Listener Q&A Format and Survey

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This episode answers three listener questions to kick off the new year. First, Dave and Andrew break down the difference between Coca-Cola (KO) and Coca-Cola Consolidated (COKE)—why the brand/business model matters, what makes bottling and distribution a different kind of investment, and what to look for before assuming a “price drop” is automatically a buying opportunity.

Next, they tackle a common beginner question: how to think about NVIDIA compared to Apple and Google. Finally, they discuss why they sold Crown Castle (CCI) in the Real Money Portfolio—what changed in the thesis, how to think about selling at a loss, and why opportunity cost and ego can quietly wreck long-term returns.

Key Topics Covered

• KO vs COKE

• Why distribution can be a moat

• NVIDIA vs Apple/Google: valuation, expectations, and forward returns

• Crown Castle sale

• Selling losers

  
  

Timestamps

00:01:32 – KO vs COKE

02:06 – What COKE actually is

03:22 – Capital intensity + ROIC differences

005:11 – COKE’s dividend changes + what’s driving the improved numbers

07:19 – Distribution matters: Celsius/Pepsi example + why moats can be distribution-driven

09:41 – How to think about analyzing distribution businesses

16:06 – KO growth expectations vs looking deeper at COKE’s recent performance

18:01 – Brand vs distributor over the long run

22:27 – Apple/Google vs NVIDIA

23:05 – Great company, but priced for huge expectations

29:34 – Valuation risk

30:17 – Why NVIDIA gets the warning vs Apple/Google

33:35 – Why they sold Crown Castle (CCI)

37:48 – Selling at a loss

Resources Mentioned

The Value Spotlight Newsletter: https://einvestingforbeginners.com/value-spotlight-newsletter/

Have questions or want your story featured? Email the show at newsletter@einvestingforbeginners.com or comment below. Your feedback shapes the podcast!

Remember, invest with a margin of safety—emphasis on the safety. Have a great week, and we’ll talk to you next time.

Timestamps are generated by artificial intelligence, and are not 100% accurate depending on the platform used for listening.

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