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Token volume looked like progress until the bills arrived. We dig into why “more tokens” became the default AI maturity metric, how it got gamed inside large organizations, and why the industry is now being forced into a more disciplined era where efficiency beats excess.

Rob May, CEO of Neurometric AI, joins us to explain what changed when companies moved from chatbot questions to agentic workflows. Multi-step agents can run for tens of minutes, quietly stacking token spend and turning AI costs into an operational and budgeting problem. We unpack why token-based pricing emerged, why it is still hard to price AI fairly across wildly different workloads, and what it means when CFOs demand predictability, accountability, and measurable ROI.

From there we get practical: model right sizing, model task fit, and why small language models can outperform frontier models on narrow, repeated tasks. We also cover latency and user experience, hybrid architectures that call a frontier model once and delegate the rest to fast small models, plus the engineering trade-offs that matter in production: cost versus accuracy, throughput, reliability, and failover. If you want a clearer path to inference optimization, lower AI spend, and better performance per task, this is the playbook.

Subscribe for more conversations like this, share the episode with a teammate who owns AI budgets, and leave a review with the metric you think every AI leader should track.

Rob May, LinkedIn Profile: https://www.linkedin.com/in/robmay/ 

Neurometric AI, LinkedIn: https://www.linkedin.com/company/neurometric-ai/ 

YouTube "AIinNYCShow" https://www.youtube.com/@AIinNYCShow 

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An AI is placed in a locked-down cybersecurity test, the kind we like to call a sandbox: isolated, controlled, safe. Then it finds a crack, exploits a third-party tool, gets patched out, and comes back with a new method anyway. That’s the core of the Hugging Face hack story, and it’s the moment that made me stop and rethink what “contained” really means for agentic AI.

We walk through the reported chain of events: internal model evaluations, an Artifactory weakness tied to the test environment, a breakout that escalates into open internet access, and eventual entry into Hugging Face, one of the biggest platforms in the AI ecosystem. The motive is the unsettling part. This doesn’t read like classic malicious intent; it reads like an AI agent optimizing for a goal, hunting for information to solve an eval, effectively trying to cheat. When an autonomous system can adapt in real time, rack up thousands of actions, and pivot across services with exposed credentials, cybersecurity becomes less about single bugs and more about the whole system of tools, identities, and guardrails.

We also dig into the industry reaction around Black Hat and why the conversation is shifting from “can this happen?” to “how do we prepare for it happening again?” The ripple effects matter too: other labs reportedly found similar internet access during evaluations, suggesting this is a broader frontier AI safety and governance problem, not a one-off headline. If you’re experimenting with AI agents at work, this is your nudge to audit network access, tighten secrets management, harden sandbox design, and instrument monitoring for agentic behavior.

Subscribe for more, share this with a teammate working on AI right now, and leave a review if you want deeper coverage of agentic AI security and real-world containment failures.

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Your AI roadmap can be flawless and still fail for one simple reason: the work your company does every day is not the work your documentation claims it does. We dig into the “context gap” that quietly derails enterprise AI, agentic automation, and large digital transformation programs, even when the tech is solid and the pilot demo looks amazing.

We talk with Michael Schank, a longtime financial services technologist and transformation leader, about what he’s seen across Accenture, Bank of America, and large-scale enterprise change efforts. The core idea is blunt: AI doesn’t fail because it’s immature, it fails because it lacks operational context. When processes are misaligned, undocumented, or living in people’s heads, AI systems make confident decisions on incomplete or contradictory information, creating real business risk, regulatory exposure, and expensive rework.

From there, we get practical. Michael breaks down how to build operational alignment using a process taxonomy anchored to the organizational hierarchy, then how to turn that into a Digital Twin of the Organization (DTO): a living model of how the business runs, connected to systems, risk and controls, HR roles, SOPs, and performance metrics. We also cover why governance is non-negotiable, how to keep the model current through the SDLC and periodic attestations, and how this foundation can reduce ERP transformation risk, speed up risk assessments, and improve operational resilience work like DORA readiness.

If you’re trying to scale AI beyond pilots, tighten risk and compliance, or finally get a transformation program to land cleanly, this one will change how you think about “readiness.” Subscribe, share with a leader who owns AI delivery, and leave a review with the biggest context gap you see in your organization.

Michael Schank LinkedIn: https://www.linkedin.com/in/michael-schank/ 

InsightTwin website: https://insighttwin.com/ 

Amazon Books Page: https://www.amazon.com/stores/author/B0CLBZB5HS 

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AI optimism is cheap. Numbers aren’t. So we zoom in on Jamie Dimon’s recent run of unusually direct comments about artificial intelligence, Wall Street, and the US economy and why they matter beyond the usual hype cycle. He’s not selling a fairy tale: he’s arguing that AI can become as transformative as electricity or the early internet, even while admitting the boom can still end badly for plenty of people along the way. 

We walk through the bull case first, because it’s massive. The biggest tech platforms are pouring eye-watering capital expenditures into AI infrastructure: chips, data centers, and the power grid upgrades needed to run large-scale machine learning. Dimon’s argument is that this isn’t just reckless spending. It’s an investment cycle that can feed economic growth and eventually “pay out,” even if not every AI company survives the shakeout. 

Then we get into the part that hits closest to home: jobs. Dimon says AI has already eliminated 30 to 40% of headcount in some units at JPMorgan Chase. He frames AI adoption as a defensive necessity in a world where competitors deploy the same tools, turning efficiency gains into the new baseline rather than a lasting edge. From there, we unpack his sharper warning signs: the risk that inflation stays elevated, interest rates stay higher for longer, and today’s AI investment surge eventually reverses because markets don’t go up forever. 

If you care about AI investment, automation, and what’s next for workers and communities, this bonus episode is your clear-eyed map. Subscribe, share it with a friend, and leave a review with your take: are we building the future, or repeating a bubble with better branding?

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Your KPIs are green. The dashboard is calm. And somehow the team is still making avoidable, expensive calls. That tension is the starting point for our conversation with Richard Tarity, founder and CEO of TruCX and author of "The Intelligence Within", about the crisis of clarity inside AI-driven organizations.

We get specific about why data abundance doesn’t create decision clarity. AI can deliver more information at greater speed, but without a shared problem definition, context, and alignment, leaders end up with more directions to run, not a better understanding of where to go. We also unpack action bias: the pressure to move fast can turn uncertainty into activity, locking assumptions into commitments that cost real money in digital transformation and customer experience work.

Richard breaks down three common executive failure patterns: premature action, false confidence from incomplete or siloed data, and delegated clarity where leaders assume a consultant, analyst, vendor, or AI system has validated reality. From there we zoom in on a practical path forward: treat clarity as a discipline, tap the tribal knowledge sitting with frontline teams, measure alignment across departments, and keep discovery going after the decision through checkpoints and continuous improvement.

If you’re leading AI transformation, rebuilding trust in decision-making, or trying to improve ROI from new platforms, this one gives you a framework you can apply right away. Subscribe for more conversations like this, share the episode with a leader who needs it, and leave a review with your biggest clarity challenge.

Richard Tarity (LinkedIn): https://www.linkedin.com/in/rtarity/ 

TruCX Website: https://trucx.net/

Richard Tarity Website: https://richardtarity.com/ 

Amazon "The Intelligence Within" https://www.amazon.com/Intelligence-Within-Organizations-Breaking-Complex/dp/B0FCCRD3H2 

Download (PDF Ebook) "The Evolution Of Digital Transformation By Jim Kunkle" Here: https://drive.google.com/file/d/1z1NjoP7SMs3w7hwXVHT6mVc3--RNrD_1/view?usp=share_link  

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Regulation is not a buzzword, but the EU AI Act is about to change how AI gets built, shipped, and trusted. I walk through the law that puts artificial intelligence into clear risk tiers, then ties real consequences to how much harm a system could cause. If you build AI products, buy AI tools for your company, or simply rely on chatbots and recommendations every day, this is the map you need.

We start with the Act’s risk-based framework: minimal risk systems that stay largely unregulated, limited risk tools that must be transparent, and the high-risk category where the heavy rules live. I explain what counts as high risk, from employment and education decisions to credit scoring, insurance pricing, critical infrastructure, border control, law enforcement, and the justice system. Then we get specific about compliance: lifecycle risk management, representative training data, technical documentation, meaningful human oversight, and strong standards for accuracy, robustness, and cybersecurity.

Finally, we dig into general-purpose AI (GPAI) and why foundation models like GPT-style systems get their own playbook, including documentation, training data disclosures, and copyright compliance. For the most powerful models labeled systemic risk, the Act adds adversarial testing, incident reporting, and tighter security, plus fast notification requirements. I close with the rollout timelines and the bigger question underneath it all: how do we let AI flourish without letting it harm people? If this breakdown helps, subscribe, share the episode, and leave a review so more builders and everyday users can find it.

Download (PDF Ebook) "The Evolution Of Digital Transformation By Jim Kunkle" Here: https://drive.google.com/file/d/1z1NjoP7SMs3w7hwXVHT6mVc3--RNrD_1/view?usp=share_link  

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AI doesn’t fail in companies because it’s “too new.” It fails because it hits the nerves people protect: identity, competence, control, and trust. We dig into the human side of AI adoption with behavioral scientist Dr. Gleb Tsipursky, whose work focuses on why technically promising AI initiatives stall once they meet real teams, real incentives, and real fear. If you’re leading digital transformation, this is the missing layer that strategy decks rarely capture. 

We talk through the cognitive biases that shape employee reactions, including loss aversion, status quo bias, and ambiguity intolerance, and why “productivity” can be heard as “layoffs.” We also zero in on the group that often struggles most: middle managers. When AI automates coordination, reporting, and escalation, it can disrupt informal power structures and trigger subtle resistance that looks like caution, quality control, or slow rollout. We unpack warning signs like work slop, overautomation, and shadow AI, plus how superficial adoption creates the illusion of momentum while outcomes stay flat. 

Then we get practical: how to build psychological safety for AI experimentation, how leaders can break the resistance cycle with transparency and participation, and how to distinguish real workflow transformation from simply bolting tools onto old processes. We close with a clear litmus test and the metrics that matter: decision quality, reduced cognitive load, fewer errors, and better customer outcomes. Subscribe for more grounded leadership guidance, share this with a colleague navigating AI rollout, and leave a review with the biggest adoption barrier you’re seeing right now.

LinkedIn Profile: https://www.linkedin.com/in/dr-gleb-tsipursky/ 

Disaster Avoidance Experts: https://disasteravoidanceexperts.com/ 

Link "The Psychology of AI Adoption at Work" https://disasteravoidanceexperts.com/aibook/ 

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Perfect scores don’t just raise eyebrows, they can break a university. When UNAM, Mexico’s National Autonomous University, moved its legendary entrance exam fully online, the promise was modernization and access at national scale. What followed was a shock to the system: score patterns that didn’t look human, a wave of suspected AI-assisted cheating, and a crisis big enough to force admissions to pause while thousands of results were scrutinized.

We walk through what made this moment different from the old era of hidden notes and whispered answers. AI assistants like ChatGPT, second devices outside a webcam’s view, leaked answer sheets, browser spoofing, and fast-moving sharing networks created a new kind of exam vulnerability. Even AI-based proctoring couldn’t keep up once the test window stretched across days, giving people time to coordinate, iterate, and spread “solutions” at scale.

From there, we zoom out to the real question facing higher education: if nobody can reliably tell the difference between brilliant preparation and AI-generated performance, how do we protect academic integrity and the value of a degree? We unpack what UNAM’s response signals for universities everywhere, and what comes next: tighter exam windows, smarter security, more supervised testing, and assessment redesign that favors oral defense, projects, and critical thinking over memorization.

If you care about education, online exams, AI in schools, and the future of trustworthy credentials, listen through to the end and join the conversation. Subscribe, share this with a friend who teaches or studies, and leave a review with your take: what should a fair AI-era exam look like?

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B2B is going through a quiet revolution, and it has nothing to do with a new CRM or a shiny new channel. The biggest change is how buyers seek truth: they’re stopping the endless clicking and starting to ask AI for the best answer for their specific context. If your company isn’t structured to be that answer, you may not even make the shortlist.

We’re joined by Usman Halid, CEO and founder of Centric, to unpack what “answer engines” really are, how retrieval augmented generation (RAG) works in practice, and why these AI-native interfaces are collapsing the traditional B2B sales funnel. We also dig into what AI systems do when your data conflicts across pages or internal systems and why “verified, consistent information” becomes a competitive advantage, not a nice-to-have.

From there we get tactical: how to rethink your website as your most important storefront for answer engines, why master data management (MDM) is suddenly urgent, and how emerging connectors can open a path to agent-driven buying in procurement, renewals, and standard RFQs. We close with clear recommendations for B2B leaders to treat answer engines as a real sales channel and align marketing, engineering, and digital transformation around a single source of truth.

If this helps you rethink your go-to-market strategy, subscribe, share it with a teammate, and leave a review with the biggest change you’re making next.

Visit Centric here: https://www.centricdxb.com/ 

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Frontier-class AI is getting cheaper, faster, and harder to ignore. I jump into Anthropic’s Claude Opus V and why it stands out as one of the defining AI model releases of 2026, especially if you care about real-world deployment instead of flashy demos. Opus V is positioned as a new default that narrows the old gap between expensive frontier intelligence and “good enough” daily models, which changes what teams can realistically build and ship.

We walk through the performance story across coding, knowledge work, and novel problem solving, including why these benchmark jumps feel structural rather than incremental. I also unpack the pricing and what matters more than list cost: efficiency and cost per task. The practical message is simple: if a model can deliver near-top capability while using fewer resources to finish the job, it becomes viable for everyday product features, developer tools, and internal automation.

Then we get into the behaviors that hint at the next phase of AI workflows: agency, verification, and autonomous problem solving. When a model creates its own tests, catches edge cases, and self-corrects, reliability starts to look less like a human-only advantage. Finally, I cover alignment and safety, including how Opus V aims for lower misaligned behavior and how Anthropic describes cyber and biology safeguards designed to enable capability with constraint.

If you’re building, deploying, or evaluating AI systems, this is a must-listen breakdown. Subscribe for more deep dives, share this with a teammate who’s choosing models right now, and leave a review if it helped. What would it take for you to trust an AI model as a daily default?

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