The Bottom Line:
- The new industrial revolution harnesses the power of internet, mobile, and AI technologies, according to Nvidia’s Jensen Wong.
- This transformation involves two phases: building infrastructure (Phase 1) and leveraging big data through AI and ontology (Phase 2).
- Phase 2 focuses on extracting and utilizing quality data, benefiting companies like Palantir, MongoDB, Snowflake, and Elastic.
- Effective use of large language models is crucial, requiring precision and appropriate application within enterprises to extract value.
- Businesses can leverage AI to improve efficiency, reduce costs, and transform operational practices, as demonstrated by various industries at recent conferences.
The Role of the New Industrial Revolution in AI Advancements
The Impact of AI Advancements in Business Transformation
The new Industrial Revolution marked by advancements in artificial intelligence (AI) has brought about significant changes in the business landscape. Companies are now adapting to the evolving technological landscape to stay competitive and relevant in the market.
Utilizing AI in Enterprise Operations
Enterprises are leveraging AI technologies such as generative AI to streamline operations, increase efficiency, and drive innovation. By harnessing quality data and implementing advanced AI solutions, businesses can make informed decisions and optimize their processes for better outcomes.
Realizing Value through AI Implementation
The successful implementation of AI solutions, particularly in sectors like construction, healthcare, and other industries, is resulting in tangible benefits for businesses. Through effective utilization of AI tools and strategies, companies are witnessing improved performance, cost savings, and enhanced capabilities across various functions.
Understanding Phase 1: Building the Infrastructure
Exploring Phase 1: Establishing the Foundation for Progress
The initial phase of the new Industrial Revolution focuses on building the necessary infrastructure to support technological advancements like AI. Companies such as Nvidia and others are investing in hardware and data centers to lay the groundwork for future growth and innovation.
Unveiling Phase 2: The Evolution into AI and Data Utilization
Phase two of the revolution delves deeper into the realms of AI, big data, and ontology. This phase involves harnessing the power of quality data to drive generative AI, emphasizing the importance of refining data for meaningful impact in businesses.
Embracing the Potential of AI Implementation
Businesses that leverage AI tools, such as large language models and ontology, stand to benefit significantly in terms of operational efficiency, cost-effectiveness, and strategic decision-making. By understanding the value AI brings, companies can transform their operations and unlock new opportunities for growth and success.
Phase 2: Leveraging Big Data with AI and Ontology
Understanding the Intersection of AI, Big Data, and Ontology
In this phase, the focus shifts towards leveraging big data with AI and ontology. This involves refining data for meaningful impact in businesses by utilizing generative AI technologies that require quality data inputs for optimal outcomes.
Realizing Business Transformation through AI Implementation
By embracing AI tools such as large language models and ontology, businesses can enhance operational efficiency, make strategic decisions, and achieve cost-effectiveness. This transformation allows companies to unlock new opportunities for growth and success by harnessing the value that AI brings to their operations.
Unlocking the Power of AI Tools for Business Growth
Utilizing quality data and advanced AI solutions enables companies to streamline processes, boost innovation, and drive efficiency. Through effective implementation and understanding the potential of AI in various sectors, businesses can witness tangible benefits and improved performance across different functions.
Palantir’s Strategic Importance in the AI Ecosystem
Palantir’s Impact on the AI Landscape
Palantir CEO, Alex Karp, emphasizes the significance of individual investors in recognizing the value generated by companies like Palantir within the AI ecosystem. By focusing on two key components – chips and ontology – businesses can effectively harness data to drive meaningful outcomes in the rapidly evolving AI environment.
The Evolution of AI Infrastructure Building
In the realm of AI, the initial phase involves establishing robust hardware and data center infrastructure, akin to the efforts made by companies like Nvidia. This phase lays the foundation for future advancements in AI utilization and optimization across diverse sectors.
Utilizing Data for AI Transformation
As companies transition into the second phase of the AI revolution, the focus shifts towards leveraging big data and ontology to power generative AI solutions. Entities like Palantir, alongside MongoDB, Snowflake, and Elastic, are poised to benefit from the strategic implementation of AI tools for enhanced business operations and decision-making.
Maximizing Business Efficiency Through Large Language Models
it’s a new Industrial Revolution this is historic moment internet mobile and now ai nvidia’s Jensen Wong said the next industrial revolution has begun who will be the winners well come talk to our 70 customers they think they’re [Music] winning we’re in a new Industrial Revolution and by now you understand phase one the infrastructure phase like Nvidia and next is phase two of AI big data and ontology but you want to know what does that mean exactly by the end of this video you’ll know exactly what I mean listen to paler CEO Alex karp the way I would explain it to my most important investors which are individual investors notice how he said individual investors he’s talking about you and me retail investors paler understands and appreciates the importance of us individual investors two parts of the market that are creating value people will pay for chips and ontology and you understand the chip side now and now you’re going to understand ontology and what does that mean for your business it means you can actually use this raw resource and process it into something that actually works so he’s talking about taking a company’s data and making use of that data in Phase One of AI it’s about the infrastructure building out the hardware and the data center so stocks like Nvidia smci broadcom public cloud services providers like Microsoft Amazon Google gole you also have others like AMD and More in phase two we’re focused on Big Data taking that data and making use of the data so generative AI is going to need quality data because garbage in garbage out so companies like paler mongod DB Snowflake and elastic will benefit in the next phase now this phase is going to take time because for example AIP is only one year old it’s a brand new product what is AIP how does it work and how are businesses lever right now I I think what everybody watching this is familiar with is you have a massive he cycle around large language models and then when you try to use them in your Enterprise you find out that it’s more like self flagellation and it’s expensive with no output and what we learned in the context of War fighting primarily but also across the uh 20 years of building software infrastructure were how do you manage an emergent natural resource called large language model models in a way where you actually get value meaning you can transform your Enterprise you can change the margins you can turn tech non-technical people into technical people on the battlefield you can do things that would have otherwise cost billions of dollars for millions of dollars meaning being very precise in how you allocate troops being precise in how you target people there’s a fundamental fallacy around large language miles people could conflate actual data of an Enterprise which is structured and can be taken apart and understood with a large language model which is much more like an emergent property something that is a can be modeled used but you need precision and all the value in the market is going to go to chips and what we call ontology and we have this antology and the antology will allow you to take a large language model and use it refine it and then impose it on your Enterprise in the logic of your Enterprise in the security model of your Enterprise now we’ve talked a lot about how some companies are going to benefit from Ai and others just simply aren’t and we’ve discussed how Enterprise is going to be the key because that’s where the money is going to be at that’s where it can be scale when you can sell licenses and software to thousands of employees at a single time of course B to C is what got us here with jet gbt it’s fun it’s interesting it’s something we can all understand but to make use of generative AI in a scalable fashion and to monetize off that of course the infrastructure makes sense this next phase is going to be the data taking the data from all the different siloed locations of a business extracting that data in a meaningful way so that artificial intelligence can make real impact with businesses and what does that mean for the 70 70 people are here presenting talking about why they’re using it how they’re using you got to remember we started uh selling this product just over a year ago uh we started with the claim that we knew a lot about the precursors of large language models and large language models and the general approach of just buying models is going to be essentially self-pleasuring for an Enterprise at the cost of the Enterprise and no one believed us and now you see 70 people saying hey we’re using this for construction we’re using it for hospitals uh the people who are not speaking but we’re using on the battlefield we’re using it to compressed margins we’re using it to build Enterprises that we’re only able to build in Asia and America we’re making Engineers uh better Engineers we’re making people are not Engineers into Engineers using our anology and a large language model and this works very very quickly and it is substantially changing the health and vitality of every business here and for as opposed to the alternative which is that you buy some large language model you party with it basically and the next day you have a hangover and again for for people just looking at this what does it mean for paler it means we are sitting on the only thing that actually creates a quantifiable transformational value in an Enterprise yes it is not understood well because everybody understands the problem incorrectly yes it is going to transform America and our clients are leing the way and and by the way it’s like to actually show things that are not understood you have to actually show them the whole purpose of this conference is I can tell you how an ontology works it’s actually quite simple you have the logic of the business you including the security logic of the business and you have something that is a proximates new knowledge in a new form called a large language model and it allows you to take the value of the new form the raw resource of a large language model largely powered by chips and put it into a precise organization in the logic of the business I can tell you that or I can show you 70 businesses saying my business is stronger healthier and much better than any other business so what are some examples of how businesses can use AIP you’re going to see people in construction using it to build buildings quicker cheaper more accurately you’re going to see people in the hospital industry saying how could I possibly ever distribute my patients ethically fairly and commercially relevantly how could I distribute resources across how can I manage my my company as not abstract units but as a portfolio these things are happening within days what I’m interested in the Alex is because you you can talk a lot of CEOs will say and they’ll say on y find they’re interested in AI but actually they’re not willing to pull the trigger yet because they have these questions about privacy security vendors but but you’re saying we’re crushing it we’re closing what I would tell any of them is how are you doing it well there’s a technical answer which I gave you the precursor of but again do you want to know how we’re doing it or do you want to enjoy it being done both really great well first you but no actually first you want to see it works like the the the the central issue I think I mean we have hundreds and hundreds of CEOs and by the way the thing about the 70 people here presenting is you know they’re presenting to other people I’m not paying them to present the reason they’re presenting is they’re like wow I didn’t really believe this could work and now it’s working really well the most important thing by the way for a normal user someone who’s going to pay is does it work then the second question is how does it work does it scale what is the commercial model you really have to establish it works I would stand by the thing that it in this case is people think of large language models as the value and of itself what they’re going to find is the large language model is much more like a chemistry experiment the outgrowth of which is a something that is useful when refined and the refinement of that for your Enterprise happens in what we call our ontology which is where we impose the logic of your business on the large language model in the security and intellectual logic of your business and this is transformative and what it means for investors and others is there is value in this market people you can identify where the value is very easily are people paying for it will they pay for it and what will there’s a pre to optimality to a lot of this stuff and that PR optimality happens when people say oh I actually getting value and that’s why people are here a lot of this goes back to basic ideas of how you build things why you got to talk to people who are getting value and ask them how are you getting value why are you getting value are you paying more or less for that value than you think you should and that’s exactly what you’re going to see today you’re going to see people saying I’m getting a lot of value it went much quicker than I thought I’m I’m out stripping what I thought I could do I’m better much better than the people it happened efficiently and I’m probably I don’t think they’ll say this but I’m paying less than I think I should and that’s why I’m very happy abou”.
