Michael Elabd
@MichaelElabd
Joined July 2020
396 Following 2.3K Followers
Intelligence Density or intelligence per token is one of the most important metrics for specialized intelligence.
As each company starts to own their own intelligence, doing a task right doesn't become the only goal but how efficiently the agent can complete the task.
Really excited about the research coming out of Trajectory on how to make models have way higher intelligence density!
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Continual learning is a bet that the retraining loop will get cheaper over time. With larger models, you can maybe run this loop once every few weeks. But with smaller models, you can run it nightly, per customer. And it keeps recursing: a model per company, then a model per client that company serves, then per matter. We’re getting closer to intelligence cheap enough to meter.
On the path to this, we received early access to, and post-trained @nvidia's Nemotron 3.5 Lightning on @harvey LAB. One click on the Trajectory platform, no new engineering. 0% to 8.3%, above Opus 4.6 at 6.6%.
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