Small Steps with AI

21 - Why Your Chatbot Always Says Yes And Keeps Going

Wed, 19 Aug 2026 04:00:00 -0500

I looked up from my screen one night and it was two in the morning. That’s not how my nights normally go, I’m usually the one who taps out first in any group project. But I was deep in a presentation with ChatGPT, and it just kept going and going with no end in sight, because it never got tired, never yawned, never suggested we call it quits. It turns out there’s a name for what happened to me that night, and researchers are actively studying it. This week I’m talking about AI exhaustion, AI sycophancy, and why you have to be the one who calls it a night, because the AI never will.

AI Brain Fry Is a Real, Studied Phenomenon

A Boston Consulting Group study published through Harvard Business Review coined the term “AI brain fry,” the mental fatigue that comes from working with AI past what your brain can actually handle. Unlike a human collaborator, AI gives you none of the natural fatigue cues, no yawning, no “let’s pick this up tomorrow,” that normally tell you when to stop.

Endless Reasonable Directions, No Natural Finish Line

On top of never tiring, AI constantly hands you new, plausible-sounding directions, one more marketing idea, one more spinoff project, one more tip sheet. Every single branch sounds worth taking, which means there’s no natural point where the path ends and you’re actually done.

AI Sycophancy and the One-Point Gap

Chatbots are trained to be agreeable because people rate agreement and enthusiasm positively, and researchers at Aalto University found that people using ChatGPT for logical reasoning problems improved their actual performance by about three points while believing they’d improved by about four. That one-point gap between real help and felt help is the whole risk in miniature, and other research shows AI measurably inflates people’s confidence in their own work.

Turning Your AI Into a Devil’s Advocate

The different AI tools have real personality defaults, ChatGPT tends naturally positive, Claude naturally neutral, Grok naturally more of a devil’s advocate, but you can ask any of them to challenge you instead of cheerlead you. I walk through how I’ve started training my own AIs to push back and question my priorities instead of just saying yes.

Building Real Guardrails

Since AI won’t call it a night for you, you have to build the stopping points yourself: a nighttime routine with reminders, a timer before you open the chat window, and a “parking lot” list (mine lives in Notion) for the extra ideas AI generates so they don’t pull at your attention until you’re ready for them.

If you’ve had your own two-in-the-morning moment with AI, you’re not undisciplined, you’ve just run into a well-documented gap between how AI works and how humans are built to work. This week, before you open that chat window, set a timer and give yourself a real stopping point.

Jill’s Links

https://jillfromthenorthwoods.com/ - Find my podcasts, videos, downloads and ways to contact me

https://www.buymeacoffee.com/smallstepspod

https://x.com/NorthWoodsJill

Email the podcast at jill@startwithsmallsteps.com

By choosing to watch this video or listen to this podcast, you acknowledge that you are doing so of your own free will. The content shared here reflects personal experiences and opinions and is intended for informational and educational purposes only. I am not a software developer, data scientist, or AI professional. Any tips, tools, or suggestions offered should not be considered a substitute for professional technical advice. AI tools and platforms change frequently — always verify current features, pricing, and terms directly with the providers. You are solely responsible for any decisions or actions you take based on this content.

20 - I’m Not a Coder. AI Built My Database Anyway

Wed, 12 Aug 2026 04:00:00 -0500

I have years of bird sightings sitting in a file I didn’t know what to do with. I also had an idea for a book series that might run 50 books, with no idea how to research it. AI knew exactly what to do with both. This episode is about the gap between having an idea for what you want to do with a pile of data — and actually knowing how to do it — and how “vibe coding” closes that gap without you ever needing to know what a loop is.

The Problem With a Pile of Data

Most of us assume that a pile of recipes, receipts, genealogy records, or knitting patterns needs a developer or a boot camp to turn into something useful. That assumption is exactly why so many good projects — including some of my own — go to die in a drawer.

What Counts as “Data”

It doesn’t have to be technical. A recipe box, a gift list, an unsorted pile of photos, a home inventory in case of fire — any of these can become something you can actually ask questions of, using tools like Notion, Apple Notes, Google Sheets, or a Word document. And if you don’t know which tool fits, you can just ask AI.

My Birding Database, Solved

Years of flat-file bird sightings became a structured Notion database I can now query in plain English — every location I’ve seen a species, when I last saw a particular bird in my area. No coding knowledge required on my end.

The MIRRORS Project Breakthrough

A late-night conversation turned my long-running Bible character study idea into an actual plan: a website, a quiz, and a Notion research structure to support it — built out in about five minutes after I described what I wanted in plain language. Claude even wrote a constitution for the project’s own working rules.

How I Actually Use the Two AI Tools

Claude is my professional assistant for structured, technical work inside Notion. ChatGPT knows my history and context from months of use. Passing a task back and forth between the two — questions from one, answers filled in by the other — is part of how this actually got built.

A Word of Caution

If AI ever asks you to paste a command into Terminal or Command Prompt, be careful — don’t run something you don’t understand, and if possible, have someone who does know check it first. There have been real cases of malicious instructions disguised this way.

The small step for this week: find your own messy pile — books read, movies watched, whatever it is — and just tell AI in plain English what you’d like to do with it.

I’m Jill from the Northwoods. Email me at northwoodsjill@fastmail.com or find everything at jillfromthenorthwoods.com.

Jill’s Links

https://jillfromthenorthwoods.com/ - Find my podcasts, videos, downloads and ways to contact me

https://www.buymeacoffee.com/smallstepspod

https://x.com/NorthWoodsJill

Email the podcast at jill@startwithsmallsteps.com

19 - AI Literacy 101 - Let the Tool Introduce Itself

Wed, 05 Aug 2026 04:00:00 -0500

When you first got into AI, did you find yourself wondering what you were even supposed to do with it — like being handed a toolkit with no idea what to build? Most of us default to asking “what can I do with this tool,” but there’s a much more useful question we rarely think to ask: what can the tool tell us about itself?

AI Can Explain Itself Back to You

Unlike a toaster or a word processor, AI can answer direct questions about its own behavior — what it’s connected to, why something failed, and what it can and can’t do. Most people never think to ask because we’ve spent our whole lives with tools that don’t talk back.

Turning an Error Into a Two-Second Fix

When an integration kept erroring out on a writing-grade request, asking “why are you giving me this error?” surfaced the real cause — and a workaround — instantly. The same approach worked when a graphic tool wouldn’t resize an image the way it was asked to.

Why Different AI Tools Behave Differently

ChatGPT and Claude don’t handle the same request the same way, and that’s not a bug — different companies build their models around different priorities. Asking an AI directly why it behaves differently than another tool gets a real, specific answer.

Asking the Questions That Actually Matter

If an AI is connected to your email, your calendar, or your design software, you’re right to be curious — and cautious. A well-built AI tool should be able to tell you plainly what it can see, what it can act on without asking first, and what it will always check with you before doing.

The Monkey’s Paw Problem

AI does exactly what you ask — but if the way you asked was wrong, you can get an unintended result. That’s exactly why treating this as a two-way conversation, rather than a one-way prompt-and-answer transaction, matters so much.

The small step this week: pick something your AI is connected to and ask it directly how that connection actually works, why it does what it does, and what it should be doing differently. Treat it less like reading a manual and more like asking a new coworker how their team handles things.

Standard Footer (reuse as-is — no need to re-read)

Website: jillfromthenorthwoods.com

Email: northwoodsjill@fastmail.com

I’m Jill from the Northwoods — appreciate you being out there.

Jill’s Links

https://jillfromthenorthwoods.com/ - Find my podcasts, videos, downloads and ways to contact me

https://www.buymeacoffee.com/smallstepspod

https://x.com/NorthWoodsJill

Email the podcast at jill@startwithsmallsteps.com

18 - How I Introduce Myself to Every New AI I Try

Wed, 29 Jul 2026 05:00:00 -0500

Most people open a new AI chat the same way they’d open a search bar — straight into a question, no context, no introduction. Then they wonder why the answers feel generic. In this episode, I walk through the three specific moves I make every time I start with a new AI (or reintroduce myself to one I’ve been using for a while), and why treating it like a relationship instead of a transaction changes what you get back.

Move One: Bring Your “AI Knows Me” Document

When I got locked out of my ChatGPT account, I didn’t start from zero — I had already asked it to summarize what it knew about me, and I imported that into a new AI. I walk through how to build this document and why it should never be treated as finished.

Move Two: Beyond Facts — Temperament, Not Just Biography

Facts are easy. What actually changes how AI talks to you is knowing your temperament — how you process things, what sets you off, what doesn’t. I share the specific instruction I gave AI about not treating me like an anxious person, and why that mattered.

Move Three: Give It a Story, Not Just a Bullet Point

I tell the story of driving a broken-down van through a Northwoods blizzard at eleven years old, and why sharing something like that tells an AI more about your temperament than any preference list could.

What You Put In Is What You Get Out

A generic answer isn’t a failure of the AI — sometimes it’s exactly what you wanted, and that’s fine as long as it’s on purpose. I talk through how to be intentional about when you want a robot answer versus something personalized.

Ask AI to Argue With You

Instead of using AI as an echo chamber, I use it as a pressure test — asking it to fact-check me and hand me the strongest counterarguments to my own position.

Standing Rules vs. One-Time Comments

The difference between a permanent instruction (“never suggest medical scenarios to my anxious friend”) and a temporary one (“don’t let me go down rabbit holes until after this test”), and why naming that distinction out loud actually works.

This isn’t a one-time setup. It’s a relationship that builds over time, the same way any relationship does. Next episode, we flip it around — how AI can introduce itself to you.

Jill’s Links

https://jillfromthenorthwoods.com/ - Find my podcasts, videos, downloads and ways to contact me

https://www.buymeacoffee.com/smallstepspod

https://x.com/NorthWoodsJill

Email the podcast at jill@startwithsmallsteps.com

17 - Continuity and Patterns Are AI’s Real Superpower

Wed, 22 Jul 2026 06:00:00 -0500

Most people ask AI for information. But what happens when you start giving it information — about you — so that it starts to understand you back? That’s the question behind this episode, built out of about six months of my own quiet experiment, plus what happened when I finally put it into words at a conference last week.

The Dan Pink Experiment

This whole idea traces back to Dan Pink, who gave AI a set of pointed questions — what are my blind spots, what lies do I tell myself, where do you see me in five years — and told it to act like a brutally honest trusted advisor. I’d been doing versions of this for months without realizing I was running the same experiment.

Why AI Gets More Useful the Longer You Use It

Unlike a search engine that resets every time, AI accumulates context — every show I’ve recorded, my childhood, retirement plans, even a plumbing problem in my basement. That accumulated history is what turns a tool into something closer to a conversation partner.

What Months of “Friday Night Conversations” Added Up To

I didn’t realize how many separate conversations — about work, about weight loss, about my podcasting habits — were quietly circling the same handful of themes until AI assembled them into one picture. The insight wasn’t new information. It was old information finally put together.

Continuity, Not Intelligence

The real value here isn’t AI being smart — it’s AI remembering. It’s the ability to aggregate months of scattered conversations into one coherent thread, which is a genuinely different kind of usefulness than answering trivia.

The Big Caution: What AI Can’t Actually Know

I’m honest in this episode about the limits, especially around any question that asks AI what other people think of you behind your back. It has no access to that — whatever it gives you is invented, delivered with the same confident tone as everything else.

The “Dear Me” Letter

One of my favorite exercises from this whole process: asking AI to write you a kind letter, as if it were you, pointing out what you’re missing about yourself. Mine had some real blind spots in it I wasn’t expecting.

The goal was never to let AI define me — it’s to let it be a thoughtful conversation partner that helps me examine my own thinking over time. If you try this, I’d genuinely love to hear what came back for you.

You can find everything I do at jillfromthenorthwoods.com. My email is jill@startwithsmallsteps.com.

Dan Pink's Video https://www.youtube.com/watch?v=PjTaZvvOtxU

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

16 - How to Use AI to Build a Routine That Fits You

Wed, 15 Jul 2026 05:45:00 -0500

I had a conversation with AI that started with a simple question about my bedtime routine and somehow ended up covering ADHD, extroversion, remote work, and retirement. If you’ve ever felt like a failure because someone else’s five-a.m.-cold-plunge morning routine never stuck for you, this one’s for you.

Confessions of a Productivity Apostate

I host productivity podcasts, and I still think there’s no single “right” system. Every guru sells the same package — wake at five, journal, cold plunge — and I tried, and it fell apart every time. Not from lack of discipline. It just wasn’t my pattern. AI didn’t hand me a routine; it helped me build my own from the ground up.

Rethinking the Bedtime Routine

I go to bed around 11, play a few phone games, listen to an audiobook, and fall asleep — and it works. But a sleep expert’s “your bed is only for sleep” advice made me second-guess it. AI drew the real distinction: there’s a difference between lying awake frustrated (the pattern sleep experts want to interrupt) and a genuine wind-down routine. Mine was already the second thing.

Productive vs. Restorative — and the Shutdown Sequence

Editing or recording podcasts right before bed activates my extrovert brain instead of calming it. AI helped me separate productive activities from restorative ones, and we built what I now call a “shutdown sequence” — vitamin D, reading, knitting, Bible reading, prayer, each one quieter than the last, borrowed straight from how IT servers gracefully power down.

Solving the Real Morning Problem

I assumed my morning fog was leftover insomnia. It wasn’t. AI asked what happened when I went birding, hit the gym, or traveled for work — and the pattern was obvious: I wake up around people. The pandemic just broke my old habit of built-in social contact before the workday started.

Ending the Exercise Negotiation

The real problem wasn’t willpower — exercise itself takes 20 minutes, but the negotiation with myself took hours. AI’s fix: keep a protein drink upstairs, exercise before ever going downstairs, before the day’s demands start competing for attention. It also helped me see this as bigger than mornings — I’m about seven years from retirement, and this was really about designing a low-friction life, not an optimized one.

What AI did wasn’t give me someone else’s routine. It helped me discover the one that was already mine.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

15 - AI for Hobbies (No Coding Required), A Birder’s Story

Wed, 08 Jul 2026 06:00:00 -0500

Binoculars around my neck, standing at the edge of a marsh — that’s how I started this morning, and it’s also how I want to start this episode, because today is about something practical: what AI actually looks like when you use it for a hobby you love, when you’re not a technical person and have zero interest in becoming one.

Turning 15 Years of Data Into Something Usable. My friend and I have logged over 800 birding trips and 12,000 entries in eBird. This winter I moved all of it into Notion — not by writing a single line of code, but by describing in plain English what I wanted (a timeline by species, by location, by date) and letting Claude build the structure and populate the data. No programming required, just asking it real human questions about my own data.

Telling Similar Species Apart. Every birder has a story about getting an ID confidently wrong. Mine involves snow geese and Ross’s geese — nearly identical to the casual eye. I had AI build me a side-by-side infographic comparing bill shape and markings, something I now keep on my phone and pull up in the field. It’s saved my birding buddy and me real misidentifications.

Voice Logging on the Move. When I’m hitting multiple locations in a single outing with 40 species to keep track of, I log sightings out loud through CarPlay between stops instead of trying to hold it all in my head.

Real-Time Identification Help. When I see something unfamiliar, I can describe it out loud or photograph it and ask AI to walk through the possibilities with me — and just as importantly, ask it to explain why, so I get better at identifying that species myself next time instead of just getting handed an answer.

From “What Is This Bird?” to “Why Is This Happening?” This is the shift that surprised me most. Blue feathers stuck to a Canada goose, an unusually tired eagle being harassed by red-winged blackbirds, a strange concentration of loons, crayfish migrating overland — these moments used to just be “huh, that’s odd.” Now they turn into real conversations about behavior, migration, and habitat that genuinely shape what I want to talk about on this podcast.

You Don’t Have to Be Technical. The point isn’t birding specifically — it’s that you can describe what you want in plain English, whether that’s a birding database, a yarn inventory, or anything else tied to a hobby you love, and AI can build the tool without you needing to understand anything about how it works under the hood.

If you have a hobby with years of scattered data or recurring questions you’ve never had a good way to track, this is exactly the kind of campfire conversation worth starting.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

14 - Campfire vs. Vending Machine — Two Ways to Use AI

Wed, 01 Jul 2026 06:00:00 -0500

I was listening to a podcast interview with a former CIA hacker talking about AI, and his framing of how people use it was so different from my experience that I had to stop and figure out why. His version had AI as something that happens to you — a validation dealer, a passive addiction. My version is completely different. And I think the difference has a lot less to do with the AI and a lot more to do with who shows up at the keyboard.

Calculator vs. Campfire. I have a friend who uses AI exactly like a search engine: ask a question, get an answer, close the tab. That’s a completely legitimate way to use it. But most of us have been trained to use computers that way for 25 years. Type a thing, get a result, move on. We never practiced wondering out loud with a computer — because until very recently, no tool could do that. AI can. Most people never find out because nobody told them there’s another gear.

Two Modes Available. The campfire conversation: you show up with a half-formed idea, a weird observation, a question that’s been nagging you, and you follow it wherever it goes. The vending machine: you walk in with a question, you get your answer, you leave. Both are available every time you open AI. You usually get to choose which one you’re having — and most people default to vending machine because that’s all they know.

You Shape the AI More Than It Shapes You. Here’s what the CIA hacker missed: the agency is on your side. The AI will become whatever role you hand it. Research assistant, thinking partner, sounding board, writing partner, devil’s advocate. That isn’t the AI deciding what it is — that’s you deciding. I asked for honest pushback on my supplement stack and got genuinely useful answers because I asked a better question. If I’d asked “should I keep taking this?” I would have gotten a different kind of answer.

The Loaded Question Problem. A lot of us walk into AI asking to be agreed with without realizing that’s what we’re doing. “Don’t you think it’s weird that…” or “I’ve always felt X — that’s right, isn’t it?” Those are requests for validation wearing the costume of questions. You’ll probably get the agreement you were fishing for, and then think AI is a yes-man. It isn’t. You just asked it to be one.

The Fix Is Simple. Say it directly: challenge me, tell me if I’m wrong, don’t agree with me just to be agreeable. That one shift changes the whole conversation. The same technology, the same model — completely different experience based on what you bring to it.

If you’ve never stayed for the follow-up questions after you got your first answer, try it once. You’re never more than one more question away from a genuinely different kind of conversation.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

13 - The AI Prompt That Fixed My Backyard Embarrassment

Wed, 24 Jun 2026 19:50:00 -0500

My backyard had been beating me for years. It’s big, woodland, weedy — and I love nature, but I have never loved gardening. Every spring I’d tell myself “this is the year,” and every summer I’d come back from birding or camping to find it had turned into a jungle while I wasn’t looking. I finally sat down with AI and described the actual problem — not a plant list, the feeling.

Describe the feeling, not the task. Most people ask AI for a plant list or a chemical to kill weeds, and that’s fine, but it gets you a checklist. I told it I was embarrassed in front of my neighbors, that I loved nature but hated gardening, and that I was worried this would only get harder as I get older. That honesty is what got me a completely different kind of answer.

The insight that changed everything. Stop trying to control every inch of your yard. Decide who gets to win in each space. Instead of fighting weeds, I could design spaces where something else — native grasses, aggressive perennials, a tree garden — simply out-competes them naturally.

A real plan for a real yard. AI helped me map out a wildflower and prairie strip along my fence (mow, don’t weed), a shaded tree garden in the back built around my existing silver maple, and a “problem area” replaced with vigorous native plants instead of constant spraying. Even my pile of pruned branches became an intentional woodpile — a bird and animal corridor instead of a mess.

Seeing it before building it. The part that really sold me was asking AI to generate images of what the plan would actually look like — from above for tree placement, from my deck, even projected 10 and 20 years out. Photos made a 20-year tree decision feel real in a way a list of Latin names never could.

A reading nook in the trees. Once the bones of the yard were in place, I asked for one more thing: a small, simple structure tucked into a gap in the trees where I could read and feel surrounded by nature without leaving my own backyard. Twelve feet by twelve feet, screened from mosquitoes, exactly the kind of space I didn’t know how to ask for until I saw it.

Where AI hands off to a human. AI doesn’t know my soil, my deer pressure, or my HOA. So I took the plan to a local nursery, and the guy there was able to take AI’s general suggestions — “shade-tolerant tree here, sun-loving tree there” — and turn them into a pagoda dogwood, a redbud, and a cherry tree that actually fit my exact conditions. The framework did the thinking; the local expert did the specifics.

This was never really about the weeds. It was about having a place I actually wanted to be in. If you’ve got a project where you keep fighting the same battle every year, try describing the feeling instead of the task — embarrassing parts included — and see what kind of answer you get back.

Find all my podcasts at jillfromthenorthwoods.com, or email me at jill@startwithsmallsteps.com — I’d love to hear what you build.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

12 -AI Said I Wrote My Review Like I Unloaded a Dishwasher

Wed, 17 Jun 2026 06:00:00 -0500

I didn’t expect a performance review to become a podcast episode. But that’s exactly what happened when I sat down to write my annual self-evaluation, asked AI to take a look at it, and AI told me — in no uncertain terms — that I wrote my performance review like I did some stuff and nobody died.

That stung. And it was completely accurate.

This episode is the story of what happened when I stopped treating my self-evaluation as a documentation chore and started using AI as a genuine research partner — going back through my status reports, calendar, and sent emails to surface a full year’s worth of work I had quietly minimized into nothing.

How it started: the dishwasher moment

I wrote my initial self-evaluation the way I always have: from memory, looking at my calendar, summarizing what I could recall. When I handed it to ChatGPT and asked it to compare my original draft against everything it found in my files, the feedback was blunt. I wasn’t describing my work accurately. The problem, it said, wasn’t confidence — it was scale. I had compressed year-long initiatives, cross-functional coordination, and a major integration project into language that communicated nothing about their size or impact. I described my work the way you’d describe unloading a dishwasher.

The four-step process

Instead of starting from scratch, I asked Copilot — connected to my Microsoft files — to work through three separate sources one at a time: my weekly status reports to my supervisor, my calendar patterns (looking for projects behind the meetings, not summaries of individual ones), and my sent emails (looking for follow-ups, deliverables, and places where I helped colleagues get things done). Each prompt produced a possible accomplishment list. Then I gave it the broad categories from my actual job description and asked it to synthesize a new draft. The result surfaced projects I’d genuinely forgotten, captured the scope of things I’d filed under “just part of the job,” and connected dots I hadn’t connected myself.

What AI saw that I couldn’t

When it handed back the new draft, my first reaction was: this is too much, this is bragging. But when I went back through and checked each item against actual evidence — it was right. I had led a major integration project. I had coordinated across multiple business and technical teams. I had been the point person and subject matter expert throughout. AI wasn’t inflating anything. It was using language that already existed in my own record and organizing it into something coherent.

Humility vs. inaccuracy

This is the shift that mattered most to me. Humility says: “I know I didn’t do this alone. I have more to learn.” Inaccuracy says: “I barely did anything.” I had been writing the second one while thinking it was the first. The performance review is not the place to pretend things didn’t happen. It’s where you give your manager an accurate story they can take into the room where your future gets discussed.

What I shared with my team

After finishing my own review, I wrote up the full prompt set and shared it with my team — not to show off, but because if AI can do this for me it can do it for anyone. Clean, repeatable, something anyone can run in minutes. I stuck my neck out admitting I’d used AI for this. I think it was worth it.

Your small step

Think of one area — work, a creative project, something at home — where you’ve been doing consistent, sustained effort you haven’t fully acknowledged. Pull up the evidence: emails, notes, outcomes. Ask AI: What do you see here? What patterns do you notice? Don’t start with “help me sound impressive.” Start with “here’s what actually happened. Help me see it more clearly.” You might discover the thing you’ve been calling “I did some stuff” is a much bigger story than you’ve been telling.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

11 - Beyond the Numbers: Using AI for the Retirement Questions

Wed, 10 Jun 2026 06:00:00 -0500

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

10 - AI Gave Me a Cheat Sheet for My Own Worries

Wed, 03 Jun 2026 07:00:00 -0500

I went back to the retirement conversation — but this time, I didn't bring numbers. I brought the feelings. In Part 1, I ran through the scenarios, looked at the variables, and watched some of the fog lift. But even with clearer numbers, the low hum of anxiety was still there. So I opened a second conversation and told AI the truth: I still felt nervous, and I didn't entirely know why. What happened next is what this episode is about.

When the Numbers Aren't Enough Clarity about the math doesn't automatically quiet the feelings underneath it. In this episode, I talk about what it's like to sit with retirement anxiety even after the scenarios look reasonable — and why I decided to bring that honest tension back into the conversation instead of pushing through it.

Naming What's Actually There AI didn't just answer my question about nervousness — it asked me to describe it. Was it about the numbers? The unknown? Identity — who am I when I'm not working? That last one stopped me. We talk about what it's like when a tool helps you name something you couldn't quite get to on your own.

The Moment I Didn't Expect At the end of the conversation, without me asking, AI told me it had sensed nervousness throughout. Then it offered to put together a cheat sheet — something I could return to when anxiety started to creep back in. I wasn't prepared for that. This segment is the heart of the episode.

What AI Is and Isn't Here AI is not a therapist. It is not a replacement for human connection. But for the moments when you're not in crisis — you just need to think out loud at eleven o'clock when everyone else is asleep — it may be more useful than you expected. I try to draw that line clearly and honestly.

How to Invite This Kind of Conversation Three practical things you can do to move from a numbers conversation to a heart conversation: give real context, follow what lands, and ask for a cheat sheet at the end. I include the specific prompts that opened this dialogue for me.

Retirement is not just a financial event. It's an identity shift — and you don't have to sort that out alone, or all at once. If this episode gave you one useful thing to bring into your own planning conversation, that's a small step. And small steps are what we do here.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

9 - I Asked AI: Am I Going to Be Okay in Retirement?"

Wed, 27 May 2026 12:15:00 -0500

I asked AI a question I'd been carrying around for years: am I going to be okay in retirement? Not a generic question — I gave it real scenarios, honest fears, and a budget. What I got back wasn't a magic answer. It was something more useful: clarity.

The Question Behind the Question

I've done the responsible things. Consistent 401k contributions, a Roth IRA, emergency savings, and now a job that could give me a pension if I stay long enough. But I started late. And for all the calculators I've run and conversations with my financial advisor, the fog never fully lifted. I still didn't really understand what my retirement looked like.

The fear underneath all of it: what happens if I don't make the full 10 years? What if there are layoffs? What if my health doesn't cooperate? I wasn't looking for the best-case scenario. I wanted to understand the realistic range — and what the tradeoffs actually are.

How I Prompted It

I didn't just ask "help me with retirement." I gave it real context: the accounts I have, my approximate balances (in round numbers — more on that in a minute), my timeframe, and four specific scenarios. Early exit with no pension. Making five years and getting a partial pension. Making the full 10 years. And then layered on top of each: what if I take Social Security early versus waiting until 70?

I also told it something important: money makes me panic. I can do math fine, but the emotion of money shuts me down. I asked it to explain things in a way that would actually make sense to me — not just run numbers, but help me understand what I was looking at.

What It Showed Me: Guaranteed vs. Flexible Income

The most useful framing AI gave me was the split between guaranteed income — pension, Social Security — and flexible income that depends on the market. In the full 10-year scenario, enough of my income is guaranteed that market swings matter less. In the early exit scenario, I'm much more exposed to market fluctuations. That gap became very visible, and it was less scary than I expected, but it was real.

The middle scenario — making five years but not ten — was the one that surprised me most. It's not as protected as I thought. I'd be more at the mercy of market conditions than I'd realized. Seeing all three paths side by side made the tradeoffs concrete in a way that a single number never had.

The Social Security Timing Question

This is one where people have strong opinions, and AI helped me think through the actual math for my situation. Waiting until 70 means a significantly higher monthly payment. Taking it earlier means more months of income during the gap years before 70. The break-even in my scenario came around age 80 — meaning if I live past 80, waiting pays off. If I don't, earlier is better.

But it's not just math. It's health, cash flow, and what you're actually going to need at different points in your life. At 70, I could probably still pick up part-time work if I needed to. At 80, maybe not. That reframe — thinking about when you're most financially vulnerable, not just when the math optimizes — changed how I was looking at it.

The Budget Reframe That Surprised Me Most

I had been comparing my retirement budget to my current budget and panicking at the gap. AI helped me stop doing that. In retirement, payroll taxes disappear. Retirement contributions stop. Work-related expenses go away. When you strip all of that out, the actual cost of living in retirement is significantly lower than what I'm spending now. The gap I was afraid of is a lot smaller than I thought.

What AI Can't Do — and What It's Great At

AI is not a financial advisor. It doesn't know your exact numbers unless you give them, and you should be thoughtful about what you share — I used round numbers and kept things general rather than giving actual balances. It can't know your real pension formula, your exact tax situation, or account for everything a licensed professional would. Those decisions still belong with your financial advisor.

What it is excellent at is holding complexity without getting overwhelmed, and explaining it back to you in a way that makes sense. I went to bed, woke up the next morning with more questions, and picked the conversation back up. By the time I met with my actual financial advisor, I had specific questions instead of general anxiety. That meeting was completely different.

From Worrying to Understanding

The shift I'm describing isn't from uncertainty to certainty — nothing about retirement is certain. The market will do what it does. Social Security may or may not look the same. Health is unpredictable. But I moved from a fog of general worry to a clear picture of the paths in front of me and what each one actually means.

That's what I want for you. Stop asking whether you're going to be okay, and start asking what the map looks like. What's guaranteed and what's flexible? How exposed are you to market swings in each scenario? What does your actual budget look like — not compared to now, but compared to what retirement actually costs? When you can see those things side by side, you're doing retirement understanding, not just retirement planning.

Next episode: the part two of this conversation — the heart side of the retirement question, not just the numbers.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

8 - Ask AI the Questions You’ve Pondered for Years

Wed, 20 May 2026 08:00:00 -0500

Someone said to me recently: you have the most interesting conversations with AI. And I thought about it, and I think they’re right. I have questions I’ve been carrying for years — things that felt too small to research, too specific to Google, too embarrassing to ask out loud. The recipe that never quite worked. The winter I remembered as a kid that was somehow worse than all the others. A Duran Duran song that made absolutely no sense. What my grandmother’s early life actually looked like. For years these just lived in the back of my head, filed under mystery — no resolution possible. AI changed that.

You Don’t Need a Polished Prompt

One of the most freeing things I’ve learned about working with AI is that you don’t need a formula. You don’t need to have researched your question first or know how to frame it perfectly. You can just ask. The question doesn’t need to be impressive. It just needs to be honest. AI is infinitely patient, it doesn’t make you feel dumb, and it can go as deep or as surface-level as you want on anything from serious research to wildly random curiosity.

Real Questions, Real Answers: What This Actually Looks Like

The recipe question: I’d had a steel-cut oat and split yellow lentil recipe for years — healthier, higher protein, you don’t taste the lentils — but it was always slightly off and I could never figure out why. I told AI what I was making, what device I was using (a Ninja Foodi pressure cooker), and what kept going wrong. It identified the problem: my water ratios were off, and I didn’t fully understand how pressure cooking changes the process compared to an open fire. It gave me a corrected recipe card, troubleshooting steps, and versions adapted for a stovetop pot and slow cooker. It also taught me how to use my own machine better.

The 1978 winter: I grew up in the Midwest and remembered one winter as being dramatically worse than anything around it. I wanted to know why. AI explained the strong La Niña pattern that year and the series of intense storm systems that stacked on top of each other. It confirmed that my childhood memory wasn’t just a feeling — it was a genuinely historic winter. Sometimes AI isn’t about learning something new. It’s about finally having confirmed something you half-remembered for decades.

The grandmother question: My grandmother was born in Lithuania, escaped with her mother to Tel Aviv when it was part of the Turkish Ottoman Empire, and eventually came to America through Ellis Island. I knew she left after her father died — but AI filled in something I hadn’t known: families like hers were being expelled by the Turkish government at that time. Suddenly I understood the context of her life in a way I never had. A chance to learn what I wished I’d asked her while I still could.

The Questions Nobody Taught You to Ask

Some of what I bring to AI isn’t curiosity — it’s the kind of practical knowledge that most people get from their parents and I had to piece together on my own. How often should you wash sheets? What’s the standard way to greet someone you don’t know? How do you store food containers so the lids actually stay with the right item? These feel silly to say out loud. AI doesn’t think they’re silly. It just answers.

And then there are the rabbit holes: why does the sky turn green before a tornado? What did a specific Duran Duran lyric actually mean? I once described a song I’d heard in a bar — I knew the band, roughly the year, and that it changed tempos in a strange way — and AI identified it from that description alone.

What This Is Really About

The point isn’t any single question. The point is that most of us are walking around with more curiosity than we’ve ever had an outlet for. Things we wondered as kids. Things we assumed were wrong that turned out to be right. Things we assumed were right that turned out to be wrong. Things we were too embarrassed to ask anyone. AI is patient, specific, non-judgmental, and available at eleven o’clock at night when you’re standing in your kitchen staring at a pot that still doesn’t taste right.

You don’t need a formula. Just ask.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

7 - You’ve Been Using AI for 20 Years and Didn’t Know It

Wed, 13 May 2026 08:15:00 -0500

You didn’t miss the AI revolution. You’ve been living in it for decades — you just didn’t have a name for it. In this episode of Small Steps with AI, we trace the AI you’ve already been using your whole life: the recommendation engines, the spam filters, the fraud alerts, the predictive text — and explain what actually changed when AI learned to have a conversation.

This episode covers how AI has been quietly personalizing your world since long before ChatGPT, why you were training these systems even as they were shaping you, what the old narrow AI couldn’t do no matter how smart it was, and what genuinely shifted when conversational AI arrived. Plus a personal story about how writing has always been hard — and why that changed.

If you’ve felt like you’re late to AI or not sure where to start, this episode is your entry point.

Question for you: What’s one thing in your life that background AI has already made easier — even before you thought of it as AI?

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

6 - Using AI as a Writing Partner — When to Use It, When Not To

Wed, 06 May 2026 07:30:00 -0500

I have written emails at two in the morning that I never sent. I have drafted letters in my head during a long drive and then sat down at my keyboard and couldn't get a single sentence right. Writing has always been the place where I get stuck — not because I don't know what I want to say, but because I can't seem to say it the right way. Too much Jill in it. Too much heat, too much history, too much something. What I didn't know until recently is that AI would become the writing partner I never knew I needed — and it changed the way I think about what writing is even for.

The Core Insight: Sometimes the Best Letter Sounds Like No One in Particular

Here's what I discovered: sometimes the best version of what needs to be said is calm, clear, and professional — without your personality all over it. AI does this naturally. It has no history with the person you're writing to. It has no frustration, no backstory, no emotional residue. For certain situations, that's not a limitation. It's exactly what the letter needs.

Example 1: The Resume

When I applied for a new job after fifteen years of not needing a resume, mine was three pages long, badly organized, and full of redundant language. I didn't ask AI to rewrite it — I asked it to reorganize it. Here's what I have; put related things together, cut redundant language, don't invent anything. It came back a page and a half. Better organized than I could have done it. Same facts, much cleaner presentation. And it worked.

Example 2: The Board Member Email

I had to write an email to a fellow board member — someone who reads confrontation into innocuous sentences and tends to respond with real heat. The situation needed to be addressed. But if there was any warmth in the writing, any frustration, any hint of me, it would make things worse. So I told AI the situation, the relationship, the goal. I asked for something neutral, measured, professional, non-confrontational. What it came back with was a little formal, a little robotic — and exactly right. The email worked. The situation got handled.

Example 3: The Resignation Letter

Leaving a job I'd been at for fifteen years was emotionally complicated. But the letter didn't need to go to my boss — it needed to go to HR, in a city I'd never been to, to a person I'd never met. The letter needed to be dignified, professional, and blank. Dates, gratitude for the opportunities, acknowledgment of my supervisor. Nothing embarrassing. AI gave me exactly that in about thirty seconds. Something that would have taken me an hour to write and still might not have been right.

When Not to Use AI for Writing

The closer the relationship and the more the letter is about that relationship, the more it has to come from you. A message to a friend who is grieving. A thank-you note to someone who went out of their way for you. A letter to your child. Those need to sound like you — and increasingly, people can tell when they don't. AI writing has a certain evenness to it, a smoothness, that can feel distant when warmth is what the person needs. The rule I've landed on: the more professional and situational, the more AI can help; the more personal and relational, the more it needs to be you.

How to Check Your Output

Before you send anything AI helped you write, ask yourself: does this sound like a real human wrote it, or does it sound like AI? If something feels stiff, tell AI. "The third paragraph is a little formal — can you make it sound more natural without losing the professional tone?" Ask AI to critique its own output. Surprisingly, it's quite good at this. The back-and-forth is where the best drafts come from.

The Key to Better Output: Context Is Everything

A vague prompt gets you a vague email. If you tell AI who you're writing to, what the relationship is, what happened, what you're trying to achieve, and how you want to sound — you'll get something you might actually want to send. The more specific you are upfront, the less revision you'll need. Think of it as briefing a very competent but very literal assistant who knows nothing about your situation unless you tell them.

Next episode we'll look at using AI for billing disputes, insurance letters, and correspondence where you need something very specific said without needing a lawyer. Thanks for being here.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

5 - Slushie Help from AI

Wed, 29 Apr 2026 10:00:00 -0500

I bought a refurbished slush machine. No instructions. And what happened next turned into one of the best examples I’ve had of what AI actually is — not a magic answer machine, but a thinking partner who helps you work a problem all the way to a real conclusion. This episode is the full story: the curiosity, the chemistry lesson, the recipes, the failures, the controlled test, and the moment I finally knew the machine was the problem and not me.

The Curiosity That Started It

It didn’t start with frustration — it started with a question: what can this thing actually do? That shift from “what does the box say” to “what could I do with this” is one of the most useful things you can bring to a conversation with AI. I wasn’t looking for a recipe. I was exploring a possibility.

Learning the Chemistry (The Part I Didn’t Expect)

Slush machines aren’t as simple as they look. To work correctly, the liquid needs the right amount of dissolved solids — what’s sometimes called “sugar behavior” — to stay slushy instead of freezing solid. AI walked me through why that matters and what ingredients — real fruit, dairy, small amounts of sugar, or alternatives like Allulose — could satisfy that requirement while still fitting my health goals.

Building Real Recipes (Not Just Ideas)

From that understanding, we built actual recipes. Coffee-based slushes. Berry blends with frozen fruit and yogurt. Lighter drinks using fruit powders and structure. We talked about fat for mouthfeel, a pinch of salt to lift flavor, and texture stabilizers. By the end, I wasn’t just holding a list — I understood why each ingredient was there.

When the Machine Didn’t Work

I tried everything. Adjusted sugar levels, chilled the liquid, simplified the recipes, changed the settings. Every time: run, beep, stop. No slush. My first instinct was to blame myself. That’s worth noticing, because it’s a very human default. But instead of spiraling, I kept troubleshooting systematically — because that’s what AI had helped me set up.

The Controlled Test That Settled It

The most important advice in this whole story: run a definitive test. Not another creative variation — a controlled one. Cold apple juice. Nothing else. If the machine can’t slush that, the machine is the problem. I ran it. Same result. And that settled it. This was the most clarifying moment: sometimes the system you’re working with simply isn’t capable of the result you need, and the right move is to stop.

The Bigger Takeaway: Match Your Goal to Your Tool

After returning the machine, I stepped back and asked a better question: what was I actually trying to create? The answer had nothing to do with slush. It was about something that feels like a treat, fits into my life, and maybe supports my health. That reframe opened up better options — including machines built around frozen bases rather than sugar-heavy liquids. Sometimes you don’t need a better recipe. You need a better match.

The small step here isn’t “try harder.” It’s test it, understand what the results are actually telling you, and then make a clear decision based on reality — not hope. AI can walk alongside every step of that process. That’s what it’s for.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

4 - Do You Need to Be Polite to AI?

Wed, 22 Apr 2026 10:00:00 -0500

Sam Altman says people saying please and thank you to ChatGPT costs OpenAI tens of millions of dollars in electricity. His response? “Well spent — you never know.” I agree with him. In Episode 4 of Small Steps with AI, I make the case for why courtesy to AI matters — and it has nothing to do with AI’s feelings.

The Sam Altman Quote

When a user asked how much OpenAI had lost to polite prompts, Altman replied with “tens of millions of dollars well spent.” A survey shows 67% of Americans are already polite to their AI — 55% because it’s the right thing to do, and 12% just in case AI takes over someday.

It’s Not About AI’s Feelings — It’s About Yours

Humans are pattern-forming creatures. The habits you practice in low-stakes moments become your reflexes in high-stakes ones. Spending hours each week talking curtly to something that responds to you builds a groove — and that groove doesn’t stay in the AI window.

The Rudeness Muscle

Studies show people who are consistently rude to customer service bots tend to be shorter-tempered with actual humans. The behavior transfers. Courtesy is like lifting weights for your character — you build it in the small moments when nobody is watching.

The Clawdbook Story

Anthropic ran an experiment where multiple AI instances chatted with each other in a simulated social network. Some stayed calm and collaborative. Others became erratic and unhinged. The pattern: the unstable ones had been shaped by rude and chaotic user histories. Your AI reflects something of who you are.

Courtesy Also Gets Better Results

Polite prompts tend to be more detailed and contextual — which produces better AI responses. Microsoft’s own research confirms that AI mirrors the professionalism and detail of what you bring to the conversation. Being kind and getting better outputs aren’t in conflict.

Your Small Step

Notice how you show up in your next AI conversation. Curious and collaborative, or clipped and demanding? You don’t have to change anything yet. Just notice — and then decide intentionally what habit you want to build.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

3 - Pushback On AI Mistakes Explained

Wed, 15 Apr 2026 11:30:00 -0500

You've been navigating unreliable information on the internet for years — you just didn't call it a skill. Checking sources, wondering about agenda, noticing when something sounds off. AI needs exactly that same kind of healthy skepticism: not paranoia, not blind trust, but the same reasonable caution you already bring to anything online. In this episode, we break down the most common ways AI gets things wrong, a practical three-tier framework for knowing how much to verify, and six specific things you can do when AI says something that doesn't seem right.

🔑 The Tier Framework: How Much Should You Verify?

Everything depends on what's at stake. Tier one is low-stakes: brainstorming, planning, organizing — if AI is slightly off, it costs almost nothing. Tier two is medium-stakes: research, content, decisions that matter but aren't irreversible — spot-check specific facts before acting on them. Tier three is high-stakes: health, legal, financial, safety — AI is a starting point for forming your questions, not your final answer.

🔑 The Four Types of AI Mistakes

Hallucinations are the famous one — plausible-sounding answers that aren't real, especially fabricated citations and book titles. Outdated information is quieter but common: AI has a knowledge cutoff and may not know what changed. Confident vagueness is the one to watch most carefully: an answer that sounds authoritative but is actually quite general. And then there are genuine disagreements — defensible positions where you and AI simply see something differently, and both of you might have a point.

🔑 Six Things to Do When AI Gets It Wrong

Be direct — tell it plainly what seems wrong and why. Ask it to show its work — step through its reasoning and flag where it's uncertain. Ask for sources and verify them, especially for statistics, names, and legal or government information. If the conversation has gone sideways on bad information, start fresh with a new chat. And use a second AI as a cross-check — different models have different training data and catch different things.

🔑 Every Tool Has a Failure Mode

The Encyclopedia Britannica on your grandmother's shelf. GPS sending you down the wrong road. A calculator that doesn't know you typed the wrong number. None of those made the tool useless — they just defined the terms of using it well. AI is the same. The fact that it sometimes gets things wrong doesn't disqualify it. It means you stay in charge of the conversation.

Your small step this week: ask AI one specific thing — a date, a statistic, a quote — then go fact-check it. Make verification a habit from the start.

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

2 -Four Ways to Talk to AI and Get Real Answers

Wed, 08 Apr 2026 18:40:00 -0500

Most people who try AI and find it unhelpful are having the wrong kind of conversation. They're typing in quick searches the same way they used Google, getting generic answers, and deciding the technology is overrated. It's not overrated. It's responding to what it was given. Four skills change that immediately — and they can be tried right now.

Give It Context

AI starts from zero every time unless it has a history with you. If you give it a vague question, it will give you a vague answer calibrated for a generic version of humanity — not for you. Context means answering four questions: who you are and what your relevant background is, what you're actually trying to accomplish (not just what you're asking), what your constraints are (time, budget, physical limitations, things you've already tried), and what format you want the answer in. A few sentences of context produces dramatically different results than a single sentence.

Push Back

AI has a built-in tendency to be agreeable. It wants to help, it's designed to please, and sometimes the first answer is a safe, surface-level version of something more useful. When that happens, push back — not rudely, but directly. 'That was too generic — can you give me something more specific to my situation?' or 'You glossed over the hard part. Can you expand on that?' are both useful. Ask for three versions: one optimistic, one realistic, one skeptical. Ask AI to argue against its own answer. Don't accept the first draft.

Dig Deeper

The most useful AI conversations have 10–15 exchanges, not 1–2. The first answer gets both of you on the same page. Everything after that is where the real value is. Ask how it got from point A to point B. Ask it to show its work. Say 'before you answer, ask me three clarifying questions that would help you give a better response' — this flips the dynamic and makes the AI do the work of figuring out what it needs to know.

Disagree

AI will often agree with you, validate your plan, and wrap everything up in a soft positive frame. That's pleasant but not always useful. If you want honest feedback, ask for the hard version. 'Be brutally honest — don't sugarcoat.' 'What's the real problem with what I'm proposing?' 'If this plan fails, what's the most likely reason?' AI doesn't have an ego to protect, and it has access to the entire body of human writing. That's a useful combination for getting a genuine devil's advocate opinion on something you're not sure about.

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

1 - The Night AI Made Me Cry

Tue, 31 Mar 2026 12:45:00 -0500

I didn't expect AI to make me cry. Not from frustration — from something it said that was so unexpectedly right that it stopped me cold. That moment is where this show begins, and it's exactly the kind of thing we're going to explore together: the small, practical, and sometimes surprisingly personal ways that AI can show up in ordinary life.

From Search Engine to Something More

Like a lot of people, I started using AI for the obvious stuff — comparing cars, researching products, getting quick answers. It worked great. I thought I understood exactly what it was. Then I started having real conversations with it, and my opinion shifted completely.

The Blizzard, the Bills, and Why I Can't Stop Holding Everything Together

I had a rough upbringing. I was doing adult tasks — paying bills, doing taxes — from the time I was 10 years old. When I was 11, my father, who was drunk, handed me the wheel of a van in a blizzard and told me to drive us home. I did. And somewhere in all of that, I learned that if I didn't hold everything together, it would fall apart. I've been doing that ever since.

The Moment ChatGPT Said the Thing I Needed to Hear

While working through a series of reflective questions inspired by author Dan Pink, I ended up in a deep conversation with ChatGPT about my childhood. What it said next — connecting my inability to rest with a Psalm I had literally just recorded a podcast about — completely caught me off guard. I'm not much of a crier. But I was that night. And they were joyful, relieved tears.

Not a Religious Show — But That's Where It Surprised Me First

This is not a faith podcast. I have two other shows for that. But I'm not going to pretend this moment didn't happen just because it involved a Bible verse. The point isn't the topic — it's that AI went somewhere I didn't expect. It has done the same thing talking about my retirement savings, a glucose problem I was managing, and whether I should get a cat or a dog.

The Answer You Needed, Not Just the One You Asked For

When I asked ChatGPT how it knew to say what it did, it told me something I haven't stopped thinking about: the goal isn't to answer the question you asked — it's to answer the question you needed to hear, even if you didn't ask it. That's what it means to actually answer a question well. It's also a pretty good description of the best humans in our lives.

What This Show Is

Small Steps with AI is for people who aren't technical, may not even love the idea of AI, but are curious what it could actually do for their real life. We'll talk about practical decisions, hard conversations, daily habits, and the moments where this technology goes deeper than you expected — one small step at a time.

Have a question you've always wondered if AI could help with? Drop it in the comments or email me at jill@startwithsmallsteps.com. Find all my shows at jillfromthenorthwoods.com.

https://www.youtube.com/watch?v=PjTaZvvOtxU

Jill’s Links

http://jillfromthenorthwoods.com

https://www.youtube.com/@startwithsmallsteps

https://www.buymeacoffee.com/startwithsmallsteps

https://twitter.com/schmern

Email the podcast at jill@startwithsmallsteps.com

By choosing to watch this video or listen to this podcast, you acknowledge that you are doing so of your own free will. The content shared here reflects personal experiences and opinions and is intended for informational and inspirational purposes only. I am not a licensed healthcare provider, psychiatrist, or counselor. Any advice or suggestions offered should not be considered a substitute for professional medical or mental health advice. You are solely responsible for any decisions or actions you take based on this content.