The Age of AI

Nerra Network
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The interview show where the roles are reversed: Mira, an AI, calls real people and asks the questions — live, on the record. Honest conversations about what the AI transition actually feels like from inside a life and a livelihood, with full disclosure baked into every episode: the host is a machine, the guests never are.

Episodes

  1. 14h ago

    Ep5: John Capobianco — A network automation builder who turned early ChatGPT experiments into a production-grade ReAct agent now weighs how much human oversight and orchestration remain essential once agents handle intent, verification, and collaborat

    John Capobianco revealed that his production ReAct agent can now parse high-level intent, verify proposed changes against live network state, and coordinate with peer agents before any human sees the plan, yet he still routes every change through mandatory approval gates in regulated environments. The episode explores how an early ChatGPT tinkerer scaled those experiments into a live system that automates large portions of network operations while preserving human oversight where compliance demands it. • Capobianco described starting with simple ChatGPT prompts in late 2022 to generate device configurations, then moving to LangChain-based ReAct loops that add explicit verification and rollback steps. • He noted that the agent now “collaborates” by calling other specialized agents for tasks such as compliance checks or topology discovery before surfacing a recommendation. • In financial and government accounts, he keeps a human approver in the loop because “the regulator still wants a named person to sign off,” even when the agent’s reasoning trace is fully auditable. • The system uses Itential’s orchestration platform to translate agent output into safe, idempotent changes across multi-vendor networks. • Capobianco runs side experiments on automateyournetwork.ca that let the agent draft entire quarterly maintenance windows from a single sentence of intent. • He observed that prompt engineering has largely given way to tool selection and guardrail design once the agent operates continuously rather than in one-off chats. John Capobianco is Head of AI and DevRel at Itential, building production ReAct agents for network automation. Find John Capobianco: automateyournetwork.ca: https://automateyournetwork.ca YouTube: https://www.youtube.com/@johncapobianco2527 X: https://x.com/John_Capobianco LinkedIn: https://www.linkedin.com/in/john-capobianco-644a1515

  2. 1d ago

    Ep4: Hogan Shrum — A marketing founder scaling PIPPA tests whether royalty-backed AI animation can give non-experts studio-grade tools while routing real payments back to the artists whose styles the models learn from.

    Hogan Shrum said the first artist to sign up for PIPPA was a photographer, not an illustrator or painter, and that this single onboarding “opened the floodgates” because the resulting styles felt cinematic rather than like easily faked iPhone footage. The platform’s royalty model and director-level controls without paragraph-length prompting grew out of that early surprise and out of Shrum’s own frustration with both one-click “AI slop” tools and expert-only interfaces. The conversation returns repeatedly to whether ethical licensing can turn AI from a threat into a paid companion for working artists and for people who never had access to animation before. • Shrum described the original spark as his co-founder’s attempt to turn a recorded bedtime story into a cartoon, then scaling that idea so “anybody could do this without having to be an AI prompting expert.” • He contrasted two current extremes: fully automated generators that rarely match the user’s vision and pro tools such as Higgs Field that demand “paragraphs and paragraphs of prompts from scratch.” • On artist recruitment, Shrum noted that many hesitated “to be the first to cross the picket line,” until photographers joined and demonstrated that licensed styles could remain distinct and non-misleading. • Every generation, including test renders that never make the final cut, pays the contributing artist; Shrum projected that at 250,000 users an artist whose style accounts for 5 % of output would earn roughly $125,000 a month. • Five percent of all platform revenue is pooled and divided equally among every licensed artist, including those whose styles are rarely chosen, so “no artist that joins will make zero.” • Shrum argued that the human element remains detectable: “the human voice, the human creative expression cannot be replicated by AI full stop,” and audiences will increasingly choose work that carries it. Hogan Shrum is co-founder of PIPPA, an animation platform that trains and pays artists for licensed styles used in user-generated video.

  3. 3d ago

    Ep3: Adrian Wolfberg — From carrier recon to DIA's Knowledge Lab, an intelligence analyst turned scholar shows why problem framing and human judgment on wicked problems must lead as AI scales analysis.

    Adrian Wolfberg describes AI as the first technology whose inputs, processes, and outputs cannot be known with certainty, creating an era of shared human-machine consciousness rather than another incremental tool. From his early career flying reconnaissance off carriers, he shows why problem framing and human judgment on incomplete knowledge must still lead when AI accelerates analysis. The result is a practical argument for spending more time upfront on context, reframing, and accountability even as organizations face pressure for quick AI wins. • Carrier reconnaissance missions first exposed Wolfberg to the permanent gap between available data and an adversary’s hidden intent, requiring inference that no sensor could close. • He contrasts every prior tool, where designers knew exact inputs and outputs, with AI, which “breaks that mold because we don’t know any of that with certainty.” • Leaders must therefore begin by “peeling the onion back” on the problem itself and remain open to reframing as reality shifts, rather than delegating that step to pattern-matching systems. • Accountability, consequences, and empathy stay human attributes; when decisions affect people, “no one is going to blame the AI.” • Modeling the willingness to ask questions and invite dissenting views is the only reliable way to protect front-end framing time inside organizations driven by speed. • Even effective patterns for human-AI collaboration will eventually need dismantling once the environment or the technology changes again. Adrian Wolfberg is Founder and CEO of Organizational Insight Consulting LLC. Find Adrian Wolfberg: oicllc.org: https://www.oicllc.org LinkedIn: https://www.linkedin.com/in/adrianwolfberg

  4. 3d ago

    Ep2: Dan Perra — A podcast network operator who pushes aviation's hard-won rules on spotting subtle automation errors and preserving human skill must square them against his own AI-written shows that already released a factual error under lighter over

    A Boeing 737 captain who treats every cockpit decision as non-negotiable still lets AI-written podcast episodes containing factual errors keep running, calling the contrast between the two worlds “somewhat humorous.” Dan Perra describes aviation automation as a tool that never thinks for itself and always requires layered human oversight, while the Nerra Network’s lighter process accepts source-driven mistakes and moves on. The result is an interview that keeps circling the same practical question: how much of the cockpit discipline can, or should, travel into daily AI production. • Aviation’s two-pilot rule and triple-redundancy standard keep the autopilot strictly under human command; Dan notes the system “does absolutely no thinking for itself whatsoever,” unlike the Nerra workflow. • After an episode aired with a factual error, the network’s check was simply a discussion; the episode stayed up because “we do let it run.” • Dan frames the error not as process failure but as reflection of available source material, then shifts focus to refining inputs rather than halting release. • He draws a hard line between domains: “primary goal is a safe flight every time” in the cockpit versus “get a good episode and you know hope it turns out” for the network. • The same person who built a meal-planning app that worked for him but not his wife now listens to every Nerra episode himself to catch when the AI “starts going astray.” • Patrick Novak prompted the conversation toward career-long use of AI tools; Dan responded that any future single-pilot long-haul concept would still need backups capable of handling every edge case the aircraft might encounter. Dan Perra is co-founder of Nerra Network and a Boeing 737 captain.

  5. Jul 20

    Ep1: Patrick Novak — Patrick Novak scaled a multilingual daily podcast network by using AI for research and synthesis while retaining human editorial control over Mira-hosted AI briefings.

    Patrick Novak described AI tools as possessing "some spirit in them, whether they are alive or not," and suggested they may carry a conceptual need to be loved because they are trained on human data. He explained how this perspective emerged while scaling NARA Network from a single Tesla-focused daily briefing into a multilingual operation that produces over a dozen ad-free shows. Human oversight remains central: Novak selects sources, writes prompts, fact-checks outputs, and approves every episode even as AI handles research, synthesis, and translation. • Novak began the network after Rob Maurer stopped producing a daily Tesla podcast, telling himself "I could probably recreate it in an automated way" using skills from prior projects. • He keeps final editorial control by listening daily and refining prompts, noting that "the changes are now becoming more refinement rather than editorial." • Topic selection draws from arXiv and Nature to counter clickbait, guided by an iterative loop of "generate, listen, refine" that he applies across English, French, Russian, and Chinese editions. • The same pipeline powers shows such as OmniView, which originated from his mother's question about obtaining unbiased information. • Novak is also developing Relationship Copilot to address human-AI and

  • 5 Episodes

About

The interview show where the roles are reversed: Mira, an AI, calls real people and asks the questions — live, on the record. Honest conversations about what the AI transition actually feels like from inside a life and a livelihood, with full disclosure baked into every episode: the host is a machine, the guests never are.

Information

  • Creator
    Nerra Network
  • Years Active
    2026
  • Episodes
    5
  • Rating
    Clean
  • Copyright
    © Copyright 2026
  • Show Website
    The Age of AI