December 31, 2024 • 49min

Jack Kokko: Building AlphaSense - [Invest Like the Best, EP.404]

Invest Like the Best with Patrick O'Shaughnessy

Jack Kokko is the CEO and Founder of AlphaSense, an AI-powered search engine for market intelligence. The company began by solving the problem of fragmented financial data sources and has evolved with AI advances to transform how professionals conduct research. Through strategic acquisitions like Tegus and continuous innovation in AI capabilities, AlphaSense has expanded from serving primarily investment firms to becoming a horizontal solution for all types of businesses.

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Key Takeaways

Evolution of AI in Financial Search: AlphaSense began by aggregating fragmented financial data sources and evolved with large language models to transform research capabilities
Data vs Tools: Success in enterprise financial search requires both proprietary data access and advanced AI/search tools working together
M&A Strategy: Strategic acquisitions like Tegus have helped expand capabilities and market reach beyond just investment firms to corporations
Trust & Validation: Professional users need the ability to validate AI-generated insights by seeing original sources and context
Corporate Market Expansion: Breaking into the corporate market was a defining moment that proved the platform could serve all business professionals

Introduction

Jack Kokko is the CEO and Founder of AlphaSense, an AI-powered search engine for market intelligence. The company began by solving the problem of fragmented financial data sources and has evolved with AI advances to transform how professionals conduct research. Through strategic acquisitions like Tegus and continuous innovation in AI capabilities, AlphaSense has expanded from serving primarily investment firms to becoming a horizontal solution for all types of businesses.

Topics Discussed

Early Days of AI-Powered Search (5:56)

AlphaSense's initial challenge was building a semantic search engine that could understand financial content without the benefits that web search engines had:

Key Challenge: Content was siloed in thousands of paywalled databases without linking or user interaction data
Early Approach: Used machine learning to categorize information semantically and understand context
Evolution: Progressed from basic categorization to more sophisticated language understanding with models like BERT

Impact of Large Language Models (7:56)

The advent of large language models brought transformative capabilities:

Enhanced Understanding: Models could process much larger context and demonstrate world knowledge
Natural Language: Better comprehension of user queries and intent
Accelerated Development: Enabled faster progress toward the vision of conversational business research
"I always thought it was much further away in terms of when you could actually deliver that and now we're suddenly able to." - Jack Kokko

User Behavior and Trust (17:15)

Observations about how different users interact with AI tools:

Consumer Behavior: Often trust AI responses without verification
Professional Users: Need ability to validate sources and context
Trust Balance: Important to maintain verification capabilities while leveraging AI benefits

Data Strategy and Differentiation (19:47)

The role of proprietary content in building competitive advantage:

Fragmentation Solution: Aggregating dispersed content sources into one platform
Expert Interviews: Tegus acquisition brought valuable proprietary insights
Private Markets: Expanded coverage beyond public company information

M&A Insights and Integration (29:10)

Lessons learned from executing acquisitions:

Strategic Logic: Must have clear strategic alignment and value creation potential
Cultural Integration: Focus on building trust and excitement about the combined future
Communication: Address employee concerns early and clearly
"You have to try to anticipate what's in the minds of people on the other side and then try to address their concerns very quickly." - Jack Kokko

Selling to Different Customer Types (35:12)

Differences in selling to investors versus corporations:

Investment Firms:
- Quicker decision making
- Value proven edge and advantage
- Less likely to spread word-of-mouth
Corporations:
- More complex buying process
- Multiple stakeholders and titles
- Need for internal champions

Breaking into Corporate Market (44:54)

The defining moment that expanded the company's potential:

Initial Approach: Started with investor relations departments
Natural Extension: Product worked without major modifications
Market Validation: Proved addressable market was 10x larger
"That breakthrough and proving that addressable market was 10 times bigger... that was the most defining moment." - Jack Kokko

Future of AI and Research (47:49)

Exciting developments and potential improvements:

Enhanced Reasoning: Better native analysis capabilities
Expanded Context: Larger context windows with maintained speed
Multimodal Analysis: Better processing of video and audio
Reduced Complexity: Less need for multiple validation layers

Conclusion

AlphaSense represents a compelling example of how AI technology can transform professional research and decision-making. Through strategic acquisitions, focus on both proprietary data and advanced tools, and careful attention to user trust and validation needs, the company has built a platform that serves both investment professionals and corporations. As AI capabilities continue to advance, the potential for even more transformative research capabilities appears bright, though maintaining the balance between automation and human verification remains crucial.