Remaking the UI for AI

Anjney Midha (0:00)

The cost required to bring a new form factor to market is just insane. We've had a reasoning breakthrough with large-language models and generative models, and they're really hungry for new kinds of input and context that the current generation of interfaces is not providing.

The North Star of computing has always been, to borrow like a Steve Jobs quote, to be the bicycle for the mind. The history of hardware has been the history of computers.

Hey, the rate limiter here is silicon, it's sand. And there's tons of sand in the world. And eventually, somebody else will figure out how to make sand that does the same thing. We have adapted to interfaces over the last 60 years, but the way we interact with computers today is actually dramatically unnatural.

Steph Smith (0:43)

Hello, everyone. Welcome back to the a16z podcast. Now, if you're regular around here, you probably have a sense for just how important the topic of artificial intelligence is to a16z.

In fact, it is so important that we decided to create a new AI-specific podcast, which to no surprise is called AI plus a16z. And no, we did not consult AI on naming. Today, you'll get to hear one of the early episodes from that podcast, hosted by venture editor Derek Harris. Derek sits down with a16z general partner, Anjney Midha, to discuss a very important question. Here it goes. If we have a new software platform through large language models, do we also need to rethink our hardware from the ground up? Do our interfaces today keep pace with the data requirements across input, reasoning and output? In other words, what does the UI look like for AI? Will it look like a phone or something completely different? And what can we learn from the millennia of human behavior, long before this technology existed, that might actually inform what's to come? Of course, there are many companies trying to solve this right now, but this conversation may actually give you clues as to why no one has quite solved this yet.

Again, this episode comes straight from our new AI plus a16z feed. So if you like this episode, don't forget to subscribe to get all of a16z's latest AI content, including episodes around the software supply chain, open source, rag and a whole lot more.

Of course, we'll include a link in our show notes. And with that, Derek, take it away.

Derrick Harris (2:13)

Hi, this is Derek Harris, and you're listening to the a16z AI Podcast, where we dig into all things artificial intelligence with our in-house team of experts, as well as the founders, engineers, and researchers working at the state of the art. In this episode, I speak with a16z general partner, Anjney Midha, about how AI hardware will look in the years to come, and why there's so much innovation yet to happen at the inference layer. Among other things, he explains how he sees wearable devices evolving to take advantage of improvements in sensors and workload specific chips, and how the introduction of big company technology, like the Apple Vision Pro, can actually lay the foundation for startups. But because we recorded right after Nvidia's big GTC event, as with our previous episode with Naveen Rao, we kick off the conversation talking about training workloads versus inference workloads, and how Nvidia came to dominate the former category.

As a reminder, please note that the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a16z fund. For more details, please see a16z.com/disclosures.

I saw recently like Olamo released support for AMD GPUs. I think I saw someone compare Nvidia to Sun Microsystems in the early days of the web, which seems like it might be wishful thinking. Are skeptics kind of underestimating the stranglehold that Nvidia has?

Anjney Midha (3:47)

There's the two schools of thought, which is that Nvidia's stronghold on this is completely transitory. Their margins are overinflated because of supply chain crises over the last 24 months, where we had this explosion in demand, but like production is going to catch up. And you know, basically those folks will tell you, that school of thought will tell you like, hey, the rate limiter here is silicon. It's sand and there's tons of sand in the world. And eventually somebody else will figure out how to make sand that does the same thing. There's tons of it on planet earth. And I personally find that view is provocative, but reductive because it doesn't on your first principles analysis of the fact that training has these idiosyncratic needs like a really robust software driver layer, right? That can orchestrate thousands of these chips acting in unison. I think yes, on that side of the debate, I'm certainly one who believes that the developer experience that Nvidia started, by the way, investing in a decade ago is in its sort of later stages of compounding right now. And it's really hard to dislodge that.