Podcast Summary: Exchanges – Can Software Survive AI?

Host: Alison Nathan, Goldman Sachs
Guest: Gabriela Borges, Software Sector Analyst, Goldman Sachs Research
Date: March 10, 2026

Episode Overview

This episode of Goldman Sachs Exchanges explores the existential risks and opportunities that Artificial Intelligence (AI), particularly generative AI, poses to the software industry. Host Alison Nathan interviews Gabriela Borges, who provides insights from investor sentiment to sector dynamics, with a focus on what determines winners and losers in an AI-disrupted software landscape. The discussion draws from Borges's recent contribution to Goldman’s "Top of Mind" report.

Key Discussion Points & Insights

1. Why AI Is Raising Existential Questions for Software (00:05–02:30)

• Recent AI advancements have spurred investor anxiety: AI has transitioned from a source of hype to a source of deep concern for software investors, especially following rapid improvements in coding automations and broader enterprise adoption.
  • Notable Example: Anthropic’s CLAUDE code, which expanded from helping developers to aiding everyday knowledge workers.
• The competitive landscape is intensifying: Major announcements from rivals like Anthropic and OpenAI led investors to question the defensibility of incumbent software companies.

Quote:
“We’re finally getting enterprise adoption of AI... It generally takes a fair number of years before you go from the ChatGPT moment in 2022... to actually starting to see the benefits in the enterprise ecosystem.”
— Gabriela Borges (01:39)

2. The Nature and Durability of Software Company "Moats" (02:30–06:00)

• Domain experience and data as defensible moats:
  Companies like CrowdStrike have built up valuable, proprietary security datasets over a decade, using human-in-the-loop methods for threat intelligence.
• Incumbents must prove their edge:
  It’s not enough for legacy software companies to simply possess data or experience; they must demonstrate that their proprietary strengths lead to better AI outcomes for customers, especially compared to new AI-first entrants.
  • Case in point: Salesforce’s “agentforce” vs. third-party tools.
• How to evaluate competitive durability:
  Borges describes a three-part checklist for enduring moats:
  1. Replatforming technology stacks to reduce legacy tech debt.
  2. Building organic innovation capabilities and M&A agility to cover gaps and stay competitive.
  3. Proving monetization capacity for AI features by tracking adoption and pricing power.

Quote:
“These are the types of things that we’re looking for today’s software leaders to figure out if they will still be a leader three, five, seven years from now.”
— Gabriela Borges (07:10)

3. What Will Stabilize the Software Sector? (07:26–09:00)

• Numbers must trump narrative:
  Stability will come when companies show positive, measurable improvement in key business fundamentals (“numbers need to contradict the narrative”).
  • Investors track metrics like ARR (annual recurring revenue), LTV/CAC, and AI usage among other KPIs.
• Sustained fundamental performance is key:
  The sector’s key metrics have deteriorated since their post-pandemic peak, but there are signs of recovery, as markets recently rewarded positive earnings performance.

Quote:
“You can start to see stabilization in the metrics…that type of engagement where you see the market respond favorably to positive data points… those dynamics tend to be pretty good for the sector to recover over time.”
— Gabriela Borges (08:15)

4. Investor Sentiment and Shifting Ownership (09:00–11:50)

• Value-based investors are entering:
  Historically, high-growth software stocks were seen as too expensive. Improved GAAP earnings and shifts in how companies treat stock compensation are making the sector more attractive to new cohorts of investors—particularly value and GARP (“growth at a reasonable price”) investors.
• Slower sector growth, but more efficiency:
  The typical company’s growth rate has dropped from over 20% to nearer 10%, which is changing the investor base. Companies, in response, are becoming more disciplined: expanding margins and using AI to boost productivity internally.

Quote:
“We’re finally at the point where you can actually start to do some interesting analysis around GAAP earnings power. And we think that’s bringing a set of investors into the space that historically have essentially written it off as being too expensive.”
— Gabriela Borges (09:35)

• Internal AI adoption:
  Software companies themselves are leveraging AI for efficiency—"drinking their own champagne."

5. How Software Companies Are Adapting to AI (12:08–13:42)

• Acquisition, not just innovation:
  Large software incumbents increasingly let venture-backed startups take big innovation risks and then acquire the best ones, integrating them into their platforms (e.g., Salesforce’s 10+ acquisitions in a year).
• Applying lessons from cybersecurity:
  The cybersecurity sector’s rapid innovation cycle—largely because of active adversarial threats—provides a model for how application software leaders might combine organic innovation and M&A for sustained strength.

Quote:
“It makes a lot more sense for the venture capital community to make those types of bets and then for the software leader today to sit back and say, okay, let me take stock of where the cookies are crumbling so, so that I can figure out which of these technologies actually has staying power.”
— Gabriela Borges (12:43)

Memorable Moments & Quotes

• On existential fears vs. actual opportunity:
  “Are their concerns actually merited? And how can investors sort out the winners from the losers?”
  — Alison Nathan (00:10)
• On the pressure for incumbent innovation:
  “You have to prove to us as an investor community and as a customer base that you can actually deliver better AI experiences because of that [moat].”
  — Gabriela Borges (03:45)
• On the new investor base:
  “You actually get to some really nice valuation support for these stocks. And it’s attracting a cohort of investors that historically would not have looked at software as a sector.”
  — Gabriela Borges (11:38)

Conclusion & Forward Outlook

• Not a static moment:
  Both established and emerging companies are highly dynamic in response to AI’s disruption. The competitive landscape will continue to evolve, with leaders determined by the ability to modernize, innovate (organically or via M&A), and monetize effectively.
• Near-term stability driven by fundamentals:
  A clear recovery in business metrics, and the arrival of new investor profiles, could underpin the next phase of sector stability.
• Long-term winners will actively adapt:
  Those who best combine existing strengths with agile adaptation—whether via engineering, acquisitions, or internal efficiency—stand to thrive.

Segment Timestamps

• 00:05–02:30 – The AI disruption thesis and investor concerns
• 02:30–06:00 – Defining and testing software company moats
• 06:00–07:26 – What makes a moat sustainable?
• 07:26–09:00 – Data/metrics needed for sector stabilization
• 09:00–11:50 – Evolving investor sentiment and sector ownership
• 12:08–13:42 – How software companies adapt: innovation, M&A, and competitive lessons