dalio founded Bridgewater Associates out of his two-bedroom apartment in Manhattan. He did so without funding, credibility, or status but simply with a plan, an enthusiasm for the markets, and an eye for reducing complex systems into simple cause-and-effect relationships. He would start small; he would bootstrap.

At launch, Dalio faced two challenges common to most first-time entrepreneurs. The first was a credibility gap–a phrase coined in 1965 by the New York Herald Tribune and most relevant to Dalio’s early fundraising efforts. The second challenge was his go-to-market strategy decision, which stands as a representation of our first universally applicable framework.

The Credibility Gap

Paraphrasing organizational psychologist and Wharton professor Adam Grant, credibility lies at the intersection of two socio-anthropologic dimensions: power and status. Management involves the authority to influence others, while rate consists of the accordance of respect by others. Any attempts by one to exert control (i.e., influence) without status–for example, attempting to fundraise, pre-revenue, or without a track record–typically fails, as was the case for not just our 26-year-old Dalio, but also for many a first-time founder struggling to find their feet.

Thus, as part of the crafting process for his go-to-market strategy, Dalio would outline and answer a series of questions that would pave the way for the establishment of his track record, thereby solving his status–and thus credibility–issue. The questions were as follows: First, what am I good at today but could become best in the world tomorrow? Second, what problem am I out to solve that the market also finds valuable? Third, what form should my solution come in, and how will it be different from substitutes? Fourth, how will I price this solution? And fifth, who will my customers be; how will I reach, engage, and communicate with them?

Go-to-Market Strategy

A go-to-market (GTM) strategy, as implied by the five questions above, is a pre-launch blueprint for how new ventures reach their desired customer and price and distribute their chosen product or service, achieve early adoption, and establish advantage. Robust GTM strategies typically take into account our second universally applicable framework, the “Four Ps” of marketing: Product, price, place, and promotion.

Bridgewater’s Early Business Model

From the onset, Dalio was clear about his strengths and where he would likely have an edge–in “visualizing, reducing and synthesizing complex systems into interdependent stocks and flows, underpinned by cause and effect relationships;” relationships that he translated into “rule-based and time-deterministic” quant models that mathematically reflected what he called “economic machines” or “machines.”

With clarity about his strengths, Dalio then decided that his value-delivery vehicle would be an advisory practice that managed risk exposures [on behalf of clients] while also offering advice and market observations. Further, he would price these services on a success fee basis, i.e., a back-end weighted pricing strategy I would recommend to any new market entrant seeking to gain share by reducing the cost barriers to adoption for prospective/new clients.

With his product and pricing strategy cemented, Dalio now had to settle on an entry market/niche with which to establish a track record. He chose the livestock, meats, grains, and oilseeds markets, which, by my analysis (hindsight bias notwithstanding), were ideal for three reasons.

First, these markets were closely correlated. My attempt at making sense of this is as follows: Livestock eats quantifiable amounts of corn (grain) and soybeans (oilseeds) before ending up on the butcher’s counter as meats; corn and soybeans compete for acreage, rainfall, and fertilizer input (all quantifiable on a yield basis), as well as share harvest cycles. Over specified periods, planted lot, rainfall (measured per week, per major U.S. growing area), and fertilizer input translate into predictable harvest sizes and carrying costs over set distances. Data which, when married with livestock inventory levels by age, weight group, location, and rate of weight gain, determine the quantity and velocity of grain feed consumption. Superimposing these correlated datasets upon retailer margin, consumer preferences (i.e., meat cuts), and slaughterhouse capacities allowed for detailed regressions and other analyses to be run, which Dalio did to the ends of programming a proprietary system of “machines” for his market. These machines would then have produced the pricing predictions he went on to trade around successfully.

The second reason why the livestock, meats, grains, and oilseeds were a great launch market for Bridgewater is that these markets were less sensitive to speculative and sentimental market distortions relative to public securities markets, another attribute that dovetailed nicely with Dalio’s strength of reducing complex systems into simple economic machines.

The third reason for choosing such niche markets to go to market with is in line with our next time-tested and frequently championed business principle: Where possible, entrepreneurs should launch into small, niche, sparsely understood demands and establish monopoly dominance before commencing with expansion. Expansion under this model subsequently leverages either the established NewCo’s brand reputation and strategic competence to grow into related markets/verticals or leverages its existing assets and infrastructure to sell new products to the same set of customers. So, just as Amazon began with books, Google with search, and Facebook with Harvard students, Bridgewater started with livestock, meats, grain, and oilseeds.

Bridgewater’s Marketing Strategy

The final piece to Bridgewater’s GTM strategy/launch business model was Dalio’s plan for reaching, engaging, and communicating his services and successes to his clients. In this regard, Dalio settled on a low-cost strategy that is still very much mainstream today: the newsletter. Every day for the better part of ten years, Dalio telexed his Daily Observations–a research circular that detailed his market analysis, observations, and risk management techniques–to a growing expanse of clients. This strategy eventually won him inbound mandates by the McDonald’s Corp, Kodak’s Pension Fund, and the World Bank, and cumulated in his first turn of outside capital. Paraphrasing an adage from Toptal Finance Expert Alex Graham, “Just because you build it doesn’t mean they will come.” Novice and experienced business folk obsess about marketing.

Product/Market Fit

In 1985, Bridgewater raised its first $5 million of outside capital, formally marking its transition to the Bridgewater we know today–i.e., a hedge fund. To be successful in this new phase, Dalio’s company would require a very different sort of organizational structure, composition, and strategy vis-a-vis the Bridgewater of years past. Most importantly, and in line with our fourth foundational business framework, Bridgewater would require a fully congruent operations strategy.

An operations strategy is a unifying framework that steers the total pattern of decisions shaping an organization’s long-term capabilities toward a singular, coherent system. Or, as defined by Harvard Business School Professor Gary Pisano, an operations strategy is a consistent and comprehensive set of operating policies specifying how an organization will arrange its resources, priorities, and processes (RPPs) to achieve its most important strategic priorities.

To accomplish the task of designing a new operations strategy, Dalio, as has every entrepreneur that has ever crossed the chasm into mass adoption and scaling, would first need to redefine the problem he was out to solve (i.e., the job to be done), and on the back of that establish his firm’s strategic priorities.

The “Job to Be Done”

Introduced by renowned scholar and Harvard University professor Clayton Christensen, the job-to-be-done framework is one designed to root out the fundamental problem that a given customer needs solved as a going business concern.

When Dalio first set out as a scrappy upstart in 1975, his job was to manage risk exposures on behalf of his clients. In his new capacity as an asset manager, this job, naturally, had changed, and he would need to pivot. Like many entrepreneurs iterating toward product/market fit, Dalio initially mistook his proposition to be “the building of data-driven systems with which to map economic machines and foretell future prices.”

It wasn’t until he suffered a series of almost impossible losses that Dalio pivoted for a final time toward his true proposition. This proposition was to use proprietary data systems to continuously react and trade on real-time market information without trying to predict where the market would go. He would do this by building a unified “economic machine” powered by tens of millions of discrete datasets, which he passed through real-time indicators that tested for shifting fundamentals. Finally, he would sieve his results through a different system of trend filters designed to confirm/reject price movements based on consistency with past outcomes. The ultimate objective of all this is to accomplish Bridgewater’s true job to be done: generating best-in-class risk-adjusted returns on behalf of its limited partners.

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