Video summary

The Mag 7 Just Became the Lag 7 — My 3 Best Stocks to BUY Right NOW!

Main summary

Key takeaways

Finance

Finance-focused summary (markets, investing framework, key claims)

Market context / narrative shift

  • The “Mag 7” is described as underperforming and being replaced by a “Lag 7” narrative.
  • Claim: $3.2 trillion in market cap loss for the Mag 7 in June.
  • Mag 7 stocks referenced:
    • Microsoft (MSFT)
    • Meta (META)
    • Google (no separate ticker mentioned)
    • Amazon (AMZN)
    • Apple (AAPL)
    • Tesla (TSLA)
    • Nvidia (NVDA)
  • Macro/positioning rationale:
    • A loud “AI fatigue” narrative is cited (attributed to economist Ed Yardeni).
    • The speaker argues this is short-term thinking; the real issue is CapEx uncertainty, not business collapse.

Core thesis / “why they’re down”

  • The lagging group is said to be underperforming mainly because they’re spending heavily on CapEx (especially AI data centers).
  • The market is framed as disliking uncertainty about whether ROI will materialize in the short term.

AI CapEx / macro numbers cited

  • Global AI CapEx (2026): $750B–$765B
  • Worldwide AI spending (services/software included): $2.52T total
  • Of that, AI infrastructure spending: $1.37T (Gartner cited)
  • Country comparison (to emphasize scale):
    • 174 countries projected to have GDP < $1T in 2026
    • Only 21 have nominal GDP > $1T

“Three layers” framework (methodology)

The speaker proposes three layers of the AI buildout and suggests one stock per layer:

  1. Chip / AI compute (the “picks/shovels” category)
  2. Cloud / software platform that monetizes compute
  3. Energy / power that enables data centers’ 24/7 operation

Investment approach emphasized:

  • Buy “against the grain” when narratives are loud.
  • Prefer long-term investors over traders.
  • Acknowledge caution: stocks can go lower; use DCA (dollar-cost averaging) and staged entries.

Key numbers, recommendations, and risk notes by stock

1) Microsoft (MSFT) — “cloud + AI monetization despite CapEx”

CapEx figure

  • Microsoft planning $190B for calendar year 2026, claimed as +61% vs prior year.

Earnings / growth metrics cited

  • Azure: surpassed $75B in annual revenue.
  • “Microsoft AI run rate”: $37B (as claimed by the speaker).
  • Q3 2026 revenue: $82.9B (+18% YoY).
  • Azure growth expectations next quarter: 39%–40%.

Valuation / profitability metrics cited

  • Mentions: “PEG … 0.77” (speaker wording; context unclear due to subtitle noise).
  • Dividend: about ~1% yield; 20 years of dividend growth.
  • Payout ratio: ~22% (as stated).
  • Net income per employee: $549,000 (as stated).
  • Qualitative note: described as A+ profitability.

Performance metrics mentioned

  • Stock down -17% YTD
  • Down -20% over the past year
  • Up +7% in the past 5 days

Explicit price guidance

  • 52-week low: $349.20
  • Preference: under $350
  • Example entry: $349.97 limit order (stated as already giving gains)

Recommendation style

  • Framed as a long-term “buy, hold, and monitor” holding (“swan/sleep well at night” style).

Caution

  • Notes the stock can still go lower.

2) Oracle (ORCL) — “neo cloud + backlog; spending scare already priced”

Market narrative

  • Claims Oracle had a parabolic 2025 run and is being “crucified” for:
    • spending too much
    • worsening FCF trend
    • margin compression risk tied to “neo cloud”

AI customers / partnerships cited

  • OpenAI allegedly chose Oracle Cloud Infrastructure (OCI) for primary compute (not AWS/Azure/Google).
  • Claims Nvidia partnership with Oracle.
  • Mentions AI ecosystem/customers: XAI, Meta Platforms, Microsoft.

Key metrics

  • Remaining performance obligation (RPO): $638B (+363% YoY), framed as backlog/contracted future revenue.

Technical/macro positioning claim

  • OCI described as specialized/high-performance AI cloud; hyperscalers are said to “partner where they can’t replace.”

Explicit price / tactical framework

  • Notes Oracle pulled back after a peak around $343, described as an “air pocket.”
  • Fibonacci levels cited:
    • S2: $115.06
    • S3: ~$74
    • April 2025 lows: $117
  • Speaker view:
    • Doesn’t think it goes to $75, but acknowledges it’s possible
    • Suggests staged entries via DCA
  • States they bought today around $139 (“$139 and change”).

Recommendation style

  • “Buying Oracle stock with my money.”

Caution

  • Acknowledges downside scenarios; uses staged entries/DCA.

3) Constellation Energy (CEG) — “nuclear power for AI data centers”

Company fundamentals

  • Described as the largest nuclear energy operator in the US.
  • Controls 22 gigawatts nuclear capacity.
  • Capacity factor: 94.7% (stated).
  • Claim: virtually 0% of core business is a regulated utility (supported via “Gemini search,” as quoted).

Demand / utility link to AI

  • Claim: one large-scale AI data center consumes electricity comparable to 50,000 homes.
  • AI inference runs 24/7 on billions of queries, increasing power demand.

Hyperscaler contracts cited

  • Microsoft: 20-year purchase power agreement (PPA)
  • Meta/Facebook: 20-year deal
  • Google and Amazon: “searching heavily” for nuclear
    • Amazon working with Talen
    • Google looking into SMRs
  • Mentions Walmart collaboration with Constellation (from Walmart site, as quoted)

Analyst target numbers

  • TipRanks claims:
    • 12 buy, 3 hold, 0 sell
    • Highest price target: $516
    • Lowest: $296
    • Average: $366 (~52.79% upside, stated)

Explicit entry guidance

  • Speaker claims price “dropped from $411 to $239 today.”
  • Fibonacci visual cited:
    • S3 about $214
    • Example “floor”: $200
  • Recommendation: buy CEG around $250 or less via DCA (“the lower the better”).

Caution

  • Notes it’s possible prices fall; uses DCA.

Disclosures / disclaimers

  • The speaker states: “This is not financial advice.”
  • Mentions personal analysis and that the stocks are “what I’m buying with my money.”
  • Risk note throughout: stocks can go lower; framework is for investors, not necessarily traders.

Presenters / sources mentioned

  • Presenter/speaker: Not named in subtitles.
  • Sources / authorities mentioned:
    • Ed Yardeni (used the term “AI fatigue”)
    • Gartner (AI spending forecasts)
    • Mentions CNBC, Bloomberg (where the “AI fatigue” narrative is discussed)
    • TipRanks (CEG analyst target stats)
    • Gemini search (used to support the claim regarding regulation exposure)

Original video