Kevin Warsh's Planned GREAT RESET | How to Prepare & WIN.

Finance-Focused Summary (Markets, Macro, Investing, Risk)

Core Macro Debate: Deflation vs. Inflation (and What It Implies for Portfolios)

Kathy Wood / “Deflation + Growth” View

• Yield curve flattening is attributed to 2-year Treasury yields rising, driven by short-term inflation pressure, while long-term markets price deflation.
• A productivity boom is expected to drive prices down (i.e., deflation).
• “True inflation” is argued to be < 2%, even if CPI is around ~4% (contrasting “Kevin CPI” vs “true inflation”).
• Bond-market behavior: “net buying of Treasuries” supports the deflation thesis, with exceptions noted when countries such as China, India, and Japan reportedly sold at times to stabilize currencies.
• Main concern raised by the narrator: the employment report undermines a clean deflation narrative:
  • Household employment rate (3-month average) went negative → typically a recession signal.
  • Employment misses / weakness cited:
    • Healthcare: ~38,000 trend vs ~22,000 (slower growth)
    • Leisure & hospitality: ~-61,000 in June, described as weaker seasonal hiring
    • Civilian labor force: -72,000 workers; also about ~1 million YoY decline
  • Labor force participation falling; the narrator rejects the idea that this is mainly people leaving for startups/AI entrepreneurship, arguing the labor market still matters most for whether recession (and thus deflation) arrives.

TS Lombard / “Inflationary During AI Buildout” View

• The AI buildout is described as inflationary during the build phase, because hyperscalers are making large infrastructure investments (framed like stimulus).
• A key mechanism is a J-curve:
  • Early inefficiency (“token maxing”) increases inflationary pressure as firms adopt AI before realizing benefits.
  • Over time, efficiency improves, and the narrator suggests the J-curve flips toward deflation as the rollout matures.

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Investing / Strategy Implications Discussed

Key driver to watch (recession vs. inflation): Employment

• TS Lombard warns that if employment re-accelerates, the debate shifts toward the hawks (i.e., “higher for longer,” more inflation risk).

Key driver to watch (AI cycle): Capex & capacity roll-off

• The narrator emphasizes capex slowing and excess supply coming online, which would be consistent with the move toward deflation/recession pressure.
• Timing windows mentioned:
  • AI supply/capacity turning points referenced as ending around 2027–2028 and 2030
  • “Waiting times on AI supplies” ending in 2028 and 2030
  • Memory supply implied earlier, around the end of 2027
• The narrator suggests 2027–2029 as the period to prepare for this shift.

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What Could Break the Economy (Risk Framing)

Main recession / deflation risk channel (narrator)

• Labor-market deterioration → recessionary deflation (or at least materially worse conditions).

AI capex “U-turn” / spending cycle risk

• Meta example:
  • April 29: described as a capex “loop” where future models require continued spending.
  • ~Two months later: Zuckerberg said AI agent progress is slower than expected, expecting bigger benefits in 3–6 months.
  • The narrator frames this as an early flip toward efficiency, potentially implying slowing capex.

“Unabsorbed layoffs” concept

• Even if the unemployment rate looks stable early, layoffs can become unabsorbed later—companies lay off faster than labor can be redeployed.
• This is framed as a recessionary escalation mechanism.

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Company / Sector Implications (Who Wins / Loses)

Potential winners named

• Large, cash-generating AI infrastructure/platform firms could benefit if the cycle turns and others struggle:
  • Microsoft
  • Google
  • Meta

Potential losers framed

• Companies bought at peak prices based on the AI buildout narrative.
• Some “compute side / neocloud / core side” is expected to collapse over the next decade (broadly framed; no specific tickers mentioned beyond the mega-caps already named).

Concentration / market power risk (TS Lombard)

• Concern that AI may “collude and consolidate” into big platforms (e.g., Google / Meta / Microsoft) enabling higher pricing via data advantages.
• The narrator calls this a weaker evidence thesis but flags it as a risk.

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Hedging / Portfolio Construction Guidance (Explicit Recommendations)

• General stance: both deflation and inflation arguments can be true, depending on phase.
• Be hedged for recession, even if long-term AI is bullish.

Hedge components stressed

• Optionality: hold cash and limited debt.
• Avoid being forced into liquidity/deleveraging at the wrong time.

Named risk management principle

• Avoid “hype trains”; “bailouts” can prop up losers.

Narrator’s discipline framing

• Reinvest with disciplined spending/allocation as the antidote to hype-driven overexpansion.

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Disclosures / Disclaimers Mentioned

• Includes: “# no guarantees don’t sue me.”
• Also contains a personal/experiential disclaimer tone (not a formal legal disclaimer, but signaling non-guarantee).

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Instruments, Tickers, Assets, and Sectors Mentioned

Macro Rates / Instruments

• 10-year Treasury (10Y)
• 2-year Treasury (2Y)
• Treasuries
• References include “Fed puts,” rate hikes, and the Fed chair (no explicit ticker).

Equities / Companies

• Meta
• Microsoft
• Google (Alphabet)
• Mentions: Cisco
• Mentions: Micron
• Mentions: Samsung
• (Note: AT&T is mentioned as not present.)

Other

• Memory prices and proxies for compute demand
• Chips / semiconductor supply chain and manufacturing (e.g., DRAM, wafer fabs, advanced packaging, HBM implied)
• Real estate and financing concepts (including scenarios where rates go to zero; buying below value / “wedge deals”)

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Methodology / Frameworks Explicitly Described

Yield Curve Decomposition Framework

• Flattening = narrowing spread between 10Y and 2Y
• Interpretation:
  • 2Y rising → near-term inflation pressure
  • Long-term deflation pricing → markets expect lower inflation later

Productivity vs. Pricing Framework

• Productivity can be:
  • Deflationary if gains reduce prices / flow to workers
  • Inflationary if companies preserve margins (keep prices up)

AI Cycle / Inflation Mechanism Framework

• AI buildout = inflationary during the capex phase
• J-curve
  • Adoption inefficiency initially increases inflationary pressure
  • Benefits later improve efficiency → potentially flips toward deflation

Recession Risk Watchpoints

• Watch for:
  • Employment deterioration
  • Capex slowing at hyperscalers
  • Memory/compute cost declines
  • Layoffs → “unabsorbed layoffs” (labor absorption breaks)

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Key Numbers and Timelines Cited

Inflation Comparison

• CPI referenced around ~4%
• “True inflation” referenced as < 2%

Employment / Labor

• Employment miss referenced: “missed by about 50%”
• Healthcare: ~38,000 trend vs ~22,000
• Leisure & hospitality: ~-61,000 in June
• Civilian labor force: -72,000
• Labor force YoY: ~1 million workers down

Capex / AI Cycle Timing

• AI supply “waiting times” ending around 2028 and 2030
• Memory timing implied around end of 2027
• Preparedness window: 2027–2029
• Meta timeline: Zuckerberg expects major AI benefits in 3–6 months

Real Estate / Financing Example Numbers (Narrator personal scenario)

• Targeting $2 to $250 million in assets (noted as not “market cap”)
• Mentions ~$250 million in cash assets
• Mentions potential 35% down payment when rates go to zero
• Down payment framed to support ~$714 million real estate
• Mentions ~20% discount leading to ~$100 million equity uplift

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Presenters / Sources Mentioned

• Kathy Wood (Arc Invest / ARK Invest referenced indirectly)
• TS Lombard
• Kevin Warsh (referred to in the framing; “Warsh” / “Kevin Worsh” in subtitles)
• Jay (Jerome) Powell / Fed policy reference (Jerome U-turn context)
• Allen Greenspan (historical comparison)
• Mark Zuckerberg
• David Sacks (via “ramp capital study” / tweet narrative)
• Micron, Samsung, SK Hynix (industry players)
• Jensen Wong (quoted as an example related to spreadsheets/accounting jobs)
• Kevin Papra / Meet Kevin (host channel mentioned at the end)