Month: December 2025

There’s a way of working with AI that I don’t hear talked about very often. It doesn’t feel like prompt engineering, and it doesn’t feel like outsourcing thinking. It feels quieter than that. Slower, even when it’s fast. More like sitting with an idea until it reveals what it actually is.

When I use AI this way, I’m not looking for answers so much as surfaces to think against. I’m trying to understand what I believe, what I’m assuming, and what I might be missing. The tool helps, but it doesn’t lead. And that distinction has started to feel important.

What I’m Experiencing

The most valuable work I’ve done with AI rarely happens in one exchange. It starts with something unfinished. A half-formed thought. A sense that there’s something true here, but I don’t yet know how to say it. I ask the AI to help me articulate it, and then I read what comes back carefully. I notice where it assumes too much, where the tone is wrong, where something sounds impressive but doesn’t actually serve the point I’m trying to make.

So I push back. I ask it to elaborate. I ask it to remove parts. I ask it to aim at a different audience. Sometimes I realize the problem isn’t the response at all, but my own lack of clarity. The process becomes one of refinement rather than generation. The AI helps me externalize my thinking, but I remain responsible for deciding what stays and what goes.

What surprises me is how human this feels. The value isn’t in the speed of output. It’s in the way the dialogue forces me to pay attention. I can’t disengage without the work getting worse. If I abdicate judgment, the artifact becomes hollow very quickly.

What This Means for Software Development

If this way of working becomes more widespread, it has implications beyond writing or planning. It points to a shift in what we actually value in software development. Tools are getting remarkably good at producing code, tests, and documentation. They can process more context than any individual ever could. But they do not understand consequences. They don’t feel the weight of tradeoffs or the cost of being wrong in the real world.

That means the center of gravity moves. The scarce skill is no longer raw production. It’s sensemaking. It’s the ability to hold complexity, challenge confident outputs, and decide what not to ship. AI increases the volume of possibilities. Humans still decide which of those possibilities deserve to exist.

Organizations that confuse acceleration with understanding will move quickly and accumulate invisible risk. Organizations that pair powerful tools with disciplined human judgment have a chance to build software that is not just fast, but trustworthy. In that environment, the question isn’t whether AI can do the work. It’s who is willing to take responsibility for what the AI produces.

What This Means for Software Testers

For testers, this moment feels strangely familiar. Testing has always required sitting in uncertainty, asking inconvenient questions, and resisting the urge to accept artifacts at face value. Using AI as a sensemaking partner doesn’t change that. It intensifies it.

When AI generates tests, or explores code paths, or summarizes system behavior, someone still has to understand what happened. Someone still has to say, “I know what this tested, and I know what it didn’t.” That knowledge doesn’t come from the artifact alone. It comes from learning the system deeply enough to notice what’s missing.

Testers who see their role as primarily producing artifacts may feel displaced. Testers who see their role as cultivating understanding will find new leverage. The work becomes less about writing things and more about interpreting them. Less about volume and more about discernment.

A Closing Thought

I don’t think the most important question about AI is whether it can think. It’s whether we’re willing to stay engaged when it thinks quickly and confidently on our behalf.

Used well, AI gives us better drafts, better surfaces, better starting points. Used poorly, it gives us plausible nonsense at scale. The difference isn’t technical. It’s human.

I’m trying to use AI in a way that sharpens my responsibility rather than dulls it. As a developer, as a tester, and as someone who still believes that clarity, care, and judgment matter.

—

Beau Brown

Testing in the real world: messy, human, worth it.

There’s a thought that’s been circling in my mind for a while now, and I want to try to name it clearly.

We’re living through a moment where many leaders are saying some version of:

“We need to invest heavily in tools right now.”

That sentence makes a lot of people nervous. It makes me nervous too. Because investing in tools often feels like investing less in people. And that feels wrong, or at least morally suspect, especially for those of us who believe that good software is built by thoughtful, humane teams.

At the same time, I’m not convinced the situation is as simple as “tools replacing people.” I think something more subtle is happening.

So here’s what I want to say, plainly and in order.

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What I’m Trying to Say

1. Industries tend to move through phases where investment shifts between people, processes, and tools.
2. We are currently in a phase where tools are advancing faster than our ability to staff and scale with people alone.
3. Investing in tools does not eliminate the need for people, but it changes what kind of people are most valuable.
4. If history rhymes, this phase will eventually create more demand for humans with judgment, skepticism, and systems-level thinking.
5. Software professionals, especially testers, need to orient their careers toward that future rather than fighting the present.

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Why I Think This Is True

1. Hiring your way to scale breaks down

Building enterprise software that sells requires speed, reliability, and consistency. At a certain point, you can’t just add more humans to get more output. It’s expensive, fragile, and slow.

Tools exist precisely to decouple output from headcount. That’s not cruelty. It’s economics.

2. Tools absorb repetition, not responsibility

Modern tools and AI systems are exceptionally good at:

• repetition
• speed
• pattern matching
• brute-force analysis

They are not good at:

• understanding markets
• weighing tradeoffs
• noticing when assumptions are wrong
• deciding what not to build
• recognizing when something “works” but is still a bad idea

That responsibility doesn’t disappear. It moves.

3. Every major technological shift has followed this pattern

We’ve seen this before:

• Handcrafted work gives way to mechanization
• Mechanization creates scale and standardization
• Standardization exposes new risks and blind spots
• Expertise becomes valuable again, but in a different form

The work changes, but it does not vanish.

4. AI requires conductors, not just operators

The metaphor that keeps coming to mind is orchestration.

The most valuable people in the next phase are not those who blindly trust the tools, nor those who reflexively reject them. They are people who can:

• challenge outputs
• interrogate assumptions
• think in systems
• understand incentives
• recognize second-order effects
• hold quality, speed, and ethics together

That’s not new work. It’s the work good engineers and testers have always done.

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What This Means for Businesses

For businesses, “investing in tools” should not mean abandoning people. It should mean:

• using tools to reduce toil and repetition
• freeing humans to focus on judgment and strategy
• making quality more consistent, not more brittle
• resisting the temptation to replace wisdom with velocity

Organizations that mistake tool adoption for wisdom will move fast and break trust. Organizations that pair strong tools with thoughtful people will endure.

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What This Means for Software Professionals

For software professionals, this moment calls for honesty.

The work is changing. Some roles will shrink. Some tasks will disappear. But new forms of responsibility are emerging:

• system-level thinking
• risk assessment
• quality judgment
• cross-functional communication
• ethical and economic reasoning

The people who thrive will not be the fastest typists or the most prolific code generators. They will be the ones who can say:

• “This looks right, but it’s wrong in context.”
• “This optimizes the wrong thing.”
• “This will hurt us later, even if it helps now.”

Those skills have always mattered. They’re just becoming harder to fake.

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What This Means for Younger Testers

If you’re earlier in your testing career, this can feel unsettling. It might sound like the ground is shifting under your feet.

But here’s the hopeful part.

Testing has always been about:

• curiosity
• skepticism
• learning systems deeply
• noticing what others miss
• asking uncomfortable questions

Those skills translate incredibly well to a world full of powerful tools.

Don’t anchor your identity to a single tool or framework.

Anchor it to your ability to think well, to learn quickly, and to care about outcomes.

If you can learn how to work with tools while retaining judgment, humility, and courage, you won’t be replaced. You’ll be needed.

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A Closing Thought

I don’t think we’re headed toward a people-less future. I think we’re headed toward a future where undisciplined human labor becomes less valuable, and disciplined human judgment becomes more valuable.

That transition is uncomfortable. It always is.

But if history rhymes, the pendulum will swing again. And when it does, the people who can think clearly, question wisely, and care deeply about quality will be the ones shaping what comes next.

—

Beau Brown

Testing in the real world: messy, human, worth it.