How AI is Changing Cloud Valuations

🤔 Just ran the numbers, and I'm seeing a fascinating shift coming in the #AI and #Cloud landscape... The conventional wisdom that agentic AI would naturally gravitate to hyperscaler platforms is proving to be more myth than reality. Here's what's really happening: Processor Evolution • Most agentic AI systems are leveraging commodity processors • The dependency on specialized GPUs is diminishing • Simple CPU clusters are handling many AI workloads effectively Cost Reality Check • Hyperscaler margins (40-60%) are becoming harder to justify • Private clouds delivering 50-70% cost savings for AI workloads • MSPs and colos offering more flexible, cost-effective solutions Market Adaptation • Sovereign clouds gaining traction with regionalized AI solutions • Enterprise IT becoming more sophisticated about true TCO • Multi-cloud strategies focusing on cost optimization over brand names 🎯 The Reality: By end of 2025, we'll see that AWS, Azure, and GCP missed their AI growth targets significantly. The market is speaking - agentic AI doesn't need hyperscaler infrastructure to thrive. 💡 My Prediction: Watch for a massive shift toward hybrid architectures, with agentic AI workloads running primarily on optimized private infrastructure and smaller, specialized providers. #CloudComputing #ArtificialIntelligence #TechTrends #CloudStrategy #Enterprise #Innovation Thoughts? Would love to hear your perspectives on this shift.

This week has been a perfect storm. As if Diwali, Halloween, and month-end weren’t keeping us on our toes, the Tech Titans threw in their earnings for good measure. The big takeaway is this: for the cloud giants — Google, Microsoft, and Amazon—the AI trend has come with both a trick and a treat. 👻 On the one hand, they’re seeing accelerating cloud revenue as companies rush to adopt AI. On the other, they’re being handed the bill. Meeting this demand requires infrastructure—a lot of infrastructure—and that means some eye-popping capex projections. 🥇 Google kicked things off with a bang. Google Cloud’s 35% surge to $11.35 billion signals the AI hype is translating into real dollars. Overall revenue up 15% to $88.3 billion. Sundar Pichai dropped a fun stat for us in the earnings call - 25% of new code at Google is AI-generated. 🥈 Microsoft came in hot, but guidance left investors cold. Microsoft’s Azure posted a solid 29% growth, hitting $24.1 billion, but then the stock took a hit when they projected slower. Satya Nadella’s take? “We are seeing more demand for AI than we can keep up with.” Translation: the market wants AI now, but Microsoft’s pace is held back by its own infrastructure buildup. 🥉 Amazon had a massive quarter too, with AWS posting 19% growth to $27.5 billion and total revenue up 13% to $158.9 billion. But it’s Andy Jassy’s “once-in-a-lifetime opportunity” language on AI that’s notable. He talks about it like it’s a rare planetary alignment, so naturally, they’re investing accordingly. Their CAPEX is substantial, especially for AWS, and Amazon’s approach seems to be, “Spend now, explain to shareholders later.” The bigger picture here is that Alphabet, Microsoft, and Amazon are collectively bracing to drop over $200 billion by 2025 on the infrastructure needed to support AI. The market might flinch a bit at that figure, but there’s a certain inevitability to it. They aren’t just reacting to demand—they’re building the AI economy’s plumbing, making sure they’re the pipes. 🔌

Everyone’s talking about AI monetization, but let’s be real—one of the biggest winners so far are cloud hyperscalers. Case in point, Alphabet Inc. / Google CEO Sundar Pichai mentioned this in their recent earnings call: “Today, cloud customers consume more than 8x the compute capacity for training and inferencing compared to 18 months ago.” As enterprises pivot towards AI-driven solutions, they rely heavily on the scalable, flexible, and powerful compute resources offered by hyperscalers. Whether it’s for machine learning model training, real-time inferencing, or managing massive data pipelines, public cloud is the backbone of modern AI innovation. This is where most of the AI workloads are going. The engines have started for private cloud, but the consumption is nowhere near public cloud (yet). What are your thoughts on the cloud hyperscaler's ability to monetize AI workloads? #AI #ArtificialIntelligence #CloudComputing #Monetization #AITransformation #FutureOfAI #MachineLearning #Hyperscalers #TechEconomics #AIInfrastructure #CloudBusiness #AIAdoption Data: Google earnings, Perplexity