BRAND.

AI’s Secret Bubble: 3 Money Problems Facing AI Businesses

As we will continue to discuss in part 2 of this series, there are two AI hype bubbles right now.

From our analysis, are set to implode nearly every company that relies too heavily on it in less than 2-3 years.

Anyone that’s been working or integrated AI into their business models have discovered a harsh reality – AI has a two profitability issue.

Currently, nearly all of the foundational generative AI models and multi-modal models are hoisted up by constant rounds of investments from organizations who’ve chosen to ride the hype train.

I understand listening to the promises of folks Like Sam Altman (CEO of ChatGPT’s OpenAI) who are running around any format of media he can get into.

They’re goal is obviously to scream from the pulpit that “AGI (artificial general intelligence) is coming right around the corner… so invest now.”

Altman is running around asking for trillions of dollars, because he and everyone who knows how this all works, understands that AI has a massive profitability issue.

Why? There are 3 profit-pits as to why the AI foundational model companies are not profitable.

This is why they will not achieve any semblance of commercially available AGI for the foreseeable future.

 

Money Pit #1: The AI Chip and GPU Shortage

There’s a reason why NVIDIA’s stocks are skyrocketing and the demand for their computer chips has exploded in demand since generative AI became the hottest topic at the end of 2022.

AI models, especially multi-modal systems, require a hefty amount of computational power to not only go through their AI training process, but also to run the AI models at scale.

And because every company is trying to get their hands on the best computer chips and graphics cards, the supply is lower than the demand.

The lack of AI-focused computer chips is slowing down the process of development.

So once they do become available, the cost will be astronomical for any business to invest in that kind of silicon, and so if most of the companies are trying to reach AGI, they will be unable to due to this limitation.

So what does the AI chips problem mean for your business?

It means that the costs for connecting to their API will increase.

Just as right now, OPENAI’s ChatGPT API is so expensive, that even enterprise users are reconsidering the viability.

So if they had to impart those costs onto their end-users per token request, their churn rate or loss of subscribers would spike considerably.

 

Money Pit #2: The High Cost of Ethical AI Training Data

The tech companies will in the near future need to license their training data from the actual copyright owners.

This is the main reason they curtailed the ethics on that to begin with and claimed “Fair Use” (when it’s not fair use the moment you commercialize it).

The costs associated with ethically licensing that data will skyrocket the costs of training and weaken the impressiveness of their models.

Training large-scale AI models incurs massive costs due to the need for substantial computational resources, data handling, and infrastructure.

There was a recent projection that the cost of training more sophisticated AI models will move into $10 billion very soon, instead of the current hundreds of millions for training.

So what does the training data problem mean for your business?

 

Training Data Transparency

It means that all the companies who are making generative AI from these foundational models, or if your business has built its own generative AI model.

And if you’re buying into the so-called “publicly available” data on the internet without compensation or consent.

Or, if you’re using the bigger offenders of uncompensated and unconsented training data from the big tech companies.

Each of the foundational AI model companies, have at least a handful of lawsuits from copyright holders, including Google, OpenAI, Anthropic, Microsoft, StabilityAI, and more.

These lawsuits started at the end of 2022, and generally, it takes 2+ years for these lawsuits to go through their process.

And all of your favorite companies are integrating the API’s of these AI foundational models into their products and services, from Salesforce (likely Chatpgt api), to Canva (using stabilityAI), Grammarly (Chatgpt api), and more.

Once the hammer drops… and it will.

The incoming legislation and ethical corrections in the market, your business will be stripped of what makes your current AI model impressive.

 

Don’t Bet The House On AI Right Now

So if  you’re betting on this type of artificial intelligence to be the main value driver of your business?

Well, once the law catches up and the litany of lawsuits are decided where companies need to strip their AI models of copyrighted and unconsented training data.

And this will drastically downgrade the capabilities of the AI models you’ve stuck into all of your products, services, and business models.

Ultimately, this can crash not only your main value driver, but your business as a whole.

This is why AI CEO’s like Sam Altman are going around trying to secure deals with publishers and film studios, when he didn’t do that before.

They are preparing for this, but the data they will acquire as they secure these deals with lower-tier publishers and film studios will pale in comparison to the billions of data they took off the internet.

 

Money Pit #3: The Massive Energy Costs of Running AI Models

Currently, the biggest cost for all AI foundational models is their energy costs to run their models.

All foundational models are spending $10 million per month in energy costs to run their AI models at scale.

It’s still not enough, which is why the majority of them are constantly limiting the power consumption.

So for those worried about Terminator Skynet or Matrix robot revolution, that’s not going to happen any time soon.

At least until they solve the lack of electricity and energy to run these models at scale, which is not just a money issue.

It’s a technology issue.

Some believe that proper commercial AGI will not be achieved until we introduce nuclear energy to run the AI foundational models.

Or, we achieve sustainable energy strong enough to power all the AI data centers.

So what does the AI energy cost issue mean for your business? It means that your usage of AI will continue to be throttled for the sake of power consumption, and the energy costs will be put into the per token request if you are connecting to the API of the AI companies.

Because when a company cannot achieve profitability, the cost will be levied on the user which means it will be more expensive over time.

Or if the AI company cuts corners, they will try to “foot the bill” by doing something with your business data that you may’ve not consented to.

 

The Reality of AGI Timelines and Hype

I’m a tech entrepreneur, and me and my team have been working on AI software for the past 4 years, and we have been watching this emerging technology since it proliferated in 2022.

From our analysis, AGI is not around the corner without these 3 problematic money pits being addressed.

The only reason folks are saying this, and in some cases, even faking the demonstration videos such as Sora’s Balloon Man video.

Or Google’s faked multimodal demo, is that they are putting out proofs of concept and stringing us all along to make specific investors believe that AGI is coming with this next investment….

and the next investment….

and the next investment…

so they can continue to play with their toys in the lab.

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Drew Lewis

Drew is a brand engineer, creative entrepreneur, digital marketer, and designer.

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