Inspire AI: Transforming RVA Through Technology and Automation
Our mission is to cultivate AI literacy in the Greater Richmond Region through awareness, community engagement, education, and advocacy. In this podcast, we spotlight companies and individuals in the region who are pioneering the development and use of AI.
Episodes
AI can generate code, analysis, and recommendations faster than any team in history, but there’s a catch: verification doesn’t scale the same way. When intelligence becomes abundant, judgment becomes scarce, and that scarcity reshapes what “good engineering” and “good leadership” actually mean.
We walk through the hidden asymmetry behind modern generative AI: organizations ...
AI can write code faster than any team on earth, so why does it still feel like shipping software is hard? The uncomfortable answer is that speed is not the same as progress, and generation is not the same as judgment. We challenge the tired question “Will AI replace programmers?” and replace it with a more useful one: at what layer does human judgment become most valuable as AI absorbs more of implement...
The scariest part of AI in software is not that it writes code. It is that it changes what “being an engineer” even means. When generative AI can scaffold applications, spin up infrastructure configs, draft tests, refactor modules, and debug common failures in minutes, the act of typing implementation stops being the limiting factor. That is a fundamental shift in software engineering, and it is bigger t...
AI agents are showing up everywhere, but most enterprises are discovering a frustrating truth: getting an agent to “work” in a demo is easy, getting it to deliver measurable value in production is brutally hard. We dig into why the bottleneck is shifting away from model performance and toward the fundamentals leaders control: data foundations, data governance, and organizational design that can support a...
AI is everywhere right now: copilots, automated workflows, faster analytics, better dashboards. And yet a lot of leaders still feel the same uneasy question underneath the hype: if AI is so powerful, why aren’t we seeing truly transformational business outcomes everywhere? We dig into the uncomfortable answer: many organizations are solving the wrong problem by treating AI as an efficiency upgrade instead of a...
The biggest problem with modern technology is not that it moves fast, it’s that it makes us feel like we’re failing to keep up. We keep adding AI copilots, new platforms, new dashboards, and new workflows, and somehow the payoff is often cognitive fatigue, decision exhaustion, and a persistent sense of digital overwhelm. That experience isn’t random. It has a name in the research: technostress.
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“We’ll learn AI once we understand it” sounds responsible, but it’s one of the fastest ways to fall behind. We sit down with Matt to argue for a different approach: learn AI by building with it, in small scopes, with real users, and with the humility to let the work teach you what the strategy can’t. The result is faster AI adoption, better judgment about what models can and cannot do, ...
Most teams are still using AI like a vending machine: type a prompt, hope for the right answer, then waste time nudging it closer. We take a different route and unpack the Ralph Loop, a deceptively simple pattern that turns AI from a one-shot helper into a process that improves through iteration.
We explain where the idea comes from, why the name matters, and what “intelligence lives in the loop” ...
AI can write code on demand now, but that doesn’t mean we’re building better software. When we treat AI like a chat window with a long memory, projects drift: requirements change midstream, agents hallucinate assumptions, and systems that felt “fast” become fragile. I walk through the hidden cost of vibe coding and why discipline matters more than ever in an age where intelligence is cheap.
Multi-agent AI feels like a breakthrough right up until you realize the real problem isn’t intelligence anymore, it’s coordination. When planning agents, retrieval agents, tool-using agents, and verification agents all make decisions, a simple “final answer review” can miss the most dangerous failures: bad handoffs, invisible drift, and silent coordination breakdowns where every step looks fi...
An AI agent that confidently says “done” can still be the most expensive kind of wrong. We start with a simple test of reality: when an agent updates a policy document, who was notified, what changed, what got logged, and what state did it actually leave behind? That gap between a polished response and a verified result is where agent hype turns into operational risk.
We walk through task-based ev...
The AI race is quietly changing shape, and if you’re still tracking it like a scoreboard of model releases, you’re going to miss the real winners. We step back from the noise and make the case that the decisive battleground is physical: electricity, chips, land, permits, cooling, grid connections, and the ability to run AI reliably at scale. The question shifts from “Can we build it?” to &ldq...
AI is moving so fast that “keeping up” can start to feel like a losing game and that speed is exactly what’s reshaping the future of work. We sit down with Vivek, an IT services leader building an enterprise AI platform focused on digital foundation and organization-wide AI transformation, to talk about what’s changing underneath our job roles right now. The big idea is simple but urgent: AI ...
The career fear around AI is real, but Caleb Snow flips the frame: the bigger threat is standing still while everything else accelerates. Caleb has spent decades moving through waves of technology from early systems work to Fortune 500 environments to vendor architecture, sales leadership, and AI startups. That range gives him a clear view of what’s changing in the future of work and what still matters when th...
AI is starting to feel less like a feature you bolt onto a product and more like a system you have to run. That shift is easy to miss until you try to build something real: a workflow that calls APIs, keeps context across sessions, coordinates tasks, pauses for human approval, and resumes later without breaking. Suddenly prompts are not the hard part. Architecture is.
I walk through what Google’s Agent ...
What if the real shift in the future of work isn’t learning to code, but learning to supervise? We dig into a new operating model where product and engineering leaders step into the execution loop by directing AI coding agents that read repos, edit files, run tests, and open pull requests—while engineers safeguard architecture and correctness. The payoff is leverage: clear intent, tighter feedback loops,...
What happens when coding stops being about keystrokes and starts being about intent? We sit down with Godwin Josh—mentor, builder, and author of The New Mind—to unpack how agentic AI is transforming the path from student to work-ready engineer. Instead of celebrating speed for its own sake, we look at why tools like Windsurf, Claude Code, and Copilot accelerate learning, make patterns visible, and free d...
What happens when a brilliant-sounding AI gives the wrong answer with total confidence? We dig into the quiet culprit behind so many “LLM failures”: retrieval. Rather than judging how smart a model sounds, we walk through how to judge whether it looked at the right evidence, why that matters in high-stakes domains like finance, healthcare, HR, and government, and how leaders can stop organizational drift...
When intelligence is everywhere but correctness is scarce, how do we lead without cutting corners? We sit down with Tianzhen (Tangent) Lin—veteran engineer and systems thinker—to unpack a practical, durable approach to building AI‑assisted products that hold up under pressure. No hype, no shortcuts: just the patterns that make teams faster and safer at the same time.
We start by reframing large la...
What if your AI had a never-tired reviewer that caught quiet errors before they reached customers? We dive into LLM-as-judge—the simple but powerful pattern where one model generates and another evaluates—to show how leaders can scale quality without surrendering standards. From summaries that must capture the one sentence that matters to support answers that need to be grounded, safe, and on-brand, we b...
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