This Founder Is Using AI To Disrupt The Trillion Dollar M&A Market

Hebia: AI for Finance - A Deep Dive into 2025 Growth & 2026 Vision

Key Concepts:

• Hebia: An AI company focused on providing solutions for investors, bankers, and lawyers.
• Assistant Use Cases: Basic AI tasks like quick information retrieval or rephrasing text (e.g., ChatGPT-like functions).
• Analyst Use Cases: More complex tasks requiring significant processing time, such as building financial models or presentations.
• MD Level Tasks: AI applications directly impacting revenue generation, like deal sourcing and faster deal processing.
• Reasoning Engine: Hebia’s approach to AI, focusing on synthesizing information to provide insights rather than generating content.
• Matrix Interface: A data grid interface allowing users to condense large amounts of information into key metrics.
• Deal Spaces: Integrated environments for managing deal data and receiving proactive AI-generated outputs.
• Lindiness: A concept referring to the durability and resilience of interfaces and collaborative networks.

I. 2025: A Year of Validation & Record Growth

2024 was characterized by AI companies struggling to gain traction and secure long-term deals. However, 2025 marked a turning point, with Hebia demonstrating tangible value and securing multi-year (2-6 year) enterprise-wide expansions, particularly within the finance sector. This resulted in record growth for the company, as leaders in finance began recognizing a competitive edge through AI implementation. The shift occurred because AI started demonstrably adding value, not just offering incremental improvements.

II. Hebia’s Platform: Use Cases & Functionality

Hebia categorizes its use cases in finance into three buckets:

• Assistant: Simple, quick tasks (5-minute tasks) like information retrieval or text rephrasing. These are considered less impactful, as they largely replicate existing AI capabilities.
• Analyst: More complex tasks (5-hour tasks) such as building Excel models from scratch or generating PowerPoint presentations in a firm’s specific style. This represents a significant step up in functionality.
• MD Level: Tasks directly contributing to revenue generation, such as identifying potential deals and accelerating deal processing. This is Hebia’s key differentiator.

Example: For an investor evaluating 50 companies, Hebia can sift through vast amounts of unstructured data (legal documents, data rooms, etc.) – a process traditionally requiring significant manual effort – to identify key indicators of investment potential. The company estimates that 3.5 trillion dollars of M&A activity annually is hampered by the friction of this manual data review process. Hebia aims to remove this friction, enabling faster, more informed investment decisions.

III. The “Reasoning Engine” Approach & Addressing Hallucinations

Hebia distinguishes itself by focusing on a “reasoning engine” rather than a “generation engine.” Instead of generating large outputs from small prompts (which can lead to “hallucinations” – factually incorrect information), Hebia synthesizes information from a comprehensive dataset to distill it into key metrics (revenue, headcount growth, etc.). This approach, termed a “capernac inversion” of typical AI functionality, aims to minimize inaccuracies by focusing on data reduction and validation. The goal is to provide confidence in the output by grounding it in existing data.

IV. Interface & User Experience

Hebia offers multiple interfaces:

• Chat Interface: A standard chatbot for conversational interactions.
• Matrix Interface: A data grid that condenses millions of pages of information into key numbers, allowing users to quickly identify relevant data points. This acts as a “sandbox” for scoping information.
• Deal Spaces: Integrated environments where data is processed proactively, generating presentation-ready graphs, Excel models, and slide decks without requiring user input. This represents a shift from reactive to proactive AI.

V. Founding Story & Focus on Finance

George Civula founded Hebia in the summer of 2020, inspired by the potential of GPT-3. He left a PhD program in Electrical Engineering at Stanford (without completing a computer science degree) believing that large language models would be the most important technology of the next 100 years.

The decision to focus on finance stemmed from the belief that truth and accuracy are paramount in financial markets. The high “marginal utility” of accurate information in finance – the potential to generate significant financial returns – made it an ideal application for AI focused on truth discovery. Civula believes that finance will ultimately be the largest market for AI applications due to this inherent need for accuracy.

VI. Market Dynamics & The “AI Bubble”

Civula believes that current AI applications are often focused on simple task completion (going from A to B) rather than generating new insights. He predicts that the companies serving AI for finance will ultimately be 10-20 times larger than those serving other sectors like law or healthcare, as finance demands a higher level of accuracy and insight.

He acknowledges the existence of an “AI bubble” but views it as a natural part of technological innovation. He believes that the long-term impact of AI will be overwhelmingly positive, driving significant societal and economic change.

VII. Scaling & Addressing Concerns

Hebia prioritizes product value and customer satisfaction over rapid scaling. The company is currently experiencing strong traction in the diligence phase of private investment, with customers actively utilizing the platform to uncover valuable insights. Expansion into banking, law, and advisory services is underway.

A key concern raised during pitches is convincing potential clients that AI can deliver on its promises, particularly in complex tasks. Another concern is the reluctance of successful firms to share their AI-driven competitive advantages.

VIII. The Future of Work & AI’s Impact on Jobs

Civula believes that fears of widespread job loss due to AI are overblown. He anticipates that AI will augment existing roles, allowing workers to focus on more creative and strategic tasks. He envisions a future where AI handles routine tasks, freeing up humans to focus on innovation and problem-solving. He believes AI will ultimately create more jobs than it displaces, particularly in roles related to AI orchestration and support.

IX. Overhyped vs. Underhyped Areas in AI

• Overhyped: AI writing all software; the idea that AI will fundamentally transform collaborative networks.
• Underhyped: AI generating revenue; AI transforming coding (though not replacing it entirely).

X. Hebia’s 10-Year Vision

Hebia’s long-term vision is to become the provider of the world’s most innovative AI interfaces, extending beyond chat-based interactions to encompass data grids, deal spaces, and other novel methods of human-AI collaboration. The goal is to integrate Hebia’s interfaces into the workflows of a significant portion of the global workforce.

Notable Quote:

“I think Hebia has always been the pioneer of the world's most interesting interfaces in AI… I don't think chat that is uh the thing that will scale for all of eternity.” – George Civula.