You think OpenClaw is helping you make money? It’s actually spending yours.

Recently, have you noticed a tool called OpenClaw?

Simply put, it’s a tool that truly brings AI into practical application.

What is OpenClaw?

Traditional AI remains confined to dialogue-based outputs like ChatGPT and Gemini.

They are limited to generating text or images; when it comes to specific execution, they merely tell you the steps rather than performing them for you.

OpenClaw, however, is an AI tool that actually “does” the work.

By receiving instructions → decomposing tasks → calling tools → executing operations, it replaces manual labor with AI automation.

Furthermore, OpenClaw runs locally, which means privacy is better protected.

Its built-in library of over 100 AgentSkills minimizes AI hallucinations during execution, and the community has contributed a vast array of skill plugins.

As of now, its basic functions cover about 80% of common scenarios — whether it’s organizing emails, sending scheduled daily reports, or tracking updates, it handles them with ease.

In terms of automation, it has certainly reached the level of a high-end productivity tool.

But is OpenClaw truly so perfect that it’s beyond reproach?

Not exactly.

The Money Trap

First, let’s talk about the topic everyone cares about: making money.

Many claim that with OpenClaw, they no longer have to worry about running social media accounts alone.

Indeed, OpenClaw acts like an elite employee working 24/7.

It can scrape trending news, filter it for your confirmation, generate content, and upload it to various platforms while you just wait for the cash to roll in.

It can do all of that. But at what cost?

• Local Hardware: If you use local models, your GPU’s computing power must keep up. If you’re trying to run this on a basic Mac mini, I’d suggest you think again.
• Cloud Costs: If you use cloud-based models, the performance becomes silky smooth, but the token consumption is astronomical.

Take a standard scenario for testing: a simple “Email Organization + To-Do List Generation” task.

OpenClaw requires about 8 cloud calls, consuming roughly 128,000 tokens. Using GPT-4o, this costs over $1 USD .

If you’re tinkering with web scrapers or debugging code? You might easily blow past $2.

Ask yourself: is your business worth that $2 per task?

Most social media accounts probably don’t even earn $2 a day.

User Experience

Then there are the practical issues.

• Compatibility: It struggles with niche software. You often have to build your own plugins, which is a daunting task even for average programmers.
• Hardware Demands: OpenClaw requires a high-performance PC to run smoothly. Given the current market prices for RAM and SSDs, I’d advise you to think twice before building a new rig just for this.
• Stability: OpenClaw still has plenty of bugs. About 60% of batch tasks exceeding 10 minutes end up stuck in loops, crashing, or losing context. Its browser automation — especially form filling — is also quite mediocre in testing.

Security Concerns

Finally, there is the massive issue of security.

OpenClaw’s usability depends heavily on its community ecosystem rather than just the official developers.

While the myriad of community-developed plugins improves ease of use, many of these third-party tools come with vulnerabilities — or worse, are created by developers with malicious intent.

Summary

To date, OpenClaw is indeed a major breakthrough; at the very least, it bridged the gap between AI “talking” and AI “doing.”

It offers local execution, privacy, and open-source customizability.

However, the token consumption is extreme, leading to potential “bill explosions” when using cloud models, and complex tasks are still prone to freezing and infinite loops.