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Daniel Romero
@HyperTechInvest
Analyst. Semiconductors, data centers & physical AI. Portfolio and investment theses on Substack Join 14,000+ readers:
Joined April 2024
348 Following    47.1K Followers
What? 1. $CBRS using a post from September now, for some reason 2. A comparison that doesn't disclose how many chips/systems were used? 3. What's the point of comparing an in-house assistant against public models? 4. They specifically optimized the assistant for this task
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Consumer agents are the next frontier of AI. But right now, they're slower than a person with an iPhone. Consumers are incredibly impatient. Google learned this years ago. Where they showed delays of a few milliseconds changed how often people searched and how long they stayed. If I ask you to wait 8 seconds for a website to load, you’re gone. Now look at today's AI assistants. Our team asked the leading ones to book a dinner reservation. A person can do it by hand in 37 seconds. The assistants took up to 7 minutes and 40 seconds. So we built a prototype assistant on @Cerebras, tuned for this task. It booked the same table in 22 seconds, faster than a person could. It checked every restaurant in parallel, rather than sequentially. We taught it how the booking site worked ahead of time, so it didn't have to guess. And every call to the model came back in a fraction of the time because it ran on the Cerebras Wafer-Scale Engine. This was just one task. There is a lot more work to do before agents can handle every errand this well. But it shows what's coming. The leverage these assistants can give is enormous. The real breakthrough will come when agents are faster than we are. Then consumers will hand them everything. Fast inference is a big part of getting there. That's the future we're building for at @cerebras.
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