AI Slop Is Costing You Trust And Power. Here Is What AI Should Be Doing Instead

Generated images and video are the most expensive, least trusted thing you can point AI at. The return sits in the dull back office.

Aidan Lambert Published 1 Sep 2026 7 min read

What AI slop actually means

AI slop is generated content published in volume because it costs almost nothing to make: images, video and text, mostly unchecked and mostly filler. The phrase spread through 2024 as the tools went mass market. It stuck because it named something people had already noticed in their feeds.

That's the whole definition.

The more useful question is what it costs. Making it costs close to nothing, which is the whole problem. The real costs are in trust and in power. Both can be measured, both are worse than the people selling generated content tend to mention, and neither shows up on the invoice you approve at the end of the month.

Problem one. Nobody trusts a picture any more

The consumer numbers are blunt. The Harris Poll, with the 4As and Infillion, reported at Cannes in June 2026: 73% of people say they're less likely to trust an ad they suspect was made by AI. 63% say they're less likely to buy from the brand behind it. 78% say AI makes advertising feel less authentic. That's the number that should worry anyone whose brand rests on being believed.

Three numbers, all pointing the same way.

A larger study lands in the same place. Klaviyo, with Datalily, surveyed more than 8,000 consumers across eight markets, Australia included, in December 2025. 32% said they trust brands less after seeing low-quality or generic AI content.

That's roughly one customer in three marking a brand down for a shortcut.

It's the trade on offer every time a generated hero image goes up to save a photography budget, and almost nobody running the campaign has seen it stated that way.

Cheap to make. Expensive to be anywhere near.

The Australian fraud problem this feeds

Cheap synthetic media has victims, and here the bill is already public. ASIC removed 19,400 scams in the 2026 financial year, a 182% rise year on year, and 33,400 across the three years since it started counting.

The wider bill is bigger. The National Anti-Scam Centre put reported scam losses at $2.18 billion in 2025, with $837.7 million of that going to investment scams, the single largest category.

The usual delivery mechanism is a deepfaked public figure. Anthony Albanese and Gina Rinehart both appear on ASIC's list of the most impersonated Australians, alongside Alan Kohler, Jacqui Lambie and Dick Smith. ASIC chair Sarah Court put it plainly: AI is making investment scams more convincing and harder to detect, and a simple online search is not enough to verify whether an opportunity is legitimate.

That's the environment a generated brand image now lands in. Every synthetic face in an ad teaches the audience to hunt for the seam. An audience trained by a year of investment scams gets better at finding it every month.

Problem two. Images are the most expensive thing you can ask AI to do

Luccioni, Jernite and Strubell measured this properly, at FAccT in 2024. An inference is one request to a model, one image or one classified email. Generating images averaged about 2.9 kWh per 1,000 inferences. Classifying text averaged about 0.002 kWh for the same volume, a spread of roughly 1,450 times. The least efficient image model drew about the same power per 1,000 images as 522 smartphone charges.

The study measured 2023-era open models, so treat the figure as the direction of the gap rather than a precise current number. The direction is the point. It's a different category of cost.

The picture holds when you zoom out. The International Energy Agency puts data centre consumption at about 415 TWh in 2024, roughly 1.5% of global electricity, and projects about 945 TWh by 2030, with AI-accelerated servers responsible for almost half of that increase.

Be careful with what that does and doesn't say. Data centres don't exist mainly to make pictures, and anyone arguing they do is one fact-check from losing the room. The claim that survives is narrower and more useful: per task, generative media is the most power-hungry thing on the menu, and text and structured data sit at the cheap end by three orders of magnitude (about a thousand times).

Where the water goes

Cooling is the other cost. Data centres use water to shed heat, and the build-out around Sydney and Melbourne has made that a live planning argument rather than an abstract one.

The numbers here are shakier. Most published water figures for AI workloads are estimates built from power draw and regional cooling assumptions, not disclosed operator data. Operators publish very little, and they rarely break it down by workload. The honest position is that the direction is clear and the precision isn't. We'd rather say that than quote a figure to two decimal places that nobody can trace to a source.

The comparison nobody in your feed is making

Energy per 1,000 inferences by task type, measured on 2023-era open models by Luccioni, Jernite and Strubell, FAccT 2024. The second panel is the same data on its own scale, because on the first one eight of the ten bars are thinner than a hairline.

Generated media against automated admin, per task
	Generated image or video	Automated admin task
Energy per task	Highest measured category, about 2.9 kWh per 1,000 generations	Lowest measured category, about 0.002 kWh per 1,000 text classifications
Who sees a mistake	Every customer, permanently	One reviewer, before it leaves the building
Effect on trust	32% of consumers trust brands less after low-quality or generic AI content	Invisible to the customer
Time returned	Minutes, on a task a person enjoyed	Hours, on a task nobody wanted

The argument in one line: the compute spent on a hero image nobody trusts could clear a week of invoice matching that everybody benefits from.

Point AI at the admin instead

This is the part that doesn't trend, which is most of why it's still sitting there. Six jobs. Every one of them is dull, and every one is worth more than a generated campaign.

None of it will win an award.

Inbox triage and routing. Every enquiry read, categorised and sent to the right person, with the odd one flagged for a human to decide. This is ordinary workflow automation with a model doing the reading. On a busy shared inbox that's usually about an hour a day back, and it's the hour that used to go before anyone had started on real work.

Quoting and proposal assembly. A template, the CRM record and the pricing rules, assembled into a draft in under a minute. The estimator checks it and sends it, with the record living in the CRM rather than a folder. Quoting stops following people home, which is most of why it gets automated first.

Invoice and receipt matching. Line items checked against purchase orders, the exceptions surfaced, the rest cleared without anyone opening a spreadsheet. In most finance teams that's days a month, and they go back to the person who was least glad to be spending them.

Meeting notes into actioned tasks. The recording becomes a list of who owes what by when, filed in the system where the work actually lives rather than a document nobody reopens.

First drafts for repeat enquiries. The same forty questions that arrive every week, answered from your own documentation rather than made up, with a person reading every one before it goes. The same pattern runs the marketing side, where the follow-up nobody sends is usually the expensive gap.

Data entry between systems that were never meant to talk. The copy-paste that exists purely because two tools have no integration between them. It's the least interesting job in any business, and one of the most expensive.

They all have the same shape. The output is text or structured data, a person can check it before it leaves the building, and a mistake costs an hour rather than a reputation.

Why admin automation is the low risk bet

Admin work sits at the cheap end of the compute curve, where it costs a fraction of what a picture does. Mistakes get caught by the person who reviews the output, because it's internal, reversible and checked before anyone outside sees it. A generated image is the opposite: public, unverifiable and permanent.

Nobody has ever felt deceived by an automated invoice reminder. That's why we build this half of the work and don't sell generated content. The return is real, and it doesn't come out of the trust a business spent years earning.

Where generated imagery still earns its place

The tools aren't worthless. They're genuinely good at a few things, and pretending otherwise to make a cleaner argument would be its own kind of slop.

Internal mockups, where the point is to show a layout rather than a photograph. Storyboards. Concept work before a real shoot, when you're choosing a direction and nobody outside the room will see the frames. Throwaway visuals nobody could mistake for a photo.

The line isn't hard to find.

Could a customer reasonably believe a real person or a real place is in front of them? Inside that line, the tools save real time and cost almost nothing. Outside it, you're spending trust to save a few hundred dollars on a shoot.

How to decide what to automate first

A test you can run on Monday. Four questions, one point each.

Is the task repetitive, done the same way every time? Is the output checkable by a person before it matters? Does a mistake cost hours rather than reputation? Would a customer be unbothered to learn AI had done it? That last question is the one an AI use policy should already answer for your team.

Four yeses and it's worth automating. One yes, leave it alone.

In our experience three or four jobs clear the bar comfortably, and they're always the dull ones. If you want a second opinion on yours, an AI audit is exactly that conversation, and we're happy to have it. Plenty of the answers we hand back are that a process is too rare or too messy to pay for itself.

For the mechanics of doing it, how to automate a business process covers the mapping and the build. For the arithmetic, is AI automation worth it works through the numbers, and operations automation is what the finished thing looks like.