Jennifer Li

speaker

607 appearances 7 recordings 1 series first heard Dec 2024 last heard 17 Sep

Jennifer Li’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 4 in all, peaking in Sep 2026 with 2.

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The a16z Show · The Race for AI—Search, National Infrastructure, & On-Device AI · 25 Dec 2024 podcast

the human like synthetic voice, but being able to handle conversations fluently with end users and to get the latency down and also to think about what type of real-time exchanges you want to have with your AI companion, your support agents, or any sort of life coach.

I think we do need to think about the modality and the latency in a much more, I guess

improved fashion.

So I won't be surprised if some of those inference workloads are running locally coming into the next twelve, eighteen months.

A hundred percent you can actually reimagine the AR experience of if I point a camera to this room and I want to see a new surface and wallpaper and furnitures, there the technology is already there.

Like we can actually leverage both generative AI and the camera and also

Prompting interaction to create new experiences already of how we interact with real physical life.

And that's where I also think a lot of on-device models will play a big role of how do we interact with the 3D world, how to interact with physical world, and not just using the camera for capture, but also using it as a projector.

Yeah, it's a great question.

And I honestly don't really have the answer because even for larger models, the inference price has been dropping really significantly.

for the optimizations to be done if it's a very workload intensive compute, let's say using a computer or phone, I think we'll still have economic benefits.

But I don't think it's a very direct answer of it's going to

substantially reduce infrastructure cost for some of these applications.

But architecting and structuring sort of the whole tool chain, it does change sort of economics on the developer efficiency and sort of iteration speed.

There is pros and cons when shipping in the cloud where you can launch more continuously.

on device has its own challenges because you'll have to go with the updates with the application and the with hardware.

So there's that set of economics that I think will have impact from how teams are being structured in launching models in the hybrid mode.

So I would encourage teams who are thinking of leveraging this technology consider it more holistically.

Right now I've uh seen more interest and uh enthusiasm from the hardware development side, whether it's chips, it's the filmmakers.

I do think there's also a lot of interest from the model developers as well of just like proliferating the model adoption across different setups and devices.

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