The Future of AI with NVIDIA Founder and CEO Jensen Huang

01:00:05
https://www.youtube.com/watch?v=q54RnCUwDuY

Sintesi

TLDRThe video presents an engaging dialogue between a host and Jensen Huang, CEO of Nvidia, regarding the transformative potential of artificial intelligence (AI) on various sectors. Jensen discusses Nvidia's role in revolutionizing computing through machine learning, emphasizing the decline of traditional software coding in favor of AI-generated intelligence. He envisions a future where AI personal agents enhance productivity for both individuals and businesses. The impact of AI on cybersecurity, education, and the energy sector is also explored, along with the importance of onboarding and training AI for effective use. Jensen encourages a focus on curiosity and continuous learning as essential skills for the workforce of the future.

Punti di forza

  • 🚀 AI is revolutionizing computing and business practices.
  • 🤖 Machine learning is replacing traditional software coding.
  • 🔍 AI tools enhance productivity and creativity.
  • 🌍 AI democratizes intelligence access for everyone.
  • 🔒 AI improves efficiency in cybersecurity.
  • 🧑‍🎓 Students should engage with AI as tutors.
  • 🔋 AI aims to optimize energy consumption.
  • 💡 Curiosity and continuous learning are key for future success.
  • 🤝 AI agents will work alongside human employees.
  • ⚖️ Sovereign AI empowers nations with their data.

Linea temporale

  • 00:00:00 - 00:05:00

    The event begins with an introduction to Jensen, a prominent figure in technology and artificial intelligence, emphasizing his impact and the honor of having him share insights with the audience.

  • 00:05:00 - 00:10:00

    Jensen expresses gratitude for the opportunity to speak and acknowledges the excitement surrounding the advancements in artificial intelligence and the technology landscape.

  • 00:10:00 - 00:15:00

    He discusses the historical context of computing breakthroughs, particularly referencing the IBM System 360, and notes that it laid the groundwork for modern computing, which has since evolved significantly, especially over the past decade.

  • 00:15:00 - 00:20:00

    Jensen explains how advancements in technology have dramatically reduced computing costs and increased performance, equating it to the drastic improvements seen under Moore's Law, emphasizing that NVIDIA has pushed these advancements even further.

  • 00:20:00 - 00:25:00

    He highlights the revolutionary shift from traditional programming to machine learning, where AI can now learn from vast amounts of data and generate new insights, using examples like ChatGPT and its ability to understand and generate language.

  • 00:25:00 - 00:30:00

    Jensen shares that AI's capabilities extend beyond language processing to areas like image recognition and protein folding, showcasing the potential to translate data across different domains, thus opening new avenues for innovation and business.

  • 00:30:00 - 00:35:00

    He envisions a future where businesses will leverage specialized AI agents for various tasks, enhancing productivity and streamlining operations, allowing individuals to focus on more strategic initiatives.

  • 00:35:00 - 00:40:00

    Jensen discusses the necessity for quick adaptation in the field of cybersecurity, where AI can help manage threats and false positives, ultimately enhancing the overall security posture.

  • 00:40:00 - 00:45:00

    He contrasts traditional programming with 'training' AI, suggesting that as AI improves, it could take over many of the more repetitive tasks people perform, leading to a more efficient workforce composed of both humans and AI.

  • 00:45:00 - 00:50:00

    The conversation emphasizes the importance of curiosity and continuous learning for future workforces, suggesting that students leverage AI tools as tutors to aid their personal development and education.

  • 00:50:00 - 01:00:05

    In closing, Jensen paints an optimistic picture of the future with AI as a partner in both personal and professional spheres, urging the audience to engage with AI as a tool that enables creativity and innovation.

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Mappa mentale

Video Domande e Risposte

  • What is Nvidia's role in AI advancement?

    Nvidia is a market maker in artificial intelligence, focusing on creating new technologies rather than competing for market share.

  • How does AI impact cybersecurity?

    AI can automate the analysis of cyber threats, improving efficiency and reducing false positives in threat detection.

  • What advice does Jensen provide for high school students?

    Jensen advises students to focus on school, embrace AI tutors, and remain curious to succeed in a rapidly changing technological landscape.

  • What does Jensen predict about the future workforce?

    He envisions a blend of biological and AI agents working together in various roles within organizations.

  • How might AI democratize access to intelligence?

    AI will lower the cost of intelligence, making it available to everyone, including underserved populations.

  • What is Jensen's view on energy consumption from AI?

    Jensen hopes that energy use from AI will increase as it helps optimize other resource uses efficiently.

  • How can AI enhance creativity and learning in children?

    AI can serve as a tutor to engage children's creativity and facilitate learning in personalized ways.

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Scorrimento automatico:
  • 00:00:00
    [Music]
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    it is my pleasure to bring up and a man
  • 00:00:11
    that needs really no introduction so I'm
  • 00:00:14
    not going to spend any time introducing
  • 00:00:16
    him I would go by now where this
  • 00:00:19
    individual is and what he's done from an
  • 00:00:21
    innovation standpoint uh thought
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    leadership just really driving
  • 00:00:26
    technology artificial intelligence
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    throughout the world
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    Jensen I will use one name come up
  • 00:00:33
    because last name is not needed with
  • 00:00:35
    this gentleman so welcome
  • 00:00:47
    Jensen I would just say very proud to
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    call Jensen a friend a great partner and
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    someone that I look at as a mentor
  • 00:00:55
    looking at how he's leading the AI
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    Revolution and how he's leading his
  • 00:01:00
    organization so we'll just grab Tech
  • 00:01:04
    thank
  • 00:01:06
    you so Jensen so great great to have you
  • 00:01:09
    here uh we have a great group of
  • 00:01:13
    customers here partners and I will also
  • 00:01:16
    say we have some of our esteemed golfers
  • 00:01:19
    here that actually I'm not sure how many
  • 00:01:22
    events we've had in the past but I don't
  • 00:01:24
    remember a lot of the professional
  • 00:01:26
    golfers coming up to me and saying hey
  • 00:01:27
    can I get into the event and I was it's
  • 00:01:30
    like well certainly you can so uh you're
  • 00:01:33
    attracting I I've seen a lot of
  • 00:01:35
    customers before this doesn't look like
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    customers you guys are all too well
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    dressed to be
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    customers we would consider them custo
  • 00:01:44
    friends friends uh friends and partners
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    and just great to be here with you so
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    great to be here you know just so many
  • 00:01:52
    things going on right now it's such an
  • 00:01:54
    exciting space so first of all I do want
  • 00:01:56
    to thank Jensen and I've said it before
  • 00:01:58
    for taking the time to come here his
  • 00:02:01
    he's he's all over the place been in t
  • 00:02:04
    you know over in Asia Taiwan you were
  • 00:02:06
    you know India speaking and and you know
  • 00:02:09
    again the venues are normally could be
  • 00:02:11
    30 40 50 60,000 people that you're
  • 00:02:14
    speaking to so to come here and speak
  • 00:02:16
    with us in a really intimate setting
  • 00:02:18
    means a lot to me and I know it means a
  • 00:02:21
    lot to uh our customers partners and
  • 00:02:23
    friends here yeah thanks thanks for the
  • 00:02:24
    invitation I've been looking forward to
  • 00:02:26
    it um maybe you could just take it from
  • 00:02:30
    you know a little bit of the past the
  • 00:02:33
    present and the future and I know that's
  • 00:02:35
    a lot but so much has changed even in
  • 00:02:38
    you know you've been driving Nvidia for
  • 00:02:40
    30 30 plus years now but so much has
  • 00:02:44
    changed you know even in the last three
  • 00:02:45
    or four years so maybe you could just
  • 00:02:47
    frame up from your view how much has
  • 00:02:49
    changed and why in the last several
  • 00:02:52
    years to where we are today and where
  • 00:02:55
    you think things are going and and and I
  • 00:02:57
    know that's a lot uh and we can take
  • 00:02:59
    break breaks and I I can give a
  • 00:03:01
    perspective but really you know I know
  • 00:03:04
    they really want to hear me talk but
  • 00:03:06
    they don't so uh I I I would just like
  • 00:03:08
    to maybe you can share with them that
  • 00:03:10
    past present future where you think so
  • 00:03:12
    well it it is an extraordinary time uh
  • 00:03:15
    we are Reinventing the computer for the
  • 00:03:17
    first time since it was introduced
  • 00:03:20
    largely in
  • 00:03:21
    1964 the IBM system
  • 00:03:24
    360 uh the architecture of of that IBM
  • 00:03:28
    system it's the most famous computer the
  • 00:03:31
    world's ever ever built it created as a
  • 00:03:34
    result the most valuable company in the
  • 00:03:36
    world IBM IBM system 360 introduced the
  • 00:03:39
    idea of central processing unit
  • 00:03:41
    multitasking the separation of software
  • 00:03:43
    and Hardware with a layer called
  • 00:03:45
    operating system IO subsystems um
  • 00:03:48
    architecture compatibility family
  • 00:03:50
    compatibility all of the concepts that
  • 00:03:52
    govern uh the computer industry today
  • 00:03:55
    was really introduced in literally one
  • 00:03:57
    press release in 1964 it's
  • 00:04:00
    an unbelievable thing and and um uh that
  • 00:04:05
    basic that basic system uh ran out of
  • 00:04:08
    steam about about a decade ago and and
  • 00:04:12
    uh we we that that was the that was the
  • 00:04:15
    idea of our company was to invent a new
  • 00:04:17
    way of doing Computing uh as as the
  • 00:04:20
    general purpose Computing was going to
  • 00:04:22
    run out of steam and one of the one of
  • 00:04:25
    the benefits of what we invented um
  • 00:04:28
    results in when you when you create
  • 00:04:31
    amazing Computing technology it
  • 00:04:33
    effectively drives down the cost and so
  • 00:04:36
    we so if you will many of you might have
  • 00:04:38
    heard of the word Moors law U Moors law
  • 00:04:42
    uh is an incredible technology force and
  • 00:04:44
    it it created the modern technology
  • 00:04:46
    industry uh it doubled in performance
  • 00:04:49
    every year and a half but the easier way
  • 00:04:51
    to think about that is it increased in
  • 00:04:52
    performance by 10 times every five years
  • 00:04:55
    so every time if it's 10 times every
  • 00:04:57
    five years every 10 years 100 times so
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    Moore's law was improving the
  • 00:05:01
    performance of Technology of computing
  • 00:05:03
    by about a 100 times every decade uh you
  • 00:05:06
    invert that and the reason for that is
  • 00:05:08
    because deflationary technology is what
  • 00:05:11
    creates an
  • 00:05:13
    abundance it was it was the fact that we
  • 00:05:16
    took our industry drove the cost of
  • 00:05:19
    computing down by a 100 times every 10
  • 00:05:21
    years that today you have you have uh
  • 00:05:24
    these supercomputers in your pocket
  • 00:05:26
    called
  • 00:05:26
    iPhones and and um
  • 00:05:30
    uh what we did was over the course of
  • 00:05:33
    the last 10 years reduce the cost of
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    computing or increase the performance of
  • 00:05:37
    computing by one million
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    times so uh a million times is a lot
  • 00:05:43
    bigger than a 100 as you can imagine and
  • 00:05:47
    and the the way to think about that is
  • 00:05:50
    is um what would happen if you could
  • 00:05:54
    travel
  • 00:05:55
    somewhere uh a 100,000 times faster or
  • 00:05:58
    10,000 times faster
  • 00:06:00
    it would change everything about how you
  • 00:06:02
    think about the problem and so if you
  • 00:06:05
    were to go lift something and it's all
  • 00:06:07
    of a sun 10,000 times lighter than it
  • 00:06:10
    used to or you go do something and you
  • 00:06:13
    could do it with 10,000 times more scale
  • 00:06:16
    than you could how you see the world
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    completely changed and to the point
  • 00:06:21
    where uh we invented this new way of
  • 00:06:23
    doing software called machine
  • 00:06:26
    learning you could just literally take
  • 00:06:30
    uh all of the observed data in the world
  • 00:06:32
    you give it to a computer and this
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    computer is about the size of this room
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    would study all of the data in the world
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    and say figured it out this is how the
  • 00:06:41
    world
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    works that is Chad
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    GPT literally took all of the data in
  • 00:06:48
    the world on the internet and over the
  • 00:06:50
    course of several months uh looked for
  • 00:06:53
    patterns and relationships that
  • 00:06:55
    eventually understood uh words and uh
  • 00:06:58
    vocabulary and
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    uh syntax and grammar and and then you
  • 00:07:03
    gave it multiple languages at one time
  • 00:07:04
    and it figured out how to correlate um
  • 00:07:09
    uh uh well I don't I don't know French
  • 00:07:13
    but but but dog and Sh I think in French
  • 00:07:17
    dog and Shen right and so dog and Shen
  • 00:07:20
    are really the same word it figured out
  • 00:07:22
    how to cor first of all understand the
  • 00:07:24
    languages of both but it also correlated
  • 00:07:26
    the two to the point where you could now
  • 00:07:28
    translate and and so we created this
  • 00:07:30
    this architecture that is now a
  • 00:07:32
    universal translator and and now you you
  • 00:07:34
    figured out if it could do it different
  • 00:07:36
    language but what what if I could do it
  • 00:07:38
    for images and you correlate uh this uh
  • 00:07:42
    patch of pixels that appears to look
  • 00:07:44
    like a dog with the word
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    dog and when you can do that then when
  • 00:07:49
    you're shown an image of a dog you say
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    dog and when you say the word uh when
  • 00:07:54
    you type in the word dog it now
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    generates an image of a dog and that
  • 00:08:00
    that idea of course uh stable diffusion
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    M Journey uh Sora is now we can do it in
  • 00:08:05
    in in video and so then you you say okay
  • 00:08:09
    well if we created a system that that
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    can do that uh what if we taught it a
  • 00:08:13
    whole bunch of other types of digital
  • 00:08:16
    information like for example chemicals
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    and proteins and video and 3D and uh
  • 00:08:23
    fluid dynamics and and now because you
  • 00:08:27
    understand the meaning of all of the
  • 00:08:29
    things you can understand the meaning of
  • 00:08:31
    a protein and you could say I would like
  • 00:08:34
    you to generate a protein and it would
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    take a sequence of amino acids and would
  • 00:08:38
    turn it into the 3D structure of a
  • 00:08:40
    protein and when you can understand the
  • 00:08:42
    3D structure of a protein you understand
  • 00:08:44
    its function what it's for and that just
  • 00:08:47
    won a Nobel Prize that is um uh that's
  • 00:08:50
    deep mind's Alpha fold and and so I just
  • 00:08:54
    gave you some examples of what you can
  • 00:08:55
    do but but but the way the big idea here
  • 00:08:58
    is is that that we've now uh learned the
  • 00:09:01
    language of almost everything we learned
  • 00:09:04
    the language of images and videos and of
  • 00:09:07
    course language human language and
  • 00:09:09
    proteins and and we could also translate
  • 00:09:11
    to anything and so for for anybody who's
  • 00:09:14
    interested in starting a company uh what
  • 00:09:16
    I just described to you uh is a
  • 00:09:19
    framework to think about what can you do
  • 00:09:21
    now that you couldn't do before and so
  • 00:09:24
    uh for example you guys are doing golf
  • 00:09:27
    let's say let's say that that
  • 00:09:29
    uh you would like um an AI just to
  • 00:09:32
    record all everybody's rounds and uh
  • 00:09:36
    instead of instead of somebody going
  • 00:09:38
    through the videos and trying to figure
  • 00:09:40
    out which one of the segments were
  • 00:09:41
    interesting and told the story of the
  • 00:09:43
    round you just have an AI watch the
  • 00:09:45
    video and the AI will watch the video
  • 00:09:46
    and say this is interesting uh and uh
  • 00:09:49
    from that video it would generate the
  • 00:09:51
    captioning uh from the captioning it
  • 00:09:53
    could even voice over it generate the
  • 00:09:56
    generate words generate speech and so
  • 00:09:58
    you would go from uh I said earlier from
  • 00:10:01
    amino acid to protein why can't you go
  • 00:10:04
    from video to speech and video to speech
  • 00:10:08
    would basically be somebody who is a a
  • 00:10:10
    commentator uh talking about uh you know
  • 00:10:13
    a round of golf and it would only talk
  • 00:10:15
    about the important things uh it's
  • 00:10:17
    interesting because because if you could
  • 00:10:20
    create that
  • 00:10:22
    AI uh after you don't as you're watching
  • 00:10:24
    that video you might say I well uh you
  • 00:10:28
    know
  • 00:10:30
    uh what was what was the uh what did
  • 00:10:33
    what did this player how did Jim do in
  • 00:10:34
    the last hole so you would literally
  • 00:10:36
    talk to the AI and the AI would talk
  • 00:10:38
    back and say the last hole you know Jim
  • 00:10:40
    did this and that and so so from this
  • 00:10:44
    framework you could see the Cambrian
  • 00:10:46
    explosion of startups and there H that's
  • 00:10:52
    hundreds and thousands of startups are
  • 00:10:53
    showing up because of this translation
  • 00:10:55
    this framework but the the important
  • 00:10:57
    idea that I wanted to to end up with is
  • 00:11:00
    is this we reinvented the computer
  • 00:11:03
    whereas we used
  • 00:11:05
    to code software we now do machine
  • 00:11:09
    learning and so instead of humans
  • 00:11:12
    programmers writing software we now have
  • 00:11:14
    computers write the software instead of
  • 00:11:17
    developing software we're now using
  • 00:11:19
    machine learning to create
  • 00:11:21
    AIS and and that is the that is the big
  • 00:11:24
    transformation of our of our industry
  • 00:11:27
    that we're not making something software
  • 00:11:29
    anymore we're now producing
  • 00:11:33
    intelligence and the production of
  • 00:11:35
    intelligence has created a whole new
  • 00:11:37
    layer of industry and this layer of the
  • 00:11:40
    industry um what it's going to augment
  • 00:11:43
    is you know measured in trillions not in
  • 00:11:45
    billions and that's the that's the
  • 00:11:47
    remarkable thing about what what we've
  • 00:11:49
    built no it's it's a it's fascinating to
  • 00:11:53
    see you know all the things going on we
  • 00:11:56
    we talked about you know we're just
  • 00:11:58
    starting this phase of translating you
  • 00:12:00
    know words to images images to words
  • 00:12:03
    your different languages you think about
  • 00:12:06
    you know on the medical side from cells
  • 00:12:09
    to to to to literally cures you know
  • 00:12:12
    outcomes that you're going to deliver uh
  • 00:12:14
    maybe for the group that that we're
  • 00:12:16
    looking at we talked a bit about you
  • 00:12:18
    know we're we're we're in such the early
  • 00:12:20
    stages also that this has really come to
  • 00:12:22
    life with chat
  • 00:12:24
    gbt uh and people you know I would look
  • 00:12:27
    at it individually everybody's been able
  • 00:12:29
    to create your own account right to it
  • 00:12:32
    you can see the power of it now we're
  • 00:12:34
    we're moving into the power of business
  • 00:12:38
    the power of driving efficiencies
  • 00:12:40
    opportunities differentiation you know
  • 00:12:43
    so maybe you could touch on a little bit
  • 00:12:44
    of where do you see it today and where
  • 00:12:48
    do you see it going around changing the
  • 00:12:50
    way organizations operate and think
  • 00:12:53
    about individuals also and businesses we
  • 00:12:56
    talked about agents everybody will have
  • 00:12:58
    an A you know their own agent maybe
  • 00:13:00
    personally uh but also as a business and
  • 00:13:02
    then you know avatars and agents that
  • 00:13:05
    individuals may have so jents and maybe
  • 00:13:07
    kind of share a little bit of your view
  • 00:13:10
    of where this is going and how it may be
  • 00:13:13
    impacting businesses and individuals as
  • 00:13:15
    we move forward well let's make it first
  • 00:13:17
    of all take it take a step back and ask
  • 00:13:20
    ourselves when we use you know what is
  • 00:13:22
    the miracle of chat
  • 00:13:24
    GPT um and these large language models
  • 00:13:27
    the the miracle is that we all know how
  • 00:13:28
    to use it
  • 00:13:31
    that's actually the
  • 00:13:32
    miracle the miracle is that we can all
  • 00:13:36
    interact with it and and what are you
  • 00:13:39
    really doing well we used to program
  • 00:13:43
    computers now you program AIS and AIS
  • 00:13:48
    are easy to program you just tell them
  • 00:13:49
    what you want and and if you're not sure
  • 00:13:54
    uh you know it takes a swing at it
  • 00:13:56
    anyways um but it could also ask you I'm
  • 00:13:59
    not exactly sure what you mean and you
  • 00:14:01
    need to be more precise and you might
  • 00:14:03
    not even know exactly how to ask it to
  • 00:14:05
    do what you want it to do and you you
  • 00:14:07
    literally say that uh I would like to
  • 00:14:10
    ask you to do this I would like to ask
  • 00:14:12
    you to write me a Python program that
  • 00:14:14
    sorts you know a thousand
  • 00:14:18
    numbers and I don't know how to ask you
  • 00:14:20
    to do that teach me how to ask you to do
  • 00:14:22
    that it'll even come back and ask you
  • 00:14:24
    and teach you how to ask it to do it so
  • 00:14:26
    that it can do it are you guys and so
  • 00:14:29
    this is the miracle and so what I what
  • 00:14:31
    what did I in that interaction what just
  • 00:14:34
    happened I programmed I programmed an AI
  • 00:14:37
    to write a program that the computer can
  • 00:14:42
    run okay and so uh this is no different
  • 00:14:45
    than you asking a software programmer um
  • 00:14:48
    to do that on your behalf now that's
  • 00:14:50
    software but it could be almost anything
  • 00:14:52
    it could be uh review this document it
  • 00:14:54
    could be write me a contract it could be
  • 00:14:57
    um uh uh uh
  • 00:14:59
    create a uh a vacation plan that you
  • 00:15:02
    know it could be anything and so the
  • 00:15:04
    first thing that we we should observe is
  • 00:15:06
    that we close the gap of Technology
  • 00:15:09
    programming a computer is really really
  • 00:15:11
    in the hands of call it 10 million
  • 00:15:13
    people 10 million people out of eight
  • 00:15:16
    billion people in the world knows how to
  • 00:15:17
    program computers and yet 100% I just I
  • 00:15:20
    can do we can do the thought experiment
  • 00:15:22
    I'm certain 100% of you can use chat GPT
  • 00:15:25
    to fairly good effect and and so that's
  • 00:15:28
    that's the first thing to to observe is
  • 00:15:30
    that the technology barrier is now gone
  • 00:15:33
    now let's let me go to The Other Extreme
  • 00:15:34
    now and let's do another thought
  • 00:15:36
    experiment what would what would it what
  • 00:15:38
    would working be like
  • 00:15:40
    someday um and and uh it would likely be
  • 00:15:45
    my experience with working and so my
  • 00:15:47
    experience you know I I work in in um uh
  • 00:15:51
    one of the most advanced one of the most
  • 00:15:53
    capable computer science company the
  • 00:15:54
    world's ever know and I'm surrounded by
  • 00:15:56
    32,000 people and the way I interact
  • 00:15:59
    with them uh you know of course there's
  • 00:16:02
    a lot of face to face but most of my
  • 00:16:04
    interactions are through Outlook and
  • 00:16:07
    Outlook I see all these little little
  • 00:16:09
    dots and and some of them are literally
  • 00:16:12
    faces and some of them are cartoon faces
  • 00:16:14
    and so on so forth in the future many of
  • 00:16:17
    those little
  • 00:16:18
    dots will just be
  • 00:16:22
    AIS and that AI would be somebody would
  • 00:16:25
    be good at marketing campaign somebody
  • 00:16:27
    would be good at sales Camp campaign
  • 00:16:28
    somebody somebody's really good at
  • 00:16:29
    customer service somebody's good at
  • 00:16:31
    software programming chip design system
  • 00:16:33
    engineering some somebody is a really
  • 00:16:35
    good mechanical engineer and they're
  • 00:16:37
    working with a whole bunch of other
  • 00:16:39
    mechanical engineers and so on so forth
  • 00:16:41
    and I'm hoping that that I'm surrounded
  • 00:16:44
    by these super agents that are so good
  • 00:16:47
    at at what what they do uh that I can I
  • 00:16:51
    can give it almost any Mission any dream
  • 00:16:54
    and I I can come up with big ideas about
  • 00:16:56
    what we would like to achieve and what's
  • 00:16:58
    the amazing thing is I would assign that
  • 00:17:01
    mission today we were assigning to
  • 00:17:02
    somebody I would assign that mission to
  • 00:17:04
    to an agent to an AI agent that AI agent
  • 00:17:07
    would invite a whole bunch of other
  • 00:17:08
    friends other agents some of them are
  • 00:17:10
    biological some of them are
  • 00:17:12
    digital and uh they'll they'll come up
  • 00:17:15
    with a plan and they'll come back and
  • 00:17:16
    you know pitch the plan to me just like
  • 00:17:19
    they do today and uh we'll break it down
  • 00:17:21
    on improve it uh you know and and we
  • 00:17:24
    work together as a team and then goes
  • 00:17:26
    off and does the job and so so if that's
  • 00:17:29
    the
  • 00:17:30
    future if that's the future then we just
  • 00:17:32
    got to work our way back and say how do
  • 00:17:34
    we go create that future yeah and it's I
  • 00:17:36
    mean we talked a little bit in the back
  • 00:17:38
    obviously our our teams work together in
  • 00:17:40
    a in a big way uh so many exciting
  • 00:17:43
    things going on but kind of going back
  • 00:17:45
    we see now and correct me if I'm wrong
  • 00:17:47
    if you agree or disagree you know you
  • 00:17:49
    think about chat GPT you think about
  • 00:17:51
    some of the large language models that
  • 00:17:53
    all of us here can go out and you have
  • 00:17:55
    you have the ability to go exercise that
  • 00:17:58
    use those
  • 00:17:59
    ask it really interesting compelling
  • 00:18:02
    things to do in information and ideas
  • 00:18:05
    and it keeps getting smarter and smarter
  • 00:18:06
    you see how fast those models are
  • 00:18:08
    changing you know the versions are going
  • 00:18:10
    up and the capabilities but then within
  • 00:18:13
    the companies as Jensen has mentioned
  • 00:18:15
    you know we see this capability that
  • 00:18:17
    organizations have to build their own
  • 00:18:19
    call it large language models or rag
  • 00:18:21
    models which is really a customized uh
  • 00:18:25
    AI platform that you're going to build
  • 00:18:27
    on an Nvidia platform in your company
  • 00:18:30
    that's going to allow you to literally
  • 00:18:32
    write apis into these large language
  • 00:18:34
    models to deliver these agents at scale
  • 00:18:37
    within an organization and that's really
  • 00:18:40
    what we're seeing and I'm kind of
  • 00:18:41
    curious you know you know Jensen give
  • 00:18:43
    your perspective but we're in a really
  • 00:18:45
    early stages of that and it's really
  • 00:18:48
    starting to come to life and those are
  • 00:18:50
    things that we're seeing so kind of your
  • 00:18:52
    view maybe a little more color on how
  • 00:18:55
    you see that transitioning over the next
  • 00:18:58
    couple six months to a couple of years
  • 00:19:00
    because uh from my view and you know
  • 00:19:03
    from what we even talked about it's like
  • 00:19:06
    everybody's going to have an agent and
  • 00:19:08
    organizations are going to have
  • 00:19:09
    thousands and thousands of agents and
  • 00:19:11
    it's not necessarily going to displace
  • 00:19:14
    you know employees it's going to enable
  • 00:19:17
    and complement in a lot of ways so maybe
  • 00:19:19
    just a little more color on well I hope
  • 00:19:20
    it displaces a lot of my current
  • 00:19:22
    work and and what I mean by that is very
  • 00:19:25
    honestly there there's there's there no
  • 00:19:28
    agents that that we know no AIS that we
  • 00:19:30
    know that could replace 100% of
  • 00:19:32
    anybody's job but we know of many agents
  • 00:19:36
    that can replace 80% of the things that
  • 00:19:39
    we do well nothing would give me more
  • 00:19:42
    joy than to have something do 80% of
  • 00:19:45
    what I do today so I can go find 80%
  • 00:19:48
    more things to go do that's the
  • 00:19:50
    definition of productivity do more with
  • 00:19:52
    less and so and and of course of course
  • 00:19:56
    uh uh we would like that to happen for
  • 00:19:58
    all of our employees and and we have ai
  • 00:20:00
    all of our companies today to uh write
  • 00:20:03
    software help us design chips help us
  • 00:20:05
    design systems and and and they all tend
  • 00:20:08
    to be inward focused because we could
  • 00:20:10
    develop the technology inside and if it
  • 00:20:12
    doesn't work that well uh we still have
  • 00:20:14
    excellent Engineers to to override
  • 00:20:17
    override their their uh uh their
  • 00:20:19
    decisions and and their suggestions now
  • 00:20:22
    as the technology gets better we're
  • 00:20:24
    starting to get to a point where we
  • 00:20:25
    could use it for outward facing um work
  • 00:20:28
    and outward facing work would be uh
  • 00:20:31
    marketing campaigns sales campaigns
  • 00:20:32
    customer service uh so on so forth the
  • 00:20:36
    the way to think about a good mental
  • 00:20:38
    model for for thinking about AI is this
  • 00:20:40
    the same exact way you think about
  • 00:20:43
    employees the first thing you have to do
  • 00:20:45
    is you have to onboard them and and
  • 00:20:47
    there's a way of onboarding AIS you you
  • 00:20:50
    give it a whole bunch of data that's
  • 00:20:51
    specific to you to your company and your
  • 00:20:53
    the work that you're trying to do many
  • 00:20:55
    examples you you just you show your
  • 00:20:58
    employee
  • 00:20:59
    this is the way we talk in our company
  • 00:21:00
    these are core values uh this is this is
  • 00:21:03
    the functionality of your job these are
  • 00:21:05
    examples of uh work products that we
  • 00:21:08
    would like you to create these is this
  • 00:21:10
    is how you this this is a a source of
  • 00:21:12
    all the resources that you can get your
  • 00:21:14
    approved to go used and then within that
  • 00:21:17
    boundary you have to go do that work and
  • 00:21:19
    so so you train the AI to do that
  • 00:21:21
    there's a methodology for doing that you
  • 00:21:23
    evaluate the AI just like we evaluate
  • 00:21:26
    employees uh we guard rail and just like
  • 00:21:29
    we guard rail employees after we onboard
  • 00:21:31
    somebody in the accounting department we
  • 00:21:33
    don't say you know please don't don't
  • 00:21:36
    you well I don't know what you tell
  • 00:21:37
    accountants not to do don't talk to
  • 00:21:41
    customers
  • 00:21:43
    so oh that's good I'm going to use that
  • 00:21:46
    where's our finance folks for example
  • 00:21:48
    you know that's a joke for you're you're
  • 00:21:51
    an engineer you're an engineer don't
  • 00:21:53
    talk to customers uh you know and and
  • 00:21:55
    you're in sales don't don't launch
  • 00:21:57
    products
  • 00:21:59
    and and and so so we guardrail them and
  • 00:22:01
    they're ai ai Technologies for Guard
  • 00:22:04
    railing things uh and then there's
  • 00:22:06
    there's a flywheel for continuous
  • 00:22:08
    Improvement and so on so forth and so
  • 00:22:10
    these these uh everything that I just
  • 00:22:13
    described is a whole bunch of other AIS
  • 00:22:15
    and these other AIS are helping us on
  • 00:22:18
    board AIS fine tune AIS um uh you know
  • 00:22:22
    soone evaluate eyes guard rail AIS you
  • 00:22:26
    know improve AIS and so all all of this
  • 00:22:29
    is is kind of like a framework for
  • 00:22:32
    employees yeah and and my sense is that
  • 00:22:35
    that um what what is likely to happen is
  • 00:22:37
    that we'll just we'll have two two very
  • 00:22:40
    significant workforces inside companies
  • 00:22:42
    one that are that are uh you know one
  • 00:22:45
    that are biological they're made of meat
  • 00:22:47
    and then and then the other ones they're
  • 00:22:50
    M electrons you know and and Flesh and
  • 00:22:53
    Bones I mean you know and and and we'll
  • 00:22:56
    have both and then they'll work together
  • 00:22:58
    and they and they'll work with each
  • 00:22:59
    other yeah no and it is it's it's
  • 00:23:02
    fascinating to to to see that maybe uh
  • 00:23:06
    talk a little bit about your vision and
  • 00:23:08
    maybe the ecosystem to enable this
  • 00:23:11
    within organizations because you talk
  • 00:23:13
    about Nvidia you know we talked about in
  • 00:23:16
    the back where and I truly believe this
  • 00:23:19
    Jensen does not look at uh Nvidia even
  • 00:23:22
    know it's now you know you look at top
  • 00:23:26
    first second you know bouncing around
  • 00:23:28
    you know largest market cap company in
  • 00:23:29
    the in the world so just an amazing job
  • 00:23:32
    honestly to to I
  • 00:23:34
    mean
  • 00:23:36
    uh I I mean this and I know it and
  • 00:23:39
    Jensen knows I I I don't say this
  • 00:23:41
    stroking Jensen on this uh but he will
  • 00:23:44
    go down as one of the great innovators
  • 00:23:46
    of our lifetime and uh and really a
  • 00:23:49
    great leader I mean a great leader that
  • 00:23:52
    is maniacal in regards to his focus on
  • 00:23:56
    Innovation and drive and really a a good
  • 00:23:59
    person from a humility perspective too
  • 00:24:01
    very humble and driven and it's kind of
  • 00:24:04
    fascinating maybe you can give a little
  • 00:24:05
    your perspective on you how you look at
  • 00:24:08
    the market because you really don't I
  • 00:24:10
    feel like that's where we should
  • 00:24:13
    end you know how you look at the market
  • 00:24:16
    because you really don't fixate on like
  • 00:24:19
    the market cap and the value you you and
  • 00:24:21
    you talk about creating markets not like
  • 00:24:25
    here's a market and here's competitors
  • 00:24:27
    and here's how much I can yet it's I'm
  • 00:24:30
    looking at creating additional markets
  • 00:24:33
    for NVIDIA but also the ecosystem talked
  • 00:24:36
    about you know with Jay and the team and
  • 00:24:38
    Craig how you're trying to pull
  • 00:24:40
    organizations with you into this AI
  • 00:24:44
    journey to to drive positive change for
  • 00:24:47
    our humanity and for business uh so
  • 00:24:49
    maybe a little bit of how you look at
  • 00:24:51
    that of being a market maker and not
  • 00:24:54
    necessarily going at a size of a market
  • 00:24:57
    that's there today
  • 00:24:59
    yeah um Nvidia is a market maker not
  • 00:25:03
    share
  • 00:25:04
    taker um we we we only do things that
  • 00:25:08
    other people don't do um there's nothing
  • 00:25:12
    wrong with with market share thinking
  • 00:25:15
    there's nothing wrong with benchmarking
  • 00:25:16
    there's there all of those are good
  • 00:25:18
    skills to to do um I I tend to not not
  • 00:25:24
    think about market share and the reason
  • 00:25:25
    for that is because because in a lot of
  • 00:25:27
    ways
  • 00:25:28
    uh you you have to ask yourself for for
  • 00:25:31
    what reason are we doing this if there
  • 00:25:33
    was somebody else who was already doing
  • 00:25:34
    it and and um we've not been around a
  • 00:25:38
    very long time we're only we're only 32
  • 00:25:39
    33 years old and so there were a whole
  • 00:25:42
    lot of technology companies that existed
  • 00:25:44
    uh already by the time that that Nvidia
  • 00:25:46
    was founded and so the question is is
  • 00:25:48
    what can we do that's unique that that
  • 00:25:51
    um adds a perspective and and a
  • 00:25:53
    contribution that if we didn't exist
  • 00:25:55
    wouldn't exist and so that that way of
  • 00:25:59
    thinking that way of thinking and the
  • 00:26:01
    Computing model we invented uh was
  • 00:26:03
    really unlikely to have been done and
  • 00:26:07
    and um uh and so the the company was
  • 00:26:10
    always the company's good at thinking
  • 00:26:13
    you
  • 00:26:14
    know what if what if the world was like
  • 00:26:16
    this what if we could do that notice
  • 00:26:20
    just now I answered a question my my
  • 00:26:22
    natural inclination is to answer the
  • 00:26:24
    question from the past and then from the
  • 00:26:26
    future the present is the only place
  • 00:26:28
    that I'm
  • 00:26:29
    uncomfortable if you ask me what's going
  • 00:26:31
    to happen tomorrow I have no clue if you
  • 00:26:33
    ask me what's going to happen in 10
  • 00:26:35
    years I have a lot of
  • 00:26:37
    confidence and and if you ask me what
  • 00:26:39
    happened 10 years ago I I'll tell you I
  • 00:26:41
    don't remember but but but the the the
  • 00:26:45
    reason the reason why I have a lot of
  • 00:26:46
    confidence in that is because you reason
  • 00:26:48
    from first principles um uh and the
  • 00:26:51
    limits of of
  • 00:26:53
    physics and and if you look far if you
  • 00:26:56
    look far out enough you don't have to
  • 00:26:58
    worry about about the the challenges of
  • 00:27:02
    getting there you just have to you just
  • 00:27:04
    imagine being there and so I am almost I
  • 00:27:07
    am completely certain that human or
  • 00:27:09
    robotics is going to be um here and and
  • 00:27:12
    there's the first principal reasons for
  • 00:27:14
    that because we understand something
  • 00:27:16
    about the technology breakthroughs that
  • 00:27:17
    we've we've invented I'm now certain
  • 00:27:20
    that it will be created um I certain why
  • 00:27:22
    it's going to be helpful and so so now
  • 00:27:24
    you have these certain Concepts you know
  • 00:27:27
    and uh for example I'm I'm certain my
  • 00:27:30
    Outlook is going to be the way I
  • 00:27:31
    described I am certain that when I'm
  • 00:27:33
    writing emails sometimes I'm writing an
  • 00:27:35
    email to uh biological employee
  • 00:27:38
    sometimes I'm writing an email to an
  • 00:27:39
    artificial int employee I'm certain of
  • 00:27:42
    that I'm certain that that my email is
  • 00:27:44
    going to have a whole bunch of toos and
  • 00:27:46
    froms and and many of them are going to
  • 00:27:49
    be intermixed and none of them are going
  • 00:27:51
    to
  • 00:27:52
    know and they're all we're all going to
  • 00:27:54
    be exchanging information and however we
  • 00:27:57
    exchange information today
  • 00:27:58
    and so once you live in that future then
  • 00:28:00
    the question is how do you get there you
  • 00:28:01
    know do you like that future is that is
  • 00:28:03
    that going to be helpful to the industry
  • 00:28:04
    is that going to be helpful to the world
  • 00:28:06
    is that going to be helpful to the
  • 00:28:07
    society and and then you work your way
  • 00:28:09
    back and uh I find that always to be
  • 00:28:12
    more helpful and that
  • 00:28:14
    future usually doesn't include um that
  • 00:28:19
    person is doing it so let's take it from
  • 00:28:21
    them it usually starts with it hasn't
  • 00:28:24
    been done before and so I I always find
  • 00:28:27
    that that thought model model is is
  • 00:28:28
    helpful to me and and then and then
  • 00:28:31
    since it doesn't exist then it's very
  • 00:28:34
    likely that the ecosystem of Partners
  • 00:28:36
    don't exist then you have to go find
  • 00:28:39
    friends to help you do it you know and I
  • 00:28:41
    don't have to go do everything myself I
  • 00:28:43
    just want to make sure it's done and so
  • 00:28:45
    and and I I actually you know like to do
  • 00:28:48
    as little as possible
  • 00:28:50
    frankly and and so so when you then that
  • 00:28:53
    future needs friends and and of course
  • 00:28:55
    in many of the areas where we have
  • 00:28:57
    Enterprise AI industrial AI uh wwt is
  • 00:29:01
    just an incredible friend and and that's
  • 00:29:03
    why our partnership is so great you know
  • 00:29:05
    I would say just in the spirit of uh you
  • 00:29:08
    know this it's
  • 00:29:09
    a it's special to have Jensen here and I
  • 00:29:12
    don't want to to hog the the the kind of
  • 00:29:15
    the mic up here so maybe will it open up
  • 00:29:17
    to to some of the audience and I know
  • 00:29:19
    Jensen actually enjoys the free flowing
  • 00:29:24
    uh question so we've got a shy guy over
  • 00:29:26
    there Mr DeWalt first time with a mic in
  • 00:29:29
    his hand uh uh I have to I have to tell
  • 00:29:31
    you I'm much more comfortable with the
  • 00:29:32
    whole bunch of engineers in the room
  • 00:29:35
    Well he kind of covers both so all right
  • 00:29:38
    hey fir first of all I just say how
  • 00:29:40
    inspiring it is to see the two of you up
  • 00:29:43
    here what is it 30 plus years both of
  • 00:29:45
    you grinding away and building
  • 00:29:48
    incredible businesses so congratulations
  • 00:29:50
    it's a great moment um I don't know if
  • 00:29:53
    you know this Jensen but I've been in
  • 00:29:54
    cyber security for 25 years and uh
  • 00:29:57
    fighting a b bad guys for a lot of time
  • 00:29:59
    at McAfee Mandy and firey talk about
  • 00:30:03
    putting fingers in the Dyke right I mean
  • 00:30:05
    the bad guys have been winning a lot
  • 00:30:07
    until I've seen kind of through your
  • 00:30:09
    eyes to Nvidia and see the future of
  • 00:30:12
    what AI can really do it's pretty cool
  • 00:30:14
    to like start to see the power of
  • 00:30:17
    autonomy and what we can do to reverse
  • 00:30:20
    kind of what the offense can do versus
  • 00:30:22
    what the defense my question is what do
  • 00:30:24
    you see I mean how do we solve some of
  • 00:30:26
    these threats and risks that we're
  • 00:30:27
    seeing every day in the world of cyber
  • 00:30:30
    and uh what's invidious part in that so
  • 00:30:32
    yeah I appreciate the question first of
  • 00:30:33
    all um artificial intelligence is going
  • 00:30:35
    to change the the the profile of cyber
  • 00:30:38
    security completely and and the reason
  • 00:30:39
    for that is this uh as you know the the
  • 00:30:42
    issue with cyber security isn't well
  • 00:30:45
    there are a lot of issues but one of the
  • 00:30:46
    issues with cyber security is the being
  • 00:30:48
    overwhelmed by false positives if you're
  • 00:30:51
    if you're too stringent on your
  • 00:30:54
    detection policy you're just flooded
  • 00:30:57
    with
  • 00:30:58
    with detections that aren't really uh uh
  • 00:31:03
    cyber threats um and the only way to
  • 00:31:06
    solve that problem is with humans to go
  • 00:31:07
    reason about it to reason about every
  • 00:31:11
    single one of the detections and ask
  • 00:31:13
    yourself through other sources of
  • 00:31:14
    information whether this is actually a
  • 00:31:17
    threat to be concerned about or not well
  • 00:31:19
    guess what that process you can now
  • 00:31:23
    automate with AI and as a result we
  • 00:31:25
    could tighten up our cyber security
  • 00:31:28
    posture and not be overwhelmed by false
  • 00:31:31
    positives because we'll just have a
  • 00:31:32
    whole bunch of you know ai ai cyber
  • 00:31:35
    security agents go and reason about
  • 00:31:37
    every single one of those and just go
  • 00:31:39
    you know yeah not an issue uh the the
  • 00:31:42
    other thing that is really cool about
  • 00:31:43
    cyber security is that that the the
  • 00:31:46
    framework that the industry put together
  • 00:31:48
    for cyber security really likely is
  • 00:31:50
    going to be the framework we use for AI
  • 00:31:53
    security um as you know AI is not one
  • 00:31:56
    giant model AI is a system of models and
  • 00:31:59
    when we finally deploy AI we're going to
  • 00:32:01
    have for every AI that we deploy we're
  • 00:32:03
    going to have probably hundreds of AIS
  • 00:32:05
    watching over it no different than air
  • 00:32:08
    traffic control watches over the planes
  • 00:32:10
    no different than inside a plane there's
  • 00:32:12
    there's three autopilot systems with two
  • 00:32:16
    pilots all the planes in the air are
  • 00:32:19
    watching over each other Air Traffic
  • 00:32:21
    Control watching all of the planes not
  • 00:32:24
    to mention the layers and layers of
  • 00:32:25
    policies and regulations and
  • 00:32:27
    methodologies and the best practices so
  • 00:32:28
    on so forth isn't that right and that's
  • 00:32:30
    just for not well that not not just for
  • 00:32:33
    autopilot and that's just autopilot okay
  • 00:32:36
    now imagine what we're going to do when
  • 00:32:38
    we deploy autopilot of this type of this
  • 00:32:41
    type of agent or you know lawyers and
  • 00:32:44
    doctors and accountants and Mark we're
  • 00:32:46
    going to have all kinds of other agents
  • 00:32:49
    inside the company that watches over all
  • 00:32:51
    of the other agents and so that's going
  • 00:32:53
    to be one of the one of the agents
  • 00:32:55
    inside the company is sort of you know
  • 00:32:58
    uh employee com you know AI employee
  • 00:33:01
    compliance agents you know and so so I I
  • 00:33:04
    think the framework that has been
  • 00:33:06
    created for cyber security over the
  • 00:33:07
    years is really a good one uh notice in
  • 00:33:10
    cyber security whenever one company has
  • 00:33:12
    a threat is shared with all of the other
  • 00:33:15
    companies we're going to do the same
  • 00:33:16
    thing with AI and so so I think I think
  • 00:33:18
    a lot of the work that on the one hand
  • 00:33:21
    uh your industry is going to help the
  • 00:33:23
    creation of the AI industry on the one
  • 00:33:24
    hand the creation of AI Industries is
  • 00:33:26
    going to come back and help your
  • 00:33:27
    industry
  • 00:33:32
    it's kind of kind of interesting also I
  • 00:33:34
    would say that the that the topic you
  • 00:33:36
    brought up uh uh Dave actually sits on
  • 00:33:39
    one of the airlin boards so to use the
  • 00:33:42
    pilot and the planes as one as uh very
  • 00:33:44
    very near and dear to Dave h Hello
  • 00:33:46
    Jensen hello Jim um my name's Aiden I
  • 00:33:49
    have ai in my name which was handy for
  • 00:33:51
    today but um question I have is that's a
  • 00:33:55
    blessing or a burden a burden uh
  • 00:33:58
    because the don't the only intelligence
  • 00:33:59
    I have is artificial but the the
  • 00:34:01
    question I have Jensen is when you think
  • 00:34:03
    about the future of of sovereign Nations
  • 00:34:07
    and you think about strategy and how
  • 00:34:09
    that's going to change how countries are
  • 00:34:11
    LED and how countries interact with each
  • 00:34:13
    other and also access to to for for poor
  • 00:34:17
    people to be upgraded in a world of
  • 00:34:19
    abundance how do you see that playing
  • 00:34:20
    out um and what's a time frame you think
  • 00:34:24
    where that can start to really impact um
  • 00:34:26
    geopolitics okay yeah I appreciate the
  • 00:34:29
    question excellent U first of all we're
  • 00:34:30
    going to take the marginal cost of
  • 00:34:32
    intelligence down to approximately
  • 00:34:34
    zero even a poor person can afford it
  • 00:34:37
    there you go that's the first
  • 00:34:38
    observation and and um remember remember
  • 00:34:42
    there was a time when when um well still
  • 00:34:46
    it still is true uh access to to clean
  • 00:34:49
    water is is is uh a great challenge for
  • 00:34:53
    many many countries and many people a
  • 00:34:55
    bottled water really re evolutionize the
  • 00:34:58
    access for for clean water uh you know
  • 00:35:01
    artificial intelligence is going to
  • 00:35:03
    modernize if you will democratize the
  • 00:35:05
    access of of intelligence uh it could be
  • 00:35:08
    uh Radiologists in a in a small village
  • 00:35:12
    tier four tier 7 Village in Africa or
  • 00:35:17
    India or you know you you you pick your
  • 00:35:19
    favorite place and so so I think I think
  • 00:35:21
    that working backwards I think that's
  • 00:35:23
    one of the great opportunities uh with
  • 00:35:25
    respect to Sovereign AI the observation
  • 00:35:27
    is this your your country's
  • 00:35:31
    data belongs to its
  • 00:35:35
    people sovereignty is no longer just the
  • 00:35:38
    land and the air above it sovereignty is
  • 00:35:41
    also your people your language your
  • 00:35:43
    culture your history that's all part of
  • 00:35:46
    your sovereignty and it's now encoded
  • 00:35:48
    digitally into ones and zeros but nobody
  • 00:35:51
    ever knew what to do with it until now
  • 00:35:54
    so now you could turn that Sovereign
  • 00:35:55
    data which belongs to you into your
  • 00:35:58
    Sovereign Ai and every country no
  • 00:36:01
    country wants to say hey take all the
  • 00:36:04
    take all the data that belongs to us and
  • 00:36:06
    import back to us an AI everybody wants
  • 00:36:08
    to process the data themselves refine it
  • 00:36:12
    into Ai and so I think this is going to
  • 00:36:14
    be a a new form of national
  • 00:36:17
    infrastructure which is you know we
  • 00:36:19
    build this thing called a this this AI
  • 00:36:22
    supercomputer and and we call it AI
  • 00:36:24
    infrastructure Computing infrastructure
  • 00:36:26
    it's going to be like energy it's going
  • 00:36:28
    to be like Communications it'll be like
  • 00:36:31
    you know part of the national
  • 00:36:32
    infrastructure uh in countries and we're
  • 00:36:35
    seeing this Awakening to Sovereign AI
  • 00:36:37
    all over the world uh I was just in
  • 00:36:39
    Denmark I was in Sweden uh from here I'm
  • 00:36:43
    going to go I'm going to go to
  • 00:36:45
    um it's surprising but Japan Indonesia
  • 00:36:48
    and Thailand uh they're all going to
  • 00:36:51
    build their own Sovereign AIS and um I
  • 00:36:55
    that that's likely what's going to
  • 00:36:56
    happen Jensen thank thanks for being
  • 00:36:59
    here uh just a question for you about
  • 00:37:01
    the future Workforce uh what advice
  • 00:37:03
    would you give to High School junior
  • 00:37:06
    seniors as they're trying to create
  • 00:37:07
    their
  • 00:37:08
    path uh F first of all I would I would
  • 00:37:11
    encourage them to do well in school in
  • 00:37:13
    all of the areas that that that school
  • 00:37:16
    is going to everything that we learned
  • 00:37:18
    is still
  • 00:37:19
    good it's all good and and and it's it's
  • 00:37:23
    not so much that everything we learned
  • 00:37:25
    was actually correct as you know
  • 00:37:28
    our history books have been uh the
  • 00:37:30
    reason why they give you so many re
  • 00:37:32
    addition you know you got to ask
  • 00:37:35
    yourself you get a tech history textbook
  • 00:37:38
    as addition
  • 00:37:39
    27 you know I get what happened to the
  • 00:37:42
    last
  • 00:37:44
    26 and and and who did it contaminate
  • 00:37:48
    and so so the fact of the matter is um
  • 00:37:51
    none none of that is is a problem just
  • 00:37:54
    it's about the learning process but the
  • 00:37:56
    one thing that I will say is that
  • 00:37:58
    everybody should get an AI tutor I have
  • 00:38:02
    three I use chat GPT I use perplexity
  • 00:38:05
    and I use Gemini and I use all three of
  • 00:38:08
    them uh on a regular basis I I use them
  • 00:38:10
    flying down here and so if I have
  • 00:38:12
    questions if I have a thought in my head
  • 00:38:14
    uh if I'm if I'm trying to explore some
  • 00:38:16
    new new area and trying to learn
  • 00:38:18
    something new the first thing I do is I
  • 00:38:20
    go to one of those AIS and I ask it some
  • 00:38:23
    a series of questions about the topic
  • 00:38:25
    I'm about to learn and and I I'll I'll
  • 00:38:28
    I'll start by saying you know Hey so uh
  • 00:38:30
    explain digital biology and computer AED
  • 00:38:33
    drug Discovery to me uh in uh as a fifth
  • 00:38:37
    grader as a starting point and then I
  • 00:38:41
    then I'll go okay well explain to me as
  • 00:38:42
    a college student and then and then
  • 00:38:44
    after that I've learned it a few times
  • 00:38:47
    then I can ask then then the next thing
  • 00:38:49
    I ask is what question should I be
  • 00:38:51
    asking um uh give me a give me a
  • 00:38:54
    framework for how I should be learning
  • 00:38:56
    about this and and so I'm asking a whole
  • 00:38:58
    bunch of questions about how to learn
  • 00:39:00
    and then and then beyond that I I can
  • 00:39:01
    learn it and it it remembers where I
  • 00:39:04
    left off left off yesterday and and so
  • 00:39:07
    so I think every every every student
  • 00:39:10
    should get a get a tutor and that tutor
  • 00:39:14
    AI tutor will teach them how to prompt
  • 00:39:17
    how to engage AIS how to how to ask an
  • 00:39:20
    AI to give to give you the information
  • 00:39:22
    you need and and so on so forth uh I I
  • 00:39:26
    think that this this J journey of
  • 00:39:28
    getting tutors into every student this
  • 00:39:31
    could very well be the most
  • 00:39:32
    revolutionary thing that that came out
  • 00:39:34
    of AI okay but everything that we're
  • 00:39:36
    learning Jon you know I I would look at
  • 00:39:38
    it i' even look at where we are today
  • 00:39:40
    and I'd be curious your thoughts on this
  • 00:39:43
    and my my advice
  • 00:39:45
    that even employees students kids uh I I
  • 00:39:50
    think a lot of success is going to be
  • 00:39:53
    around individuals that are
  • 00:39:55
    curious uh that that are very thoughtful
  • 00:39:59
    and know how to ask good questions
  • 00:40:01
    inquisitive questions because you think
  • 00:40:04
    about how even today and it'll continue
  • 00:40:06
    to evolve using AI models you know
  • 00:40:09
    perplexity open AI uh it's about
  • 00:40:13
    learning with that and being inquisit
  • 00:40:15
    enough you know and having that desire
  • 00:40:17
    to continuous learning and continuous
  • 00:40:19
    asking and probing uh those are
  • 00:40:22
    individuals I think within our company I
  • 00:40:25
    want them to be pushing the models and
  • 00:40:28
    have a curious mind if you don't have a
  • 00:40:30
    curious mind you're not going to learn
  • 00:40:32
    and I think that's one thing for for
  • 00:40:34
    students and for employees is that we're
  • 00:40:37
    in a world that if you want to be static
  • 00:40:41
    you're going to be in trouble if if
  • 00:40:43
    you're going to continue if you have a
  • 00:40:44
    desire to to learn and push and and
  • 00:40:48
    innovate and challenge yourself and be
  • 00:40:50
    curious I think you're going to succeed
  • 00:40:52
    so I yeah I completely agree I
  • 00:40:55
    completely agree hi Jensen uh scart with
  • 00:40:58
    gxo Logistics um we've been talking a
  • 00:41:00
    lot about a lot of the great things AI
  • 00:41:02
    will bring to the Future to us the other
  • 00:41:04
    things that I worry about is on the flip
  • 00:41:06
    side of that where you start to think
  • 00:41:07
    about the Bad actors and we think even
  • 00:41:09
    about cyber security well the Bad actors
  • 00:41:11
    will they not be also using AI
  • 00:41:12
    technology to be those attack weapons
  • 00:41:14
    and be even smarter than they are today
  • 00:41:16
    number one and then number two what
  • 00:41:18
    about the energy uh aspect of this too
  • 00:41:19
    when I try to go into certain data
  • 00:41:21
    centers today even light up new servers
  • 00:41:23
    and things like that sometimes I have
  • 00:41:24
    problems getting to places that have
  • 00:41:25
    energy so all of those two elements I'd
  • 00:41:28
    love to hear what your thoughts are uh
  • 00:41:31
    there there's only one solution to the
  • 00:41:32
    bad actor problem uh and and that is
  • 00:41:36
    that is we have to go way
  • 00:41:39
    faster that there's you know uh for for
  • 00:41:43
    Dave in the cyber security industry uh
  • 00:41:47
    worrying about it is the wrong answer
  • 00:41:48
    you just got to make sure that you're
  • 00:41:49
    ahead on Cyber secur Technology and and
  • 00:41:52
    it's it's not just as just as it is it
  • 00:41:56
    is not like likely we lose we lose our
  • 00:41:59
    job to AI it's very likely we'll lose
  • 00:42:01
    our jobs to somebody who uses AI it's
  • 00:42:04
    not likely that AI does something to us
  • 00:42:07
    it's likely that somebody uses AI to do
  • 00:42:09
    something to us and so I think your
  • 00:42:11
    concern is is is um uh well placed and
  • 00:42:14
    the answer is you got to go fast make
  • 00:42:16
    sure our AI technology is way better
  • 00:42:17
    than theirs and we democratize it
  • 00:42:19
    meaning it's available to
  • 00:42:21
    everybody okay and so you could you
  • 00:42:24
    could pick on one you can pick on
  • 00:42:25
    somebody but you can't pick on everybody
  • 00:42:27
    and so so I think the the um uh uh your
  • 00:42:32
    second question was power energy yeah
  • 00:42:35
    yeah uh uh two two answers for that
  • 00:42:39
    first of all you want to think about AI
  • 00:42:42
    from a longitudinal perspective the goal
  • 00:42:44
    is not to train the AI the goal isn't to
  • 00:42:46
    go to school the goal is to apply what
  • 00:42:49
    you learned now um you could teach an
  • 00:42:53
    AI uh how to predict physics like for
  • 00:42:56
    example weather prediction we've taught
  • 00:42:58
    in AI how to predict weather almost
  • 00:43:01
    10,000 times more energy efficiently
  • 00:43:05
    than using a supercomputer to do it
  • 00:43:06
    every single country every single region
  • 00:43:08
    around the world is running
  • 00:43:09
    supercomputers right now as we speak to
  • 00:43:12
    try to figure out what tomorrow's
  • 00:43:14
    weather is and what next week's weather
  • 00:43:16
    is because there might be a storm and
  • 00:43:18
    somebody just wants to know whether to
  • 00:43:19
    wear sweater or not okay and so that
  • 00:43:22
    supercomputer is running 24/7 all the
  • 00:43:24
    time now we could replace that by
  • 00:43:26
    teaching it in AI the laws of physics
  • 00:43:29
    well not the laws of physics you teach
  • 00:43:31
    an AI the patterns of physics and then
  • 00:43:33
    now it could predict it and and people
  • 00:43:35
    ask me how could you teach an AI physics
  • 00:43:37
    and the answer is actually s
  • 00:43:39
    surprisingly simple you know I
  • 00:43:42
    I my dogs were just talking about my
  • 00:43:45
    dogs my dogs don't understand the laws
  • 00:43:48
    of physics they don't understand
  • 00:43:49
    Newtonian physics they don't understand
  • 00:43:51
    spring Theory but boy they could catch
  • 00:43:53
    balls like you can't believe and and the
  • 00:43:55
    reason for that is because they learn
  • 00:43:57
    they learn physics by observing it and
  • 00:44:01
    uh uh we do we we're basically doing the
  • 00:44:03
    same thing by giving it so much
  • 00:44:05
    information that it observed the
  • 00:44:08
    patterns of physics and multiphysics and
  • 00:44:10
    it does it even better than principled
  • 00:44:12
    solvers okay so so you have to think
  • 00:44:15
    about the longitudinal the goal the goal
  • 00:44:17
    is not to train the model the goal is to
  • 00:44:19
    go discover a new material that could be
  • 00:44:21
    better for uh energy capture uh better
  • 00:44:24
    for carbon capture and we we train a
  • 00:44:26
    model that predicts Wells that are
  • 00:44:28
    better at Carbon capture we want to go
  • 00:44:30
    create a model that that um does a
  • 00:44:32
    better job uh um designing a new
  • 00:44:35
    material for
  • 00:44:36
    car okay you know make cars more
  • 00:44:38
    aerodynamic or make wind farms more uh
  • 00:44:41
    more productive make uh Electro voltaic
  • 00:44:45
    cells more productive for for example
  • 00:44:47
    that's the goal does that make sense and
  • 00:44:49
    so you have to you have to put the AI to
  • 00:44:51
    school in order to get the benefits out
  • 00:44:53
    of the AI and when we deliver the
  • 00:44:55
    benefits out of the AI it takes it out
  • 00:44:58
    of my goodness right if data centers
  • 00:45:02
    consume call it 3% or 4% of the world's
  • 00:45:06
    energy today you got 96% to go solve for
  • 00:45:10
    that's the benefit of AI and then the
  • 00:45:11
    last thing that I'll say is
  • 00:45:13
    this I hope that the amount of energy
  • 00:45:17
    that is used for artificial
  • 00:45:19
    intelligence goes up as a percentage of
  • 00:45:23
    human consumption over time and the
  • 00:45:25
    reason for that it's currently 4% out of
  • 00:45:28
    the world the world's total use I'm
  • 00:45:32
    hoping that artificial intelligence is
  • 00:45:35
    you know solid 10
  • 00:45:36
    15% and the reason for that is remember
  • 00:45:40
    what that 96% is doing the 96% of the
  • 00:45:43
    world's power is our
  • 00:45:46
    Industries it's making things it's
  • 00:45:48
    moving things around it's all the things
  • 00:45:51
    that we need to to live well what we
  • 00:45:54
    would like to do is instead of producing
  • 00:45:56
    all of the those other things we like to
  • 00:45:58
    produce intelligence and the
  • 00:46:00
    manufacturing of intelligence we hope
  • 00:46:03
    give you the examples that I was
  • 00:46:05
    mentioning earlier the positive benefits
  • 00:46:07
    everywhere else and so my my prediction
  • 00:46:11
    is that we have invented a new
  • 00:46:14
    industry I mentioned
  • 00:46:16
    earlier that we now produce this thing
  • 00:46:19
    called intelligence the idea that we're
  • 00:46:21
    manufacturing intelligence is a concept
  • 00:46:23
    that's insanely hard to understand and
  • 00:46:26
    so let me give you an analogy so 300
  • 00:46:28
    years ago there was a machine that was
  • 00:46:30
    created today that machine is called
  • 00:46:31
    Nvidia but 300 300 years ago the machine
  • 00:46:34
    was created was called a
  • 00:46:35
    Dynamo and people would pour water into
  • 00:46:38
    this thing and you light it on fire and
  • 00:46:41
    what comes out of it is
  • 00:46:44
    electricity and so you apply energy to
  • 00:46:47
    this thing to this machine and what
  • 00:46:49
    comes out of it is is an invisible thing
  • 00:46:51
    and the way you pay for the electricity
  • 00:46:53
    is dollars per kilowatt hours
  • 00:46:58
    okay now we have this new machine this
  • 00:47:02
    Nvidia machine that that we build you
  • 00:47:05
    apply energy to it and what comes out of
  • 00:47:07
    it is tokens that depending on how you
  • 00:47:10
    reconstitute these tokens turns into
  • 00:47:13
    either words or proteins or videos or
  • 00:47:17
    you know so on so forth and you know or
  • 00:47:20
    articulation for uh driving driverless
  • 00:47:23
    cars or you know whatever it is okay and
  • 00:47:26
    so so so these tokens that come out as
  • 00:47:28
    intelligence are monetized dollars per
  • 00:47:32
    token dollars per million tokens dollars
  • 00:47:35
    per kilowatt hour can you see that and
  • 00:47:38
    so this energy creation industry is the
  • 00:47:42
    beginning of an industrial revolution we
  • 00:47:45
    have now we have we're at the beginning
  • 00:47:47
    the reason why Jim and are so excited
  • 00:47:49
    about this is we're literally at the
  • 00:47:51
    beginning of a new Industrial Revolution
  • 00:47:54
    and this new industrial revolution has a
  • 00:47:56
    new new machinery and this new Machinery
  • 00:47:59
    produces something that's never been
  • 00:48:00
    produced before just like 300 years
  • 00:48:04
    ago and it's forming these incredible
  • 00:48:07
    companies and it's forming all these
  • 00:48:09
    Incredible use cases just like 300 years
  • 00:48:12
    ago when the electricity was discovered
  • 00:48:15
    all kinds of new things came out for
  • 00:48:17
    example consumer electronics appliances
  • 00:48:20
    Appliance the appliance industry didn't
  • 00:48:22
    exist that's the reason why GE was
  • 00:48:24
    making electricity on the one hand app
  • 00:48:26
    Ian is on the other hand Appliance was
  • 00:48:29
    the consumption of the energy they were
  • 00:48:32
    creating and so in a lot of ways to
  • 00:48:35
    together between us and
  • 00:48:40
    wwt did you guys hear
  • 00:48:44
    that this this AI is easier to train and
  • 00:48:47
    so so between us and wwt we have the
  • 00:48:51
    agents and so on so forth which are
  • 00:48:53
    essentially if you will the consumer the
  • 00:48:56
    appliances that consumes the tokens that
  • 00:49:00
    are being produced by this Factory and
  • 00:49:02
    so you know the one the one I would add
  • 00:49:04
    Jensen is is fascinating about that
  • 00:49:06
    while I'm listening to you and I'm
  • 00:49:07
    thinking I'm like you know sounds like
  • 00:49:09
    nonsense right now but in 300 years I'm
  • 00:49:12
    just no well that's exact honestly
  • 00:49:15
    that's exactly where I was going with
  • 00:49:16
    this if you think about how long it took
  • 00:49:18
    for the energy to to to literally take
  • 00:49:21
    hold and scale and you know it it was in
  • 00:49:24
    one little pocket of the world and
  • 00:49:26
    literally took forever for that energy
  • 00:49:29
    to be prevalent ubiquitous around the
  • 00:49:31
    world so if you think about where AI is
  • 00:49:34
    literally almost overnight you've
  • 00:49:36
    created this this intelligence that was
  • 00:49:40
    opened up to the world uh overnight so
  • 00:49:43
    you think how how long some of these
  • 00:49:45
    Transformations and these these
  • 00:49:47
    Industrial Revolution and now the AI
  • 00:49:50
    Revolution it's happening the the
  • 00:49:52
    difference is you know it was a speed is
  • 00:49:54
    incredible yeah it's it's it's it's
  • 00:49:57
    almost Unthinkable to compare and
  • 00:50:00
    contrast the two and the magnitude of
  • 00:50:03
    change it's going to create in such a
  • 00:50:05
    per I mean I I just think that just
  • 00:50:07
    listening to what you were walking
  • 00:50:09
    through and I think about how fast the
  • 00:50:11
    speed of Change is Going to Be
  • 00:50:13
    unimaginable so the only advice is
  • 00:50:15
    engage it as soon as you can if
  • 00:50:16
    something is moving super fast you know
  • 00:50:19
    if you if you have a train that's moving
  • 00:50:20
    super fast don't watch it get on it you
  • 00:50:22
    know and so here's yeah here's a ET ship
  • 00:50:27
    and it's going super fast get on it go
  • 00:50:29
    ahead go ahead yeah yeah yeah sorry here
  • 00:50:33
    back here sorry um first of all amazing
  • 00:50:36
    thank you so much and I think on behalf
  • 00:50:38
    this entire audience and our 40 401ks
  • 00:50:40
    thank you a lot as well too give me your
  • 00:50:42
    stock price but in all seriousness
  • 00:50:44
    you've been talking about the
  • 00:50:45
    supercomputer you're talking about how
  • 00:50:47
    things move so fast and like today is
  • 00:50:49
    arguably the slowest day we'll ever see
  • 00:50:51
    in our lifetime given how fast things
  • 00:50:52
    are moving but when you think about the
  • 00:50:55
    the next generation of the supercomputer
  • 00:50:56
    computer if and how do you think about
  • 00:50:58
    Quantum Computing as we start to use
  • 00:51:01
    that and whether or not that's going to
  • 00:51:02
    replace GPU compute or complemented or
  • 00:51:05
    assisted yeah Quantum Computing is going
  • 00:51:07
    to be really good at something so for
  • 00:51:09
    example let me give you an example of a
  • 00:51:11
    is everybody everybody you guys know
  • 00:51:13
    what a Quantum so for example let me
  • 00:51:15
    give you an example of what quantum
  • 00:51:16
    computers really good at so suppose we
  • 00:51:19
    suppose we have
  • 00:51:20
    a like like a wedding party like like
  • 00:51:22
    today like tonight well I'm not sure how
  • 00:51:24
    you did it but suppose we had a wedding
  • 00:51:26
    party
  • 00:51:27
    and and the wedding party had you know
  • 00:51:29
    let's say 300 people and you have to
  • 00:51:32
    figure out uh what are the best ways to
  • 00:51:34
    seat 300
  • 00:51:36
    people and there are a lot of
  • 00:51:38
    complexities as you could imagine some
  • 00:51:40
    some of the some of the some of the some
  • 00:51:42
    of the people are more important than
  • 00:51:44
    the others uh and like for example uh
  • 00:51:48
    you know my college friends like they're
  • 00:51:50
    in the
  • 00:51:51
    back isn't that right and and then and
  • 00:51:54
    then you're you're uh your your your
  • 00:51:57
    wife's uh you know friends they're in
  • 00:51:59
    the front you know and then for for
  • 00:52:02
    example and so you got to go through
  • 00:52:04
    this combination so so turns out 300
  • 00:52:06
    people the number he is he is human and
  • 00:52:09
    he is very know very smart but uh he has
  • 00:52:11
    to deal with the well I'm trying to
  • 00:52:13
    explain a complicated complicated idea
  • 00:52:15
    very simply and so you have these 300
  • 00:52:17
    people the number of combinations is
  • 00:52:19
    more than the number of atoms in the
  • 00:52:21
    universe okay and so so you're a
  • 00:52:24
    computer trying to figure out all all
  • 00:52:26
    the different permutations and all the
  • 00:52:28
    different combinations trying to figure
  • 00:52:29
    out which one is the most optimal as you
  • 00:52:32
    know that comp that Computing problem it
  • 00:52:34
    will run
  • 00:52:36
    forever or you could ask your
  • 00:52:40
    mother-in-law and
  • 00:52:45
    and and the reason for that is very
  • 00:52:47
    simple because she's going to use
  • 00:52:48
    artificial intelligence to to to you
  • 00:52:51
    know these people absolutely must sit
  • 00:52:53
    together these people absolutely must
  • 00:52:54
    sit in the in the front uh you know the
  • 00:52:57
    they brought a really expensive gift so
  • 00:53:00
    on so forth and then and then you know
  • 00:53:02
    your college friends in the back you
  • 00:53:04
    know what I'm saying and so so you got
  • 00:53:06
    to sort through all that I just gave you
  • 00:53:07
    an example of where artificial
  • 00:53:09
    intelligence can find a perfectly good
  • 00:53:12
    answer that we thought quantum computers
  • 00:53:15
    were necessary in the
  • 00:53:17
    past and and the the the simple idea is
  • 00:53:21
    that quantum computer is really good at
  • 00:53:23
    small data large combination
  • 00:53:26
    problems Ai and classical Computing
  • 00:53:30
    doesn't really care and then number one
  • 00:53:32
    number two the two of them will have to
  • 00:53:34
    live together someday because quantum
  • 00:53:36
    computers are good at certain things and
  • 00:53:39
    quite poor at many other things and the
  • 00:53:41
    big idea is that AI has kind of punted
  • 00:53:44
    quantum computer down the road by about
  • 00:53:46
    20 30 years okay but we're we work on
  • 00:53:49
    quantum computers uh we work with just
  • 00:53:51
    about every quantum computer company in
  • 00:53:53
    the world uh because we want it to
  • 00:53:54
    happen but it's still going to be a
  • 00:53:56
    decade or plus you know far away from me
  • 00:53:59
    anyways that's the idea okay one last
  • 00:54:01
    question as a 30-year veteran in this IT
  • 00:54:04
    industry I absolutely love technology
  • 00:54:06
    and really enjoy watching it how makes
  • 00:54:08
    the world better but also as a father of
  • 00:54:11
    two um I've also watched in a lot of
  • 00:54:13
    ways how Technologies work to you know
  • 00:54:15
    degrade interpersonal skills Humanity
  • 00:54:18
    time with people and you said something
  • 00:54:20
    interesting earlier tonight when you
  • 00:54:21
    said um you love how AI is given you
  • 00:54:24
    time back to go do the things you want
  • 00:54:26
    to do do I just thought maybe you could
  • 00:54:27
    share a little light into what your
  • 00:54:29
    vision is of how AI might be able to
  • 00:54:30
    help our children in the future in terms
  • 00:54:32
    of the humanity aspect the inter
  • 00:54:34
    personal skills and and a more fun way
  • 00:54:36
    of life well I I think that that um I
  • 00:54:39
    appreciate the question I think I think
  • 00:54:40
    Jim and I kind of approached it uh
  • 00:54:44
    approached that that topic in in a
  • 00:54:46
    slightly different way but we both
  • 00:54:47
    approached it uh which is which is um
  • 00:54:51
    the the AI is an is an instrument of
  • 00:54:54
    knowledge you know whereas your car is
  • 00:54:56
    an instrument of moving atoms from point
  • 00:54:58
    A to point B the AI is an instrument
  • 00:55:01
    it's also an instrument it's it's a
  • 00:55:03
    toaster it's just happens to be a a
  • 00:55:05
    super smart
  • 00:55:06
    toaster and you could talk to the
  • 00:55:08
    toaster about anything you want and ask
  • 00:55:10
    it to help you do things and today of
  • 00:55:13
    course the the toaster the AI that you
  • 00:55:15
    know is it's information based but that
  • 00:55:18
    same AI is about to become embodied as
  • 00:55:21
    well you know your ex your version of
  • 00:55:24
    R2-D2 uh and C3 CPO is right around the
  • 00:55:27
    corner and and I can't I can't wait I
  • 00:55:31
    can't wait um and so I know I know that
  • 00:55:33
    my AI my AIS are are um having a
  • 00:55:37
    dialogue with me on a regular basis and
  • 00:55:40
    helping me learn and helping me do tasks
  • 00:55:43
    that that in a lot of ways um you know I
  • 00:55:46
    find quite arduous like for example I
  • 00:55:49
    have to write a lot of
  • 00:55:50
    speeches and and um the context of the
  • 00:55:53
    speeches are all always different um uh
  • 00:55:56
    but the core information and my tone is
  • 00:56:00
    often very is is the same and so you
  • 00:56:04
    know I I didn't do this today everything
  • 00:56:07
    I just want to let you know everything
  • 00:56:08
    here I didn't prepare for but but but in
  • 00:56:12
    in many prepared speeches I'll give it
  • 00:56:15
    the context I'll give it the context um
  • 00:56:19
    and and I'll I'll tell it these are my
  • 00:56:21
    themes and then I'll I'll tell my AI to
  • 00:56:24
    refer to all of my other writings and
  • 00:56:27
    all of my other speeches and I say write
  • 00:56:30
    me a six-minute speeech that addresses
  • 00:56:34
    these points but highlights the themes
  • 00:56:37
    behind the things I talk about and it
  • 00:56:40
    comes back within one second literally
  • 00:56:44
    one second later I have a six minute
  • 00:56:46
    speech and then from that I refine it
  • 00:56:49
    into something that well that that is
  • 00:56:52
    you know hopefully better than what it
  • 00:56:55
    provided and often times it generates
  • 00:56:58
    one and I'll say is that the best you
  • 00:57:00
    can
  • 00:57:01
    do literally I just go Chad gbt is that
  • 00:57:04
    the best you can do and it goes I can do
  • 00:57:07
    better and I love that I love that and
  • 00:57:11
    and and then it gives me another one and
  • 00:57:13
    just to just to mess with it you know
  • 00:57:16
    since it's the same price anyways I I I
  • 00:57:19
    don't think so I think you could do even
  • 00:57:21
    better than that and comes back with yet
  • 00:57:22
    another one and each one of them
  • 00:57:24
    actually improves on the last one and
  • 00:57:26
    then I take that one and I modified it
  • 00:57:28
    saved me a ton of time writing speeches
  • 00:57:30
    is is painful and it's arduous and um
  • 00:57:33
    and but it's important and so you know
  • 00:57:36
    those are kind of examples of how I use
  • 00:57:37
    it um but but I think the most important
  • 00:57:40
    thing is I have a Learning Partner um I
  • 00:57:43
    have an age I have a little AI That's My
  • 00:57:45
    R2-D2 that's remembering the things that
  • 00:57:48
    I'm learning it's teaching me things
  • 00:57:49
    bring information to me uh enriching me
  • 00:57:52
    and making me a a smarter CEO so so I
  • 00:57:55
    think for your kids I would highly
  • 00:57:57
    recommend that they do that and and um I
  • 00:58:00
    would pay for the professional one it's
  • 00:58:02
    only $20 a month and and um it's a tutor
  • 00:58:06
    literally a tutor for you know a
  • 00:58:09
    Christmas present um I would right for
  • 00:58:12
    every one of them just give him a
  • 00:58:13
    Christmas present here's your Christmas
  • 00:58:15
    present a tutor they go
  • 00:58:17
    [Applause]
  • 00:58:20
    oh I love you Dad you're the best dad in
  • 00:58:24
    the world I will say with getting off I
  • 00:58:26
    was talking to a CTO of a large Fortune
  • 00:58:30
    Fortune 50 bank and the CTO was talking
  • 00:58:33
    about his son 5-year-old son that was
  • 00:58:36
    literally voice
  • 00:58:38
    interacting with a you know large
  • 00:58:41
    language model and and going back and
  • 00:58:44
    forth about dinosaurs so he was
  • 00:58:47
    literally listening or learning about
  • 00:58:49
    the history of dinosaurs but then you
  • 00:58:51
    know you get the creativity of a kid
  • 00:58:53
    that is unlimited was talking about well
  • 00:58:55
    what about the dinosaur with I want with
  • 00:58:57
    pink hair and they would literally you
  • 00:59:00
    know all of a sudden it would kick
  • 00:59:02
    something back around so if you think
  • 00:59:04
    about in ways that you know kids have
  • 00:59:08
    you know there is no guardrail around
  • 00:59:10
    their creativity and how they think but
  • 00:59:12
    it was fascinating to me that the
  • 00:59:14
    individual saying I was blown away by
  • 00:59:17
    the interaction that my 5-year-old had
  • 00:59:19
    with the large language mod how much he
  • 00:59:21
    was learning around the history of
  • 00:59:24
    dinosaurs but also he had no limitations
  • 00:59:27
    about what he was asking and it was
  • 00:59:30
    playing back with him so I I think you
  • 00:59:33
    know from a creativity standpoint and
  • 00:59:35
    Innovation uh it it really is unlimited
  • 00:59:38
    so maybe just in closing out thank you
  • 00:59:40
    that was an excellent last question yeah
  • 00:59:43
    well I just want to want I want to say
  • 00:59:45
    that that uh it's it's a great pleasure
  • 00:59:47
    working with you Jim and and your team
  • 00:59:49
    and wwt is a fantastic organization uh
  • 00:59:52
    you could tell that wwt was built by
  • 00:59:54
    hand from the ground up
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