Tech Infrastructure Becomes Strategic Weapon For Asset Managers

In Brief:

Private markets firms face a critical technology architecture decision that will determine their ability to compete as the industry undergoes massive structural changes including retailization, insurance partnerships, and new product launches. David Nable, Managing Director and Head of Client and Partner Development at Arcesium, warns that asset managers who built systems around single strategies or asset classes now find themselves "handcuffed" when trying to expand, forcing expensive system overhauls or fragmented operations. With two decades of experience spanning Goldman Sachs, Credit Suisse, and BNP Paribas before joining the technology firm that emerged from Two Sigma's operational infrastructure, Nable argues that firms must adopt a "data-first approach" to future-proof their businesses. Speaking on the Alt Goes Mainstream podcast, he emphasized how technology has evolved from a back-office necessity to a strategic competitive advantage, particularly as artificial intelligence transforms how investment teams analyze opportunities and serve investors in an increasingly complex regulatory environment.

Big Picture Drivers:

• Retailization Wave: Private markets expanding beyond institutional investors to wealth management channels, requiring new data architectures to handle thousands of smaller accounts versus traditional large institutional relationships

• Insurance Integration: Major asset managers partnering with or acquiring insurance companies, creating complex regulatory and accounting requirements that stress existing technology systems

• Product Innovation: Explosion of new structures including evergreen funds, interval funds, and hybrid products that don't fit traditional closed-end fund operational models

• AI Implementation: Artificial intelligence creating step-function improvements in operational efficiency while requiring unified data architectures to realize full potential

Key Topics Covered:

• Data Architecture Strategy: Moving from system-centric to data-first approaches that enable flexibility across multiple asset classes and product structures

• Operational Scaling Challenges: How firms handle growth from managing dozens of institutional relationships to thousands of retail accounts

• Technology Leadership Evolution: CTOs transitioning from operational support roles to strategic business advisors who influence investment committee decisions

• Platform vs Point Solutions: Balancing comprehensive platforms against specialized tools while managing integration complexity and costs

Key Insights:

• Technology Timing Paradox: The best time to implement proper data architecture is before launching any products, but firms typically wait until they hit inflection points like new strategies, geographies, or distribution channels.

• Data Silos Create Competitive Disadvantage: Firms with fragmented systems spend enormous resources on integration and reconciliation rather than gaining insights, while unified data architectures enable faster decision-making when competitive opportunities are widest.

• Scale Benefits Follow Barbell Distribution: Large firms can afford comprehensive technology investments while small firms can remain nimble with specialized solutions, but mid-sized firms get trapped without sufficient resources for either approach.

• System vs Data Distinction: Traditional accounting and operational systems are designed for specific functions, but modern firms need underlying data models that can feed multiple current and future systems across different regulatory and business requirements.

• AI Amplifies Architecture Decisions: Artificial intelligence requires unified data to deliver meaningful insights, making fragmented point solutions significantly more expensive to maintain and less strategically valuable.

• True Technology Costs Hidden: The real expense of multiple point solutions isn't licensing fees but the exponentially growing integration complexity, operational reconciliation requirements, and constant system maintenance that diverts resources from strategic initiatives.

Memorable Quotes:

• "The best time to do it is before you launch anything. The next best time to do it is yesterday. The next best time is today." - David Nable, on when firms should implement proper data architecture

• "You can approach it as being monotonous or you can approach it as meditative and really pay attention and be present in what's going on and try to tie it to a broader story." - David Nable, describing how his operations background taught him to spot industry trends

• "When you design your organization around the thing you do today, you can get locked in. You can get stuck and when you try to do the next thing, you either have to rip out the original thing or build something alongside it." - David Nable, warning about technology architecture decisions

• "There's a paradox with data, which is that when you're making a decision... when your window for decision-making and the opportunity to make the broadest possible set of decisions is wide, you actually have the least amount of data." - David Nable, explaining why speed of data access creates competitive advantage

• "Be careful with vibe coding. It's fun. You're running serious businesses, often regulated... it's one thing if you're Facebook and a user presses the like button and it doesn't go somewhere, it fails to like. It's another thing in financial markets when you push the button for a $500 million wire transfer." - David Nable, cautioning about artificial intelligence implementation

The Wrap:

Nable's analysis reveals that technology architecture has become the defining factor separating winners from losers in private markets' next evolution. As the industry democratizes through wealth channels and integrates with insurance companies, firms face a fundamental choice: invest in unified data architectures that enable rapid adaptation to new opportunities, or remain trapped by legacy systems that fragment operations and limit strategic flexibility. The emergence of AI as a transformative force only amplifies this divide, as firms with proper data foundations can leverage machine learning across their entire business while those with fragmented systems face exponentially higher costs and complexity.