Podcast Summary: Capital Allocators – Jack Kokko – Building the Google of Finance at AlphaSense (EP.461)

Date: September 25, 2025
Host: Ted Seides
Guest: Jack Kokko, Co-founder and CEO of AlphaSense

Episode Overview

This episode features a conversation with Jack Kokko, the entrepreneurial mind behind AlphaSense—a platform often called "Google for Finance." Ted and Jack cover the founding journey from analyst frustrations to building an AI-powered research platform used by top asset managers, banks, and corporations. The discussion explores the evolution of search and intelligent research in finance, the advent of Large Language Models (LLMs), integration of expert opinion data, and Jack’s vision for an “always-on” intelligence machine that could revolutionize business and investment decision-making.

Key Discussion Points & Insights

Jack Kokko’s Background and Founding Story

• Early Career & Motivation

  • Jack grew up in Finland, studied electrical engineering, and developed an interest in finance ([02:53]).
  • Exposure to inefficiencies as an analyst at Morgan Stanley during the dot-com boom sparked the idea for AlphaSense:
    • "The pace was really fast...but the tools just weren’t up for it. That ultimately planted the seeds in my mind for what I’d one day want to build with AlphaSense."
      — Jack Kokko ([03:57])

• Analyst Frustrations with Research Tools

  • A sense of “sweating and barely awake” in boardrooms; fear of missing critical information
    • "Still every day when I walk into a boardroom I have flashes from those situations that really was because of the lack of technology to help an analyst who needed to consume so much information..."
      — Jack Kokko ([04:24])

Evolution of AlphaSense: From Search to Intelligence Platform

• Semantic Search Vision

  • Early vision: "Google for Finance" which would understand analyst intent, link financial concepts, and search across global documentation ([05:58]).
  • Early tech involved manual tagging and basic AI for sentiment and content classification ([07:39]).
  • First traction found with hedge funds, driven by the need for speed and efficiency in analysis ([07:07]).

• Incorporation of Proprietary and Alternative Data

  • Began with public sources: SEC filings, global filings, earnings calls, etc.
  • Shifted to creating value by acquiring expert transcript platforms (Stream, then Tegus) to generate proprietary, qualitative research—especially on private companies
    • "We feel like we've got the richest source of insights on private companies."
      — Jack Kokko ([11:28])

User Experience and AI Capabilities

• How AlphaSense Works Today

  • LLM-powered narrative answers with granular citations to underlying documents
  • Key differentiator: Professional users can trace narratives back to original documents for verification ([12:17])
  • Significant efficiency gains:
    • "A single deep research report that our AI produces now gives them the same 10 pages that they spent three weeks producing with a team of people."
      — Jack Kokko ([13:55])

• Impact on Professional Research & the Quest for Alpha

  • Raises table stakes for information access, shifting the edge to who asks the smartest questions and interprets data creatively ([15:37])
  • Importance of prompt engineering to extract value from the system ([17:01])

Adoption of LLMs and Product Advancements

• The LLM Revolution and Product Transformation

  • LLM breakthroughs enabled the realization of Jack’s long-held vision of a research oracle ([19:08])
  • All core functionalities reworked with new AI, though some specialized legacy models outperform general LLMs in niche tasks like sentiment analysis ([20:32])

• AlphaSense AI Interviewer

  • Launch of an AI that conducts expert interviews, increasing research scale and efficiency
    • "I was quite shocked about what I was seeing in the first interviews..."
      — Jack Kokko ([21:51])
  • Clients now offload routine calls to AI, freeing time for higher-value analysis ([22:40])

• Technical and Operational Challenges

  • Model selection is a complex, iterative process, balancing accuracy, context retention, and style ([23:42]; [25:13]; [26:28])
  • Flexibility and constant adaptation to new model breakthroughs is essential ([26:34])

AlphaSense's Business, Customers, and Metrics

• From Hedge Funds to a Broad Client Ecosystem

  • Now serves numerous corporate functions beyond finance, including strategy, marketing, engineering, and the C-suite ([28:01])
  • Information needs are ecosystem-wide: “Dozens or hundreds of people have been using AlphaSense from all the different angles around that same deal.” — Jack Kokko ([29:17])

• Pricing and Intelligence ‘Token’ Costs

  • Evolution from fixed user-based pricing to flexible enterprise and API-driven models to accommodate massive, automated research workloads ([30:25])
  • Current pricing still largely fixed, but may evolve further with increasing automation and intelligent processes ([32:07])

• Acquisition Strategy

  • Opportunistic, not required; content-rich assets like Tegus are rare but extremely valuable ([32:46])

• Key Company Metrics

  • Main focus on SaaS recurring revenue and growth; strong margins despite high investment in AI intelligence ([34:06])

Leadership and Future Vision

• Jack’s Role and Culture

  • Jack has become “ultimate head of product,” staying close to teams and decision-making to maintain agility and drive growth ([35:26])
  • Removing organizational obstacles, maintaining ambition as the company grows beyond 2,000 employees ([36:12])

• Vision: Always-On Intelligence Machine

  • Envisions AlphaSense as a 24/7 “intelligence factory,” proactively researching and alerting clients about important information
    • "If you think about every user having kind of 1,000 analysts in their pocket... the system can be sort of thinking 24/7 and then informing our clients more proactively what should you know right now?"
      — Jack Kokko ([37:47])

Notable Quotes & Memorable Moments

• On Analyst Life and Tools

  • "It was very hard to consume [data] at the scale and speed that was needed... In those days, you already had technology for consumers, but this wasn't available for the professionals who needed it every minute of the job."
    — Jack Kokko ([05:18])

• On Research Efficiency

  • "A single deep research report that our AI produces now gives them the same 10 pages that they spent three weeks producing with a team of people."
    — Jack Kokko ([13:55])

• On LLMs as a Breakthrough

  • "When language models actually started to be able to do that, that was incredible. ... Now that the system can just understand what's on our users’ minds..."
    — Jack Kokko ([19:08])

• On AI Interviewer

  • "I was quite shocked about what I was seeing in the first interviews..."
    — Jack Kokko ([21:51])

• On Prompt Engineering

  • "If you want to be very clear about what you're looking for, then it pays to be detailed in what you're asking about. But you can also be iterative."
    — Jack Kokko ([17:20])

• On Building the Future

  • "As I see it... it's about building this always on machine that is working for all of the investment firms, public and private markets and banks and consultancies and corporations across every industry."
    — Jack Kokko ([37:37])

Memorable Closing Reflections ([39:48] on)

• Work Ethic Lessons
  • Jack's first paid jobs: delivering newspapers, cleaning furnaces—"I learned the value of trying to do your best in whatever you're doing." ([39:48])
• Best Advice Received
  • "Follow your passion...I'm doing something that I wake up excited about every day..." ([40:41])
• Greatest Joy
  • "Just being able to think what doesn't exist yet. What would help a lot of people?" ([41:38])
• On Collaboration
  • "There's so much that you can do by working with others that surpasses what you could do on your own." ([41:59])
• Next Chapter
  • "It's about playing in this sandbox...it feels like we can reinvent everything." ([43:06])

Timestamps for Key Segments

• [02:53] – Jack’s background & analyst career
• [05:18] – Analyst frustrations; birth of AlphaSense idea
• [05:58] – Early product vision: semantic search for finance
• [09:35] – Data sources and alternative qualitative data
• [12:17] – How AlphaSense works today, user experience
• [13:55] – Efficiency gains from AI research reports
• [15:37] – Impact on alpha generation; changing research process
• [17:01] – Importance of prompt engineering
• [19:08] – Adoption of LLMs and big product leap
• [21:51] – Launch of AI interviewer product
• [23:42] – Technical challenges with LLMs and AI interviewer
• [25:13] – Model evaluation and tuning process
• [26:34] – Learning to stay flexible and iterate
• [28:01] – Expansion beyond hedge funds to corporations
• [30:25] – Pricing, unit economics, and enterprise models
• [32:46] – Acquisition philosophy
• [34:06] – Important business metrics
• [35:26] – Jack’s leadership role and product culture
• [37:17] – Vision for "always-on" intelligence and AlphaSense's future
• [39:48] – Personal reflections, early jobs, best advice

Conclusion

This episode offers a rich, candid exploration of the intersection of AI, information access, and investment research. Jack Kokko details the technical evolution, business strategy, and pioneering culture at AlphaSense, while Ted Seides draws out lessons relevant for entrepreneurs, allocators, and anyone engaged in knowledge work. The conversation highlights not just the tools, but the philosophical and practical shifts underway as AI turns financial research into an “intelligence factory.”