Re-Founding Incumbents for the AI Era with Sequence Holdings Co-Founder and CEO Michael Lee

The podcast discusses the transformative potential of AI in reshaping industries through deep organizational change rather than incremental automation. The central thesis is that true value lies in reengineering businesses around AI's capabilities, particularly by leveraging incumbents with established advantages such as brand, scale, and regulatory positioning. To achieve this, a permanent holding company model is proposed - one that aligns long-term capital with sustained technological transformation, enabling the recruitment of top engineering talent and the restructuring of workflows to fully harness AI.

This model contrasts with traditional private equity, consulting firms, or off-the-shelf software providers, all of which are seen as limited in driving fundamental change due to misaligned incentives or short time horizons. Instead, the focus is on acquiring and refounding companies like Bank South and Baldwin, using a shared technology platform (Atlas) and adaptive playbook to improve performance across operations - such as reducing loan underwriting times and increasing capacity without loosening standards. The approach prioritizes one high-impact deal per year, emphasizes engineering excellence, and seeks to build $100 billion+ market leaders in sectors where AI can amplify existing strengths, such as banking and insurance.

• What if you restructured your solo software business around AI as the central engineering persona?

  • Move: Redesign your product development workflow to treat AI agents as first-class team members - automate code generation, testing, and deployment using a custom agent stack built on reusable patterns (e.g., data ontology + agent builder).
  • Why Now?: Transformers and post-2022 AI advancements have made infinite scaling of AI labor practical; waiting means ceding ground to solopreneurs who operate like mini-holdings with AI leverage.
  • Expected Upside: 3 - 5x faster iteration cycles, reduced burn rate by automating ~70% of repetitive dev tasks, and creation of a defensible, legible system that can be reused across future products.

• What if you acquired a small, stagnant but cash-flowing SaaS tool in a relationship-heavy niche (e.g., insurance brokerage workflows) and refounded it as an AI-native leader?

  • Move: Identify and acquire a bootstrapped B2B SaaS with high retention (>85%) but slow innovation; integrate your Atlas-like platform to rebuild core workflows with AI agents for underwriting, client reporting, or compliance.
  • Why Now?: Incumbent advantages (retention, data hygiene, regulatory moats) are undervalued by traditional investors focused on growth - this creates acquisition opportunities before AI-driven roll-ups dominate.
  • Expected Upside: Transform a $100K/year maintenance burden into a $1M+ ARR AI-augmented business within 18 months, leveraging existing customer trust while embedding differentiated automation.

• What if you shifted from selling AI tools to owning the businesses you transform - starting with one micro-vertical?

  • Move: Pick one domain where you've delivered AI services (e.g., loan processing automation), propose an equity partnership with the founder/owner to co-refound it as an AI-first entity, and reinvest profits into platform reuse across similar targets.
  • Why Now?: Service models cap revenue and influence; ownership aligns incentives, lets you capture full upside, and mirrors Sequence's model of long-term compounding via engineering-led transformation.
  • Expected Upside: Escape the service grind, gain control over product direction, attract better talent by offering mission-driven ownership, and build a portfolio of interconnected AI-refounded businesses with shared infrastructure.

• Prioritize industries where AI can amplify existing advantages like scale, brand, or regulatory moats - focus your product development on incumbent-heavy sectors (e.g., insurance, banking) rather than fragmented markets.
• Build reusable technical infrastructure (like a data ontology and agent orchestration layer) that can be adapted across domains to accelerate deployment and reduce redundant engineering work in future projects.
• Align your business model with long-term technological transformation by designing systems that integrate into current human workflows instead of betting on speculative future behaviors.
• Attract and retain top engineering talent by offering decision-making influence and mission-driven work, differentiating yourself from short-term-focused vendors or capital-constrained startups.
• Partner selectively with strong management teams already adopting frontier technologies (e.g., cloud, AI APIs), using their operational expertise as a force multiplier for deep AI integration.