The wealth creation movement led by AI continues, with a leading figure. $NVIDIA (NVDA.US)$ Riding far ahead. $Broadcom (AVGO.US)$ 、 $Marvell Technology (MRVL.US)$ These types of Semiconductors companies have also become the biggest beneficiaries of the initial wealth creators. However, in the recently concluded 2024, the biggest winner in the Capital Markets is likely a company named. $Palantir (PLTR.US)$ The company's Market Cap increased nearly fourfold over the year.

The name Palantir comes from the crystal ball in "The Lord of the Rings" that perceives everything, perfectly encapsulating Palantir's positioning — data technology. A decade ago, when everyone was discussing Big Data, Palantir made a name for itself by successfully tracking down Bin Laden through Big Data technology, becoming a well-known figure in various stock trading Communities.

In 2020, Palantir successfully went public, jumping onto the fast track of AI. One of the biggest changes brought about by AI is that data is no longer a patent for high-tech companies to boast about. As entrepreneurs from all walks of life began to frequently mention AI, the importance of data has unprecedentedly surged.

The race that Palantir is in is becoming increasingly heated, certainly because more and more companies are beginning to realize the importance of using data to assist in Operational decision-making.

But more importantly, many have already seen a more challenging future. In the AI era, it's not just about making decisions; more often than not, achieving a leading position requires the ability to foresee the future — a combination of prediction and decision-making.

Among these, how to acquire data and how to make good use of it is probably the core key that every enterprise wishes to possess, and it is also the most important question that the business world needs to answer in the era of AI.

AI transforms the SaaS industry.

In 2017, the Transformer architecture was introduced, marking a significant step forward in natural language understanding. Specifically, advancements in AI have brought about two changes:

First, the emergence of Transformer architecture and large models has exponentially improved AI's ability to process unstructured data, allowing technology to become "platform-based," breaking free from the trap of project-based construction teams.

Second, the integration of AI with data analysis allows a shift from mere data analysis to intelligent decision-making.

In 2020, Palantir integrated AI capabilities into its two data products, Gotham and Foundry, beginning to provide intelligent recommendations for business operations through data. It can be said that Palantir's development has witnessed the transformation of data analysis by AI.

Palantir is not the only beneficiary of this wave of AGI; in fact, many established SaaS companies have found paths to leapfrog growth with the help of AI— even if they can't find it themselves, Wall Street Analysts can help them.

March 2023, $Adobe (ADBE.US)$ Announced its generative AI tool "Firefly". Subsequently, more than 100 AI feature updates were announced for Creative Cloud (including the subscription package for Illustrator, Photoshop, Lightroom, and Premiere Pro), such as using AI to intelligently expand images in Photoshop. With a terrifying number of existing subscription users, Adobe has gone crazy in the Capital Markets.

In October of the same year, Time magazine published "The Best Inventions of 2023." Among the 14 applications selected in the AI category, Adobe's Generative Fill surpassed OpenAI's GPT-4 and ranked first in its sub-category.

Coincidentally, $Salesforce (CRM.US)$ CEO Marc Benioff disclosed a frightening statistic during an interview earlier this year: in the customer service field, Salesforce's AI agent can already independently handle nearly 90% of cases.

The key variable here is that AI has changed a series of standards defined by SaaS products in business operations.$Microsoft (MSFT.US)$What CEO Nadella said [1]:

Intelligent agents have broken the limitations of single Saas applications and their data, enabling tasks to be executed and intentions to be coordinated across multiple Saas applications. By calling APIs and integrating various tools, intelligent agents have built a unified model to integrate and utilize the functionalities of multiple Saas applications.

The most direct impact is that businesses no longer need a large IT department and top data scientists to make intelligent decisions based on data. This has indeed become a collective issue in the industry: using data to drive business growth. Most vertical sectors of the industry inevitably find themselves in the proposition of 'digitalization.'

The flourishing of large models has re-priced the vast amounts of data stored in datacenters and hard drives.

What has AI changed?

The capability provided to enterprises after deep integration of data and AI is not exclusive to Palantir, nor to American companies.

As AI begins to rapidly penetrate business operations, software companies and Internet Plus-Related companies that have accumulated a wealth of practical experience in the previous era are leading the way into various specific vertical industries. In China, the once quiet Saas industry is now stepping onto the stage with a brand-new posture.

Among the rare and unusual creatures in Alibaba's zoo, Lingyang might be the youngest but also the most cutting-edge. From its birth, it positioned itself as 'DaaS' (Data as a Service), providing intelligent services for various industries.

The core of DaaS lies in transforming data from being merely existing to becoming good data that can be utilized, allowing the internal data of enterprises to be genuine assets that permeate every operational aspect and drive the growth of the enterprise itself.

$Alibaba (BABA.US)$Peng Xinyu, the Vice President of the group and CEO of Lingyang, proposed an AI growth formula for enterprises in the intelligent era at the Yunqi Conference last September: (Algorithm + Computing Power + Data) x Scenario. The first three elements are the three cornerstones of AI development, while specific scenarios represent the directions of AI penetration, which is also what Lingyang is working on.

Similar to rapidly growing companies like Palantir, the birth of Lingyang also cannot be separated from a significant backdrop: the advancement of AI has made it possible for platforms based on technology and solutions to be constructed, and business and decision-making systems built on data are no longer limited to a few "native" internet companies based on data and cloud.

This trend resembles the "turn key" solutions that MediaTek introduced during the feature phone era, integrating multiple types of chips (such as audio, video decoding, and signal processing) into a single chip while providing systems and development platforms, so that mobile manufacturers only need to purchase a set of MediaTek solutions and then add their own casing and camera to create a mobile phone.

If the key to the turn key solution is MediaTek's chip design and system integration capabilities, then correspondingly, whether it is Lingyang or Salesforce, companies that can provide mass AI era solutions for enterprises must possess sufficiently powerful data processing capabilities.

Lingyang's team's accumulation in the data field is actually not brief; it evolved from Alibaba’s data platform, which once became a textbook in the industry. Currently, many CDOs, CIOs, and CTOs at top Chinese enterprises have career histories that indicate they have fought in this team, making Lingyang the Huangpu Military Academy in the field of digital intelligence in China.

In January, Lingyang, together with Tsinghua University, held the second "Data Classmate Association." As one of the rare top-tier data gatherings in the country, this "Data Classmate Association" gathered more than 70 data leaders, including those from...$CHINA MOBILE (00941.HK)$Adidas,$Chongqing Changan Automobile (000625.SZ)$This includes digital leaders from companies such as Procter & Gamble, Yili, Haier, China Duty Free Group, Bawang Tea, as well as Professor Huang Lihua from Fudan University's School of Management, who participated in the "20 Data Articles" project, An Xiaopeng, Executive Committee Member of the China Informationization Hundred People Association and Vice President of Alibaba Cloud Intelligence Group, Chen Long, Secretary-General of Luohantang and founder of Weixi, along with well-known business consultant Liu Run.

The discussions among this group regarding data and AI will largely determine the direction of digital intelligence in China over the next one to three years, while the data assets and methodologies they represent are practically equivalent to a comprehensive and detailed report on socio-economic operations and the history of business digital transformation.

Also at this year's "Data Fellowship Meeting", An Xiaopeng, Executive Committee Member of the China Informationization Hundred People Association and Vice President of Alibaba Cloud Intelligence Group, made a highly generalized statement: "Large models are activating all data, and AI has evolved from being able to answer enclosed problems with limited solutions to providing accurate solutions for open problems; while agents have become a significant breakthrough for the commercialization of AI."

Liu Run, founder of Runmi Consulting, shared his insights on digitalization in the AI era, stating that the migration of enterprises from the physical world to the digital world can be understood as digitalization, and that digitalization is merely the foundation for intelligence. Today, a large number of Chinese enterprises are completing the simultaneous "digital intelligence" process.

If the emergence of Lingyang signifies that AI is beginning to structurally transform data, then the "Data Fellowship Meeting" is more about confirming a consensus: using data to assist in operational decision-making and utilizing data to better predict the future is becoming the industry's top choice.

In the era of AI, there are three obvious trends under the proposition of "digital transformation of enterprises":

(1) The value of data depends on the scenario.

Palantir has evolved from a trendy unicorn to a publicly traded company with a market cap of hundreds of billions, with its core offering a universal and low-threshold solution for "intelligent decision-making." For enterprises, only when their own data can be utilized will that data generate corresponding value.

For example, in 2020, Swire Coca-Cola collaborated with Lingyang to complete the DTC transformation of "traffic generation - activation - full-domain conversion," successfully building a private domain pool with millions of members. For Swire Coca-Cola, in the traditional retail system, a large amount of user data is difficult to reach; without the corresponding methods and tools, transformation is out of the question.

(2) More and more Palantirs will emerge one after another.

As early as 2015, Palantir launched the Machine Learning Toolkit (MLTK), but its limitation lies in the high IT capability requirements for customers, lacking universality.

Most companies excel in their business areas but are probably novices in the field of AI; they possess data but lack the ability to use it. Moreover, not all companies have sufficient funds and influence to establish a star-studded AI department. Therefore, there will definitely be ready-to-use AI solutions on the market, which is also the value of Lingyang.

(3) Making business decisions based on data will be an "essential consumer choice" for enterprises.

Why are most SaaS companies with attractive business models and immensely profitable concentrated in the USA? Natural factors such as market and institutional reasons play a role, but a key factor is that American companies early on realized the need to squeeze out efficiency from management amid high labor costs and slowing market growth.

To this day, many Chinese companies are gradually realizing in the same context that they need to seek growth through management. As more and more enterprises discover that their growth may not come from hiring another team or creating another business, but rather from improving overall operational efficiency through data, intelligent decision-making will become an essential Consumer choice for businesses.

In the words of Peng Xinyu: 'In the AI era, the importance of data surpasses any time before. This is the best era for data people, as what society needs aligns perfectly with our capabilities. This is also the original intention of holding the data study meeting.'

Different eras, different practices.

Let’s rewind to 1982 when Merrill Lynch began experimenting with a device called the 'Bloomberg Terminal,' which provided timely and accurate financial information while offering specialized data and charts, integrating news, data, analytical tools, and research reports into a unified platform that was well-received by financial traders.

In the subsequent 20 years, the number of institutional investors in the USA surged unprecedentedly, making Bloomberg the largest provider of financial information globally. The terminal has dominated virtually every professional investor's office, even being installed in investors' homes.

Using data to assist in decision-making is not a new concept, but its connotation and extension have continually expanded with technological advances.

The advantage of the Bloomberg Terminal lies in its ability to aggregate a wide variety of financial market data, assisting traders in making investment decisions. Therefore, even with a steep subscription cost, it remains popular in the investment industry.

The derivatives market andMutual FundIn the rapid growth cycle, Bloomberg terminals and Wall Street's extravagant allure shine together; as time goes by, in an era where every person in Silicon Valley and Beijing Centergate Technologies has a copy of 'Reinventing Teams', Internet Plus-Related companies born from Big Data outline new growth theories. When the wave of AI arrives, a variety of unstructured data begins to be transformed continuously, and the behaviors in the business world are increasingly becoming data-driven.

The key variable here is that a large number of non-structured aspects in corporate operation are 'datafied', and more and more difficult-to-quantify aspects and processes can be accurately captured by AI, thus serving business growth.

This is also why major buyers of NVIDIA GPUs, in addition to Microsoft,$Amazon (AMZN.US)$these cloud computing giants, also include chain dining companies like Domino's Pizza that seem less high-tech.

While OpenAI invests hundreds of millions of dollars to build data centers for training models, Domino's Pizza is also using AI in its data room to optimize delivery routes for riders, continuously practicing the commitment of '30 minutes or it's free'.

According to Silvio Savarese, chief scientist at Salesforce: Agents are shifting from soloists to orchestras, as a business manager can not only manage and schedule their "human employees", but also allocate their "digital workforce".

Companies like Adobe and Salesforce have defined the way people work in the Internet Plus-Related era. Today, Lingyang and a group of "data people" at the forefront of technology are exploring the new rules for business social growth in the AI era.

Fifty years ago, management masters might have resembled philosophers, but today, CEOs managing business operations are increasingly becoming "data people", and more and more companies are joining this era's "data society".

Editor/Somer