AI Is Amazing at Some Tasks — and Still Behind at Others

 Writing, coding, creating images, and editing video all take humans a long time. AI is already dramatically faster than us at some of this work - but not all of it. Understanding why reveals a lot about where AI is actually heading.

Where AI Wins: Text and Code

AI is much faster than humans at writing and programming. Part of the reason is how it processes information: text and code are raw data, and AI can move through that data more efficiently than a human reading and typing it out manually.

Where AI Still Struggles: Visual Processing

Processing images and video is a different story. It's costly, slow, and far less mature than text-based AI work - for a few overlapping reasons.

Humans are built for this. We process visual information constantly, every waking hour, and we do it at very low cost to us. For the vast majority of cases, it currently isn't worth handing visual tasks over to AI - humans are already highly efficient at them.

Training data is uneven. AI has been trained on huge amounts of images, and image and video generation has improved a lot as a result. But that's different from AI being trained on how to use the websites and programs we use every day. Every time it navigates a new interface, it's often doing so visually for the first time, even where plenty of documentation exists. That will likely improve with time.

Most tools weren't built for AI. They were built for human visual input. When AI was purely web-based, it was limited - reliant on humans to feed it information a piece at a time. Moving onto the desktop freed it up considerably, especially for text and code, since it no longer had to wait on us to relay everything manually. But anything graphical has remained a struggle.

What Might Change

• Better CLI and text-based tooling - more ways for AI to work through interfaces via commands rather than visuals
• Restrictions elsewhere - some websites or platforms may start locking AI out rather than opening up
• Cheaper visual processing - costs coming down to the point where speed stops being a barrier
• More training data - enough exposure to close the gap with text and code

Until AI reaches that point on cost and training, humans are likely to remain better at these visual tasks - and it makes more sense to point AI at the work it's already efficient at, rather than force it into tasks it isn't ready for yet.

The Deeper Reason: Hardware, Not Just Training

There's a more fundamental issue underneath all of this. Transistors are much faster than neurons - it's part of why a calculator will always outpace a human at arithmetic. But current AI models run as a virtual network of knowledge, simulated in software, rather than the physical, hardwired connections our brains use. For tasks that lean on that kind of architecture, this makes AI less efficient by design.

Better techniques may narrow that gap over time. But without them, closing it fully may require more than smarter software - it may take a fundamental redesign of AI hardware to truly compete with the brain.