There’s a quiet frustration shared by many in this community.
You raised concerns about AI before it was cool. You explored edge cases before they were dangerous. You built frameworks to understand systems that didn’t yet exist.
And for years, you were ignored.
Now the world is listening—but not to you. The language of alignment, safety, and control is everywhere— …but the incentives are still misaligned, and the people driving policy are often just chasing headlines.
Worse, the platforms and systems you once shaped—through code, critique, and deep analysis—have become closed-off, gamified, and optimized for scale over substance.
This isn’t new.
It happened to musicians. It happened to developers. And now, it’s happening in AI.
Power users—the ones who push systems to their limits by actually using them—are being sidelined again.
But if those users are ignored, we lose our most important feedback loop. We don’t just lose control of the technology. We lose contact with reality.
What Is an AI Power User?
The term “AI power user” isn’t widely defined yet—but it needs to be.
We can borrow the concept from the early days of computing. A computer power user wasn’t a programmer or hardware engineer. They were the people who pushed machines to the edge of their capabilities—automating tasks, scripting shortcuts, bending off-the-shelf tools to fit real workflows.
They didn’t build the system. They made it do more than it was designed for.
An AI power user is the same kind of person, in a new context.
They’re not researchers. Not alignment theorists. They’re people trying to get real work done with AI.
That includes: - Writers structuring arguments or summarizing hours of transcripts - Analysts exploring huge datasets with natural language queries - Entrepreneurs gluing together GPT and Zapier to build microservices - Designers prototyping content in minutes instead of hours
They don’t care about the model’s elegance. They care whether it does the job.
Power users aren’t hired to “test” systems—they stress them by using them. And when something breaks, they find a workaround, file a complaint, or switch tools.
Their value isn’t academic. It’s operational.
They reveal what no benchmark can: Where the tool actually fails people.
The Power Users We Left Behind
This has happened before.
Musicians: From Creators to Content Musicians once drove innovation in digital audio—DAWs, plugins, workflows. But as platforms centralized (Spotify, TikTok), discovery became algorithmic, tools got dumbed down, and real creators were pushed aside for “content producers.”
Developers: From Builders to Users Developers helped build open ecosystems—Linux, Android, early web platforms. Now those ecosystems are being closed, APIs deprecated, and the users who once extended systems are locked out.
Search Power Users Boolean logic and advanced filters gave way to ads, AI summaries, and SEO spam. Precision was sacrificed for engagement.
Wikipedia Editors Early editors built the system. Now they struggle with bureaucracy and burn out while misinformation spreads.
Gamers & Modders Modding communities kept games alive for years. Now they’re often throttled by microtransactions and closed ecosystems.
The Pattern 1. Power users build early value 2. Platforms scale 3. Customization gives way to simplicity 4. Investor logic replaces user logic 5. Real users get ignored
And now, AI is at risk of repeating it.
Why Investors Should Care
The sidelining of power users isn’t just a cultural loss—it’s a business risk.
Power users are the early warning system. They reveal what works under pressure, what breaks in the wild, and what features actually matter over time. They don’t just test limits—they define them.
Ignore them, and you get tools that scale fast but fail quietly—until the failure is public, expensive, or existential.
This has happened before. Developers, musicians, and rationalist thinkers have all seen the systems they helped refine get hijacked by growth-at-all-costs logic. Investors saw short-term returns—but lost the communities that made those systems viable long term.
If AI follows the same arc—chasing engagement, ignoring depth—it won’t just lose users. It’ll lose its edge.
How AI Could Break the Pattern
AI doesn’t have to repeat history.
Unlike static tools, AI is adaptive. It can improve based on how people use it—if that feedback is allowed to shape development.
But here’s the catch: real feedback comes from power users. From people trying to hit real goals and encountering real failure.
That means: - The teacher who found the AI’s summary subtly wrong - The researcher who lost an hour to hallucinated sources - The founder who hit scale and found the tool cracked under load
These aren’t bugs in isolation. They’re friction points. And they matter.
If we integrate that feedback—if we treat users as co-creators, not just consumers—then AI can become something rare:
A tool that actually gets better the harder you push it.
But that means breaking the pattern now—not after it’s too late.
A Final Thought: Feedback as Function
If AI continues to evolve without deep feedback from those using it under real constraints, we risk designing tools that are theoretically impressive but operationally brittle.
Some of the most valuable information about these systems won’t come from benchmarks or red teaming—it will come from friction. From the places where the tool almost works, then fails in ways no spec predicted.
Those insights typically come from what we might call power users: people pushing AI systems not to explore them, but to achieve goals—under pressure, in context, with real consequences.
Integrating that kind of feedback isn’t just a product design question. It may be central to building AI systems that don’t silently drift away from their intended purpose.
The Power Users We Forgot: Why AI Needs Them Now More Than Ever
The People Who Were Right Too Early
There’s a quiet frustration shared by many in this community.
You raised concerns about AI before it was cool.
You explored edge cases before they were dangerous.
You built frameworks to understand systems that didn’t yet exist.
And for years, you were ignored.
Now the world is listening—but not to you.
The language of alignment, safety, and control is everywhere—
…but the incentives are still misaligned, and the people driving policy are often just chasing headlines.
Worse, the platforms and systems you once shaped—through code, critique, and deep analysis—have become closed-off, gamified, and optimized for scale over substance.
This isn’t new.
It happened to musicians.
It happened to developers.
And now, it’s happening in AI.
Power users—the ones who push systems to their limits by actually using them—are being sidelined again.
But if those users are ignored, we lose our most important feedback loop.
We don’t just lose control of the technology.
We lose contact with reality.
What Is an AI Power User?
The term “AI power user” isn’t widely defined yet—but it needs to be.
We can borrow the concept from the early days of computing. A computer power user wasn’t a programmer or hardware engineer. They were the people who pushed machines to the edge of their capabilities—automating tasks, scripting shortcuts, bending off-the-shelf tools to fit real workflows.
They didn’t build the system.
They made it do more than it was designed for.
An AI power user is the same kind of person, in a new context.
They’re not researchers. Not alignment theorists.
They’re people trying to get real work done with AI.
That includes:
- Writers structuring arguments or summarizing hours of transcripts
- Analysts exploring huge datasets with natural language queries
- Entrepreneurs gluing together GPT and Zapier to build microservices
- Designers prototyping content in minutes instead of hours
They don’t care about the model’s elegance. They care whether it does the job.
Power users aren’t hired to “test” systems—they stress them by using them.
And when something breaks, they find a workaround, file a complaint, or switch tools.
Their value isn’t academic.
It’s operational.
They reveal what no benchmark can:
Where the tool actually fails people.
The Power Users We Left Behind
This has happened before.
Musicians: From Creators to Content
Musicians once drove innovation in digital audio—DAWs, plugins, workflows.
But as platforms centralized (Spotify, TikTok), discovery became algorithmic, tools got dumbed down, and real creators were pushed aside for “content producers.”
Developers: From Builders to Users
Developers helped build open ecosystems—Linux, Android, early web platforms.
Now those ecosystems are being closed, APIs deprecated, and the users who once extended systems are locked out.
Search Power Users
Boolean logic and advanced filters gave way to ads, AI summaries, and SEO spam.
Precision was sacrificed for engagement.
Wikipedia Editors
Early editors built the system. Now they struggle with bureaucracy and burn out while misinformation spreads.
Gamers & Modders
Modding communities kept games alive for years. Now they’re often throttled by microtransactions and closed ecosystems.
The Pattern
1. Power users build early value
2. Platforms scale
3. Customization gives way to simplicity
4. Investor logic replaces user logic
5. Real users get ignored
And now, AI is at risk of repeating it.
Why Investors Should Care
The sidelining of power users isn’t just a cultural loss—it’s a business risk.
Power users are the early warning system. They reveal what works under pressure, what breaks in the wild, and what features actually matter over time. They don’t just test limits—they define them.
Ignore them, and you get tools that scale fast but fail quietly—until the failure is public, expensive, or existential.
This has happened before. Developers, musicians, and rationalist thinkers have all seen the systems they helped refine get hijacked by growth-at-all-costs logic.
Investors saw short-term returns—but lost the communities that made those systems viable long term.
If AI follows the same arc—chasing engagement, ignoring depth—it won’t just lose users. It’ll lose its edge.
How AI Could Break the Pattern
AI doesn’t have to repeat history.
Unlike static tools, AI is adaptive.
It can improve based on how people use it—if that feedback is allowed to shape development.
But here’s the catch: real feedback comes from power users.
From people trying to hit real goals and encountering real failure.
That means:
- The teacher who found the AI’s summary subtly wrong
- The researcher who lost an hour to hallucinated sources
- The founder who hit scale and found the tool cracked under load
These aren’t bugs in isolation. They’re friction points. And they matter.
If we integrate that feedback—if we treat users as co-creators, not just consumers—then AI can become something rare:
A tool that actually gets better the harder you push it.
But that means breaking the pattern now—not after it’s too late.
A Final Thought: Feedback as Function
If AI continues to evolve without deep feedback from those using it under real constraints, we risk designing tools that are theoretically impressive but operationally brittle.
Some of the most valuable information about these systems won’t come from benchmarks or red teaming—it will come from friction. From the places where the tool almost works, then fails in ways no spec predicted.
Those insights typically come from what we might call power users: people pushing AI systems not to explore them, but to achieve goals—under pressure, in context, with real consequences.
Integrating that kind of feedback isn’t just a product design question. It may be central to building AI systems that don’t silently drift away from their intended purpose.