I used to think catastrophic memory loss was something to fear with age. In AI, catastrophic forgetting turns out to be less existential and more engineering: difficult, but solvable.
One of the biggest advantages of building as a small founder-led team is decision speed. You can respond to problems quickly, adjust direction without organisational drag, and keep momentum through uncertainty. In 2025, that speed is no longer a bonus. It is the baseline.
From Black Art to Repeatable Process
As part of the platform we are taking to market, we built orchestrated AI frameworks backed by expert-trained, locally hosted small models. For a while, fine-tuning these models felt unpredictable. After sustained experimentation, we now have a repeatable process for injecting domain knowledge in a way that scales across increasingly complex tasks.
That progress did not come from perfect planning. It came from short feedback loops, willingness to absorb temporary pain, and consistent iteration under real constraints. Catastrophic forgetting, in its technical form, refers to a model losing previously acquired knowledge when learning something new — a significant challenge when fine-tuning on domain-specific data. The breakthrough for us was a better-structured training pipeline that preserved core capability while layering in specialist knowledge, rather than overwriting it.
Why This Matters
With our current trainer workflow, tuning that once took days, sometimes weeks, now takes hours. The downstream time savings are substantial, but the bigger gain is strategic responsiveness: we can identify a problem, adjust models, and redeploy capability quickly.
In modern software and AI delivery, the ability to move through problems rather than around them is not optional. It is now a core operating requirement. The same applies to team structure. Small teams with clear ownership and fast iteration cycles will outpace larger, slower-moving operations in this domain for as long as model access remains broadly available. Size is no longer the advantage it once was when the quality ceiling is determined by how quickly you can learn and redeploy, not how many people you can assign.