Bret Taylor

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184 appearances 1 recordings 1 series first heard Oct 2024 last heard Oct 2024

Bret Taylor’s voice in public audio — every appearance, attributed to the second.

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: Bret Taylor: The AI Bubble and What Happens Now | How the Cost of Chips and Models Will Change in AI | Will Companies Build Their Own Software | Why Pre-Training is for Morons | Leaderships Lessons from Mark Zuckerberg · 2 Oct 2024 podcast

I think the large hyperscalers are in a challenging position where there's a big difference between owning and operating one of the best frontier models and not. As a consequence, I think that I'd probably make very similar decisions to all of those firms because there's so much value for consumer products,

infrastructure as a service providers to have a differentiated frontier model available to their customers that betting on the future and then similarly betting on breakthroughs and AGI I think is really rational. But the reason I was talking about the sort of Moore's law part of it is I think that

Sort of like in the infrastructure as a service market, it really consolidated around a very small handful of companies. And much like AI, you know, building data centers like scale helps. So, you know, the more data centers you operate, the more you can afford the CapEx to expand your data center footprint.

You know, it should be financed and built by the large hyperscalers because of the CapEx requirements to do so. And I think as the training market sort of consolidates and people start, I think it will probably help because the revenue will sort of consolidate, you know, around those providers as well. So it is a complex situation.

I think these companies have an imperative because of the potential impact of AI to spend. And, you know, the CapEx numbers are mind boggling, but I probably would do the same thing.

One of the things that I have changed my mind on over the past year is how quickly open source foundation models would be impactful. So I had a thesis when we started CIRA in March of last year that eventually we'd end up with a

few frontier model frontier models essentially built and financed by some of the hyperscalers in partnership with the research labs and that we would eventually have a meaningful open source model or two the equivalent of postgres and mysql in the database market

that would come out, eventually be adopted by one of the larger tech companies that wasn't one of the hyperscalers, just in the same way Google adopted Linux or Facebook adopted MySQL and Memcache and contributed a lot of patches upstream to those projects. And I would say Mark Zuckerberg sort of accelerated that by a meaningful amount. Not only the timing of when that happened, but the quality.

You know, Lama 3.1 is a really high quality model. I think it comes from what you said, you know, without a cloud business to finance it, his incentives are different than, you know, the cloud providers. And I think he wrote, no need for me to say it. I mean, if you just read his post on why he believes that this is the right strategy, I thought it was a really well articulated post.

I think it's probably good for the AI market overall. Just look at the cloud infrastructure market. You have a lot of proprietary solutions like DynamoDB to store data, and you have a lot of open source things like Kubernetes to manage your infrastructure. And then you have commercial companies commercializing those open source projects like Confluent with Kafka.

So I think that a healthy AI market probably needs all of the above. You know, you're going to have the frontier models that are the best of the best that are licensed directly. The cloud providers will probably provide both, you know, both options. And, you know, if you're building these frontier models, you need to maintain a quality lead on the rest.

And I think it's really great for the ecosystem that there's a super high quality open source model available right now.

You know, one of the big questions is, you know, what scale of supercomputer and what methodology and what data is required to create something that might resemble AGI or create that breakthrough in economic value that would justify the investment? No one really

knows that you have a lot of theories you know about it but i think when you look at these companies investing this kind of capex in that future i think it's absolutely great you know i think it's totally understandable investors would look you know at the capex and say give me the spreadsheet that justifies the returns um well that's completely rational and i'm sure there's folks doing that the idea that we have this potential to create something that benefits humanity this much

to have this kind of impact on the economy, to create something that valuable. I'm very grateful that there are some bold CEOs investing in that future. I think at every stage you end up with that sort of increasing resolution about how it will be monetized, what the great products will be. In the first wave, it was lots of co-pilots. Now you have, as I said, agents.

My sense is there could be a ton of value created here. And I think you're in this position now where you you don't want to be sort of penny wise pound foolish when you're sort of investing in this future.

It doesn't mean that, as I said, that's why when I mentioned I'm skeptical of startups doing pre-training, like that's a risk that I find irrational because you don't have the capital structure to take on that risk. It's probably running towards a cliff that you probably won't, you know, have wings to fly off of by the time you get there.

If you're a, you know, one of the larger companies you're referring to, and you think about how do you grow your revenue by a meaningful amount over the 10-year period, tell me the better option than this. And so I think there's a lot of understandable skepticism, but I also think it's a very exciting future.

I think we'll see consolidation of companies pre-training their own models. I think that the cost structure of the tools and applications companies are different and perhaps more sustainable. So like any market, you'll see consolidation when there's winners, but I think it will happen over a more measured time period.

So let me just describe what Sierra does, and then I'll tell you why I think it's very exciting. So At Sierra, we help primarily consumer brands build branded, customer-facing AI agents. So if you buy a new Sonos speaker or you're having a problem with your speaker, you'll chat with the Sonos AI powered by our platform.

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