How AI Will Taste Your Cake

Every month, like clockwork, there appears a post on the r/QualityAssurance subreddit that reads something like this:

• Do you believe AI could take over your role in software testing?
• Use of AI in testing
• AI is exhausting, I'm out
• Why do QA jobs still exist? AI anyone? (This one is the funniest because it looks like AI wrote it)

QAs are nervous. They see AI everywhere and worry they are just one agent away from being obsolete.

The truth is, they are right to be worried. They are one agent away from being obsolete.

The Baker's Neural Network

One of the most common defensive positions in the r/QualityAssurance subreddit is what I'll call "The Baker's Neural Network Fallacy." As one Redditor put it:

"I compare it to a baker. AI can tell you the ingredients match the recipe, but it can't tell you if the cake tastes good."

This argument fundamentally misunderstands the exponential learning curve of modern AI systems. The assumption that "tasting"—the subjective quality assessment of software—requires some ineffable human intuition is like claiming GPUs would never beat humans at chess. Plot twist: they did that decades ago.

Modern AI doesn't need taste buds to determine if users will enjoy an experience. It just needs data—and holy shit, does it have data. We're talking about systems that can simultaneously analyze:

• Millions of user interaction patterns across thousands of applications
• Historical bug patterns and their severity impacts
• Sentiment analysis from user complaints
• Visual UI misalignments at a pixel-perfect level that human eyes miss
• Code path execution frequencies that highlight real user behavior

This isn't simple pattern matching; it's a sophisticated multi-dimensional representation of "digital taste" that outperforms human intuition in specific domains. TDD is back, baby—but the T now stands for Tensor.

The "Not Yet" Syndrome

Reading through QA subreddits reveals a fascinating psychological pattern. Many QA professionals acknowledge AI's inevitable takeover but conveniently place it in some safely distant future:

"Not anytime soon, but it doesn't matter what I think it matters what executives think"

"What I can see happening in the future is it replacing all juniors and manual testers, even when needed leadership will just take that risk for the price reduction."

This "not yet" argument is the classic response of every profession facing disruption. Typists said word processors wouldn't replace them until they did. Taxi drivers said ride-sharing wouldn't work until it did. A 30-year QA veteran unwittingly outlined this pattern perfectly:

"I've been doing QA for nearly 30 years now. I started in the 90s doing mostly manual with a little bit of automation. The automation percentage has slowly increased over the years."

The transition from manual to automated testing didn't happen overnight, but it happened. The AI revolution in QA will follow the same pattern, compressed from 30 years to about 30 months. While QAs debate whether it will happen in 3 years or 10, the transition isn't coming—it's already here, just unevenly distributed.

Nobody Wants This Job Anyway

Perhaps the most telling evidence comes from the QA professionals themselves, who frequently express how much they dislike aspects of their own role:

"I use it to help with the boring, mundane and repetitive part of my job. Like writing test cases, documentation and other boring stuff I don't like doing."

"I don't know what to do. Lately I feel totally trapped in this career"

Even the staunchest defenders of human QA inadvertently advocate for AI replacement when they describe parts of the job they hate. If testing involves so many "boring, mundane, repetitive" tasks that humans actively avoid, it's the textbook definition of what we should automate.

This brings us to an uncomfortable truth: AI don't care, bro. It doesn't get bored running the same regression test for the 500th time. It doesn't dream of opening yogurt shops while maintaining test suites. As one burned-out QA engineer put it:

"Am I the only one who's tired of all this AI hype and just wants to actually do my job? When I get the money -- probably when my first employer IPOs soonish -- I'm ditching all this tech insanity to start a local yogurt shop."

When your best people are fantasizing about dairy-based career pivots, you might have a motivation problem that AI simply doesn't share.

The Exponential Edge Case Cliff

The QA community seems oddly split on how quickly this transition will happen:

"For the next 1-3 years QA will see increased openings, devs will start to test more than code. In 3-5 years we will see a drastic decline in human needs as QA will optimized up by a factor of 10"

Others see a more abrupt change:

"It doesn't need to be good, it just needs to be good enough. Exit criteria has already fallen off a cliff since software stopped being sold on discs."

This last point is crucial. The threshold for "good enough" testing isn't perfection—it's just marginally better than what we currently achieve with human QA. And for companies drowning in testing backlogs with release deadlines looming, "good enough" AI testing that catches 95% of issues without complaining about working weekends is already looking mighty attractive.

From Testing Code to Testing AI

The delicious irony in all this anxiety about AI replacing QA is that testing will become more critical, not less, as AI generates more code. As one Redditor observed:

"AI can produce the code but testing will still be needed to check that the code fulfills the requirements."

But who will perform this testing? Increasingly, it will be specialized AI systems designed for verification. The future isn't QA professionals testing code; it's AI testing AI in a digital ouroboros of quality assurance.

This creates a legitimately massive opportunity for QA professionals who see the shift coming. Instead of clinging to manual or even automated testing roles, forward-thinking QAs are positioning themselves as AI orchestrators—professionals who know how to direct, improve, and validate AI testing systems rather than performing the tests themselves.

When the Last Human QA Turns Off the Lights

A QA professional with 30 years of experience made this telling observation:

"I don't think QA is going away any time soon. The role continues to evolve. The unfortunate part is that most people above manager really don't understand QA."

The role isn't going away—it's being transformed beyond recognition. The executives who "don't understand QA" may actually understand something the practitioners don't: the essence of quality assurance can be achieved through fundamentally different means than having humans write and maintain test suites.

As AI continues its exponential improvement in testing capabilities—moving from basic test generation to sophisticated user behavior modeling to autonomous bug fixing—we'll reach an inflection point where keeping humans in the loop becomes economically irrational and technically unnecessary. When that happens, the last human QA will indeed turn off the Playwright—and quite possibly the last switch they'll flip will be the one that activates the AI agent that replaces them.

The cake will still be tasted. The bugs will still be found. The software will still be validated. But the bakery will have no human bakers, just machine learning systems with a digital sense of taste refined across billions of samples—far more than any human could ever experience in a lifetime.

And that is precisely why they should be worried. Because, unlike humans, AI's taste gets better with every single byte.