If you're a creative freelancer using AI and still not making more money, you're not imagining it — the math in fact doesn't work out the way everyone promises. This episode is the honest, unfiltered version of that problem, starting from a night I couldn't sleep doing that exact math in my head.

I'd been lying awake doing math in my head, realizing I was repeating the exact same overcommitting pattern I swore off in 2025 - saying yes to clients randomly, never actually productizing my own service even though I tell other creative professionals to do it constantly. And the math got worse from there: with everything AI has supposedly changed about how I work, I'm charging roughly the same as I was before any of it.

This one's for my editors, designers, writers, video people, social media managers - anyone whose work sits a step or two away from a client's actual revenue, which makes "just charge more" a lot harder to pull off than the advice makes it sound.

I get into why the work itself slowly gets worse when AI lets you take on more than you can actually hold, what I've started doing differently (including capping my client roster and bundling deliverables instead of taking on more people), and the harder question underneath all of it - whether the real fix is productizing, upskilling into something closer to the money, or stepping away from production work entirely.

I don't wrap this one up with a bow. But if you've had your own version of the 2am loop, I think you'll feel it here.

If this one hit home, hit subscribe on Apple Podcasts or Spotify so the next episode lands in your feed automatically.

The written version of this plus a couple of things that didn't make it into the recording — is up on my newsletter and Substack.

Come find me on Instagram or Substack.

If you've got a minute, a review on Apple Podcasts helps other creative pros find the show. Even just a rating helps more than you'd think.

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Here's my actual AI-assisted podcast production workflow - no editor, no team, recorded to fully scheduled in under two hours most weeks. If you're a solo creator wondering how AI tools actually fit into a real production process (not a fantasy one), this is the unglamorous, specific version. Okay so I'm gonna be real with you... when most people think about podcast production, they picture a whole operation. Studio time, an editor, someone writing show notes, someone else doing socials. Days between recording and publishing. And I get it, because that's how a lot of shows run.

Mine doesn't work that way. It's just me. No team, no production days, no back and forth with anyone. And from the moment I finish recording to the point where everything is scheduled — show notes, blog post, newsletter, the whole thing — it's under two hours most weeks.

In this episode I walk through exactly how I do it. The tools, the order I do things in, and why that order actually matters. I talk about how I plan episodes quarterly so I'm never starting from a blank page, the 70/30 split I keep between keyword-researched episodes and the ones I just make because I feel like it, and the Claude skill I built that takes one transcript and spits out every repurposing output in a single pass.

I'm also testing the whole thing on a second podcast right now in a completely different niche. Same system, different content, different audience. If it holds up there, it's actually a system. If it only works here, it's just something I got good at through repetition, which is a different thing.

The principles at the end apply no matter what tools you're using, so those are worth sticking around for.

RESOURCES & LINKS MENTIONED → Audacity – Free audio recording and editing software, used for noise reduction, compression, and EQ immediately after recording → Descript – Transcript-based podcast editing software for content polish, cutting dead air and tangents, adding transitions → Podbean – Podcast hosting platform for upload, metadata, scheduling, and distribution

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The written version of each episode, plus the occasional thing that didn't make it into the recording, lives on the newsletter. Link below if you want in.

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"AI slop" isn't just a meme - it's a real design failure pattern, and it's got a 1985 ancestor most designers have never heard of. This episode breaks down the "ransom note effect," why AI image tools are heading toward smooth sameness instead of visible chaos, and what that means for anyone using AI tools for designers right now. Okay so you know that specific visual, the AI-generated poster with the floating shapes and the busy text that almost looks designed but doesn't quite hold together? There's actually a name for that. A typography term from 1985, coined the first time professional design tools landed in untrained hands. It's called the ransom note effect, and this episode is about why we keep living through the same moment over and over with different tools.

I counted four times it's happened (there's probably more but I focused on these four): desktop publishing in the '80s, early web in the '90s, Canva in the 2010s, and now ChatGPT Image 2.0 in 2026. The difference this time is that the output is smooth enough that we're not heading into the obvious chaos phase. We're heading somewhere more like WordPress sameness: everything competent, everything identical, and way harder to spot than blinking text and mismatched fonts.

I also get into the failure mode that I think is actually the most common one of all, and it's not chaos or sameness, but someone taking something the tool made pretty well and then going in and wrecking it through small edits they didn't know would cost them anything.

And for designers specifically, I talk about where I think the real value gap is sitting right now. Because AI image generation optimizes for looking complete. Design is something else - it's a series of decisions, each one ruling things out. Those two things can look similar on the surface and be totally different underneath.

RESOURCES & LINKS MENTIONED → ChatGPT Image 2.0 – OpenAI's image generation tool released April 2026; renders readable text, generates matching image sets, searches the web mid-process → PageMaker – Desktop publishing software from 1985 that triggered the original ransom note effect when combined with the Macintosh and LaserWriter → Canva – Design platform; My closest historical parallel to the current AI image moment

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If you've felt exhausted before you've even opened an app, you're not imagining it — content overload is a real, measurable problem, and it started well before AI showed up. This episode traces it back to 2015, debunks the "humans have shorter attention spans than goldfish" myth that never had real science behind it, and gets into why AI didn't create the oversupply problem, it just stepped on the accelerator.

The first thing I found is that this problem didn't start with AI at all. A TV exec named it back in 2015, when there were already 400 scripted original series airing in the US in a single year. AI didn't create the oversupply problem, it just stepped on the accelerator.

I pulled stats on what's happening right now: 74% of new web pages contain detectable AI-generated content, a startup is producing 3,000 AI podcast episodes a week for a dollar each, and consumer trust in AI content has dropped from 60% to 26% in three years. I also get into the attention span myth, because it turns out the whole "humans now have shorter attention spans than goldfish" thing didn't really come from any real science. People aren't broken. They just got pickier. And pickier is a very different thing to compete against.

The practical section at the end is what I'd actually want you to take away, specifically what this means if you're a creative professional making content for clients or for your own brand right now.

→ Ahrefs study – Analysis of nearly a million web pages measuring AI-generated content prevalence → Gartner projection – Forecast on synthetically generated content by 2026 → Nielsen survey – Research on streaming subscriber overwhelm and subscription behaviour → McKinsey research – Data on UK media consumption trends and attention plateauing → Adobe digital trends report – Survey on marketer adoption of generative AI workflows in 2026 → Inception Point AI – The startup producing 3,000 AI podcast episodes a week

If this resonated, hit follow on Apple Podcasts or Spotify. New episode every week.

The written version of this one, plus extra thoughts that didn't make it into the recording, is over on the newsletter. Link below.

And if you've got 30 seconds, a rating on Apple or Spotify helps new listeners find the show more than you'd think.

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These are the AI tools for designers I actually use for real client work — not the ones I wish I had time to master. This episode covers AI image generation tools for creatives who need usable results fast, with an honest breakdown of what's worth your time and what isn't.

Just got back from Greece (yes, I was working on holiday because my brain literally cannot turn off when I'm following AI newsletters and finding new tools to test). Today we're discussing AI image generation - specifically the tools I actually use for real work, not the ones I wish I had time to master.

I'll be honest - I'm not an AI art expert. I use these tools for specific work needs (mainly Instagram carousels) and that's exactly what I recommend to everyone else. Don't try to master everything - pick what serves your actual work and get good at that.

• ChatGPT image creator deep dive - Why this has become my go-to tool (spoiler: convenience + quality)
• Prompting that actually works - 4-element framework that gets you usable results, not weird blobs
• Copyright considerations - You can't use "in the style of [living artist]" anymore - here's what to do instead
• Keep it realistic and manageable - Focus on solving actual work problems, not becoming an AI art master
• Tools by creative role - Because you don't need to try everything, just what works for your needs

80% of my AI image use: Instagram carousel graphics for clients 20% split between: Blog headers, concept visuals, client presentations

I don't use it for: Complex illustrations, professional photography, detailed technical work

• Pick ONE tool - Don't try to master everything. Choose based on your actual work needs
• Test the 4-element framework - Create 3-5 images using subject + style + setting + technical details
• Save what works - Start building your personal prompt library for future use
• Focus on real needs - Pick one type of visual content you create regularly and experiment with AI for that
• Iterate, don't perfect - First attempts won't be amazing, and that's normal

You don't need to become an AI art expert. You need tools that solve real problems in your work. Start with what you actually need, not with trying to master every new tool that launches.

These are means to an end - creating functional visual content - not ends in themselves.

• ChatGPT Image Creator
• DALL-E, Midjourney, Adobe Firefly
• Recraft, Leonardo.ai, Runway ML
• Figma AI tools, Canva AI features

How creative careers are evolving in the AI era - who's thriving, who's struggling, and what skills are becoming more valuable.

P.S. - I spent way too much time testing new AI tools instead of enjoying Greek sunsets. Learn from my mistakes and focus on what you actually need for work!

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