Missed NVIDIA? Don't Worry—The Second Wave of AI is Bringing Doubling Opportunities!

If history is any guide, from the electricity revolution a century ago to the internet boom in the 1990s, groundbreaking tech revolutions tend to follow three distinct phases. Let’s take the internet revolution as an example:

Infrastructure Buildout Phase
In the late '80s and early '90s, the internet was still in its infancy, with very limited applications. The biggest beneficiaries were infrastructure-focused companies like Cisco (building networks, routers, and switches) and Intel (making chips). For AI, NVIDIA and other chip giants have played this foundational role in building the compute power needed for the first wave of AI adoption.

Enterprise Application Boom Phase
By the early '90s, the internet began to find its way into enterprise applications, with software like CRM and supply chain management boosting corporate productivity. AI is currently entering this phase, as businesses leverage the technology to optimize operations, reduce costs, and increase efficiency.

Consumer Application Explosion Phase
In the late '90s, consumer-focused, game-changing applications started to take off—think Amazon, PayPal, and Yahoo.

Right now, the first wave of AI has stabilized. So the big question is: when will the second wave, focused on enterprise applications, fully take off?

Many people perceive AI applications as limited to tools like ChatGPT, believing the truly disruptive, killer apps are still years away. Some argue AI investing is premature and full of hype.

While it’s true that consumer AI applications may take time to mature, enterprise-level AI applications are already scaling rapidly, delivering tangible results in specific sectors. The key for investors is to stay ahead of the curve, as enterprise adoption will drive the second wave of AI growth.

According to a ranking by Bank of America, software companies are the biggest beneficiaries of the AI wave.

The chart below shows the performance of IGV, the software company ETF. Notice the sharp upward divergence in software stocks starting in November.

Is this a sign of the second wave of AI adoption, or just a coincidence?

I dug into the earnings reports of several software companies, and the data tells a clear story: AI is significantly improving operational efficiency. Here are three standout cases:

Snowflake is another standout example. The company focuses on providing AI-powered intelligent operational support for enterprises. This quarter, its RPO surged from $5.2 billion to $5.7 billion, reflecting growing enterprise trust in its AI capabilities. The CEO’s “All in AI” strategy not only boosted data-mining efficiency but also led to a 30% rally in the stock post-earnings.

The insurance sector, with its data-intensive nature, is a prime candidate for AI transformation. Companies are racing to adopt AI-driven solutions.

AIFU Intelligent Future has taken the lead with its flagship “Du Xiao Bao” platform, co-developed with Baidu. By combining big data and AI, the platform delivers personalized insurance solutions, enhances agent productivity, and significantly reduces operating costs.

In 2023, AIFU reported $3.198 billion in revenue (+14.98% YoY) and $289 million in net profit (+237.25% YoY). Its TTM P/E ratio is just 3.5x—much lower than industry peers like Prudential Financial (15x) and Prudential UK (12x), which have more traditional, slower-growth business models.

Last Friday, BGM announced the acquisition of two AIFU subsidiaries, including the core assets behind the “Du Xiao Bao” platform. BGM, a global pharma and chemicals giant, has been aggressively pivoting toward AI, especially in enterprise-level cost optimization. This acquisition strategically positions BGM in the high-potential insurance and health sectors while aligning with the second phase of AI adoption. The market recognized this bold move, sending BGM shares up over 20% following the announcement.

So, what kind of companies are best positioned to succeed in this wave? Here are the key traits I look for:

Strong Moat: Companies that use AI to continually strengthen their competitive edge.

Exclusive Data: Firms with access to high-quality proprietary data, rather than relying on public datasets.

Flexible Business Models: SaaS platforms with pay-as-you-go models are more scalable and profitable.

Execution-Oriented Leadership: Agile and decisive management teams that can quickly bring AI applications to market.

Final Thoughts: The Future Belongs to the Bold At this stage, AI's enterprise applications are just getting started, with massive growth potential ahead. It's crucial to identify promising companies and build positions early. While risks remain, as AI adoption expands, this could be the beginning of the next NVIDIA-like growth stories.