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Don't Miss the Second Wave of AI Opportunities!
Are you still regretting missing out on NVIDIA's 10x growth? This time, you may not need to worry. The second wave of AI is forming, and this time, the opportunities are not limited to hardware, but are fully penetrating enterprise-level applications. For investors, this is an unparalleled new window of opportunity.
A Look into the Future: The Development Patterns of AI
Reviewing history, from the power revolution a century ago to the internet revolution in the 1990s, we see similar development patterns. Each revolutionary technology wave will go through three key stages. Let's take the internet revolution as an example:
Infrastructure Construction Stage
In the late 1980s and early 1990s, the internet was just emerging, and its applications were still very limited. The companies that benefited most were those in the foundation layer, such as Cisco and Intel.
The first stage of AI development was similar, with chip giants like NVIDIA driving the construction of AI infrastructure.
2. B2B Application Rise Stage
In the mid-1990s, the internet gradually entered the enterprise-level application field, with CRM and supply chain management software emerging, improving corporate production efficiency.
AI is currently entering this stage, with companies optimizing operational processes using AI technology to achieve cost reduction and efficiency improvement.
3. C2C Consumer-Level Application Popularization Stage
In the late 1990s, various C2C killer applications began to emerge, such as Amazon, PayPal, and Yahoo!, which became familiar companies.
Now that the first wave has stabilized, the question is: when will the second wave arrive in B2B applications?
Many ordinary people have a feeling that AI applications are limited to chatbots like ChatGPT, and that true killer applications have not yet arrived or will take a long time to develop.
As a result, some people believe that AI investment is still too early, and that what's being blown up now is just a bubble.
Indeed, we can see that C2C applications are still in development and will take a long time to mature. However, in B2B applications, AI has already been widely deployed and has shown significant effects in certain specific fields. It's just that ordinary people haven't yet felt it.
As investors, we must be more sensitive than ordinary people because corporate changes will be critical to the second wave of AI.
The Second Wave of AI: The Golden Era of Enterprise-Level Applications
The following graph is a summary of the top-ranked industries in which AI-driven companies are most likely to benefit.
[Graph: Top-ranked industries for AI-driven companies]
As for software companies like ETFIGV, we can see from their financial reports that AI is driving significant improvements in corporate operating efficiency.
The following graphs show the gross margin and EBITDA margin of three typical software companies: Shopify, Salesforce, and ServiceNow.
[Graphs: Gross Margin and EBITDA Margin of Shopify, Salesforce, and ServiceNow]
Explaination:
Gross margin reflects the main product profit of software companies, while EBITDA margin reflects the company's operating profit after deducting depreciation and interest.
In other words, it represents a company's ability to generate profits from limited resources.
We can see that these three companies have seen significant improvements in their EBITDA margins over the past two quarters while maintaining stable gross margins.
Data does not lie; this may indicate that AI is already seeing effects in enterprise-level applications.
• Shopify: By optimizing internal processes using AI, it maintained stable gross margins while improving EBITDA margins and directly driving stock price growth by 30% after reporting earnings.
• Salesforce: It launched its "INS Instant" AI tool to automate 370,000 tasks, saving 50,000 hours of labor time and significantly improving employee efficiency.
• ServiceNow: Its AI accelerated data extraction speed by 53%, work flow efficiency by 27 times, and RPO growth by 26%, providing more powerful workflow optimization services for enterprises.
These data clearly show that AI is not just a buzzword but brings actual efficiency and profitability improvements to enterprises.
Snowflake: A Breakthrough in Enterprise Data Analysis
Snowflake's case is more representative. This data analysis platform focuses on providing intelligent operational support to enterprises using AI technology.
This quarter's RPO increased from $52 billion to $57 billion, reflecting enterprise trust in its AI capabilities. CEO's "All-in-AI" strategy not only drives data mining efficiency but also drove its stock price up by 30% after reporting earnings.
Insurance Industry Digital Transformation: AIFU and BGM's Strategic Cooperation
The insurance industry is an important target area for AI transformation due to its information-intensive nature. It is at the forefront of digital transformation, especially with AI technology driving it forward.
AIFU's smart future has already achieved insurance industry transformation through its core product "Duxiao" platform.
"Duxiao" is an AI-driven insurance platform developed jointly by AIFU and Baidu. By combining big data and AI technology, it can provide personalized insurance solutions for customers.
The platform analyzes customer health insurance needs, education planning, and wealth management needs in depth and generates highly customized insurance configuration plans. This has significantly improved agent productivity and accuracy while reducing operating costs.
As of December 2023, AIFU's revenue reached $31.98 billion, with a year-on-year growth rate of 14.98%. Net profit was $2.89 billion with a year-on-year growth rate of 237.25%.
AIFU's PE ratio (TTM) is only 3.5 times. In comparison to industry giants such as Prudential (PUK) and AXA (AXAHY), which have PE ratios above 12 times or even higher than AIFU.
AIFU's strategic acquisition of two subsidiaries by BGM on Friday includes core technology assets such as "Duxiao" platform. BGM is a global pharmaceutical and chemical company that has actively promoted its AI strategy in recent years.
By integrating AI with data analysis, BGM is reshaping its business model towards a more intelligent future.
How to Seize Opportunities in the Second Wave of AI?
What kind of companies will ultimately succeed? I can share with you my thoughts on what kind of companies need to possess these characteristics:
Strong Competitive Moat: Companies that can continuously strengthen their competitive barriers through AI.
Data Monopoly Advantage: Companies that build models using high-quality private data rather than public data.
Flexible Business Model: SaaS platforms with pay-as-you-go pricing models have more scalability and profitability potential.
Strong Execution Ability: Agile and decisive management teams that can quickly deploy technology.
Conclusion:
The future belongs to those who dare to layout!
A Look into the Future: The Development Patterns of AI
Reviewing history, from the power revolution a century ago to the internet revolution in the 1990s, we see similar development patterns. Each revolutionary technology wave will go through three key stages. Let's take the internet revolution as an example:
Infrastructure Construction Stage
In the late 1980s and early 1990s, the internet was just emerging, and its applications were still very limited. The companies that benefited most were those in the foundation layer, such as Cisco and Intel.
The first stage of AI development was similar, with chip giants like NVIDIA driving the construction of AI infrastructure.
2. B2B Application Rise Stage
In the mid-1990s, the internet gradually entered the enterprise-level application field, with CRM and supply chain management software emerging, improving corporate production efficiency.
AI is currently entering this stage, with companies optimizing operational processes using AI technology to achieve cost reduction and efficiency improvement.
3. C2C Consumer-Level Application Popularization Stage
In the late 1990s, various C2C killer applications began to emerge, such as Amazon, PayPal, and Yahoo!, which became familiar companies.
Now that the first wave has stabilized, the question is: when will the second wave arrive in B2B applications?
Many ordinary people have a feeling that AI applications are limited to chatbots like ChatGPT, and that true killer applications have not yet arrived or will take a long time to develop.
As a result, some people believe that AI investment is still too early, and that what's being blown up now is just a bubble.
Indeed, we can see that C2C applications are still in development and will take a long time to mature. However, in B2B applications, AI has already been widely deployed and has shown significant effects in certain specific fields. It's just that ordinary people haven't yet felt it.
As investors, we must be more sensitive than ordinary people because corporate changes will be critical to the second wave of AI.
The Second Wave of AI: The Golden Era of Enterprise-Level Applications
The following graph is a summary of the top-ranked industries in which AI-driven companies are most likely to benefit.
[Graph: Top-ranked industries for AI-driven companies]
As for software companies like ETFIGV, we can see from their financial reports that AI is driving significant improvements in corporate operating efficiency.
The following graphs show the gross margin and EBITDA margin of three typical software companies: Shopify, Salesforce, and ServiceNow.
[Graphs: Gross Margin and EBITDA Margin of Shopify, Salesforce, and ServiceNow]
Explaination:
Gross margin reflects the main product profit of software companies, while EBITDA margin reflects the company's operating profit after deducting depreciation and interest.
In other words, it represents a company's ability to generate profits from limited resources.
We can see that these three companies have seen significant improvements in their EBITDA margins over the past two quarters while maintaining stable gross margins.
Data does not lie; this may indicate that AI is already seeing effects in enterprise-level applications.
• Shopify: By optimizing internal processes using AI, it maintained stable gross margins while improving EBITDA margins and directly driving stock price growth by 30% after reporting earnings.
• Salesforce: It launched its "INS Instant" AI tool to automate 370,000 tasks, saving 50,000 hours of labor time and significantly improving employee efficiency.
• ServiceNow: Its AI accelerated data extraction speed by 53%, work flow efficiency by 27 times, and RPO growth by 26%, providing more powerful workflow optimization services for enterprises.
These data clearly show that AI is not just a buzzword but brings actual efficiency and profitability improvements to enterprises.
Snowflake: A Breakthrough in Enterprise Data Analysis
Snowflake's case is more representative. This data analysis platform focuses on providing intelligent operational support to enterprises using AI technology.
This quarter's RPO increased from $52 billion to $57 billion, reflecting enterprise trust in its AI capabilities. CEO's "All-in-AI" strategy not only drives data mining efficiency but also drove its stock price up by 30% after reporting earnings.
Insurance Industry Digital Transformation: AIFU and BGM's Strategic Cooperation
The insurance industry is an important target area for AI transformation due to its information-intensive nature. It is at the forefront of digital transformation, especially with AI technology driving it forward.
AIFU's smart future has already achieved insurance industry transformation through its core product "Duxiao" platform.
"Duxiao" is an AI-driven insurance platform developed jointly by AIFU and Baidu. By combining big data and AI technology, it can provide personalized insurance solutions for customers.
The platform analyzes customer health insurance needs, education planning, and wealth management needs in depth and generates highly customized insurance configuration plans. This has significantly improved agent productivity and accuracy while reducing operating costs.
As of December 2023, AIFU's revenue reached $31.98 billion, with a year-on-year growth rate of 14.98%. Net profit was $2.89 billion with a year-on-year growth rate of 237.25%.
AIFU's PE ratio (TTM) is only 3.5 times. In comparison to industry giants such as Prudential (PUK) and AXA (AXAHY), which have PE ratios above 12 times or even higher than AIFU.
AIFU's strategic acquisition of two subsidiaries by BGM on Friday includes core technology assets such as "Duxiao" platform. BGM is a global pharmaceutical and chemical company that has actively promoted its AI strategy in recent years.
By integrating AI with data analysis, BGM is reshaping its business model towards a more intelligent future.
How to Seize Opportunities in the Second Wave of AI?
What kind of companies will ultimately succeed? I can share with you my thoughts on what kind of companies need to possess these characteristics:
Strong Competitive Moat: Companies that can continuously strengthen their competitive barriers through AI.
Data Monopoly Advantage: Companies that build models using high-quality private data rather than public data.
Flexible Business Model: SaaS platforms with pay-as-you-go pricing models have more scalability and profitability potential.
Strong Execution Ability: Agile and decisive management teams that can quickly deploy technology.
Conclusion:
The future belongs to those who dare to layout!
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