Snowflake Summit 2023 Keynote: Generative AI’s Impact on Data Innovation in the Enterprise

Summary

在Snowflake Summit上，前Greylock合伙人、Conviction AI投资公司的创始人Sarah Guo欢迎了Snowflake和Nvidia的CEO Frank Slootman和Jensen Huang。他们讨论了生成式AI的革命性影响，强调了数据在其中的重要性，并宣布了两家公司合作扩展，旨在为客户提供加速创建定制生成式AI应用的途径。该合作利用Nvidia的AI平台和Snowflake的数据能力，推动企业使用自有数据开发智能应用，预示着企业将转变为AI工厂，不断生产智能。

Takeaways

• 🌟 Snowflake和Nvidia宣布扩大合作伙伴关系，允许客户在Snowflake平台上利用Nvidia AI合作平台开发自定义生成AI模型。
• 🤖 强调生成AI（Generative AI）的重要性，它正在改变世界，需要数据来实现其功能。
• 🔍 讨论了AI如何帮助企业，特别是通过自然语言处理和增强查询来简化与数据的交互。
• 🛠️ 强调了Nvidia在计算革命中的作用，特别是在加速计算和AI应用开发方面。
• 💡 讨论了AI如何帮助企业解决复杂问题，例如消费者流失、供应链管理和定价优化。
• 📈 强调了AI在提高企业效率和降低成本方面的潜力，特别是在处理大型数据集和复杂计算任务时。
• 🛑 讨论了AI应用开发中的挑战，包括数据安全、合规性和版权问题。
• 🚀 提到了AI如何加速产品开发，例如Nvidia使用AI来设计下一代GPU。
• 💼 讨论了企业如何通过AI改变其业务模型，特别是在金融服务、零售和制造业中。
• 🔑 强调了数据作为企业最宝贵的资产，以及如何通过AI将其转化为智能和洞察力。

Q & A

什么是生成性人工智能（Generative AI）？

生成性人工智能是一种能够创建新内容而不仅仅是识别或标记数据的人工智能。它能够生成文本、音乐、图像等内容，代表了人工智能领域的一个重大进步。

为什么数据对于生成性AI至关重要？

数据是生成性AI的基石，因为AI模型需要大量的数据来训练和学习，以便生成准确和有用的输出。没有数据，生成性AI就无法理解和创造与人类需求相关的新内容。

Snowflake和Nvidia宣布了什么样的合作？

Snowflake和Nvidia宣布了一个大规模的合作扩展，该合作使客户能够在Snowflake上利用Nvidia AI平台开发定制的生成性AI模型，并使用他们自己的专有数据。

Nvidia在AI领域扮演了什么角色？

Nvidia是AI领域的关键参与者，提供了强大的计算平台和加速技术，使得AI模型能够更快速、更高效地进行训练和推理。

Snowflake作为数据平台，它的核心优势是什么？

Snowflake是一个云数据平台，它提供了一个安全、可扩展且易于使用的环境来存储、处理和分析大量数据。Snowflake的优势在于其能够处理各种规模的数据，并支持实时分析。

为什么Snowflake和Nvidia的合作对于企业来说很重要？

Snowflake和Nvidia的合作为企业提供了一个集成的解决方案，使他们能够在自己的专有数据上快速开发和部署定制的生成性AI应用，这有助于企业提高效率、降低成本并创造新的商业机会。

什么是软件1.0、软件2.0和软件3.0？

软件1.0指的是传统的、由工程师一行一行编写代码的软件。软件2.0涉及到通过大量标记好的训练数据来优化神经网络。而软件3.0则是与基础模型一起工作，这些模型本身就非常强大，但仍然需要与企业数据和自定义数据集一起工作。

为什么说Snowflake是存储和处理数据的最安全、最好的地方？

Snowflake提供了一个高度安全的环境，具有强大的数据治理和合规性功能。此外，它的架构设计允许高效的数据处理和分析，使其成为企业存储和处理数据的理想选择。

Nvidia如何帮助企业加速其AI应用的开发？

Nvidia通过提供预训练的基础模型和专业知识，帮助企业在Snowflake平台上快速构建和部署AI应用。这些预训练模型已经在Nvidia的AI工厂中进行了优化，可以显著减少企业开发AI应用的时间和成本。

企业如何从AI中获得最大的价值？

企业可以通过利用AI来优化运营、改善客户体验、开发新产品和服务以及提高决策效率来获得最大的价值。关键是要找到正确的数据集，并与像Snowflake和Nvidia这样的合作伙伴合作，以确保AI应用的成功部署。

Outlines

00:00

🌟 雪湖峰会开幕与生成式AI的前景

Sarah Guo，前Greylock合伙人及Conviction创始人兼CEO，在雪湖峰会上致开幕词。她强调了生成式AI在过去一年中的快速发展，并介绍了Frank Slootman和Jensen Huang，分别作为Snowflake和Nvidia的CEO，讨论了数据和生成式AI如何为企业带来变革。两位CEO讨论了他们对AI的看法，Jensen Huang认为这是前所未有的技术革命，而Frank Slootman则强调了与数据打交道的重要性。他们还宣布了Snowflake和Nvidia之间新的合作伙伴关系，旨在帮助企业利用自己的专有数据创建定制的生成式AI应用程序。

05:03

🤝 Snowflake与Nvidia的合作及其对企业的意义

Snowflake和Nvidia宣布扩大合作伙伴关系，允许客户在Snowflake平台上使用Nvidia AI合作平台开发自定义的生成式AI模型。Frank Slootman提到，Snowflake与Nvidia的合作是天作之合，双方在提供计算能力、服务和数据方面有着天然的一致性。Jensen Huang强调了将计算引擎带到数据所在的地方的重要性，并解释了如何通过结合数据、AI算法和计算引擎来帮助客户使用他们的专有数据开发AI应用程序。

10:04

🛠️ 利用生成式AI模型和企业数据

Jensen Huang和Frank Slootman讨论了如何将预训练的基础模型与企业数据结合使用。Jensen Huang提到，预训练模型需要根据特定行业进行微调和专业化，以适应企业的具体需求。他们讨论了如何通过监督学习和增强学习来训练模型，并强调了在Snowflake平台上创建AI应用的目标，即帮助企业解决特定问题。此外，他们还讨论了如何通过使用大型语言模型将任何数据库转换为应用程序。

15:04

💡 企业如何从AI中快速获得价值

讨论集中在企业如何快速从AI中获得价值。Frank Slootman和Jensen Huang认为，最直接的应用将是增强查询，因为这是一种相对容易实现的方式，可以让更多人以更自然的方式与数据互动。他们还提到，一旦解决了这些初步问题，企业将开始关注更复杂的问题，如消费者流失、供应链管理和定价优化等，这些问题需要更深入地挖掘企业数据。

20:05

🚀 Snowflake和Nvidia如何革新数据处理

Jensen Huang强调了Snowflake和Nvidia合作的目标，即通过加速计算来革新数据处理。他们计划将Nvidia的GPU计算能力整合到Snowflake中，以提高数据处理、训练、推理和部署的效率。这种整合将使企业能够在Snowflake中快速开发AI应用，而不需要传统的编程技能。

25:05

💼 企业如何利用AI改变业务模式

讨论转向企业如何利用AI改变业务模式。Frank Slootman提到，企业将能够通过AI更深入地了解数据，从而改变利润结构并扩大对客户的服务范围。Jensen Huang分享了Nvidia如何在自己的数据上应用AI来解决复杂的工程问题，例如GPU设计和软件编译优化。

30:06

🏆 为什么选择Snowflake和Nvidia进行AI和ML工作负载

最后，讨论了为什么企业应该选择Snowflake和Nvidia来处理他们的AI和机器学习工作负载。Jensen Huang和Frank Slootman强调了他们提供的服务的独特价值，包括安全的数据存储、处理和AI应用开发。他们承诺将帮助企业将数据转化为智能，创造出前所未有的价值。

Keywords

💡生成式AI

生成式AI指的是能够生成文本、图像、声音等内容的人工智能技术。视频中提到的生成式AI在过去一年里席卷了全球，被认为是一项革命性的计算技术。Nvidia和Snowflake的合作将利用生成式AI技术，帮助企业创建定制的应用程序。

💡数据

数据是指各种形式的信息集合，是生成式AI的核心。视频中强调了数据对于启用生成式AI的重要性，并介绍了Nvidia和Snowflake的合作如何利用企业的专有数据来开发生成式AI模型。

💡Nvidia

Nvidia是一家著名的计算机图形处理器制造公司，近年来在人工智能和生成式AI领域取得了重大进展。视频中提到的Nvidia与Snowflake合作，将其强大的计算引擎与Snowflake的数据平台结合，提供生成式AI解决方案。

💡Snowflake

Snowflake是一家提供云数据存储和分析服务的公司。视频中介绍了Snowflake与Nvidia的合作，利用其平台的数据优势，加速生成式AI应用的开发和部署。

💡合作伙伴关系

合作伙伴关系指两个或多个公司为了共同的目标而进行的合作。视频中提到，Nvidia和Snowflake通过合作，为企业提供一个加速生成定制AI应用的路径，并强调了这种合作的巨大潜力。

💡大语言模型

大语言模型是一种经过大量文本数据训练的AI模型，能够理解和生成自然语言。视频中提到，将大语言模型与企业数据结合，可以实现对数据的自然语言查询和处理，创造出智能的AI应用。

💡人工智能应用

人工智能应用是指利用AI技术解决特定问题的软件。视频中讨论了如何使用生成式AI和企业数据来开发这些应用，显著提高业务效率和效果。

💡定制模型

定制模型是指为特定企业或用途调整和优化的AI模型。视频中解释了为什么需要定制模型而不是通用模型，特别是在企业数据分析和应用中，这种定制能够带来更高的准确性和效率。

💡查询引擎

查询引擎是一种用于搜索和检索数据的系统。视频中提到，通过生成式AI和大语言模型，可以将查询引擎提升到一个新的水平，使用户能够以自然语言与数据进行互动，获取更有价值的信息。

💡数据治理

数据治理是指对数据的管理和控制，确保其质量、隐私和合规性。视频中提到，在使用生成式AI和企业数据时，数据治理至关重要，以确保数据的安全和有效使用。

Highlights

生成性人工智能（AI）在过去一年席卷全球，成为人们关注的焦点。

生成性AI需要数据的支持，Snowflake和Nvidia的CEO讨论了数据与生成性AI的结合如何为企业带来变革。

Snowflake和Nvidia宣布扩大合作伙伴关系，共同提供加速创建定制生成性AI应用程序的途径。

Nvidia的Jensen Huang认为，当前的计算革命是前所未有的，AI能够自我编写软件，对行业产生深远影响。

Snowflake的Frank Slootman强调了与数据的自然和富有成效的关系的重要性，以及AI如何帮助实现这一点。

通过结合Snowflake的数据和Nvidia的计算能力，企业可以在自己的专有数据上开发定制的生成性AI模型。

AI技术的发展使得人们可以用自然语言与数据库交互，就像与人交谈一样。

Nvidia和Snowflake的合作将推动企业利用AI优化成本结构，例如通过减少呼叫中心的规模。

投资者认为，软件3.0时代的到来意味着企业将能够以更低的成本开发AI应用程序。

定制的AI模型与通用模型相比，可以更专注于特定领域的复杂数据集，从而降低计算成本。

Nvidia将提供预训练的基础模型和专业知识，以帮助企业在Snowflake平台上与企业数据互动。

企业将能够快速在Snowflake上开发AI应用程序，而不需要从头开始构建大型语言模型。

Nvidia和Snowflake的合作将使企业能够在Snowflake上训练、开发模型，并运营他们的AI，实现数据到智能的转化。

Snowflake和Nvidia的合作是关于将世界上最强大的计算引擎带到世界上最有价值的数据旁边。

通过Nvidia的AI基础服务，企业可以在Snowflake上以更经济高效的方式微调和增强预训练模型。

Nvidia和Snowflake的合作将加速AI在企业中的应用，使企业能够快速构建和部署AI应用程序。

Snowflake Summit上，Nvidia和Snowflake展示了他们的合作成果，包括Nvidia AI Enterprise在Snowflake上的运行。

Nvidia和Snowflake的合作将帮助企业将数据转化为智能，这是最有价值的商品，为企业带来前所未有的变革。

Transcripts

please welcome former Greylock General

partner and founder and CEO of

conviction Sarah Gore

hello Las Vegas

I have the pleasure to officially kick

off snowflake Summit I'm Sarah Gua

former Greylock General partner and

founder of conviction a venture

investing firm purpose-built for

partnering with AI entrepreneurs from

idea to IPO I also host no priors a Tech

podcast focused on the AI opportunity

ahead of all of us

generative AI has taken the World by

storm over the past year and is likely

top of mind for everyone in this room

but what do you need to enable

generative AI That's Right data

there are no two people better on the

entire planet to talk about what

generative Ai and data can do for you

than Frank slitman and Jensen Huang the

legendary CEOs of snowflake and Nvidia

we'll also learn more about their new

partnership to provide businesses with

an accelerated path to creating custom

generative AI applications built with

their own proprietary data please

welcome to the stage Jensen Huang and

Frank slootman sir

[Applause]

[Applause]

[Music]

here come here Frank hugs hugs all

around yeah

he never once get left out

okay

um we will start with a question about

what you two are seeing

um the hype and excitement around AI is

extraordinary this past year uh is it

real is it oriented Jensen let's start

with you

well this is the biggest Computing

Revolution we have ever seen

um no one has ever seen a technology

like this

a computer that writes software

itself

and now the computer can write software

for other computers by itself

when you take a step back and think

about the implications it's really

really profound

and and all of our experiences every

single one of ours I'm sure

has proven that this is the easiest

application to use in the history of

computing because it's so smart

it understands what you mean even when

you say what you mean poorly

and it does things that you tell it to

do that exceeds your expectations

and you can connect it to almost

anything it's a universal translator

and so when you when you take a step

back and think about its implications

I think we can all we can all uh be

fairly excited about the all of us in

the computer industry and what we've

done

Frank is It Something That Matters to

Enterprises and and should they be

paying attention

well yes

um you know we we have a long strenuous

uh relationship with data you know we're

old enough actually Jensen and I might

be you will not be you know where I was

learning COBOL and COBOL stood for

common business oriented language and

there was nothing common or business

oriented about it

um you know in the 80s you know we had

SQL structured data that helped a lot

because data now kind of made sense rows

and columns

um but still it's it's it's been hard to

have a relationship with data that's

natural and productive and insightful

all these things and all of a sudden you

know we have this huge step function

from where we've been all this time and

it's like it's just an absolute wow

which is why we're all so infatuated

with the experiences that we're seeing

it's a perfect segue to um news hot off

the presses in the last two minutes

Nvidia and snowflake just announced a

massive expansion to their partnership

where you know customers can Leverage

The Nvidia AI partnership platform on

snowflake for developing custom

generative AI models on their own

proprietary data can you tell us more

about this partnership

yeah you know you know snowflake

disorder things that we do there's a lot

of people trying to kill us so uh

finding partners that have natural

alignment with us and have extraordinary

capabilities

um that doesn't come along you know

every day and uh you know obviously

Nvidia is an extraordinary place you

know in time and in history in terms of

the role they play in the entire history

you know for us you know to be able to

bring data and relationships with large

Enterprises everybody that you see here

in the room if we combine that you know

with the Computing fabric that that we

need to enable this technology as well

as the entire stack of services to use

it productively yeah that's you know I

don't want to use the say it's a match

made in heaven but it's about as close

as you as you're going to get we have

full alignment there's no conflict and

uh that's that's that's a heck of an

opportunity for for all of us in the

room here so we're lovers not Fighters

so

I woke up this morning without a voice I

have no idea where it went I'm going to

find it here somewhere and so but the

the big deal here Sarah is that we're

we're going to bring the world's best

compute engine

to the world's most valuable data you

know back in the old days when we first

started our career and and uh Frank

Frank mentioned how long he's been

working I've been doing my job a long

time I'm not that old you're old

they think that's funny well they know

it's true and and

so

so back in the old days we used to bring

data to the computer

and the reason for that is because

there's so little data to move but these

days for all the reasons that we know

well the data is gigantic the data is

valuable it has to be secure

it's regulated

governance has to be incredible and so

it's tough to move data around the data

gravity is real and so it's a lot easier

for us to bring our compute engine to

Snowflake and our partnership is about

about accelerating snowflake but it's

also about bringing AI to snowflake you

know that at the core

the big revolution is about the

combination of data plus AI algorithms

plus compute engine

our combination our collaboration here

our partnership brings all of those

three things together

incredibly valuable data incredibly

great AI incredibly great compute engine

and the thing that we could do together

is to help customers use their

proprietary data and write AI

applications with it you know the big

breakthrough here and and this is I'm

sure you guys are going to learn a lot

about this during this week is that for

the very first time

you could develop a large language model

you stick it in front of your data and

you talk to your data

you talk to your data like you talk to a

person

and that data will be augmented with the

would augment the large language model

and you'll ask it all kinds of questions

about what and how and when and why all

of the things that you might query the

database in the future you'll talk to

the database in the future the

combination of a large language model

plus knowledge base equals an AI

application

it's as simple as that

a large language model turns any data

knowledge base into an application and

just think about all of the amazing

applications that people have written

it's always at the core of it some

valuable data now you have a query

engine Universal query engine in front

of it that's super intelligent and you

can get it to of course respond to you

but you can also connect it to an agent

as you know and this is uh the

Breakthrough of of Lang chain plus

Vector databases Plus data

from large language models

groundbreaking stuff is happening

everywhere now everybody's going to do

it and Frank and I are going to help

everybody do that yeah we we've seen

um you know in our investing a huge

explosion of chat Bots search

summarization and Frank you and I were

talking a few weeks ago about how much

the interface to business data can

change and what a big opportunity that

is for customers is that um something

you expect to show up in the snowflake

product or or P or applications that

people build themselves

well we we expect to be you know

visiting with our customers at some

point you know relatively soon you know

I would say I said look you know we're

we're going to push a boundary in terms

of what kind of questions you know

you'll be able to ask of your data I

mean just just connecting natural

language I think it's amazing and I I

love it right because many more people

can be productive and all this kind of

stuff but asking really hard questions

of your business is a whole you know

different matter and I I hope you know

we're all right here that the

intelligence is in the data and the

models are able to extract the the

reasoning and intelligence from that

data and that will lead to those you

know incredibly insightful predictive

relationships with data but if not you

know we're going to sort of unpack and

break it down and understand it so we

can keep pushing those boundaries people

ask very very hard questions of their

data sometimes they have to you know

launch whole teams for weeks on end to

come back with answers that we even have

difficult be trusting there's no Frank

it's like this this is I mean there's

there are tons of customers in the

audience who have large customer support

organizations yeah and all that data is

going into snowflake

could you imagine if you had hundreds of

your customer support agents sitting in

front of you and you just asked them a

whole bunch of questions

that's the future you're going to put it

you're going to put a large language

model in front of that customer service

database and you're going to talk to it

yeah you're going to ask you know what

are the most frequent things that are

happening to our customers that we could

improve on what are the things that they

seem to find really delightful about

products you could ask those questions

in normal human language and it will

talk back to you that's the miracle

you're going to turn customer

service data into a customer service

application

that's the that's the incredible

opportunity a lot of the uh you know the

originals of large telcos and Banks I

mean they're looking to redefine the

economics of their businesses which is

really something they have not been able

to do and they're thinking you know that

that opportunity is coming large call

centers that's the examples you you just

used right sales organizations that feel

incredibly unwieldy and expensive you

know to them

um so redefining cost structures

um I mean people get heady just thinking

about the uh the possibility and

opportunity yeah we're we're going to be

part of this of this journey and I it's

a good time to be alive in this business

you know one of the ways that we um you

know as investors think about this

transition is going from software uh 1.0

which is uh you know very deterministic

code written function by function by

Engineers to software 2.0 which is you

know you're optimizing a neural network

with um you know carefully collected

labeled training data and I think the

opportunity that you guys are really

helping people Leverage is what I think

of as software 3.0 which is we are

working with this set of foundation

models that are incredibly capable on

their own but they still need to work

with Enterprise data and custom data

sets it's just much cheaper to go

develop those applications against them

right you can think of them as the

world's best query engine on top of your

most important data so I think that's

like a really attractive opportunity to

how much the cost comes down for

customers so actually this is a this

raises an interesting question for

people who are paying deep attention to

this area

um you know one question is foundation

models are very general uh

can they just do everything like why do

we need custom models and Enterprise

data anyway

well I mean there's there's a school of

thought in there it's like maybe but it

will be incredibly expensive right so we

we have you know very broad General

models that can do poetry and process

the Great Gatsby and summarize it and

all these and math problems broken down

how I wish I had that as a kid right but

you know in business we don't need that

right we're looking for a co-pilot that

has

extraordinary Insight in a very narrow

set of data but a very complex Sarah

data we need to understand business

models and business Dynamics now that's

that is computationally not as expensive

because you just don't need to be

trained on a million things you need to

be trained on you know very few and very

very deep subject matter so if we think

of these models as

the general knowledge of the

from these AI applications that is not

in the general knowledge of the internet

yeah well I'll give you an example

um you know one of my uh I'm on the

board of instacart and you know one of

the topics a great customer of ours as

well and you know one of the topics that

comes up in businesses like doordash and

all the others is you know they drive

Top Line with huge amount of marketing

expense and it's very very hard to do

and then they onboard a customer and

then the customer places an order and

then the customer either doesn't come

back or he comes back 90 days later it's

very erratic and they call that churn

churn is the most you know most

incredibly complex problem to analyze

because you may not come back for one

reason I may not come back for another

reason right so people want to find

answers to these questions it is in the

data it is right but but try to reason

your way you know to that right that's

that that's an example that is worth an

enormous amount of money you know right

and the answer to you know why is this

instacart customer churning is not in

the general knowledge of the internet no

somewhere in the instacart data we hope

yes it is and we we see other software

companies and within in product

marketing and so on they try to you know

understand behaviors to the point where

they can then because if you understand

the problem you can do something about

it and be productive in changing the

turn ratios if you're wrong everything

you're doing is is ineffective and that

that is literally what's going on you

know

Jensen part of the partnership is you

guys bringing your pre-trained

foundation models and expertise

to uh you know the snowflake platform

right so how do you think about how

those models should interact with

Enterprise data

uh first of all I've never met

um an employee that was too smart

you know just it embarrassed me how

smart they were

I've never hired a new college grad I

just got too smart too smart

so we can make the pre-trained models as

smart as we can and then we still have

to onboard them we still have to

fine-tune them we still have to you know

we have to still specialize them into

our particular craft and so our strategy

and our offering is state-of-the-art

pre-trained models of a variety of sizes

and sometimes you need to create a very

large pre-trained model so that it can

generate prompts so that you could teach

the smaller models which generates

prompts to teach even smaller Pro Models

and the smaller models could run almost

almost anything and maybe the latency is

really really low however it doesn't

generalize as well it's you know zero

shot capability is probably a lot more

limited and so you might have several

different types of different different

sizes of models but in every single case

you have to do supervised fine-tuning

you have to do reinforcement learning

human feedback so that you could keep it

and keep it aligned to your your um are

your goals and principle and you need to

guard rail it you need to augment it

with Vector databases and things like

that and and so all of that comes

together in a platform and we have the

skills and the knowledge and the basic

platform to help them create their own

AI

and um and then connected to the

incredible data that Frank has in uh

snowflake now ultimately what every

Enterprise customer will do is not

ultimate their goal shouldn't be to

think about how do I build a large

language model their goal should be how

do I build an AI application that solves

this particular problem and it might

that application might require 17

questions in previous prompts that it

finally came to the right answer and

then you might say now I would like to

take these I would like you to to to

write me a program and make it could be

a SQL program it could be a Python

program so that I could do this

automatically in the future and so you

still have to guide this AI through as

you know so that it could ultimately

give you the right answer but then after

that you can create an application that

could run as an agent 24 7. Looking for

circumstances like this and revealing it

to you in advance and so our job is to

help customers build these AI

applications that are guard railed and

specific and customized to them and then

lastly we're all going to be

intelligence manufacturers

in the future we'll hire or hire

employees of course and um but we're

going to create a whole bunch of agents

and these agents could be created with

Lang chain and or something like that

that

connects models and knowledge bases and

other apis and you deploy in the cloud

and connected to all the snowflake data

and and you'll operate these AIS operate

these AIS at scale and you'll

continuously refine these AIS and so

every one of us are going to be

manufacturing AIS and we're going to be

running AI factories

we're going to put that infrastructure

in Snowflake and so customers could work

with their data there

train and develop their models there and

then operate their AIS there and so

snowflake will will be your data

repository and your bank

and Frank is sitting on a gold mine of

data as you guys know your gold mine of

data and all and and snowflake is going

to help you turn that gold mine of data

into incredible intelligence and all of

you will be running AI factories all of

it in Snowflake that's the goal

one of

yeah

there's a lot of gold mines out there

I I actually saw the the Nvidia Nemo

stack you know running inside a

snowflake on the park near pavilion

floor at three o'clock this afternoon so

this is not just Pie in the Sky I mean

this is literally happening as we sit

here so that's what's so exciting I mean

the deployability of these Services is

quite High yeah

one of the things that res

ident is talking to some of the

technical

working on this and I said you know

where did the idea come from

came from these two leaders but also

came from seeing you know large

snowflake customers struggled to get

data out to GPU Computing to do machine

learning and then get it back into

Snowflake and you know seeing that you

have the most sophisticated customers

already going down that path and just

making it possible to consolidate in one

place with one stack that's obvious to

operate on seems like a huge win

you should be able to run on Nvidia

everywhere and anywhere you know like I

said we're lovers and so so if

if you if you need if you need Nvidia

GPU Computing we'll come find you

and all of you are in snowflakes so we

found you

but I do have one important question on

this

um if we pay attention to these

companies that are you know training

this generation of llms it is very

expensive to do so on huge numbers of

gpus is you know what should customers

expect about

um you know GPU Computing spend

it's not free uh and and uh that's the

wrong answer yeah I I know and by the

way everybody in this room uh is is

running consumption models uh it's very

different from a capacity model

um you have to really Master the model

you have to have levels of discipline

around it it's hard to come to terms

with so you know when you when you're

running workloads queries or AI

factories whatever you want to call it

there's a cost so in other words it's

not just fun and games you can ask the

question what are we having for dinner

tonight but is that a question worth

asking just because a generation

interesting answer right so there

there's the consumption model is already

you know an enormous evolution in in the

world of software this is going to you

know take that to another level I mean

Nvidia are wonderful products but

they're not free last time I checked you

know

I think Frank better let me let me

answer this one

sympathetic CEO so so for

for first of all he's announcing they're

all free actually first of all first of

all there's a coupon coming

first of all

as

as you know

um we we built we we have five AI

Factories at Nvidia and uh four of them

are in the world's uh top 500

supercomputers and another one is coming

online as we speak we use those super

computers to do pre-trained models and

so when you use our Nemo AI Foundation

service inside snowflake the first thing

you're going to get

is a state-of-the-art pre-trained model

that tens of millions of dollars of

expense has already gone into it not to

mention all the r d that went into it

and so it's pre-trained and it's like a

new college grad pre-trained super smart

and and not so not so smart that you're

scared not so smart well not so smart as

me but not I'm just

smart super smart

and so so if they're pre-trained and

then there's a whole bunch of other

models around it and those models for

fine-tuning for uh you know human human

feedback reinforcement learning human

feedback for augmentation all of those

models are a lot more cost effective

to train and so now you've adapted that

pre-trained model to your functionality

to your guard rails to be able to

optimize for the type of skills or

functionality you would like it to have

and and augment it with your data and so

that is going to be a lot more cost

effective and the the important thing is

here what I'm what I hope what I hope

and I believe this this will happen is

that you'll be you'll be able to develop

AI applications connected to your data

at snowflake in a matter of days not

months

you should be able to build AI

applications quickly in the future and

the reason for that is because we're

seeing it happening in real time right

now as we speak isn't that right

communities are

um you know almost enthusiasts and

developers are creating all kinds of

applications with Lang chain and uh

Pinecone and uh and whatever data that

they have uh chat chat PDF is pretty

pretty terrific you load a bunch of PDFs

into it you talk to your PDF and uh

instead of reading a research paper this

morning I was asking this chat PDF about

this particular piece of research which

is a lot more interesting having a

conversation about it than having to

read the whole thing and it happens to

be like 49 pages and so being able to

ask a few questions about it is really a

lot more productive you're going to be

able to do this almost everywhere yeah I

think one of the things that's

encouraging if you just think about

software 2.0 versus software 3.0 is one

way to think about the foundation models

is

you know 95 of the training costs has

been born already by somebody else

that's right right you know as early

stage investors did you hear that

thank you thank you Jensen another

incredible Frank Jensen yeah incredible

it's 95 95 off yeah

can't imagine a better deal slightly

different than what I said but

but I think it's I think it's a real

Dynamic right because you know we're

investors in companies like seek in the

analytics automation space Harvey in the

legal space where uh you know these are

very young companies but they're

applications that have gone to real

business value in six months or less

yeah and part of it is because they're

starting with these pre-trained models

and I imagine that's a massive

opportunity for the Enterprise yeah

every single company will have hundreds

if not thousands of AI applications and

you're just connected to all kinds of

data in your company yeah so all of us

have to get good at building these

things

one of the questions I've been hearing

from you know large Enterprise

practitioners is

we have to go invest in this AI thing do

we need a new stack like how should we

think about this in relation to our

existing data stack how do you how do

you think about that you know I think

the uh it's it's evolving actually

something that we're going to talk about

you know tomorrow is to sort of you know

show all the models that are becoming

possible some are already possible and

already being used are in production and

then you know they're getting

progressively you know tighter more

secure more governed

um and and and and all of that so you

know we don't have a real clear view of

this is the reference architecture that

everybody's going to use you know some

people are going to have some set Of

Central Services you know Microsoft has

its you know open AI you know version

running in Azure and a lot of their

customers uh you know are interacting

it's Azure to Azure it's not exactly you

know uh you know where where we want it

to be but we're not clear on what model

is going to be sort of the dominance you

know form that this is going to take

which is why we will be traversing many

paths because I think the market is

going to sort itself on what is the

trade-off between how hard is it and and

and how easy is it to use and what does

it cost and all these kinds of things so

we're we're speculating a little bit we

see people rigging things up like that

right now and asking cute little

questions why is my portfolio down today

you know all this kind of stuff that

that's all fine right but that's not

that's the beginning that's not going to

be the end State you know in terms of

the sort of production Deployable

applications because the whole the whole

security side of the house is going to

weigh in and and Enterprises like are we

violating any copyrights here when we

use this this kind of all these

questions

um you know we're very enthralled right

now with the technology and and and for

good reason but there's going to be a

level of reality that's going to start

to settle in as well you know well Sarah

you're at the core of your question and

and I agree with everything that Frank

said

um but you know that that we're living

through right now the First Fundamental

Computing platform change in 60 years

if you just read the press release of

the IBM system 360 you will just you

will hear about central processing units

i o subsystems dma controllers virtual

memory multitasking it's scalable

Computing forward and backwards

compatibility you're going to hear about

all those literally written in 1964 and

those words have carried us with CPU

scaling right for

60 years but that has run its course we

can't scale CPUs anymore and that is now

well understood at exactly the same time

that all of a sudden software is

different the way that software is

written the way that software is

operated and what software can do is

profoundly different than it was before

isn't that right you call it software

2.0 and now we're talking about software

3.0 the fact of the matter is Computing

has fundamentally changed and so we're

seeing two fundamental Dynamics

happening at the same time that's the

reason why things are shaken up so hard

on the one hand you can't just keep on

buying CPUs you know that and the reason

for that is because if you bought a

whole bunch more CPUs next year your

Computing throughput would be no more

than if you didn't touch it at all

because it's the end of CPU scaling you

would spend a whole bunch more money you

wouldn't get a lot more throughput and

so the answer is you have to go

accelerate it the touring Award winners

spoke about acceleration Nvidia has

pioneered acceleration and and so

accelerated Computing is now here the

second part of it of course is the whole

operating system on top of that computer

is profoundly different we have a layer

called Nvidia AI Enterprise Nvidia AI

Enterprise data processing training

inference deployment that entire

end-to-end is now integrated into or in

the path of being integrated into

snowflake so that the compute engine

underneath from the beginning of data

processing all the way to the large

language models are now going to be

completely accelerating we're going to

turbocharge The Living Daylights out of

snowflake that's our that's our mission

and and you'll be able to do more and

you'll be able to do more with less and

so so the the um and it's it's it's uh

very very evident if you go to any of

the clouds you will see that Nvidia gpus

are the most expensive compute instances

but if you put a workload on it you will

find

that we do it so fast it's as if you've

got a 90 discount you said 90 earlier is

in fact ten to one and so what's what's

interesting is that we're the most

expensive instance but we're the most

cost effective TCO

so if your job is to run workloads then

the best way to do it is acceleration an

example workload is training large

language models an example workload is

fine-tuning large language models if you

want to do that

absolutely do not

do not not accelerate it please do

accelerate it so accelerate every

workload you can and that's that's the

reinvention of the whole stack of the

processor is different the operating

system is different the large language

model is different and now the way you

write AI applications is different isn't

that right software 3.0 you don't have

to write it at all

AI applications we're all going to write

applications we're all going to connect

our prompts and our contacts and our few

python commands right we're going to

connect it to a large language model

we're going to connect it to our own

database or the corporate database and

we'll develop our own application

everybody's going to be an application

developer

but the constant

is still your data and you still need to

fine tune it yeah you know I I think you

know what in the early days of stuff

like we really because it's a cloud

computing fabric faster is cheaper and

faster has always been more expensive

and all of a sudden faster is cheaper

right it's an inverted

mentality so there's sometimes people

want to under provision thinking it's

cheaper ends up being more expensive

right so this this counter

counterintuitive the other thing you

mentioned in terms of compute paradigms

I mean the idea of

uh the data the work going to the data

instead of the data going to the work we

are completely retracing ourselves and

you know for

60 years pick a number we've been

pushing the data to the work and it has

led to massive siloing and segmentation

that's going to be really difficult if

you want to have ai factories right

that's right so we we have to bring that

work to the data and you know we're

doing that now together and I think

that's we're doing it the right way you

know so yeah that's right yeah

uh uh maybe just as advice for the

audience about what you're seeing from

your own customers or your predictions

where do you expect that customers will

get the most value from AI most quickly

well most quickly it's not the most

value but the most quickly yeah two

separate tanks I mean most quickly is

that

um we're going to be you're going to see

augmented query all over the place

because that's just relatively easy to

add you don't even have to be literate

to get value out of data now which is

quite an amazing uh thing so the

ultimate democratization of your

relationship and interaction so that's

that's all great right it's like search

on steroids right and chat and stuff you

think that's more people interacting

with the data yeah but but you know it's

it's like you're at these cute little uh

you know questions to your dashboards

you know I mean dashboards they are

relatively static and the data is one of

this but then I can bring these

questions to that data and that's that's

low-hanging fruit and then people are

going to go to that board meeting and

say look at what we've got you know

everybody will be applauding and this is

really exciting

um but then I'll be like phase one and

then then it really starts you know once

we sort of get over to low hanging fruit

we start really focusing on a much

harder type questions I'm talking about

proprietary Enterprise data and

combinational structured unstructured

all of it right how do we mobilize that

what's an example of a hard question in

phase two in whatever industry well I I

mentioned the problem of churning

consumer Industries the the problems

that we have in Supply Chain management

because they they when Supply chains can

be extraordinarily complex and if there

is an event then you know how do we you

know re-configure rejigger a supply

chain they're running these extremely

intense models you have to ask these

questions what do I do now and that does

the event does happen and bioid Supply

chains are comprised of many many

different entities it's not a single

Enterprise right so by the way this is a

problem that has never been solved in

the in the history of computing Supply

Chain management has never been platform

it's pretty much an email you know

spreadsheet you know type of a business

you know with small exceptions here and

there so so it's exciting you know

collapsing these these enormous call

center Investments that we we have

pricing optimizations in the world of

retail right like I said it's a

redefinition of business models that

people are going to see and it's it's

exciting but that's that's the real

potential that I think you know CEOs of

large institutions are after you know

Jensen where do you where do you see the

most impact coming in the next five

years across the Enterprise uh I would

ask myself number one what is my single

most valuable database

and the second thing I would ask myself

is

if I had a super super super smart

person in front of it

and everything goes through that super

intelligence

scary scary smart person what would I

ask that person

and and um uh it is can I guess is it

the customer database customer database

in the case because Frank has a lot of

customers I don't have that many

customers

um but but the asset database if you're

manufacturing supply chain yeah so my

supply chain is super complicated and

and my design database is super

complicated

uh it's impossible for NVIDIA to build

our gpus without AI anymore

because none of our Engineers can go

through the number of iterations and

exploration that AIS could do for us and

so when we were coming up with AI our

first application was ourselves

and and so

um uh you know Hopper couldn't have been

designed without Ai and our next

Generation cannot be designed without AI

none of our optimizing compilers could

be done without AI just optimizing the

graph is so complicated because there

are so many targets uh so many gpus so

many different neural network models uh

so many different constraints that

you're optimizing for no human could

possibly compile for that optimize for

that and so we have ai just run

continuously looking for the best

Optimal Solutions and so so we we apply

our our AIS to our own data we would do

we would do that of course our bugs

database is a perfect place because we

have so much code we've got to go and

figure out where

um how to how to how to as quickly as

possible enable our Engineers we have um

you know we're a full stack company if

you look at the amount of code that goes

through Nvidia AI is some several

hundred packages of software that comes

together so that an application can sit

up here be accelerated by a factor of 50

on top of a GPU and then scale across a

full data center that piece of software

called Nvidia AI Enterprise is insanely

complicated

we need in the future and this is these

are some of the things that we're

working on now is how do we use an AI to

go figure out how to security patch it

how to best how to best maintain it so

that we can keep compatibility so that

we don't have to disturb the entire

upper layer on the in the meantime

support backwards compatibility maybe

support a particular security hole

etc etc and these are questions that AI

is capable of answering for you that's

right exactly we can use a large

language model to go answer those

questions and then find the answer for

us or reveal the exposure to us and then

the engineer will be able to go in and

fix it

yeah or recommend a fix and then we can

be the human in the loop to go

acknowledge whether this is a good fix

or there's a better fix yeah yeah when

we talk to practitioners we hear

um understand our data better across the

organization so that's kind of phase one

that you talked about like how can we

just ask more questions and let more

people ask those questions we hear

change the margin structure of our

business right and then expand the scope

of what we are doing for our customers

um I think that what you're talking

about with Nvidia actually using it

within engineering across problems that

are too complex for humans to handle

today I think that's actually new but it

makes sense to be honest it makes a lot

of sense because because you have to

Traverse across so many layers

no person could see all those layers and

and so wherever we can when the data is

so so valuable so complex and so I just

asked myself what's my most valuable

data

oh you have we have great customers and

you know for example auto loans you know

um every time they they price alone you

know the amount of computation and data

that gets involved in pricing that loan

I mean you're a market of one to them

right and the one basis point here or

there at the scale that they operate you

know becomes real money and it's only

computationally understandable you look

at auto insurance right they use

Telemetry data from the devices that are

in your car to price risk because that's

the data that determines you know

whether you're going to have claims or

not in other words that business can't

be run without Telemetry data anymore

because you know the competition drives

the prices down but how do you need to

drive profits up at the same time so you

need to really really understand the the

cost of the risks you're taking so we

can already not live with data uh in

many many businesses that we're in you

know impossible

I can't let you guys get away without

asking at least like one more

challenging question here so uh you know

we have a lot of portfolio companies

they need uh you know they have ML and

AI workloads there's also a series of

cloud providers out there like why

Snowflake and Nvidia for their ml

workloads

what was the question hang on a second

her mind's I think cocktail you want

this one too no no yours is water I

think mine's not yeah

I was just saying I'm the guest Frank

was I also wanted a cocktail what did

you say what was the last part the

question is like there's a bunch of

cloud providers out there who are vying

for your ml or your AI workload business

like why why Nvidia and snowflake

well we're the best

that's a good answer yes

and and we're gonna we're gonna come to

a theater near you and prove it of

course you know and uh and and prove Us

in the putting you know we we always aim

for outcomes your outcomes not ours

yours right and uh you know that we're

gonna leave it all in the field I mean

this is this is I find it's the most

interesting time to be alive you know in

this business because the ability to aim

for superlative outcomes is is there now

that's for a long time you know um I I

we talk about data warehousing it used

to be begging for 2 30 a.m time slot

three months from now that was the state

of the business and look at where we are

now it's just unbelievable right you

know the types of problems that we're

talking about now so it's it's shipping

an intelligent application in days and

weeks yeah I I think not not everybody

realizes how much intelligence is

embedded in the data that they look at

every day how much there is to be

unlocked you know in insights and and

impact so that's where we all are going

to come in and make that happen yeah

you know Sarah there's a reason

that everybody is in Snowflake

there's a reason for that because this

is the safest best place to store your

data

to process your data and and so for

everybody who's in Snowflake

we are your best answer

and for the very first time if you date

a warehouse we're going to connect the

AI Factory next to it

and you're going to be producing

intelligence the most valuable commodity

the world's ever produced

you are sitting on a gold mine of

Natural Resources your company's data

proprietary data we're now going to

connect it to an AI engine

and you're gonna on the other end of

that is just intelligent spewing out

every single day

unbelievable amounts of Intelligence

coming out the other end even while you

sleep

this is the best thing ever so if you're

if you're a snowflake customer as many

many are

Frank and I are going to help you

turn your data into intelligence

that's a great way to summarize I can't

improve on that

it was a great conversation guys welcome

to snowflake Summit congratulations on

your partnership thank you so much thank

you so much

right here let's do it thank you great

job oh one more thing

oh

Jensen we're not going to let you go

home empty-handed

I mean this is one of the most valuable

wow wow it's snowflake Nvidia Brandon

look at this wow

[Applause]

this is awesome thanks awesome let it go

everybody thank you for your office okay

you can get on it you have to stand on

it actually I don't want to ruin it

what's up