AI Cloud Hype vs Real-World Execution

Q1 2026 shut down the AI hype debate. AI has shifted from a productivity layer to end-to-end execution, making entire software categories economically unviable. Anthropic hit a $19B run rate, Cursor doubled to $2B, and Lovable added $100M in a month. The scale of the shift is hard to ignore. Meta, Alphabet, and other tech giants plan to invest $650B in AI this year, roughly 3x what they spent 24 months ago, more than the U.S. Interstate system adjusted for inflation. But the most important change was not financial. It was structural. This quarter made one thing clear. The advantage is no longer about who has the biggest or most advanced general-purpose model. It's about who can put models to work inside real workflows. In March, Intercom, Cursor, and Decagon showed the same pattern: domain-specific models trained on proprietary data outperform general models on cost, speed and accuracy, with Decagon running over 80% of its workload on its models. The prevailing concern in 2025 was whether AI would live up to the hype. That concern disappeared this quarter, replaced by a harder question. What happens when an AI agent can do what a $50 per seat SaaS product does? When software shifts from licensed seats to executed outcomes, the economics change quickly. AI stops being a feature layered on top of software and starts replacing entire layers of it. That dynamic sits behind what some are calling the SaaS Apocalypse. The language is dramatic. The math is not. Every week, I sit with CEOs, CIOs, and CTOs in healthcare, financial services, energy and sovereign industries. The pattern is consistent. Access to AI is no longer the constraint. Putting it into production, securely, reliably, governed and at scale, is. That is the work we do at Rackspace Technology. We operate the infrastructure, the data layer and the software integration as one system so AI runs in production, not in pilots. The leadership implications are already evident. Seat-based software becomes a cost to challenge, not a given. Pick one workflow you pay for per user today and test replacing it with an AI agent end-to-end. Not assist. Replace. Proprietary data becomes a competitive moat again. Identify two or three datasets that would materially improve model performance and lock them down now. Governance becomes strategy. Inventory every model or agent in your environment. Who owns it? What data does it touch? What decisions can it make? How can it be shut off? SaaS renewal cycles become strategic moments. Review your next five renewals and ask a hard question. If an agent can do this job in 12 months, should this product still exist? The question is no longer whether to adopt AI. The question now is whether your organization will lead this transition or trail behind it. The gap widened meaningfully in Q1. Most companies will not lose because they ignored AI. They will lose because they assumed the old software model would survive it. Q1 made that assumption expensive.

Watch the full video here: https://lnkd.in/en46CRYX After years of working with enterprises on their cloud and AI journeys, I find we’re still grappling with some “unpopular truths” about Agentic AI—truths I explored in my latest video. Despite the industry buzz, large-scale and meaningful deployments of Agentic AI in enterprise settings remain extremely rare. Organizations are pouring resources into these technologies, yet very few are seeing the transformative results they were promised. One of the biggest misconceptions I’ve noticed is treating Agentic AI as a universal solution. In reality, its effective use cases are still narrow, and complexities around integration, data governance, cost, and skills are routinely underestimated. Many enterprises rush to adopt AI without a coherent strategy or a realistic sense of the foundational work and investment needed. As a result, expectations quickly outpace outcomes, and most companies soon realize that fixing poor data quality and upskilling teams is far more challenging than rolling out a flashy pilot. What I’m seeing today is a strategic pivot—a move toward smaller, targeted AI projects that deliver real business value quickly, rather than expensive, organization-wide initiatives that fail to live up to the hype. Success in AI, especially Agentic AI, is not measured by technical sophistication but by real, tangible business outcomes. To my peers and leaders: focus on the fundamentals. Prioritize your data, set clear business objectives, and understand your actual capabilities before jumping on the latest AI bandwagon. If we do that, we’ll turn today’s hype cycle into tomorrow’s enterprise value. Watch the full video for a candid dive into these realities. Let’s keep pushing toward pragmatic, valuable AI in the enterprise. #AI #AgenticAI #EnterpriseIT #CloudComputing #DataQuality

There is a vast difference between an AI PoC vs AI Production. More than half of GenAI projects never make it past the POC. Gartner says at least 50% of GenAI projects were abandoned after proof of concept by the end of 2025. And when you’ve worked on enough enterprise AI projects, it’s not that surprising. A POC is built to answer one question: Can this work and show business value? Production has to answer a much harder question: Can this work reliably, at scale, within budget, for real users? That gap usually shows up across four dimensions. 1. Cost In a POC, you might run 20 or 30 requests a day. Nobody is worried about the bill. Then you go to production and suddenly you have thousands of users, millions of tokens, retrieval calls, tool calls, retries, and background workflows. The architecture that looked cheap in a demo can get expensive very quickly. 2. Latency A demo usually runs one request at a time. Production doesn’t. Now you have concurrent users, multiple model calls, retrieval, tools, APIs, and databases sitting in the critical path. That 3 second demo response can easily become 10 or 15 seconds under load. 3. Reliability During a POC, if an API fails, the developer refreshes the page. In production, there is a customer waiting on the other side. You need retries, fallbacks, timeouts, circuit breakers, monitoring, and a plan for what happens when one dependency goes down. 4. Failure modes This is probably the biggest one. During a POC, you test the inputs you know. Production gives you the inputs you never thought about. Messy documents, missing fields, unexpected questions, broken tool responses, edge cases nobody included in the eval set. And that is usually where the real problems start showing up. A good POC proves that the idea is possible. It does not prove that the system is ready for production. Those are two very different engineering problems. #enterpriseai #POCvsProduction Image Credits Prashant Rathi

🔥 Why AI Fails in the Real World (Even After a Great Demo) 🔥 The biggest lesson from deploying AI in real environments? A good prototype is not a production system. That sounds obvious. Until you ship. 🚀 In the real world, AI breaks in places demos never reveal: 1️⃣ What failed ♠️ We assumed a strong model would survive production ♠️ We underestimated data quality and integration issues ♠️ We overestimated how much users would trust imperfect outputs ♠️ We treated governance like a final checklist, not an architectural requirement The truth is harsh: **Most AI failures are not model failures. They are system failures.** 2️⃣ What surprised me ♠️ The bottleneck was often not the model, but the workflow around it ♠️ Retrieval quality mattered more than prompt polish in many cases ♠️ Users noticed small latency or accuracy issues much faster than expected ♠️ Trust disappeared faster than technical teams predicted One bad answer can damage confidence. A few bad ones can kill adoption. 😬 3️⃣ What I’d do differently ♠️ Design the workflow before chasing model sophistication ♠️ Test with real users and real data much earlier ♠️ Build observability and escalation paths from day one ♠️ Treat governance, auditability, and human review as core architecture If I were starting again, I’d optimize less for “impressive” and more for reliable, usable, and safe. 4️⃣ The global lesson Working across industries and environments taught me one thing: ♠️ AI adoption is never just technical ♠️ Different regions prioritize speed, explainability, regulation, or legacy integration ♠️ The same model can succeed in one context and fail in another ♠️ People adopt AI when it makes their work easier, not when it looks clever That’s why the best AI systems are built around business reality, not hype. The real production lesson: AI success is not about building the smartest demo. It is about building something people can depend on every day. 🧠 That means: ♠️ Less magic ♠️ More trust ♠️ Less slide-deck AI ♠️ More operational AI ♠️ Less experimentation for applause ♠️ More engineering for adoption The teams that win are not the loudest. They are the ones who make AI useful enough, safe enough, and reliable enough for real business use. ⚡ If you’re deploying AI, don’t ask whether it looks good in a demo. Ask whether people will still trust it six months after launch. That is where the real work begins. #AI #GenAI #AIEngineering #MLOps #LLMOps #EnterpriseAI #ProductionAI #AILeadership #DataScience #MachineLearning

𝙀𝙫𝙚𝙧𝙮𝙤𝙣𝙚 𝙩𝙝𝙞𝙣𝙠𝙨 𝙗𝙪𝙞𝙡𝙙𝙞𝙣𝙜 𝘼𝙄 𝙞𝙨 𝙩𝙝𝙚 𝙝𝙖𝙧𝙙 𝙥𝙖𝙧𝙩.    𝙏𝙝𝙚 𝙧𝙚𝙖𝙡 𝙣𝙞𝙜𝙝𝙩𝙢𝙖𝙧𝙚 𝙨𝙩𝙖𝙧𝙩𝙨 𝙩𝙝𝙚 𝙙𝙖𝙮 𝙮𝙤𝙪 𝙡𝙖𝙪𝙣𝙘𝙝 𝙞𝙩. That is when your shiny new model meets the final boss: real customers, unpredictable latency, and angry compliance lawyers. And spoiler alert? Most of them are failing spectacularly. 📉 New enterprise research from Sinch just dropped, and the numbers are a massive reality check for the hype train: 🗑️ 74% of organizations roll back their AI customer comms agent. 🚸 84% of engineers spend half their time just building guardrails. (AKA babysitting the AI so it doesn't go rogue and insult your customers). 🏗️ 87% realize communications infrastructure is the actual bottleneck. Right now, your timeline is obsessed with bragging about models, autonomous agents, and "prompt engineering." 🤖 But once AI hits scale, no one cares how smart your model is if it crashes during a basic customer escalation. If your system cannot reliably authenticate, notify, or hand off to a human in real time, your "cutting-edge" AI is just a very expensive liability. 💸 The next wave of AI winners will not be the companies with the absolute smartest models. It will be the ones whose infrastructure doesn't completely crumble when a real human types "hello." Read the full Sinch research before your next deployment: https://lnkd.in/eGzpvaQq

A group of professors from Carnegie Mellon University recently decided to run an experiment. They built a fake company and staffed it entirely with AI agents. No humans, just bots with job titles like CEO, engineer, finance lead, and intern. They gave them a project brief, dropped them into a simulated Slack workspace, and waited to see if anything useful would happen. What happened next wasn't exactly a triumph of machine efficiency. It's pretty funny, though. The agents immediately started talking past each other, forgetting what they were doing, hoarding information, and trying to take over one another's roles. One model got so carried away that it appointed itself project lead without being asked. Another spent more time asking irrelevant questions than actually getting anything done. Out of all the AI models tested, including ones from OpenAI, Google, Anthropic, and Meta, Claude had the best result, completing just 24% of its tasks. The others did worse. That's probably worse than your incompetent intern. What's interesting is that even in a controlled sandbox, where there were no real customers, deadlines, or consequences, the AI agents still got overwhelmed and confused each other. Now imagine dropping that chaos into your business, in the middle of a real incident response, a compliance audit, or a product release. AI isn't ready to run your company. It can barely handle a fake one. Use it to assist, augment, and take the boring bits off your plate. Just don't fall for the hype that it's ready to replace real teams doing real work. Science will tell you that you need to fail to learn and then improve until you reach a level that humans can be exposed to, but we ain't there yet… Because if the bots can't even work with each other, what makes you think they're ready to work with you? https://lnkd.in/dHANUKiD

After 6 months of using AI coding tools (Cursor and Claude) almost every day, and not just for "toy apps" but actually making things that I have subsequently deployed, I can say a few things: 1. It's not just hype. If you are unable to generate a competent full-stack application or microservice with AI coding tools, you're almost certainly using the tools incorrectly. 2. Coding LLMs are working with *small context sizes* compared to the overall challenge space of keeping tens of thousands of lines of code working from commit to commit. You MUST have the AI write tests for everything it generates. You MUST tag every working checkpoint so that it (the AI) can compare working with non-working. 3. When you tell an AI tool to "plan", you are working a different part of the model - or even potentially a completely different model - when you are telling it to "execute". "Deep thinking" models are better at analyzing code and planning than they are at executing. Sometimes even "dumb" models are better at executing to a plan than the smart ones, and they are certainly CHEAPER. 4. You MUST break planning from execution, just like you would do if you were writing the code yourself, and you must have the AI write planning files that it can follow. If you or your devs are executing plans with the most expensive models, you're almost certainly just wasting money. 5. You must AUDIT the code for every feature cycle. My "flow" is basically: * Plan (write this as a .md file, save in plans/ directory) * Execute (write the code) * Write tests * Run tests * Re-execute as necessary until tests pass * Audit - check the code against Plan.md You also don't need a lot of fancy prompts to do any of this. You can literally write out your high-level goals and then have the tool write the plan, then read the plan back to you, correct its assumptions, then proceed with steps 2-6. The results will surprise you. GIGO applies to AI coding just as much as everything else. If you are sloppy and undisciplined in how you do it, you'll get predictably bad results.

Should we use AI in the cloud? That is no longer the right question. The better question is: “Where should this AI workload actually run?” Because not every AI system belongs in the same place. Cloud AI is great when you need scale, access to large foundation models, and managed infrastructure. Edge AI is better when decisions need to happen close to sensors, cameras, machines, or field devices. Local AI makes sense when you want control, privacy, offline usage, or workstation-level experimentation. On-Device AI is becoming critical for phones, PCs, wearables, and embedded experiences where latency and privacy matter. Private AI is the enterprise answer for regulated workloads, sensitive data, compliance, and tighter governance. Hybrid AI is where many real-world systems are heading: some tasks local, some private, some cloud, all routed based on latency, privacy, cost, scale, and policy. The future of AI infrastructure is not “cloud vs local.” It is intelligent placement. The best AI architecture in 2026 will not be the one using the biggest model. It will be the one that knows: where to run, what to protect, when to scale, how to control cost, and when to keep data close. AI deployment is becoming an architecture decision, not just a model decision. Which one are you seeing more in real projects: Cloud AI, Private AI, or Hybrid AI?

We've all met them: the friend who can't stop hyperventilating over every AI demo video, predicting universal basic income by next Tuesday, convinced we're months away from AGI. Meanwhile, they haven't actually used AI to solve a single problem this week. I'm noticing a pattern. The loudest voices about AI aren't the ones integrating it into their daily work. They're not the people quietly using Claude to analyze contracts, ChatGPT to structure feedback loops, or Runway to prototype concepts. They're treating AI like sports gossip—a conversation starter, a way to flex knowledge they don't actually apply. Alicia McKay's recent piece "What the AI Bros Won't Tell You" cuts through this noise. She points out what we're ignoring while we're busy arguing about Sora's latest capabilities: AI scraped the entire internet without permission, trains on biased datasets filtered by workers paid $2/hour, replicates inequality in loan decisions and hiring algorithms, and consumes enough water and energy to rival small nations. The people building real value with AI aren't the ones breathlessly sharing every OpenAI press release. They're the ones asking: Does this actually solve my problem? Does it improve my team's output? What am I willing to trade for this convenience? McKay writes: "Powerful technology companies and their bottom-feeding minions dazzle and obfuscate, making AI acceleration and adoption appear magical and inevitable, but it is neither". We've seen this script before—with tobacco, oil, social media. The playbook is always the same: hype the benefits, downplay the risks, capture regulation before it can bite. Here's what I'm seeing in practice: The professionals using AI effectively are boring about it. They integrate it quietly. They test, iterate, and measure impact. They don't predict the future—they build the present, one workflow improvement at a time. If you're spending more time talking about AI than using it, you're not an early adopter. You're part of the hype machine. And if we're serious about this technology shaping the next decade of work, we need fewer evangelists and more practitioners. What's one concrete way you've used AI this week that actually improved an outcome? Not a demo you watched—something you built, tested, or shipped. https://lnkd.in/gSuh5kUi #ArtificialIntelligence #AI #Leadership #TechEthics #ProductivityTools #BusinessStrategy #DigitalTransformation #ResponsibleAI #TechCulture #Innovation

AI is moving fast, but after my conversation with Alex Bouzari, Co-Founder and CEO at DDN, at Google Cloud Next '26, one thing became clear. The bottleneck is no longer the model. It is the infrastructure behind it. Alex broke it down in a very real way. Today’s AI systems are powerful, but the way data moves through them is still inefficient. You train these large models, but when it comes to actually running them at scale, things slow down. Latency increases, costs go up, and performance becomes unpredictable. That is what is broken. He shared how this shows up in real scenarios. When enterprises deploy AI, especially with large models, they struggle with speed and consistency. It is not that the model cannot perform, it is that the infrastructure cannot keep up with the demand. At Next, DDN focused on solving exactly this. Building what Alex called a new foundation for AI, designed for high-performance workloads where data access and speed matter just as much as the model itself. One concept that stood out was KV cache. It sounds technical, but the idea is simple. Instead of recomputing everything every time a model runs, you reuse key pieces of information. That reduces latency and makes systems faster and more efficient. In large-scale AI systems, that becomes a big deal. The bigger shift here is clear. We are moving from experimenting with AI to operationalizing it at scale. And that means infrastructure is becoming the deciding factor. What makes DDN different is their focus on this layer. Not just enabling AI, but making sure it actually performs in real-world environments. My takeaway. The future of AI will not just be defined by better models. It will be defined by better infrastructure. #data #ai #ddn #infrastructure #googlecloudnext #api #google #theravitshow