Frederic Lardinois
Frederic Lardinois is Senior Editor for AI at The New Stack, leading its coverage of large language models, AI agents and the software stack that supports them. His work connects new AI capabilities with the practical realities of software engineering, deployment and operations at scale. He brings long experience covering enterprise technology to filter hype and focus on the trade-offs that matter for teams building and running AI-powered systems.
AI Agents and the Web Stack
The through-line in his recent coverage is how AI agents change the way software interacts with the web and cloud infrastructure. In his reporting on making the web agent-ready, he follows how browser and platform teams are redesigning the web so autonomous agents can navigate, transact and integrate with services without human micromanagement.
He ties these changes back to the broader rise of agentic workloads, including episodes that examine “agentic workloads in production on Amazon EKS” and what it means to run AI agents as first-class workloads in cloud-native environments. Across these pieces and conversations, he treats agents not as a novelty feature but as a new execution model that must be supported by standards, APIs and operational tooling.
LLM Infrastructure and Developer Workflows
Lardinois spends much of his time on the mechanics of building and operating applications on top of large language models. His coverage of Traceloop’s observability tool for LLM-based applications and its seed funding round is typical: he explains how the product works, why observability for prompt-driven systems is hard, and how investors are framing this new layer in the stack.
He extends that infrastructure lens into developer workflows, hosting discussions on “drowning in AI pull requests” and dedicated Git repos built for agents, where he draws out how AI-assisted code review and agentic automation change day-to-day engineering practices.
In interviews with guests from GitLab and others, he presses on why AI is “not helping enterprises ship code faster” yet, surfacing bottlenecks in data quality, governance and organizational process instead of treating model quality as the only variable. Episodes on “AI can write your infrastructure code” and “breaking data team silos is the key to getting AI to production” show his focus on deployment pipelines, platform engineering and cross-team coordination as prerequisites for successful AI projects.
Enterprise AI Strategy and Platform Features
His beat sits at the intersection of AI product news and enterprise strategy, so he frequently covers major platforms as they roll out AI-driven features. The YouTube story on testing AI hosts is one example: he looks past the surface announcement to what automated presenters mean for content workflows, audience trust and the long-term direction of creator tools. Author spotlights from The New Stack describe his particular interest in open source models, the impact of on-device AI chips on cloud economics, and the strategic debates around where AI workloads should run. He uses those themes to frame coverage of events like Nvidia’s GTC, connecting hardware roadmaps, model trends and enterprise adoption patterns rather than treating them as separate domains. Across these stories, he emphasizes the practical value of AI features and the core decisions technology leaders face, such as openness versus control, cloud dependence versus local capability, and how much autonomy to give agents in production systems.
Interviews and Agentic AI Podcast
Beyond written coverage, Lardinois plays a central role in The New Stack’s podcast and livestreams on agentic AI. The masthead introduced “The New Stack Agents” podcast as part of an expanded focus on agentic AI, with him as host and editorial lead.
In that series he speaks with executives, architects and practitioners from companies such as Harness, GitLab, Thoughtworks, Spacelift, PyTorch and others, using structured interviews to surface the realities of running AI in production.
Conversations range from how to manage “AI slop” and its impact on jobs, to why PyTorch “won” in the machine learning ecosystem, to what it will take for 2026 to become “the year of agentic workloads in production.” His questions often return to deployment risk, observability, organizational change and the fit between AI capabilities and existing engineering culture, reinforcing the same stack-focused perspective that runs through his writing.
Before joining The New Stack in 2025, Lardinois spent more than a decade covering enterprise technology and startups at other tech publications, which informs his current focus on the strategic and operational sides of AI adoption. That background shapes a consistent style: detailed reporting on new tools and features, anchored in interviews and analysis about how they slot into the broader software stack and what they mean for the teams that have to run them.
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