Episoder
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Most non-technical business owners and founders assume that building software still requires a developer. You might use AI to sketch something out, but at some point you hand it to someone who actually knows what they're doing. For a lot of use cases in 2026, that assumption is out of date.
Vibe coding was Collins Dictionary's Word of the Year for 2025, and it has moved well beyond the developer community. Founders, operators, and small business owners are using these tools to build working internal tools, automations, and web apps for themselves. The tools have matured to the point where knowing what to build and being able to describe it matters far more than knowing how to write the code.
This episode is the first in a new series on vibe coding. Jess and Kyle cover what the tool landscape looks like today, which starting point fits your situation, how much the agent now does versus how much you need to manage, and what Anthropic's Claude Fable 5 means for where all of this is heading.
If you've been curious about whether these tools are actually ready for someone without a technical background, this episode is the place to start.
What You'll Learn
Why vibe coding has become genuinely usable for non-technical founders What the difference is between the tool you work in and the model underneath itWhat the tool landscape looks like nowThe difference between the harness and the modelWhy the brief matters more now than ever What Claude Fable 5 is built for, and what the US government directive that suspended public access on 13 June 2026 means for you right nowWhy the skills that matter most for building with AI in 2026 are the same ones you use when you brief a designer or hand a project to a team memberUse these links for a discount on the tools we recommend (and it supports the pod!)
Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptrGranola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptrResources :
Claude Code: https://claude.ai/codeCursor: https://cursor.comLovable: https://lovable.devBolt: https://bolt.newReplit: https://replit.comBase44: https://base44.comGitHub Copilot: https://github.com/features/copilotVellum: https://www.vellum.aiHow to make vibe coding sustainable inside the enterprise: https://martech.org/how-to-make-vibe-coding-sustainable-inside-the-enterprise/AI vibe coding boosts output but strains oversight: https://itbrief.co.uk/story/ai-vibe-coding-boosts-output-but-strains-oversightMCP, vibe coding and harness engineering: https://www.infoq.com/podcasts/mcp-vibe-coding-harness-engineering/Amazon AI coding outage review: https://www.fintechweekly.com/news/amazon-ai-coding-outage-reviewStack Overflow Developer Survey 2025: https://shiftmag.dev/stack-overflow-survey-2025-ai-5653/A quarter of startups in YC's current cohort have codebases almost entirely AI-generated: https://techcrunch.com/2025/03/06/a-quarter-of-startups-in-ycs-current-cohort-have-codebases-that-are-almost-entirely-ai-generated/Google: 75% of code is now AI-generated: https://www.businessinsider.com/google-ai-generated-code-75-gemini-agents-software-2026-4Get in Touch
TikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
LinkedIn: https://www.linkedin.com/company/early-adoptr/
Get in touch with Early Adoptr: [email protected]
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We've all seen the stats. Women are adopting AI tools at a 25% lower rate than men. 33% of men use AI daily at work versus 27% of women. Men are 23% more likely to be encouraged by their managers to try it, and 27% more likely to be praised when they do.
Most of the conversation about women's lower AI adoption rates focuses on what women need to do differently. Instead, we're looking at the conditions that produced the gap in the first place, and why closing it matters far more than just productivity.
This week on Early Adoptr, Kyle is away so Jess is joined by Nadia Koski, digital growth expert at The Marketeer Group, and Stefanie Beach, founder and CEO of The Marketeer Group, to get into what's actually driving the adoption gap and what women founders and small business owners can do about it. Because when women use AI tools less, that eventually shapes what the tools look like. It costs the economy. And it grows over time.
We'll work through the stats, the barriers, and the guilt that comes with using AI at work. We also get into how to start without the overwhelm, the mental load, and why the caution women tend to bring to AI turns out to be an asset.
If you're going to Cannes, make sure to check out the sessions that Nadia and Stefanie are running:
Cannesversations Series - LIVE FROM CANNES
YouTube Channel or LinkedIn for contextFor Cannes 2026 - Cannesversations Interest FormThe Digital Marketeer podcast:
Follow Nadia:
Nadia Koski on LinkedIn: https://www.linkedin.com/in/nadiakoskiStill Human: Real Talk in the Age of AI Podcast: https://open.spotify.com/show/6xCZdhBOerROCuLatJoNay?si=FeYj2JidTtiA9xsREd9FtgFollow Stefanie:
Stefanie Beach on LinkedIn: https://www.linkedin.com/in/stefanieg/[email protected]Use these links for a discount on the tools we recommend (and it supports the pod!)
Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptr
Granola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr
What You'll Learn:
Why corporate AI challenges often exclude women without meaning toWhy finishing a project faster with AI does not mean charging less for it, and why women are more likely to think it doesWhat a team leader needs to have in place before running internal AI training, and why protected experimentation time mattersWhy more women in their 40s are leaving corporate life for entrepreneurship, and what the AI revolution has to do with itTimestamps:
00:00 Where's Kyle + Women & AI
03:23 Introductions: Nadia & Stefanie
08:19 The Data on Women and AI Adoption
10:55 How Women in Tech Engage with AI Differently
12:02 The Perfectionism Problem
15:36 How to Give Your Team a Safe Space to Try AI
19:00 How to Actually Educate Yourself on AI
21:13 Who Really Has Time to Learn AI
24:31 Why the Founders Furthest Ahead on AI Have a Support Network at Home
25:53 Why the AI Revolution Is Pushing More Women Into Entrepreneurship
28:23 The Unpaid Work Research That Explains the Women's AI Adoption Gap
29:46 Why Using AI to Brainstorm and Polish Your Work Is Not a Shortcut
31:27 Fighting the Guilt and Redistributing the Mental Load as a Female Founder
33:23 The Glass Cliff: Why Women Face Higher Stakes When AI Goes Wrong
36:22 Why Doing Work Faster with AI Doesn't Mean You Should Charge Less
38:20 What Workplaces Can Do to Give Women Equal Access to AI Learning
42:45 Is Being Cautious About AI Actually a Business Advantage?
47:24 Why Women Questioning AI Accuracy Is Good for Business
47:39 Don't Be Afraid to Start: Final Advice for Women Using AI
Resources:
Harvard Business School studyLean In studyWhy women aren't âmissingâ the AI trainMalin Frithiofsson (Daya Ventures)Maya Betron (PowHer Data)Sinead BovellGet in Touch:
Email: [email protected]
TikTok / Instagram / YouTube: @early_adoptr
LinkedIn
Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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Manglende episoder?
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Running a smaller business or start up means you're also doing payroll. And chasing invoices. And trying to figure out if last month closed in the black, while also building a proposal that's due tomorrow and following up with the three leads who went quiet last week. Not to mention marketing! The admin never stops, and you're always the one doing everything.
Anthropic's new Claude for Small Business package doesn't fix that, but it does something almost more useful. It runs your most time-consuming jobs directlly in the tools you're already paying for like, QuickBooks, HubSpot, PayPal, Canva, and more.
In this episode, Jess and Kyle get into what Claude for Small Business actually includes, how the workflows and skills fit together, what it looks like in practice, and what you need to know before you connect anything. If you've listened to our Cowork, Skills and MCP episodes, this is where those pieces click into place.
They also give an update on last week's jobs episode, which already has multiple updates in the week since it was published.
Full list of workflows & skills: https://claude.com/plugins/small-business
The setup command: /smb-onboard
ï»ż
Tools we use:
Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptr
Granola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr
What You'll LearnWhy Claude for Small Business isn't just another AI chatbot layerWhat the 15 pre-built workflows are and what they actually doThe difference between a workflow (the full job you trigger) and a skill (the reusable technique running underneath it)How the approval model worksThe permissions issue to be aware ofHow to work out whether this is the right tool for you right nowRelated Episodes:
Claude Skills: https://www.earlyadoptr.ai/episodes/claude-skills-explained-how-to-stop-repeating-yourself-in-every-sessionModel Context Protocol (MCP): https://www.earlyadoptr.ai/episodes/stop-switching-tabs-how-to-connect-your-ai-to-your-business-tools-safely-using-model-context-protocol-mcpClaude Co-Work: https://www.earlyadoptr.ai/episodes/claude-cowork-explained-can-ai-really-organize-your-files-and-dataReference material:
https://www.anthropic.com/news/claude-for-small-business https://www.forbes.com/sites/jodiecook/2026/05/24/run-your-whole-business-from-one-tab-with-claudes-new-update/ https://medium.com/@sebuzdugan/how-to-use-claude-to-automate-your-small-business-in-a-weekend-15c749aac5e0 https://www.technologyreview.com/2026/05/26/1137855/a-reality-check-on-the-ai-jobs-hysteria/https://fortune.com/2026/05/26/sam-altman-dario-amodei-walking-back-ai-jobs-apocalypse-prophecies-ipo/https://www.gov.ca.gov/2026/05/21/governor-newsom-signs-first-of-its-kind-executive-order-to-prepare-workers-and-businesses-for-potential-ai-disruption/https://fortune.com/2026/05/03/chinese-court-layoffs-workers-ai-replacement-labor-market/Get in Touch:
Email: [email protected]
TikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
LinkedIn:
Timestamps:
00:00 What We've been Up To
05:22 What Is Claude for Small Business?
07:11 Understanding Connectors and Workflows
09:33 Claude's Pre-Built Workflows and Skills: What's Included
15:35 Why the Approval Step Is So Important
17:52 Data Security: What Claude Can and Cannot See
19:43 Understanding Permissions and Access Levels
21:50 Row-Level Access and How Permissions Actually Work
23:23 Garbage In, Garbage Out: Why Data Quality Matters
25:19 How to Set Up Claude for Small Business (Step by Step)
31:02 Pros and Cons of Claude for Small Business
33:20 Cost Considerations for Small Businesses
35:35 The Approval Mechanic: Smart Product Design or Speed Bump?
36:31 How This Differs from ChatGPT and Microsoft Copilot
37:25 Data Retention and Privacy: What You Need to Know
43:33 Key Takeaways and How to Get Started with Claude for Small Business
46:15 AI News: More Jobs News from Last Week
56:25 Fifteen Street
Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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AI layoffs are dominating the news, but the story being told isn't the full story. In this episode, Jess and Kyle break down what's really driving the cuts, and what it means for your job and your business.
When Meta announced 8,000 job cuts, the coverage landed the same way it always does: AI is replacing people, the future is here. But the numbers don't support that story. Meta's savings from the cuts amount to roughly three billion dollars. Their AI infrastructure spend this year runs to multiples of that. So what's actually going on?
In this episode, Jess and Kyle work through the real economics behind the layoff headlines, from the infrastructure bets driving the cuts, to the compute costs that are now exceeding what companies spend on their own people, to the quietly alarming data on what's happening to early-career workers. They also cover the Musk v. Altman verdict, what it means for OpenAI's upcoming IPO, and why Anthropic keeps coming out looking like the adult in the room.
The episode closes with practical guidance on what founders, team leaders, and employees can actually do right now, including why waiting to feel ready is the worst strategy available.
What You'll Learn:
Why Meta's 8,000 job cuts are better understood as a budget-clearing exerciseWhat AI washing is and how to spot it in a layoff announcementWhy infrastructure spending at Amazon, Meta, and Microsoft is projected to exceed total payroll costs by $50 billion this yearWhat MIT research actually found when it tested whether AI is economically viable compared to keeping humans in the roleWhy 43% of CEOs plan to reduce junior roles over the next two yearsWhy IBM's contrarian bet on junior hiring may look very smart in ten yearsWhy smaller businesses are better placed than large firms to make the same moveWhat AI fluency actually means in practiceTools we use and recommend:
We only recommend tools we actually use. Both links below are affiliate links â if you sign up, it costs you nothing extra and helps support the show.
Wispr Flow â AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptr
Granola â The best AI meeting notes! New users get 100% off for their first month.
https://www.granola.ai?via=early-adoptr
Timestamps:
ï»ż00:00 Introduction and Personal Updates
04:58 Why the AI Layoff Headlines Don't Tell the Whole Story
08:58 When Companies Cut Staff to Fund AI and Call It Efficiency
18:16 How Meta Extracted Its Employees' Knowledge Before Letting Them Go
22:40 Why the Productivity Gains Don't Justify the Scale of the Cuts
28:12 When Running AI Costs More Than Paying Your Team
29:58 MIT Research: AI Is Only the Cheaper Option in 23% of Cases
32:14 The Disconnect in AI Implementation
33:52 Why Junior Roles Are Being Cut First
35:44 The Talent Pipeline Problem Nobody Is Planning For
38:28 Redesigning Early-Career Roles Instead of Cutting Them
40:27 The Skills Gap in Education
42:29 Key Takeaways
Resources:
https://www.shrm.org/topics-tools/news/technology/ai-layoffs-transformation-scapegoathttps://www.linkedin.com/news/story/ceos-plan-to-reduce-junior-roles-8108505/https://www.forbes.com/sites/danrunkevicius/2026/05/20/meta-layoffs-signal-ai-bill-is-coming-due/https://fortune.com/2026/03/31/marc-andreessen-ai-layoffs-silver-bullet-excuse-overhiring/https://techcrunch.com/2026/05/19/openai-co-founder-andrej-karpathy-joins-anthropics-pre-training-team/https://www.bbc.co.uk/news/articles/cewpyv79pw1ohttps://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.htmlhttps://www.pymnts.com/artificial-intelligence-2/2026/jpmorgan-prioritizing-ai-hires-over-bankers/https://fortune.com/2026/05/22/microsoft-ai-cost-problem-early%20adoptr%20-agents/www.moneycontrol.com/technology/mark-zukerberg-s-leaked-viral-audio-clip-suggest-meta-is-tracking-employees-to-train-ai-article-13924715.htmlGet in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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A year in AI doesn't feel like a normal year, it feels like about five.
In honour of Early Adoptr's first anniversary, we thought it was worth stopping to take stock of what's changed in the last year.
When we launched Early Adoptr, there was a pretty clear hierarchy, with ChatGPT was at the top, everyone else was catching up, and agents were something people talked about at conferences without anyone being entirely sure what they meant. Twelve months later, almost none of that is still true.
In this episode, we take a proper look back at what actually changed. From the model landscape and the slow collapse of the "ChatGPT is the Google of AI" era, to reasoning becoming the baseline rather than the premium tier, to context windows going from a genuine operational headache to essentially a non-issue, the foundations shifted faster than most businesses could keep up with. We also get into what happened with agents once MCP â Model Context Protocol â gave them a shared language to work with, why agentic commerce looks completely different to how we predicted it would, what Answer Engine Optimisation means for any business that needs to be found online, and what has changed with the security picture once agents got access to real tools.
We close out with a look ahead at what's actually worth paying attention to: outcome-based pricing, orchestrated multi-agent systems reaching smaller businesses, and what we're calling agent debt, the accumulating consequences of workflows that were built in a hurry and haven't been stress-tested yet.
Thanks for being with us for the last year, and here's to the next 12 months!
What You'll LearnWhy reasoning models went from a premium add-on to the default , and what that shift enabled for agents and complex workflowsHow context windows grew from a operational constraint to a non-issue, and what that unlocks for businesses working with large volumes of documents, contracts, or correspondenceWhy smaller, more focused AI tools regularly outperform general-purpose models on the tasks they're built for, and what that means for how you structure your own stackWhat MCP actually solved â and why it's the reason agents went from demo-quality to deployable for non-technical teamsWhat the two-tier internet looks like in practice and why it mattersWhy ChatGPT's instant checkout failed commercially and what it tells us about how brands are learning to use AI for discoveryWhat AEO â Answer Engine Optimisation â means for any business that needs to be found onlineHow the security risk picture changed once agents got real access to real tools via MCPWhat agent debt isWhat outcome-based pricing meansResources and LinksAll previous episodes of Early Adoptr can be found here or via your podcast player of choice: https://shows.acast.com/early-adoptr
Get in Touch:
TikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
Timestamps:
00:00 What We've Been Up to This Week
04:14 One Year of Early Adoptr: What We Got Right (and Wrong)
06:27 The Model Landscape: Why ChatGPT Lost the Top Spot
11:01 AI Pricing Is Changing â and Your Bill Is Going Up
16:20 Context Windows: From Headache to Non-Issue
20:03 When Smaller Is Better: The Case for Specialist AI Tools
20:35 Use Cases for Small Language Models
22:53 gents: From Conference Buzzword to Actually Useful
29:02 What MCP Did for the Agent Ecosystem
31:29 Agentic Commerce and the Two-Tier Internet
37:55 How Brands Are Using AI for Discovery Without Losing the Customer
39:05 The Evolving Landscape of AI Security
44:17 The Shift in AI Risks and Management
48:26 From Subscriptions to Outcome-Based Pricing
50:22 AI Regulation, Memory, and the GDPR Question Nobody's Asking Yet
52:20 The Agent Debt Problem
56:12 Where Does AI Go From Here?
Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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Most people start their AI journey by asking how to save time. That is not a wrong question â but Anthropic's latest research, based on open-ended interviews with over 81,000 Claude users across 159 countries, suggests it may not be the most important one.
The most commonly reported productivity gain in the study was not speed. It was scope. Not doing existing work faster, but doing things that you simply couldn't before, because of budget, skills, or just the assumption that certain capabilities belonged to someone else.
This episode is about the difference between saving time with the boring middle and asking what is now possible that wasn't before, and why that second question is where the real opportunity lies.
What You'll Learn
Why the Anthropic study's methodology is unusualThe difference between efficiency gains and capability gainsHow to identify your "boring middle" and what to do once you have sorted itHow to prevent your freed-up time from get absorbed back into more of the sameHow a delivery driver and landscape gardener from the illustrate capability gainsWhat the Pocket OS incident reveals about AI agent permissions, and the simple rule that would have prevented itTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:
00:00 Introduction and Travel Plans
01:37 About the Anthropic Research
03:37 How the Study Actually Worked
07:36 The Headline Productivity Stat
10:35 The Four Types of Productivity Gain from AI
11:54 What the Data Says About Job Displacement
13:32 The Efficiency Game: What It Gets You and What It Misses
16:45 Why Automating the Wrong Things Makes You Faster at the Wrong Things
21:27 The Boring Middle: Why Consistency Is the Point
25:00 Capability Gains: Doing Things That Were Previously Off the Table
28:54 The Wrong Question: Efficiency vs. Capability
31:39 How Efficiency and Capability Feed Into Each Other
35:09 Practical Takeaways: What to Try This Week
38:09 AI News of the Week: Lessons from Pocket OS Incident
Resources:
What 81,000 people told us about the economics of AI: https://www.anthropic.com/research/81k-economics https://www.linkedin.com/posts/oguzaliacar_anthropic-just-published-findings-from-80000-activity-7440462065959366657-IqJj/ https://medium.com/activated-thinker/an-ai-interviewed-81-000-people-what-it-discovered-exposes-our-deepest-insecurities-c231c7d2f77e https://www.reddit.com/r/AI_Agents/comments/1ssz7v3/anthropic_surveyed_81000_claude_users_about_ais/Get in Touch:
TikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
What 81,000 people told us about the economics of AI: https://www.anthropic.com/research/81k-economics https://www.linkedin.com/posts/oguzaliacar_anthropic-just-published-findings-from-80000-activity-7440462065959366657-IqJj/ https://medium.com/activated-thinker/an-ai-interviewed-81-000-people-what-it-discovered-exposes-our-deepest-insecurities-c231c7d2f77e https://www.reddit.com/r/AI_Agents/comments/1ssz7v3/anthropic_surveyed_81000_claude_users_about_ais/Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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This is the second part of our interview with Rob Webster, who has spent over 20 years in media and marketing, ran data and technology at MediaCom for some of the world's biggest brands, built and sold a MarTech and AdTech consultancy, and now works with enterprise businesses on how they actually adopt AI.
In this episode, Jess and Kyle talk to Rob about everything from navigating the messy middle to what the future of work for juniors, where AI should play in your business, what it means for how you hire and develop people, why so many organisations are stuck between experimenting and scaling, and what it actually takes to move forward.
We also cover the OpenAI vs Elon Musk trial, and how South Africa's AI Policy offers a useful reminder to always check your citations.
What You'll Learn
How to identify your best AI use cases by starting with outcomes rather than tasksWhy AI multiplies what you're doingHow junior employees can move faster and take on more accountability earlier when they have AI as a working layerHow smaller businesses are now better placed to train entry-level hires than they've ever been.Why real-world wisdom is the skill that can never be replaced.What the messy middle of AI adoption looks like in practice and why most organisations are stuck in itHow leaders can model AI adoption in a way that actually moves teams forwardï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:
00:00 Intro & What We've Been Up To
04:09 The 10,000 Foot View: Why You Should Start Broad Before Picking an AI Use Case
10:42 What AI Means for Hiring and Training Junior Employees
14:55 Why Smaller Businesses Can Now Compete on Talent Development
16:20 Human in the Loop: Why Oversight Still Matters
19:35 The Messy Middle: Why Most Businesses Get Stuck Between Testing and Scaling
23:17 How Leaders Can Drive AI Adoption
34:02 AI News of the Week: OpenAI vs Elon Musk
34:31 AI Gone Wrong: South Africa's AI Policy Debacle
37:55 Wrapping Up for the Week
Resources:
Rob Webster - https://www.linkedin.com/in/digitalstrategyleader/ TAU Marketing Solutions - https://taums.ai/ AI Agents in Media Planning and Buying: https://taums.ai/ai-agents-in-media-planning-and-buying/Judging the Coming Wave of Agentic Adtech: https://taums.ai/judging-the-coming-wave-of-agentic-adtech/Mastering the Messy Middle: https://taums.ai/mastering-the-messy-middle-with-a-balanced-approach-to-ai-implementation/10 Predictions for Marketing and AI in 2026 (Futureweek): https://futureweek.com/rob-webster-10-predictions-for-marketing-and-ai-in-2026/The AI Land Grab interview (AdWorldNews): https://www.adworldnews.com/news/robert-webster-tau-marketing-ai-land-grab
Get in TouchTikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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It's here! The culmination of our series on agents, and if you've ever wondered how to make the most of the AI agents in your business (without a huge budget or a team of developers), this is the episode for you.
This week on Early Adoptr, we are joined by Rob Webster, who has spent 25 years working at the intersection of data, machine learning, and marketing, including working on data and technology for brands like Dell, Tesco, and Coca-Cola. He now runs Tau Marketing Solutions, where he helps businesses adopt AI and build agents to solve real marketing problems. In this episode he joins Jess and Kyle and shares everything he has learned about making agents that actually work. From the "Fisher Price Agent" to building a daily action plan, the four components every working agent needs and why most agents fail, this episode is a goldmine of tips from years of experience.
We also cover a major deal between SpaceX and Cursor, and what it tells us about where the real competition in AI is playing out right now.
What You'll Learn
The two-prompt method Rob uses to turn a vague goal into a concrete daily action planThe four components every working agent needs and the reason most agent setups fail to produce useful outputWhy the most valuable skill in AI right now has nothing to do with technology, and how anyone can develop itWhat human-in-the-loop looks like as a working habit rather than a safety conceptHow to start with a "Fisher Price Agent"Rob's tips for getting unstuck when you hit a wallï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:
00:00 What We've Been Up to This Week
03:16 Interview with Robert Webster
05:07 AI News: SpaceX and Cursor Deal
05:47 Meet Rob Webster
10:51 The Two-Prompt Method: Going from Vague Goal to Concrete Plan
13:28 The Four Components Every Working Agent Needs
18:14 Why Knowing What Good Looks Like Is the Real Skill
20:26 Human-in-the-Loop in Practice
26:20 Building a Co-CEO Agent: From Fisher Price to Advanced
33:01 Where to Start If You Are Not Technical
37:42 Takeaways
40:07 AI News of the Week: SpaceX & Cursor
Resources:
Rob Webster - https://www.linkedin.com/in/digitalstrategyleader/ TAU Marketing Solutions - https://taums.ai/ AI Agents in Media Planning and Buying: https://taums.ai/ai-agents-in-media-planning-and-buying/Judging the Coming Wave of Agentic Adtech: https://taums.ai/judging-the-coming-wave-of-agentic-adtech/Mastering the Messy Middle: https://taums.ai/mastering-the-messy-middle-with-a-balanced-approach-to-ai-implementation/10 Predictions for Marketing and AI in 2026 (Futureweek): https://futureweek.com/rob-webster-10-predictions-for-marketing-and-ai-in-2026/The AI Land Grab interview (AdWorldNews): https://www.adworldnews.com/news/robert-webster-tau-marketing-ai-land-grabGet in TouchTikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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A new client signs...congrats, you're excited! And then the onboarding begins process with all the tedious tasks: folder creation, the welcome email, the kickoff scheduling, the project setup, the intake form you'll need to chase twice. None of it is difficult, all of it takes time, and it always seems to happen at the exact moment you're least available to do it well.
This week on Early Adoptr, we walk one real, familiar business process through every single rung of the Ladder of Autonomy, from fully manual through to fully autonomous, using real examples at each stage. By the end, you'll know what each level actually looks like in practice, which rung your current setup sits on, and what a realistic next step looks like for your business.
This is episode three in Early Adoptr's ongoing series on AI agents. If you haven't listened to our previous episodes (links below), it's worth starting there.
What is an AI Agent: https://shows.acast.com/early-adoptr/episodes/what-is-an-ai-agent-a-plain-english-guide-for-business-owner
AI Agent Frameworks Explained: https://shows.acast.com/early-adoptr/episodes/ai-agent-frameworks-explained-the-five-things-every-agent-sy
What You'll LearnHow to tell which steps in your workflow genuinely benefit from AI and which ones are better handled by a simple automationWhat it actually means to add an AI agent to a business workflow, and how that differs from using a chat tool like Claude or ChatGPTHow AI agents become more capable and more autonomous at each level â and what that progression looks like applied to a single, familiar business processWhy keeping a human in the loop isn't just a safety measure, and how the way you structure that oversight changes as your setup becomes more sophisticatedWhat the real security and risk considerations are when AI starts taking actions on your behalf, with practical guidance on how to approach permissions and accessWhy the most advanced level of AI autonomy is worth understanding and what goes wrong for businesses that skip the basicsï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:
00:00 What We've Been Up to This Week
03:46 Exploring Client Onboarding and Automation
07:27 The Ladder of Autonomy: A Quick Recap
13:11 Before You Start: Why Workflow Mapping Comes First
14:55 Rung One: Basic Automation and Where It Falls Apart
20:32 Rung Two: Adding AI Into Your Onboarding Workflow
28:41 Rung Three: Handing the Agent a Goal, Not a Task
39:19 Rung Four: Full Autonomy and What Can Go Wrong
47:08 Your Action Plan: How to Start Without Overcomplicating It
51:57 AI News of the Week: Anthropic Launches Claude Design & Allbirds AI Pivot
Follow Us:
Email: [email protected]
LinkedIn: https://www.linkedin.com/company/early-adoptr/
TikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
Resources:
https://www.reddit.com/r/AI_Agents/comments/1nv3sd4/case_study_client_onboarding_issue_how_i_fixed_it/https://www.linkedin.com/pulse/how-agentic-ai-can-re-define-customer-onboarding-product-upadhye-7ikje/https://www.mindstudio.ai/blog/ai-powered-client-onboarding-tools-workflowshttps://churnzero.com/blog/customer-onboarding-with-ai/ https://techcrunch.com/2026/04/17/anthropic-launches-claude-design-a-new-product-for-creating-quick-visuals/ https://slate.com/technology/2026/04/ai-allbirds-pivot-silicon-valley.htmlGet in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
Hosted on Acast. See acast.com/privacy for more information.
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Most business owners have heard the word "framework" thrown around a lot lately and filed it under "probably technical, not my problem." In this episode, we make the case that it is your problem, not because you need to build one, but because understanding what a framework actually does is what helps you evaluate any agent tool being pitched to you, spot where an agentic workflow is likely to break down, and make smarter decisions about what to hand over and what to keep human.Most business owners have heard the word "framework" thrown around a lot lately and filed it under "probably technical, not my problem." In this episode, we make the case that it is your problem, not because you need to build one, but because understanding what a framework actually does is what helps you evaluate any agent tool, spot where an agentic workflow is likely to break down, and make smarter decisions about what to hand over and what to keep human.
In this episode, Jess and Kyle walk through a complete practical example, that shows how you move from writing down your decision logic to deploying a real working agent, step by step.
What You'll LearnWhat an AI agent framework actually isThe five components every agent system needs to work â model access, tools, memory, coordination, and human oversightWhy decision logic mapping is important before you build anythingHow to automate a real business process, using inbound enquiry handling as a worked example, from writing your decision logic through to rolling out with guardrailsThe difference between CrewAI, LangChain, and LangGraph, and which situations each one is suited toInbound enquiry automation as a practical use caseHuman-in-the-loop and why it matters as agents gain more accessï»żï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:
00:00 Introduction and Personal Updates
06:42 Recapping AI Agents and the Ladder of Autonomy
12:07 Frameworks 101: The Five Capabilities Every Agent Needs
18:41 How Framework Design Affects Performance and Cost
19:20 Frameworks vs. Capabilities in Agent Systems
25:40 How to Get Started with Frameworks: A Five-Stage Path
40:10 Real-World Example: Automating Inbound Sales Enquiries
47:52 Safety Considerations in Agent Use
50:21 Key Takeaways for Agent Frameworks
52:28 AI News of the Week: Claude Mythos and Third-Party Harnesses
58:42 AI Gone Wrong : Meta's Expensive Model Mistake
Instagram: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
LinkedIn: https://linkedin.com/company/early-adoptr
Resources: https://linktr.ee/early_adoptr
đ© [email protected]
Resources:
https://www.anthropic.com/engineering/building-effective-agents https://www.moveworks.com/us/en/resources/blog/what-is-agentic-framework https://www.linkedin.com/pulse/turning-your-icp-framework-working-ai-agent-without-code-aird-mash-ni1me/ https://www.moxo.com/blog/agentic-ai-framework-comparison https://www.freecodecamp.org/news/the-agentic-ai-handbook/ https://huggingface.co/learn/agents-course/unit2/introduction https://www.reddit.com/r/AI_Agents/comments/1pcrjgn/trying_to_learn_agentic_ai_please_suggest_me_a/ http://aiagentskit.com/blog/agentic-ai-frameworks/https://www.datacamp.com/blog/agentic-ai https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained https://techcrunch.com/2026/04/04/anthropic-says-claude-code-subscribers-will-need-to-pay-extra-for-openclaw-support/https://futurism.com/artificial-intelligence/first-model-zuckerberg-superintelligence-labs-flopshttps://thenewstack.io/anthropic-claude-mythos-cybersecurity/Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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"AI Agent" has become one of those phrases that means everything and nothing depending on who's using it. It gets attached to basic customer service bots, to tools running overnight making business decisions, and to everything in between. And then there's "agentic AI," which most people use interchangeably with "AI agent" but shouldn't. If you've been nodding along while wondering what actually separates any of these things from a very clever chatbot, this episode is for you.
In this episode, Jess and Kyle revisit the basics of AI agents: what an AI agent actually is, what makes something agentic, why those two things are different, and a much needed update to our Ladder of Autonomy, the framework that helps you figure out which level of AI autonomy actually makes sense for your business right now.
This is part one of a multi-part series on AI agents and agentic AI.
What You'll LearnWhat actually makes something an AI agentThe difference between an AI agent and agentic AI and why the two get confused constantlyHow to use the Ladder of Autonomy to assess any AI tool or workflow â and where Cowork, OpenClaw, and Perplexity Computer each sit on itWhat the key failure modes are for agents in live business environments, and the questions you should be asking before you deploy anythingThe Anthropic Claude Code source code leak and what it revealed about unreleased features and how far ahead the labs are buildingï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:
00:00 What We've Been Up To
04:25 Why we're revisiting agents
07:26 What Is an AI agent?
11:11 LLMs vs agents: understanding what each component does
12:29 From demos to deployment: where agents actually stand right now
16:20 Will AI agents be reliable by 2035? What the experts are saying
17:11 AI adoption and the digitally native advantage
18:17 What is the difference between an AI agent and agentic AI?
19:56 Agents vs orchestrators
25:27 The Ladder of Autonomy: a framework for understanding AI capability
26:25 Rung 1: Basic Workflows and Automations
29:06 Rung 2: Task Agents and Their Capabilities
30:49 Rung 3: Outcome-Based Task Management
36:18 Rung 4: The Future of Agentic AI
39:51 Why most agent failures are setup problems, not AI problems
41:40 Which rung of the autonomy ladder is right for your business?
43:38 Pros of AI agents for small business: what actually holds up
46:32 Cons of AI agents: what to watch out for before you deploy
46:57 How to avoid the most common AI agent failure modes
50:31 The most important question to ask before building any AI agent workflow
51:24 AI News of the Week: Anthropic's Code Leak
đČ **FOLLOW EARLY ADOPTR**
Email: [email protected]
Instagram: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
LinkedIn: https://linkedin.com/company/early-adoptr
Resources: https://linktr.ee/early_adoptr
Resources:
https://www.anthropic.com/research/measuring-agent-autonomy https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agentshttps://www.maryammiradi.com/blog/build-ai-agents-anthropic-lessons https://medium.com/@speaktoharisudhan/ai-agent-vs-agentic-ai-understand-the-actual-difference-4580a4b01dd4 https://www.bcg.com/capabilities/artificial-intelligence/ai-agents https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explainedhttps://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025https://www.theguardian.com/technology/2026/apr/01/anthropic-claudes-code-leaks-aiGet in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
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YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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Claude Skills is one of the most useful features available to Claude users right now, and solves something that you almost definitely have encountered.
You start a new conversation, and Claude has no idea how you like things done. You end up re-explaining your tone, pasting in your brand guidelines, or manually correcting the output back into something that actually sounds like you. Every. Single. Time.
Claude Skills fixes that but allowing you to build your preferences, your rules, your formats, and your style into a reusable package that Claude can pull in automatically whenever it is relevant. Set it up once, and stop repeating yourself.
In this episode, Kyle and Jess break down what Skills actually are, how they sit alongside Model Context Protocol (MCP), and the pros and cons. They also get into where to find pre-built Skills, how to build your own without any technical knowledge, and what to watch out for when you are browsing the public marketplaces.
If you're fed up with constantly repeating yourself to Claude, this is the episode for you.
PS. Kyle's audio is a little weird on this one, apologies in advance!
What You'll Learn
What Claude Skills are, how they differ from custom GPTs and Google Gems, and why portability gives them a longer shelf life than eitherHow Skills, MCP, Projects, and memory all fit together and when to reach for each oneWhere to find pre-built Skills, what to check before you install anything from a public marketplace, and how to build your own without any technical knowledgeWhy the skill description is an activation condition, not a title, and what to do if your skill is not triggeringWhat OpenAI shutting down Sora and consolidating its products signals about where the money is actually flowing in AI right nowWhy the window where small businesses can run the same AI stack as enterprises is real, and why it probably will not stay open indefinitelyWhat are Claude Skills and how are they different from custom GPTs or Google Gems?How do Claude Skills and MCP work together?How do I find and install Claude Skills without needing any technical knowledge?Are public Claude Skills safe to install, and what should I check before using one?How do I write a Claude Skill that actually activates when I need it?ï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:
00:00 Introduction and Weekly Updates
04:19 Quick Recap: What Model Context Protocol (MCP) Does and Why Skills Come Next
06:28 What Are Claude Skills and Why Do They Matter
11:17 Inside a Skill: How It Is Built and How It Knows When to Activat
16:51 Where to Find Skills and How to Install Them Without Any Technical Knowledge
17:59 Memory, Projects, and Skills: Which One Does What
20:21 Projects vs Skills: How to Use Both Without Getting Confused
24:09 Where to Find Skills: Build vs Pre-made
26:50 Free, Portable, and Consistent: The Pros of Claude Skills
30:18 Skills Are Not Perfect: The Limitations Worth Knowing About
35:27 Real Business Use Cases: Brand Voice, Sales Prep, and More
42:39 Getting Started with Claude Skills: Tips and Tricks
46:14 AI News of the Week: Sora and OpenAI's SuperApp
55:12 AI Gone Wrong: Robot Hot Pot Chaos
Resources:
Anthropic Skills repositoryskills.sh Find Skills SkillVoice DNA skillAnthropic's official guidance on skillsMCP Episode Part 1 MCP Episode Part 2Disney Exits OpenAI Deal After AI Giant Shutters SoraOpenAI Plans Launch of Desktop âSuperappâ to Refocus, Simplify User ExperienceFollow Us:
Email: [email protected]
TikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
LinkedIn: @early_adoptr
Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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MCP â Model Context Protocol â is the AI infrastructure that's quickly becoming the key layer underneath almost every serious AI setup. It's a big part of why AI is shifting from something where you copy and paste from one tab to the other, to something that actually can act on your behalf, and is the foundation that makes agentic AI possible.
Last week in part one of this series, we covered the what MCP is and why it's such a big deal. In part two, we get into what MCP actually looks like in practice, from the easy non-technical entry points (no technical knowledge required!) already built into Claude to automation tools like Zapier to Kyle's own more advanced setup that allows you to have plain-English conversation with your business data in 30 minutes. We cover a range of options so you can find the right starting point for where you are right now, and understand how far you can take it from there.
If you are a founder, operator or small business owners who is tired of manually looking at data across all your different systems and tools, this is the episode for you.
What You Will Learn
The easiest way to get started with MCP with no technical knowledge requiredWhat a more advanced setup looks like using BigQuery and ClaudeWhy clean data still matters â MCP removes the barrier between you and your data, but it can't fix what is broken underneathThe safety rules that apply to every MCP setupWhat multi-agent systems look like next, and why MCP is the infrastructure that makes them possibleï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
ï»żTimestamps:
00:00 What We've Been Up To This Week
04:43 What Is MCP and Why Does It Matter? A Quick Recap
08:58 Why AI Agents Need MCP to Actually Be Useful
11:28 The Easy Wins: MCP Connectors Already Built Into Claude
18:12 Zapier, n8n and Make: The Next Step Up
23:40 The Advanced Setup: Talking to Your Data Warehouse With Claude
26:28 The Problem MCP Solves: Getting Answers Without a Developer
28:47 Asking Your Data Questions in Plain English
32:41 Democratizing Data Analysis for All Businesses
34:33 Garbage In, Garbage Out: Why Clean Data Still Matters
36:31 Having a Real Conversation With Your Data: Memory and Context in Data Conversations
39:35 Pulling From Multiple Systems in a Single Question
42:27 Where MCP Is Heading in the Next 12 Months
47:03 AI News of the Week: What 81,000 Claude Users Actually Want From AI
49:06 AI Gone Wrong: The Importance of Human Oversight
52:34 Wrapping Up for the Week
Get in Touch
Email: [email protected]
TikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
LinkedIn: @early_adoptr
Resources:
Official Anthropic MCP server list: github.com/anthropics/mcp-serversGitHub MCP server (what we used): github.com/github/github-mcp-serverBigQuery MCP server options: search Smithery.ai for âbigqueryâZapier MCP (no-code entry point): zapier.com/mcpSmithery.ai â browse and discover MCP serversOWASP MCP Top 10 â security reference: owasp.org/www-project-mcp-top-10AI News of the Week: https://www.anthropic.com/features/81k-interviewsAI Gone Wrong: https://fortune.com/2026/03/18/ai-coding-risks-amazon-agents-enterprise/AI Gone Wrong: https://techcrunch.com/2026/03/18/meta-is-having-trouble-with-rogue-ai-agents/Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
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YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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MCP â Model Context Protocol â is the open standard that is quickly becoming the infrastructure layer underneath almost every serious AI tool you will encounter in 2026. It's one of the main reasons that AI is shifting from something you consult to something that acts on your behalf. And like most big developments in this space, it has arrived with both significant opportunity and risks.
In this episode, Kyle and Jess do a full deep dive into what MCP is, why the whole industry has moved on it faster than almost any standard in modern tech, and what the upside looks like for a small business that has never had access to serious AI integrations before. We also cover the cons including some new security risks.
This is part one of two. This week we're tackling what it is, the pros, the cons and some quick wins to make sure you understand what you are dealing with before next week's episode gets into the practical setup, the safety framework, and Kyle's actual tech stack. If you are going to connect AI to your business systems â and increasingly, you will be â this is the episode to start with.
What You Will LearnWhat MCP isHow MCP differs from APIs, and why that distinction mattersWhy OpenAI, Google, and Microsoft all adopted a competitor's open standard within six monthsWhy agentic AI only delivers on its promise if the AI can move fluidly across multiple systemsThe real business advantages: cost efficiency, flexibility, the ecosystem of ready-made connections, and why a cheaper model with good connections beats an expensive one working blindThe risks that matter: over-permissioned access, supply chain vulnerabilities, and a novel attack type called tool poisoninSome practical rules for staying safe with MCP before next week's full setup guideï»żï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:
00:00 What We've Been Up To
06:41 What Is Model Context Protol (MCP) and Why Is Everyone Suddenly Talking About It
17:34 Why MCP Is the Missing Piece for AI That Actually Does Things
22:40 The Real Advantages of MCP for Small Businesses
24:07 The Importance of Your Tool Integrations
27:05 Competitive Advantage through Connected Workflows
29:31 Pros of MCP
31:22 The Downsides to MCP
39:31 Best Practices for Safe MCP Implementation
42:26 AI News: Meta Acquires Molt Book
49:43 AI Gone Wrong: Amazon Pauses AI-Generated Code After Costly Outages
Get in Touch
Email: [email protected]
TikTok: @early_adoptr
Instagram: @early_adoptr
YouTube: @early_adoptr
Get in touch with Early Adoptr: [email protected]
Follow Us on Socials & Resources:
IG: https://instagram.com/early_adoptr
TikTok: https://tiktok.com/@early_adoptr
YouTube: https://www.youtube.com/@early_adoptr
Substack: https://substack.com/@earlyadoptrpod
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OpenClaw is one of the biggest AI stories of the year, and it is generating equal parts excitement and concern. Unlike every other AI tool you have probably used, it does not just respond to questions...it takes action.
In this episode, Kyle and Jess get into what OpenClaw actually is, why the use cases are compelling enough that people are buying spare laptops just to run it, but (most importantly) why the security risks are serious enough that they were both nervous about discussing it at all. From prompt injection attacks to a skills marketplace where nearly one in seven tools has been found to contain malicious code, and a new category of threat called cognitive context theft â this is not a light risk profile. The episode exists because the tool is worth understanding, and because understanding the risks is the only responsible way to approach it.
This episode is slightly more technical than most, and that is intentional. The goal is not to scare you off, but to make sure that if you do decide to experiment with OpenClaw, you know exactly what you are handing over and how to protect yourself.
What You Will LearnWhat OpenClaw is, how it launched, and it's chaotic journey so farWhy the developer behind OpenClaw was acqui-hired by OpenAI and what that signals about where the industry is headingThedifference between AI that advises and AI that actsHow OpenClaw's skills system and the ClawHub marketplace workWhat prompt injection is, how attackers are already exploiting it against OpenClaw users, and why there is no clean solution to it yetWhat cognitive context theft is and why OpenClaw creates a new category of security risk that did not exist beforeReal business use casesHow OpenClaw compares to Claude CoworkWhy setting OpenClaw up safely is a technical undertaking â and what to do if that is not your skill setThe SAVE framework: practical rules for using OpenClaw responsiblyï»żTry GranolaIf you've ever sat in a meeting, taken what felt like decent notes, and then opened them afterwards and they didn't capture anything, Granola is the tool for you. It runs in the background, captures everything, and turns your notes into something you can actually use. Both Jess and Kyle use it, it plays really nicely with Claude, and it is one of our most highly recommended tools
New users get 100% off their first month using our link: granola.ai?via=early-adoptr
Timestamps:00:00 Introduction and What We've Been Up to This Week
02:33 What Is OpenClaw and Why Is Everyone Talking About It?
05:17 Understanding OpenClaw: How OpenClaw Actually Works
08:05 OpenClaw vs. Cloud Cowork: Key Differences
10:55 Exploring OpenClaw's Skills System
13:26 Use Cases and Potential Applications of OpenClaw
16:28 Pros of Using OpenClaw
19:13 Cons of Using OpenClaw
21:40 Final Thoughts on OpenClaw
28:17 Cons of OpenClaw
36:49 Practical Guidance for Safe Usage
47:54 Framework for Safe OpenClaw Usage
50:32 AI News of the Week: Perplexity Launches Perplexity Computer
54:30 AI Gone Wrong: Woolworth's Chat Bot
57:54 Wrapping up for the week
Get in TouchEmail: [email protected]
Follow us: @early_adoptr on TikTok, Instagram, YouTube, and LinkedIn
Resources:
https://www.malwarebytes.com/blog/news/2026/02/openclaw-what-is-it-and-can-you-use-it-safely https://www.gendigital.com/blog/insights/leadership-perspectives/how-to-use-openclaw-safely https://medium.com/@srechakra/sda-f079871369ae https://shawnkanungo.com/blog/how-to-use-openclaw-safely-best-practices-and-security-tipshttps://www.perplexity.ai/hub/blog/introducing-perplexity-computerhttps://www.bbc.co.uk/news/articles/cy7jeyeyd18oGet in touch with Early Adoptr: [email protected]
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The QuitGPT movement has been spreading across Reddit and Instagram, with people canceling their ChatGPT subscriptions for reasons ranging from political concerns to product frustration to simple curiosity about what else is out there. Whatever you think of the movement itself, it has done something actually useful: it has made a lot of people stop and ask whether ChatGPT is actually the best tool for what they need.
In this episode, Kyle and Jess break down four of the strongest ChatGPT alternatives â Perplexity, Gemini, Mistral, and Claude (yes, we know about DeepSeek and Grok and we have reasons for not covering them) â covering what each one is actually good at, who it is for, and where it falls short. This is not a ChatGPT takedown. It is a practical guide to understanding the alternatives, and why sometimes ChatGPT isn't the best tool for the job.
If you are a founder, operator, or small business owner who has been defaulting to ChatGPT out of habit, this episode will help you make a more deliberate choice.
For a deep dive into Claude Co-work, check out our recent episode - https://shows.acast.com/early-adoptr/episodes/claude-cowork-explained-can-ai-really-organize-your-files-an
Key Topics CoveredWhat the QuitGPT movement is and why it startedHow to build a practical AI stack on a limited budgetThere is no universally best AI tool. There is the best tool for your specific job, your budget, and where your team already works.Tools Covered in This EpisodePerplexity (perplexity.ai) â AI-powered research with cited sourcesGoogle Gemini â AI integrated into Google WorkspaceNotebook LM â Google's document-based research tool, free to useMistral / LeChat â open weight AI models with EU hosting optionsClaude (Anthropic) â deep reasoning, long document analysis, agentic capabilitiesClaude Cowork â desktop AI agent for file and document managementClaude Code â AI-assisted coding for developers and technical foundersClaude for Excel â spreadsheet automation within Microsoft ExcelTimestamps:
00:00 What We've Been Up To
03:18 So You're Thinking of Breaking Up with ChatGPT?
09:35 Perplexity: The Best AI Tool for Research
16:07 Google Gemini: The Strongest AI Option for Teams Already in Google Workspace
21:59 Mistral: The Best AI Choice for European Businesses and Regulated Industries
33:07 Claude: The Strongest AI Tool for Deep Analysis, Long Documents, and High-Stakes Work
37:23 Building Your AI Tech Stack
41:51 AI News: Anthropic's Safety Policy Shift
47:20 AI Gone Wrong: Robot Vacuum Army & Even AI Safety People Go Wrong
53:38 Wrapping Up
Get in TouchTikTok: @early_adoptr
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A new study from the National Bureau of Economic Research made headlines with a blunt claim: AI has had no measurable impact on productivity. Kyle and guest co-host Sean (filling in for Jess) do what most people never bother to do...they actually read the full 70-page report! What they find is far more interesting, and far more useful, than the headline suggests.
Here's what the headline buried: firms with the highest productivity (measured by sales per employee) have AI adoption rates of around 80%. The lowest performers? Closer to 40%. Companies generating $500K per employee are nearly twice as likely to be using AI as those generating $10K. The gap is already widening, and it has nothing to do with which tools you're buying.
This episode breaks down why flat productivity numbers are completely normal for a technology only three years into mainstream adoption, what history tells us about what comes next (spoiler: the Solow Paradox predicted this exact moment back in 1987), and why the organizations that move now are setting themselves up for the J-curve surge that's coming. It is not a story about failure. It is a story about timing, organizational readiness, and what you should be doing right now to be on the right side of that gap.
If you are a founder, operator, or small business leader wondering whether AI is actually delivering (or whether you have been wasting your time_ this episode gives you the honest, grounded answer. Plus practical frameworks you can start using this week.
Our guest this week is Sean, a partner at Breakthrough Growth Partners, where he advises founders, operators, and leadership teams on growth strategy and AI adoption. Website: https://breakthroughgrow.com
What You'll LearnWhy flat AI productivity numbers are expected and what history tells us about what comes nextThe key difference between companies seeing results and those that are not (it is not the tools)What the "agility gap" is and why smaller, newer organizations have a structural advantage right nowHow to assess whether your organization is actually ready to benefit from AIFive practical frameworks for accelerating real AI adoption in your businessWhy many high-profile "AI-driven" layoffs were actually driven by macroeconomic factorsTimestamps:
00:00 Introduction
01:59 The NBER Study Everyone Misread (And What It Actually Says)
06:07 78% of US Firms Are Using AI â So Why Aren't We Seeing Results?
09:46 The Solow Paradox: We've Seen This Productivity Lag Before
13:20 High Performers vs. Low Performers: The AI Adoption Gap Is Already Widening
17:08 The Agility Gap: Why Smaller, Newer Companies Have the Upper Hand Right Now
20:43 AI and Job Losses: Separating the Real Data from the Corporate Narrative
24:26 What Happens When You Automate Away Entry-Level Roles
28:34 The J-Curve: Are We Finally Coming Out of the Dip?
32:05 Model Wars and Falling Prices: What Fierce AI Competition Means for Your Business
36:01 Same Cost, 10x the Capability: How to Think About AI Value Today
36:56 The Tool Is Becoming a Commodity â Your Implementation Strategy Is Not
37:54 Five Frameworks for Getting Real Productivity Gains from AI
39:36 The Three Frameworks That Turn AI From a Buzzword Into a Business Process
46:34 Is Your Business Ready for AI to Accelerate It â or Just Accelerate Its Problems?
50:15 The Productivity Surge Is Coming â Here's How to Be Ready When It Lands
51:06 AI News: OpenClaw Goes to OpenAI: What It Means for Agentic Security
56:36 AI Gone Wrong: Grok's Nutrition Initiative - A Case Study in Missing Guardrails
Get in Touch with Early AdoptrFollow us: @early_adoptr on TikTok, Instagram, and YouTube
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Claude Cowork is generating serious buzz as Anthropic's latest feature, but the name undersells what it actually does. This isn't collaboration software, it's a desktop AI agent that can read, create, edit, organize, and manage files on your local computer through plain English instructions.
In this episode, Kyle and guest co-host Sean break down what Claude Cowork actually is, how it works, and why it represents a major change in how we interact with AI tools. They explore what Cowork is, how it works, practical use cases and the real risks of giving AI access to your local files. We also cover the Super Bowl's AI advertising blitz and the spectacular failure of AI.com's $85 million launch.
What You'll LearnHow to set up and use Claude Cowork safely on your desktop without risking your filesPractical workflows for expense reports, file organization, research synthesis, and data cleanupWhy Cowork represents a major step up the "ladder of autonomy" from advisor AI to active participantThe real security risks of local file access and how to mitigate them with narrow permissionsBest practices for testing AI automation: start small, supervise closely, expand slowlyWhy the automation trap is more dangerous than dramatic failuresHow to create dedicated working folders and maintain oversight as AI handles more tasksKey TakeawaysClaude Cowork makes agentic AI accessible to everyone.Start with dedicated folders, not your entire hard drive.The automation trap is more insidious than obvious errors.Prior proper planning prevents poor performance.We're shifting from doing work to directing work.Timestamps:
00:00 What We've Been Up to This Week
03:12 What is Claude Cowork and What Does It Actually Do?
06:45 Claude Cowork: Moving Up the Ladder of Autonomy
08:43 What Cowork Actually Does: Reading, Creating, and Organizing Files
10:46 The Infinite Intern Gets Smarter
13:19 How to Set Up Cowork
16:21 Why Cowork Only Sees What You Allow
18:02 Why Now? The Tech Behind Agentic Workflows for Non-Technical Users
27:35 Practical Cowork Use Cases
35:32 Should You Label AI-Generated Content?
36:17 AI Tools: Features vs. Products
36:59 What Are the Risks of Using Cowork?
44:58 Best Practices for Using Cowork
51:12 From Clicking Buttons to Describing Outcomes: The Shift in AI Interaction
53:05 AI News of the Week: The Super Bowl Hype Cycle
59:38 AI Gone Wrong: AI.com
Get in touch with Early Adoptr: [email protected]
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ChatGPT âappsâ have been getting a ton of hype since OpenAI opened submissions in December. The pitch is simple: this is the iPhone App Store moment for AI â build once, tap into hundreds of millions of users, and ride the distribution wave.
In this episode, Jess and Kyle unpack what ChatGPT apps actually are (and what they arenât). They break down the difference between apps, plugins, and custom GPTs, why the Apple comparison falls apart fast, and what the underlying architecture (MCP servers + in-chat widgets) means for builders who care about customer ownership, data, and monetization.
We also cover the buzziest news story in a while: Moltbook and OpenClaw (formerly âClawdbotâ), the viral âagents social networkâ story.
What Youâll LearnWhat a ChatGPT app is and how it differs from plugins and custom GPTsWhy âApp Store momentâ is an oversimplification and what the real opportunity isThe mall kiosk vs storefront analogy: distribution without owning the customer relationshipWhere ChatGPT apps genuinely reduce friction (and where they add it)The practical constraints developers are hitting right nowHow MCP changes the game for interoperabilityWhat the Moltbook/OpenClaw incident reveals about security, hype, and âagent cultureâ narrativesTIMESTAMPS:
00:00 Introduction and Weekly Updates
06:41 ChatGPT App Store Launch and Overview
19:25 Understanding ChatGPT Apps vs. Plugins and Custom GPTs
28:57 The Model Context Protocol and Its Implications
33:00 The Future of AI Models and Ecosystems
36:05 Invisible Apps and Personal AI Agents
38:54 Navigating the ChatGPT App Submission Process
39:49 Exploring ChatGPT Apps for Users
43:02 Building ChatGPT Apps: Key Considerations
51:04 Evaluating the Viability of ChatGPT Apps
52:53 Moltbook and ClawdBot/Openclaw
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Synthetic data is often pitched as a shortcut around slow, expensive market research. In this episode, we break down when that promise holds, and when it falls apart.
This week, we welcome our guest Lee Henshaw, founder and AI marketing guru, to share how he actually uses synthetic respondents in real business decisions. From testing pricing and sales messaging to simulating focus groups of UK media buyers and retail CMOs, Lee walks through what works, what doesnât, and where founders can get into trouble if they over-trust the output.
This episode introduces a practical, risk-based approach: use synthetics for speed and direction, validate with real people when the stakes are high, and design research around decisions, not curiosity. If you want better customer insight without a six-figure research budget, this episode shows whatâs realistically possible right now.
Listen to our previous episode for the basics on synthetic data - https://shows.acast.com/early-adoptr/episodes/synthetic-data-without-the-hype-practical-uses-and-real-risk
Make sure to check out Lee's course on Maven:
https://maven.com/dino-myers-lamptey-lee-henshaw/the-marketer-in-the-loop https://www.linkedin.com/in/leehenshaw/ What Youâll LearnHow synthetic respondents differ from traditional synthetic datasetsWhen synthetic research is useful for fast decision-making, and when itâs riskyHow to design synthetic focus groups that mirror real buyer segmentsA decision-first approach to market research that reduces wasted effortHow to validate synthetic insights against real customer feedbackKey Topics CoveredSynthetic respondents vs synthetic datasetsPrompting and validation strategies for synthetic focus groupsRisk-based decision frameworks for using AI research toolsBackward market research and the âphantom reportâ methodIterative follow-up in synthetic interviewsLarge-scale qualitative analysis using AI agentsAccuracy, bias, and trust issues in synthetic dataHow agencies are incorporating synthetic research into client workGaps in market research training among marketersTimestamps:
00:00 What We've Been Up to This Week
03:41 Synthetic Data Explained: A Quick, Practical Recap
07:45 Meet Lee Henshaw: Using AI for Real Market Research
10:28 âBrains in a Jarâ: What Synthetic Respondents Actually Are
12:42 Predicting The Traitors With Synthetic Data
15:22 Pricing With Synthetic Focus Groups: A Real Synthetic Research Example
19:37 Talking to Retail CMOs Using Synthetic Focus Groups
23:20 Can You Trust Synthetic Data? Accuracy, Bias, and Validation
28:18 How to Build and Engineer Synthetic Respondent Audiences
31:44 Why Secondary Market Research Still Matters
35:15 Backward Market Research: Start With the Decision
38:57 Common Mistakes & Top Tips When Using Synthetic Respondents
50:16 AI News of the Week: World Models and Whatâs Next
01:00:31 AI Gone Wrong
01:03:29 Where to Find Us
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