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.

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 Uma, 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.

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.

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?"

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.

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

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.

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.

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.

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.