AI at Work

Every organization with 50 or more people probably has someone quietly building AI tools right now. The question isn't whether it's happening it's whether there are any guardrails around it. Kevin Williams and Matt Graham get into the governance gap that almost nobody in the 'just start building' conversation is talking about honestly.

Matt runs a security firm and watches the dark web. What he's seeing in terms of exposed API keys, hard-coded credentials, and applications built without basic security hygiene has reached a level he's never seen before. This episode is the conversation business owners and executives need to have before the next thing gets built.

The conversation covers the full picture: why citizen development concentrates key-man risk instead of reducing it, how to think about deterministic versus probabilistic workflows, what the Anthropic copyright settlement means for companies plugging their data into frontier models, and the practical governance steps any organization can take right now.

✅ Why vibe coding without documentation creates key-man risk

✅ How API keys get exposed and why it doesn't require a hacker

✅ The five-minute cost cap task most organizations haven't done

✅ Deterministic vs. probabilistic workflows: why the distinction matters for accuracy, liability, and cost

✅ What the $1.5B Anthropic copyright settlement signals for SMBs

✅ The Basecamp framework for governing citizen developers before things get complex

Resources & Links:
https://isclaudedumb.today/
resources.ascendlabs.ai
https://ascendlabs.ai/basecamp/
https://assessment.ascendlabs.ai/

Check 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 comparison
• Book a Call: Talk to Kevin on TidyCal

Check out Matt’s stuff:

• LinkedIn: Follow Matt on LinkedIn
• Company: Rapid Dev on LinkedIn
• Direct Email: Reach out to Matt at [email protected]

Anthropic 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.

• 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.

• Website: Ascend Labs
• LinkedIn: Follow Kevin on LinkedIn –
• Free Tool: Take the AI Readiness Assessment
• Deep Dive: Read the GPT Teams vs. Anthropic Teams comparison
• Book a Call: Talk to Kevin on TidyCal

• LinkedIn: Follow Matt on LinkedIn
• Company: Rapid Dev on LinkedIn –
• Direct Email: Reach out to Matt at [email protected]

Most 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 flowers

Check 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 Distilled
https://news.zeitgeistdistilled.com/

Most 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 solution

Check 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/kevinwilliams

Check out Matt’s stuff:
→ mailto:[email protected]
→   / rapid-dev  

The 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 work

Approximate timestamps:

00:00 — Intro and Matt's background

05:25 — No-code evolution and the AI transition

12:30 — Workflow problems vs. AI problems

18:45 — Build vs. buy framework

28:15 — SaaS disruption and data access issues

36:00 — Pricing model evolution and output-based work

45:30 — Job displacement and labor market impacts

50:45 — The 10-year-old entrepreneur story

53:20 — Personal tech stacks and tools

Check 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/kevinwilliams

Check out Matt’s stuff:
→ [email protected]
→ https://www.linkedin.com/company/rapid-dev/

95% 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 implementation

Timestamps:

00:00 — Intro and Brett's background

05:30 — From AI resistance to AI mandates: two-year evolution

12:00 — The model selection dilemma and governance concerns

18:45 — Scaling AI education beyond workshops

25:30 — The "frozen middle" problem explained

32:15 — ROI data across departments and functions

38:00 — Marketing productivity vs. content quality concerns

45:20 — Bringing marketing and sales together with AI

52:00 — Weekend project recommendations

57:00 — Brett's essential tools and where to connect

Check 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.ai

Schedule a conversation with Kevin: tidycal.com/kevinwilliams

Connect with Brett: AI First Leadership on LinkedIn:
https://linkedin.com/in/bschklar
Website: brettschklar.com
Book: https://bit.ly/AIWithoutBS

When 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 implementation

TIMESTAMPS:

00:00 — Intro and Sora commercial viability discussion

05:30 — Video generation landscape: Veo, SeeDance, Higgs Field

15:00 — Model quality regression and hallucination experiences

25:00 — Skills vs snippets: workflow evolution deep dive

35:00 — Enterprise governance and validation requirements

45:00 — Tech stack consolidation and platform comparisons

53:00 — Eli's transition and mental health focus

Show Notes & Links: https://www.spark6.com/podcast

Submit listener questions: 

[email protected]

[email protected] 

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/newsletter

Follow Elijah on LinkedIn:

https://www.linkedin.com/in/elijahszasz/

Kevin 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 wins

Timestamps:

00:00 — Platform switching and AI adoption decisions

08:00 — Microsoft integration advantages and limitations

15:00 — Team adoption and accessibility considerations

25:00 — AI work density and cognitive load crisis

35:00 — Human connection vs AI interaction

42:00 — What to automate vs what to preserve

48:00 — Organizational capture of AI innovations

55:00 — Enterprise data integration challenges

Explore practical AI implementation: https://assessment.ascendlabs.ai/

Book a conversation with Kevin: tidycal.com/kevinwilliams

Show Notes & Links: https://www.spark6.com/podcast

Submit listener questions: 

[email protected]

[email protected] 

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/newsletter

Follow Elijah on LinkedIn:

https://www.linkedin.com/in/elijahszasz/

Kevin 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 wins

Timestamps:

00:00 — Platform switching and AI adoption decisions

08:00 — Microsoft integration advantages and limitations

15:00 — Team adoption and accessibility considerations

25:00 — AI work density and cognitive load crisis

35:00 — Human connection vs AI interaction

42:00 — What to automate vs what to preserve

48:00 — Organizational capture of AI innovations

55:00 — Enterprise data integration challenges

Explore practical AI implementation: https://assessment.ascendlabs.ai/

Book a conversation with Kevin: tidycal.com/kevinwilliams

Show Notes & Links: https://www.spark6.com/podcast

Submit listener questions: 

[email protected]

[email protected] 

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/newsletter

Follow Elijah on LinkedIn:

https://www.linkedin.com/in/elijahszasz/

What 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 careers

Get practical AI guidance: https://assessment.ascendlabs.ai/

Book a conversation with Kevin: tidycal.com/kevinwilliams

TIMESTAMPS:

00:00 — Introduction and Aaron's background

03:00 — AI evolution: Chat bot to context to orchestration eras

08:00 — The probabilistic nature problem and trust calibration

15:00 — Current AI stack and team adoption at Pulse V

22:00 — Risk management and process controls

28:00 — The SaaSpocalypse and internal capability building

31:00 — Organizational challenges and people dynamics

38:00 — Developer resistance and team composition

42:00 — Future education and closing thoughts

Show Notes & Links: https://www.spark6.com/podcast

Submit listener questions: 

[email protected]

[email protected] 

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/newsletter

Follow Elijah on LinkedIn:

https://www.linkedin.com/in/elijahszasz/