What's Up with Tech?

• How A Decentralized GPU Network Beats The Cloud On Price And Flexibility

  Interested in being a guest? Email us at [email protected] happens when thousands of retired Ethereum GPUs meet a tidal wave of AI demand? We sit down with Hidde Hoogland Founder at Decent to unpack how a former mining operation evolved into a decentralized edge provider and why connecting to Aethir’s GPU marketplace changed the math on utilization, cash flow, and scale. It’s a candid look at turning stranded hardware into a real business with clearer ROI targets and a pricing story that can undercut hyperscalers by 60–80 percent for many workloads.We trace the journey from proof-of-work mining to a three-part operating model: sourcing hardware directly from vendors, hosting and maintaining systems, and owning the facility layer. Hine explains how decentralized GPU-as-a-service works for both sides of the market: buyers get location choice, hourly rentals, and on-chain payments; providers get a single platform that aggregates demand. The kicker is stability. Aethir’s static lease fees create a baseline even when GPUs sit idle, letting operators scale into actual demand rather than guessing. That foundation supports a portfolio approach to revenue: pay OPEX in fiat, stack tokens for growth, and invest in GPU SKUs with clear utilization histories and 12–24 month payback windows.We also zoom out to the European landscape. The Netherlands offers elite fiber and interconnect density but faces expensive power and a saturated grid. That reality shifts strategy toward storage-heavy growth while onboarding next-gen GPUs like 5090s where the numbers make sense. The medium-term vision is compelling: pair decentralized compute with distributed storage so teams can run inference, fine-tune LLMs, and manage data gravity without surrendering cost control or flexibility. If AI demand keeps rising, this “house of DPin” model could redefine how we think about cloud alternatives at the edge.If this conversation sparked ideas about your own GPU strategy or you’re curious about decentralized infra economics, follow the show, share it with a teammate, and leave a quick review to tell us what you want to hear next.Support the showMore at https://linktr.ee/EvanKirstel

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• How Agentic AI Turns Calls Into Continuous Learning

  Interested in being a guest? Email us at [email protected] don’t call to chat with a bot—they want outcomes. In my chat with Jim Palmer Dialpad's Chief AI Officer we dive into how Dialpad’s AI-first platform turns real-time voice, video, and digital interactions into agentic workflows that act on behalf of users, learn from every result, and hand off seamlessly to humans when it counts. No black boxes. No bolt-ons. Just a unified stack where speech recognition accuracy, in-domain adaptation, and transparent evaluations compound into trust you can measure.We talk through the nuts and bolts of building AI where it belongs: inside the media layer, not tacked on after the fact. That foundation unlocks higher-quality transcripts, reliable summaries, and precise suggestions that downstream models can use without drowning in errors. From there, in-domain training becomes the engine for better answers: when support questions share patterns across industries, the system adapts faster and resolves issues with fewer escalations. And when a case does escalate, the agent sees the distilled context—intent, key points, attempted steps, and recommended next actions—so the conversation keeps moving.The flywheel is the real breakthrough. As new issues surface in a rolling window of conversations, the platform quantifies impact and proposes automations. Humans review, approve, and refine. The system executes, measures completion and containment, and folds learnings back into models. Layer on governance—clear data policies, observability, and rigorous evaluation—and you get AI that leaders can trust and teams love to use. We also share how internal hackathons and applied research push practical features over the line, keeping innovation and execution in lockstep.If you care about customer experience, agent performance, and measurable ROI from AI, this conversation brings a clear blueprint. Subscribe, share with a colleague who owns CX or support, and leave a review with your biggest automation challenge—we might feature it next.Inspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifySupport the showMore at https://linktr.ee/EvanKirstel

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• How An Agentic Platform Turns First-Party Data Into Loyalty

  Interested in being a guest? Email us at [email protected] brands pay again and again to win back customers they already won. We wanted to fix that. In this candid conversation with Sandeep Menon Co-Founder Auxia we unpack how an agentic AI platform can sit on top of rich first-party data and finally make it work: autonomous decisions about timing, creative, channel, and offer that meet people at the right moment across web, app, and owned media. No more brittle, rules-based journeys that spam everyone. Think continuous learning, clear objectives, and experiences that build trust.We talk through the reality of today’s martech sprawl and why so many marketers feel like systems integrators. Sandeep shares a practical model for role compression, where a suite of agents handles the repetitive work and analysts’ chores, while the human team sets goals, brand guardrails, and strategy. He breaks down the “reacquisition treadmill” and why acquisition gets the spotlight while retention gets sidelined, even though the biggest gains often come from deepening relationships with people who already converted.You’ll hear a tangible story: a large C2C marketplace used cross-category nudges to move fashion buyers into electronics, driving an 84% lift in customer lifetime value. We dig into how the decision agent and analyst agent work together, how marketers can query outcomes in plain language, and why the next two years of AI—especially improved reasoning—will reward teams that measure success by revenue impact, not pilot buzz. If you’re ready to turn first-party data into loyalty instead of noise, this one’s for you.If you enjoyed this conversation, follow the show, share it with a colleague who lives in lifecycle or growth, and leave a quick review so others can find it.Support the showMore at https://linktr.ee/EvanKirstel

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• How Enterprises Turn AI Agents Into Real Value With Modern Observability

  Interested in being a guest? Email us at [email protected] is moving faster than any wave we’ve seen—and the hidden cost is complexity. We sit down with New Relic’s leadership to unpack how observability must evolve to keep up with agents, vibe coding, and probabilistic systems that don’t fail like traditional software. From the early SaaS days to hyperscale cloud and now AI-native architectures, we connect the dots on where value breaks, why pilots get stuck, and what changes when real users meet real AI in production.We challenge the headline that “95% of GenAI projects fail” and explain the real blockers: adoption, verification, and integration into messy enterprise workflows. Then we dive into the rise of AI agents—microagents and nanoagents coordinating around-the-clock—to offload repetitive tasks and accelerate delivery. With that power comes a new ops reality: tracing prompts and tool calls, monitoring hallucinations, enforcing safety policies, and tying every hop back to user and business outcomes. You’ll hear practical guardrails for vibe coding, including evaluation suites, prompt versioning, canary releases, role-based access, and real-time model quality monitoring.Finally, we look at outages through a clear lens: even the smartest teams can’t outpace system complexity without better visibility and faster remediation. New Relic’s two-pronged roadmap—observability for AI and AI for observability—aims to give teams both the data and the decisions: model-aware telemetry, agent state insights, automated incident summaries, and recommended fixes that protect revenue and trust. If you’re planning for 2026, take this as your cue to embrace the era with discipline: ship agents, instrument everything, and let observability become the backbone of safe, scalable AI.If this conversation helps you think sharper about AI in production, follow the show, share it with your team, and leave a quick review so others can find it.Support the showMore at https://linktr.ee/EvanKirstel

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• From IVR To AI: How Teneo 8 Scales Multilingual Contact Centers And Revenue

  Interested in being a guest? Email us at [email protected] if your contact center could speak 46 languages, automate millions of calls, and still hand tricky moments to the right human in seconds? We dig into the launch of Teneo 8 with CEO Per Ottosson and reveal how a hybrid AI approach—combining LLM-powered conversation with deterministic logic—delivers scale, governance, and measurable results without sacrificing warmth or control.We walk through the architecture that makes this possible: long-form, human-style dialogue guided by LLMs, paired with rules and business processes that capture facts, validate entities, and keep flows on track. You’ll hear how a major global rollout built in English, then used language objects to expand into dozens of markets over a weekend, and why public APIs let providers embed high-scale automation under their own brands. Real outcomes anchor the story—like Medtronic’s virtual assistant in healthcare, which lifted customer satisfaction by eight percentage points and drove revenue gains while meeting strict compliance needs.Beyond replacing legacy IVR, we explore omnichannel reality and persistent memory that unifies voice with channels like WhatsApp and iMessage. We share a practical stance on human-in-the-loop: AI first for routine steps, expert agents for edge cases, leading to happier teams and better resolutions. Looking ahead, we map the shift to voice-to-voice conversations supervised by a control layer that enforces compliance and intent understanding. If you’re evaluating Dialogflow, Genesys, or custom stacks, you’ll get a clear view of how to scale, how to avoid the “easy demo” trap, and how to measure value through CSAT and revenue—not just cost cuts.If this resonates, follow the show, share it with your CX and engineering teams, and leave a review with one question you want us to tackle next.Support the showMore at https://linktr.ee/EvanKirstel

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