What's Up with Tech?

• Sensors, AI, and Incentives Are Making Our Roads Safer

  Interested in being a guest? Email us at [email protected] technology in your pocket—sensors in your everyday smartphone—is quietly revolutionizing road safety in ways most of us never realize. In this eye-opening conversation, MIT professor Hari Balakrishnan shares the remarkable journey of Cambridge Mobile Telematics (CMT) from university research project to global safety leader.What began as a simple idea to use mobile device sensors to measure driving quality has evolved into technology that's prevented over 90,000 crashes and saved 50,000 lives worldwide. Balakrishnan reveals how CMT's DriveWell platform fuses data from phones, windshield-mounted tags, connected vehicles, and dash cams to create a comprehensive picture of driving behavior that's transforming the insurance industry.The implications are profound. For the first time in insurance history, companies can price policies based on how people actually drive rather than demographic assumptions. Young drivers no longer automatically face punishing premiums—they can earn discounts through demonstrably safe driving. This creates a virtuous cycle where better driving leads to lower costs, fewer crashes, and ultimately, lives saved.Perhaps most fascinating is CMT's breakthrough in real-time crash detection—something once deemed impossible using only smartphone sensors. Unlike solutions that focus only on catastrophic accidents, CMT's technology works across all crash severities, providing immediate assistance when needed most.Looking toward the future, Balakrishnan shares his vision of data-driven road infrastructure improvements and the gradual integration of autonomous vehicles. All while maintaining a refreshingly human-centered approach to privacy: "Don't do anything with the data that you wouldn't want done with your own."If you're interested in how technology can create safer roads while giving people more control over their driving costs and safety, this episode offers an inspiring glimpse into that future—one that's already arriving on roads worldwide.PodMatchPodMatch Automatically Matches Ideal Podcast Guests and Hosts For InterviewsSupport the showMore at https://linktr.ee/EvanKirstel

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• AI Governance: ModelOp's Approach to Enterprise Trust

  Interested in being a guest? Email us at [email protected] remains the central challenge for enterprise AI adoption, as revealed in ModelOp's comprehensive AI Governance benchmark report. CTO Jim Olsen explains why so many generative AI projects remain stuck in development limbo, with 56% taking 16-18 months to reach production.The disconnect stems from AI's non-deterministic nature - unlike traditional software, models can't be fully predicted or verified in the same ways. "One bad recommendation is harder to overcome than a thousand correct ones," Olsen notes, highlighting how organizations struggle to build confidence in systems that sound convincingly authentic even when delivering incorrect information. This challenge becomes particularly acute in regulated industries where the stakes are highest.Financial services companies have developed the most mature governance practices out of necessity, having faced multi-billion dollar fines for improper model management. However, healthcare faces even greater complexity with "life or death" decisions and patchwork regulations across different jurisdictions. In both cases, fragmentation within enterprises compounds governance challenges, with different teams pursuing siloed approaches that prevent organizations from learning collectively.ModelOp addresses these challenges through centralized model lifecycle management that provides visibility, consistency, and automated governance. Their "minimal viable governance" approach enables organizations to start with essential controls and iterate, rather than waiting for perfect solutions. As AI evolves toward autonomous agents with decision-making authority, governance becomes even more critical.Ready to accelerate your AI implementation without compromising on trust or compliance? Discover how leading organizations are cutting deployment times in half while building stronger governance foundations. The key isn't waiting for perfect solutions, but starting the governance journey now before complexity overwhelms your AI initiatives.Support the showMore at https://linktr.ee/EvanKirstel

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• The Internet Has Become Your Enterprise Network, And That Changes Everything

  Interested in being a guest? Email us at [email protected] security is facing a fundamental paradigm shift that most organizations haven't fully recognized. According to Aviatrix CEO Doug Merritt, we're overlooking a staggering 50-80% of our attack surface by failing to properly secure cloud workload communications.The problem stems from three critical changes in how computing works today. First, the internet has essentially become our enterprise network – when your application calls an S3 bucket, that communication happens over the internet, not a controlled private network. Second, the traditional security perimeter hasn't disappeared; it's atomized from a handful of entry points to thousands or even hundreds of thousands of mini-perimeters. Every VPC, Kubernetes cluster, and API endpoint now requires its own security strategy. Third, modern workloads are largely ephemeral rather than long-lived, making them harder to secure with traditional approaches.This security challenge is further complicated by multi-cloud environments, where security policies must consistently follow workloads across cloud boundaries. Meanwhile, the rise of generative AI creates both defensive opportunities and heightened risks, as attackers leverage these same technologies to map enterprise environments and find vulnerabilities with unprecedented speed and effectiveness. As Merritt explains, "Attackers think in graphs" – constructing comprehensive maps of your organization's resources to identify any possible entry point.The solution requires a paradigm shift in how we approach cloud security. Aviatrix advocates for a "cloud native security fabric" built on zero trust principles specifically designed for cloud workloads. This approach focuses on four critical elements: controlling egress to prevent command-and-control communications, implementing east-west macro-segmentation to block lateral movement, applying micro-segmentation for granular control, and ensuring comprehensive encryption to protect data even if network infrastructure is compromised.Ready to rethink your cloud security approach? Discover how zero trust principles can be applied to your cloud workloads to close critical security gaps and protect your most valuable digital assets.PodMatchPodMatch Automatically Matches Ideal Podcast Guests and Hosts For InterviewsSupport the showMore at https://linktr.ee/EvanKirstel

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• Who Will Build the Builders? Inside Emergence AI's CRAFT Platform

  Interested in being a guest? Email us at [email protected] quiet revolution happening in enterprise AI isn't about flashy robots or sci-fi scenarios—it's about solving a $200 billion problem hiding in plain sight. Outdated, manual data pipelines are costing businesses astronomical amounts and keeping your smartest people stuck doing digital busywork.Emergence AI's CRAFT platform stands at the forefront of this revolution with its "agents creating agents" approach. The concept sounds deceptively simple: AI systems that can dynamically create specialized AI tools to handle specific tasks. But the implications are profound. Data scientists spend up to 90% of their time wrangling data rather than analyzing it. CRAFT flips that equation, cutting analysis time from days to minutes through intelligent automation that requires only natural language commands.What makes this particularly powerful is CARFT's self-improvement capabilities. The platform continuously learns through three dimensions: knowledge acquisition (gaining domain expertise), skill acquisition (optimizing workflows), and agent acquisition (building new capabilities). Like a new employee gaining experience, the system becomes increasingly valuable over time, adapting specifically to your business context.Real-world results already demonstrate the impact. Semiconductor manufacturers using CARFT identify yield issues in minutes instead of days, potentially saving millions weekly. Telecommunications companies report 70% reductions in data governance tasks. An online forum reduced unsafe image postings by 60 million monthly. The platform balances this power with built-in governance features, ensuring human oversight remains for critical decisions while shifting roles toward strategic direction rather than execution.Available now in private preview and launching broadly this summer, CARFT represents a fundamental shift in how enterprises handle data. The question isn't whether AI will transform your business operations—it's whether you'll be among the first to capitalize on this transformation or watch competitors race ahead while you're still stuck with yesterday's manual workflows.PodMatchPodMatch Automatically Matches Ideal Podcast Guests and Hosts For InterviewsSupport the showMore at https://linktr.ee/EvanKirstel

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• The Enterprise AI Revolution: From Science Project to Mission Critical

  Interested in being a guest? Email us at [email protected] cutting-edge experimentation to business-critical infrastructure, the AI landscape has undergone a dramatic transformation. Ron from KungFu.ai shares an insider's perspective on this evolution, drawing from his experience dating back to AI research in the 1990s.The conversation reveals the pivotal factors driving AI's enterprise breakthrough: exponentially increased computing power, unprecedented data availability, and the democratizing effect of open-source libraries. These elements have converged to create capabilities early researchers could scarcely imagine, requiring millions of times more resources than initially anticipated.What distinguishes KungFu's approach is their unwavering focus on production-grade AI systems that deliver tangible business value rather than impressive but unreliable demos. Ron shares a striking success story of a financial services client whose AI implementation reduced loan decisioning time from 48 hours to just 9 seconds while simultaneously reducing fraud rates – all without eliminating human jobs but rather redirecting human attention to the complex cases requiring judgment.The discussion tackles the profound challenges enterprises face during implementation. AI systems differ fundamentally from traditional software in their probabilistic nature, making human-like mistakes that can be difficult to predict or debug. Data quality emerges as the critical determinant of success – "garbage in, garbage out" applies more powerfully to AI than to any previous technology. Ethical considerations, especially regarding bias and explainability in regulated environments, demand sophisticated approaches that go far beyond typical software development concerns.Looking ahead, Ron provides a sobering yet optimistic assessment of agentic AI systems, suggesting that failure rates may exceed Gartner's 40% prediction while maintaining that these technologies will ultimately revolutionize business faster than most anticipate. For companies navigating this complex landscape, the talent equation remains daunting – building effective AI systems requires a blend of mathematical expertise, domain knowledge, and hard-won intuition that remains in critically short supply.Ready to transform your business with AI that delivers real results rather than just impressive demos? Connect with Ron at [email protected] or explore their "Hidden Layers" podcast for deeper technical insights into the future of enterprise AI.PodMatchPodMatch Automatically Matches Ideal Podcast Guests and Hosts For InterviewsSupport the showMore at https://linktr.ee/EvanKirstel

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