In the AI era, the winners won’t be the tools you adapt to — they’ll be the tools that adapt to you.
Let's take Linear. It is a beautiful, well-designed, simple but inflexible tool with little room for AI to add value. AI thrives in messy, open-ended spaces where it can design, assemble, and adapt — but in Linear, the major design choices have already been made. At best, AI might shave a few seconds off repetitive tasks or auto-fill a few fields, but it can’t reinvent the core process, because the tool doesn’t allow it.
Let's take Fibery. It is somewhat beautiful, quite well-designed, complex and flexible tool 😝. However, it is relatively hard to setup Fibery for your needs. LLMs turn complexity from a barrier into an advantage, collapsing weeks of setup into a few prompts. In a world where “how” disappears, the most adaptable tools will win.
Problem vs. Solution
The biggest shift LLMs bring to malleable software is moving the focus from designing the solution to defining the problem.
In the past, when you had a problem in mind (the what), you still had to figure out the how — which meant learning the tool, assembling components, and translating your needs into its language.
Now, in many cases, LLMs can handle the "how" for you. You describe what you want in plain language, and the system works like a programmer or system analyst: breaking your problem into building blocks, mapping a flow to solve it, and creating the first version. You review the result, give feedback, and iterate. The entry barrier drops dramatically, and the loop from idea to working prototype becomes fast.
Malleable software FTW
Historically, malleable software was a niche for tinkerers. It demanded time, patience, and a willingness to wrestle with complexity. Learning it took effort. Building with it was cognitively heavy.
That’s why simple, vertical solutions thrived. You picked something popular, used it as-is, and avoided customization altogether. Linear, for example, gives you a well-designed, opinionated process for software development. It’s a good process — but not the right one for everyone.
This changes when customization becomes fast and easy. If AI can bend a tool to your process in minutes, why settle for a rigid default? At some point, your needs will evolve, and with a locked-in tool, you’ll hit a wall. With malleable software, you just keep going and bend it to fit your new needs.
When you can have a tool shaped to your process in minutes, why would you accept one that shapes your process instead?
My take is that malleable software will replace less flexible hardcoded tools in some observable future. When configuration and setup is fast, easy (and fun), there is no way back to rigid tools.
This shift won’t happen overnight, but the trajectory can be like this:
2025–2027 – AI starts removing the steepest parts of the learning curve in malleable tools. Teams still pick rigid tools for speed, but migrations accelerate when processes evolve.
2028–2030 – The default buying question changes from “How fast can we start?” to “How easily can we change later?” Rigid tools lose ground in competitive evaluations.
2030–2035 – Malleable platforms, powered by AI assistants, reach a point where setup feels like a conversation, not a project. Switching costs collapse, and most rigid vertical SaaS tools become niche or legacy solutions.
Rigid tools won’t vanish completely — some industries will still prize absolute standardization over adaptability — but for most use cases, they’ll be relics of a pre-AI era.
The future belongs to software that bends without breaking.