Modern AI is built on one bet: train a model to predict the next token across enough data, at enough scale, and a system you can trust with real decisions will emerge.
Centenum Labs, a new African AI research lab focused on intelligent simulations, argues in its founding thesis that this bet is wrong. Titled “Likelihood Is Not Truth,” the document takes a direct position against the industry’s core objective.
The Wrong Objective
Likelihood, the mathematical property that large language models are trained to maximize, measures how plausible an output sounds given the training data. It does not measure whether the output is correct, causally grounded, or structurally valid. The difference is invisible in familiar problems. It becomes catastrophic for the ones that matter most.
The lab names four properties of the current training objective:
No truth mechanism. The training objective rewards what sounds right, not what is right. No
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