What’s Going on With AI Software Stocks

Colin’s Note: Enterprise software stocks had a rough run last week…

Across the board, software companies are reporting weaker-than-expected results.

Artificial intelligence hardware darling Nvidia has been posting exceptional results quarter after quarter after quarter. So, why hasn’t this massive spending on hardware translated to software gains?

We believe fully in our thesis for the spread of AI. It starts with hardware… picks up with software… then, eventually, the technology will be everywhere.

But it won’t happen overnight. We’ve hit a snag, no doubt, as all these companies compete for AI dominance and hit some familiar obstacles that all innovative tech movements inevitably have to hit – and hurdle – to see the tech take root.

We’ll get into some of those obstacles today… and how the evolution of this technology isn’t stopping just because we’ve hit a bump in the road to mass adoption. It’s all in the video. Just click below to watch…

Last week was brutal for enterprise software stocks. Salesforce reported that customers were delaying deals and closely scrutinizing budgets. As a result, Salesforce shares fell nearly 20% due to the weaker-than-expected outlook.

Database provider MongoDB slashed its sales forecast with the CEO stating, “Everyone’s kind of watching their pennies.” MongoDB shares ended the week down over 30%.

Other software providers like Snowflake, ServiceNow, and Paycom all saw declines of 10% or more. Across the board, software companies are reporting weaker-than-expected results. And these companies are lowering expectations that artificial intelligence (AI) will rapidly boost sales and profits for the sector.

What is going on here? Nvidia has been reporting exceptional numbers as data center providers have been snapping up Nvidia GPUs as quickly as they can be manufactured. So why hasn’t this massive spending on hardware translated to software gains? And more importantly, will it ever?

What is going on, investors? Hopefully, guys are doing well out there. Welcome back to The Bleeding Edge.

As you know, we’re in the midst of a familiar technology pattern. It begins with hardware, then it progresses to software. This leads eventually to the technology being everywhere.

Hardware, software, everywhere.

Similar to the personal computer era in the 1970s and 1980s that paved the way for software giants like Microsoft and Adobe. The internet era saw Cisco and Motorola usher in Google and Amazon. Smartphones powered new applications like Uber.

When we look back in a few years, a similar pattern will play out in artificial intelligence… but it doesn’t mean it’s going to happen overnight.

So I read through the commentary from executives of software companies over the past week.

A few common issues are slowing down the adoption of AI.

First is competition. Companies like Google and Microsoft, they’re engaged in a fierce battle to maintain the leadership position in their respective categories.

Since rolling out AI features on Bing last year, Google has been frantically trying to keep up with Microsoft, often rolling out AI features that are producing embarrassingly poor search results.

For Google, keeping pace with these innovative new features outweighs the negative press that the company is receiving for the imperfect product.

But that won’t be the case for most companies. Financial institutions, healthcare providers, schools, and education centers… they can’t suffer the same mistakes that Google can.

Not only that, most don’t have competitors already producing and making groundbreaking AI products like Microsoft. So there’s no pressure for these organizations to rush anything to market like Google has to.

The next hurdle with AI is the cost.

These days, OpenAI is seen as a Goliath in the world of AI with its popular ChatGPT product producing record-setting traffic levels, even to this day.

But just last year, OpenAI struck a deal with Microsoft for a $10 billion investment. But there’s one very surprising aspect to this deal. Microsoft has wired over very little of this $10 billion investment in cash.

Instead of taking the investment in cash, OpenAI instead took the bulk of the investment in the form of cloud computing credits.

Running AI applications is far more expensive than traditional computing tasks. Most software providers that reported dismal earnings this last week cited that consumption-based pricing is keeping some customers on the sideline.

Consumption-based pricing simply means charging companies based on the actual usage. So this variability and uncertainty of what the cost will be is making executives pause before they spend on fancy new AI features offered by the software makers.

And the last thing, it’s not something that these executives likely want to admit, but it’s true. Most products that I’m seeing from existing enterprise software providers, they’re just not innovative enough.

Most software stocks have layered on AI to speed up certain tasks and provide recommendations. But that’s not what the software phase of the technology revolution is all about.

Spreadsheets and word processing software of the 1970s and 1980s was groundbreaking. Internet search engines of the 1990s changed how we found information. Uber and mapping applications changed how we travel and find destinations using our smartphones.

Salesforce adding an AI chatbot to its software isn’t revolutionizing anything.

But look, that doesn’t mean AI is all hype. There is groundbreaking AI software coming. It just needs more time and, in reality, more money, to be developed.

Tesla’s full self-driving software… a multitude of medical research projects that we’ve been looking at… and industry-specific AI chatbots are all in various forms of testing and production.

So, while last week was harsh for these enterprise software stocks, it’s clear we are witnessing a familiar technology evolution. This cycle started with hardware… it’s progressing slowly through software… and eventually, it’ll be everywhere. And it mirrors past technology advancements.

Despite the current challenges – fierce competition, high cost, and the need for truly innovative AI applications – the potential for AI to revolutionize industry across the board remains strong.

But this transformation is going to take time. The cost of running AI is significantly higher than traditional computing tasks. This will lead to cautious adoption due to the consumption-based pricing models.

For investors, for you, this gradual evolution is ideal. Costs will begin to come down. And, gradually, more companies will begin using AI tools more regularly.

I suggest using this time to get into many of the stocks in this sector as the familiar cycle of technology plays out again.

It’s already started with hardware. It’s slowly moving to software. Eventually, in a few years, we’ll look around, and it’ll be everywhere.

Folks, that was The Bleeding Edge for today. Be back later this week. Bye for now.