The New Stack Podcast

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300

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Jun 2026

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The New Stack Podcast is all about the developers, software engineers and operations people who build at-scale architectures that change the way we develop and deploy software. For more content from The New Stack, subscribe on YouTube at: https://www.youtube.com/c/TheNewStack

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June 11, 202650 min

WeAreDevelopers is coming to the US to give unsung developers a bigger voice

WeAreDevelopers, the Berlin-based developer conference founded in 2015, has grown into a major global event, attracting 15,000 developers from over 70 countries each year. In 2026, it expands beyond Europe with new editions in San Jose, California, and Bengaluru, India. Co-founder and CEO Sead Ahmetovic says the conference was created to give developers a stronger voice in an industry where marketers, salespeople, and entrepreneurs often receive more recognition. He believes developers, despite being less vocal, build the products that power the modern world. The event began as a small meetup that quickly gained popularity, filling a gap between highly specialized technical gatherings and broader business-focused conferences. Former GitHub CEO Thomas Dohmke highlights another benefit: giving developers a platform to share the stories behind their work and inspire peers. Discussing the future of software development, Dohmke predicts AI agents will handle much of the coding, while developers focus on managing ideas, prompts, and workflows. Ahmetovic agrees, arguing that developers will remain essential, spending less time typing code and more time thinking, orchestrating, and creating new solutions. Learn more from The New Stack around the latest in developer community growth: How Community Helps Developers Grow Empowering Developers Is Critical to Drive AI Innovation 3 Ways Organizations Can Redefine the Developer Experience Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 27, 202627 min

Why MotherDuck refuses to fork DuckDB

At a recent MCP developer summit, The New Stack spoke with Till Döhmen, AI lead atMotherDuck, about the company’s growing role in the evolving DuckDB ecosystem. Backed by investors includingTomasz Tunguz, MotherDuck is commercializing the open-source analytical databaseDuckDBwhile also expanding how employees interact with data through AI agents rather than traditional dashboards. Döhmen emphasized the company’s close collaboration withDuckDB FoundationandDuckDB Labs. Because MotherDuck operates what he described as the world’s largest fleet of DuckDB databases, the startup regularly pushes the database to its limits and feeds insights back to the core maintainers. Rather than forking DuckDB to create proprietary advantages, MotherDuck instead extends the platform through its existing architecture while contributing core improvements upstream when needed. The conversation highlighted the delicate but productive relationship between venture-backed companies and the open-source projects they commercialize, positioning MotherDuck as another example of startups driving both OSS adoption and strong business growth simultaneously. Learn more from The New Stack around the latest in DuckDB: DuckDB: Query Processing Is King DuckDB: In-Process Python Analytics for Not-Quite-Big Data Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 21, 202626 min

JetBrains is selling independence as the rest of AI coding picks sides

JetBrains is positioning itself as the last major independent AI coding-tool vendor in a market increasingly tied to hyperscalers and foundation model labs. Speaking at Google Cloud Next, JetBrains VP of business developmentMikhail Vink argued that competitors such as Microsoft Copilot, Anysphere Cursor, and Windsurfare all tied to either AI labs or cloud providers. By contrast, JetBrains says its independence allows customers to switch freely between models fromOpenAI,Anthropic, andGoogle Cloudwithout being locked into one ecosystem. That flexibility underpins JetBrains’ broader AI strategy. Rather than building its own foundation model, the company is focusing on orchestration and governance through JetBrains Central, announced in March as a management layer for AI agents, usage controls, analytics, and consumption-based billing. Vink said the company’s profitability, 16 million users, and 300,000 commercial customers from its long-running IDE business have allowed it to remain venture-free and model-neutral. JetBrains argues that as developers increasingly swap between AI models, neutrality may become more valuable than owning the models themselves. Learn more from The New Stack around the latest in AI coding-tools: JetBrains ‘Agentic’ AI Agent Helps Automate Coding Tasks JetBrains: AI agents are about to repeat the cloud ROI crisis JetBrains names the debt AI agents leave behind Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 15, 202619 min

Why Block handed Goose to the Linux Foundation

What began as an internal developer tool atBlockhas evolved into a broader open-source initiative with industry backing. Goose, Block’s AI coding agent, followed a path similar to Amazon’s transformation of internal infrastructure intoAmazon Web Services. After deploying Goose companywide, Block open-sourced the tool under a permissive license, leading to rapid adoption across the developer community. But according to Manik Surtani, Office of the CTO, Block and Co Founder of Agentic AI Foundation, early momentum exposed governance challenges. Although Goose was technically open source, Block retained trademark ownership, creating concerns for enterprises seeking truly independent governance. To address this, the team partnered with the creators ofAnthropicand the Model Context Protocol community to establish theAgentic AI Foundationunder the umbrella of theLinux Foundation. Goose, MCP, and Agents.MD became the foundation’s initial projects, chosen largely to accelerate the launch of the new organization and create a collaborative ecosystem around agentic AI development. Learn more from The New Stack around the latest in open-source AI: Anthropic extends MCP with a UI framework Why the Linux Foundation adopted MCP, with Jim Zemlin and Mazin Gilbert Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 13, 202622 min

Fivetran's CPO: closed data stacks won't survive the agent era

At Google Cloud Next 2026, Fivetran Chief Product Officer Anjan Kundavaram argued that enterprise data systems are unprepared for the scale of AI-driven analytics. Unlike humans, AI agents can generate exponentially more queries, often routing them through the same expensive compute infrastructure. Kundavaram compared it to “using a Lamborghini to mow the lawn.” To address this, Fivetran introduced its “Open Data Infrastructure” vision and a benchmark designed to expose hidden AI workload costs in closed ecosystems. Kundavaram said agents can optimize for cost instead of speed, choosing cheaper compute engines when appropriate — but only in open architectures with multiple options. Closed systems force every query through high-cost paths. He also warned that fragmented data and weak context create a “triple whammy” of poor AI responses, soaring analytics bills, and wasted compute. While many organizations respond by tightening controls, Kundavaram argued the better path is investing in open infrastructure, interoperability, and strong semantic data practices before AI costs spiral further. Learn more from The New Stack around the latest in enterprise data systems: Enterprise AI Success Demands Real-Time Data Platforms AI Agents Are Morphing Into the 'Enterprise Operating System' Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 12, 202628 min

The new FinOps problem isn't cloud bills

At Google Cloud Next 2026, Finout co-founder and CEO Roi Ravhon and Google Cloud FinOps lead Pathik Sharma discussed how FinOps is rapidly evolving for the AI era. Ravhon argued that while cloud FinOps had a decade to mature, AI economics are forcing the industry to adapt within a year. Unlike traditional cloud workloads, AI costs are unpredictable because token usage varies even for identical prompts, while advanced reasoning models consume significantly more tokens despite falling prices. Both emphasized that effective AI FinOps requires intelligent orchestration, routing workloads to the cheapest capable models instead of defaulting to expensive frontier models. Sharma noted that AI costs extend beyond APIs to GPUs, storage, training, and organizational adoption. They also cautioned against relying solely on LLMs for operational automation. Deterministic systems, observability metrics, and human approvals remain essential guardrails. Ultimately, both stressed that FinOps is primarily an organizational and cultural discipline, recommending newcomers start with the FinOps Foundation before investing in tools. Learn more from The New Stack around the latest in FinOps: Why FinOps Isn’t About Saving Money FinOps Foundation’s FOCUS 1.2 Expands to SaaS, PaaS Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 7, 202625 min

How Microsoft is governing thousands of Kubernetes clusters without manual intervention

Managing Kubernetes at fleet scale introduces significant complexity, especially as organizations expand from a few clusters to hundreds or thousands across cloud, on-premises, and edge environments. While GitOps remains the dominant model for declarative management, its traditional one-to-one repository-to-cluster approach struggles to handle multi-cluster realities such as global traffic routing, shared secrets, and unified observability. AsStephane Erbrech, Principal Software Engineer at Microsoftexplains, the challenge shifts from deployment to governance—maintaining consistency, security, and compliance across a vast distributed system without manual intervention. This need is amplified by the rise of AI workloads at the edge, where inference is increasingly decentralized. To address these challenges,Microsoft Azure Kubernetes Fleet Managerenables coordinated, staged rollouts across clusters, allowing teams to validate updates in lower-risk environments before production. Supporting this,Cilium Cluster Meshprovides seamless cross-cluster connectivity, enabling workload mobility and efficient resource use, especially for scarce GPU capacity. Together, these tools help modern platform teams manage lifecycle, networking, and orchestration at scale. Learn more from The New Stack around managing Kubernetes at fleet scale: KubeFleet: The Future of Multicluster Kubernetes App Management Why Microsoft is betting on temporary identities to stop autonomous agents from going rogue Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 6, 202631 min

Why long-running AI agents break on HTTP and how Ably is fixing it

In this episode ofThe New Stack Makers, Matthew O’Riordan, CEO of Ably, explains how infrastructure originally built for human collaboration is now well-suited for long-running AI agents. While Ably initially resisted positioning itself as an AI company, the rise of agents that reason, call tools, and operate over extended periods revealed a natural fit for its real-time communication platform. O’Riordan highlights the limitations of HTTP for these use cases. While effective for short, request-response interactions, HTTP struggles with persistent, stateful experiences—such as handling dropped connections, multi-device usage, or mid-task interruptions. To address this, a new “durable session” layer is emerging, enabling continuous synchronization between agents and users through shared state, presence, and recovery mechanisms. Ably’s solution, AI Transport, augments existing architectures by keeping HTTP for requests while shifting responses to durable sessions. Features like mutable message streams and “live objects” allow seamless reconnection and collaboration. The goal is to provide a drop-in layer that developers can adopt without rethinking their stack—moving beyond traditional pub/sub models. Learn more from The New Stack around Ably and AI Transport: How MCP Uses Streamable HTTP for Real-Time AI Tool Interaction Ably Touts Real-Time Starter Kits for Vercel and Netlify AI Agents Need Help. Here’s 4 Ways To Ship Software Reliably Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 6, 202632 min

Why the Linux Foundation adopted MCP, with Jim Zemlin and Mazin Gilbert

Agentic AI is advancing rapidly, with open-source projects racing to keep pace with real-world deployment. To accelerate progress, the Linux Foundation consolidated key technologies—Model Context Protocol (MCP), Goose, and AGENTS.md—under the newly formed Agentic AI Foundation (AAIF) in late 2025. At the MCP Dev Summit in New York City, Linux Foundation CEO Jim Zemlin and newly appointed AAIF executive director Mazin Gilbert discussed this transition. Zemlin explained that leading both organizations was unsustainable, prompting a careful search for a leader with both technical expertise and collaborative leadership skills. Gilbert now takes on the challenge of guiding AAIF as it shapes the emerging agentic AI ecosystem. While the foundation currently oversees three projects, its broader mission involves defining the future architecture of agent-driven systems—deciding what to build, when, and why. These decisions will influence the trajectory of open-source AI development. The conversation also highlights the importance of open collaboration, funding dynamics, and early adopters in shaping the agentic stack’s evolution. Learn more from The New Stack around the latest in open-source projects and The Linux Foundation: Anthropic Donates the MCP Protocol to the Agentic AI Foundation SAFE-MCP, a Community-Built Framework for AI Agent Security Google Donates the Agent2Agent Protocol to the Linux Foundation Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

May 1, 202623 min

Fresh data has us asking, does AI demand Kubernetes?

Kubernetes is rapidly emerging as the de facto operating system for AI, with two-thirds of organizations using it for generative AI inference and 82% adopting it in production. Its ecosystem — including tools like Kubeflow — enables organizations to build, scale, and retain control of AI systems through open, community-driven infrastructure. Bob Killen of CNCF and Liam Bollmann-Dodd of SlashData shared insights from recent reports showing that AI success still hinges on strong engineering fundamentals—especially internal developer platforms and overall developer experience. While AI-generated code accelerates development, it shifts bottlenecks to DevOps, reliability, and security, increasing operational complexity. As a result, operator experience and well-defined guardrails have become critical to safely scaling AI. These controls help constrain both human and AI developers, reducing risk while enabling speed. At the same time, organizations are evolving team structures, expanding platform engineering groups to support internal users more effectively. Despite growing complexity, the core lesson remains consistent: open source innovation thrives on people, processes, and collaboration as much as on technology itself. Learn more from The New Stack around the latest in Kubernetes and its emergence as an operating system for AI: Kubernetes and AI: Are They a Fit? How AI Is Pushing Kubernetes Storage Beyond Its Limits Kubernetes and AI Are Shaping the Next Generation of Platforms Join our community of newsletter subscribers to stay on top of the news and at the top of your game.