Welcome to episode 314 of The Cloud Pod, where your hosts, Matt and Ryan, are holding down the fort in Justin’s absence and bringing what’s left of our audience (those of you still here after the last time they were left in charge) the latest and greatest in cloud and tech news. We’ve got undersea cables, vector storage, and even some hobos – but not the kind on trains. Plus AWS S3 Let’s get started! 

Titles we almost went with this week:

• S3 Gets Direction: AWS Points to Vector Storage


• Vector? I Hardly Know Her! S3’s New AI Storage Play


• S3 Finds Its Magnitude and Direction


• Claude Goes to Wall Street


• Anthropic’s Bull Run Into Financial Services


• AI Assistant Gets Its Series 7 License


• Nova Scotia: AWS Brings Regional Flavor to AI Models


• The Fine-Tuning of the Shrew: Teaching Nova Models New Tricks


• Nova-caine: Numbing the Pain of Model Customization


• AgentCore Blimey: AWS Gives AI Agents Their License to Scale


• The Agent Infrastructure: Mission Deployable


• From Zero to Agent Hero: AWS Tackles the Production Problem


• SageMaker Gets Its Data Act Together


• From Catalog to QuickSight: A Data Love Story


• The Great Data Unification of 2024


• AWS Free Tier Gets a $200 Makeover


• EKS-treme Makeover: Cluster Edition


• #⃣100K Nodes Walk Into a Cluster…


• S3 Gets Direction: Amazon Points to Vector Storage


• Amazon S3: Now with 90% Less Vector Bills and 100% More Dimensions

Follow Up

01:03 SoftBank and OpenAI’s $500 Billion AI Project Struggles to Get Off Ground

• The $500 billion AI effort unveiled at the White House has struggled to get off the ground and has scaled back its near-term plans. 


• It’s been six months since the announcement, where they said they would spend $100B almost immediately, but now they have a more modest goal of building a small data center by the end of the year in Ohio.


• Softbank committed to $30 billion earlier this year, and it is one of the largest ever startup investments by them, which led them to take on new debt and sell assets.  


• This investment was made alongside Stargate, giving them a role in the physical infrastructure needed for AI. 


• Altman, though, has been eager to secure computing power as quickly as possible and has proceeded without Softbank. 


• Publicly, they say it’s a great partnership, and they look forward to advancing projects in multiple states


• Oracle was part of Stargate, but the recent 30B deal just signed with includes a commitment of 4.5 gigawatts of capacity, and would consume the equivalent power of more than two Hoover Dams, or about 4 million homes. 


• Oracle was also named part of the deal with UAE firm MGX as a partner, but Oracle CEO Safra Catz said that Stargate hadn’t been formed yet, as of last month. 

02:31 Matthew – “…everyone’s like, how hard can it be to build a data center? But it’s city zoning, power consumption, grid improvements, water for cooling… getting communities to approve – and these things end up being a massive undertaking. And it takes the hyperscalers a long time to get these things up and operational. So it doesn’t surprise me that a small data center by the end of the year is probably something that was already in the works beforehand; they’re just taking over other plans. Most data centers take a couple of years to really get up and operational.”

General News

04:55 A Transatlantic Communications Cable Does Double Duty – Eos

• You know how much we love a good undersea cable story, and this one is especially nerdy. Strap in! (Thanks, Matt)


• Scientists have developed a new instrument that transforms existing undersea fiber-optic telecommunications cables into ocean sensors by measuring variations in light signals between repeaters, enabling monitoring of water temperature, pressure, and tide patterns without disrupting internet or phone service.


• The technology uses fiber Bragg gratings at cable repeaters (positioned every 50-100km) to reflect light signals, allowing researchers to measure changes in travel time that indicate how surrounding water conditions affect cable shape and properties.


• This distributed sensing approach is more cost-effective than previous methods as it uses standard, nonstabilized lasers rather than expensive ultrastable ones, and can monitor individual cable subsections rather than treating the entire cable as a single sensor.


• The 77-day test on the EllaLink cable between Portugal and Brazil successfully measured daily and weekly temperature variations and tide patterns across 82 subsections, demonstrating the potential for the global submarine cable network to serve dual purposes.


• The technology could enable early tsunami warning systems and long-term climate monitoring by leveraging millions of kilometers of existing infrastructure, providing valuable ocean data without requiring new sensor deployments.

06:30 Ryan – “It feels like our version of like getting into World War Two or something.”

AI Is Going Great – or How ML Makes Its Money 

08:55 Amazon-backed Anthropic rolls out Claude AI for financial services

• Anthropic launched Claude Financial Analysis Solution, a tailored version of Claude for Enterprise specifically designed for financial professionals to analyze markets, make investment decisions, and conduct research using Claude 4 models with expanded usage limits.


• The solution integrates with major financial data providers, including Box, PitchBook, Databricks, S&P Global, and Snowflake, for real-time financial information access, with availability through AWS Marketplace and Google Cloud Marketplace coming soon.


• This represents Anthropic’s strategic push into enterprise AI following their $61.5 billion valuation in March, targeting financial services as businesses increasingly adopt generative AI for customer-facing functions.


• The offering includes Claude Code capabilities and implementation support, positioning it as a specialized alternative to general-purpose AI assistants for complex financial analysis tasks requiring domain-specific accuracy and reasoning.


• Cloud providers benefit from this vertical-specific AI approach as it drives compute consumption through AWS and Google Cloud marketplaces while demonstrating how foundation models can be packaged for specific industry needs.

10:22 Matt – “It’s literally why we named this section this! AI is how ML makes money!”  

14:35 TwelveLabs video understanding models are now available on Amazon Bedrock | AWS News Blog

• TwelveLabs brings two specialized video understanding models to Amazon Bedrock: Marengo for video embeddings and search, and Pegasus for generating text from video content. These models enable natural language queries like “find the scene where the main characters first meet” to locate specific moments in video libraries.


• The models were trained on Amazon SageMaker HyperPod and support both synchronous and asynchronous inference patterns. 


• Pegasus uses the standard Invoke API while Marengo requires the AsyncInvoke API for processing video embeddings.


• Key technical capabilities include video-to-text summarization with timeline descriptions, automatic metadata generation (titles, hashtags, chapters), and vector embeddings for similarity search. The models accept video input via S3 URIs or Base64-encoded strings.


• Practical applications span multiple industries: media teams can search dialogue across footage libraries, marketing can personalize content at scale, and security teams can identify patterns across