Venture capital firms are using AI the wrong way, argues Gilion's Henrik Landgren, who says that building better data infrastructure and connecting directly to sources like financial, payment and accounting systems would improve due diligence, help investors identify overlooked startups, and make investment decisions both faster and more accurate.
Amidst a maturing AI landscape, venture capital firms are seeking more robust and defensible methods for due diligence and investment decisions, driven by past missteps and increasing competition.
A strategic reader should care because improving data infrastructure in venture capital could lead to more efficient capital allocation, faster innovation, and the rise of previously overlooked startups, impacting future economic leaders.
The focus shifts from superficial AI application to deep integration of AI with core financial data systems for better analytics, potentially altering how investment decisions are made across the venture landscape.
- · Startups with strong data infrastructure
- · Gilion (and similar data analytics providers)
- · Early-stage investors (with improved due diligence)
- · High-growth startups (identified earlier)
- · VC firms relying on 'bad data'
- · Startups with opaque financials
- · Incumbent VC firms slow to adapt
- · AI tools lacking direct data integration
Venture capital firms begin prioritizing direct integration with financial and accounting systems for enhanced due diligence.
This improved data quality leads to more accurate startup valuations and a reduction in high-risk investments based on incomplete information.
The overall venture capital ecosystem becomes more meritocratic, with capital flowing more efficiently to genuinely promising ventures who can demonstrate clear, verifiable value through their data.
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