AI for VC: Build vs Buy | CarriedAI Blog

Key takeaways

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👉 Build When Unique, Buy When Standard: Develop custom AI only where no vendor solution exists, and switch to commercial tools as they catch up.

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👉 Predict Budget and Maintenance Costs: Custom AI requires high upfront investment and ongoing maintenance, straining typical VC management fee structures.

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👉 Consider Talent Dependency Risk: Reliance on key technical leaders makes custom tools vulnerable during leadership transitions.

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👉 Differentiate Smartly: Focus internal resources on AI that enhances your unique investment edge; use vendor tools for everything else.

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What is unique today may not be tomorrow

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When evaluating AI solutions for venture capital, it's crucial to consider that today's competitive advantage may become tomorrow's industry standard. The AI landscape is evolving rapidly, with technologies that were cutting-edge just months ago becoming widely available through commercial solutions. This technological democratization means that custom-built AI tools providing unique capabilities today may be replicated by vendors within 12-18 months (1, 2).

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VC firms must therefore identify the cutting edge of technological innovation and continuously build upon it to maintain their competitive position. As AI capabilities rapidly transition from novel to commonplace, successful investors need to anticipate these shifts, abandoning self-built platforms when they become industry standards and pivoting to emerging technologies that offer new differentiation opportunities (4).

Strategic considerations should include the pace of innovation in specific AI domains
Evaluate whether your competitive edge comes from proprietary data rather than algorithms
Consider a modular approach that combines purchased solutions for state of the art functions with custom development for truly differentiating capabilities (2).
Assess whether the value of perceived uniqueness justifies ongoing investment as the technology landscape evolves

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The leadership transition problem

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The leadership transition problem presents a significant challenge when implementing AI solutions in venture capital firms. When a VC firm builds custom AI tools, they often become dependent on the specific technical leaders who championed and developed these systems. If these key personnel leave, firms frequently struggle to maintain and evolve their proprietary technology, creating operational challenges. Moreover, venture capital firms are not used to managing technology teams and often the leadership at the firms lacks the experience and understanding to do a proper transition, learning months after the fact that they should have done a better job (3).

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While custom AI solutions require continuous maintenance and updates to keep pace with rapidly evolving technology, which becomes especially challenging when development teams in VC firms tend to be small, purchased solutions distribute this risk across vendor organizations with established succession planning.

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Hybrid approaches that combine vendor platforms with proprietary data strategies can provide a balanced solution. When evaluating options, firms should consider that custom solutions typically require 3-5x more upfront investment than purchased alternatives, with total cost of ownership calculations needing to factor in this continuity risk.

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The time spent on maintenance grows over time

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When VC firms build custom AI solutions, they often underestimate the growing maintenance burden that accumulates over time. What starts as a promising investment in proprietary technology frequently evolves into a resource-draining commitment, with maintenance requirements typically increasing 15-20% year-over-year as systems become more complex and technical debt accumulates (1). This maintenance tax diverts valuable technical resources away from innovation and new feature development.

Off-the-shelf AI tools from vendors distribute maintenance costs across their entire customer base thereby achieving a much lower cost per unit (5).
The rapid pace of AI development means custom solutions require continuous updates to remain competitive.
Vendor solutions often provide regular updates that incorporate emerging technologies without additional implementation effort.

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The management fee constraints

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Venture capital management fees typically range from 2% to 2.5% of committed capital, creating a fixed budget constraint for operational expenses including AI implementation (6, 7) This fee structure presents a significant limitation when considering AI investments, as the standard "2 and 20" model means a $100 million fund generates only about $2 million annually to cover all operational costs including salaries, office rent, travel, and technology infrastructure (8).

AI implementation requires substantial investment, with many firms reporting budget constraints as a major adoption blocker (67% according to some studies, 9).
VC firms must carefully evaluate whether to allocate their limited management fees toward buying ready-made AI solutions (faster deployment, lower upfront costs) or building custom tools (higher initial investment, potential long-term savings, 10).
As AI becomes increasingly essential for competitive fund operations, managers face difficult tradeoffs between investing in technology and maintaining other operational priorities within their fixed fee structure

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Strategic differentiation through evolution

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The most successful VC firms maintain their competitive edge by focusing AI investments on capabilities that truly differentiate them in the market. This strategic approach means identifying your firm's unique value proposition-whether it's specialized industry knowledge, proprietary data assets, or distinctive investment methodologies-and selectively building custom AI tools only where they enhance these differentiators. For everything else, purchased solutions typically offer better economics and lower maintenance burdens.

Consider a modular approach that combines vendor solutions for routine tasks with targeted in-house development for your firm's "secret sauce" (11).
Continuously reassess your technology portfolio, abandoning custom tools when they become commoditized and shifting resources to emerging opportunities (12).
Establish a regular technology review cycle to evaluate whether your AI investments are still delivering differentiated value or have become maintenance liabilities

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Build where vendors don't exist yet, buy when they catch up

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AI in venture capital is evolving at breakneck speed, with today's cutting-edge tools becoming tomorrow's standard offerings. Forward-thinking VC firms are gaining competitive advantages by identifying capability gaps in commercial AI solutions and developing proprietary tools to fill these voids. The most successful firms maintain a "build where vendors don't exist yet, buy when they catch up" philosophy, continuously scanning the horizon for emerging AI applications that aren't yet commercially available (13, 14).

Focus development resources on AI capabilities that address unique VC workflows not yet served by vendors, such as specialized founder evaluation frameworks or industry-specific risk assessment models.
Implement modular architectures that allow for rapid pivoting when vendor solutions mature, enabling seamless transitions from custom to commercial tools .
Consider fine-tuning existing models with proprietary and domain-specific data rather than building from scratch, which offers differentiation at lower cost.
Establish regular technology review cycles (quarterly is recommended) to evaluate whether custom tools still provide unique advantages or if vendor solutions have caught up.
Create cross-functional teams that combine investment expertise with technical knowledge to quickly identify and capitalize on emerging AI applications before they become widely available.

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Resources:

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Guillem Sague

CEO