Move fast and fix things
Your BitBakery update for July 2025
Software development and video games have more in common than zeros and ones. Like an exceedingly difficult level in your favourite game, sometimes a developer comes up against a bug that they just can’t get past without a bit of help.
Video games have hundreds of blog posts with tips, tricks, and advice. The same challenges exist for software developers, but over the last few months, developers have been turning to generative AI tools to help overcome seemingly impossible bugs.
A developer on our team recently described using an AI tool for help with a particularly ugly bug. In the time it took them to sip their coffee, the model spit back a multi-step patch, unit tests, and a commit message.
“My eyes hadn’t even scanned the stack-trace* yet, and the AI had already resolved it.”
While our developer was still considering where to start, the AI had already figured it out and provided a working solution.
The mismatch between human processing speed and AI output left him feeling oddly out of sync, both impressed and mentally overloaded. Industry reporters are starting to label this phenomenon “AI burnout,” noting that the triple load of context switching, hyper-speed iteration, and constant vigilance is uniquely taxing for developers.
Remember the early-pandemic Zoom marathons?
Think back to spring 2020, when our calendars turned into a series of back-to-back video calls and we couldn’t wait to slam our laptop lids shut.
Stanford researchers later unpacked why those days left us exhausted. Enlarged faces triggered fight-or-flight responses—we were looking at ourselves nonstop, stuck in our seats and performing on camera.
AI tools trigger a similar overload, just in reverse. Instead of too many of us on screen, we’re staring at a stream of machine-generated answers that arrive faster than we can evaluate them.
The result is the same: mental fatigue, decision paralysis, and a creeping sense that we’re the bottleneck in our own workflow.
Why does this cognitive dissonance happen?
When a tool’s velocity eclipses our cognitive pace, every interaction feels like sprinting to keep up. Over time, that tension accumulates as stress, second-guessing, or the urge to double-check everything.
3 ways to demystify the machine
Walk with a guide. Partner with a team that’s been down the road (that’s us). We help teams design workflows where humans remain in charge—from prompt engineering playbooks to human-in-the-loop review cycles—so you gain speed without surrendering comprehension.
Sprint before you marathon. Scope a two-week proof-of-concept that answers one business question. Small bets surface integration snags, ownership gaps, and governance issues early, saving you from enterprise-wide refactors later.
Build “mental margins” into your AI stack. Adopt meeting-hygiene habits for tooling:
Time-box prompting sessions the way you schedule meetings
Insert reflection checkpoints. After every major AI suggestion, require one human validation pass before merging or publishing
Rotate “driver” roles so no teammate is on continuous prompt duty. These micro-buffers slow the process just enough for your cognition to catch up, preventing the lag that fuels fatigue
If AI velocity has you feeling like you’ve skipped a beat, let’s talk. BitBakery’s AI Enablement Team turns “whoa, that was fast” into “wow, that was manageable.”
Hit 'Reply' or book a 30-minute discovery call. Your brain will thank you.
Until next time, keep your prompts sharp and your coffee warm.
* A stack-trace is like the program’s breadcrumb trail: when something goes wrong, it lists each function or method the code stepped through until the error occurred. By showing the exact line numbers and call order, it helps developers pinpoint where the bug surfaced and what led up to it, much like retracing your route on a map to find where you took a wrong turn.- Wes
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