In November when Google introduced its new Gemini 3 Pro model, it sent reviewers and insiders on a long loop of praise for its impressive benchmark profile. As expected afterwards, OpenAI’s CEO Sam Altman declared a “code red” in a leaked internal memo to his engineers where he instructed everyone to drop everything else to focus on delivering superior improvement to their core product.
Since then the company has pushed out a new version of its flagship GPT-5 model as well as an updated image generation model to keep pace with an increasingly competitive Google.
However, as many observers may point out, these efforts are either becoming less effective or are not sufficient, considering the recent slump in traffic to the chatGPT website since the last quarter of 2025.
In fact, Gemini now holds 21.5% of the generative AI market share, up from 5.7% a year ago and 12.9% three months prior, while ChatGPT’s share fell from 87.2% to 68% in December 2025, and then further to 64.5% by early January 2026, according to Similarweb’s Global AI Tracker.
In this article, we want to quickly bring you up to speed with what’s going on with AI, the players and their armies, their investments as well as backgrounds to tie it all together.
The Background
When the plot of this ai drama we examine today started, it did so with ChatGPT’s launch in 2022.
Almost immediately after launch, the website and app garnered exponential download and signup numbers, much to the horror of legacy tech giants such as Microsoft and Google.
For Google in particular, ChatGPT seems to be an immediate and serious threat considering much of the same web search tasks where it generates its revenue can be done in chat.
And for a long time, this dilemma seemed to have weighed Google down.
As Chatgpt grew and more and more search customers moved to other platforms including YouTube, TikTok, and ChatGPT particularly amongst younger demographics, the threat to Google’s faith seemed all but sealed.
But, as we’re now seeing, the company has turned things around and this appears to be the muted reason for all the praise it is getting with the latest iteration of its generative ai product.
For technology enthusiasts and anyone else familiar with the inherent contradiction that exists between Google’s core business model which sells banner advertising to webpage-visiting eyeballs, and this new chat-styled search interface in generative ai applications, Google’s recent turnaround in position may indicate a core paradigm shift.
On OpenAI’s “Code Red”
Part of the reason why this is significant could be debated; however, there is a consensus and fear that the web of investments in AI through OpenAI looks more like a bubble than a real emerging market.
For context, since its widespread acceptance, OpenAI has positioned itself as the one company which underpins the entire AI trade with deals spanning several of the big players on the generative AI value chain such as datacenter (Oracle) and chipmaking (Nvidia) companies.
As one analyst says, “It’s [OpenAI] linked up with AMD, it’s linked up with Nvidia and if open AI is going to fall behind here, what does that mean to the broader AI ecosystem and the various trades that investors have fallen in love with?”
Google’s Gemini benchmark leadership is a huge deal because it threatens to make OpenAI and ChatGPT substantially less relevant despite its commitment to spending over a trillion dollars in the coming years to fund data centers in hopes of significant adoption and turnover.
If all of a sudden this revenue prospect is reduced by Google, which is already a mature company with a huge presence everywhere, then this means that OpenAI is not going to have the money to fund all of the commitments it has made.
Commitments which, as another analyst puts it, “the stocks have already rallied on.”
Why Google Leads
Although we’ve talked about ChatGPT declaring code red following Google’s Gemini launch in jest up to the present moment in this article, however looking back on Google’s past effort in the AI race through the spectacular failure of their first AI model, Bard, to their struggle with antimonopoly lawsuits and what seemed like an inevitable split of its business, it might be safe to say Google’s recent turn around in fortune equally caught everyone by surprise.
Prior to the spectacular launch of Gemini 3, Google’s past failures and the flaws of the previous Gemini models made everyone conclude that perhaps the company had become too bureaucratic to innovate and keep pace with the evolution of tech and consumers’ preferences but as we’ve now seen, this couldn’t be further from the truth.
Perhaps a good way to rationalize why it took so long for Google to get into the AI ring is succinctly captured in the quote of tech YouTuber, Breaking Even:
“Google is now competing with Nvidia, Oracle, Microsoft, Meta, AMD, [and] basically every AI company you could name. And they’re doing it in a way that no other company possibly could. The more I research this, the more it starts to look like no matter what happens in AI going forward, Google is going to win.”
This conclusion is down to what insiders aptly call the ‘vertical integration’ power of the company.
When you think about the AI value chain and where each of the big players sit in it, it becomes really clear with even a bit of knowledge about Google that it plays in several of these layers.
Google is dominating the AI race. Source
For instance, Google has long made its own chips, called Tensor, which could now be tuned for AI training. It also is a datacenter powerhouse with its Google Cloud operations generating a decent portion of its revenue. The same is true for the distribution layer where it owns and maintains not just its own smartphone but also the Android operating system which is the largest on the planet as well as the Play Store for app downloads.
See also: Meta vs Google: The Billion-Dollar Battle for AI Compute Supremacy
That said, I mentioned that Tensor could be ‘fine-tuned’ for AI but, this is not exactly accurate.
This is because Google’schips are TPUs or Tensor Processing Units as opposed to Nvidia’s GPUs or Graphics Processing Units, and are specifically built to do tensor maths which are the backbone of ai models.
As a result, thanks to their increasing focus on AI model training, Counterpoint Research reports that Google is well-positioned to navigate a predicted 7% decline in the smartphone chip market in 2026.
Understanding Gemini’s Internals
Coming back to the Gemini’s recent homerun. Apart from the incredibly perfect setup of its parent company’s operations, one could argue that another cog in its recently improving luck is the marketing strategy behind the model.
Ibid.
Critics have argued that its focus on adoption by cost cutting while targeting the student demographics may lead to greater long term success.
As a fully multimodal system, Gemini was designed from the ground up to process text, images, audio, and code within a single unified model. This architectural decision—rooted in Google’s long history of transformer research—gives Gemini a coherence advantage.
More importantly, internal testing of Gemini 3.5 (codenamed Snow Bunny and successor to the recently launched Gemini 3) reveals a step-change in capability.
According to benchmark analyses, Gemini 3.5 can:
- Generate up to 3,000 lines of functional code in a single prompt
- Outperform GPT-5 and Claude Opus in complex reasoning benchmarks
- Maintain near-instant response times while executing deep multi-step reasoning (“Deepthink” mode)
This combination—reasoning depth, speed, and creative versatility—marks a shift from its initial goal as an “assistive AI” to something closer to autonomous problem-solving infrastructure.
Adoption Strategy: Where the Battle Will Be Won
Although despite its technical edge, Gemini still trails ChatGPT in raw usage numbers, reaching roughly 600 million monthly users, compared to ChatGPT’s estimated 900 million weekly active users. But, if history is anything to go by, then it’s safe to say that this gap may soon close quickly since Google’s strategy prioritizes:
- Aggressive pricing and cost efficiency
- Deep integration into education, productivity, and mobile
- Embedding AI into workflows users already inhabit
This is not a land-grab for attention—it’s a slow absorption of daily digital life.
Once Gemini becomes the default layer across Search, Docs, Android, and Workspace, usage metrics may become less relevant than dependency.
What This Means for the AI Industry
As we’ve seen, the AI race is no longer about who launches the flashiest chatbot—— Those days are long gone.
It’s now about who can:
- Sustain infrastructure at scale
- Embed AI into existing economic systems and
- Monetize without breaking trust or usability
Google now appears uniquely positioned to do all three.
Of course this does not mean OpenAI is finished. But it does suggest that the market is entering into a new phase—one where AI leadership is determined less by hype and more by curing structural advantage.
And in that phase, Google’s long game may finally be paying off.
Final Thought
The irony here is hard to miss.
The company once accused of being too slow, too cautious, and too bureaucratic may end up defining the next era of artificial intelligence—not by disruption, but by integration.
If Gemini continues on its current trajectory, the AI race may already be closer to its endgame than most people realize.
