AI added models stack artificial intelligence on top of existing systems instead of weaving it into the foundation.Sure,this speeds things up in the early stages,but it almost always leads to headaches down the road think sluggish performance and scaling problems that just won’t go away.When you bolt AI on as a separate module,it keeps shuttling data back and forth with the main system.That back and forth slows everything down and eats up extra resources.And synchronization?That’s a mess.Data pipelines break apart,so teams end up constantly patching together AI outputs with the core application logic.The whole process drags,bugs get harder to track down,and the system as a whole becomes less reliable.In fields like financial automation,predictive analytics,or real time decision engines,tiny inefficiencies snowball fast.Suddenly,you’re dealing with serious performance losses.Resource duplication is another big problem. AI added models bring their own memory, compute,and storage separate from what the main system uses.So instead of pooling resources,you get two parallel setups burning through capacity.Costs go up,and when things get busy,the system starts to choke.To really make AI work at scale,it needs to be part of the core.Native integration lets teams optimize workloads from the ground up. Everything runs smoother:data access is simpler,processing is faster,and every layer of the system benefits.Blurring the line between AI and core logic isn’t a nice to have it’s essential for building smart,future proof platforms.

@Vanarchain $VANRY #vanar