Consumer AI is Broken and It’s Not Your Fault

Despite the hype cycles, the billion-dollar valuations, and the breathless coverage of GPTs, Soras, and agents, two-thirds of U.S. adults have yet to try ChatGPT. According to Menlo Ventures State of Consumer AI Report, only 19% of Americans use generative AI daily, and that number includes passive interactions like Siri and Alexa. In the UK, it’s even lower: just 10% report daily use.

Report after report confirms the same pattern: consumer engagement is slowing, workplace adoption is uneven, and outside of tech-forward circles, AI feels more like a novelty than a tool.

We are not in the middle of a consumer AI revolution. We’re in the middle of a consumer AI stall-out.

And the question isn’t: Why aren’t people trying harder?

It’s: Why were the tools built in a way that made trying so hard in the first place

Prompt marketplaces promise magical results if you copy the right incantation and don’t ask why it works.

The Mirage of Consumer AI

Let’s start with a basic truth: most AI tools marketed to consumers aren’t really consumer products. A lot of them are just developer tools dressed up with some friendlier fonts, to sell a product. Interfaces that technically work, but collapse under the weight of real-world use.

And users feel it

• Prompt marketplaces promise magical results if you copy the right incantation and don’t ask why it works.
• YouTube tutorials walk through workflows that depend entirely on context you don’t share: a different job, a different goal, a different toolset.
• Paid courses overpromise transformation, then bury users in technical vocabulary and generic exercises.

In all these cases, the pattern is the same: the burden is on the user to figure it out, to adapt to the system, to reverse-engineer workflows, to test and tweak and guess their way into success. They teach you to think like the system, rather than helping the system work the way you think.

Welcome to the Low-Affordance Trap

There’s a name for this in HCI (human-computer interaction) research: a low affordance state. It describes a situation where a person is technically allowed to take actions in a system, but has has no clear sense of what the system will do, how it responds, or why it behaves the way it does.

It’s like being dropped in a cockpit and told, “Go ahead. Fly.”

You can push buttons. You can toggle switches. But you don’t know which combinations will get you where you want to go, and more importantly, there’s no one flying alongside you.

That’s the current state of consumer AI:

• Able to issue commands (prompts)
• Unable to predict behavior
• Lacking feedback, scaffolding, or a reliable sense of cause-and-effect

This leads to an exhausting trial-and-error loop: prompt, fail, rephrase, fail, repeat. And when something finally works, it’s unclear why, so the learning doesn’t compound. There’s no progression. No carryover. No infrastructure for building on success.

The Illusion of “Beginner Friendly”

Take Microsoft’s Generative AI for Beginners, an 18-episode YouTube curriculum promoted as accessible education for the masses. In reality, it’s a technical primer disguised as a learning resource. Well-produced? Sure. But also dense, time-consuming, and optimized for people who already speak the language of models, prompts, and probability.

This is the fundamental design flaw in most consumer AI resources today: they confuse access with accessibility.

It’s not just Microsoft. Across the board, AI “onboarding” is stuck in a mindset where teaching someone how the engine works is more important than getting them to their destination. But most people don’t want to study AI, they want to use it to do something they already care about.

And when tools fail to deliver on that, when the time-to-value stretches into hours of tutorials, technical context, and trial-and-error, you don’t get engagement. You get drop-off.

The Real User Need is Forward Motion

Let’s be clear: people want to use AI. But they want it in the same way they want electricity or Wi-Fi or their camera roll to work, in the background, in flow, in service of something else.

They’re not asking for a deep dive into token prediction or latent space.

They’re asking for:

• Tools that scaffold their thinking, not just respond to it
• Interfaces that remember what they’re trying to do
• Workflows that adapt to their role, their goal, their style
• Onramps that feel like momentum, not a second job

The average user doesn’t want to become an AI engineer. They want to send the email, summarize the doc, organize the project, or generate the first draft, without spending half their day deciphering prompts.

They don’t want AI literacy.

They want output literacy: “Can this help me do the thing I care about right now?”

They need a workspace that knows how they work.

Why Haven’t We Built This Yet?

It’s tempting to blame the gap on novelty. “We’re still early,” people say. “The consumer experience will get better over time.”

But this is not just a question of maturity. It’s a question of orientation.

Much of today’s tooling has been shaped by a supply-side imagination: built by developers for other developers, with consumer access layered on top like an afterthought. There’s been more effort put into model training than human onboarding. More energy spent on pushing capability forward than pulling understanding up.

Because most companies still don’t see the 99% as their core user.

They’re building for developers, enterprise leads, or early adopters. They’re optimizing for benchmarks and technical fluency. The dominant interfaces still reflect this: wide-open boxes that prioritize possibility over usability.

Even the most celebrated GenAI tools today require the user to do the work of translation. The system doesn’t ask: What are you trying to do? It asks: How well can you tell me what you’re trying to do?

That is a design failure. Not a user failure.

And until it’s addressed, we will continue to see adoption stall, frustration rise, and people quietly disengage, not because the technology didn’t work, but because the experience never did.

When Power Isn’t Usable, People Walk Away

It’s no wonder users are stalling out. The novelty wears off. The magic fades. Not because the technology failed, but because the infrastructure to support real, long-term use was never built.

We’ve seen this movie before. New tools arrive. Early adopters thrive. The rest of the world tries to catch up with patchwork workarounds. Eventually, frustration outweighs the initial excitement, and adoption plateaus.

That’s the risk AI faces now.

Unless we address the usability crisis, we’re going to repeat the same cycle, this time with even higher stakes.

The Path Forward: Design for Real People

Here’s the good news: none of this is inevitable.

We’re not waiting on better models. We’re waiting on better interfaces. Better assumptions. Better product decisions.

The next wave of GenAI products won’t win because they’re more powerful. They’ll win because they actually deliver value, quickly, consistently, and in context.

They’ll treat intelligence as a service, not a spectacle. They’ll lower cognitive overhead, not raise it. They’ll remember your goals, not just your words.

Because if someone gave up on AI, it wasn’t a lack of curiosity. Or effort. Or talent.

It’s because no one built a bridge to meet them where they stood.