Podcast Summary: Thoughts on the Market

Episode Title: The Next Turning Points in Tech

Date: October 22, 2025
Host: Brian Nowak (Head of US Internet Research, Morgan Stanley)
Guests: Keith Weiss (Head of US Software Research), Matt Bombasi (Morgan Stanley US Internet Research)

Episode Overview

This episode explores the evolving landscape of disruptive technologies and investment opportunities in the tech sector, drawing heavily from insights at the Morgan Stanley Spark Private Company Conference. The conversation focuses on divergent industry perspectives, early adoption hurdles, cybersecurity trends, and the growing generalization of AI and autonomous vehicle technologies, with a particular emphasis on the role of private companies in shaping the tech future.

Key Discussion Points & Insights

1. Divergence in Industry Perspectives on Tech Evolution

Timestamp: [00:50] – [04:37]

• Conference Context: Spark Conference hosts 85+ private tech companies and 150+ investor firms, emphasizing innovation across sectors (energy, health, finance, cybersecurity).
• Lack of Consensus:
  • Traditionally, innovation cycles align industry direction; currently, there’s divergence between software vendors and investors.
  • Keith Weiss:
    • "To me, one of the big takeaways was we don't have that agreement today. There's different players that are looking at this market evolution differently."
    • Application vendors (especially SaaS providers) focus on refining AI to deliver deterministic, enterprise-ready tools with governance and business context.
    • VC and early-stage investors foresee a “phase shift” where traditional application layers may be subsumed by new, more disruptive AI models, increasing potential for rapid change and upheaval.
• Early Days for Agentic AI in Enterprise:
  • At a recent CIO gathering, only 4 out of 150 CIOs felt confident understanding "agentic AI."
  • Keith Weiss:
    • "Still very early days in understanding how this is going to evolve, how we're going to actually deliver these capabilities into the enterprise." ([03:32])
• Federal Government as a Customer:
  • Increasing interest in selling to the federal government, with startups bypassing traditional procurement cycles.

2. Cybersecurity: Consensus and Drivers for Growth

Timestamp: [04:58] – [07:51]

• Generative AI Drives Security Spend:
  • Industry consensus that rapid AI development is accelerating demand for cybersecurity solutions.
• Three Main Driver Factors:
  • Expansion of Surface Area:
    • Building more software (via code generation tools) expands the digital domain needing protection, especially with new AI “agents” operating across enterprises.
    • "CIOs are thinking about this future state where you have tens, thousands, maybe hundreds of thousands of agents operating in the environment... How do we secure that side of the equation?" ([05:49])
  • Elevated Threat Environment:
    • Malicious actors already exploiting generative AI, increasing sophistication and attack velocity.
    • "You're going to have to use generative AI to counter generative AI." ([07:09])
  • Regulatory Complexity:
    • Fragmented, rapidly evolving data governance and security regulations create business opportunities for compliance-focused cybersecurity vendors.
    • "A lot of complaining going on at the conference about the lack of consistency in that regulatory environment." ([07:21])
• Sector View: Cybersecurity is seen as a bright spot—startups and investors anticipate strong growth and spending.

3. Early Adoption Trends & Barriers in Tech

Timestamp: [07:51] – [09:21]

• Adoption Patterns:
  • Early signs of cost efficiencies and productivity gains in accounting, legal, and engineering sub-verticals through tailored, smaller AI models.
  • These models achieve similar impact at about 1/50th the cost of larger foundational models.
• Barriers:
  • Change management and organizational resistance remain the largest hurdles.
  • Matt Bombasi:
    • "You can lead a horse to water, you can't make it drink. Right. And so getting people to actually deploy these technologies is something that organizations are thinking through." ([08:36])

4. The Future of Autonomous Driving Technologies

Timestamp: [09:21] – [10:42]

• Generalization of Neural Nets:
  • Neural networks powering autonomous vehicles are increasingly generalizable, with up to 80–90% of the core software transferable across verticals (e.g., ride-sharing to trucking).
  • Matt Bombasi:
    • "Not only can you make an autonomous car drive, you can make a truck drive and a bunch of other physical equipment... 80 to 90% of the software... is applicable across these verticals." ([09:43])
• Strategic Implication:
  • Success in one mobility vertical positions companies well for expansion into others, driving long-term scalability and competitive advantage.
  • Focus on accumulating total miles driven to meet safety standards remains critical.

Notable Quotes & Memorable Moments

• Keith Weiss on Industry Division:
  "One of the big takeaways was we don't have that agreement today. There's different players that are looking at this market evolution differently." ([01:35])
• On Early Stage of Enterprise AI:
  "Of the 150 CIOs, four raised their hands. Still very early days in understanding how this is going to evolve." ([03:20])
• Matt Bombasi on Adoption Hurdles:
  "You can lead a horse to water, you can't make it drink. Right. And so getting people to actually deploy these technologies is something that organizations are thinking through." ([08:36])
• On Autonomous Tech Generalization:
  "80 to 90% of the software, the underlying neural net is applicable across these verticals." ([09:47])

Timestamps of Important Segments

• [00:50] – Divergence in market views and the current innovation cycle
• [04:58] – Cybersecurity consensus & core growth drivers
• [07:51] – Adoption signals, cost efficiencies, and organizational barriers
• [09:35] – Generalization and scaling of autonomous driving technology

Tone & Takeaway

The episode conveys a thoughtful, cautious optimism about the future of enterprise technology. While major industry segments like cybersecurity are unified in forecast and opportunity, much of the broader tech landscape is at a crossroads—marked by both uncertainty and potential. A central theme is the importance of adaptability: both in organizations' willingness to embrace new tech and in how AI models—and the companies that build them—are increasingly versatile across different domains.