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.
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The "token maxing" phenomenon is reshaping how organizations think about AI budgets, but most companies are asking the wrong questions about AI spending.
In this episode, Kevin and Eli explore the reality behind engineers burning through massive token budgets - sometimes exceeding their own salaries - and what it means for practical AI adoption in mid-market companies.
From Stockholm engineers outspending their paychecks on Claude to Jensen Huang's $250K token requirements, we break down why most organizations need output-focused spending strategies, not ego-driven token consumption.
Key topics covered:
✅ The token maxing phenomenon and what's driving it
✅ Why most mid-market companies don't need massive AI budgets
✅ The difference between productive AI spending and token burning
✅ How to build sustainable AI strategies that survive subsidy endings
✅ Real-world examples of agents running amok overnight
✅ Microsoft's new agentic capabilities in Office suite
✅ Platform comparison: OpenAI vs Anthropic vs Google for different use cases
What if you could run your entire workday through one AI conversation? Kevin and Eli explore the emerging 'monothread' format that's revolutionizing how teams operate - plus the hidden security risks that amateur AI builders are creating.
In this episode, we dive deep into how the monothread approach eliminates app switching by connecting your email, calendar, tasks, and CRM into one continuous AI conversation. But we also cover the reality: it's still janky to set up, the security vulnerabilities are real, and most organizations aren't ready.
We also discuss Claude Design's launch that sent Figma's stock tumbling, why Canva is positioned to survive the AI design revolution, and the critical security practices every AI experimenter needs to know.
✅ Key Topics Covered:
✅ The monothread revolution and how to build your AI chief of staff
✅ Why Claude Design might end Figma (and what that means for designers)
✅ Security nightmares: API key protection and the amateur builder problem
✅ Platform comparison: Claude vs GPT vs Gemini for business use
✅ The hidden costs of AI tool proliferation
✅ Voice-first AI workflows and their psychological impact
Timestamps:
00:00 — Intro and sleep tracking with AI
05:15 — Main quest vs side quest in AI adoption
12:40 — The monothread format explained
20:30 — Voice-first AI and dream psychology
25:15 — Claude Design launch and Figma's response
35:45 — Platform comparison and subscription costs
45:20 — Security vulnerabilities in amateur AI apps 55:00 — API protection and credential rotationShow Notes & Links:
When Your Entire AI Team Calls in Sick at 6 AM
Season 1 · Episode 22
Thursday, April 16, 2026 • Duration 57:31
When Anthropic's Claude went down at 6:15 AM on tax day, it exposed a critical blind spot that most AI-adopting organizations haven't considered: vendor dependency risk.
In this episode, Kevin and Eli dive deep into what happened when their entire AI-powered workflow ecosystem crashed simultaneously, revealing the hidden dangers of building business operations around single AI providers without backup plans.
This conversation goes beyond the surface frustration of a service outage to explore the fundamental readiness gap that exists when organizations treat experimental AI services like established infrastructure. You'll discover why this isn't just a technology problem, but an organizational planning problem that requires immediate attention.
Key topics covered:
✅ The "heroin dealer problem" - what happens when AI dependency meets reality
✅ Hidden costs of API pricing vs subscription models and recent Anthropic changes
✅ Why AI subsidies are ending and what it means for your budget
✅ Building redundancy into AI-powered business operations
✅ The Mythos model leak and cybersecurity implications for all businesses
✅ Practical security steps every organization must take now
Approximate timestamps (verify against recording):
00:00 — Introduction and the morning Claude went down
02:49 — The heroin dealer analogy and dependency realization
07:15 — Understanding AI subscription vs API pricing models
15:19 — Anthropic's April 4th changes and the end of workarounds
28:11 — Real cost examples: $200/month to $7000/month overnight
40:05 — Content creation, AI slop, and the attention economy
52:32 — Mythos model leak and cybersecurity implications
The First Day You Stop Opening Apps.
Season 1 · Episode 21
Wednesday, April 8, 2026 • Duration 53:55
Kevin Williams & Elijah Szasz demonstrates the end of interface friction by consolidating his entire workflow into a single Claude conversation. No more jumping between ClickUp, HubSpot, and Slack - everything happens through natural conversation with AI connectors pulling and pushing data to the right systems.
This episode explores a fundamental shift happening right now: your SaaS tools are becoming expensive databases with unnecessary user interfaces. The real productivity breakthrough isn't better tools - it's eliminating the need to context-switch between tools entirely.
Kevin shares his six-day experiment of running his entire business through one chat window, including project management, CRM updates, team coordination, and strategic planning. The result? Massive time savings and the elimination of what he calls "administrative fiddliness."
✅ Key Topics Covered:
✅ How to build Claude connectors for seamless workflow management
✅ Why most productivity problems are actually interface problems
✅ The coming SaaS revolution and what it means for business software
✅ Practical strategies for consolidating multiple tools into single conversations
✅ The psychology of context-switching and why it kills momentum
✅ Future predictions for AI-powered workflow consolidation
TIMESTAMPS:
00:00 — Intro
04:49 — The SaaS apocalypse conversation
18:15 — Creative industry disruption
24:49 — The end of fiddliness breakthrough
35:07 — Building the single-interface workflow
41:09 — Platform connectors and automations
53:33 — The future of business software interfacesShow Notes & Links:
The Best Productivity Hack Right Now? Dictation
Season 1 · Episode 20
Wednesday, April 1, 2026 • Duration 46:54
Kevin and Eli dive deep into the productivity unlock everyone's missing: dictation. Kevin reveals he's crossed one million words dictated and shares why voice input isn't just faster - it generates 3-5x more context-rich data that dramatically improves AI responses.
This episode explores the gap between AI hype and practical implementation, covering computer use capabilities in Claude, the challenges of brittle workflows, and why the best AI adoption strategies focus on reducing input friction rather than upgrading models.
The conversation touches on platform friction, the emerging agent economy, and why successful AI implementations meet people where they already communicate best - through speech.
Google's AI overviews now dominate 84% of retail searches, fundamentally changing how customers discover businesses. In this episode, Kevin Williams and Eli explore the shift to zero-click commerce and what it means for organizations that depend on organic traffic.
The traditional SEO playbook is dying as AI-powered search results provide answers without sending users to websites. This creates a winner-take-all dynamic where only one or two brands get recommended, making the stakes higher than ever for businesses that rely on search traffic.
We dive deep into the practical implications: how schema markup matters more than keywords, why Answer Engine Optimization (AEO) differs from traditional SEO, and what measurement tools actually work in this new landscape. Plus, the surprising upside - while traffic volume decreases, conversion rates for clicks that do come through are jumping to 11.5% versus the industry average of 2.5%.
Key Topics Covered:
✅ Zero-click commerce and the death of the 10 blue links model
✅ Answer Engine Optimization vs Generative Engine Optimization
✅ Schema markup and structured data strategies
✅ Why external reputation building now matters more than website optimization
✅ Measurement tools and share of voice tracking
✅ The winner-take-all dynamics of AI search results
✅ High-intent traffic conversion advantages
✅ Black hat tactics emerging in AI search manipulation
TIMESTAMPS:
00:00 — Intro and Spring Cleaning with AI
05:30 — Google AI Overviews Dominating Search
12:40 — Zero-Click Commerce Reality
18:20 — High Intent vs Volume Trade-offs
What Vibe Coding and Watching Paint Dry Have in Common
Season 1 · Episode 18
Tuesday, March 17, 2026 • Duration 56:56
Kevin and Eli dive deep into Claude Cowork's browser automation capabilities and uncover a significant shift happening right now in white-collar work. What starts as excitement about AI controlling browsers quickly turns into a sobering discussion about immediate workforce displacement.
In this conversation, Kevin demonstrates his weekend vibe-coding project a complete podcast processing tool while Eli shows how Claude Cowork tasks are replacing hours of manual work with automated browser workflows. They explore the critical decision tree between custom GPTs, Cowork tasks, and full applications, and why browser automation represents a fundamental shift from API-dependent workflows to human-like task execution.
The core tension they identify: administrative workers aren't facing displacement from advanced AGI in some distant future, but from increasingly capable task automation happening right now. The "Sally Problem" represents the immediate threat to entry-level and mid-level administrative roles from automation that simply follows instructions reliably.
✅ Key topics covered:
✅ Claude Cowork browser automation capabilities and setup
✅ The decision tree: when to use GPTs vs tasks vs custom apps
✅ Real-world examples of complex workflow automation
✅ The immediate threat to white-collar administrative work
✅ Why browser automation beats API-based workflows for edge cases
✅ Cost analysis: $125K human vs $200/month automation
✅ Organizational readiness for workforce displacement conversations
✅ Practical steps for getting started with task automation
You’re Using Meeting Recordings Wrong, Shadow AI and How Block Cut 4,000 Jobs
Episode 17
Tuesday, March 10, 2026 • Duration 48:36
Shadow AI use is creating massive compliance risks that most organizations don’t even know exist yet. In this episode, Kevin Williams and Eli explore why the Anthropic-Pentagon situation should be a wake-up call for every business leader dealing with AI adoption.
We dive into the reality that every company has employees using free AI models for work tasks, creating exposure that legal teams don’t understand. From Jack Dorsey cutting 4,000 jobs at Block to the emergence of new roles like AI Product Managers and Workflow Engineers, this conversation covers both the risks and opportunities of our current AI moment.
The episode also explores the enterprise context graph opportunity - how virtual companies have an advantage in surfacing “dark arts” knowledge that only lives in individual heads, and why most companies will need to start recording everything.
Key Topics Covered: Shadow AI compliance risks and vendor volatility Why AI policies don’t match reality in most organizations The enterprise context graph and capturing organizational knowledge New AI-era roles: Product Managers, Process Engineers, Workflow Engineers Jack Dorsey’s 4,000-person layoff at Block and AI washing Legal compliance challenges with recording and surveillance tech The gap between AI capability and actual adoption
APPROXIMATE TIMESTAMPS: 00:00 — Intro and weekend updates 03:13 — Anthropic vs Pentagon situation breakdown 11:23 — Shadow AI compliance risks in organizations 16:24 — Recording technology and legal implications 25:02 — Jack Dorsey’s Block layoffs and AI washing 33:48 — Middle management displacement and knowledge sharing 36:07 — Enterprise context graph and dark arts knowledge 42:13 — Jobs of the future: AI Product Managers and Workflow Engineers45:32 — AI capability vs adoption gap analysis
The dead internet, Claude Co-Work Tasks, and a World After SEO
Episode 16
Tuesday, March 3, 2026 • Duration 55:18
Summary
Elijah and Kevin zoom out from the weekly AI frenzy to a more uncomfortable truth: most leaders still do not believe what is coming. They unpack why big organizations misread adoption signals, why an AI manifesto from the very top matters, and how “cheap” offshore workflows can paradoxically delay automation. The conversation then turns to the new wave of agents, scheduled tasks, and browser automation that replaces endless button-clicking. Finally, they map the shift from SEO to AEO and GEO, the rise of the zero-click internet, and why brands that structure data early may win the next training run.
Takeaways
Enterprise adoption stalls when senior leaders get filtered, negative signals from layers below them.
A CEO-level AI manifesto creates alignment faster than any bottom-up pilot program.
“Too cheap to change” is real, low-cost call centers can delay AI even when disruption is inevitable.
Scheduled tasks plus connectors turn simple agents into always-on workflows that publish to your tools automatically.
Zero-click discovery means you must feed machines structured, verifiable data, not just pretty pages.
Chapters
00:00 Introduction and Weekend Reflections
02:06 AI's Impact on Society and Politics
04:44 Leadership Challenges in AI Adoption
07:34 Disruption in Customer Service and AI Implementation
10:40 The Economics of AI in Business
13:11 Navigating AI in Nonprofits and Organizations
16:18 VibeCoding and AI Tools
19:16 The Future of AI and Automation
26:38 The Rise of AI Influencers
How a weekend session of vibe coding led to a fully-baked SaaS product
Episode 15
Tuesday, February 24, 2026 • Duration 53:56
Summary
Kevin spends a family-free weekend “vibe coding” and accidentally builds a full SaaS: a multi-tenant lead magnet builder that creates custom, multi-step lead magnets and then generates personalized nurture emails tied to each prospect’s exact inputs. The bigger lesson is not just the tool, it’s the shift in what’s now possible for non-developers with the right AI workflow: Claude Code plus a simple stack (Supabase, Vercel) can compress months of work into days. They also dig into the new marketing floor, hyper-personalization, rising content noise, and why authenticity still wins.
Takeaways
A “dynamic lead magnet” can generate a custom report plus a personalized nurture sequence for every single lead. Vibe coding is crossing the line from toy demos to real products, if you can iterate hard and think in systems. The economics are shifting: variable AI costs can be pennies per output, but you must monitor scale risk. Paid acquisition is getting AI-optimized fast, but competition and creative volume are exploding. Authentic voice is the differentiator, AI can amplify your take, but slop loses in a noisier world.
Chapters
00:20 Kids, viruses, and a weekend lost to vibe coding
00:39 Teaching teenagers to code and accidental vulnerabilities
01:47 From simple lead magnet to something much bigger
04:20 Generative reports customized to every user
05:17 Hyper-personalized email sequences tied to real use cases
06:24 Token costs, model choices, and AI economics
10:25 The elevator pitch for a lead magnet building SaaS
14:34 CRM integrations, HubSpot automation, and product expansion
27:35 Meta ads, AI optimization, and the future of paid traffic
Find the Right AI Project. Scope It. Launch It. Prove ROI. This playbook shows you exactly how to identify high-impact internal AI opportunities and turn them into measurable business results
Find the Right AI Project. Scope It. Launch It. Prove ROI. This playbook shows you exactly how to identify high-impact internal AI opportunities and turn them into measurable business results
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