Welcome to episode 316 of The Cloud Pod, where the forecast is always cloudy! This week we’ve got earnings (with sound effects, obviously) as well as news from DeepSeek, DocumentDB, DigitalOcean, and a bunch of GPU news. Justin and Matt are here to lead you through all of it, so let’s get started! 

Titles we almost went with this week:

• Lake Sentinel: The Security Data Monster Nobody Asked For


• Certificate Authority Issues: When Your Free Lunch Gets a Security Audit


• Slash and Learn: Gemini Gets Command-ing


• DigitalOcean Drops Anchor in AI Waters with Gradient Platform


• The Three Stages of Azure Grief: Development, Preview, and Launch


• E for Enormous: Azure’s New VM Sizes Are Anything But Virtual


• SRE You Later: Azure’s AI Agent Takes Over Your On-Call Duties


• Site Reliability Engineer? More Like AI Reliability Engineer


• Azure Disks Get Elastic Waistbands


• Agent Smith Would Be Proud: Google’s Multi-Agent Matrix Gets Real


• C4 Yourself: Google Explodes Into GA with Intel’s Latest Silicon


• The Cost is Right: GCP Edition


• Penny for Your Cloud Thoughts: Google’s Budget-Friendly Update


• DocumentDB Goes on a Diet: Now Available in Serverless Size


• MongoDB Compatibility Gets the AWS Serverless Treatment


• No Server? No Problem: DocumentDB Joins the Serverless Party


• Stream Big or Go Home: Lambda’s 10x Payload Boost


• Lambda Response Streaming: Because Size Matters


• GPT Goes Open Source Shopping


• GPT’s Open Source Awakening


• When Your Antivirus Needs an Antivirus: Enter Project Ire


• The Opus Among Us: Anthropic’s Coding Assistant Gets an Upgrade


• Serverless is becoming serverful in streaming responses

General News 

02:08 It’s Earnings Time! (INSERT AWESOME SOUND EFFECTS HERE) 

02:16 Alphabet beats earnings expectations, raises spending forecast

• Google Cloud revenue hit $13.62 billion, up 32% year-over-year, with OpenAI now using Google’s infrastructure for ChatGPT, signaling growing enterprise confidence in Google’s AI infrastructure capabilities.


• Alphabet is raising its 2025 capital expenditure forecast from $75 billion to $85 billion, driven by cloud and AI demand, with plans to increase spending further in 2026 as it competes for AI workloads.


• AI Overviews now serves 2 billion monthly users across 200+ countries, while the Gemini app reached 450 million monthly active users, demonstrating Google’s scale in deploying AI services globally.


• The $10 billion increase in planned capital spending reflects the infrastructure arms race among cloud providers to capture AI workloads, which require significant compute and specialized hardware investments.


• Google’s cloud growth rate of 32% outpaces its overall revenue growth of 14%, indicating the strategic importance of cloud services as traditional search and advertising face increased AI competition.

03:55 Justin – “I don’t know what it takes to actually run one of these large models at like ultimate scale that like a ChatGPT needs or Anthropic, but I have to imagine it’s just thousands and thousands of GPUs just working nonstop.”

04:31 Microsoft (MSFT) Q4 earnings report 2025

• Microsoft reported Q4 fiscal 2025 earnings with revenue of $76.44 billion, up 18% year-over-year and beating expectations, marking the fastest growth in over three years.


• Azure revenue grew 39% in Q4, significantly exceeding analyst expectations of 34-35%, with Microsoft disclosing for the first time that Azure and cloud services exceeded $75 billion in annual revenue for fiscal 2025.


• Microsoft’s AI investments are showing returns with 100 million monthly active users across Copilot products, driving higher revenue per user for Microsoft 365 commercial cloud products.


• Capital expenditures reached $24.2 billion for the quarter, up 27% year-over-year, as Microsoft continues aggressive data center buildout for AI workloads alongside peers like Alphabet ($85B annual) and Meta ($66-72B annual).


• Microsoft’s market cap crossed $4 trillion in after-hours trading, becoming only the second company, after Nvidi,a to reach this milestone, driven by strong cloud and AI momentum.

06:33 Amazon earnings key takeaways: AI, cloud growth, tariffs

• Things weren’t quite as great for Amazon…


• Amazon’s capital expenditure could reach $118 billion in 2025, up from the previous $100 billion forecast, with spending primarily focused on AI infrastructure alongside competitors Meta ($66-72B) and Alphabet ($85B).


• AWS revenue grew 18% year-over-year, trailing Microsoft Azure’s 39% and Google Cloud’s 32% growth rates, though AWS maintains a significantly larger market share with the second player at approximately 65% of AWS’s size.


• Amazon’s generative AI initiatives are generating multiple billions in annualized revenue for AWS, with potential monetization through services like Alexa+ at $19.99/month or free for Prime members.


• Despite initial concerns about tariffs impacting costs, Amazon reported 11% growth in online store sales and 12% increase in items sold, with no significant price increases or demand reduction observed.


• The company expects Q3 revenue growth of up to 13%, suggesting tariffs have been absorbed by suppliers and customers, though uncertainty remains with the U.S.-China trade agreement deadline on August 12.

08:08 Justin – “They’re not there yet. And they, they haven’t been there for a while, which is the concerning part. And I don’t know, you know – I haven’t really heard much about Nova since they launched.  They talk a lot about their Anthropic partnership, which makes sense. But I don’t feel like they have the swagger in AI that the others do.”

AI Is Going Great – or How ML Makes Its Money 

11:23 Gemini 2.5: Deep Think is now rolling out

• Google’s Gemini 2.5 Deep Think uses parallel thinking techniques and extended inference time to solve complex problems, now available to Google AI Ultra subscribers in the Gemini app with a fixed daily prompt limit.


• The model achieves state-of-the-art performance on LiveCodeBench V6 and Humanity’s Last Exam benchmarks, with a variation reaching gold-medal standard at the International Mathematical Olympiad, though the consumer version trades some capability for faster response times.


• Deep Think excels at iterative development tasks like web development, scientific research, and algorithmic coding problems that require careful consideration of tradeoffs and time complexity.


• The technology uses novel reinforcement learning techniques to improve problem-solving over time and automatically integrates with tools like code execution and Google Search for enhanced functionality.


• Google plans to release Deep Think via the Gemini API to trusted testers in the coming weeks, signaling potential enterprise and developer applications for complex reasoning tasks in cloud environments.

13:02 Justin  – “…these deep thinking models are the most fun to play with, because you  know, you don’t need it right away, but you want to go plan out a weekend in Paris, or I want you to, uh, go compare these three companies products based on public data and Reddit posts and things like that. And it goes, it does all this research, then it comes back with suggestions. That’s kind of fun. The more in depth it is, the better it is in my opinion.So the deep thinking stuff is kind of the coolest, like heavy duty research stuff.”

14:17 Introducing Gpt OSS

• OpenAI is releasing the new GPT-OSS-120b and GPT-oss-20b open weight language models that deliver strong real-world performance at low costs. 


• They’re both available under the flexible Apache 2.0 license; these models on reasoning tasks demonstrate strong tool use capabilities and are optimized for efficient deployment on consumer hardware. 


• Gpt-oss-120b model ac