The Datastorage.com Podcast: Building for Tomorrow's Cloud Infrastructure

In this episode of the DataStorage.com podcast, John sits down with Lu Zhang, Founder and Managing Partner of Fusion Fund, to talk about where AI infrastructure, data quality, cloud architecture, and venture capital are headed next.Lu shares her perspective as both a former founder and an early-stage investor backing deep tech, healthcare AI, infrastructure, and enterprise startups. The conversation explores what actually matters in AI today: high-quality data, inference cost, architecture design, multimodal data, edge AI, governance, and the growing importance of specialized agents over one-size-fits-all models.They also discuss how startups should think about hyperscalers vs. neo-cloud providers, why data curation is becoming mission-critical, how regulated industries like healthcare and finance are approaching AI adoption, and what Lu learned from this year’s NVIDIA GTC.Topics covered: • What Lu Zhang looks for in early-stage AI founders • Why data quality matters more than data quantity • AI infrastructure vs. AI applications • Inference cost, model efficiency, and architecture design • Edge AI and the future of AI deployment • Data governance, compliance, and regulated industries • Multimodal data and enterprise memory graphs • Otter, MCP, conversational data, and AI agents • Hyperscalers vs. alternative cloud and storage providers • The future of consolidation in cloud infrastructure • AI’s environmental impact and the role of efficient data systems • NVIDIA GTC, healthcare AI, and where venture is heading nextIf you’re a founder, investor, infrastructure leader, or enterprise operator trying to understand where AI is really going, this episode is packed with practical insights.Subscribe to DataStorage.com for more conversations on AI infrastructure, data storage, cloud, and the future of enterprise technology.#AIInfrastructure #LuZhang #FusionFund #DataQuality #EdgeAI #CloudInfrastructure #VentureCapital #HealthcareAI #MultimodalAI #DataStorage

Russ Artzt, co-founder of Computer Associates (CA Technologies), joins the DataStorage.com podcast to discuss how AI is reshaping enterprise infrastructure.Drawing on decades of experience spanning the mainframe era, the rise of SaaS, cloud computing, and today’s AI boom, Russ explains why the current shift may be the most transformative yet.In this episode we cover:• Why GPUs changed the economics of AI infrastructure• The rise of specialized neo-cloud providers like CoreWeave• How hyperscalers, multi-cloud, and hybrid architectures will evolve• Why data and storage are becoming the anchor of the AI ecosystem• The role of alternative storage providers and the impact of egress fees• How companies should design infrastructure for the next decade of AIIf you’re building AI applications, managing infrastructure, or evaluating alternatives to hyperscaler cloud providers, this conversation offers a strategic look at where the industry is heading.

Synthetic data is rapidly becoming one of the most important building blocks in AI — from training autonomous vehicles to powering robotics on Mars.In this episode, we sit down with Brian Geisel, Founder of Symage from Geisel Software, to explore how synthetic data, robotics, machine learning, and large language models (LLMs) are reshaping AI infrastructure.We cover: 🚀 How NASA trained Mars rovers using synthetic environments 🤖 Why humanoid robots may not be the future 📦 How robotics is transforming warehouse automation and micro-fulfillment centers 🧠 Why foundation models must rely on synthetic data going forward 📊 The hidden storage challenges behind AI training 🔐 How synthetic data solves PII and regulated industry problems ⚡ The balance between real-world data and AI-generated dataBrian explains why synthetic data isn’t just about generating more data — it’s about generating better, targeted data that improves model performance while reducing waste.We also dive into: • AI model training strategies • GPU vs object storage considerations • High-throughput data movement • Robotics + physical AI • The future of multimodal synthetic generationIf you’re building AI systems, training models, managing data infrastructure, or investing in AI and robotics, this conversation is packed with insights.Explore more content like this at datastorage.com Learn more about Symage: https://geisel.software/synthetic-data-storage-strategies/?utm_source=datastorage&utm_medium=podcast

What does cloud infrastructure look like after hyperscalers—and how does AI change the rules?In this episode of the Datastorage.com podcast, we’re joined by Seth Helgesen, Solutions Architect at IONOS, to unpack how cloud, object storage, and AI infrastructure are evolving—and why simplicity, performance, and transparency are becoming the real differentiators.IONOS started as a domains and hosting company in Europe and has grown into a full-stack cloud provider offering always-hot object storage, CPU and GPU compute, block storage, and hybrid cloud infrastructure—all at a fraction of hyperscaler costs.What we cover in this episode: • Why always-hot object storage matters in the AI era • How IONOS delivers $4.99/TB object storage with low egress fees • S3 compatibility, IAM support, and migrating from AWS or other hyperscalers • The rise of hybrid and multi-cloud architectures for AI workloads • Why data proximity is critical for GPU training and inference • Edge computing, smaller LLMs, and the future of multimodal AI • How IONOS simplifies cloud infrastructure without hyperscaler complexity • Who IONOS is best positioned to serve: MSPs, SaaS platforms, media, AI, and enterprise ITIf you’re evaluating alternatives to AWS, thinking about hybrid cloud strategies, or building AI-driven products that require fast, affordable access to data, this conversation is for you.👉 Learn more at Datastorage.com👉 Explore IONOS cloud infrastructure and object storage: 👉 Subscribe for more conversations on cloud, AI, and modern data infrastructure

AI is great at pattern matching — but what happens when the most valuable insights don’t fit the pattern?In this episode of DataStorage.com, we sit down with Abhishek "AJ" Jha "(AJ), Founder & CEO of Elucidata, to break down why traditional AI approaches fail in drug discovery — and how data-centric AI is reshaping the future of pharma, healthcare, and beyond.AJ shares Elucidata’s journey from resisting the “AI” label to fully embracing it in the post-LLM era — not by building bigger models, but by focusing on data quality, governance, and out-of-distribution problems that actually matter in regulated industries.🔍 In this conversation, we cover: • Why pattern-matching AI breaks down in drug discovery and healthcare • What “out-of-distribution” problems are — and why they’re so valuable • How data-centric AI differs from model-centric AI • The role of human-in-the-loop AI in high-stakes industries • Preparing multimodal data (text, tabular, imaging) for AI-ready use cases • Why starting with the use case beats “cleaning all the data” • AI infrastructure decisions, cloud costs, GPUs, and egress challenges • Deploying AI securely in regulated environments (HIPAA, SOC 2, GDPR) • Whether companies should build their own models or fine-tune existing onesThis episode is a must-watch for: • Pharma & biotech leaders • AI and data infrastructure teams • Founders building AI products in regulated industries • Anyone questioning whether bigger models = better AI⸻🎧 Subscribe to DataStorage.com for deep conversations on AI, cloud infrastructure, data storage, and the real systems powering the AI economy.🔗 Learn more about Elucidata: https://elucidata.io🔗 More episodes: https://datastorage.com

In this episode of The Data Storage Podcast, we sit down with the CEO of Backblaze to discuss the broader state of cloud computing — from hyperscaler dominance to the rise of AI infrastructure and multi-cloud strategy.We explore:How the cloud market is evolving in the AI eraWhether hyperscaler pricing models are sustainableThe pressure egress fees create for modern workloadsThe rise of alternative cloud providers (“neoclouds”)What founders and infrastructure leaders should consider in 2026This conversation goes beyond product features to examine the structural shifts reshaping cloud economics and infrastructure strategy.If you’re building or managing AI workloads, evaluating multi-cloud architecture, or watching the next phase of cloud competition unfold, this episode provides a high-level executive perspective.Produced by Datastorage.com.