Podcast Summary: Tech Brew Ride Home – (Portfolio Profile) Hypercubic.AI

Date: November 15, 2025
Host: Brian McCullough (Morning Brew)
Guests: Sai (Co-founder, Hypercubic.AI), Ayush (Co-founder & CTO, Hypercubic.AI)

Episode Overview

This episode spotlights Hypercubic.AI, a startup on a mission to help Fortune 500 enterprises understand, preserve, and modernize their aging but mission-critical legacy systems, particularly those running on COBOL and mainframes. The co-founders, Sai and Ayush, break down why legacy systems—and the institutional knowledge that keeps them running—pose an existential risk for modern businesses, and how Hypercubic.AI’s human-in-the-loop, AI-powered solutions aim to capture and digitize this knowledge before it walks out the door.

Key Discussion Points & Insights

1. The Legacy Systems Crisis

• Prevalence and Challenge of Legacy Systems

  • Mainframes and COBOL, both relics from the 1960s, still run critical infrastructure in airlines, banks, logistics firms, and major government agencies (IRS, Social Security, etc.).
  • Ayush: "70% of all Fortune 500 use mainframes for something... There’s something like between 200 to 800 billion lines of COBOL still running out there." (04:07)
  • The average age of developers who keep these systems running is 55+, with many in their 60s and 70s. (04:37)

• The Double Problem: Code and Knowledge

  • Institutional knowledge is as important as the code itself, residing in the minds of senior experts. As these experts retire, critical know-how risks vanishing—a massive enterprise vulnerability.
  • Sai: “When they leave the workforce, all of that simply vanishes or leaves with them. And so capturing all of that is the key mission that we're going after.” (05:32)

2. Hypercubic.AI’s Solution

• Two Pillars: Hyperdocs & Hypertwin

  • Hyperdocs: An AI-powered documentation platform that ingests entire legacy codebases (millions of lines, tens of thousands of files) and generates highly structured, auditable documentation for engineers, business analysts, and leadership.

    • Uses both deterministic and generative AI, tying new documentation directly back to source code for human verification.
    • Ayush: “For every statement or...diagram that we generate...we sort of link back to...the code blocks...where we're extracting this business logic from.” (09:30)

  • Hypertwin: AI “digital twin” of a subject matter expert, capturing and replicating their mental models, workflows, and tribal knowledge.

    • Sai: Information is gathered via:
      • Ingesting existing written documentation (SharePoint, GitHub, Jira, etc.)
      • Interactive, AI-driven interviews that ask highly targeted, case-based questions
      • Workflow capture (screen recording critical processes, then algorithmically encoding the “how” of key tasks)
      • Sai: “By combining these three modalities...we can create almost a digital twin of the subject matter expert, and the way they work, problem solve, and architect solutions.” (11:10)

  • Both systems are designed for ongoing, incremental updates—continual learning, not a one-off transfer.

3. Customer Onboarding & Value Proposition

• Crawl-Walk-Run Approach
  • Crawl: Start with code/document ingestion and AI-driven interviews.
  • Walk: Deeper integration and continual workflow capture.
  • Run: Future vision is full AI migration/modernization of critical systems. Hypercubic.AI is currently focused on steps one and two (07:42–08:36).
• Value Proposition
  • Main: Major risk mitigation—enterprises cannot afford failures in systems handling billions in transactions.
  • Side benefits: Enhanced productivity, efficiency, and onboarding for new engineers.
  • Early pilots show strong demand, with initial conversations always driven by the “risk” problem. (14:38, 15:22)

4. The Limits of Large Language Models

• Not Just a ChatGPT Problem
  • While LLMs can “know” COBOL, they lack company-specific, context-dependent expertise.
  • Ayush: “Every company has its own cultural way of doing certain things and that is almost never going to be in the language models. And that is one of the key things we're focusing on right now.” (06:31)

5. Proactivity, Product Maturity & Future Vision

• Hypercubic's tools are currently for guidance and rapid context retrieval, not automatic incident resolution. Human engineers remain in the loop.
  • Ayush: “Our interface is very minimal...you quickly ask a question, it’s able to look at your screen...and just give you some idea of what's happening very, very quickly.” (17:59)
• Longer-term: Full autonomous migration and modernization of legacy systems as a “digital twin” accrues enough fidelity over time (18:34).
• Product status: Active design partners in financial services, aerospace, utilities, government; early pilots open to new partners and customization to fit specific enterprise needs (23:41–24:20).

Memorable Quotes & Moments

• On the urgency of the problem:
  “There's plenty of 70 year olds working on these systems today. They simply cannot retire.” – Sai (04:37)

• On institutional knowledge vs. code knowledge:
  “It’s not just legacy systems, it's legacy knowledge. It's knowledge that can age out of an organization.” – Brian (04:52)

• On building digital twins:
  "By combining these three modalities of information, we can create almost a digital twin of the subject matter expert." – Sai (11:10)

• On product-market fit and startup growth:
  “If you just build something with intention and are really in the space full time and just focusing, the answers sort of just write themselves.” – Ayush, reflecting on Y Combinator application experience (34:14)

Timestamps for Key Segments

• [00:55] Hypercubic overview: modernizing legacy infrastructure
• [03:01] Elevator pitch repeated by Sai
• [04:07] Scale of legacy systems problem (COBOL/mainframes stats, workforce demographics)
• [05:32] Institutional knowledge loss as existential risk
• [07:42] "Crawl, Walk, Run" solution rollout explained
• [08:36] Hyperdocs—AI documentation engine
• [10:29] Ensuring trustworthiness: combining deterministic and generative AI, proof linking
• [10:54] Hypertwin—building the digital twin of experts
• [12:24] Ongoing versus one-time knowledge capture
• [14:38] Real-world outcomes: risk mitigation as enterprise driver
• [17:59] Limitations and future of “agentic” automation
• [19:50] Founders’ backgrounds, origin story, why knowledge retention matters
• [23:41] Product maturity and pilot availability
• [25:31] Leaving Apple for startup life, personal motivations
• [28:18] Connecting the dots: LLM backgrounds and right place, right time story
• [29:40] Y Combinator application journey, persistence, and “the one” idea
• [33:16] Clarity and progress leading to YC acceptance
• [36:37] Inside Y Combinator: pressure, ambition, and resources
• [38:32] YC and fundraising: investor introductions and advantages
• [40:16] Looking forward to Demo Day and active fundraising
• [41:03] How enterprises and engineers can engage with Hypercubic.AI
• [42:22] Call for referrals, especially among mainframe ecosystem leaders

Tone & Style Highlights

• Candid, fast-paced, and transparent—typical “Silicon Valley water cooler” fare.
• Founders display a sense of urgency but remain methodical: “Crawl, walk, run.”
• Down-to-earth and occasionally self-deprecating about earlier rejections, corporate origins, and startup grind.
• Consistently mission-driven about safeguarding legacy knowledge and preventing catastrophic failures in critical industries.

How to Get Involved

• Prospective Enterprise Clients:
  • Visit hypercubic.ai, book a demo—guaranteed follow-up.
• Engineers Looking to Join:
  • Contact Sai or Ayush directly via LinkedIn or their team email (team@hypercubic.ai)—especially if interested in ground-floor, high-impact founding roles.
• Referrals:
  • Introductions to mainframe system leaders or large enterprise IT decision-makers are highly sought; listeners can also contact Brian for warm intros.
  • Sai: "If you have any leaders, connections to leaders in the mainframe ecosystem or large enterprises...please feel free to reach out." (42:22)

For Fortune 500s, techies, investors, or anyone intrigued by the looming crisis of the “aging mainframe brain drain”, this episode offers a clear window into how Hypercubic.AI is working at the intersection of AI, enterprise risk, and mission-critical IT transformation—and how the founders’ relentless focus and unique backgrounds are poised to meet this moment.