The New Stack Podcast

AI coding assistants are boosting developer productivity, but most enterprises aren’t shipping software any faster. GitLab CEO Bill Staples says the reason is simple: coding was never the main bottleneck. After speaking with more than 60 customers, Staples found that developers spend only 10–20% of their time writing code. The remaining 80–90% is consumed by reviews, CI/CD pipelines, security scans, compliance checks, and deployment—areas that remain largely unautomated. Faster code generation only worsens downstream queues.

GitLab’s response is its newly GA’ed Duo Agent Platform, designed to automate the full software development lifecycle. The platform introduces “agent flows,” multi-step orchestrations that can take work from issue creation through merge requests, testing, and validation. Staples argues that context is the key differentiator. Unlike standalone coding tools that only see local code, GitLab’s all-in-one platform gives agents access to issues, epics, pipeline history, security data, and more through a unified knowledge graph.

Staples believes this platform approach, rather than fragmented point solutions, is what will finally unlock enterprise software delivery at scale.

Learn more from The New Stack about the latest around GitLab and AI: 

GitLab Launches Its AI Agent Platform in Public Beta

GitLab’s Field CTO Predicts: When DevSecOps Meets AI

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Sean O’Dell of Dynatrace argues that enterprises are unprepared for a major shift brought on by AI: the rise of the developer. Speaking at Dynatrace Perform in Las Vegas, O’Dell explains that AI-assisted and “vibe” coding are collapsing traditional boundaries in software development. Developers, once insulated from production by layers of operations and governance, are now regaining end-to-end ownership of the entire software lifecycle — from development and testing to deployment and security. This shift challenges long-standing enterprise structures built around separation of duties and risk mitigation. 

At the same time, the definition of “developer” is expanding. With AI lowering technical barriers, software creation is becoming more about creative intent than mastery of specialized tools, opening the door to nontraditional developers. Experimentation is also moving into production environments, a change that would have seemed reckless just 18 months ago. According to O’Dell, enterprises now understand AI well enough to experiment confidently, but many are not ready for the cultural, operational, and security implications of developers — broadly defined — taking full control again.

Learn more from The New Stack about the latest around enterprise developers and AI: 

Retool’s New AI-Powered App Builder Lets Non-Developers Build Enterprise Apps

Solving 3 Enterprise AI Problems Developers Face

Enterprise Platform Teams Are Stuck in Day 2 Hell

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In the era of agentic AI, attention has largely focused on data itself, while metadata has remained a neglected concern. Junping (JP) Du, founder and CEO of Datastrato, argues that this must change as AI fundamentally alters how data and metadata are consumed, governed, and understood. To address this gap, Datastrato created Apache Gravitino, an open source, high-performance, geo-distributed, federated metadata lake designed to act as a neutral control plane for metadata and governance across multi-modal, multi-engine AI workloads. 

Gravitino achieved major milestones in 2025, including graduation as an Apache Top Level Project, a stable 1.1.0 release, and membership in the new Agentic AI Foundation. Du describes Gravitino as a “catalog of catalogs” that unifies metadata across engines like Spark, Trino, Ray, and PyTorch, eliminating silos and inconsistencies. Built to support both structured and unstructured data, Gravitino enables secure, consistent, and AI-friendly data access across clouds and regions, helping enterprises manage governance, access control, and scalability in increasingly complex AI environments.

Learn more from The New Stack about how the latest data and metadata are consumed, governed, and understood: 

Is Agentic Metadata the Next Infrastructure Layer?

Why AI Loves Object Storage

The Real Bottleneck in Enterprise AI Isn’t the Model, It’s Context

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Chris Aniszczyk, co-founder and CTO of the Cloud Native Computing Foundation (CNCF), argues that AI agents resemble microservices at a surface level, though they differ in how they are scaled and managed. In an interview ahead of KubeCon/CloudNativeCon Europe, he emphasized that being “AI native” requires being cloud native by default. Cloud-native technologies such as containers, microservices, Kubernetes, gRPC, Prometheus, and OpenTelemetry provide the scalability, resilience, and observability needed to support AI systems at scale. Aniszczyk noted that major AI platforms like ChatGPT and Claude already rely on Kubernetes and other CNCF projects.

To address growing complexity in running generative and agentic AI workloads, the CNCF has launched efforts to extend its conformance programs to AI. New requirements—such as dynamic resource allocation for GPUs and TPUs and specialized networking for inference workloads—are being handled inconsistently across the industry. CNCF aims to establish a baseline of compatibility to ensure vendor neutrality. Aniszczyk also highlighted CNCF incubation projects like Metal³ for bare-metal Kubernetes and OpenYurt for managing edge-based Kubernetes deployments.

Learn more from The New Stack about CNCF and what to expect in 2026:

Why the CNCF’s New Executive Director Is Obsessed With Inference

CNCF Dragonfly Speeds Container, Model Sharing with P2P

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API sprawl creates hidden security risks and missed revenue opportunities when organizations lose visibility into the APIs they build. According to IBM’s Neeraj Nargund, APIs power the core business processes enterprises want to scale, making automated discovery, observability, and governance essential—especially when thousands of APIs exist across teams and environments. Strong governance helps identify endpoints, remediate shadow APIs, and manage risk at scale. At the same time, enterprises increasingly want to monetize the data APIs generate, packaging insights into products and pricing and segmenting usage, a need amplified by the rise of AI.

To address these challenges, Nargund highlights “smart APIs,” which are infused with AI to provide context awareness, event-driven behavior, and AI-assisted governance throughout the API lifecycle. These APIs help interpret and act on data, integrate with AI agents, and support real-time, streaming use cases.

IBM’s latest API Connect release embeds AI across API management and is designed for hybrid and multi-cloud environments, offering centralized governance, observability, and control through a single hybrid control plane.

Learn more from The New Stack about smart APIs: 

Redefining API Management for the AI-Driven Enterprise 

How To Accelerate Growth With AI-Powered Smart APIs 

Wrangle Account Sprawl With an AI Gateway 

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A CloudBees survey reveals that enterprise migration projects often fail to deliver promised modernization benefits. In 2024, 57% of enterprises spent over $1 million on migrations, with average overruns costing $315,000 per project. In The New Stack Makers podcast, CloudBees CEO Anuj Kapur describes this pattern as “the migration mirage,” where organizations chase modernization through costly migrations that push value further into the future. Findings from the CloudBees 2025 DevOps Migration Index show leaders routinely underestimate the longevity and resilience of existing systems. Kapur notes that applications often outlast CIOs, yet new leadership repeatedly mandates wholesale replacement. 

The report argues modernization has been mistakenly equated with migration, which diverts resources from customer value to replatforming efforts. Beyond financial strain, migration erodes developer morale by forcing engineers to rework functioning systems instead of building new solutions. CloudBees advocates meeting developers where they are, setting flexible guardrails rather than enforcing rigid platforms. Kapur believes this approach, combined with emerging code assistance tools, could spark a new renaissance in software development by 2026.

Learn more from The New Stack about enterprise modernization: 

Why AI Alone Fails at Large-Scale Code Modernization

How AI Can Speed up Modernization of Your Legacy IT Systems

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IBM’s recent acquisitions of Red Hat, HashiCorp, and its planned purchase of Confluent reflect a deliberate strategy to build the infrastructure required for enterprise AI. According to IBM’s Sanil Nambiar, AI depends on consistent hybrid cloud runtimes (Red Hat), programmable and automated infrastructure (HashiCorp), and real-time, trustworthy data (Confluent). Without these foundations, AI cannot function effectively. 

Nambiar argues that modern, software-defined networks have become too complex for humans to manage alone, overwhelmed by fragmented data, escalating tool sophistication, and a widening skills gap that makes veteran “tribal knowledge” hard to transfer. Trust, he says, is the biggest barrier to AI adoption in networking, since errors can cause costly outages. To address this, IBM launched IBM Network Intelligence, a “network-native” AI solution that combines time-series foundation models with reasoning large language models. This architecture enables AI agents to detect subtle warning patterns, collapse incident response times, and deliver accurate, trustworthy insights for real-world network operations.

Learn more from The New Stack about AI infrastructure and IBM’s approach:  

AI in Network Observability: The Dawn of Network Intelligence 

How Agentic AI Is Redefining Campus and Branch Network Needs 

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Ari Zilka, founder of MyDecisive.ai and former Hortonworks CPO, argues that most observability vendors now offer essentially identical, reactive dashboards that highlight problems only after systems are already broken. After speaking with all 23 observability vendors at KubeCon + CloudNativeCon North America 2025, Zilka said these tools fail to meaningfully reduce mean time to resolution (MTTR), a long-standing demand he heard repeatedly from thousands of CIOs during his time at New Relic.

Zilka believes observability must shift from reactive monitoring to proactive operations, where systems automatically respond to telemetry in real time. MyDecisive.ai is his attempt to solve this, acting as a “bump in the wire” that intercepts telemetry and uses AI-driven logic to trigger actions like rolling back faulty releases.

He also criticized the rising cost and complexity of OpenTelemetry adoption, noting that many companies now require large, specialized teams just to maintain OTel stacks. MyDecisive aims to turn OpenTelemetry into an enterprise-ready service that reduces human intervention and operational overhead.

Learn more from The New Stack about OpenTelemetry:

Observability Is Stuck in the Past. Your Users Aren't. 

Setting Up OpenTelemetry on the Frontend Because I Hate Myself

How to Make OpenTelemetry Better in the Browser

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Former GitHub CEO Thomas Dohmke’s claim that AI-based development requires progressive delivery frames a conversation between analyst James Governor and The New Stack’s Alex Williams about why modern release practices matter more than ever. Governor argues that AI systems behave unpredictably in production: models can hallucinate, outputs vary between versions, and changes are often non-deterministic. Because of this uncertainty, teams must rely on progressive delivery techniques such as feature flags, canary releases, observability, measurement and rollback. These practices, originally developed to improve traditional software releases, now form the foundation for deploying AI safely. Concepts like evaluations, model versioning and controlled rollouts are direct extensions of established delivery disciplines. 

Beyond AI, Governor’s book “Progressive Delivery” challenges DevOps thinking itself. He notes that DevOps focuses on development and operations but often neglects the user feedback loop. Using a framework of four A’s — abundance, autonomy, alignment and automation — he argues that progressive delivery reconnects teams with real user outcomes. Ultimately, success isn’t just reliability metrics, but whether users are actually satisfied. 

Learn more from The New Stack about progressive delivery: 

Mastering Progressive Hydration for Enhanced Web Performance 

Continuous Delivery: Gold Standard for Software Development 

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Most enterprises today run workloads across multiple IT infrastructures rather than a single platform, creating significant operational challenges. According to Nutanix CTO Deepak Goel, organizations face three major hurdles: managing operational complexity amid a shortage of cloud-native skills, migrating legacy virtual machine (VM) workloads to microservices-based cloud-native platforms, and running VM-based workloads alongside containerized applications. Many engineers have deep infrastructure experience but lack Kubernetes expertise, making the transition especially difficult and increasing the learning curve for IT administrators. 

To address these issues, organizations are turning to platform engineering and internal developer platforms that abstract infrastructure complexity and provide standardized “golden paths” for deployment. Integrated development environments (IDEs) further reduce friction by embedding capabilities like observability and security. 

Nutanix contributes through its hyper converged platform, which unifies compute and storage while supporting both VMs and containers. At KubeCon North America, Nutanix announced version 2.0 of Nutanix Data Services for Kubernetes (NDK), adding advanced data protection, fault-tolerant replication, and enhanced security through a partnership with Canonical to deliver a hardened operating system for Kubernetes environments.

Learn more from The New Stack about operational complexity in cloud native environments:

Q&A: Nutanix CEO Rajiv Ramaswami on the Cloud Native Enterprise 

Kubernetes Complexity Realigns Platform Engineering Strategy 

Platform Engineering on the Brink: Breakthrough or Bust? 

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