Cloud Computing Trends

About a year ago, I created a comprehensive graphic comparing the major cloud providers. As I revisit it now, I'm struck by the rapid evolution of the cloud landscape. While each provider's core competencies remain largely unchanged, there have been some significant developments and emerging trends. Let's dive in! 1. 𝗧𝗵𝗲 𝗥𝗶𝘀𝗲 𝗼𝗳 𝗠𝘂𝗹𝘁𝗶-𝗖𝗹𝗼𝘂𝗱: Increasingly, businesses are adopting a multi-cloud approach, cherry-picking services from different providers to optimize costs, avoid vendor lock-in, and take advantage of each platform's unique offerings. This shift towards a more diverse and flexible cloud strategy is a testament to the growing maturity of the market. 2. 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗧𝗮𝗸𝗲𝘀 𝗖𝗲𝗻𝘁𝗲𝗿 𝗦𝘁𝗮𝗴𝗲: In response to the pressing need for environmental action, the big three cloud providers have all stepped up their sustainability efforts. From renewable energy initiatives to tools that help customers monitor and reduce their carbon footprint, the cloud is becoming greener. 3. 𝗧𝗵𝗲 𝗔𝗜/𝗠𝗟 𝗕𝗼𝗼𝗺: Artificial intelligence and machine learning have seen explosive growth, with each provider offering an expanding array of AI/ML services. These tools are becoming more user-friendly and accessible, democratizing AI and enabling businesses of all sizes to harness its power.     4. 𝗧𝗵𝗲 𝗘𝗱𝗴𝗲 𝗘𝘅𝗽𝗮𝗻𝗱𝘀: Edge computing has come into its own, with Azure Arc, AWS Outposts, and Google Anthos all seeing significant enhancements. This development is crucial for IoT, real-time data processing, and low-latency applications. As the intelligent edge continues to evolve, it's opening up exciting new possibilities. 🚀 5. S𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆: Serverless computing has been a game-changer, abstracting away infrastructure management and enabling developers to focus on writing code. Over the past year, serverless offerings have continued to mature, with improved tooling, easier integration, and more robust functionalities. As always, the "best" cloud provider is the one that aligns with your unique requirements, existing infrastructure, and long-term objectives. It's crucial to periodically reassess your cloud strategy to ensure it remains optimized for your evolving needs. I'm curious to hear your thoughts! What notable changes or trends have you observed in the cloud ecosystem recently?

Until yesterday, no company worth over $500B had ever gained more than 25% in a single trading day. Then came Oracle. In a move that defied both gravity and historical precedent, Oracle stock surged 40% today, adding over $300B in market cap overnight. The company now hovers just shy of the trillion-dollar mark, and Larry Ellison - armed with a 41% stake - woke up as the world’s richest man, suddenly $100 billion wealthier. Yes, Oracle. The perennial punchline of “legacy software.” The company most of us had filed away in the footnotes of tech history is suddenly the market’s cool kid. For those paying attention, this moment has been years in the making. Oracle’s pivot into cloud and AI wasn’t impulsive - it was deliberate, capital-intensive, and decidedly unsexy. They didn’t chase developer mindshare; they banked contracts. And those contracts just hit the ledger all at once. ➰ The Q1 revenue headline - $14.9B, up 12% YoY - wasn’t what lit the fuse. ➰ Even IaaS revenue at $3.3B, up 55% is strong, but not frenzy-worthy. ➰ The magic number was buried deeper: $455B in Remaining Performance Obligations (RPO), up 359% YoY. That’s nearly 8 times Oracle’s current revenue run-rate, a backlog so large it borders on the surreal. RPO isn’t a flashy number. It doesn’t trend on CNBC tickers. But in enterprise software, it’s gospel. It represents revenue already won but not yet recognized. In plain English: Oracle just told Wall Street, “We’ve already signed nearly half a trillion dollars’ worth of business. All that’s left is execution.” Oracle expects cloud infrastructure revenue which came in at $3.3B this quarter to hit $18B this fiscal year and ramp to $144B within four years. They noted that “most of the revenue in this forecast is already booked in our reported RPO”. It’s less of a forecast and more of a countdown at this point. The market isn’t just reacting to a quarter. It’s reacting to a company that rewired its DNA and is now producing receipts. In a space dominated by Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, Oracle carved out an edge not through branding or developer love, but through being the only one willing to say yes to what AI-native enterprises actually wanted: custom infrastructure, multi-cloud deployments, sovereign regions, long-term capacity, and massive scale contracts. What we witnessed today is the rarest thing in markets: a narrative inversion. Oracle went from legacy to legend not by shouting louder but by building slower, selling longer, and letting the numbers speak. The company that once stood for on-prem databases is now one of the most valuable cloud businesses in the world. TikTok and Twitter are obsessing over the ‘Great Lock-In’ without agreeing on what it means. Oracle just showed the only version that matters: half a trillion in contracts, signed and sealed. King of the Lock-In.

NEWS: Oracle just dropped massive AI updates. They’re building the entire stack. Most companies are betting on one layer. Oracle is building all three: Layer 1: Infrastructure ↳ 800,000 NVIDIA GPUs (Zettascale10) - largest AI supercomputer in the cloud ↳ 50,000 AMD MI450 GPUs launching Q3 2026 - first hyperscaler at scale ↳ Powers Stargate project with OpenAI in Abilene, Texas Layer 2: Platform & Data ↳ Database 26ai: AI embedded directly into the database with automated vector indexing ↳ AI Agent Studio: Support for OpenAI, Anthropic, Meta, Cohere, Google, xAI ↳ AI Agent Marketplace: 24+ partners, one-click deployment, no-code customisation Layer 3: Applications ↳ Pre-built AI agents embedded natively in Oracle Fusion Applications ↳ Deploy where employees already work inside ERP, HCM, SCM, and CX workflows ↳ No app switching required I spoke with Pradeep Vincent (SVP & Chief Technical Architect, OCI) and Natalia Rachelson (VP of Cloud Applications) this week. The strategy is clear: 1. Own the infrastructure layer Training ground for Grok, ChatGPT, and frontier models 2. Stay model-agnostic at the platform layer Partner with the best instead of building proprietary models 3. Embed AI agents natively in applications Deploy where customers already work with no app switching My takeaway: Larry Ellison said it best at the keynote: "This AI training... is the largest, fastest growing business in human history. Bigger than the railroads, bigger than the industrial revolution." Most companies are fighting for a piece of it. Oracle is building the entire stack. → Infrastructure that powers the frontier models. → Platforms where enterprises can actually use them. → Applications where the work gets done. They're not betting on one layer winning. They're betting they can win across all three. Follow me Alex Banks for daily AI highlights and insights.

The Death of SaaS (as We Know It) Satya Nadella recently shared a fascinating perspective: AI is poised to replace traditional application layers, embedding business logic directly at the database level. This marks a profound shift: one that could redefine the very foundation of SaaS. Imagine a future where AI doesn’t just power apps but replaces them. Business logic, instead of flowing through multiple layers of UI, middleware, and APIs, is orchestrated directly with the database. This means the end of bloated, layered software and the beginning of lean, AI-native architectures. The ripple effects are massive. SaaS as a subscription model may lose relevance as modular AI-driven workflows dominate. Interfaces will transform, shifting away from dashboards and fixed workflows to adaptive, real-time experiences—think voice commands, conversational AI, or neural interfaces. Even the app store economy may collapse under the weight of this new paradigm, replaced by marketplaces for AI-driven workflows instead of apps. This could imply the extinction for the SaaS we know today. For developers, businesses, and consumers, this shift will reshape how software is built, sold, and used. The question isn’t if SaaS is dying; it’s what comes next. What do you think? Is this the end of SaaS, or the beginning of something even more disruptive?

Salesforce just fired the starting gun on a seismic shift in how we pay for software. At Salesforce #Agentforce, they announced they’re moving away from the traditional per-seat SaaS model to a consumption-based pricing for their AI agents. This is huge. Why? Because it signals the end of paying just to have access to technology. Instead, we’re moving toward paying for outcomes—the actual value delivered. Think about it. In a world where AI agents can perform the job functions of entire departments, does it make sense to charge per seat? Probably not. Here’s what’s changing: - From access to outcomes: Companies will pay for what the AI actually accomplishes. - From subscriptions to value: Pricing adjusts based on usage and results. - From Software-as-a-Service to Agent-as-a-Service: Technology that collaborates with you as a partner This isn’t just a tweak in pricing—it’s a radical upending of commercial models for large SaaS companies. What does this mean for businesses? - Budgeting will evolve: Costs align directly with value received. - ROI becomes clearer: Easier to measure the direct impact of technology investments. - Greater flexibility: Scale usage up or down based on needs without worrying about seat counts. It’s an exciting time, but also a challenging one. Is every SaaS company ready to embrace a model where companies pay directly for the value they receive? At Uniti AI, we’ve been thinking along these lines. We price our AI agents based on the amount of work they do, not on how many seats a company has. I believe this is the future. What do you think? Is the per-seat model on its way out?

Oracle Didn’t Beat Earnings Because of Software. It beat because it became the cloud vendor for AI builders who couldn't get NVIDIA chips anywhere else! For years, Oracle was the cloud underdog. But this quarter? Oracle flipped the script. 📈 Q4 Earnings Highlights:  • Cloud infrastructure revenue up 42% YoY (faster growth than AWS or Azure)  • Forecasting 70% cloud growth next fiscal year (pretty amazing for a 4th place cloud provider)  • $138B in signed cloud deals yet to deliver (RPO) — up from $97B just six months ago So what changed? It’s not SaaS. It’s GPUs. While the cloud giants battled over workloads, Oracle quietly preordered and reserved NVIDIA’s best AI chips—then built bare-metal GPU access, which is nearly impossible for companies to get from AWS or Azure. Now, when AI startups need serious compute fast, they’re going to... Oracle. Who’s renting Oracle’s power?  • xAI (Elon Musk)  • Meta  • Mistral, Cohere, MosaicML, Twelve Labs, Adept  • …and probably many more under NDA. These aren’t nostalgic Oracle fans. They’re chasing the fastest path to AI horsepower—and Oracle actually has it. 4th place in cloud? Maybe. But in AI infrastructure, Oracle’s suddenly the one to beat.

Where Did Oracle Cloud Come From? When Oracle launched Oracle Cloud Infrastructure (OCI) in 2016, few thought it could compete with hyperscalers like AWS, Microsoft Azure, or Google Cloud. Oracle was seen as a legacy on-premises software company—too late to the cloud race. Today, that perception has radically changed. Oracle found success by differentiating itself rather than imitating its competitors. With innovations like modular cloud infrastructure, cost efficiency, and flexibility, Oracle offered high-performance AI and enterprise workloads at lower costs. For example, its “butterfly” OCI instance delivers private cloud capabilities for just $6M—versus hundreds of millions for comparable hyperscaler solutions. This strategy made Oracle a key player in the alt cloud movement, wherein enterprises seek alternatives to hyperscalers. These include GPU-focused clouds, sovereign clouds, and private clouds that better fit AI and non-AI workloads. Oracle’s flexible infrastructure—integrating with legacy systems and other hyperscalers—has positioned it as the go-to provider for AI training, inferencing, and beyond. At the heart of this transformation is Larry Ellison, whose leadership and Silicon Valley connections have secured multibillion-dollar AI deals. Oracle’s success shows how innovative disruption can shake up even the most saturated markets. What do you think about Oracle’s rise as a cloud leader? #CloudComputing #AI #AltCloud #Innovation #Oracle https://lnkd.in/efpYgiNm

Salesforce didn’t just promote AEs with AgentForce. They took Solutions Engineers—some of them second-line leaders—and moved them into quota-carrying sales roles. That wasn’t just a hiring decision. It was a signal. For years, SaaS has been built around strict role segmentation. AEs sell. SEs validate. CS retains. Every function has its swim lane, and every team has its handoff. Now it appears Salesforce is collapsing those lanes entirely. If this model works, sales orgs will look completely different in a few years. SEs and technical specialists won’t just support revenue, they’ll own it. AEs will be expected to go deeper on technical execution. Comp plans will shift from closed-won to adoption-driven incentives. Buyers are already losing patience with sellers who don’t understand their own product. This move also eliminates another buyer frustration: countless handoffs to yet another support person. Instead, it gives them one person who can sell them the solution and make sure it actually works If Salesforce is making this move, mid-market SaaS will follow. The question isn’t whether the AE role is changing. It’s whether companies can adapt before they get left behind.

Rumors of SaaS’s death by AI agents are wildly exaggerated. Let’s get clear on what’s really happening: We're headed toward three distinct software categories: 1. Personal Software (1:1) - Why use software built for thousands when AI can spin up YOUR perfect solution in minutes for your exact needs, whether for personal use or use in your day-to-day job? - AI means hyper-personalized tools become a big part of the software industry. This is the most disruptive trend for SaaS. It replaces a lot of single-use case point solutions. 2. Complex Software - Healthcare EMRs, financial trading platforms, regulatory systems, platforms and so on—they’re too intricate for AI autonomous agents. - SaaS excels here because it packages complexity into standardized, reliable workflows and user experiences. While AI agents can access data directly, the nuanced interpretation, accountability, and human judgment required in these areas mean agents enhance rather than fully replace the structured solutions SaaS provides. 3. Network-Driven Software - Slack, GitHub, Zoom, Atlassian, HubSpot—these platforms become more valuable as their communities grow, making their network effects incredibly enduring and resistant to disruption by AI alone. - Their value endures because users invest deeply in the relationships, workflows, and communities these platforms support. AI agents will be a big part of the software industry: - Initially emerging as single-point solutions solving specific tasks efficiently. - Eventually integrated and bundled within complex and network-driven software platforms, enhancing capabilities rather than replacing them entirely. AI Agents have a lot of hurdles to jump to replace all SaaS. Businesses crave reliability, consistency, and reduced complexity. Custom-built AI solutions are great until you need to fix them and scale them. AI agents will need to scale, but maintaining them at scale will be hard. (I invested in a SaaS company building the Workday for AI agents to do just that). SaaS works because it offers something businesses deeply value: predictable outcomes, standardized processes, and a proven model for managing complexity, compliance, and risk. AI will disrupt SaaS - but it will also enhance it, making SaaS solutions even more valuable by embedding intelligence directly into trusted workflows. In other words: SaaS isn't going away. It's getting smarter.

Quantum-classical computing is transforming how we approach complex challenges by combining cloud-based quantum acceleration with traditional systems to improve speed, precision, and efficiency while enabling scalable innovation. This convergence is a key step toward making quantum computing a practical tool rather than a distant goal. Hybrid architectures allow organizations to experiment safely through APIs and SDKs that connect classical systems with Quantum Processing Units in the cloud. This setup reduces the barrier to entry and opens new paths for optimization, simulation, and predictive modeling in sectors such as finance, logistics, and materials science. Early experimentation is essential. Small proof-of-concept projects can reveal measurable gains in performance and cost-efficiency while helping teams develop internal expertise and new partnerships. Each iteration builds readiness for a future in which quantum computing will become an integral part of enterprise infrastructure. #QuantumComputing #DigitalTransformation