Podcast Summary: Thoughts on the Market – Future of Work: AI’s Impact on Industries

Host: Katie Huberty (Morgan Stanley’s Global Head of Research)
Guests: Steven Bird (Global Head of Thematic Research), Jeff McMillan (Head of Firmwide AI)
Date: November 4, 2025

Episode Overview

In this first installment of a two-part special, the panel explores how artificial intelligence is poised to transform work at an industry-wide level. The discussion spans the scale of AI’s earnings impact, the timeline for adoption across sectors, surprising insights from Morgan Stanley’s research, and challenges facing corporations—especially in highly regulated industries like banking. The speakers critically compare AI’s trajectory to previous technology cycles, highlighting both parallels and ways in which this transformation may be broader and deeper.

Key Discussion Points & Insights

1. AI’s Economic Impact: Headline Numbers

• Earnings Upside and Market Cap Potential

  • Steven Bird projects “a little over $900 billion” in net benefits from current AI adoption across the S&P 500, translating to “well over 20% increased earnings power” and potentially over $13 trillion in market cap (01:18).
  • The technology is evolving rapidly; upcoming models could be “twice as capable” as today’s with ten times the computational power being marshaled in leading labs.
  • Quote (Steven Bird, 01:18):
    “The net benefits based on where the technology is now would be about a little over $900 billion… that could generate over $13 trillion of market cap upon adoption.”

• Nonlinear Progress

  • The pace of AI improvement outstrips what linear projections might suggest, implying that capabilities will expand suddenly in new areas.
  • Quote (Steven Bird, 02:39):
    “Nonlinear improvement… so what looks like areas where AI cannot perform a task—six months from now—will look very different.”

2. Adoption Timelines & Industrial Shifts

• Timeline: Fast and Slow

  • Implementation is measured in years; while some companies take time to prepare, others are rapidly entering full-scale adoption (02:39).
  • 2026 is predicted to be a key inflection point for broader industry adoption.

• Industries Most Impacted – Surprising Leaders

  • Contrary to expectations, not “high-tech” sectors but rather low-margin, labor-intensive industries (healthcare, consumer staples, real estate management) show the largest potential for AI-driven gains.
  • High-tech firms’ profit per employee already high, so marginal impact is less significant.
  • Quote (Steven Bird, 03:37):
    “Think instead of sectors where there's fairly low profit per employee...healthcare, Staples came to the top...high tech sectors actually had some of the lowest numbers.”

3. Banking Sector: Use Cases & Transformations

• Software Development as a Leading Use Case

  • For banks like Morgan Stanley, AI’s earliest and most profound effects are in software development, impacting more than 20,000 employees (about 25% of staff).
  • Quote (Jeff McMillan, 04:28):
    “Software development...was probably the first genius case out of the gate. Not only was it first, but it continues to be the most rapidly advancing.”

• Automation of Paperwork & Content Generation

  • AI excels in automating document processing (account opening, contracts) and content generation (presentations, research reports), both high-volume, high-effort areas in banking.

4. Enterprise Challenges in AI Adoption

• The Human Bottleneck: Change Management and Education
  • The key challenge is not technical limitation but employee understanding and preparedness. The gap is between AI’s capabilities and firms’ capacity to use them.
  • Quote (Jeff McMillan, 06:19):
    “I've often made the analogy that we own a Ferrari and we're driving around circles in a parking lot...The technology has so far advanced beyond our own capacity to leverage it.”
  • Basic AI literacy, such as knowing how to prompt, is essential for extracting value.
  • Quote (Jeff McMillan, 06:19):
    “If you have $100 to spend, you should start spending $90 on educating your employee base, because until you do that, you cannot effectively get the best out of the technology.”

5. Looking Ahead to 2026: Key Trends and Strategies

• Agentic Solutions and Early Experimentation
  • The organizational landscape is shifting rapidly, with firms moving toward agentic (autonomous, workflow-integrated) systems, though much remains aspirational. Experimentation and establishing new governance frameworks are priorities (07:31).
  • Morgan Stanley is piloting 20+ use cases as groundwork for future full-scale adoption.
  • Quote (Jeff McMillan, 07:31):
    “That step where we are right now is really about experimentation...As things settle down and the vendor landscape really starts to pan out, we'll be in a position to fully take advantage.”

6. Historical Perspective: AI Compared to Past Tech Cycles

• AI as the Next Iteration in Computing Scale

  • The progression from mainframe to minicomputer, PC, internet, mobile, and now AI has consistently scaled computation by a factor of ten.
  • Quote (Katie Huberty, 08:48): “Each compute cycle is roughly 10 times larger in terms of the amount of installed compute...The numbers are bigger because we keep 10xing, but the pattern is the same.”

• Early Innings: Underestimated Impact

  • Historically, Morgan Stanley’s bullish early forecasts have tended to underestimate technology’s eventual market impact; this may prove true for AI as well.
  • Quote (Katie Huberty, 08:48):
    “When we go back and look at our forecasts, we always underestimated the potential...what we've seen with the upward earnings revisions for the AI enablers and soon the AI adopters is likely to continue.”

7. What’s Different This Time? The Breadth of AI’s Impact

• Scale Across Corporations
  • AI’s impact will be felt by an unprecedented proportion of companies—one third of Morgan Stanley’s ~4,000 covered companies already see material AI implications.
  • Quote (Katie Huberty, 11:14): “What may be different…is just the sheer number of corporations that will be impacted by the theme.”

Notable Timestamps

• 01:18 – Steven Bird on earnings and market cap upside
• 02:39 – Nonlinear progress and timeline of AI improvement
• 03:37 – Surprising sector findings from AI adoption research
• 04:28 – Jeff McMillan: Software development as transformative in banking
• 06:19 – The “Ferrari in a parking lot” analogy for organizational readiness
• 07:31 – Early experimentation with agentic solutions
• 08:48 – Katie Huberty’s comparison of AI to previous tech cycles
• 11:14 – Breadth of impact across covered companies

Memorable Quotes

• Steven Bird (01:18):
  “The net benefits...would be about a little over $900 billion...could generate over $13 trillion of market cap upon adoption.”

• Jeff McMillan (06:19):
  “We own a Ferrari and we’re driving around circles in a parking lot...The technology has so far advanced beyond our own capacity to leverage it.”

• Katie Huberty (08:48):
  “Each compute cycle is roughly 10 times larger in terms of the amount of installed compute...The numbers are bigger because we keep 10xing, but the pattern is the same.”

Tone & Style

The conversation is analytical and forward-looking, blending empirical data with real-world corporate perspective. Speakers balance optimism about AI’s transformative potential with realism about the internal challenges and historical context.

Episode Takeaways

• The financial upside of AI for industries is massive, but realization depends heavily on education and internal capacity.
• Industries with high labor intensity and lower profits per employee stand to gain most, not just tech firms.
• Banks are using AI most successfully in software development, paperwork automation, and content generation.
• Change management—especially employee education—is the critical lever for extracting value from AI.
• AI’s scale and impact are likely to outstrip previous cycles, touching more companies simultaneously and more deeply.
• The discussion will continue in part two, focusing on AI’s impact on individual workers and workforce transformation.