The New Stack Podcast

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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