What's Up with Tech?

Interested in being a guest? Email us at [email protected] turns into a living blueprint for the future as we sit down with CTA’s Gary Shapiro and best-selling author Jim Harris to unpack why CES 2026 is more than a showcase—it’s where strategy gets decided. We dig into how AI has moved from headline hype to the connective tissue of every sector, shaping robotics, health, mobility, and enterprise workflows. Gary lays out the big picture: a record wave of innovation colliding with real constraints like energy supply, fragmented regulation, and national industrial strategies. The tension is exciting—and actionable.We walk through what’s truly new this year: a surge in humanoid and wearable robotics, patient-centered health tech powered by continuous sensing, and a startup scene in Eureka Park designed to compress months of business development into days. CES Foundry expands into AI and quantum with live demos, while new categories highlight enterprise tech, filmmaking, logistics, and travel. Keynotes—from Dr. Lisa Su to global brands at the Sphere—frame where compute, platforms, and partnerships are headed next. It’s a global stage, with 40% of attendees from abroad, and a clear signal that innovation is now a team sport.If you’re an enterprise leader, this isn’t a gadget tour. Nearly half the action is B2B: partner summits, private suites, and tracks that turn tech trends into operating plans. We share practical tips to win your week—map your days by venue, leverage the upgraded AI-driven app, hydrate, and focus on one theme per day to avoid context switching. Looking ahead, we explore how quantum computing, agentic AI, and autonomy could reshape supply chains and healthcare, and why energy and policy choices will determine how fast the future arrives.Subscribe for more deep dives from the front lines of innovation, share this with a colleague who’s planning their CES agenda, and leave a quick review to tell us which track you’re most excited to explore.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] journalist screams, “I’m wasting four hours a day transcribing,” and a product is born. We sit down with Lasse Finderup, CEO of Good Tape to unpack how a newsroom pain point turned into a privacy-first platform used by millions—and why saying no to feature bloat matters more than chasing every shiny AI trick.We trace the spin-out origin story and the “instant product-market fit” that came from building for colleagues who needed reliable, fast transcripts yesterday. Lasse explains the decision to never train on user data and to host models in-house, trading flashy add-ons for deep security, ISO-grade compliance, and trust. We explore global AI adoption gaps—from Denmark’s “I’ll just ChatGPT this” culture to regions where automated speech-to-text still feels like magic—and why context matters when you’re designing tools for journalists, podcasters, and creators handling sensitive sources.From a tech perspective, we dive into an open-source stack centered on Whisper V3 Large and the heavy lifting around the model: optimization, infrastructure, and the real costs of self-hosting LLMs. Lasse lays out a sharp distinction between “record-everything meetings” tools and workflows where the transcript is the output itself. That sets the stage for Good Tape’s next big leap: an “artificial memory” that surfaces relevant past notes at the right moment, with user-controlled reminders that feel helpful, not invasive. We also touch on multilingual transcription’s surge across contact centers and newsrooms, market consolidation on the horizon, and founder advice: build for real needs, not just because AI makes it possible.If you care about accuracy, confidentiality, and simple tools that get out of your way, this conversation will sharpen how you evaluate transcription tech and where the industry is heading. Subscribe, share with a teammate who fights transcripts, and leave a quick review to help more builders and storytellers find the show.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] can suggest the next click, but they rarely deliver the finished job. We dive into a different path: enterprise AI agents that integrate with your systems, understand your business rules, and execute end-to-end workflows with governance, accuracy, and reliability. Rob Bearden co-founder and CEO Sema4.ai shares how their platform moves beyond brittle scripts and UI macros to reasoning-driven automation that adapts to changing contracts, policies, and supply constraints—turning strategy into repeatable, measurable outcomes.We trace the journey from big data to autonomy: insights and KPIs used to point the way, but humans still had to do the work across dozens of apps and tabs. Agents close that last-mile gap by reading documents, joining data across ERPs and CRMs, and following rule-bound reasoning paths to finish the task. You’ll hear concrete wins like multi-page invoice reconciliation done in minutes with higher accuracy, AP help desk cases resolved without swivel-chair searches, and quote-to-cash automated across fragmented systems. The result is less toil, fewer errors, and outcomes you can audit and scale.If you’re stuck in AI pilot purgatory, the way out is a platform strategy and tight guardrails. We break down a crawl-walk-run approach: pick a high-leverage use case, define the outcome, run a focused proof, measure ROI, then rinse and repeat. We also scan the broader agent ecosystem—Salesforce, ServiceNow, and hyperscalers are leaning in—while making the case for an enterprise-wide layer that spans SaaS apps, data warehouses, and data lakes. Finance operations lead early adoption, but healthcare, insurance, and manufacturing are close behind, wherever people juggle multiple systems to make a decision.Ready to trade tab hell for trained agents and predictable outcomes? Follow the show, share this episode with your ops and finance leaders, and leave a review to help more teams find it.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] leaps in AI rarely come from one breakthrough. They emerge when hardware design, open software, and real workloads click into place. That’s the story we unpack with AMD’s Ramine Roane: how an open, developer-first approach combined with high-bandwidth memory, chiplet packaging, and a re-architected software stack is reshaping performance and cost from the edge to the largest data centers.We walk through why memory capacity and bandwidth dominate large language model performance, and how MI300X’s 192 GB HBM and advanced packaging unlock bigger contexts and faster token throughput. Ramin explains how Rocm 7 was rebuilt to be modular, smaller to install, and enterprise-ready—so teams can go from single-node experiments to fully orchestrated clusters using Kubernetes, Slurm, and familiar open tools. The highlight: disaggregated and distributed inference. By splitting prefill from decode and adopting expert parallelism, organizations are slashing cost per token by 10–30x, depending on model and topology.The conversation ranges from startup-friendly workflows to hyperscaler deployments, with practical insight into VLLM, SGLang, and why open source now outpaces closed stacks. We also look ahead at where inference runs: the edge is rising. With performance per watt doubling on a steady cadence, AI PCs, laptops, and phones will take on more of the work, enabling privacy, responsiveness, and lower costs. Ramin shares a sober view on quantum computing timelines and a bullish take on the broader compute shift—moving once-sequential problems into massively parallel deep learning that changes what’s even possible.If you care about real performance, total cost of ownership, and developer velocity, this conversation brings a grounded blueprint: open ecosystems, smarter packaging, and inference architectures built for high utilization. Subscribe, share with a colleague who cares about LLM throughput and cost, and leave a quick review to help others find the show.PodMatchPodMatch Automatically Matches Ideal Podcast Guests and Hosts For InterviewsSupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] billion-dollar talent marketplace doesn’t happen by accident—it happens when access, scale, and experience come together. We sit down with  Michael Morris the head of Torc at Randstad Digital to unpack how a once-independent platform now matches millions of professionals with work, why the talent market feels like a “teenage” phase, and how AI is reshaping recruiting without removing humans from the loop.The conversation gets practical fast. Automation now handles the busywork—sourcing, screening, scheduling—so recruiters and hiring managers can focus on what actually drives outcomes: culture fit at the team level, long-term growth, and a great candidate experience. We challenge sacred cows like resumes and rigid job descriptions, and explore how a next-generation marketplace will let customers express needs via prompts, voice, and examples. The result is faster, clearer matching that opens doors for more diverse, AI-enabled talent, from Python developers to marketers fluent in prompt-driven workflows.We also dig into the skills that matter most as enterprises chase AI readiness. Coding is no longer the bottleneck; user experience and domain fluency separate good from great. That’s why soft skills and communication sit alongside credentials from Microsoft, Google, and OpenAI, and why personalized learning beats one-track training. Whether you’re building a data team in healthcare, prototyping fintech apps, or scaling a platform, the path forward is the same: invest in people, modernize how you describe work, and use AI to upgrade—not replace—the human touch.If this resonates, follow the show, share it with a colleague, and leave a quick review. Your support helps more builders, leaders, and learners find the ideas—and opportunities—that move their careers forward.Support the showMore at https://linktr.ee/EvanKirstel