Turbine Capacity, Efficiency and Production Constraints

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This episode is a Bird’s Eye View breakdown of GE Vernova (ticker: GEV), a newer standalone company spun out of General Electric. Andrew and Dave walk through what the business is, how it makes money, and why it’s showing up in investor conversations—especially around electrification and power demand.

They cover GE Vernova’s three operating segments and explain why electrification is the most exciting long-run opportunity. The conversation then shifts to the power segment and the AI/data center demand story, including how gas turbines are being positioned as a practical solution for stable, sustained power.

Key Topics Covered:

• What GE Vernova is and why it was spun out of GE

• The 3 segments

• Backlog as a runway indicator

• AI/data centers and the demand for sustained electricity

• Valuation & execution risk

Timestamps:

00:15 – Bird’s Eye View on GE Vernova (GEV)

01:38 – What is GE Vernova and how does it make money?

02:46 – “Energy to change the world” 

07:07 – $26B and what backlog means

08:12 – Book-to-bill style thinking

09:43 – AI/data centers driving demand for stable power

13:37 – Capacity booked out to 2028 & building more capacity

16:21 – Small modular reactors (SMRs) and why they matter

18:51 – Revenue growth context & profitability trend

24:05 – Balance sheet strength

34:32 – Oracle comparison

35:19 – Other electrification demand drivers

Resources Mentioned

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

Bird’s Eye View of Brookfield Asset Management (BAM) with Adrian of Stratosphere: https://einvestingforbeginners.com/birds-eye-view-of-brookfield-asset-management-bam-with-adrian-of-stratosphere-podc/

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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