Your AI bill can climb fast even when you feel like you’re asking “one quick question.” The real driver is token usage, and once you understand it, you can finally control it. We walk through what AI tokens are in plain English, why they’re not always full words, and a practical rule of thumb you can use when estimating cost (think 1,000 tokens is about 750 words). 

From there, we explain the part most teams miss: you’re paying for both sides of the conversation. Input tokens include your prompt, pasted docs, emails, and transcripts. Output tokens include everything the model generates back, from summaries and reports to analysis and code. That’s why pasting a long document and requesting a long report becomes expensive so quickly. It’s usage based pricing, more like cloud compute than a one time fee. 

Then we share three practical, immediately usable tactics for managing AI token consumption and reducing LLM costs: stop sending entire documents when you only need one section, use short reusable prompt templates instead of rewriting background context, and request focused outputs with strict lengths and formats (bullet limits, word caps, or tables with specific columns). These prompt engineering habits help you cut waste, improve clarity, and keep AI spend predictable as you scale usage across your team. 

If this helped you think differently about AI pricing and token budgeting, subscribe, share this with a teammate who owns the AI tools bill, and leave a review with your best token saving tip.

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AI can feel like a cold calculator until you realize the “human” part was never in the machine. It was in the way you ask, the way you react, and the space you give yourself to think. Seth Wylie - Owner of The Wild Edge - and I start in a surprising place: summer heat, showers, naps, and why calling rest “lazy” is often just your inner critic replaying an old script. That thread matters because the same pressure to always be on shows up in how we use technology at work.

Then we move into a practical, human-centered approach to AI in the workplace. Instead of treating ChatGPT or Claude like a magical intern that spits out answers, we talk about using it as a thought partner: Socratic questioning, creative reframes, and prompts that help you choose the kind of thinking you need. We also get real about “agency” and the modern temptation to abdicate it, like leaving a job for freedom and immediately asking AI what business to start. The goal is not blind trust or total rejection, it is using AI as a lever that expands your options without stealing your judgment.

We close with what this looks like for leaders and teams: executives often use AI to challenge their own strategy, but roll it out to everyone else as a workflow and ROI problem. Adoption changes when people have room to explore meaning, motivation, and how they want their work to feel. If you’re building a second brain, reinventing your career, or trying to lead AI adoption without losing the plot, this conversation gives you language and frameworks you can actually use. Subscribe, share this with a teammate, and leave a review, then tell us: what would you ask AI if you started with purpose first?

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AI used to feel like a flat-fee superpower. Now it is starting to behave like electricity: metered, variable, and tied to exactly how you use it. We dig into what that shift means for the people actually trying to run AI in the real world, from small businesses building their first workflows to sales, ops, and dev teams pushing tools like Microsoft Copilot, ChatGPT, Claude, and agents into daily work.

Josh unpacks the move from per-seat pricing to usage-based billing, where cost is driven by tokens, context retrieval, tool calls, and how long a model “thinks” behind the scenes. That change forces a new set of business questions: which workflows burn the most tokens, when premium reasoning is worth it, and how to stop paying for AI habits that feel productive but do not produce ROI. We also talk about the uncomfortable incentives that come with token-based revenue models and why customers need clearer visibility into what they are being billed for.

Then we zoom out to the market dynamics: if the best models become scarce at scale, enterprises with huge contracts may secure better pricing and reserved capacity while smaller teams get caps or slower access. Finally, we make it practical with an AI FinOps mindset: map workflows, set internal model tiers, put guardrails around agents, train teams to prompt efficiently, and tie spend to measurable outcomes.

If you are trying to budget for AI, prove business value, or keep your AI bill from quietly exploding, this one will give you a clear framework. Subscribe, share this with a teammate who owns the budget, and leave a review with your biggest question about AI costs.

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Richard Stiennon is back with the Word on the Street! AI isn’t just a chatbot tab anymore. It’s quietly becoming the interface inside the software we already rely on, and that shift changes how fast we can build, operate, and secure modern systems.

We talk about what it feels like when AI is embedded directly into tools like AWS Route 53, guiding you through confusing workflows step by step instead of forcing you to bounce between docs and prompts. From there we zoom out to the “SaaS is dead” noise and what’s actually happening: products racing toward AI-native experiences that help you onboard, connect data, and get value fast. We share a concrete CRM example, why traditional CRMs feel painful, and how newer tools make setup and integrations like website form-to-CRM routing take minutes instead of hours.

Then we get into the limbo stage a lot of teams are in with advanced AI engines like Mythos and the buzz around Fable 5. We break down a real cybersecurity use case: mapping an entire security stack to frameworks like MITRE ATT&CK, MITRE D3FEND, and the NIST Cybersecurity Framework, producing structured outputs that used to take consultants months. That leads to the hard question every software company asks next: how do you build fast with AI without touching customer data and putting HIPAA compliance or SOC 2 controls at risk? We walk through practical guardrails, including using synthetic data and keeping development environments separate.

Finally, we sort out where “AI security vendors” actually fit, from governance and model protection to AI-powered email security, DLP, and SOC automation, and why this category may soon just be called security again. If you’re deciding how to adopt AI safely while still moving fast, this is for you. Subscribe, share this with a teammate, and leave a review with the biggest AI adoption blocker you’re facing.

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Fraud doesn’t hide because it’s clever. It hides because our models keep looking for “normal” instead of interrogating “wrong.” That’s where this conversation with Dr. Chase Cunningham (known to many as “Dr. Zero Trust”) gets practical fast. We talk about his recent patent work using deterministic math to surface fraud inside huge systems, and why the usual data science playbook of finding enough “good” data to train on can be a trap when the real signal is buried in what shouldn’t be happening at all.

From there, we zoom out to the AI toolchain most teams are using today. LLMs like Claude and ChatGPT can be powerful, but they’re often optimized to produce the next best-looking answer, not the most adversarial truth. We dig into how to “turn the system on its head” with better prompting, correlation thinking, and agentic AI swarms that behave like a room full of specialists attacking one problem from different angles. If you’re wondering what the next step is beyond using AI for emails, this is the blueprint: orchestration, role clarity, and tight feedback loops.

Then we hit the elephant in the room: AI security. Shadow AI, sensitive data leakage, and agents that can accidentally expose HR documents or internal secrets all come back to fundamentals like identity and access management, least privilege, micro-segmentation, and non-human identities. Zero trust principles still apply, but the speed and scale of agentic systems make every gap matter more. We close with what keeps Chase up at night, including deepfakes and the erosion of shared reality, plus where to follow for more.

Subscribe, share this with a teammate who’s rolling out AI, and leave a review if it helps. What’s the one AI use case you want, but you’re not deploying yet because security feels messy?

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AI feels like a shortcut until it becomes a liability. We sit down with Nikki Robinson, STSM of AI and Platform at IBM and co author of Human Factors and Cybersecurity, plus security and risk executive Jennifer Baca, to get brutally practical about responsible AI use, data privacy, and what “secure” even means when the tooling changes weekly.

We start with the basics that too many teams skip: due diligence, responsible disclosure, and why you should learn prompt engineering by experimenting in safe sandboxes. Then we dig into the real world problem of families and coworkers pasting sensitive data into chatbots. Nikki explains why LLMs are company owned systems, why terms of service matter, and why you cannot treat AI outputs as truth. Hallucinations, made up citations, and overconfidence are not edge cases, they are daily hazards that demand critical thinking and verification.

From there, the conversation turns to work and careers. We talk about LLMs as interactive partners that can accelerate cloud learning, generate Terraform, and turn developers into “10x” builders when used thoughtfully. The employment takeaway is clear: you may not be replaced by AI, but you can be replaced by someone who knows how to use AI to improve workflows and communicate outcomes to leadership.

Finally, we connect human factors, psychology, and cybersecurity culture. Instead of blaming people, we explore secure by default design, psychological safety, broken metrics in SOC environments, and how emerging frameworks like the NIST AI Risk Management Framework point toward future AI compliance. If you lead a small or mid sized business, we close with concrete steps using AI features already inside tools like Copilot and Slack AI without creating dangerous tool sprawl.

Subscribe, share this with a teammate, and leave a review if it helps. What is one AI rule you want your organization to adopt this month?

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Ten months of job hunting can mess with your identity, even when you know you’re good at what you do. We sit down with Jennifer “Jen” T. Baca, a cybersecurity risk leader and single mom, to talk honestly about what it feels like to be qualified, visible, and still stuck in the silence of today’s hiring market. We get into the mental weight of layoffs, the endless loop of applications, and the weird reality that finding a job now demands its own set of skills: LinkedIn strategy, resume tactics, interviewing stamina, and relentless networking.  

Jen also shares the silver linings she didn’t expect: conference exposure, community leadership, and the kind of communication and empathy that can turn a solid cybersecurity manager into a truly effective security leader. We talk imposter syndrome, culture fit, and why “overqualified” is often just a polite way of saying “not our person.” If you’re in governance, risk, and compliance (GRC) or cybersecurity leadership, you’ll recognize the tension between wanting meaningful work and needing stability now.  

Then we shift into the work itself: real-world healthcare cybersecurity risks, the business cost of phishing and account compromise, and how to translate security risk into board-level language tied to revenue, trust, and HIPAA. We also dig into women in cybersecurity, minority experiences in male-dominated rooms, and why soft skills and emotional intelligence are not optional in modern security teams.  

If you’ve ever been ghosted after a great interview, this one will hit. Subscribe to Cybernomics, share this with someone in the job hunt, and leave a review if you want more conversations that tell the truth and push the industry to do better.

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Most scams don’t start with “bad technology” they start with a perfectly normal human impulse to trust. Josh Bruyning sits down with security speaker Robert Siciliano to get honest about why cybersecurity still doesn’t stick for everyday people, even when the stakes are obvious to CISOs and security teams. We dig into the uncomfortable truth: denial feels good, and compliance training often turns security into a chore instead of a skill that protects real lives.

Robert explains his idea of the “human blind spot” the biological default to trust what seems familiar, even when the message arrives by email, text, phone call, or a convincing deepfake. From there, we get practical about the basics that still move the needle: unique passwords, password managers, and two-factor authentication for critical accounts, especially email. If attackers “own the email,” they can reset passwords, take over financial apps, and cause damage that looks a lot like you doing it.

We also reframe security as something healthy, not paranoid. Think seatbelts, home locks, and proactive protection rather than fear. Robert lays out the shift from security awareness (knowing) to security appreciation (caring), plus the “strategic human firewall” mindset that turns people into an active layer of detection at work and at the kitchen table at home.

Then we look ahead: AI fraud, voice cloning, deepfakes, and pig butchering scams are scaling fast, and the old red flags are disappearing. If you want to follow Robert, find him across social media and at protectnowlc.com. Subscribe to Cybernomics, share this with someone who still says “why would they target me,” and leave a review so more people learn how to verify before they trust.

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We dig into why security awareness fails even when the risks are obvious, and why the real challenge is human behavior rather than tools. Robert explains the “human blind spot” and how AI deepfakes and voice cloning make trust harder to manage at every level. 

• CISOs and tech executives as unsung heroes protecting modern life 
• why attackers target people instead of systems 
• the human blind spot as biology overriding digital suspicion 
• how social engineering uses trust and mental shortcuts 
• AI deepfakes and voice cloning raising the stakes for fraud 
• denial and convenience driving weak password habits 
• low adoption of unique passcodes and two-factor authentication 
• basic digital literacy questions like judging safe links 

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We talk with Robert Cicliano about why cybersecurity matters for people who do not work in security and why scams keep working even on smart, capable adults. We dig into how fraudsters build trust, why denial is so common, and why compliance-style training fails to create real risk awareness. 

• why everyday people are targets, not exceptions 
• fraud as a business model built on trust tricks 
• common attack channels like phone, text, and email 
• denial and “it can’t happen to me” thinking 
• why compliance training fails under real pressure 
• what a better approach looks like for spotting risk 

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