AI Is Abundant. Judgment Is Scarce.

For the last 1.5 to 2 months, I have been hitting AI rate limits constantly. Daily limits. Weekly limits. Everything.

That experience led me to a realization that feels uncomfortable, but increasingly hard to ignore:

A meaningful share of AI usage today is not creating leverage. It is creating noise.

That does not mean AI is fake. It does not mean the technology is overrated. In fact, the opposite is true.

AI is now powerful enough that waste matters at a physical level, not just a productivity level. The cost is no longer abstract. It is electrical, environmental, infrastructural, and financial.

That is why this conversation matters.

AI is useful. But usefulness is not the same as value.

I have used Claude and ChatGPT across full-stack app development, Zapier automations, financial modeling, Excel plugins, presentations, internal tools, n8n workflows, AI agents, logs, local machine setups, and cloud deployments.

From that kind of close exposure, one thing becomes obvious very quickly:

AI can compress execution time dramatically, but it does not automatically improve the quality of what is being executed.

That is the trap.

The current AI wave makes it incredibly easy to act on impulse. You get an idea at 2 a.m., open your laptop, spin up a workflow, test a stack, connect APIs, clone a UI, deploy something to the cloud, and convince yourself that speed itself is value.

Sometimes that works.

Sometimes it creates real leverage.

But often, it just allows people to industrialize low-quality thinking.

In other words, the cost of “just trying things” is no longer just your time.

The real waste is not prompting.

It is building without judgment.

Motion is being mistaken for substance

A lot of today’s AI culture celebrates motion:

vibe coding
autonomous agents
wrappers
dashboards
“Bloomberg-like terminals”
personal copilots
fully automated work systems

Some of that is real progress. Some of it is genuinely transformative.

But a lot of it is repetition dressed up as innovation.

A polished interface is not the value.

A cloned terminal is not the value.

An agent is not the value.

The value is in judgment:

knowing what should exist
knowing what problem is actually worth solving
knowing what workflow deserves automation
knowing what still requires human reasoning
knowing where AI creates asymmetry instead of just output

That distinction matters even more now because the infrastructure underneath AI is heavy.

So when people casually burn tokens, recompute the same low-value outputs, or spin up expensive workflows that solve nothing important, the waste is not metaphorical.

It is real infrastructure being spent on low-conviction activity.

AI lowers friction. That is both its strength and its danger.

This is the paradox of the current moment.

The best thing about AI is that it lowers the barrier between thought and execution. It allows an individual to test, prototype, write, summarize, automate, and deploy with extraordinary speed.

That is a real shift.

But lower friction also means lower filtering.

Before AI, many weak ideas died naturally because they required too much effort to build.

Now they survive long enough to consume resources.

And because the outputs can look polished, people mistake completion for value.

This is why so much of the AI ecosystem feels simultaneously exciting and bloated.

The models are powerful. The interfaces are slick. The demos are impressive.

But a nontrivial share of what is being built is still operational theater:

systems that automate unimportant work
apps that repackage existing models without real depth
“agentic” products that are more visual than useful

That does not mean wrappers are useless.

It means wrappers without substance are useless.

The environmental and economic cost makes bad judgment more expensive

One reason this matters more now is that AI does not run on abstraction alone.

It runs on electricity, cooling, chips, data centers, supply chains, land, and capital.

That changes the moral texture of the conversation.

When AI was mostly a software novelty, waste felt harmless.

Now it increasingly sits on top of real-world infrastructure.

So bad judgment at scale is no longer just annoying.

It is materially expensive.

That is the part many people still underestimate.

The easier it becomes to generate output, the more important it becomes to ask whether that output deserved to exist in the first place.

Because pointless output is not free.

It is paid for somewhere:

in power consumption
in hardware demand
in cooling systems
in capital expenditure
in environmental strain
in human attention

The next edge is not more output. It is better selection.

We are entering a phase where AI output is abundant.

That means raw generation is becoming commoditized.

Text is cheap.

Code is cheaper.

Interfaces are easier.

Prototypes are faster.

So the scarce thing is shifting.

The scarce thing is no longer the ability to produce.

It is the ability to decide:

what should be built
what should not be built
what should stay human
what deserves automation
where intelligence actually compounds

That is why domain knowledge matters.

That is why logic matters.

That is why taste matters.

That is why technical understanding still matters, even in a world of vibe coding.

Because vibe coding without understanding is not the future.

It is just faster confusion.

Final thought

AI should absolutely be used.

It should be learned deeply.

It should be deployed aggressively where it creates real leverage.

But we need a more serious culture around it.

Not just how to prompt.

Not just how to connect tools.

Not just how to host on cloud.

Not just how to call something an agent.

We need to know what is worth building in the first place.

The models are getting stronger.

The infrastructure is getting larger.

The cost of pointless output is becoming more real.

AI is abundant now.

Judgment is scarce.