What's Up with Tech?

Interested in being a guest? Email us at admin@evankirstel.com
AI is making decisions that shape real lives, yet most people cannot see how those decisions get made. We sit down with Scott Zoldi, Chief Analytics Officer at FICO, to unpack what “trustworthy AI” actually requires when the stakes include fraud, credit risk, and customer outcomes in heavily regulated financial services. If you have ever wondered why black box models create so much fear and backlash, this conversation puts clear language around the real issues: data provenance, explainable AI, ethical testing, robustness, and the ability to audit a decision after the fact. 

We go beyond buzzwords and get specific about AI governance. Scott explains why responsible AI starts with a shared model development standard, so a large organization is not running a hundred different approaches that no one can consistently defend. We talk about why monitoring is often the weakest link in real world machine learning, when to retire models that drift, and why enterprises need to stay in control instead of outsourcing critical decisions to models they did not build. 

Then we dig into a practical enforcement mechanism: coupling AI governance with blockchain to create an immutable record of requirements, testing, verification, and release decisions. Think of it as an operating manual that travels with the model and can be inspected years later by regulators, customers, or internal teams. We also look ahead to what changing regulation could mean, including the push toward interpretable models, trust scoring for generative AI, and focused language models or small language models built for narrow tasks with auditable data. 

If you care about responsible AI, AI transparency, and building systems people can actually trust, hit play, then subscribe, share this with a friend who works in AI or compliance, and leave a review with the one governance rule you think every model should follow.
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at admin@evankirstel.com
Drug discovery is one of the hardest engineering problems on Earth, except it has not always been treated like engineering. Costs can hover around $2 billion per successful drug, timelines can run 10+ years, and too many patients still wait without a cure. We sit down with Rory Kelleher, who leads global business development for life sciences at NVIDIA, to talk about what changes when accelerated computing meets foundation models, generative AI, and agentic AI that can actually do work.

We break down how scientific agents differ from chatbots, and why tools matter as much as models. Rory explains NVIDIA’s BioNEMO Agent Toolkit and the idea of turning core life sciences capabilities into “agent skills” so biologists and chemists can run complex workflows through natural language. We talk protein design and protein binder design, co-folding, bioinformatics, target identification, and ADMET prediction for toxicity and safety, plus why this wave can “democratize” computational drug discovery for scientists who were never trained as programmers.

You’ll also hear a real example from Bristol Myers Squibb, where foundation models trained on proprietary sequences and compound libraries helped improve a sickle cell molecule profile until it reached first-in-human testing. We dig into what an “AI factory” looks like inside pharma, why teams want to run open models and local LLMs on secure infrastructure, and why scientific judgment becomes more important, not less, as agents increase throughput.

If you care about biotech, pharma R&D, life sciences AI, and the future of medicine, this conversation is for you. Subscribe, share the episode with a friend in research, and leave a review with the one workflow you want agents to tackle next.
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at admin@evankirstel.com
Everyone is obsessed with bigger models and faster GPUs, but the part that decides whether AI actually works in the real world is the data behind it. We talk with Paul Speciale CMO at Scality, about what happens when enterprises try to store, protect, and serve AI data at petabyte and exabyte scale, and why the rise of huge context windows turns “just a chat” into a massive storage and latency problem.

We walk through the full enterprise AI pipeline, from data collection and cleansing to inference and the long archive tail most teams never plan for. Paul breaks down which AI phases truly need the fastest storage, why inference can demand microsecond access, and how tiered designs (hot, warm, cool) align to modern GPU stacks. We also dig into what’s changing in the market right now: flash price spikes, constrained supply, and the reality that power per rack often matters more than raw capacity.

From there, we get practical about operations and risk. We explore autonomous infrastructure as “tell the system what you want, not how to do it,” including human-in-the-loop recommendations that can move data to cheaper tiers and cut power bills. We also cover cyber resilient storage, immutability for ransomware defense, and the growing pressure around data sovereignty and even code transparency. If you’re building an AI infrastructure plan, this is the blueprint mindset that keeps GPUs busy and data trustworthy. Subscribe, share this with your infrastructure team, and leave a review with the biggest AI data challenge you’re facing.
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at admin@evankirstel.com
AI is cranking out more code than ever, but that doesn’t mean teams are shipping the right outcomes. From PegaWorld, I sat down with Steph Lewis, Senior Director of Community Developer Programs at Pega Systems, to unpack what’s actually changing for developers, architects, and business stakeholders as AI, agents, and LLMs reshape enterprise delivery. 

We get specific about the Solution Designer role Steph helped build and why it’s showing up now. We talk about Pega Blueprint as a practical way to turn business ideas into a live application earlier, and how that changes the skills teams need across low-code development, workflow automation, and digital transformation. Steph explains why the goal never changes (delivering value), but the day-to-day work does, especially as “hands-on keyboard” tasks shrink and the need to challenge AI outputs grows. 

A big thread is the human edge: judgment, creativity, and the discipline to align what you build with what stakeholders truly need. We dig into the skills gap Steph sees most often, why workshops and real conversations beat passive training, and what sits behind eye-catching outcomes like 80% of projects going live in 90 days. We also zoom out to what Steph wants for the developer community over the next year: guiding architecture decisions, placing Pega in the right spots, and incorporating other agents only when it makes sense. 

If you care about modern software delivery, developer enablement, and the real-world impact of AI on building systems, this one’s for you. Subscribe, share this with a teammate, and leave a review with your take: what “human skill” will matter most as AI becomes default?
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at admin@evankirstel.com
Your CRM knows a lot about your customers, so why does it still feel like a place you go to do homework? We talk with SugarAI CEO David Roberts about the hard truth many sellers live every week: CRM has mostly evolved into a management tool, not a system that makes reps better in the moment that matters, right before a call, a meeting, or a critical follow-up.

We dig into why the “360-degree view of the customer” often backfires. More dashboards and more reports can create more overwhelm, especially when a rep is managing hundreds of accounts, dozens of deals, and a product catalog with thousands of SKUs. David lays out a more practical promise for AI in CRM: move from data straight to action. Instead of forcing sellers through a rigid process, AI can interpret unstructured sales work like emails and meetings, spot gaps like missing executive sponsors, and recommend the next best step that improves retention, expansion, or new business.

We also explore contextual intelligence and “precision selling” and why generic AI advice is easy to ignore. The best guidance comes from combining customer data with the real selling context: sales methodology, pitch decks, competitive battle cards, and objection handling. For manufacturing and distribution teams, connecting front-office CRM with back-office ERP data like inventory and shipping can surface reorder opportunities without sending reps into endless spreadsheets.

If you care about sales productivity, revenue team performance, AI CRM, and faster sales onboarding, you’ll get a lot from this conversation. Subscribe, share this with a sales leader or RevOps partner, and leave a review with the biggest “next step” you wish your CRM would tell you.
Support the show
More at https://linktr.ee/EvanKirstel