Podcast Summary: Full Signal with Phil Rosen

Episode Title: AI is KILLING Big Tech stocks! | Luke Kawa
Guest: Luke Kawa (Markets Editor at Sherwood News)
Release Date: March 9, 2026

Overview of Episode

In this episode, host Phil Rosen interviews Luke Kawa, a leading markets editor, to dissect the impact of artificial intelligence (AI) on big tech stocks, particularly the “Magnificent Seven.” They explore market sentiment, the ongoing AI-driven software sell-off, changes in free cash flow, shifting investment narratives, and the broader macro and credit environment. Real-world data, expert charts, and candid insights bring nuance to the question: is AI boosting or disrupting the tech juggernauts—and what does that mean for investors?

Key Discussion Points & Insights

1. Current Market Dynamics & Investor Sentiment

• Market Flatness, Safety, and “Hidey Holes”
  • Luke observes that despite a new all-time high early in the year, major indices have been "meandering." Investors are searching for perceived safety in both consumer staples (e.g., Walmart and Costco) and certain AI-related memory stocks (e.g., Sandisk).
  • Significant price increases in select memory stocks, despite low P/E ratios, signal that earnings have risen much faster than share prices, highlighting heightened interest and confidence in specific corners of the AI narrative.
• Quote:
  • “The main story in markets has been a lot of looking for hidey holes in kind of expected and unexpected places, as we continue to debate how we feel about AI Capex and returns on Capex.” (Luke, 01:58)

2. AI Capex Boom and Big Tech’s Falling Cash Flows

• The “Capex Binge” and Cash Flow Challenges
  • Luke unpacks the shift among the Magnificent Seven from high free cash flow to aggressive capital expenditures (mainly for AI infrastructure), leading to falling cash flow projections and underperformance of big tech stocks.
  • He likens the ongoing investment spree to “a teenage kid…getting into heavy metal and goth music,” suggesting this “phase” is lasting longer than markets expected.
• Notable Moment:
  • “[Capex] is lasting a lot. Still a lot of black eyeliner… and a lot of falling cash flows and not necessarily a return to what you’ve expected these stocks to always be.” (Luke, 04:12)
• Nvidia as a Bellwether:
  • Nvidia is no longer simply “upstream”; its valuation depends on the success of its customers (the “hyperscalers”), making its fate far more interdependent with Big Tech buyers.
  • “You want the GPUs to be able to earn the return that you’re paying for them. And so far…the evidence is not supportive of that so far.” (Luke, 06:34)

3. Valuations and Potential Inflection Point

• When Do Tech Stocks Look Attractive Again?
  • Luke believes that with declining prices and valuations, a re-entry point for investors is approaching, but points out that a catalyst, not just low valuations, is usually necessary to trigger a rebound.
  • Narratives such as “memory over everything” and “the many over the few” (equal-weight over S&P 500) have recently reversed, possibly providing support for a tech-based rally soon.
• Quote:
  • “When you get to valuations…you need, oftentimes, valuation plus catalyst.” (Luke, 07:23)

4. Bubble or Breakthrough?

• Personal and Market Perspectives on AI’s Trajectory
  • Phil and Luke explore the AI “bubble” debate, both admitting they swing between excitement and skepticism based on daily experiences and market moves.
  • Luke stresses that much AI-related capex is defensive: hyperscalers are spending to avoid being left behind, not just to seek new returns. This can lead to “deadweight loss” for over-spenders.
• Insightful Quote:
  • “There are days I wake up going, ‘I better come up with something good today or else my bosses are gonna figure out that an AI could probably do 75-80% of what I do in two weeks.’ There are other days where I’m like, ‘This thing can’t even scrape the damn chart I’m looking for.’” (Luke, 09:58)
• Winners and Losers:
  • Luke notes that value is accruing to memory stocks and chipmakers rather than hyperscalers, and that private markets (startups, unlisted firms) may increasingly benefit from the AI boom compared to public markets.

5. AI Disruption’s Spillover into the Rates and Credit Markets

• Peter Williams’ Research and Rate Expectations
  • Unusual divergence: Short-term rate cuts are being “priced out,” but long-term rates (end of 2027) are expected to be lower, reflecting concerns about AI-induced labor and credit disruptions.
  • Thesis: Large AI capex may trigger job and income losses, leading to eventual Fed easing.
• Luke’s Take:
  • More likely, AI will introduce credit stress as disrupted companies struggle and private credit faces losses. “There will be losers and losers will come with job losses.” (Luke, 15:45)

6. Credit Stress, Software Disruption, and AI Startups

• Private Credit Jitters
  • The intersection of AI startups needing cash and established software companies facing disruption creates risk for lenders: “Either the AI disruptors get funded…and software companies are disrupted, or software companies hold up…because AI companies don’t get the funding.” (Luke, 17:12)

7. Macro Perspective: Stock Market vs. Job Market

• Historical Correlation—Erosion Ahead?
  • Historically, the stock market and job market move together 75% of the time for six-month periods. While the age-old assertion is that “the stock market is not the economy,” the evidence suggests a far stronger connection—at least, for now.
• Quote:
  • “When your timeframe is anything more than a day…stock market and job market are going to go in the same direction. The big discrepancies are…generational inflation or lagged policy effects.” (Luke, 18:57)
• Bear Markets and Earnings Drawdowns:
  • Bear markets usually coincide with recessions or major inflation, largely explained by drops in corporate earnings.

8. The Political Dimension

• Stock Market as a Political Scoreboard
  • Phil raises how public figures, notably President Trump, have explicitly tied economic wellbeing to stock market performance, blurring the lines between Main Street and Wall Street perception.

9. Outlook for the Rest of the Year

• Base Case: Cautious Optimism with Caveats
  • Luke remains constructive for now, noting that, historically, "the stock market usually goes up" without a clear catalyst to the contrary.
  • However, if AI doesn’t “work”—meaning hyperscalers aren’t rewarded for massive spending—he warns, “I don’t think anything works. The past five months are your proof.” (Luke, 25:30)
• What “AI working” Means:
  • “The companies spending the most [on AI] getting rewarded for spending…Market rewarding investment.” (Luke, 26:13)
  • If AI investment remains purely defensive (avoiding disruption), margin compression will follow, dampening multiples and stock price potential.

Notable Quotes & Memorable Moments

• On Market Sentiment:

  • “Sentiment has been everything this year…Even the Citrini piece causing the huge sell-off in pretty much everything, especially in tech.” (Phil, 02:32)

• On Capex Binge:

  • “This phase is lasting a lot. Still a lot of black eyeliner… and a lot of falling cash flows and not necessarily a return to what you’ve expected these stocks to always be.” (Luke, 04:12)

• On Nvidia’s Unusual Role in AI:

  • “Nvidia… is being treated and valued as a play on the success of its customers.” (Luke, 06:12)

• On Personal Use of AI:

  • “AI has been a fabulous editor and sparring partner for me. Love to get it to fight me and tell me why I’m wrong about things.” (Luke, 12:40)

• On Defensive Capex:

  • “A lot of this hyperscaler spending in Capex is defensive in nature. We’re spending because we can, and if we spend and we’re the leaders, the odds of us getting disrupted go down.” (Luke, 27:36)

Important Timestamps

• 00:36 – Phil sets up the markets context, “big picture view”
• 01:58 – Luke defines the market’s search for safety
• 02:51 – Forward cash flow estimates, Magnificent Seven analysis
• 04:12 – Capex binge and shifting expectations
• 06:34 – Nvidia and hyperscalers’ interdependence
• 09:55 – AI bubble fears and personal/market sentiment
• 13:32 – AI’s impact on rates & labor/credit disruption thesis
• 16:31 – Private credit stress, AI startups vs. traditional software
• 18:49 – Stock market/economy correlation and historical analysis
• 24:19 – Macroeconomic and investment outlook for rest of year
• 26:09 – What “AI working” actually means in today’s market
• 27:36 – Defensive Capex, margin pressures, and future risks

Where to Find Luke Kawa

• Sherwood News:
  “Best place to find me and all the folks I work with, great colleagues at Sherwood News.” (Luke, 28:26)
  (Sherwood is part of Robinhood but operates independently.)

Summary Takeaway

This episode unpacks the underlying mechanics of the current AI-driven transformation in markets, arguing that while AI is a real and powerful force both for productivity and technological change, its financial rewards are unevenly distributed and, in some cases, may be hampering the returns investors expect from Big Tech. Credit stress, market sentiment, and defensive spending all create a complex investment environment that will require careful navigation—a message delivered with candor, data, and wit by both guest and host.