( Six minute read)

We are now looking at a Caught Two situation with these companies.

As programmers make more and more use of generative AI in the workplace, their responsibilities are quickly transitioning from creating new products from scratch to babysitting a large language model that cobbles together code.

It’s gotten to the point where some engineers are no longer able to make sense of the code being spat out by an AI.

That doesn’t bode well, considering researchers are finding that the widespread use of AI can lead to cognitive decline and skill atrophy, including most computer scientists.

Developers are afraid that software engineers are drifting further and further away from the act of actually writing code and learning new skills required to architect larger projects as their employers continue to push for more AI automation.

“ They might a whole bunch about the concepts in the system, but they just have no idea what this MLA kernel that they’re showing us for DeepSeek actually does. “

“ At the time, Nanos argued that the “actual code is not necessarily something that a human has to reason about deeply,” because the “AI knows how to manipulate the data movement and the processing elements on the hardware that you’ve given it.”

AI has advanced to the point where there’s no need to worry about many things anymore,” he wrote.

“Developers simply need to define the architecture and correctness criteria; the neural network will handle the rest.”

But whether such optimism will hold out as software developing becomes ever more unrecognizable remains an open question.

A new generation of engineers are growing up in an environment that’s actively undermining the value of understanding the code itself.

And that doesn’t bode well for an increasingly digital world order that’s putting AI front and center.

The Beady eye says it’s not the intelligence we should be looking at but its application in the real world.

Intelligence—whether human or artificial—has always carried a cost.

The financial exchange is already here:

Take Training and running advanced frontier models.

It requires massive data centers, specialized chips, immense electricity, and water for cooling.

Because the physical foundation of AI is capital-intensive, high-end capabilities are naturally commercialized.

Basic access to foundational models may remain free or ad-supported (much like internet search),

But the most capable models, specialized reasoning agents, and deep research tools are increasingly placed behind subscriptions, API fees, or enterprise pricing.

Even when you don’t pay with cash, you often pay with data, attention, or privacy, which tech companies monetize to fund further intelligence.

High-level human cognitive ability relies heavily on economic investment—access to quality education, nutrition, stable environments, and early childhood enrichment.

As biotechnology, brain-computer interfaces (like Neuralink), and cognitive-enhancing therapies progress, direct access to enhanced human memory, focus, or processing power may become commercial products.

The real challenge isn’t just whether intelligence costs money, but whether that cost creates a cognitive divide:

As hardware costs drop (following trends like Moore’s Law), baseline intelligence becomes a cheap, highly accessible commodity—democratizing access to expert-level medical advice, tutoring, and technical skills worldwide.

We will be left with a scenario where the wealthiest individuals and corporations have exclusive access to the highest-tier AI models and cognitive enhancements, compounding economic and political power over time.

Intelligence is unlikely to become entirely paywalled, but high-end cognitive power—both artificial and biological—will remain tied to resources and capital.

In the final analysis we are heading for a world run by an Artificial Intelligence that will have no human oversight or overlord.

Digital Slaves.

All human comments appreciated. All like clicks and abuse chucked in the bin.

Contact: bobdillon33@gmail.com