Alphabet Inc.’s shares slipped following the company’s latest quarterly results, which included a massive $75 billion Capex guidance for 2025. However, Alphabet bulls believe these investments will pay off in maintaining its dominance in search. The company’s Gemini model has an edge over competitors due to Alphabet’s large ecosystem.
So far, AI competition hasn’t significantly impacted the company’s search revenue. In the fourth quarter, Alphabet’s operating margin rose 32%, YouTube ad revenue jumped 14%, and Cloud revenue increased by 30.1%. Alphabet generated $12.8 billion in free cash flow, marking roughly 215% growth from the same period last year despite heavy investments in AI.
The stock has a forward (2026) P/E ratio of 20.8x, making it about 22% cheaper than the average company in its sector. Bretton Fund stated that Alphabet Inc. was a major contributor for the quarter, adding 1.3%.
Alphabet reports strong Q4 growth
In recent news, Jim Cramer stated on CNBC that he has been selling shares of Alphabet Inc. at his investing club, citing his bearish outlook.
“Members of my club know I’ve been selling it down. Would it be the first one that I leave of the mag? Yeah, except it does sell only for 20 times earnings.
But I think they really need to rethink their game plan because I feel like Gemini competes with Google search, and the ads are endless, and they don’t know how to monitor YouTube,” Cramer said. Overall, Alphabet is on the list of top buzzing AI stocks to watch now. While it has significant potential as an investment, some believe that under-the-radar AI stocks could offer higher returns within a shorter timeframe.
The market continues to evolve, and as always, investors should conduct thorough research before making any investment decisions.
Image Credits: Photo by Andrea De Santis on Unsplash
Cameron is a highly regarded contributor in the rapidly evolving fields of artificial intelligence (AI) and machine learning. His articles delve into the theoretical underpinnings of AI, the practical applications of machine learning across industries, ethical considerations of autonomous systems, and the societal impacts of these disruptive technologies.
