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AI agents bring new demands to the infrastructure connecting them to models, tools, and data. Each agent can have its own permissions and state, and spend much of its time asleep before waking up to make another request. Supporting that behavior at scale changes how the network routes traffic, loads policies, and manages resources. In this episode of Alexa’s Input (AI), I sit down with Yan Avlasov, Staff Software Engineer at Google and a senior maintainer of Envoy, to talk about building the networking stack for agents. We get into why Google is building on Envoy, what changes between serving inference and supporting agents, and why enforcing policies for individual agents requires a more dynamic control plane. Yan also shares what he’s seeing from AI-powered security scanning, including how models combine subtle bugs into serious vulnerabilities and why fixing them can mean changing behavior that production systems have relied on for years. From the episode: - Model selection, cost controls, and fallback across providers - Loading policies dynamically as agent traffic changes - Managing personalized state and agents that suspend and resume - Where Model Context Protocol (MCP) fits alongside the protocols agents already use - Semantic routing and choosing models based on what a request needs - Security findings involving policy enforcement, path normalization, and JSON parsing - The engineering work and cost of making security scanning continuous Chapters 00:00 Introduction 01:12 Welcome, Yan 02:30 How AI is changing Google 07:56 Yan’s networking background 10:00 New networking layers for agents 13:11 AI gateways, cost controls, and fallback 16:38 Why Google builds on Envoy 22:21 Per-agent policies and a dynamic control plane 26:56 Inference versus stateful agents 31:58 Project Substrate: running agents at scale 33:31 MCP and the other protocols agents use 37:32 Semantic routing and model costs 40:40 AI security scanning and the work of fixing bugs 43:55 Policy timing, path normalization, and JSON 47:11 Making security scanning continuous 50:40 What’s next for agentic networking 53:32 Closing Read the episode article on Substack Watch on YouTube

In this episode of Alexa's Input (AI), I sit down with Alex Zenla, founder and CTO of Edera. Alex grew up in a small town in Alabama, found a computer young, and started building. Her story is unlike many in tech. She taught herself to program and got a job in tech at 14 years old. Since then, she's been actively building and involved in open source. She's currently the founder and CTO of Edera, a company whose product integrates security into the lowest layers of the platform without sacrificing performance or velocity. In this episode, we get into where that path started, what it costs to be different in founder and venture rooms, and what breaks when infrastructure still ships with security off by default. From the episode: Growing up in small-town Alabama without a path into techSouthern niceness as theory versus practiceFull-time work at fourteen and presenting to executives as a teenagerBeing one of very few trans founders in venture rooms, and the tension between visibility and being treated as a tokenElevator pitches that change with the audienceDetection and response after a problem has already occurredCommon Vulnerabilities and Exposures becoming untenable when tools like Mythos surface hundreds of findings per project per dayKubernetes and vendors selling yet another layer while the foundations underneath are misalignedSecure defaults as the path of least resistance for teams that just need a cluster that works Alex's mission is to make secure computing the default. Today you work hard to get a secure environment, and she's building Edera to invert that. What stays with you is how personal that work is for her. The path from a small Alabama town into those rooms is not separate from the product. It's why the default being broken bothers her enough to build a company around fixing it. GENERAL PODCAST LINKS Watch: https://www.youtube.com/@alexasinput Read: https://alexasinput.substack.com/ Listen: https://creators.spotify.com/pod/profile/alexagriffith/ More: https://linktr.ee/alexagriffith LEARN MORE ABOUT THE HOST Website: https://alexagriffith.com/ LinkedIn: https://www.linkedin.com/in/alexa-griffith/ FIND OUT MORE ABOUT THE GUEST LinkedIn: https://www.linkedin.com/in/azenla/ Bluesky: https://bsky.app/profile/alex.zenla.io Edera: https://edera.dev/ GitHub: https://github.com/edera-dev RESOURCES Edera docs: https://docs.edera.dev/

AI agents have created more possibilities for engineers than ever before. But what does daily life actually look like for the builders who've gone all in? In my very first panel episode, I sit down with Adam Anzuoni from Cursor, Taylor Dolezal from Dosu, and Peter Bell from Gather.dev. Three builders running agents every day for real work, not demos. Peter runs nine API plans across three Mac Minis, with built-in adversarial review. Adam manages cloud agents from his phone. Taylor is building the context infrastructure that makes agent knowledge portable across teams. We get into deterministic pipelines, skill systems that become their own technical debt, and what all three kept coming back to: attention is now the bottleneck. When agents can do everything, deciding what deserves your focus is the actual hard problem. Three setups. One shared constraint. A conversation worth hearing. Topics discussed: Attention as the real bottleneck when agents can do everythingDeterministic pipelines vs. agentic orchestration — when to use scripts and when to use agentsPeter's system: nine API plans, three Mac Minis, adversarial review, self-improving contextAdam on Cursor cloud agents and managing builds from his phoneTaylor on context distribution — making accumulated knowledge available to ephemeral agentsThe skill maintenance problem and why agent systems become their own technical debtThe ADHD-like productivity loop that agent-driven work createsPlans matter more than prompts — all three panelists converged on thisIntermediate artifacts as the key to quality outputProduct mindset as the engineer's next high-value skillSandboxes, governance, and why "approve, approve, approve" puts your hard drive at riskGeneral podcast links Watch: https://www.youtube.com/@alexasinputRead: https://alexasinput.substack.com/Listen: https://creators.spotify.com/pod/profile/alexagriffith/More: https://linktr.ee/alexagriffith Learn more about the host Website: https://alexagriffith.com/LinkedIn: https://www.linkedin.com/in/alexa-griffith/X: https://x.com/alexa_griffith_ Find out more about the guests Adam AnzuoniLinkedIn: https://www.linkedin.com/in/adamanz/Website: https://www.adamanzuoni.com/Cursor: https://www.cursor.com/ Taylor DolezalLinkedIn: https://www.linkedin.com/in/onlydole/Website: https://onlydole.dev/Dosu: https://dosu.dev/ Peter BellLinkedIn: https://www.linkedin.com/in/peterfbell/Gather.dev: https://gather.dev/O'Reilly Book: Scaling AI Adoption in Engineering Resources mentioned in this episode Cursor: https://www.cursor.com/Dosu: https://dosu.dev/Gather.dev: https://gather.dev/Anthropic Claude: https://www.anthropic.com/The Phoenix Project (book reference by Taylor)Kelsey Hightower productivity survey (referenced by Taylor)

Most engineers think reliability means avoiding outages. Vlad Leyberov learned the opposite lesson: sometimes you have to intentionally cause a 100% outage to fix the system faster. Vlad is a Site Reliability Engineer (SRE) at Google, running systems that handle billions of requests per second. Before Google, he kept critical infrastructure running at Meta (billions of events a day) and Amazon (millions of Alexa devices). In this conversation, we dig into cascading failures, incident responses, why consistency beats speed, how AI changes reliability engineering, and the philosophy behind running systems where downtime doesn't feel like an option. Topics Discussed: How cascading failures propagate unpredictably in distributed systems (like nature, not machines)Incident responses: virtual panic rooms, on-call, paging procedures, and how to narrow down failure pointsThe Alexa incident: why dropping an entire DynamoDB table was the right callCritical User Journeys (CUJ): measuring end-to-end customer experience vs individual SLOsCareer journey from the USSR to maritime academy to business degree in Australia to SRE at Amazon, Meta, and GoogleWhy consistency in API response times beats raw speedHow AI makes it dangerously easy to create complex systems with poorly understood interactionsScience fiction, the Borg as a distributed system, and the Three Body Problem trilogyHot takes on reliability: all software development is maintenance, overrated 9s, underrated global failure modes General Podcast Links Watch: https://www.youtube.com/@alexasinput Read: https://alexasinput.substack.com/ Listen: https://creators.spotify.com/pod/profile/alexagriffith/ More: https://linktr.ee/alexagriffith Learn more about the host Website: https://alexagriffith.com/ LinkedIn: https://www.linkedin.com/in/alexa-griffith/ Find out more about Vlad Leyberov LinkedIn: https://www.linkedin.com/in/vladleyberov/ Google SRE NYC Tech Talks Resources Google SRE Resources: Google SRE Book: https://sre.google/books/Google Cloud Platform: https://cloud.google.com/Google Cloud Build: https://cloud.google.com/build (service discussed in outage story)Google Cloud Pub/Sub: https://cloud.google.com/pubsub (Vlad's previous role, billions of requests/second)Sci-Fi Books Mentioned: Three Body Problem trilogy by Liu Cixin (Vlad's current favorite)Foundation series by Isaac AsimovLeft Hand of Darkness by Ursula K. Le GuinSnow Crash by Neal StephensonInternal Google Systems Referenced: Borg: Google's internal cluster management system (Kubernetes predecessor), named after Star Trek BorgDynamoDB: AWS distributed key-value store (used in Alexa poison pill incident) Intro Music:PR1BVOV7R4F1ASZC

In this episode of Alexa's Input (AI), I sit down with David Aronchick, co-founder and CEO of Expanso and former product lead for Kubernetes at Google. Data is growing everywhere outside your data center. Solar panels in remote across a country. Security cameras at retail stores. IoT sensors across factory floors. And moving that data to the cloud for processing? It's expensive, slow, and often restricted by compliance. David is an expert when it comes to solving distribution problems. He led Kubernetes product at Google, co-founded Kubeflow to bring ML to production, and now he's building Expanso to tackle a difficult constraint: when your data can't move, how do you process it where it lives? We discuss: - The need for distributed data orchestration -Upstream data control: filtering and transforming at the source - Three forces making edge computing inevitable (physics, regulations, economics) - How to build successful open source infrastructure projects- Customer discovery and finding real pain points - His transition from Protocol Labs to founding Expanso - ETL pipelines: moving the first four steps closer to the data - Context loss and lineage in distributed systems - Processing 400,000 signals per second with 150MB agents - AI observability: attaching source metadata to training data - Running ML pipelines at the edge- Real-world deployment challenges (bandwidth, regulations, cost) Expanso is rethinking how we process data in an AI-native world—moving compute to data instead of data to compute. If you want to understand where distributed systems and edge computing are heading, this is a deep dive into the infrastructure layer beneath modern AI applications. General Podcast Links Watch: https://www.youtube.com/@alexasinput Read: https://alexasinput.substack.com/ Listen: https://creators.spotify.com/pod/profile/alexagriffith/ More: https://linktr.ee/alexagriffith Learn more about the host at Website: https://alexagriffith.com/ LinkedIn: https://www.linkedin.com/in/alexa-griffith/ Find out more about the guest at LinkedIn: https://www.linkedin.com/in/aronchick/ Twitter/X: https://x.com/aronchick GitHub: https://github.com/aronchick Expanso Website: https://expanso.io/ Resources Expanso Website: https://expanso.io/ Kubernetes: https://kubernetes.io/ Kubeflow: https://www.kubeflow.org/ CNCF (Cloud Native Computing Foundation): https://www.cncf.io/ Protocol Labs: https://protocol.ai/ Keywords David Aronchick, Expanso, Kubernetes, Kubeflow, distributed systems, edge computing, data pipelines, ETL, upstream data control, Google Kubernetes Engine, open source, CNCF, observability, log processing, data lineage, provenance, schema enforcement, IoT, edge AI, distributed data, machine learning infrastructure, Protocol Labs, IPFS, Filecoin, data governance, compliance, GDPR, bandwidth optimization, data aggregation, AI infrastructure, multi-cloud, hybrid cloud, real-time processing

In this episode of Alexa’s Input (AI), I sat down with Rob Shaw from Red Hat to talk about how AI inference evolved from a simple model serving problem into a large-scale distributed systems problem. We explored the infrastructure shifts behind modern LLM serving, including how vLLM and PagedAttention changed the economics and efficiency of inference, why KV cache management became one of the most important bottlenecks in production AI systems, and how orchestration layers like llm-d are emerging to coordinate distributed inference. We also discuss: how LLM inference differs from traditional model serving runtimes KV cache, prefix caching, and cache-aware routing why throughput and latency became major infrastructure challenges long-context agents and repeated inference calls distributed inference on Kubernetes intelligent routing, flow control, and load balancing prefill/decode disaggregation enterprise AI deployment realities vLLM has become one of the most important open-source projects in AI infrastructure, and llm-d represents a newer shift toward treating inference as a coordinated distributed system rather than just a single runtime problem. If you want to better understand the systems layer beneath modern AI applications, this episode is a deep dive into where inference infrastructure is heading next. General Podcast Links Watch: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠ Listen:⁠⁠ ⁠⁠https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠ More: ⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠ Learn more about the host at Website: ⁠⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠⁠ Find out more about the guest at: LinkedIn: https://www.linkedin.com/in/robert-shaw-1a01399a/ Red Hat Articles: https://developers.redhat.com/author/robert-shaw Github: https://github.com/robertgshaw2-redhat Resources vLLM Website: https://vllm.ai/ vLLM GitHub Repository: https://github.com/vllm-project/vllm llm-d Website: https://llm-d.ai/ llm-d GitHub Repository - https://github.com/llm-d/llm-d Keywords AI inference, VLLM, LMD, distributed inference, GPU optimization, open source AI, Kubernetes, multi-cluster deployment, AI infrastructure, enterprise AI AI infrastructure, Kubernetes, model optimization, speculative decoding, mixture of experts, AI deployment, performance tuning, AI systems, neural network scaling Key Topics Evolution of vLLM and llm-d Distributed inference and routing GPU utilization and performance optimization Open source AI infrastructure Enterprise deployment challenges and solutions Standardization in Kubernetes for NIC exposure Performance optimizations: quantization and speculative decoding Mixture of experts architecture and parallelism strategies Flow control and request scheduling in AI systems Emerging hardware for AI inference, Cerebras processor Reinforcement learning and AI system support Modular architecture of vLLM and ecosystem projects

On this episode of Alexa’s Input (AI), I sit down with Emilio Andere, co-founder and CEO of Wafer, to talk about the future of AI infrastructure, inference optimization, and the economics driving the AI compute race. We discuss: why “intelligence per watt” may become one of the defining metrics of the AI erathe current GPU and accelerator landscape across NVIDIA, AMD, TPUs, and emerging hardware startupswhy software optimization is becoming just as important as hardware itselfinference optimization strategieswhy AI infrastructure companies are racing up the stackwhat it’s actually like building an AI infrastructure startup todayand more! Emilio also shares lessons from founding Wafer, thoughts on the future of open-source AI infrastructure, and why he believes optimizing intelligence itself could become one of the most important engineering problems. General Podcast Links Watch: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠ Listen:⁠⁠ ⁠⁠https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠ More: ⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠ Learn more about the host at Website: ⁠⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠⁠ Find out more about the guest at: LinkedIn: https://www.linkedin.com/in/emi-andere/ Wafer Website: https://www.wafer.ai/ Wafer AI / Y Combinator Article: https://www.ycombinator.com/companies/wafer Chapters 00:00 Exploring AI Conversations and Recent Podcasts 02:14 Intelligence per Watt: A New Metric for AI 07:35 The Manifesto: Efficiency in Civilization 12:40 Founding Wafer: The Journey Begins 18:08 The GPU Hardware Landscape and Market Dynamics 23:07 AMD's Growing Presence in the GPU Market 24:07 Emerging Competitors in the AI Hardware Space 26:04 Comparing TPUs and GPUs 27:21 Acquisition and Availability of TPUs 28:33 Navigating the GPU Marketplace 30:05 Understanding Neo Cloud Economics 33:30 The AI Bubble Debate 36:25 Optimizing AI Models for Performance 44:46 Bottlenecks in AI Model Performance 48:08 Future Directions in AI Hardware Optimization 54:39 Balancing Speed and Cost in AI Performance 56:54 Kernel Arena: Benchmarking AI Performance 01:03:45 Lessons from Founding: Sales and Emotional Resilience 01:07:38 The Future of AI: Trends and Predictions 01:13:03 Outro Keywords AI hardware, inference optimization, intelligence per watt, GPU market, AI infrastructure, Wafer, AI bubble, TPU, GPU bottleneck, AI efficiency AI optimization, large language models, AI hardware, quantization, speculative decoding, benchmarking, AI infrastructure, model training, AI startups

In this episode of Alexa’s Input (AI), I sit down with Sal Furino to explore the hidden engineering work that keeps modern systems reliable. We break down what Service Level Objectives, Indicators (SLOs/SLIs), and error budgets actually mean in practice, why reliability is as much a cultural problem as a technical one, and how teams can better measure real user experience instead of just infrastructure health. Sal also explains reliability engineering and the challenges of reliability at scale, like: Why latency and correctness become harder to measure with GenAIThe difference between a bad incident and a fundamentally bad systemHow observability and telemetry shape modern engineering organizationsWhy most teams focus too much on infrastructure metrics and not enough on user happiness Why “the best systems are the ones nobody notices.”If you work in AI infrastructure, distributed systems, platform engineering, observability, or SRE, this episode is a must listen! SRECon Talk Dashboards & Dragons: Reliability Magic for AI Platforms by Alexa Griffith and Sal Furino: https://youtu.be/aWMB_7ksbkc?si=S49nPyAl_hCUIH7y General Podcast Links Watch: ⁠⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠ Listen:⁠⁠ ⁠https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠ More: ⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠ Learn more about the host at Website: ⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠ Find out more about the guest at: LinkedIn: https://www.linkedin.com/in/salvatore-furino/ Rootly Interview: https://rootly.com/humans-of-reliability/salvatore-furino Reliability at Scale Talk: https://youtu.be/J-VrU5JHPlk?si=8aV8acy57NWX30KA Bloomberg Careers: https://bloomberg.avature.net/careers/SearchJobs Chapters 00:00 - Introduction: Reliability in a world reshaped by generative AI 02:22 - The importance of seamless, background system design 04:41 - Becoming a Customer Reliability Engineer at Bloomberg 05:17 - Clarifying the CRE role and its customer focus 08:02 - The importance of observability and high-scale performance in finance 09:00 - Balancing technical and cultural aspects of reliability 10:19 - Coaching teams to be proactive using error budgets and SLIs 12:21 - The social-technical system: People, processes, and tools 13:06 - Mediation of differing opinions on reliability practices 15:06 - The nuanced approach to alerting and incident response 17:08 - The significance of tiered SLOs and the concept of error budgets 21:08 - Using signals like latency, correctness, availability, saturation in system measurement 22:53 - The impact of service level "nines" on system design and resilience 28:00 - Handling non-determinism and trust in AI responses 33:01 - Error budgets and their role in managing deployments 34:10 - The challenge of achieving five nines and data durability considerations 40:03 - Adapting SLOs for GenAI systems: core principles remain intact 42:23 - Measuring non-deterministic AI responses and quality proxies 44:41 - The ongoing importance of reliability even in AI/ML contexts 47:25 - Reacting to error budget exhaustion and proactive mitigation 50:42 - The significance of involving cross-functional teams during outages 55:36 - Advocating reliability investment to leadership 56:24 - The customer perspective: reliability as a fundamental feature 58:42 - Connecting with Sal Furino: where to follow his work and learn more about Bloomberg's engineering culture 59:20 - Final advice: Focus on user happiness to avoid common pitfalls in adopting SLOs

In this episode of Alexa’s Input (AI), I sit down with Kaan Divitoğlu, founder of Laila — a New York based startup rethinking online dating as a social marketplace centered around real plans instead of endless swiping. We talk about why traditional dating apps struggle to create real-world connection, how marketplace dynamics shape modern dating behavior, and why Kaan believes the future of dating products is less about “matching soulmates” and more about helping people actually get out on first dates. Kaan shares what he’s learned building a product around something emotional, unpredictable, and deeply human: connection. We also get into: • The metrics behind dating products and user behavior • Why most matches never turn into real dates • Designing around human psychology and social incentives • AI in dating apps — where it helps and where it shouldn’t • The process of building Laila • Social media growth, creator strategies, and startup distribution • Why Kaan thinks apps themselves may eventually disappear Links Watch: ⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠ Listen:⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠ More: ⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠ Learn more about the host at Website: ⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠ Find out more about the guest at: LinkedIn: https://www.linkedin.com/in/kaan-divitoglu-152779105/ Laila Website: https://laila.nyc Laila Instagram: https://www.instagram.com/laila.social Chapters 00:00 Introduction to Layla and Its Concept 04:10 The Journey of Building Layla 08:43 User Feedback and Validation 13:35 Metrics of Success in Dating Apps 18:23 Differentiation in the Dating App Market 22:54 Understanding User Behavior and Expectations 27:37 Challenges in the Dating Landscape 29:50 Loneliness and Social Skills in Modern Dating 30:51 AI's Role in Dating Apps 34:20 The Future of Dating Apps and User Experience 38:19 Building Community Through Events and Social Media 42:54 Navigating Social Media Marketing 46:00 Rapid Fire Insights on Dating and Relationships 53:33 Outro Keywords dating app, AI, product design, real-world connections, marketplace, user engagement, social media, social tech, startup, innovation

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 Links Watch: ⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠ Listen:⁠⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠⁠ More Links: ⁠⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠⁠ Find 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/ Chapters 00:00 Introduction to Brady Jordan and His Journey 06:45 The Birth of Clip Play Media 14:58 Quality vs. Consistency in Content Creation 24:51 Y2Cam: A Solution to Frustration 30:51 Cost and Infrastructure of App Development 35:30 Navigating the Challenges of Self-Employment 42:51 Marketing Strategies for App Success 49:04 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 breaks The difference between unavoidable failure and negligence How modern software supply chains amplify small mistakes The role of leadership and culture in preventing breaches Why verification systems like TUF and in-toto matter more than ever As 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 Links Watch: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠ Listen:⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠ More: ⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠ Website: ⁠⁠⁠⁠⁠⁠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-cappos NYU page: https://ssl.engineering.nyu.edu/personalpages/jcappos/ Wikipedia: https://en.wikipedia.org/wiki/Justin_Cappos Chapters 00:00 Introduction to Justin Cappos and His Work 01:17 The Importance of Security in Software Systems 03:50 Understanding Security Breaches: Mistakes vs. System Design Problems 06:34 Cultural Factors in Security Failures 09:25 Justin's Journey in Software Security 12:03 The Role of Academia in Enterprise Security 14:10 Evaluating Enterprise Security Systems 16:58 Foundational Projects in Software Security 19:21 AI Security Concerns and Future Directions 24:59 The Need for MCP 2.0 28:57 Security Challenges with LLMs 32:33 Designing Secure AI Systems 37:14 Ethical Dilemmas in AI Decision-Making 40:17 The Role of AI in Open Source 43:44 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 Links Watch: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠ Listen:⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠ More: ⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠ Website: ⁠⁠⁠⁠⁠⁠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/ Chapters 00:00 Introduction to AI Security 01:16 Understanding Data Poisoning 04:38 The Dangers of Malware in AI 07:46 AI's Moral Dilemmas and Decision Making 08:45 Building Empathy in AI 13:07 The Role of Good Data in AI Training 17:02 PureCypher's Artificial Immune System 22:34 Digital Twins and Their Implications 25:22 Nurturing AI Like a Child 30:53 Data Therapy for AI 36:13 The Future of AI and Human Interaction 38:45 The Dark Side of AI: Hacking and Security 45:03 Global Perspectives on AI Security 48:11 MCP Agents and Security Concerns 51:41 Philosophical Implications of AI and Human Connection 01:00:04 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 Links Watch: ⁠⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠ Listen:⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠ More: ⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠ Website: ⁠⁠⁠⁠⁠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/promptfoo Ian’s LinkedIn: https://www.linkedin.com/in/ianww/ Chapters 00:00 Introduction to AI Security Challenges 02:06 Funding and Growth of PromptFu 06:16 The Genesis of PromptFu 11:05 Career Journey and Lessons Learned 12:53 Understanding AI Red Teaming 17:36 Recent AI Security Vulnerabilities 19:46 The Dual Nature of AI in Security 21:47 Understanding the Lethal Trifecta in AI Security 24:22 Exploring Model Context Protocol (MCP) and Its Security Implications 26:22 Common Security Issues in MCP Systems 28:17 The Role of Identity and Permissions in AI Security 30:00 Practical Implications of Using PromptFoo for Developers 31:33 Evaluating Language Models: Challenges and Techniques 36:34 The Limitations of Guardrails in AI Security 38:25 Best Practices for Engineers in AI Development 39:58 Future Trends in AI and Security 42:28 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 Links Watch: ⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠ Listen:⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠ More: ⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠ Website: ⁠⁠⁠⁠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/githedgehog Chapters 00:00 Rethinking AI Infrastructure 02:49 The Role of Networking in AI 05:54 Marc's Journey to Hedgehog 08:46 Lessons from Big Companies 11:38 Requirements for AI Networks 14:48 Advancements in AI Networking 17:33 Future Challenges in AI Infrastructure 20:46 Creating a Cloud Experience On-Prem 23:32 The Shift to Hybrid Multi-Cloud 28:10 Evolving AI Infrastructure and Efficiency 30:57 AI Workloads and Network Configurations 32:41 Zero Touch Lifecycle Management 35:12 Support for Hardware Devices 35:45 Networking Paradigms and Vendor Lock-in 38:42 The Rise of Neo Clouds 41:31 Demand for AI Infrastructure 43:57 Open Source and Cloud-Native Networking 47:27 Challenges of Building a Networking Startup 50:46 Proud Accomplishments at Hedgehog 52:41 Future Excitement in AI Inference

Five years ago, Kelsey Hightower helped me find my voice in tech as the guest for my fifth podcast episode. Today, the man who taught the world Kubernetes and became a legend for his live demos returns for a conversation that goes far beyond infrastructure and code. Now retired-ish, Kelsey has transitioned into a new chapter. In this episode, we explore what it means to be not only a senior engineer, but also a "senior human" in an industry obsessed with speed. Kelsey shares his unique perspective on: Real vs. Artificial Intelligence: Why we must stop ignoring real intelligence and focus on providing humans with the same context and clarity we give to AI.The Future of Engineering: Why your value will shift from writing code to making stylistic, high-impact decisions as AI levels the technical playing field.Impact Over Activity: How to stop being a "busybot" and start asking the difficult questions about why we are building in the first place.The Senior Human Unit Test: Building communities with integrity, leading with empathy, and staying balanced in a world that always wants more.Whether you are just getting into your career or a seasoned veteran, this episode is a masterclass in curiosity, craft, and the art of staying grounded while building the future. Podcast Links Watch: ⁠⁠⁠⁠https://www.youtube.com/@alexasinput⁠⁠⁠⁠ Read: ⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠ Listen:⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠ More: ⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠ Website: ⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠ Find out more about the guest at: Bluesky: https://bsky.app/profile/kelseyhightower.com LinkedIn: https://www.linkedin.com/in/kelsey-hightower-849b342b1 GitHub Profile: https://github.com/kelseyhightower Kubernetes the Hard Way: https://github.com/kelseyhightower/kubernetes-the-hard-way No Code (The minimalist project): https://github.com/kelseyhightower/nocode Kubernetes: Up and Running (Book): https://www.oreilly.com/library/view/kubernetes-up-and/9781492046523/ Chapters 00:00 Introduction and Background 01:10 Transitioning from Engineer to Tech Philosopher 04:00 The Importance of Being a Senior Human 07:23 AI's Impact on People Skills 10:12 The Future of Engineering in an AI World 15:04 Navigating the AI Shift 21:21 Finding Impact Over Activity 25:47 Creating Meaningful Products 29:57 The Power of Listening and Connection 35:21 The Importance of Listening in Discussions 35:55 Embracing the Learning Journey 36:58 Understanding Imposter Syndrome 39:33 Creating Supportive Learning Environments 40:31 Learning in Public and Sharing Experiences 41:31 Finding Your Own Voice 43:26 The Power of Emotion in Presentations 47:29 Crafting Engaging Stories 48:40 Improvisation in Public Speaking 55:10 The Evolution of Presentation Styles 01:03:28 Legacy and Impact in the Tech Community