What You Don’t Know About Google VIEW IN BROWSER
By Jeff Brown, Founder and CEO, Brownstone Research If we were to ask most people on the street, “What kind of company is Alphabet (GOOGL)?” The majority would reply: “It’s a search company.” And they wouldn’t necessarily be wrong. That’s certainly the “product” that Google initially became so well known for. Some might also say, “It’s famous for search, and it also has Gmail, Drive for storage, Docs, Sheets, all that Google Workplace stuff.” Not wrong again. A response like that would likely come from someone in the workforce whose company uses Google’s cloud-based enterprise software to save money on software services. A more tech-focused response would also include a reference to Google’s Cloud services, one of the largest cloud service providers after Amazon Web Services. It is an absolutely massive business that generated $58.7 billion in revenue for Google in 2025. And from a financial analyst on Wall Street, we’d likely get a completely different answer. They’d say, “Google is first and foremost an advertising company.” After all, about 85% of all of Google’s revenues came from selling access to Google’s decades of data collection on all of us to generate advertising revenues. That’s Google’s business model. The other 14.5% is Google Cloud revenues. The remaining amounts are insignificant. So it’s an advertising company, right? If 85% of its revenues are from advertising… Ironically, the most accurate answer is, “None of the above.” Pmax: The Flagship Technology You’ve Never Heard of Google is one of the most successful artificial intelligence companies in the world. It’s an AI company. Consider this: Google’s flagship advertising technology, known as Performance Max, or “PMax,” is entirely driven by AI. It uses AI to manage ad bidding, audience targeting, and creative ad placement across all of Google’s media assets, like YouTube, Google Search, Gmail, etc. Google uses artificial intelligence to analyze millions of real-time bits of information about consumers to autonomously set the bid price for each individual advertisement. This bidding and matching process using machine learning, a form of AI, started around 2015 when Google replaced the old manual keyword-based bidding with an automated system using machine learning. Naturally, AI employment became increasingly sophisticated as the years passed. Google’s AI was soon able to understand the context and intent of a user’s search query, enabling even more precise matching of ads most relevant to a search query. Today, almost all of Google’s advertising platform is empowered by AI. And it’s not just advertising software. Back in 2016, Google developed the first of what is now seven generations of semiconductors, known as Tensor Processing Units (TPUs), that were invented for machine learning and optimized for Tensor Flow, an open-source software stack designed by Google for building AI models. 
Source: Google Above is a picture of Google’s seventh-generation TPU – Ironwood – Taiwan Semiconductor (TSMC) manufactures these semiconductors. But aside from Google’s AI ad technology and its custom AI semiconductors, the largest and most ambitious project using AI has been developing its frontier AI models, the latest of which is known as Gemini 3 Deep Think. Blowing Through Benchmarks Without question, Google will be one of a handful of companies that achieve artificial general intelligence (AGI) within the next 12 months. In fact, some are already arguing that it’s here. It’s not, but it is very close. I don’t blame them, though, for having that opinion… Just look at the performance metrics that Google’s Gemini 3 Deep Think has generated. The first are the ARC-AGI benchmarks. The ARC-AGI benchmarks are designed to be a measure of general intelligence. The ARC-AGI organization defines AGI as: A system that can efficiently acquire new skills outside of its training data. It’s a reasonable, and surprisingly simple, definition of AGI. After all, AGI isn’t about the kind of rote memorization and regurgitation that large language models (LLMs) are so well known for. AGI is about using all that knowledge for fluid intelligence: self-learning, adaptation, reasoning, and eventually self-directed research. Gemini 3 Deep Think scored an incredible 96% this month on the ARC-AGI-1 benchmark, effectively making the benchmark obsolete. These tests require reasoning, and none of the problem sets can be solved by conducting a simple Google search. 
The ARC-AGI-1 benchmark was launched in 2019 by the ARC Prize Foundation. And the organization recognized how much progress AI companies like OpenAI, Anthropic, xAI, and Google were making against that benchmark. That was the catalyst for launching the ARC-AGI-2 benchmark in March 2025. For context, this was less than a year ago. The team at ARC-AGI developed ARC-AGI-2, which it believed to be a far more challenging, far more accurate representation of general intelligence, which it expected would take years to master by any of the frontier AI models. Think again. At the time ARC-AGI launched this second benchmark, no frontier AI model had solved a single problem in the challenge. This month, Gemini 3 Deep Think scored 84.6%. Simply astounding. For context, prior to what has happened in the last few weeks with Google, Anthropic, and OpenAI, the highest score on ARC-AGI-2 was Grok 4 at 29.4%. 
The benchmark is quickly becoming obsolete. And the implications are stunning. We are weeks away from not only ARC-AGI-1 benchmark seeing its first 100% score, but also ARC-AGI-2 seeing the same. The team at ARC-AGI has been scrambling to publish a third benchmark, designed to be even more challenging and designed to measure human-level intelligence in AI models. They are continually having to move the goalposts because progress has been so rapid. The ARC Prize team is launching that on March 25. Masquerading at the AI Ball It’s not just the raw performance or the raw general intelligence demonstrated by the latest benchmark scores that is so incredible. It’s how quickly each frontier AI model is improving. 
Shown above is Google’s own comparison chart for key AI benchmarks. On the left, we’ll see that the precursor to Gemini 3 Deep Think was Gemini 3 Pro Preview, which scored just 31.1% on the ARC-AGI-2 test. Gemini 3 Pro was released in November 2025, about three months ago. In just a few months, the performance jumped from 31.1% to 84.6%. And remember, the time that it takes for each frontier model to double its performance is shrinking. Another extremely complex benchmark is Humanity’s Last Exam. Humanity’s Last Exam is unique in that it is composed of 2,500 very complex problems in more than 100 subject areas that require expert academic-level knowledge in each field to solve. It is literally impossible for any one person, even with a computer at their disposal, to even come close to completing the exam. Gemini 3 Deep Think scored an incredible 48.4% this month, significantly besting Anthropic’s Claude Opus 4.6 at 40%. Another incredible example is on the far right of the image above, Gemini 3 Deep Think also scored a 3455 on Codeforces, which is equivalent to the eighth-best programmer in the world. How much longer do we think it will take to rank #1? Weeks? Or perhaps a period measured in days? I don’t know how you feel about everything that is happening right now, but I can tell you I wake up with a pang in my stomach every day these days… caused by the excitement of wondering what breakthrough will happen today. Will tomorrow bring the release of artificial general intelligence? Will it be the next release of Google’s Gemini? Or how about Grok 4.2, whose beta version was released just this week? 
Google is one of the most significant AI companies in the world. It’s just masquerading as an advertising company due to its business model. And no matter how it makes its money, it has always been a secret AI stock, hiding in plain sight. Sincerely, Jeff Brown
Founder and CEO, Brownstone Research P.S. I’ve spent 25 years in tech – as an executive, angel investor, and an advisor to some of the most innovative companies in the world. But when it comes to spotting the next wave of breakthrough stocks, I trust one person above all others: Jason Bodner, former Head of Equity Derivatives at Cantor Fitzgerald and editor of TradeSmith’s Quantum Edge Pro. I find opportunities through my Silicon Valley network. Jason does it the Wall Street way – through a proprietary “quant” system that spots massive institutional buying before the rest of the market catches on. Together, we’ve identified what we call “Secret AI Stocks” – companies that don’t look like AI plays on the surface but are becoming key players in the AI buildout. These are small companies in similar positions to Nvidia and Tesla before their trillion-dollar runs. I hope you’ll join us Feb. 25 at 8 p.m. ET for our Secret AI Stocks Summit. You’ll discover the hidden plays that could become the next group of trillion-dollar super stocks. When you join us at this free webinar, Jason and I will reveal one stock each that is critical to AI’s future… yet still flies under the radar. | 
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