Logixly Live Unsupervised Learning Podcast

NLP Logix

Raw takes on AI, Tech and Innovation. Join NLP Logix's Logixly Live team as they break down the biggest tech news, emerging trends, and industry hot topics while sharing our insights and perspectives all under 30 minutes.

Episodes

  1. 2d ago ·  Video

    Is AI Making Us Less Skilled? The De-Skilling Debate

    Is AI making us smarter, or are we slowly losing the skills that make us human? As artificial intelligence becomes part of our everyday lives, a new concern is emerging: AI de-skilling. When we rely on AI to complete tasks, are we becoming more productive, or are we losing the ability to do those things ourselves? In this episode of Logixly Live Presents: Unsupervised Learning, Matt Berseth and Katie Bakewell debate whether AI is weakening human expertise or creating opportunities to develop entirely new skills. From research on AI-assisted medical procedures to students using ChatGPT, hackathons, and the future of software development, they explore what happens when technology starts doing the work we once had to learn ourselves. In this episode: What AI de-skilling means and why people are talking about itResearch raising questions about AI dependence and human expertiseWhether AI is limiting creativity and critical thinkingHow students can use ChatGPT to learn rather than simply get answersWhy re-skilling may be more important than de-skillingCould voice-based AI eventually replace the keyboard?What skills will matter most in an AI-powered future?The big question: Should we be worried about the skills AI is replacing, or focused on the new skills it allows us to develop? Join the conversation in the comments! Do you think AI is making us more capable or more dependent? RESOURCES MENTIONED: Research Study: AI-Assisted Colonoscopies and the Risk of De-Skilling: https://www.thelancet.com/journals/langas/article/PIIS2468-1253(25)00133-5/abstract MEET THE HOSTS: Matt Berseth:   / mattberseth  Katie Bakewell:   / katiebakewell  CONNECT WITH NLP LOGIX: Learn more about NLP Logix: https://nlplogix.com/ Explore the Unsupervised Learning Podcast: https://nlplogix.com/insight/logixly-... Follow NLP Logix on LinkedIn:   / nlp-logix  Subscribe on YouTube:    / @nlplogix  New episodes of Unsupervised Learning every Wednesday! Subscribe and turn on notifications to stay up to date on the latest conversations in artificial intelligence.

  2. Oct 21 ·  Video

    Exploring the AI Adoption Funnel

    AI feels like it’s everywhere and nowhere at the same time. One person is still figuring out how to ask for a better email draft, while another is building agentic workflows that quietly reshape an entire operating model. That tension is why Andrej Karpathy’s AI adoption funnel is so useful, and we unpack it from a real-world angle: where people actually are on the spectrum and how that shapes what they believe about “AI hype.” We also get practical about what stops adoption cold. Buying licenses for ChatGPT, Claude, copilots, or internal assistants doesn’t automatically create capability, and we explain why “the license is not a capability” should be the mantra for enterprise AI rollout. Special guest Ashton Gideon, Senior UX/UI Designer at NLP Logix, shares the experience design lens: sit with people, watch the messy workflow details, listen for hesitation, and design the smallest changes that create an “aha” moment. That’s how AI moves from novelty to habit. Then we tackle the UX problem everyone feels but rarely names: today’s chat interfaces and constant mode decisions create friction, and the output is starting to overwhelm the humans who have to review it. We talk about proactive assistants, the challenge of managing hundreds of hours of generated work, and why the next layer is orchestration, not more tokens. If you care about AI adoption, AI user experience, prompt strategy, and building the right things (not just shipping fast), you’ll get a lot out of this one. Connect with the Unsupervised Learning Team! Matt Berseth, Co-Founder & Chief AI Officer at NLP LogixTed Willich, Co-Founder and CEO at NLP LogixAshton Gideon, Senior UX/UI Designer at NLP LogixSend our team a message here to discuss more topics from this week's episode or a topic you'd like them to dive into for a future episode!

  3. Oct 15 ·  Video

    The State of AI in 2026: What's Working and What's Not

    The 2026 State of AI Report is here, but what does it reveal about what's actually working in artificial intelligence? In Episode 10 of Unsupervised Learning, Matt Berseth and Katie Bakewell of NLP Logix break down some of the most interesting findings from the latest State of AI Report, exploring the evolution of AI models, the importance of real-world applications, and where AI still has room to improve. From OpenAI and Anthropic's competing approaches to innovation to the role of AI in scientific research and decision-making, this conversation goes beyond the headlines to explore what these developments mean for the future of AI. In this episode: OpenAI vs. Anthropic: Different approaches to AI innovationWhy better AI performance isn't always about better modelsThe role of prompts, tools, and supporting systems in AI performanceAI-generated research and the challenges of scientific discoveryWhy AI predictions don't always translate into real-world successRESOURCES MENTIONED 2026 State of AI Report by Nathan Benaich of Air Street Capital: https://www.stateof.ai/State-of-AI-Report-2026.pdf Nathan Benaich on LinkedIn: https://www.linkedin.com/in/nathanbenaich/ MEET THE HOSTS Matt Berseth: https://www.linkedin.com/in/mattberseth/ Katie Bakewell: https://www.linkedin.com/in/katiebakewell/ CONNECT WITH NLP LOGIX Learn more about NLP Logix: https://nlplogix.com/ Explore the Unsupervised Learning Podcast: https://nlplogix.com/insight/logixly-live/ Follow NLP Logix on LinkedIn: https://www.linkedin.com/company/nlp-logix Subscribe on YouTube: https://www.youtube.com/@NLPLogix New episodes of Unsupervised Learning every Wednesday! Subscribe and turn on notifications to stay up to date on the latest conversations in artificial intelligence. #UnsupervisedLearning #StateOfAI #ArtificialIntelligence

  4. Oct 8 ·  Video

    OpenAI's Math Breakthrough

    OpenAI’s latest math results make us wonder what “progress” really means when a model can generate proofs quickly but still struggles with the messy human work of deploying AI inside a business.  We talk through why mathematicians care about elegant proofs, why counterexamples are a perfect job for computers, and why enterprise AI still needs people who can integrate, validate, and adapt systems over time.  • OpenAI’s reported jump in math output and why the pace feels exponential  • What it means for us to be an OpenAI select partner and what the application emphasizes  • The Wired-reported tension around pacing research releases and mathematicians’ concerns  • Beauty in mathematics versus “a proof that works” and the Erdős number detour  • Why counterexamples and bound improvements play to machine strengths  • Human factors in business change management and why emotional intelligence still matters  • Data drift, monitoring, and how bad inputs and shifting conditions break systems  Hit us up on LinkedIn. Keep it up if there’s something you want us to cover specifically. Matt Berseth Ted Willich Katie Bakewell NLP Logix Unsupervised Learning Podcast  Follow us on LinkedIn and YouTube Things the team talked about in the podcast: OpenAI Sharing AI Profess in Mathematics: https://openai.com/index/sharing-ai-progress-in-mathematics/OpenAI is Frustrating Mathematicians Again: https://www.wired.com/story/openai-is-pissing-off-a-bunch-of-mathematicians-again/Git Hub OpenAI Math Repository: https://github.com/openai/math

  5. Oct 1 ·  Video

    Anthropic IPO Reality Check

    We break down the reported details behind Anthropic’s private S-1 and why an Anthropic IPO conversation quickly turns into a debate about switching costs, and the brutal economics of frontier AI. We also zoom out into what actually drives business value with generative AI, from narrow models and unbundled tools to the real-world quality-of-life gains that don’t make headlines. • Anthropic’s reported revenue ramp and what “$65B run rate” implies • Putting AI revenue scale next to Amazon, Walmart, and Apple • Frontier models versus small language models for narrow workflows • Unbundling AI into task-specific tools like decisioning and coding • Why Claude wins mindshare with developers and why that matters • Switching LLM providers compared with switching internet or streaming • AI as a utility, regulation, and the tradeoffs of standardization • Is AI more important than electricity or the internet over time • Practical optimism on productivity and quality of life, including healthcare documentation Things the team talked about in the podcast: Anthropic's IPO Prospectus  All-In Podcast We want to hear from you: Matt Berseth Ted Willich Katie Bakewell NLP Logix Unsupervised Learning Podcast  Follow us on LinkedIn and YouTube

  6. Sep 23 ·  Video

    JEV And The Rise Of Decision Models

    We dig into JEV, a decision-focused AI model that trades open-ended chat for constrained, software-ready outputs. We talk through why that shift matters for enterprise automation, cost, speed, calibration, and trust when AI starts choosing the next action in a workflow.  • Why JEV is positioned as a decision model rather than a chatbot  • The split between human assistants and automated business processes  • Unbundling frontier LLM capabilities into specialized models  • Why light models feel risky for enterprise classification accuracy  • Cost, quality, and speed trade-offs when AI runs at scale  • What confidence scores and probabilities should mean in production  • Calibration differences between general LLMs and task-trained models  • How decision models can bridge toward fine-tuning on proprietary data

  7. Sep 17 ·  Video

    Why Forward Deployed Engineers Turn AI Into ROI

    Everyone has an opinion about the next frontier model. That is why we’re zeroing in on the rise of the forward deployed engineer: the person who embeds with a customer, learns the operation up close, adapts the tech to fit real constraints, and stays long enough to make sure the solution actually gets used. We unpack where the term comes from (including Palantir’s view of forward deployed engineers and the “devs vs deltas” split), and why the role is suddenly showing up in hiring conversations like “data scientist” did years ago. We also wrestle with the weird split in the market: The debate on AGI and whether progress should slow down, while those same companies build services and deployment teams because the biggest bottleneck is not intelligence, it is execution. For most organizations, a good model deployed beats a perfect model on a shelf. Along the way, we get concrete about what last mile AI deployment really takes: workflow mapping, change management, domain learning, and tight iteration with the people doing the job. We share field stories that highlight why specs miss critical context, from financial trading floors to dangerous manufacturing environments, plus a cautionary tale about “snake vision” and why cool technology can still be the wrong solution. If you’re building enterprise AI, leading digital transformation, or trying to ship automation that sticks, this one will sharpen how you think about roles, loops, and outcomes.

    Why Forward Deployed Engineers Turn AI Into ROI

  8. Sep 10 ·  Video

    The AGI Claim Nobody Feels

    We unpack why recent AI headlines feel less like progress and more like a trust breakdown, from Open AI's GPT-6 Astra becoming less transparent to big claims about hitting AGI (artificial general intelligence). We also dig into the Millennium Prize math controversy and what it suggests about telemetry, governance, and whether anyone should hand over their workflow data to an AI provider. • GPT-6 Astra getting less transparent and why traceability matters • What AGI should mean versus passing tests • Why “AGI is here” should feel world-changing • The Millennium Prize problems and why they matter • Questions about prompts, drafts, and telemetry influencing outcomes • Trust, audits, and governance as the real battleground • Marketing demos that don’t match how people use AI

  9. Sep 4 ·  Video

    What The 2026 McKinsey AI Survey Reveals About Real-World ROI

    AI is getting adopted at a speed most enterprise software can only dream about, and the numbers finally show it. We dig into McKinsey’s 2026 State of AI survey (fielded May to June 2026 across nearly 100 countries) and react to the stat that stopped us cold: reported AI use in at least one business function rose from 55% in 2023 to 90% in 2026. That’s not a gradual rollout, that’s saturation, and it raises a bigger question than “are you using AI?” We talk through what isn’t surprising anymore and why it still matters for your AI strategy: agents are everywhere, coding agents keep showing up as the practical killer app, and token costs are a real concern. Yet even with cost pressure, nobody seems to be budgeting less for AI. That tension leads us into the choices teams are already weighing, including when scale pushes you toward open-weight models, local models, or hybrid deployments. Then we hit the real problem: about 80% of people say AI improves productivity, but only 6% say it significantly impacts profitability. We unpack why “time saved” often fails to turn into ROI, how vague goals like “deploy an agent” create activity without outcomes, and what changes when you set targets like reducing customer onboarding time while maintaining compliance and quality. We also get into workflow redesign, breaking silos, and a sobering stat on job cuts where people expect impact for others more than themselves. If you want practical, outcome-driven guidance on enterprise AI adoption, AI ROI, AI agents, token economics, and workflow redesign, hit play. Subscribe, share this with a teammate, and leave a review with the biggest AI outcome your org is chasing.

  10. Sep 3 ·  Video

    The Data Center Power Crunch

    We dig into the uncomfortable truth behind the AI boom: data centers are limited by electricity, not imagination. We pressure-test the numbers on gigawatts, frontier model training demand, and what it means if tokens become a constrained resource.  • Current US data center power explained in gigawatts and what a gigawatt means in household terms  • Buildout projections through 2027 and why doubling capacity may still not be enough  • Frontier model training vs inference demand and why the math starts to break  • Efficiency as the pressure valve, from better training methods to more performant smaller models  • Space-based data centers as an idea, plus reasons the near-term payoff may be elsewhere  • State and local resistance to new builds and what that means for US competitiveness  • Repurposing closed military bases with bring-your-own-power infrastructure as a siting strategy  • Open weight models, self-hosting, and “dark tokens” that are hard to measure  • Token scarcity scenarios, surge pricing, outages, and why non-gen solutions still matter

  11. Sep 3 ·  Video

    AI In The Witness Box

    An expert witness gets paid about $90,000, submits a 30-page report, and later admits under oath that roughly 85 to 90 percent of it was generated by ChatGPT. This is a real legal story and it forces an uncomfortable question: when AI helps write the “expert” opinion, what are juries and judges actually evaluating?  We walk through the timeline from a 2020 Houston facility explosion to a 2026 trial, then unpack how discovery surfaced a five-page document that looked AI-written and led to hundreds of pages of ChatGPT history. We debate the most controversial detail, a prompt that aims to defend the defendant and argue for zero percent fault, and why that can look like outcome-first reasoning instead of technical analysis. From there, we get practical about what expert witness work is supposed to be, how incentives skew neutrality, and why AI in law creates new attack surfaces for cross-examination.  We also dig into the messy gray areas: using ChatGPT to prepare for testimony, brainstorm questions, and stress-test arguments can be legitimate, but disclosure, authorship, and data handling matter. We talk about privilege risk when confidential information goes into an AI tool, what “clean room” expert work could look like, and whether future automated reasoning engines could ever serve as neutral courtroom experts given the constant problem of missing context. If you care about AI governance, legal ethics, expert testimony, or how courts adapt to generative AI, this conversation is for you.  Subscribe for more deep dives, share this with someone in law or tech, and leave a review if it helps. Where should the line be for AI-assisted expert reports?

  • 11 Episodes

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Raw takes on AI, Tech and Innovation. Join NLP Logix's Logixly Live team as they break down the biggest tech news, emerging trends, and industry hot topics while sharing our insights and perspectives all under 30 minutes.

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