2026 is the year the magic stops and the engineering begins.
For the last two years, AI got noticeably better every few months. That phase is ending.
Text reasoning is plateauing because the two engines driving progress are hitting limits.
Pre-training is tapping out. We are running out of high-quality web data to feed the models.
Post-training is hitting a wall. RL is expensive and only truly scales for tasks with clear right answers, like math or code, not the messy real world.
So we cannot expect the same massive jumps in performance we saw in the past few years. Not even close.
Since AGI is likely 5–10 years away, we have to ask: Why is AI not driving GDP at the pace expected?
The missing keys
It comes down to two gaps: on-the-job learning and generalization.
When Trump announced tariffs in April, trading logic changed overnight. A human analyst read the news, talked to a few people, and instantly updated their mental model. They generalized from a few data points and adapted.
An AI can’t do that. It is frozen in its training past. It cannot “learn on the job” or generalize to a totally new regime without massive retraining or engineering.
The long-tail problem
This is the core issue. Most real-world jobs are full of these "long-tail" scenarios. They are context-heavy and constantly changing.
Because you cannot pre-train for every possible future scenario, general-purpose models fail to capture the specific nuances needed to do the job. That is why we aren’t seeing the GDP explosion yet.
Fix the context problem
This is where better engineering comes in. If we can’t make the model smarter, we have to make it better informed.
You cannot have reliability if the model is guessing about your specific business context. That is exactly why we are building retrieval engines. Once we start reliably feeding the right facts at the right time to our agents, they stop guessing and start executing.
The goal isn't to replace humans. It's to stop burning creative minds on administrative tasks. Let the agents execute the process so the humans can create the value.
That's exactly what we're building here at EaseFlows AI.
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