AI_Cloud Essentials

About AI_Cloud Essentials

Breakthroughs stall when leaders are forced to build the future on foundations from another era. Modern AI demands new thinking, tooling and decision patterns, yet many executives feel trapped by outdated playbooks. AI Cloud Essentials clears that bottleneck. Hosted by Independent AI Value Strategist Ritu Jyoti, the show delivers practical guidance for leaders navigating trillion-parameter models, real-time adaptation and fast-moving AI ecosystems. Each episode offers clear frameworks to help teams innovate faster, scale smarter and reduce friction without jargon or recycled thinking.

Publisher

CoreWeave

Category

business

Language

en

Explicit

No

First episode

16 Dec 2025

Latest episode

6 Oct 2026

Latest episodes

19 episodes in the feed.

1. 

   6 Oct 2026

   The Future of AI Infrastructure

   How do leading AI companies scale massive GPU workloads without getting buried in infrastructure complexity?In this episode of AI Cloud Essentials, Christian from Anyscale breaks down how Anyscale and CoreWeave are combining AI software and high-performance cloud infrastructure to help organizations build, train, and run AI workloads at scale.Anyscale is the company behind Ray, the open-source framework used by leading AI teams for data preparation, model training, inference, and distributed computing. Christian explains how pairing Ray and the Anyscale platform with CoreWeave’s GPU infrastructure can give AI teams greater control, observability, scalability, and faster time to value.The conversation explores what it actually takes to orchestrate large-scale GPU clusters, why building AI infrastructure entirely in-house can create unnecessary complexity, and how organizations can take advantage of specialized infrastructure and software platforms to move faster.They also discuss where AI infrastructure demand is accelerating—including robotics, physical AI, life sciences, biotech, financial services, quantitative trading, ecommerce, and recommendation systems—and why multi-cloud infrastructure could become a major competitive advantage for AI companies.In this episode, you’ll learn: How Anyscale and CoreWeave combine AI software and GPU infrastructure How Ray helps orchestrate distributed AI workloads at scale Why GPU infrastructure creates new challenges for Kubernetes environments How organizations can reduce AI infrastructure complexity and accelerate deployment Why multi-cloud AI infrastructure is becoming increasingly important How enterprises are using AI across robotics, biotech, financial services, and ecommerce Why forward-deployed engineers can help companies move from infrastructure to business outcomes faster What AI leaders should prioritize as they scale their infrastructure into 2027 Christian also shares how the teams were able to stand up a large-scale GPU cluster running data-generation workloads in under 24 hours, demonstrating what becomes possible when specialized AI infrastructure and software are tightly integrated.If you're building AI applications, managing GPU infrastructure, working with distributed computing, or thinking about how to scale machine learning workloads, this conversation offers a practical look at the infrastructure powering the next generation of AI.

2. 

   22 Sept 2026

   Architecting for Speed and Reliability

   AI cloud infrastructure is not just a capacity decision - it is a customer partnership. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Tara Madhyastha, Senior Field Engineer and Solutions Architect at CoreWeave, to unpack what AI teams are really asking for as they move from ambition to architecture. Tara describes customers building across training, inference, healthcare, physical AI, robotics, and other demanding workloads - each with different goals, constraints, and urgency. The common thread: teams need performance, reliability, cost control, and speed, but they also need a trusted technical advisor who can help them put the full system together. This conversation is for infrastructure leaders, technical buyers, platform teams, and AI builders who need more than raw compute - they need a partner who can help them design, iterate, troubleshoot, and scale the right architecture. What you'll take away: Why AI customers often know the outcome they want before they know the architecture they need How CoreWeave field engineering turns cloud engagement into joint solutioning Why performance is not enough without trusted technical guidance How AI-native cloud brings compute, data, storage, networking, lifecycle, and operations together Why hands-on support, POCs, Slack channels, and real-time collaboration help customers move faster Learn why the next phase of AI cloud is not only about more capacity - it is about helping teams build the right architecture, faster.

3. 

   8 Sept 2026

   Insights from the Frontline of AI

   Agentic AI is moving from demos into real work - but autonomy only matters if teams can trust what the agent is doing. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Umar, Nico, and Brandon for a panel-style conversation about what builders need to make agents reliable across specialized domains, long-running workflows, and production environments. The group unpacks why the most exciting agent use cases are also the hardest to build: agents that take more steps, operate over longer trajectories, and work in specialized settings like drug development, customer support, robotics, and enterprise workflows. That shift raises a new set of infrastructure and tooling questions around online evaluation, offline benchmarking, observability, monitoring, and optimization. The conversation also explores the emerging agent stack: Weave for prompt, context, and harness engineering; model training and policy improvement through SFT or GRPO; CoreWeave under the hood to run models effectively; and MCP/skills as a way to bring coding agents into the development process itself. This conversation is for AI builders, platform teams, ML engineers, and enterprise leaders trying to move from agent experiments to production-grade systems. What you'll take away: Why longer agent trajectories make evaluation and reliability harder How online evaluation and offline benchmarking work together for agent systems Why observability is becoming a must-have layer for models, agents, and production AI How the agent stack is expanding across prompt engineering, context engineering, harness engineering, and model optimization Why coding agents, MCP, and skills are becoming part of the workflow for everyone - not just developers Learn how to build agentic AI around evaluation, optimization, and observability - before autonomy becomes a liability.

4. 

   25 Aug 2026

   More Shots on Goal for Enterprise Video

   Enterprise video is being rebuilt around speed, trust, and volume. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Faizan, Co-Founder and CEO of Roll.ai, to unpack how AI can help teams move from recording to polished video faster - without crossing the line into synthetic or misleading content.Faizan traces the problem from his own creative background - logos, websites, video editing, and a long relationship with media tools - to the way businesses still produce video today: expensive shoots, long post-production cycles, and a tendency to treat every customer story or campaign like a Super Bowl ad. Roll.ai is taking a different approach: capture the real person, let agents reason through the edit, send the media to the cloud, and use GPU-powered enhancement to make the result look more human and more compelling.The conversation also digs into the trust barrier around AI video. Faizan is clear that enterprise customers do not want fake video or unauthorized training on their content; they want a faster way to make authentic assets. This conversation is for marketing leaders, enterprise AI buyers, infrastructure teams, and video teams deciding how AI can increase output without sacrificing credibility.What you'll take away: Why enterprise video teams need faster paths from recording to finished assets How AI agents and AI cloud GPUs can help compress the production workflow Why the line between enhancement and fake video matters for enterprise trust How lower-friction video creation gives teams more shots on goal Why AI adoption often starts with small wins before teams earn license to change more Learn how AI video can shift enterprise content from rare, expensive productions to repeatable, credible output.

5. 

   11 Aug 2026

   Every Enterprise Decision Is a Prediction

   Enterprise AI is moving past retrieval and into prediction. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Ben Turtel, CEO and founder, to unpack why the next generation of enterprise models needs to understand which signals actually lead to which outcomes - not just memorize company information. Ben explains how predictive analytics reframes everyday business choices: every enterprise decision is a prediction about what will happen next. From financial services use cases like forecasting earnings surprises from SEC filings to private equity and sales predictions, the conversation explores why outcome-oriented models require the right data, the right training approach, and infrastructure that can move as fast as the experiments demand. The episode also gets practical about what slows AI teams down: access to proprietary data, the need to containerize full stacks, the cost of training runs, and the importance of being able to spin GPUs up and down quickly. This conversation is for enterprise AI leaders, technical founders, and infrastructure teams who are deciding whether their AI strategy is still searching for information - or starting to reason like an expert. What you'll take away: Why every enterprise decision can be understood as a prediction How models can learn which signals and factors lead to which outcomes Why access to proprietary data is often the biggest friction point for enterprise AI How containerized small-model architectures can help teams run closer to customer data Why the enterprise needs to move from retrieval toward trained experts that know what matters Learn how predictive AI changes the enterprise question from "where is the information?" to "what is likely to happen next?"

6. 

   28 Jul 2026

   AI Is a Marathon Where You Sprint the Entire Time

   The next frontier of AI may start inside a gaming clip. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Pim, CEO and Co-Founder of General Intuition, to unpack how player-generated gaming data became the foundation for a new class of general models - and why scaling that bet requires infrastructure built for high-velocity research.Pim explains the insight behind General Intuition's work: a saved gameplay clip is not just a video; it is the end of a sequence of decisions, actions, and reasoning. That data flywheel lets the team train models that can move from information-dense gaming environments to physical robots, while a new CoreWeave pre-training cluster gives the company the performance, uptime, and support needed to keep a lean research team moving.This conversation is for AI founders, research leaders, and infrastructure teams trying to scale faster without burning out the people doing the work.What you'll take away:- Why gaming clips can function like reasoning chains for model pre-training- How a consistent controller input space helps models transfer across simulators and robots- Why CoreWeave's Kubernetes implementation and GPU uptime mattered for a lean research team- How infrastructure reliability directly affects team sleep, morale, and research velocity- Why reinforcement learning may bring the CPU stack back into the center of AI infrastructureLearn why the AI race is not a marathon or a sprint - it’s both at once, and every infrastructure decision makes the next step easier or harder.

7. 

   14 Jul 2026

   Move Fast, Fail Forward—and Clone the Win

   AI cloud infrastructure is the difference between a viral moment and a missed opportunity. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Jake Rothman, who leads Go-To-Market and Partnerships at Decart AI, to unpack how a single viral LinkedIn demo of virtual try-on turned into a commercial roadmap — and why an AI-native cloud partner like CoreWeave made it possible to say yes. Jake traces Decart's journey from Oasis One — a Minecraft-inspired, prompt-generated world model — to real-time video products demanding sub-40 millisecond latency, and shares the go-to-market playbook his team uses to turn one obsessed customer into a repeatable motion. This conversation is for AI builders, infrastructure leaders, and GTM teams deciding how fast they can afford to move. What you'll take away: Why sub-40 millisecond latency is the threshold that makes real-time AI experiences possible How burst capacity removes the internal debate when a product spikes overnight The "clone the win" playbook: find one customer who loves the product, then exhaust that track Why Decart treats GPU optimization as a commercial product, not a cost center How world models like Oasis One point toward the physical AI and robotics opportunity ahead

8. 

   30 Jun 2026

   How AI Is the Ultimate Horizontal Enabler

   AI agents are reshaping enterprise infrastructure, and AI-native cloud is becoming the foundation for what comes next. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Chen Goldberg to explore how businesses are moving from models to agents—and why traditional cloud environments can’t keep up. If you want to understand how CoreWeave’s AI-native cloud drives faster experimentation, better production scale, and real business outcomes, this episode gives you the roadmap. Ritu and Chen break down how CoreWeave helps customers move beyond AI experimentation into full production, supporting everything from enterprise search to large-scale inference and agentic workflows. They discuss customer examples, revealing how bottlenecks shift from compute to data movement, why speed matters more than ever, and how organizations can “lean in” instead of falling behind. Whether you’re a CIO, CTO, platform leader, or AI innovator, this conversation will change how you think about infrastructure in the AI era. Learn why AI-native cloud is critical for moving from experimentation to production Understand how AI agents are changing the demands on infrastructure and data pipelines Discover how companies like MercadoLibre and Cohere are scaling AI workloads faster See why “speed matters” is becoming the most important strategy for enterprise AI teams Learn how leaders can avoid pilot purgatory and accelerate innovation with confidence Don’t risk building tomorrow’s AI strategy on yesterday’s infrastructure. Learn how to move faster, scale smarter, and lead the shift from models to agents before your competitors do.

9. 

   16 Jun 2026

   How Marketing Is Moving to an AI-First Approach

   AI product marketing is changing fast—and the teams that win will be the ones using AI for more than just content generation. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Susanne Seitinger, VP of Product Marketing at CoreWeave, to unpack how AI product marketing is transforming everything from messaging and naming to persona development and synthetic research. If you want to understand how to increase marketing velocity without sacrificing strategy, this conversation delivers practical insights you can use immediately. Ritu and Susanne explore how product marketing teams can move beyond surface-level AI use cases and rethink foundational practices like validation, decision-making, and customer feedback loops. They break down how synthetic research helps teams test messaging in days instead of months, why “AI slop” is a real risk for marketers, and how human discernment still separates great marketing from automated noise. Susanne also shares how her team at CoreWeave is building an AI-first product marketing function—one that balances speed, consistency, and real customer resonance. Learn how AI product marketing improves naming, messaging, and persona mapping Understand when synthetic research works—and when traditional research still matters Discover how to create faster feedback loops without losing message consistency Avoid “AI slop” by strengthening human judgment, discernment, and taste See how leading product marketing teams are building AI-first workflows that scale Don’t risk falling behind while your customers and competitors move faster than ever. Learn how to rethink product marketing from the ground up and use AI to create real business impact.

10. 

    2 Jun 2026

    How AI Is Transforming DevOps and the Developer Experience

    AI-powered engineering is reshaping how software gets built, and this episode breaks down exactly what that means for teams today. In this episode of AI Cloud Essentials, we explore AI-powered engineering through real-world use cases, revealing how autonomous agents are changing the role of developers and accelerating innovation. If you’re trying to understand where AI coding tools deliver real value—and where they still fall short—this conversation gives you a clear, practical roadmap. Hosted by Ritu Jyoti, this episode features Camille Fournier, VP of Engineering at CoreWeave, who shares how teams are moving beyond simple code completion into fully agent-driven workflows. From greenfield development to operational automation inside complex AI cloud environments, Camille explains how organizations are adopting these tools, where bottlenecks are emerging (like code review and validation), and what it takes to actually get value from AI today. You’ll walk away with a grounded understanding of how AI is transforming engineering workflows—not just in theory, but in practice. What you’ll learn: How AI agents are shifting engineering from writing code to orchestrating systems Where AI coding tools work best today (greenfield vs. legacy systems) Why code review, validation, and workflows are becoming the new bottlenecks How non-engineers are already using AI tools to build and automate work The mindset engineers need to stay relevant as AI evolves rapidly Don’t risk falling behind as AI reshapes engineering. Learn how to adapt your workflows, rethink your role, and get real value from AI—before the gap widens.

11. 

    19 May 2026

    How Wall Street Can Drive The Next AI Breakthrough

    AI in financial markets is moving beyond incremental gains and into core decision-making. It’s no longer just about back-office automation, it’s beginning to reshape pricing, risk, and trading systems. In this episode, you’ll learn how to apply AI in financial markets to move beyond alpha and drive real market transformation. Hosted by Ritu Jyoti, an independent AI strategist, this episode features Dimitris Tsementzis, Founder and CEO of hLevel. Together, they explore why traditional AI approaches struggle in non-stationary markets and what it takes to build systems that can adapt, learn, and operate under constant change. From the limits of interpretability to the importance of controllability, this conversation breaks down how financial leaders can rethink models, manage risk, and apply AI to core market functions—not just incremental efficiency gains. In this episode, you’ll learn how to: Apply AI to pricing, risk, and trading systems Design AI systems for non-stationary, constantly changing markets Prioritize controllability over interpretability in high-risk environments Move beyond back-office automation to core market transformation Identify where AI can create real competitive advantage in finance Don’t risk falling behind by limiting AI to incremental improvements. Learn how to apply AI in financial markets to move beyond alpha and lead the next wave of innovation.

12. 

    5 May 2026

    How Physical AI is Streamlining Engineering

    AI-powered engineering is transforming how organizations solve complex physical problems. AI-powered engineering is no longer about theory, it’s enabling faster simulation, real-world testing, and better decision-making across industries. In this episode, you’ll learn how to apply AI-powered engineering to accelerate innovation in the physical world. Hosted by Ritu Jyoti, an independent AI strategist, this episode of AI Cloud Essentials features Richard Ahlfeld, SVP for Physical and Scientific AI at CoreWeave. Together, they explore how AI is moving beyond digital systems into real-world applications like automotive, manufacturing, and robotics. From using transformers to dramatically improve simulation accuracy to learning directly from physical testing data, this conversation breaks down how leaders can move faster by combining AI with real-world iteration instead of relying on theory alone. In this episode, you’ll learn how to: Use AI to accelerate physical simulations and engineering workflows Combine simulation and real-world testing to improve outcomes Apply transformer models to complex physical systems Overcome data challenges in engineering and scientific AI Identify high-impact use cases across automotive, manufacturing, and robotics Don’t risk falling behind by relying only on traditional simulations or outdated workflows. Learn how to apply AI-powered engineering to move faster, test smarter, and build better systems.

13. 

    21 Apr 2026

    How AI augments artistry

    AI in creative workflows is moving from experimentation to execution. AI in creative workflows is no longer about hype or isolated pilots, it’s about integrating AI into real production environments. In this episode, you’ll learn how to operationalize AI in creative workflows to drive real value across media and entertainment. Hosted by Ritu Jyoti, an independent AI strategist, this episode of AI Cloud Essentials features Mac Moore, Head of Media and Entertainment at CoreWeave. Together, they break down how studios are shifting from disconnected AI experiments to fully integrated, artist-centric workflows using platforms like CoreWeave Conductor for AI. From debunking the “prompt-to-movie” myth to explaining why assistive AI is the real future, this conversation unpacks how creative teams can scale production, reduce friction, and unlock new levels of collaboration without sacrificing artistry. In this episode, you’ll learn how to: - Integrate AI into creative workflows instead of running isolated experiments - Identify the right use cases for AI in media and entertainment production - Use assistive AI to augment creative teams, not replace them - Simplify complex AI infrastructure for artists and studio teams - Move from AI hype to real, production-ready value Don’t risk falling behind by treating AI as a side experiment. Learn how to integrate AI into your creative workflows and start realizing value today.

14. 

    24 Feb 2026

    Future-Proof Your Enterprise AI: The Decisive Strategic Choice You Must Make Now

    AI native cloud strategy now determines who can scale AI and who stalls out. AI native cloud decisions directly impact cost control, speed to market, and competitive advantage as agentic AI moves from pilots to production. In this episode, leaders explain why the wrong AI foundation quietly becomes a business risk. In this episode of AI Cloud Essentials presented by CoreWeave, host Ritu Jyoti, Independent AI Strategist, is joined by Jean English, Chief Marketing Officer of CoreWeave, to take AI out of the infrastructure weeds and into the boardroom. They break down why general purpose cloud platforms were never designed for AI at scale and how AI native cloud architectures unlock faster innovation, better economics, and real business velocity. In this episode, you will learn how to: Tell if your current AI stack is enabling growth or slowing it down Understand the real differences between general purpose cloud and AI native cloud Move AI from prototype to production without friction Improve GPU utilization and cost predictability at scale Balance speed, security, and governance for enterprise AI Align AI infrastructure decisions with executive and board level outcomes Future proof your organization for agentic AI and next generation workloads The gap between AI leaders and laggards is widening fast. Do not risk rising costs, stalled innovation, or strategic lock in. Learn how to build an AI native foundation that protects speed, control, and competitive advantage while there is still time to lead.

15. 

    10 Feb 2026

    Migration Risk? Debunking the Myths to Get to AI-Native Cloud, Fast

    AI cloud migration no longer has to be risky, disruptive, or slow. In this episode of AI Cloud Essentials, we break down the biggest myths holding enterprises back from AI cloud migration and show why moving to an AI-native cloud is far easier and more strategic than most leaders believe. If your organization is overspending on legacy cloud infrastructure while still feeling unprepared for AI, this episode delivers a clear path forward. Host Ritu Jyoti sits down with Corey Sanders, SVP of Strategy at CoreWeave, to dismantle the fear around AI cloud migration and reframe it as replatforming for intelligence, not a risky lift-and-shift. Together, they explore why general-purpose clouds create hidden cost, operational drag, and innovation bottlenecks for AI workloads, and how an AI-native cloud enables faster experimentation, zero-downtime transitions, and real business velocity. Brought to you by CoreWeave, this conversation is built for CIOs, CTOs, and enterprise leaders who need to move decisively without breaking what already works. In this episode, you’ll learn: Why AI cloud migration is not a “big bang” data center evacuation How AI-native clouds eliminate wasted GPU cycles and legacy cloud tax Why incremental, parallel migration enables zero downtime How AI workloads shift cloud strategy from cost center to revenue driver Why inactivity is the biggest risk enterprises face with AI How to prioritize the right workloads to accelerate AI ROI fast Don’t risk falling behind by delaying your AI cloud migration. Learn how to move to an AI-native cloud with confidence, cut hidden costs, and unlock real AI value before competitors do.

16. 

    27 Jan 2026

    The AI Risk Blind Spot: Are General-Purpose Clouds Leaving Your Enterprise Exposed?

    AI cloud security is no longer a future concern, it is an urgent enterprise risk hiding in plain sight. In this episode of AI Cloud Essentials, brought to you by CoreWeave, we expose the AI cloud security blind spot most organizations miss and explain why traditional perimeter defenses fail modern AI workloads. If your AI strategy touches proprietary data, models, or business logic, this conversation could save you from costly exposure. In Episode 4, host Ritu Jyoti is joined by James Higgins, Chief Security Officer at CoreWeave, to unpack how AI fundamentally changes the shared responsibility model in the cloud. They explore why AI risk now lives inside the model, the prompt, and the data flows, not just the network, and why AI native infrastructure, governance, and zero trust, data centric security are quickly becoming non negotiable as regulators, attackers, and autonomous systems outpace legacy security tools. In this episode, you’ll learn: Why the shared responsibility model breaks down for AI workloads How prompt injection, data poisoning, and model drift create new security risks Why general purpose cloud security tools fail AI systems How AI native infrastructure changes the security equation The top actions enterprise leaders should take in the next 90 to 180 days Don’t risk your AI models becoming your biggest liability. Learn how to secure AI workloads properly before blind spots turn into breaches.

17. 

    13 Jan 2026

    The AIOps Black Hole: Escaping the Complexity Trap

    AI native infrastructure is no longer optional; it is the foundation enterprises need to scale AI reliably, securely, and cost effectively. In this episode, Independent AI Strategist Ritu Jyoti sits down with Lavanya Shukla, Senior Director of AI at CoreWeave, to expose the AI ops black hole and explain why GPUs alone will never get AI models safely into production. You will learn how hidden complexity, fragmented tooling, and legacy AIOps quietly drain AI ROI and stall even the most ambitious AI roadmaps. Together, Ritu and Lavanya unpack why general purpose clouds create an operational trap for modern AI workloads. They break down how probabilistic models, multi cloud deployments, and disconnected observability tools increase cognitive load, slow experimentation, and introduce serious business and compliance risk. Drawing on real world experience with large scale AI deployments, they outline how AI native cloud architecture and model aware observability restore trust, speed, and control across the entire AI lifecycle. In this episode, you will learn: Why the AI ops black hole is the real reason AI initiatives fail at scale How general purpose cloud infrastructure creates hidden time and complexity costs Why traditional AIOps breaks down for probabilistic and generative AI systems What model aware observability looks like and why it is non negotiable How AI native cloud architecture reduces integration debt and developer burnout The concrete steps leaders can take to move from fragile prototypes to production ready AI Do not risk stalled deployments, burned out engineers, and AI systems you cannot trust. Learn how to escape the AI ops black hole and build AI platforms that scale with confidence, clarity, and measurable business impact.

18. 

    30 Dec 2025

    Beyond GPUs: What True AI-Native Infrastructure Really Means

    AI-native infrastructure is no longer optional; it is the foundation every modern enterprise needs to scale AI reliably and cost effectively. In this episode, Independent AI Strategist Ritu Jyoti sits down with Jacob Feldman, Lead Solutions Architect at CoreWeave, to break down what true AI-native infrastructure really means and why GPUs alone will not get your models into production. You will learn how the right architecture can eliminate bottlenecks, reduce training time, and unlock the performance your AI teams have been missing. Together, Ritu and Jacob unpack the full AI stack: networking, storage, orchestration, security, and observability, and explain how each layer impacts speed, cost, and long term ROI. They share real-world insights from working with leading CIOs, CTOs, and Heads of AI, giving you a clear blueprint for moving from pilot experiments to scalable AI deployment. In this episode, you will learn: Why general purpose cloud slows AI teams down How InfiniBand, bare metal compute, and AI optimized storage transform performance What AI-native actually means across the full lifecycle (training to inference to continuous refinement) The operational pitfalls that stall enterprise AI and how to avoid them How to build for reliability, scale, and predictable cost in every AI workload Do not risk stalled pilots, rising GPU costs, and fragile architectures. Learn how to build AI systems that truly scale and give your teams the infrastructure advantage they need now.

19. 

    16 Dec 2025

    The Hyperscaler Tax: Why Your AI is Bleeding Budget (and How to Stop It)

    The hyperscaler tax is draining more AI budget than most teams realize. If you’re fighting rising cloud bills or trying to make AI ROI make sense on paper, this episode walks through the hidden fees, blind spots, and cloudy forecasting that cause companies to miss their AI cost targets by a wide margin. In this episode, host Ritu Jyoti, an independent AI Value Strategist, breaks down why general-purpose clouds struggle with AI workloads and how inefficiencies in resource mapping, data movement, and day-to-day operations quietly chip away at margin and innovation. You’ll also hear a practical path forward, how AI-native cloud architecture changes the math, what predictable pricing actually looks like, and the simple first step that helps teams uncover where their spend is really going. It’s a clear, grounded conversation designed for leaders who need results, not hype. Don’t risk budget creep or stalled progress, learn how to take control of your AI costs and turn them into real ROI.

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