InfoWorld AI·
It is tempting to date cloud computing from the launch of Amazon S3 in 2006 and the rise of infrastructure as a service (IaaS) that followed. That was certainly the moment the market changed in a visible, irreversible way. But the truth is that cloud began earlier, in the 1990s, when software as a service (SaaS), application hosting, managed services providers, and various forms of remote subscription computing started to reshape how enterprises thought about owning and operating technology. Even then, the core value proposition was familiar: Let someone else run the infrastructure, abstract the complexity, deliver capability as a service, and allow the business to consume only what it needs. What AWS changed was the scale, accessibility, and precision of the execution. Amazon turned infrastructure into a programmable utility. It made compute and storage available in ways that were elastic, self-service, API-driven, and globally reachable. That was the breakthrough. Enterprises had out
Read full articleAWS turned on AI traffic monetization inside AWS WAF on Monday, letting any site behind Amazon CloudFront charge AI agents per request in USDC through Coinbase's x402 protocol. It is the first time a hyperscale cloud has wired onchain settlement into its content-delivery edge.
For many developers, the hard part of building an AI application isn’t the model anymore. It’s keeping the application’s knowledge current. Retrieval-augmented generation (RAG) has become a popular technique for grounding AI applications in enterprise data, but it also introduces a steady stream of operational work, including tasks such as updating embeddings and indexes, synchronizing data sources, and tuning retrieval performance. AWS is seeking to remove much of that burden with Bedrock Managed Knowledge Base, a new managed service that automates the retrieval layer behind enterprise AI applications. “By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself,” Daniel Abib, senior solutions architect at AWS, wrote in a blog post. In order to help maintain data pipelines without building and managing custom integrations
Expanded collaboration enables customers to adopt and scale agentic AI as they modernize and run mission‑critical workloads on AWS NEW YORK, June 18, 2026 — Kyndryl, a leading provider of […] The post Kyndryl and AWS Expand Partnership to Accelerate Enterprise Agentic AI Adoption appeared first on AIwire.
AWS is in talks to sell its chips to other data centers. CEO Andy Jassy has said this represents a $50 billion opportunity for the company.
Accelerated grid connections for AI data centers could reshape energy markets, impacting cloud computing, crypto mining, and digital infrastructure. The post US government to fast-track grid connections for AI data centers appeared first on Crypto Briefing.
The EU probe could reshape cloud market dynamics, potentially enhancing competition and impacting AWS and Azure's strategic operations. The post Microsoft, Amazon Web Services face EU probe over cloud dominance appeared first on Crypto Briefing.
After helping turn cloud computing into essential infrastructure, Sivasubramanian is now leading AWS’s push to make agentic AI easier for companies to deploy at scale.
June 17, 2026 — Amazon Bedrock Managed Knowledge Base, a fully managed retrieval-augmented generation (RAG) service, is now generally available. With Managed Knowledge Base, developers can build production-ready AI agents grounded […] The post AWS Launches Amazon Bedrock Managed Knowledge Base for Enterprise RAG Applications appeared first on AIwire.
