AI's Gold Rush - Strike Rich or Get Burned?

Remember the dot-com bubble? Many investors lost fortunes, but a few but a few who read the signs early reaped huge rewards. Fast forward, in 2023 alone, AI startups raised over $50 billion. Is this déjà vu—another gold rush, or a bubble waiting to burst? Should you jump in with your hard-earned money? Not so fast. We just need to look back at what history has to offer, and we don’t even have to peer that far back to know if it's the right time.

B2B vs B2C: The Hidden Key to AI's Financial Future

First, let's decode tech's DNA. Companies fall into three camps:

• B2C (Business to Consumer): Think Apple's iPhone, driving consumer tech through end-user products.
• B2B (Business to Business): Like Amazon's AWS, offering utility tech for other businesses to build upon.
• Hybrids: Google's search for users, ads for businesses.

With the basics out of the way, let's take a look on how investor trends have evolved over time.

A Case Against Following the Herd

Innovations in consumer tech usually garner a lot of hype that capture the imagination of investors. With enough critical mass in the hype machine, fundamentals fly out the window.

During the dot-com boom (1995-2000), internet startups without clear revenue models saw a 300% surge in investment, only to lose most of it in the following crash. "We got caught up in the exuberance," admits Mark Cuban, who sold Broadcast.com just months before the bubble burst. "Today's AI hype feels eerily similar." In social media's early days (2005-2008), platforms like Myspace and Friendster attracted billions in funding without solid monetization plans. By 2009, they were largely forgotten.

But not all B2C hype is hot air. The iPhone's 2007 debut was easy to grasp—people wanted it, and it had a price tag. Result? Apple's stock soared 1,100% over the next decade.

In contrast, utility tech driven by B2B companies often flies under the radar. There is usually little hype as the link between operations and revenue isn't clear until later. Take Amazon in 1997, trading at a split-adjusted $0.25. Most investors missed its e-commerce potential until profitable models like Amazon Ads emerged in 2012. Similar caution met early cloud computing and IoT—until their business value became undeniable.

So if not for the ‘hype herd’, how do we gauge AI's financial future? To understand the 'how', let's look at the 'what'.

AI: Straddling Murky Waters

AI while still in its early days has shown promise in B2C as well as B2B domains. Before we make sense of it, let's look at its close cousin, FinTech - another hybrid domain.

Like AI, FinTech also spans consumer and utility sectors, with a news cycle on steroids. B2C players in digital payment like PayPal and Stripe validated their promises. Why? Clear revenue: a cut from each transaction. In contrast, crypto (Coinbase) and BNPL platforms (Klarna) face volatility due to murky monetization and regulatory hurdles.

AI straddles a similar hybrid space:

1. B2C AI: ChatGPT and Google Gemini target end-user productivity. But revenue? Volatile. OpenAI, for instance, offers a subscription model, yet released their most capable ChatGPT version - 4o for free. Signaling a focus on growth over income.
2. B2B AI: LLM (Large Language Model) APIs are making inroads with content creation businesses. Yet, as Reed Sturtevant, CEO of Gradient AI, warns, "Training costs are outpacing Moore's Law. Each new model is a multimillion-dollar gambit."

Legal hurdles are lurking around the corner as well. Getty Images' lawsuit against Stability AI for copyright infringement is just the start. These costs will trickle down to businesses relying on AI services.

The AI investment Roadmap

Note: This isn't financial advice, but strategic insights from an industry observer.

Given the fog around AI's revenue models, directly investing in LLM or chatbot companies is risky. But that doesn't mean missing the AI wave. Instead, I look for businesses with proven value that AI can supercharge.

1. B2B Data Handlers: Databricks, Snowflake, and Fivetran have loyal customers from cloud computing era. "AI doesn't change our core value—data optimization," says Ali Ghodsi, Databricks' CEO. "It just makes us do it faster, cheaper, better." As data handling standards become more stringent, these companies will become even more crucial for businesses.
2. AI's Engine Room: Chip companies like NVIDIA are already trading hot, and players like Intel and Qualcomm could stand to benefit from the AI boom as well. Cloud titans AWS and Azure are primed for growth as AI's appetite for compute explodes. Further, their enterprise lock-in is a moat, as customers are more likely to try their cross-model AI offerings.
3. Digital Highways: While storage and compute costs have enjoyed steady declines in cost as the underlying tech improves, network costs have only risen. With the explosion of video streaming, internet capacity is like gold dust. CDN leaders Akamai and Fastly are poised to leverage AI for smarter traffic management. DigitalOcean and Cloudflare's granular pricing models are suited for this high-demand era.

What does the future hold?

It's 2024, and you're reading this piece, perhaps challenging some assertions. That's the point. AI's landscape shifts daily. But remember:

• Learn from History: Hype cycles come and go. Value endures.
• Follow the Money: In emerging tech, clear revenue models are rare gems.
• Bet on Enablers: Companies that make AI more accessible, efficient, and scalable have staying power.