Alexa's Input (AI)

In this episode, Alexa Griffith interviews Brady Jordan, a creative director and entrepreneur, who shares his journey from aspiring software engineer to the founder of Clip Play Media and the photo app Y2Cam. Brady discusses the intersection of creativity and technology, the importance of storytelling in video production, and the challenges of self-employment. He emphasizes the need for resilience, adaptability, and a consumer-first approach in product development, while also exploring the significance of networking and community building in achieving success.Podcast LinksWatch: ⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠⁠More Links: ⁠⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠⁠Find out more about the host, Alexa Griffith, at:Website: ⁠⁠⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠Find out more about the guest at:Website: https://www.bradyjordan.com/Chapters00:00 Introduction to Brady Jordan and His Journey06:45 The Birth of Clip Play Media14:58 Quality vs. Consistency in Content Creation24:51 Y2Cam: A Solution to Frustration30:51 Cost and Infrastructure of App Development35:30 Navigating the Challenges of Self-Employment42:51 Marketing Strategies for App Success49:04 The Value-Based Approach to Creation

Modern software is built on layers and layers of code. So how do we know we can trust it?In this episode of Alexa’s Input (AI), Alexa Griffith sits down with Justin Cappos, professor of computer science at NYU and a leading expert in software supply chain security, to unpack what trust really means in today’s digital infrastructure.From package managers and dependency chains to large-scale outages and AI systems built on inherited code, Justin explains why many security failures aren’t random accidents, they’re predictable consequences of weak process, misaligned incentives, and insecure design.They discuss:Why security only becomes visible when something breaksThe difference between unavoidable failure and negligenceHow modern software supply chains amplify small mistakesThe role of leadership and culture in preventing breachesWhy verification systems like TUF and in-toto matter more than everAs AI accelerates development and increases system complexity, the need for verifiable trust only grows. This episode is a practical look at the invisible infrastructure that keeps modern software, and increasingly, modern AI, from collapsing under its own complexity.Podcast LinksWatch: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠More: ⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠Find out more about the guest at:Website: https://engineering.nyu.edu/faculty/justin-capposNYU page: https://ssl.engineering.nyu.edu/personalpages/jcappos/Wikipedia: https://en.wikipedia.org/wiki/Justin_CapposChapters00:00 Introduction to Justin Cappos and His Work01:17 The Importance of Security in Software Systems03:50 Understanding Security Breaches: Mistakes vs. System Design Problems06:34 Cultural Factors in Security Failures09:25 Justin's Journey in Software Security12:03 The Role of Academia in Enterprise Security14:10 Evaluating Enterprise Security Systems16:58 Foundational Projects in Software Security19:21 AI Security Concerns and Future Directions24:59 The Need for MCP 2.028:57 Security Challenges with LLMs32:33 Designing Secure AI Systems37:14 Ethical Dilemmas in AI Decision-Making40:17 The Role of AI in Open Source43:44 Trust and Mindset in AI Security

As AI systems grow more autonomous, the question is no longer just what they can do, but whether we can trust the data and models behind their decisions. In this episode of Alexa’s Input (AI), Alexa Griffith talks with Wendy Chin, CEO of PureCipher, about building what she calls an artificial immune system for AI, a framework designed to make data, models, and inference tamper-evident across the AI lifecycle.They unpack what data poisoning really means (training data, weights and biases, inference inputs), why small amounts of targeted poison can create outsized model misbehavior, and how generative AI lowers the barrier to sophisticated malware. The conversation expands into the security implications of agent-to-agent communication via MCP, digital twins, and why we don’t have the luxury of “shipping now and securing later.” It’s a wide-ranging discussion that moves from practical threat models to the philosophical frontier of what happens as AI becomes more human-like, and more autonomous.Podcast LinksWatch: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠More: ⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/wendy-chin-ctg/Website: https://www.purecipher.com/Chapters00:00 Introduction to AI Security01:16 Understanding Data Poisoning04:38 The Dangers of Malware in AI07:46 AI's Moral Dilemmas and Decision Making08:45 Building Empathy in AI13:07 The Role of Good Data in AI Training17:02 PureCypher's Artificial Immune System22:34 Digital Twins and Their Implications25:22 Nurturing AI Like a Child30:53 Data Therapy for AI36:13 The Future of AI and Human Interaction38:45 The Dark Side of AI: Hacking and Security45:03 Global Perspectives on AI Security48:11 MCP Agents and Security Concerns51:41 Philosophical Implications of AI and Human Connection01:00:04 The Sci-Fi Future of AI and Humanity

As LLM apps evolve from simple chatbots to tool-using agents, the attack surface explodes, and the old security playbooks don’t hold. In this episode of Alexa’s Input (AI), Alexa Griffith sits down with Ian Webster, co-founder and CEO of PromptFoo, to break down what AI security actually looks like in practice: automated red teaming, prompt injection and jailbreak testing, evaluation workflows that scale, and why “guardrails alone” is not a security strategy.Ian shares how PromptFoo grew from a side project into a widely adopted open-source standard, what it means to raise multi-millions in a fast-moving market, and how enterprises are approaching the full vulnerability lifecycle, from finding issues to triage, remediation, and validation. Ian also discusses the “lethal trifecta” that makes agents fundamentally risky (untrusted input + sensitive data + exfil path), and why MCP security isn’t just about users and tools, it’s about dangerous tool combinations and rogue servers.Podcast LinksWatch: ⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠More: ⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠Find out more about the guest at:PromptFoo Website: https://www.promptfoo.dev/Github: https://github.com/promptfoo/promptfooIan’s LinkedIn: https://www.linkedin.com/in/ianww/Chapters00:00 Introduction to AI Security Challenges02:06 Funding and Growth of PromptFu06:16 The Genesis of PromptFu11:05 Career Journey and Lessons Learned12:53 Understanding AI Red Teaming17:36 Recent AI Security Vulnerabilities19:46 The Dual Nature of AI in Security21:47 Understanding the Lethal Trifecta in AI Security24:22 Exploring Model Context Protocol (MCP) and Its Security Implications26:22 Common Security Issues in MCP Systems28:17 The Role of Identity and Permissions in AI Security30:00 Practical Implications of Using PromptFoo for Developers31:33 Evaluating Language Models: Challenges and Techniques36:34 The Limitations of Guardrails in AI Security38:25 Best Practices for Engineers in AI Development39:58 Future Trends in AI and Security42:28 Everyday Applications of AI and Language Models

Most AI infrastructure today is hitting a breaking point. Marc Austin, CEO of Hedgehog, reveals how open source networking and cloud-native solutions are revolutionizing how enterprises build and operate AI at scale. This episode addresses issues many building AI infrastructure today are facing — expensive proprietary systems, overwhelming complex network configurations, and ways to make on-prem AI infrastructure feel just like the public cloud.We discuss how networking is the hidden bottleneck in scaling GPU clusters and the surprising physics and hardware innovations enabling higher throughput. Marc shares the journey of building Hedgehog, an open source, cloud-native platform designed for AI workloads that bridges the gap between complex hardware and seamless, user-friendly cloud experiences. Marc explains how Hedgehog's software abstracts and automates the networking complexity, making AI infrastructure accessible to enterprises without dedicated networking teams.We break down the future of AI networks, from multi-cloud and hybrid environments to the rise of Neo Clouds and the open source movement transforming enterprise AI infrastructure. If you're a CTO, data scientist, or AI innovator, understanding these network innovations can be your moat. Listen to this episode to see how open source, cloud-native networking, and physical innovation are shaping the AI infrastructure of tomorrow.Podcast LinksWatch: ⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠Listen:⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠More: ⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠Website: ⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠Find out more about the guest at LinkedIn:  https://www.linkedin.com/in/austinmarc/Website: https://hedgehog.cloud/Github: https://github.com/githedgehogChapters00:00 Rethinking AI Infrastructure02:49 The Role of Networking in AI05:54 Marc's Journey to Hedgehog08:46 Lessons from Big Companies11:38 Requirements for AI Networks14:48 Advancements in AI Networking17:33 Future Challenges in AI Infrastructure20:46 Creating a Cloud Experience On-Prem23:32 The Shift to Hybrid Multi-Cloud28:10 Evolving AI Infrastructure and Efficiency30:57 AI Workloads and Network Configurations32:41 Zero Touch Lifecycle Management35:12 Support for Hardware Devices35:45 Networking Paradigms and Vendor Lock-in38:42 The Rise of Neo Clouds41:31 Demand for AI Infrastructure43:57 Open Source and Cloud-Native Networking47:27 Challenges of Building a Networking Startup50:46 Proud Accomplishments at Hedgehog52:41 Future Excitement in AI Inference