AI is moving fast, but most advice about AI for small businesses or founders assumes you already know what you’re doing. Early Adoptr is a weekly AI podcast for founders, entrepreneurs, and small business owners who want to understand how to actually use AI tools in their business, without needing a technical background.
Hosts Jess and Kyle spend their weeks testing tools like ChatGPT, Claude, Perplexity, and Gemini, working out what they actually do for real businesses. Each episode breaks down the most important trends in AI tools, automation, and workflows, answering one simple question: is this worth your attention, and how do you use it in practice?
From prompt engineering and AI agents to vibe coding and AI search optimisation, they cut through the hype and jargon to give you clear, practical guidance. Think of it as having two slightly obsessed friends who try everything first and report back.
New episodes every week on Spotify, Apple Podcasts, YouTube, and wherever you listen to podcasts.
Just Because You CAN Vibe Code....SHOULD You Vibe Code? (w/ Sean Bhardwaj)
Season 1 · Episode 63
Wednesday, July 29, 2026 • Duration 01:06:51
Vibe coding can build you almost anything now. A customer database, a little internal tool, a whole CRM in an afternoon. But some of those things could land you in a world of GDPR pain, and one or two you should never build at all.
In this final episode of the vibe coding series, Jess and Kyle are joined again by Sean Bhardwaj to work out where the line sits for a non-technical founder. They get into why building has become the easy part while distribution (aka getting people to actually use what you make) is still the hard part. They cover what belongs in the never-vibe-code pile, how much time it really takes to learn this properly, and why the answer to whether you should comes down to what you are actually trying to do.
Vibe coding gives almost anyone the ability to build a working app in an afternoon. In fact, around 63% of people building this way right now have no coding background at all. But once that app is live, the job of keeping it secure, monitored, and running for real users is as hard as it has always been, and if you don't know what you're looking for, you can open yourself to all sorts of risk.
In this episode, Jess and Kyle cover what it actually takes to keep an AI-built app safe and running once it's in front of real users. They break down the concept of vibe slop, what type of security risks are common for vibe-coded apps, and the operational layer that sits between deploying an app and running it safely, including configuration, access control, monitoring, and dependencies. They also walk through a security checklist you can run before anyone else uses what you've built, and more importantly, when it's time to hand what you've built over to a professional.
What You'll Learn
What vibe slop is, and why most AI-built apps have it without their builders realising
Why AI-generated code carries a specific category of security risks
The operational layer between building something and running it safely, covering configuration, access control, monitoring, and dependencies
A practical five-step security checklist to run through before anyone else touches what you've built
How to tell when your app has outgrown what you can safely manage, and what that decision actually looks like in practice
Use these links for a discount on the tools we recommend (and it supports the pod!)
Granola - The best AI meeting notes! New users get 100% off for their first month -
A Year in AI: What Changed, What We Got Wrong, and What's Coming Next
Season 1 · Episode 53
Wednesday, May 20, 2026 • Duration 58:22
A year in AI doesn't feel like a normal year, it feels like about five. The AI landscape has shifted faster in the last twelve months than most businesses could track, let alone act on. For Early Adoptr's first anniversary, we're taking the opportunity to map what actually changed: the model landscape, the rise of MCP, what happened to agents, and what's worth paying attention to in the next twelve months.
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 Learn
Why reasoning models went from a premium add-on to the default , and what that shift enabled for agents and complex workflows
How 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 correspondence
Why 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 stack
What MCP actually solved and why it's the reason agents went from demo-quality to deployable for non-technical teams
hat the two-tier internet is and why it decides whether AI recommends your business or your competitor's.
Why ChatGPT's instant checkout failed commercially and what it tells us about how brands are learning to use AI for discovery
What AEO — Answer Engine Optimisation — means for any business that needs to be found online
How the security risk picture changed once agents got real access to real tools via MCP
What agent debt is
What outcome-based pricing means
Resources and Links
What Should You Actually Be Asking About AI?
Season 1 · Episode 52
Wednesday, May 13, 2026 • Duration 46:51
Most people start their AI journey by asking how to save time. That is not a wrong question — 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 unusual
The difference between efficiency gains and capability gains
How to identify your "boring middle" and what to do once you have sorted it
How to prevent your freed-up time from get absorbed back into more of the same
How a delivery driver and landscape gardener from the illustrate capability gains
What the Pocket OS incident reveals about AI agent permissions, and the simple rule that would have prevented it
Try Granola
If 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
What It Really Takes to Move Your Team Forward With AI (w/ Rob Webster)
Season 1 · Episode 51
Wednesday, May 6, 2026 • Duration 40:04
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 tasks
Why AI multiplies what you're doing
How junior employees can move faster and take on more accountability earlier when they have AI as a working layer
How 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 it
How leaders can model AI adoption in a way that actually moves teams forward
Try Granola
If 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:
Get in Touch
Stop Building AI Agents the Hard Way: Lessons from 25 Years in AI (w/ Rob Webster)
Season 1 · Episode 50
Wednesday, April 29, 2026 • Duration 51:53
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 plan
The four components every working agent needs and the reason most agent setups fail to produce useful output
Why the most valuable skill in AI right now has nothing to do with technology, and how anyone can develop it
What human-in-the-loop looks like as a working habit rather than a safety concept
How to start with a "Fisher Price Agent"
Rob's tips for getting unstuck when you hit a wall
Try Granola
If 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
Get in Touch
AI Agents Explained Through One Real Business Use Case
Season 1 · Episode 49
Wednesday, April 22, 2026 • Duration 59:26
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 Learn
How to tell which steps in your workflow genuinely benefit from AI and which ones are better handled by a simple automation
What it actually means to add an AI agent to a business workflow, and how that differs from using a chat tool like Claude or ChatGPT
How AI agents become more capable and more autonomous at each level — and what that progression looks like applied to a single, familiar business process
Why 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 sophisticated
What the real security and risk considerations are when AI starts taking actions on your behalf, with practical guidance on how to approach permissions and access
Why the most advanced level of AI autonomy is worth understanding and what goes wrong for businesses that skip the basics
Try Granola
AI Agent Frameworks Explained: The Five Things Every Agent System Needs
Season 1 · Episode 48
Wednesday, April 15, 2026 • Duration 01:02:38
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 Learn
What an AI agent framework actually is
The five components every agent system needs to work — model access, tools, memory, coordination, and human oversight
Why decision logic mapping is important before you build anything
How to automate a real business process, using inbound enquiry handling as a worked example, from writing your decision logic through to rolling out with guardrails
The difference between CrewAI, LangChain, and LangGraph, and which situations each one is suited to
Inbound enquiry automation as a practical use case
Human-in-the-loop and why it matters as agents gain more access
Try Granola
If 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
What Is an AI Agent? A Plain-English Guide for Business Owners
Season 1 · Episode 47
Wednesday, April 8, 2026 • Duration 01:01:15
"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 Learn
What actually makes something an AI agent
The difference between an AI agent and agentic AI and why the two get confused constantly
How to use the Ladder of Autonomy to assess any AI tool or workflow — and where Cowork, OpenClaw, and Perplexity Computer each sit on it
What the key failure modes are for agents in live business environments, and the questions you should be asking before you deploy anything
The Anthropic Claude Code source code leak and what it revealed about unreleased features and how far ahead the labs are building
Try Granola
If 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:
Claude Skills Explained: How to Stop Repeating Yourself in Every Session
Season 1 · Episode 46
Wednesday, April 1, 2026 • Duration 59:36
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 either
How Skills, MCP, Projects, and memory all fit together and when to reach for each one
Where 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 knowledge
Why the skill description is an activation condition, not a title, and what to do if your skill is not triggering
What OpenAI shutting down Sora and consolidating its products signals about where the money is actually flowing in AI right now
Why the window where small businesses can run the same AI stack as enterprises is real, and why it probably will not stay open indefinitely
If 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
What 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?
If 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
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