GOOGL CLOUD HAS A RULE-OF-40 SCORE OF 78

1.

$Alphabet(GOOGL)$ CLOUD HAS A RULE-OF-40 SCORE OF 78

Google spent a decade building TPUs before anyone cared about custom AI chips, built BigQuery before enterprises modernized data stacks & kept investing in Gemini while the market fixated on OpenAI.

Now those bets are converging into a full-stack AI platform of custom silicon (TPUs), data (BigQuery), models (Gemini), enterprise AI (Vertex) & global network infrastructure.

No other company has all five at scale and it’s the only credible $NVIDIA(NVDA)$ alternative.

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2.

$NVIDIA(NVDA)$ is re-entering the laptop market this year by pushing CUDA onto AI PCs.

Margins won’t look like data center but Nvidia now spans the full AI stack:

• Training (H100, Blackwell)

• Inference (data center)

• Edge (Jetson)

• Device (AI PCs)

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3.

DRAM prices are up nearly 10x over the past year which is wild when you look at what's driving it:

• Memory shortages now expected to persist through 2027–2028

• HBM TAM now pegged at ~$100B by 2028 (pulled forward by two years)

• $MU guided Q2 ~30% above consensus & is turning away business due to capacity constraints

• $600B+ in 2026 $Meta Platforms, Inc.(META)$ , $Microsoft(MSFT)$ , $Amazon.com(AMZN)$ & $Alphabet(GOOGL)$ CapEx while every $NVIDIA(NVDA)$ Blackwell rack + inference server is memory-hungry

HBM gets the headlines but the real story is that the entire memory stack is supply-constrained as AI demand overwhelms capacity across the board.

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