Video summary

9 Best Stocks To Buy In July

Main summary

Key takeaways

Finance

Finance-focused summary (stocks / investing / macro)

Theme & timing (July)

The host argues that while the market is “racing up,” returns are being driven disproportionately by semiconductors. He then claims there’s a valuation opportunity in other high-quality companies that have been “left behind,” particularly parts of Big Tech.

Macro / market rotation thesis

The video’s core setup is that the market has become a reallocation game:

  • Capital moves from most sectors (“everything else”) into semiconductors
  • That inflates semis’ valuation and momentum
  • While other quality sectors/companies lag despite their underlying strength

Methodology used (company selection)

The approach combines long-term quality with short-term valuation timing:

  1. Prefer “compounding machines”
    • Durable earnings growth
    • Strong moats
    • Rising margins over long periods
  2. Use valuation as timing
    • Buy great companies when priced attractively
  3. Positioning / holding style
    • Target companies suited for large concentrated positions
    • Plan for multi-year holding periods (even “forever” conceptually)

Key market datapoints / numbers cited

Semiconductors in the S&P 500 (weighting)

Approximate historical weighting figures cited:

  • ~1% in 1995
  • ~8% during earlier peak levels (pre–dot bubble)
  • ~2% most of the time from ~2005 to ~2020 (never over ~5%)
  • ~12% in 2024
  • ~19.7% by ~2025
  • “Today” around ~20% of the S&P 500

Market breadth effect

  • “Everything else” weight falls from ~98% to ~80%
  • Interpreted as money flow into semiconductors

Semiconductor momentum & risk framing (cautions)

The host acknowledges positives (high demand, “scarcity pricing,” dramatic price increases), but cautions that semiconductors remain:

  • Historically cyclical
  • Volatile

He also highlights momentum dynamics—investors tend to buy what’s already rising (described as “TikTok trader” behavior / momentum factor behavior). He references Bloomberg writing that “buying what’s going up” is working due to momentum.

“Buy in July” stock recommendations (valuation / performance numbers mentioned)

1) Meta Platforms (META)

  • Valuation: ~18x forward P/E
  • Why it’s attractive (AI / capex optionality):
    • Frontier AI training via Meta Super Intelligent Labs (referenced)
    • Scaling ad recommendation systems: complexity improvement described as “over 10x”
    • Possible access to a private Claude instance via Anthropic (build/scale AI capabilities)
    • Potential “NeoCloud” angle: on-demand compute-style deals; claims “a few hundred MWs” could drive ~$10B/year in revenue
  • Recommendation: “First company… buy in July” = Meta

2) Amazon (AMZN)

  • Core edge: logistics + Prime (two-day delivery) plus AWS
  • AI angle implied: expected AWS demand acceleration; capex described as monetized quickly
  • Recommendation: top July buy alongside Meta and Microsoft

3) Microsoft (MSFT)

  • Share price / valuation: trades below $400; valuation described as “below a 24p ratio” (interpreted as <24x P/E)
  • Context vs history:
    • Often around 30–38 trailing P/E
    • Past up to ~40 trailing P/E (2024 reference)
    • “5-year low” around ~23 trailing P/E (seen during 2022 big-tech selloff)
  • Earnings growth estimate (projection): analyst EPS growth expectations described as high teens rate
  • Narrative: being sold “with the rest” (treated as Big Tech/capex exposure, not a semis beneficiary)
  • Recommendation: top July buy

4) Netflix (NFLX)

  • Price / performance: $75.83/share; down 15% YTD; about down ~40% from highs
  • Valuation: ~22x P/E on 2027 estimates; claims it’s effectively below 20 based on “true” earnings being higher than estimates
  • Primary bear-case diagnosis (why it’s cheap):
    • “No big hit in a while” (content cycle concern)
    • Viewer retention problems: viewers “abandon shows after the first season”
    • Examples of audience drops:
      • One Piece: >30% loss in season 2
      • Beef (season 2): >70% drop
      • The Night Agent: 50% drop (season 2) and 35% drop (season 3)
      • Avatar: The Last Airbender: >60% drop in week 1
  • Company response noted: studying data; “sharp drop in viewers” is “major source of concern.”
  • Thesis: mispricing driven more by “market dynamics” (capital rotation funded by semis) than by fundamentals alone
  • Recommendation: suggested as a buy (cautious but attractive tone)

5) Uber (UBER)

  • Price levels referenced:
    • Prior peak around $100/share
    • Fell to about $70/share (host considered “opportunity”)
  • Scale argument vs competitor:
    • Competitor (“Wimo/Whimo” in subtitles): ~500,000 trips/week
    • Uber: 3.64B trips/quarter
    • Claim: Uber is ~570x the trips of Wimo
  • Recommendation: added to portfolio; positioned early in growth path

6) DoorDash (DASH)

  • Price levels referenced:
    • Peak around $281
    • Dropped to ~$150 (host says “that’s where I bought”)
    • Back up to ~$190
  • Recommendation: added / early growth path despite drawdown

7) Copart (CPRT)

  • Price / drawdown: ~$62 down to ~$28 (host says “down over 50% from highs”)
  • Business quality argument: solid business model, strong balance sheet, long-term growth
  • Recommendation: interested / attractive to look at

8) Constellation Software (CSU; ticker not clearly shown)

  • Rationale: serial acquisitions of vertically integrated software
  • Thesis: sold off due to broad market “distaste for software,” but host argues the selloff is overdone and that “Claude” won’t replace most of their software assets
  • Recommendation: attractive / held as high-quality

9) Mastercard (MA)

  • Price / valuation:
    • From ~$600 to ~$530
    • ~25x P/E (subtitles show “254 PE 23 based off 2027’s earnings,” interpreted as mid-20s forward on 2027 estimates; exact formatting unclear)
  • Recommendation: “Left behind,” attractive valuation

Portfolio / performance mentions

  • The host mentions a personal milestone: the “passive income portfolio” “cracked a million dollars” (not framed as a return metric tied to stock performance).

AI operating leverage example (used to support Big Tech)

The host uses EBIT and headcount trends to argue operating leverage from AI spend/capex translates into improved profit efficiency.

Google (example figures)

  • Adjusted EBIT rising each quarter; headcount stable
  • EBIT per employee:
    • $393k (2022) → $488k (2023) → $613k (2024) → $676k (2025)
  • Expected to keep rising through 2026–2028

Microsoft (example figures)

  • EBIT up by billions; headcount slightly down
  • EBIT per employee: $389k → past $1M

Meta (example figures)

  • Strongest growth so far
  • EBIT per employee projected to ~$1.78M

Amazon (example figures)

  • Headcount roughly same; EBIT per employee improves
  • EBIT per employee: ~$8k → ~$24k, expected ~$112k by 2028

Step-by-step / framework elements explicitly described

  1. Step 1: Long-term quality screen
    • Look for: moat/duration, durable earnings growth, margin expansion
    • Company becoming “more financially prosperous” over many years
  2. Step 2: Short-term timing
    • Buy when valuation is “very attractive”
    • Specifically the combination of “great company + attractive price”
  3. Step 3: Positioning / risk posture
    • Hold as large positions for multi-year periods
    • Avoid a “3-month flip” mindset

Disclosures / disclaimers

  • Risk disclosure: “No guarantees… Investing always takes on risk. That’s why we buy more than one company.”
  • No explicit “not financial advice” disclaimer is present in the subtitles provided.

“Fail of the week” (crypto / leveraged equity warning)

MicroStrategy (MSTR) and Bitcoin (BTC)

  • Stock down ~80% from highs (subtitles later say ~78%; numbers inconsistent)
  • Price cited: ~$450+ high area down to ~$98
  • Cause: leveraged Bitcoin exposure
    • As Bitcoin fell 30–40%, MicroStrategy fell more dramatically

Critique of Michael Saylor’s strategy

  • The strategy relied on MicroStrategy trading at a premium to the value of its Bitcoin holdings
  • Failure: the market shifted to valuing Strategy at a discount, “unraveling” the roll-up logic
  • Mentions Saylor’s rhetoric about never selling Bitcoin, with implication of contradiction when MicroStrategy did sell

Warren Buffett / Berkshire Hathaway reference

  • An argument that shifting $320B into Bitcoin would have been disastrous given:
    • ~3% after-tax yield vs
    • ~15% cost of capital
    • negative real yield (~12%)
  • Exact calculations described conversationally in the subtitles

Tickers / instruments mentioned

  • Stocks: META, AMZN, MSFT, NFLX, UBER, DASH, CPRT, MA, ASML, Micron (MU), TSLA
  • ETFs / indexes: QQQ, S&P 500
  • Crypto: Bitcoin (BTC)
  • Leveraged crypto proxy: MicroStrategy (MSTR)
  • AI references (not tickers): OpenAI / Anthropic (also “Claude” referenced)

Constellation Software ticker was not provided clearly in the subtitles; a “Mobility Global” reference appeared as a spin-off from S&P Global, but the ticker wasn’t given.

Presenters / sources

  • Host / presenter: Joseph Carlson
  • Referenced external sources / guests: Tom Lee (CNBC), Bloomberg, Michael Saylor (MicroStrategy)
  • Referenced executives: Sam Altman (OpenAI), Dario Amodei (Anthropic)
  • Referenced investor: Bill Ackman (subtitles: “Bill Aman” likely Ackman)
  • Macro comparison: Warren Buffett / Berkshire Hathaway

Original video