Elementary physics gives us a simple definition: a machine is anything that reduces effort and makes work easier. Every major leap forward has come from a new kind of machine, one that reduced human effort and expanded what was possible. The wheel moved physical weight, the printing press moved information, and lithography machines made modern computers possible.

AI is simply the next step in that chain. It doesn’t replace the machine, it extends it. While earlier machines amplified human muscle and speed, AI amplifies cognition, helping computers handle judgment, pattern recognition, and decision-making at a scale humans can’t. Long before AI became a headline or a product label, it was already at work, quietly, in the background. Powering search rankings, filtering spam, recommending content, detecting fraud. It was not framed as a tool to increase sales or for pushing products, it was ground breaking tech doing what machines do best – reducing effort in places where humans couldn’t operate at scale.

At the time of writing this, AI seems to be everywhere. And when you think of it as just another machine built to reduce effort, the reason is obvious. It’s fast, convenient, and generally, it saves time.

Due to Big Tech and their deep pockets, AI is democratised, easy to use, cheap to try, and powerful enough that almost anyone can plug it into a product or workflow, or even to write an email or to ideate for a blog :’).

But availability doesn’t automatically mean applicability. That’s where we think the conversation has gone slightly off track. Somewhere along the way, ‘simple’ started to sound like ‘unsophisticated’. That’s a mistake. For the most of us historically, solid engineering was good enough to make a product or project successful. In academics, solid engineering was necessary to develop critical thought, problem solving capabilities and understanding design. Today, however, having a complex, black-box solution for simple problems has become the status quo for managers, clients, and academicians. When complexity became the norm, common sense and good engineering took the fall.

Just Because AI Is an Option Doesn’t Mean It’s the Right One.

We work in AI, We work with AI, We build with it. One of us deployed it
( ͡° ͜ʖ ͡°) and debugged it when it broke. Being close to the actions and participating in the Ai revolution first hand, we’ve started noticing a pattern: AI is becoming the default solution, even when it doesn’t need to be. We have seen simple, well-defined problems turned into complex systems just because “AI can do it.” Calling a meeting doesn’t automatically solve a problem, dropping AI into a workflow doesn’t automatically make it smarter, efficient or faster. 

We’ve seen AI used in places where it increased operational overhead and solved a problem that didn’t exist.

AI is an incredible tool, but it’s also another dependency, another cost, another source of unpredictably.

If it doesn’t clearly reduce effort or unlock something genuinely new, it’s worth asking why it’s there at all.

Why We Started beReasonable.ai

As passionate engineers, we want to bring back critical thinking to the workflow, and utilise the fundamentals of engineering solve problems that truly matter. We didn’t start beReasonable.ai to tell people not to use AI. We started it to help teams use AI with intent. We help determine when AI adoption adds real value and when integration may do more harm than good.

Our mission is to bring clarity and intentionality to AI, ensuring machines work for us and with us, not against us.

We need to relearn the value of simple, elegant and sustainable engineering solutions. Building things that work – That is what we care about.