Some notes from Stansberry Research on GOOG

The Weekend Edition is pulled from the dailyStansberry Digest.
Alphabet's New Chip Could Be the Next AI Breakthrough
By Mike DiBiase, editor, Credit Opportunities
Alphabet is looking to "freeze" its place in history...
Alphabet (GOOGL), the parent company of Google, may have just made one of the most important moves since ChatGPT kicked off the artificial-intelligence ("AI") boom. According to a recent report from the Information, Alphabet is developing a new custom chip it calls "Frozen v2" to run its AI model.
And this chip has the potential to change what matters most in the AI race.
Alphabet will embed architectural elements of the Gemini AI model directly into the chip. The "frozen" name comes from the idea of permanently etching part of the model into the physical silicon.
This minimizes the physical movement of data and reduces the volume of calculations needed to generate a response. According to the report, this will make the chip 6 to 10 times more efficient than Alphabet's current AI chips... which are already world-class.
The company could begin using the chips as early as 2028. If those efficiency numbers hold up, it could change the economics of AI.
AI has a cost problem...
It's one of the most expensive computing projects humans have ever undertaken.
So far, it has been a massively unprofitable pursuit. OpenAI, Anthropic, and xAI – a division of SpaceX (SPCX) – have lost tens of billions of dollars trying to build the best model.
The next phase of AI won't be won by the company with the smartest model. It's going to be won by the company that figures out how to deliver AI at the lowest cost.
And creating the model isn't the only expense. Once a model is built, the next step is rolling it out and putting it to work. Generating responses when users prompt the model is called "inference" in industry jargon.
Training – the process of "teaching" the models – only happens for a limited period. Inference happens continually.
And importantly, AI data centers are not only expensive to build... They also consume staggering amounts of electricity as they continuously run AI calculations on the cloud. The price tag soars into the billions before the AI model is ever used.
Alphabet aims to lower that cost with its Frozen v2 chips... because they're designed specifically for inference.
Every time someone asks Gemini to summarize a document or create an image, Alphabet must perform a fresh round of calculations. They consume computing power, electricity, memory, and networking capacity. Multiply that by billions of requests every day, and the costs add up fast.
That's why Alphabet's latest Frozen v2 chip is so important. These chips can potentially reduce the cost of each AI interaction. With this design, they should use less electricity, respond faster, and handle more requests simultaneously.
Alphabet is making a clear statement about where it believes AI is headed. The next phase of AI will be about lowering its cost.
Soon, AI companies will be obsessing about the cost of producing one more answer rather than getting to PhD-level reasoning.
AI engineers have already started talking more about things like "inference efficiency" and "cost per token" (a token is the smallest unit of text AI processes, around four characters).
The less expensive it is to produce each AI response, the more profitable a business becomes.
That's what Alphabet has its sights set on.
As long as its AI models are roughly competitive in capabilities while serving AI requests at a greatly reduced cost, then it can lower prices and win market share.
Recommended Link:
My Nearly $20 Million 'Bar Tab' With Stansberry Research
I'm an ordinary guy – a reader like you. But a few years ago, I landed an extraordinary position: unmasking a priceless secret from inside Stansberry Research, unfiltered and on camera. In the years since, my ongoing efforts have COST Stansberry nearly $20 million and counting – money that has stayed in readers' bank accounts instead. Today, I'm speaking out one FINAL time.
Alphabet's Advantage Most AI Companies Can't Match
Alphabet is investing extraordinary sums in AI...
It expects capital expenditures this year to approach $205 billion, a figure that would have seemed almost unimaginable only a few years ago.
Few companies can afford that level of investment. But Alphabet generates massive amounts of cash because it dominates nearly every industry it's in...
The company made $165 billion in cash from its operations last year. And it's expected to make close to $200 billion this year.
Billions of people use Alphabet's products before they ever think about using AI directly.
And Alphabet plans to use AI to continue growing these dominant businesses.
Advertising is Alphabet's biggest source of revenue. It accounted for more than 75% of its sales last year. Most of this advertising revenue is generated by its Google search engine. So, in large part, Alphabet is using AI to protect its golden goose...
Instead of simply returning links, the company now uses AI Overviews and Gemini-powered search features to answer questions, sum up information, and compare products. AI can understand a user's search intent better, so Google can show more relevant ads.
The cloud is also proving to be crucial to Google's future...
Specifically, AI is now powering Alphabet's fastest-growing segment – its Google Cloud business.
Google Cloud sells computing, storage, and access to AI tools to businesses. Last year, the cloud segment generated nearly $60 billion in revenue, around 15% of Alphabet's sales.
It's also capturing market share from its competitors. Its cloud sales grew 36% last year, nearly double the 20% growth of Amazon's and Microsoft's cloud businesses.
And this growth is accelerating thanks to AI. Last quarter, Google Cloud sales grew 82%. The company's Gemini AI app now has 950 million monthly active users, and its AI models process 22 billion tokens per minute.
Other AI companies rely on Alphabet. AI-model startups can't afford to build their own massive data centers, so they rent Alphabet's infrastructure. For example, Anthropic is a Google Cloud customer. It recently committed to $200 billion in Google Cloud spending over the next five years.
AI data centers are where the big money will be made. They provide both the training and the inference that AI models need... so lowering inference costs will be a major advantage.
That's why this latest chip announcement is worth paying attention to. Alphabet doesn't just care about building a better chip. It's thinking bigger... It's lowering the cost of intelligence.
Alphabet rarely wins by inventing an entirely new market. Instead, it finds ways to do things faster, cheaper, and at a much larger scale than its competitors. That's what this Frozen chip is all about.
Wall Street has spent the past three years trying to identify the company with the best AI.
It may soon discover that it has been asking the wrong question. The better one is: Who can give the world AI at the lowest cost?
Good investing,
Mike DiBiase