Podcast
AI at Work
Hosted by Elijah Szasz, Kevin Williams · EN
AI at Work is hosted by Elijah Szasz (CEO of SPARK6) and Kevin Williams (CEO of Ascend Labs). Each week delivers actionable insights on how artificial intelligence is reshaping jobs today, and how you can use it to advance your career. We unpack real-world tools, automation workflows, and emerging roles so you can stop chasing hype and start using AI to get things done. Whether you’re an individual contributor, team lead, or business owner, we show you what to build, what to ask for, and what to look for in a workplace moving at machine-speed.
24episodes
Episodes
Newest firstAll episodes
When Your Vibe Coder Doesn't Know What They Don't Know
6d ago00:56:20Tap to summarizeAnthropic and OpenAI aren't embedding engineers at major firms because they want to do IT consulting they need to drive massive token consumption to pay for a trillion dollars in AI infrastructure. But while McKinsey guides the Fortune 500 through this shift, mid-market and SMB companies are being left completely behind.In this episode, Kevin Williams and Matt Graham break down the "forward-deployed engineer" land grab, what it means for mid-market businesses, and the hidden cost of letting internal teams build fast without governance. They also dive into the technical realities of building agentic systems with precision, shifting from probabilistic outputs to deterministic reliability, and the true switching costs between major AI platforms.Key Topics Covered:The Token Economics Play: Why tech giants are embedding engineers it's a race for volume, not a consulting play.The Mid-Market Gap: The structural divide leaving mid-sized companies without access to embedded AI expertise.Vibe Coding vs. Governance: What happens when internal teams build fast without rails, and the hidden costs of cleaning up undocumented pipelines.Deterministic AI: How to configure agentic systems to deliver reliable outputs for sensitive financial and operational workflows.Platform Switching Costs: The real impact of committing to team-level platforms (GPT Teams vs. Anthropic Teams).2026 Strategy: Why this is the official year organizations must stop piloting and start activating.Resources & Links:https://www.linkedin.com/posts/pneppalli_agentic-ai-adoption-is-on-fire-at-uber-and-share-7480367288781746176-Ji7t/https://resources.ascendlabs.ai/openai-vs-anthropic-for-teamsCheck out Kevin’s stuff:Website: Ascend Labs LinkedIn: Follow Kevin on LinkedIn – Free Tool: Take the AI Readiness Assessment Deep Dive: Read the GPT Teams vs. Anthropic Teams comparisonBook a Call: Talk to Kevin on TidyCalCheck out Matt’s stuff:LinkedIn: Follow Matt on LinkedIn Company: Rapid Dev on LinkedIn –Direct Email: Reach out to Matt at mgram@rapiddevelopers.com
Transcribe →
The Head of AI Role Everyone Wants Doesn't Report to IT
2w ago00:58:16Tap to summarizeMost leaders assume they need to hire technical talent before their organization can move seriously on AI. Wyatt Barnett runs AI at an 80-person trade association with no engineering staff and no CTO and he's built more working AI capacity than most companies twice his size. The model is simpler and more replicable than you'd expect.Wyatt Barnett is Head of AI (Technology Enablement) at the NCTA, the Internet and Television Association in Washington, D.C. In this conversation, Kevin and Wyatt get into the Tiger Team model Wyatt built from scratch ten cross-functional people embedded across departments, authorized to catch problems early, run experiments, and start solving things before they become formal requests.They also cover tool selection for mid-market orgs, token cost management, why the AI engineering community is shifting focus from frontier models to the surrounding tooling ecosystem, and what Wyatt would tell a CEO who wants to build this kind of capacity with a blank slate.Key topics covered:✅ The Tiger Team model: how to build AI capacity without hiring engineers✅ Why forward-deployed problem-catchers outperform centralized IT approaches✅ Tool stack for mid-market orgs: Notion, Airtable, Claude, and what's actually working✅ Token cost management and the real budgetary risks of AI at scale✅ What Wyatt observed at the AI Engineer World's Fair and what it signals about where the industry is heading✅ How to structure AI experimentation so you're watering the right flowersCheck out Kevin’s stuff:→ See where your organization stands on AI readiness: launchpad.ascendlabs.ai→ Talk with Kevin about building this in your org: tidycal.com/kevinwilliams→ Ascend Labs: https://ascendlabs.ai/→ Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevinguywilliams/ Check out Wyatt’s Stuff:→ Follow Wyatt on LinkedIn: https://www.linkedin.com/in/wyattbarnett/→ The Zeitgeist Distilledhttps://news.zeitgeistdistilled.com/
Transcribe →
Why Claude Code Has Nothing to Do With Code
4w ago00:46:44Tap to summarizeMost people heard "Claude Code" and assumed it was a developer tool. Kevin Williams and Matt Graham from Rapid Dev are here to correct that assumption and explain why the goal-oriented loop logic at the core of Claude Code might be the most underused productivity feature available to business leaders right now.This conversation started with a weekend of building and a realization: the same recursive, self-correcting loop that hardens software can harden a marketing plan, a strategic brief, or a financial analysis. The name was always the problem, not the tool.Kevin and Matt also get into the hacker house model how Rapid Dev gets more done in two focused weeks than two unfocused months and what happened when Kevin spent two days at a Los Angeles dining room table taking a high-end fashion brand from zero AI literacy to a functioning chief-of-staff agent.The episode covers the awkward middle most organizations are stuck in: past individual AI use, not yet at production-grade systems, and not quite sure how to close the gap. And with recent regulatory pressure on AI providers and real questions about vendor stability, they get practical about fallback architectures and what it actually means to build on a foundation that might shift.✅ Why Claude Code is not a coding tool and what it actually does✅ How /goal changes the quality and depth of any AI output✅ The hacker house model: structure, composition, and expected outcomes✅ The journey from individual AI use to organizational AI systems✅ Vendor risk, provider instability, and the case for multi-model fallback✅ Why organizational friction outlasts every technical solutionCheck out Kevin’s stuff:→ Ascend Labs: https://ascendlabs.ai/→ Follow Kevin on LinkedIn: / kevinguywilliams → Get practical AI guidance: https://assessment.ascendlabs.ai/→ Book a consultation: http://tidycal.com/kevinwilliamsCheck out Matt’s stuff:→ mailto:mgram@rapiddevelopers.com→ / rapid-dev
Transcribe →
Is Your Brain Worth Building or Should You Just Buy Glean?
Jun 1600:51:34Tap to summarizeThe economics of building versus buying software just fundamentally changed. In this conversation with Matt Graham, CEO of Rapid Developers, we explore how the cost of custom development has dropped 90% and what that means for business strategy.Matt brings a unique perspective, having lived through the no-code revolution and now seeing the AI-native development wave. His company works with CEOs of $20-500M revenue businesses, and the patterns he's seeing are remarkable.From his 10-year-old son building a profitable e-commerce site over a weekend to enterprise clients rebuilding their ERPs from scratch, the landscape is shifting faster than most people realize.✅ Key topics covered:• The evolution from no-code to AI-native development• Framework for build vs. buy decisions in the AI era• Why SaaS platforms walling off data changes the equation• Real cost breakdowns and ROI considerations• Job displacement concerns and the changing labor market• Personal tech stacks and tools that actually workApproximate timestamps:00:00 — Intro and Matt's background05:25 — No-code evolution and the AI transition12:30 — Workflow problems vs. AI problems18:45 — Build vs. buy framework28:15 — SaaS disruption and data access issues36:00 — Pricing model evolution and output-based work45:30 — Job displacement and labor market impacts50:45 — The 10-year-old entrepreneur story53:20 — Personal tech stacks and toolsCheck out Kevin’s stuff:→ Ascend Labs: https://ascendlabs.ai/→ Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevinguywilliams/→ Get practical AI guidance: https://assessment.ascendlabs.ai/→ Book a consultation: tidycal.com/kevinwilliamsCheck out Matt’s stuff:→ mgram@rapiddevelopers.com→ https://www.linkedin.com/company/rapid-dev/
Transcribe →
The One Percent Problem That Creates 68% Productivity Gains
Jun 900:53:08Tap to summarize95% of AI pilots fail to reach production, but it's not because of the technology. Brett Schklar, CEO of AI First Leadership and author of "AI Without the BS," has tracked AI adoption across 1,800 companies over two years and discovered the real bottleneck: the "frozen middle."In this conversation, Brett reveals why senior managers and directors represent both the highest resistance and highest potential impact for AI adoption. We explore his data showing marketing teams achieving 2.3x productivity gains while others fall behind, and his practical approach to building AI capacity through 1% weekly improvements that compound to 68% productivity gains.Key topics covered:✅ Why the "frozen middle" blocks AI adoption✅ How intelligence becomes a commodity but wisdom remains human✅ The K-shaped productivity curve emerging in organizations ✅ Department-by-department ROI data from 1,800 companies✅ Why focusing on 1% problems beats transformation strategies✅ Building internal AI champions and centers of excellence✅ The identity crisis driving AI resistance✅ Practical tactics for mid-market AI implementationTimestamps:00:00 — Intro and Brett's background05:30 — From AI resistance to AI mandates: two-year evolution12:00 — The model selection dilemma and governance concerns18:45 — Scaling AI education beyond workshops25:30 — The "frozen middle" problem explained32:15 — ROI data across departments and functions38:00 — Marketing productivity vs. content quality concerns45:20 — Bringing marketing and sales together with AI52:00 — Weekend project recommendations57:00 — Brett's essential tools and where to connectCheck out Kevin’s stuff:Ascend Labs: https://ascendlabs.ai/Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevinguywilliams/Get practical AI guidance: launchpad.ascendlabs.aiSchedule a conversation with Kevin: tidycal.com/kevinwilliamsConnect with Brett: AI First Leadership on LinkedIn:https://linkedin.com/in/bschklarWebsite: brettschklar.comBook: https://bit.ly/AIWithoutBS
Transcribe →
When Models Lie and You're Still Paying for the Tokens
Jun 300:55:50Tap to summarizeWhen AI models hallucinate confidently while burning through your token budget, you need more than better prompts you need better processes.In this episode, Kevin and Eli explore the reality of working with AI that lies while you pay, the evolution from saved prompts to voice-triggered workflows, and why validation loops matter more than model updates. From enterprise governance requirements to tech stack consolidation, this conversation cuts through the hype to reveal what actually works when deploying AI in real business contexts.Key topics covered:✅ Why model reliability requires systematic validation, not perfect AI✅ The shift from skills to snippets and voice-triggered workflows ✅ Enterprise governance requirements when litigation risk is involved✅ Tech stack consolidation as AI tools mature beyond experimentation✅ Cost economics driving procurement strategy changes✅ Building quality control loops that catch hallucinations before implementationTIMESTAMPS:00:00 — Intro and Sora commercial viability discussion05:30 — Video generation landscape: Veo, SeeDance, Higgs Field15:00 — Model quality regression and hallucination experiences 25:00 — Skills vs snippets: workflow evolution deep dive35:00 — Enterprise governance and validation requirements45:00 — Tech stack consolidation and platform comparisons53:00 — Eli's transition and mental health focusShow Notes & Links: https://www.spark6.com/podcast Submit listener questions: elijah@spark6.comkevin@ascendlabs.ai Check out Kevin’s stuff:Ascend Labs: https://ascendlabs.ai/Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevinguywilliams/ Check out Eli’s Stuff:SPARK6 Agency: https://www.spark6.com/Sign up for FREE AI Framework Friday Newsletter: https://www.spark6.com/newsletterFollow Elijah on LinkedIn:https://www.linkedin.com/in/elijahszasz/
Transcribe →
Why Your AI Productivity Wins Are Making You Miserable
May 2800:56:50Tap to summarizeKevin and Eli dive deep into the hidden costs of AI productivity gains, exploring how cognitive overload and social isolation are creating a new form of workplace burnout that organizations aren't prepared to address.In this candid conversation, they share their personal experiences with AI work density accomplishing 80-100x more work daily while feeling more exhausted than ever. The discussion reveals why the promise of AI efficiency is backfiring without proper human-centered implementation.Key topics covered:✅ AI work density and the cognitive load crisis✅ Why context switching between AI tasks creates decision fatigue✅ The importance of pacing in AI-forward organizations✅ How to identify what should (and shouldn't) be automated✅ The role of human connection in an AI-augmented workplace✅ Platform comparison: OpenAI vs Claude vs Gemini for team adoption✅ The hidden costs of AI agent proliferation✅ Why most organizations miss the pedestrian productivity winsTimestamps:00:00 — Platform switching and AI adoption decisions08:00 — Microsoft integration advantages and limitations15:00 — Team adoption and accessibility considerations25:00 — AI work density and cognitive load crisis35:00 — Human connection vs AI interaction42:00 — What to automate vs what to preserve48:00 — Organizational capture of AI innovations55:00 — Enterprise data integration challengesExplore practical AI implementation: https://assessment.ascendlabs.ai/Book a conversation with Kevin: tidycal.com/kevinwilliamsShow Notes & Links: https://www.spark6.com/podcast Submit listener questions: elijah@spark6.comkevin@ascendlabs.ai Check out Kevin’s stuff:Ascend Labs: https://ascendlabs.ai/Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevinguywilliams/ Check out Eli’s Stuff:SPARK6 Agency: https://www.spark6.com/Sign up for FREE AI Framework Friday Newsletter: https://www.spark6.com/newsletterFollow Elijah on LinkedIn:https://www.linkedin.com/in/elijahszasz/
Transcribe →
Is Your Organization Ready or Just Excited?
May 1900:43:05Tap to summarizeWhat does AI implementation actually look like when you move beyond the hype? Aaron Wilt, CEO of 40-person Pulse V Holdings, shares the unfiltered reality of deploying AI in a mid-market business where technical sophistication meets organizational complexity.This conversation reveals why even leaders who deeply understand AI struggle with organizational deployment, and what it really takes to bridge the gap between AI demos and business transformation. Aaron breaks down the "trust calibration" problem, the three-way intersection most companies lack, and why winners use AI to win harder while others get left behind.Key topics covered:✅ The implementation bottleneck - why technical knowledge isn't enough✅ Trust calibration - when 85% AI accuracy is acceptable vs. dangerous ✅ Building AI teams - pairing skeptics with dreamers for maximum impact✅ Control vs. scale - centralized innovation vs. distributed adoption✅ The SaaSpocalypse - how AI enables internal capability building✅ Risk management - deterministic vs. probabilistic AI applications✅ Team dynamics - getting non-technical staff to adopt AI tools✅ Future-proofing - what to tell kids about AI and careersGet practical AI guidance: https://assessment.ascendlabs.ai/Book a conversation with Kevin: tidycal.com/kevinwilliamsTIMESTAMPS:00:00 — Introduction and Aaron's background03:00 — AI evolution: Chat bot to context to orchestration eras08:00 — The probabilistic nature problem and trust calibration15:00 — Current AI stack and team adoption at Pulse V22:00 — Risk management and process controls28:00 — The SaaSpocalypse and internal capability building31:00 — Organizational challenges and people dynamics38:00 — Developer resistance and team composition42:00 — Future education and closing thoughtsShow Notes & Links: https://www.spark6.com/podcast Submit listener questions: elijah@spark6.comkevin@ascendlabs.ai Check out Kevin’s stuff:Ascend Labs: https://ascendlabs.ai/Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevinguywilliams/ Check out Eli’s Stuff:SPARK6 Agency: https://www.spark6.com/Sign up for FREE AI Framework Friday Newsletter: https://www.spark6.com/newsletterFollow Elijah on LinkedIn:https://www.linkedin.com/in/elijahszasz/
Transcribe →
The Microsoft-Claude Connection That Actually Works
May 1300:38:06Tap to summarizeKevin Williams breaks down the Microsoft-Claude integration that's transforming productivity workflows, plus the legal risks and model competition shaping AI adoption decisions.The Claude add-on for Microsoft Office enables something most people haven't seen yet: lateral document communication. Your email can talk to Word, Word can talk to Excel, Excel can talk to PowerPoint—all seamlessly connected. Kevin explains why this integration matters more than having the smartest AI model, and how it's changing his perspective on Microsoft's productivity suite.The conversation also covers the current state of AI model competition, why different models excel at specific tasks, and the emerging legal liabilities around AI agents making autonomous decisions in organizations. How the Microsoft-Claude integration actually works Why document integration beats AI intelligence for most businesses Current state of GPT vs Claude vs Gemini capabilities Legal risks of AI transcripts and agent decisions Shadow AI use and corporate policy implications The "dead internet" problem with AI-generated content Why boring AI integration creates competitive advantage Practical next steps for Microsoft Office usersAPPROXIMATE TIMESTAMPS:00:00 — Intro and Microsoft-Claude revelation03:00 — How lateral document communication works08:00 — Current AI model comparison and capabilities15:00 — GPT agents vs Claude computer use20:00 — Team plans and organizational AI adoption25:00 — Risk tolerance for AI agent deployment30:00 — The "dead internet" and content authenticity35:00 — Legal liabilities and AI policy implications40:00 — Wrap-up and next episode preview→ Get practical AI guidance: https://assessment.ascendlabs.ai/→ Book a strategy conversation: tidycal.com/kevinwilliamsShow Notes & Links: https://www.spark6.com/podcast Submit listener questions: elijah@spark6.comkevin@ascendlabs.ai Check out Kevin’s stuff:Ascend Labs: https://ascendlabs.ai/Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevinguywilliams/ Check out Eli’s Stuff:SPARK6 Agency: https://www.spark6.com/Sign up for FREE AI Framework Friday Newsletter: https://www.spark6.com/newsletterFollow Elijah on LinkedIn:https://www.linkedin.com/in/elijahszasz/
Transcribe →
When Your Team Says Claude But Means GPT
May 501:02:16Tap to summarizeThe AI industry loves throwing around the word 'agents,' but most teams are still stuck in basic prompting mode. Kevin and Eli cut through the semantic noise to reveal what actually matters: sophisticated automation is now accessible through natural language, not technical configuration.In this episode, they explore the practical reality of moving from one-off prompts to systematic workflows, why the 'agent' versus 'automation' debate misses the point, and how natural language interfaces are removing technical barriers that used to require specialized workflow knowledge.Key topics covered:✅ Why most people are still just prompting instead of building workflows✅ How natural language makes complex automation accessible✅ The practical difference between projects, automations, and agents✅ Real examples of workflow automation without technical expertise✅ Why focusing on results beats debating terminology✅ Moving from ChatGPT tabs to systematic AI integrationThis isn't about the latest AI hype – it's about practical transformation that works Monday morning.TIMESTAMPS:00:00 — Future of AI and robotics discussion08:16 — Current state of enterprise AI adoption16:30 — Job displacement and economic impact25:40 — Moving beyond basic prompting35:20 — Context and platform lock-in42:30 — Agents vs automations semantics52:00 — OpenAI agents vs Claude workflows58:30 — Real-world automation examplesShow Notes & Links: https://www.spark6.com/podcast Submit listener questions: elijah@spark6.comkevin@ascendlabs.ai Check out Kevin’s stuff:Ascend Labs: https://ascendlabs.ai/Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevinguywilliams/ Check out Eli’s Stuff:SPARK6 Agency: https://www.spark6.com/Sign up for FREE AI Framework Friday Newsletter: https://www.spark6.com/newsletterFollow Elijah on LinkedIn:https://www.linkedin.com/in/elijahszasz/
Transcribe →