Is Everyone in AI Wrong?

🏝 TheTechOasis 🏝

part of the:

In AI, learning is winning.

This post is part of the Premium Subscription, in which we analyze technology trends, companies, products, and markets to answer AI's most pressing questions.

You will also find this deep-dive in TheWhiteBox ‘Key Future Trends in AI’, for future reference if necessary and to ask me any questions you consider.

As you may have realized, over the past few weeks, I’ve been sharing the unpleasant reality of AI, that side that is rarely—if at all—shared with us through mainstream media but that you should be well aware of.

With a lot of money in the line and AI-led valuations at indecent multiples compared to tangible AI revenues, there’s no interest in sharing anything but an ‘AI is changing the world’ narrative that keeps the party going.

For that, today, I’m bringing a manifold of ‘AI dogmas,’ including the uber-mentioned: “LLMs will lead to AGI” or “LLMs Are All You Need,” with both, in reality, being far from certain, as we’ll see today.

I will also provide potential consequences if these fears materialize so that you can have an unbiased, no-hype, no-bullshit view of the space that reading mainstream media and influencers simply do not provide, giving you an edge over the majority.

“Frontier AI Will Lead Demand”

With success stories like OpenAI, with revenue growth increasing steadily every year since the launch of ChatGPT to a whopping $3.4 billion current run rate, it’s easy to assume that better AI models will lead to the ‘great adoption’ that many incumbents and investors patiently await to justify the insane valuations.

A CAPEX Problem

But as discussed in two previous Leaders segments (here and here), current Generative AI products aren’t as good as some will tell you, which has translated into extremely poor demand.

Every tech enthusiast’s psychologist right now.

Nevertheless, if energy constraints are the mid-to-long-term elephant in the room for AI, product demand is unequivocally the main issue in the short term.

But how much demand do we need to avoid a catastrophe?

In a very recent Sequoia update on their “$200 billion question,” their first try at coming up with a number, that value is now closer to $600 billion.

This means that the AI market is heavily overinvested and that Generative AI revenues need to grow into those values to justify the CAPEX investments led by big tech companies and the valuations at which private and public investors have invested.

In other words, unless revenues grow to that number, companies aren’t even breaking even, let alone earning money.

The worst part is that David Cahn from Sequoia is referring explicitly to non-hardware revenues; meaning NVIDIA’s revenues don’t count.

Although we don’t have an exact value, seeing as the hyperscalers don’t reach $20 billion combined for 2024, that number is probably much closer to $30-50 billion for 2024 (a value I still feel is inflated).

Scarily, if hyperscalers are recognizing as revenue their auto-generated demand on their compute clouds…

Enjoyed the introduction? Subscribe or sign in to continue with this edition.