Geisel Software, Inc.
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07/16/2026
AI may increase output while destroying developer focus.
Developers can now generate code faster than ever.
But generation speed is only one part of productivity.
A better way to evaluate AI-assisted development is to track three separate metrics:
How quickly can the tool produce usable code?
How long does it take to review, test, integrate, and deploy that code?
How much attention is required to prompt, validate, correct, and maintain the output?
AI can improve the first metric while making the other two worse.
More generated code can mean:
More context switching.
More review fatigue.
More subtle defects.
More time spent understanding decisions the engineer did not make.
Productivity is not how much code enters the repository.
It is how much correct thinking reaches production.
The best AI workflow is not the one that generates the most.
It is the one that helps engineers maintain focus, make better decisions, and ship reliable systems.
Have you ever looked at an AI-generated image and thought, “Something feels off, but I can’t quite explain why?”
That is often because AI does not see an image the way we do.
It focuses on what matters for the task it was trained to perform, and everything else can become secondary.
For example, if a model is trained to detect faces, it may not care much about the background.
A wall might look slightly warped.
A pattern might repeat strangely.
A hand might feel unnatural.
The lighting might not fully make sense.
But if the face looks enough like a face, the system may still classify it correctly.
Those small inconsistencies do not necessarily break the illusion.
They are not critical to what the model was trained to identify.
That is what makes AI-generated images so fascinating, and sometimes a little unsettling.
Humans look for harmony, context, realism, and cohesion.
AI often focuses on the signal it was trained to prioritize, even if the rest of the image feels distorted.
And that raises important questions:
What are the limits of AI perception?
How do these artifacts affect our trust in AI systems?
How can we improve training so models become more accurate without losing visual authenticity?
As AI becomes more present in design, media, robotics, and decision-making systems, these details matter. They remind us that accuracy is not just about getting the main object right.
It is also about understanding the full picture.
What is the most interesting AI artifact you have encountered?
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67 Millbrook Street, Suite 520
Worcester, MA
01606