The AI Discount Trap: Why Creators Still Matter

Clients are using AI as an excuse to discount creative work. This episode breaks down what is really happening underneath that pressure, why the hype cycle matters to your business, and why reliability is the advantage that keeps creators valuable even as tools get faster.

In This Episode

•The “AI discount” moment: when the scope is clear, but your value gets questioned anyway

•Why the AI hype cycle affects budgets, expectations, and how clients justify pricing

•What creators are actually selling (hint: not the tool)

•The return to reliability: why dependable delivery becomes the differentiator

•The hidden risk of tool dependence, subscriptions, cloud access, outages, and workflow fragility

•A practical way to audit your workflow so you can deliver even when tools fail

Key Takeaways

•Speed is not value by itself. Outcomes are.

•AI can change costs, but it also increases the demand for taste, consistency, and accountability.

•Your edge is reliability: the ability to deliver on time, on brief, and in a way that is legally safe and professionally consistent.

•If your workflow depends on one tool or one login, you have a single point of failure.

•The best “AI strategy” for creators is resilience plus a clear value story clients can understand.

Chapters

•00:00 The moment you feel the discount pressure

•01:11 The AI bubble question and why it matters to creators

•05:15 Why companies struggle to get ROI from AI (and what that signals)

•06:33 The corporate budgeting lens that shapes how clients think

•08:20 Return to reliability

•09:48 What clients are really buying when they hire a creator

•10:24 Tool dependence and the cloud risk most creators ignore

•14:16 The one question to audit your workflow

Try This: The Reliability Audit (10 minutes)

1.List the 3 tools your turnaround time depends on.

2.For each tool, answer: If this disappears tomorrow, what is my backup?

3.Identify the single point of failure (login, subscription, cloud processing, internet).

4.Create one offline fallback path you can execute under pressure.

5.Update your client value language: reliability, consistency, and outcomes.

Next Episode and Subscribe

If this episode hit close to home, subscribe on your favorite podcast platform and on YouTube. New episodes are built for working creators who need practical decisions, not hype.

Transcript

[01:00:00:09 - 01:00:07:11]
 You send a proposal for a brand film or a headshot campaign or a big retouching job. The scope is clear.

[01:00:31:10 - 01:00:39:01]
 It's someone putting a price tag on your value and AI is the excuse.

[01:00:40:23 - 01:00:45:05]
 And if you've been doing this long enough, you know, what comes next.

[01:00:46:14 - 01:00:53:15]
 It's not just about this job. It's about what your clients think your work should cost from here on out.

[01:00:54:20 - 01:00:56:09]
 So zoom out with me.

[01:00:57:11 - 01:01:10:04]
 That mindset is helping drive hundreds of billions of dollars into AI is pushing valuations higher. It's shaping how clients think about creative work, including yours.

[01:01:11:20 - 01:01:15:12]
 So the question isn't only, are we in an AI bubble?

[01:01:16:18 - 01:01:21:23]
 The real question is what happens to your career if the bubble pops?

[01:01:21:23 - 01:01:43:09]
 Hi, I'm Daniel Douglas. I'm a working photographer, filmmaker, and content creator. I also have a background in technology and yes, I use AI tools inside real client work.

[01:01:44:15 - 01:01:46:22]
 I'm not talking about this from the sidelines.

[01:01:48:05 - 01:02:03:00]
 I've also lived through the.com boom and the crash. I've watched what happens when hype outruns reality. I watched real people paid a price after the early winners already took their profits and moved on.

[01:02:04:09 - 01:02:06:09]
 So I see this from both sides.

[01:02:07:09 - 01:02:15:07]
 I care what the tools can do and I care what they're doing to the way clients value our work.

[01:02:17:00 - 01:02:24:08]
 This episode is in theory, it's about protecting the craft, the business, and the people doing the work. So let's get to

[01:02:29:19 - 01:02:34:14]
 When people say AI bubble, they're usually pointing to a familiar pattern in

[01:02:35:14 - 01:02:58:13]
 the late 1990s, the internet felt new and unstoppable. Investors got so excited that money started chasing anything with.com in the name. It didn't always matter if the company had a plan to make a profit for a few years. Everything connected to the internet felt like it could only go up stock

[01:02:59:14 - 01:03:34:04]
 prices, sword company spent money. Like the good times would never end expensive offices, big launch parties, Superbowl ads, et cetera, all based on the idea that the details would somehow work themselves out later. Then reality showed up. The market turned a lot of those companies collapsed. Their stock prices crashed. People lost jobs. A huge amount of investor money just disappeared.

[01:03:35:11 - 01:03:38:03]
 Here's the simplest definition of a bubble.

[01:03:39:18 - 01:03:47:09]
 A bubble is what happens when hype and money race ahead of what the business can actually support.

[01:03:48:18 - 01:03:58:12]
 People stop asking basic questions like, does this make money? Is this sustainable? What happens if conditions change?

[01:03:59:20 - 01:04:05:16]
 And then when the answers catch up, the correction is fast and it's painful.

[01:04:07:10 - 01:04:19:11]
 So when people ask if we are in an AI bubble today, that's the picture they're comparing it to hype, easy money, very little patience for boring fundamentals.

[01:04:26:10 - 01:04:32:19]
 So where are we right now? Big tech companies like Amazon, Google, meta,

[01:04:33:19 - 01:04:40:21]
 and Microsoft are on track to spend hundreds of billions of dollars on AI over the next few years.

[01:04:42:06 - 01:04:47:18]
 A lot of that is going into massive data centers that power these models.

[01:04:48:23 - 01:05:14:14]
 It's a huge bet and it's a bet on infrastructure from the outside. It looks unstoppable, but when you look at the numbers, the picture is messier. The research tells two different stories and that doesn't mean either is wrong. They're measuring different things and they're using different definitions of our OI.

[01:05:16:00 - 01:05:24:14]
 One report from MIT suggests most organizations aren't seeing meaningful return yet from their AI investments.

[01:05:26:03 - 01:05:54:15]
 Another report from the Wharton school at the university of Boston, report from the Wharton school at the university of Pennsylvania suggests many companies already see positive returns and they expect more over the next few years. Both can be true at the same time because they're not measuring the same thing. And that's the point. There isn't a settled answer yet on whether this wave of spending is paying off at a scale that the market is pricing in.

[01:05:55:15 - 01:05:58:11]
 And here's where it gets uncomfortable.

[01:05:59:22 - 01:06:02:15]
 Even with that uncertainty on the table,

[01:06:03:20 - 01:06:04:21]
 the money keeps flowing.

[01:06:06:02 - 01:06:09:19]
 And some of the spending is easier to hide than people realize.

[01:06:11:04 - 01:06:12:22]
 Let me say this carefully.

[01:06:13:23 - 01:06:16:12]
 I'm not saying anyone's committing fraud.

[01:06:17:14 - 01:06:31:17]
 I'm saying the way costs are categorized, capitalized and spread across partnerships can make the spend look lighter in one place while the total bet keeps growing.

[01:06:33:10 - 01:06:36:18]
 If you aren't used to corporate accounting, here's the takeaway.

[01:06:38:13 - 01:06:51:09]
 The headline numbers can look cleaner than the underlying risk. At the same time, a few AI companies have raised billions of dollars without a mature part product in the market.

[01:06:52:22 - 01:06:56:11]
 That should sound familiar if you remember the dot com era.

[01:06:57:22 - 01:07:00:04]
 The difference now is exposure.

[01:07:01:16 - 01:07:07:05]
 A lot of the retirement money lives inside tech heavy index funds and four one K's.

[01:07:08:09 - 01:07:21:17]
 So if this wave of AI investment blows up, it isn't just venture capitalists who feel it. It's teachers, nurses, it's regular workers and yes, it's creatives like us.

[01:07:27:05 - 01:07:39:11]
 Let's bring this down to earth. If you are a photographer, filmmaker or content creator, what does an AI bubble or crash actually mean for your work?

[01:07:40:14 - 01:07:42:09]
 The obvious fear is simple.

[01:07:43:20 - 01:07:56:13]
 If the market turns companies panic and they start cutting costs, marketing budgets, shrink content, budgets, shrink freelance contracts dry up. And we're already seeing it.

[01:07:57:14 - 01:08:05:17]
 Some clients are leaning harder on AI. Some are keeping more work in house.

[01:08:07:02 - 01:08:13:01]
 Some are just asking for more for less because they assume the tools made everything faster.

[01:08:14:15 - 01:08:19:21]
 That's one possible path, but there's another path that doesn't get talked about enough.

[01:08:21:01 - 01:08:38:12]
 I call it the return to reliability. Right now, big tech is pouring money into the infrastructure that runs these tools. Most of the AI we use lives in remote data centers, not on our own machines.

[01:08:40:00 - 01:08:56:20]
 So here's the part people selling you AI for everything skip over. If the company behind your favorite AI tool fails, your subscription can disappear. Overnight, no login, no access, no support.

[01:09:02:01 - 01:09:32:18]
 A while back, I watched a creator build a workflow around one specific AI feature. It wasn't just a nice to have it with a user. It wasn't just a nice to have it was baked into their turnaround time. Then the tool updated the feature moved behind a higher tier. The pricing change, the whole promise to clients changed with it. This isn't evil. That's business, but it's also why dependence is risky.

[01:09:33:23 - 01:09:48:00]
 If you're a creative director with a six figure campaign on the line, you aren't going to gamble the schedule, their approvals and the brands reputation on a tool that might slow you down, glitch or go offline mid project.

[01:09:49:03 - 01:09:58:03]
 You want a human team that can commit to that date and deliver a file that's legally safe and visually on point.

[01:09:59:07 - 01:10:03:03]
 That's where the return to reliability happens.

[01:10:04:08 - 01:10:10:05]
 Demand snaps back to people who can't get the data. You can create solve problems and adapt.

[01:10:11:21 - 01:10:23:20]
 Let me give you a small version of this from my own work. In my episode, the human algorithm I talked about a shoot where I worked an AI tool into my workflow.

[01:10:25:03 - 01:10:57:04]
 The tool itself didn't fail. The problem was the tech around it. The wifi dropped and because the AI ran fully in the cloud, it might as well have not existed at all. I lost access to the tool in the middle of the work and the client didn't care. Why they didn't care if it was the model or the internet, they cared that the images were delivered on time and at the quality we promised.

[01:10:58:19 - 01:11:03:21]
 So in that moment, I fell back on my standard process and my own skills to get it done.

[01:11:05:08 - 01:11:11:00]
 That isn't a story about a stock market bubble. It's a story about dependency.

[01:11:12:07 - 01:11:27:12]
 When your workflow leans too hard on a single point of failure, the weakest link decides whether you deliver or you don't. In the end, the only reliable backup was me, not the tool.

[01:11:29:03 - 01:11:32:17]
 Now let's zoom out. That was just a wifi failure.

[01:11:33:17 - 01:11:42:06]
 If we see major funding cuts or platforms shutting down that same fragility scales across entire creative teams.

[01:11:43:12 - 01:11:46:22]
 So yeah, I use AI and yes, I'm careful.

[01:11:52:07 - 01:11:58:22]
 If you listen to my episode on ethical AI, you know, I am not anti AI.

[01:12:00:00 - 01:12:31:12]
 I use these tools in my own workflow and I think they can be genuinely useful. The problem isn't the tools. The problem is blind dependence dependence on a wave of investment that may be overheated dependence on platforms. You don't own platforms. You can audit platforms. You can't control as creators. We can't outsource our value to systems that can change the rules overnight.

[01:12:32:13 - 01:12:37:23]
 AI should amplify your judgment and your craft, not replace them.

[01:12:39:09 - 01:12:49:18]
 So the question isn't is AI good or bad. The question is how do you stay valuable no matter what happens to this wave of AI investment?

[01:12:55:08 - 01:12:56:21]
 So here's the takeaway.

[01:12:58:01 - 01:13:18:22]
 This current wave of AI investment carries real financial risk. If the bubble bursts, budgets will get cut. Some of the work you rely on may disappear for awhile. And at the same time, that same shock could push companies back towards reliable human driven work.

[01:13:19:23 - 01:13:28:09]
 If the money slows, fragile tools become unreliable or vanish and companies lean on human creators again.

[01:13:29:18 - 01:13:32:04]
 Your job is to be ready for both.

[01:13:33:10 - 01:13:50:08]
 Build a practice that can work with AI without depending on it completely. Strengthen the skills. Nobody can strip away from you. So whether the market fuse the machine or shuts it down, you still have a role and you still have options.

[01:13:55:12 - 01:13:57:06]
 Here's what I want you to do next.

[01:13:58:08 - 01:14:12:01]
 First audit your workflow. This week, take one real client project or one personal project and map every point where you depend on a single AI tool or platform.

[01:14:13:16 - 01:14:15:15]
 Then answer one blunt question.

[01:14:16:18 - 01:14:19:01]
 If this tool disappears tomorrow,

[01:14:20:04 - 01:14:22:10]
 what do I do instead?

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 Second, tell me what you find.

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 If you're watching on YouTube, leave a comment below.

[01:14:33:03 - 01:14:38:08]
 Tell me where you're most exposed and what backup you're putting in place.

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 If you're listening on a podcast app, use share your thoughts link in the show notes to message me.

[01:14:48:06 - 01:14:54:23]
 Tell me what you discovered and what you want me to cover next around AI business and creative work.

[01:14:56:23 - 01:15:04:10]
 Third, if this episode challenged how you think about AI in your career, help this show reach more creators.

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 On YouTube, subscribe and turn on notifications. On your podcast app, follow the show and leave a rating and review.

[01:15:14:18 - 01:15:22:15]
 And wherever you are, share this episode with one creator you know is leaning hard on AI with no safety net.

[01:15:23:18 - 01:15:29:00]
 Your voice matters here. I want this to be a conversation, not a lecture.

[01:15:30:01 - 01:15:35:05]
 Your feedback, your questions, and your stories will shape future episodes.

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