Is this the AI ​​revolution? Gemini 3.0 vs. OpenAI: It's not about the better model, but about the better strategy.

Why OpenAI is now really under pressure – and what strategy could make Google the winner

The artificial intelligence market is approaching a crucial turning point. While OpenAI, with ChatGPT, has been considered the undisputed symbol of the generative AI revolution for the past two years, Google is preparing a strategic counterattack that could reshape the balance of power. The imminent release of Gemini 3.0, announced by CEO Sundar Pichai before the end of the year, is far more than an incremental product improvement. It marks the provisional culmination of a three-year catch-up effort aimed at cementing Google's technological and commercial leadership in the AI ​​age.

At the heart of this attack lies not only a more powerful AI model with superior capabilities in critical areas such as professional code generation and multimodal processing of text, images, and audio. Google's true, difficult-to-replicate advantage lies in its "full-stack" approach: complete control over the technological chain—from the development of proprietary AI chips (TPUs) and the most advanced AI models to deep, native integration into an ecosystem of billions of Android devices and widely used services like Google Workspace and Google Search.

While OpenAI benefits from its first-mover advantage, it increasingly faces structural problems: The recent release of GPT-5 was disappointing for many users, its reliance on expensive, external infrastructure remains a strategic weakness, and its subscription-based business model is more vulnerable than Google's ability to seamlessly integrate AI capabilities into its existing, highly profitable revenue streams. The coming months will reveal whether Google's strategy of gradual but profound integration is sufficient not only to challenge OpenAI's dominance but also to fundamentally reshape the AI ​​market.

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The realignment of the AI ​​market: Why Google's next step is crucial

Google is at a critical juncture in its AI strategy. While ChatGPT has dominated as the symbol of generative artificial intelligence for the past two years, Google is preparing to release Gemini 3.0, a model with the potential to fundamentally transform the dynamics of AI competition. This is not an incremental step within an already established product segment, but rather a strategic repositioning aimed at cementing Google's position as the technological and commercial leader in artificial intelligence.

CEO Sundar Pichai's announcement at the Dreamforce 2025 conference that Gemini 3.0 would be available before the end of the year garnered significant attention in the industry. But this is more than just a product announcement. It signals the culmination of a three-year catch-up effort involving substantial organizational restructuring, massive investments in proprietary hardware, and a fundamental reassessment of Google's business model. The then-prevalent perception of a sluggish, backward company, caught off guard by startups like OpenAI, has shifted dramatically.

According to insiders, the upcoming Gemini 3 model is already available in beta versions, which are being tested by select users and developers. Initial reports indicate that its technical capabilities are impressive, particularly in the areas of code generation and multimodal processing. Google traditionally tests its models with the utmost discretion, so the existence of working versions is not surprising. However, the fact that these versions are available beyond the normal research channels signals a deliberate strategy to gather early feedback and build expectations.

Gemini 3 and its technical promises: Where the model becomes competitive

Gemini 3.0 is positioned as an even more powerful AI model, offering substantial improvements over its predecessor, Gemini 2.5, not only in natural language processing but especially in two critical domains: professional code generation and multimedia generation. This focus on specific performance areas is a deliberate strategic choice, as these two functionalities are becoming increasingly business-critical in modern companies.

The coding capabilities of AI models have become a key differentiator between leading systems. In recent benchmarks like SWE-Bench Verified, Gemini 2.5 Pro already achieves 63.8 percent, placing it at the top of available systems in this area. Gemini 3.0 is expected to bring further significant improvements. The practical implications are considerable: development teams that rely on cutting-edge AI-based programming support could have a stronger incentive to choose Google's ecosystem. This is particularly relevant because programming is an area where engagement often leads to loyalty. A developer who works effectively with an AI tool will continue to use and recommend it.

In the area of ​​image generation, Gemini 3.0 is expected to integrate an improved version of Nano Banana, Google's tool for creating viral images and content. This tool has already demonstrated considerable success, attracting millions of users who utilize it for the rapid creation of marketing content, social media posts, and creative projects. Integrating these capabilities into the core model would make Gemini 3.0 a multimodal tool that not only processes text but also generates high-quality visual content. This addresses one of the most critical use cases in today's content economy.

Gemini's multimodal design, built from the ground up for the seamless use of text, images, video, audio, and code, gives Google an inherent advantage. Unlike OpenAI, which for a long time trained models with separate components for different data types, Gemini's architecture is natively multimodal. This allows the system to make connections between different modalities, resulting in more creative and contextualized output.

At the 2025 International Collegiate Programming Contests, Gemini 2.5 Deep Think demonstrated impressive capabilities by solving ten out of twelve highly complex algorithmic problems, a feat that would have earned it a gold medal in the official rankings. The model even found solutions to problems that had stumped all 139 participating top human teams. Although OpenAI later revealed that its experimental model had solved all twelve problems, Gemini's performance demonstrates that Google can technically compete with OpenAI. More importantly, however, Gemini achieved this feat using universal reasoning models that operated in natural language, rather than specialized mathematical models. This suggests a fundamentally different and potentially more flexible architecture.

The silent takeover: Google's full-stack advantage as insurmountable

What many observers of the AI ​​market overlook is that the real competition doesn't primarily take place in the lab, but rather in sales channels and infrastructure. Google has an advantage that is structurally difficult to replicate: a complete technological stack spanning from semiconductor fabrication and software development to global distribution.

This isn't simply a technical superiority. It's a superiority in operational efficiency. Google not only develops the models but also possesses Tensor Processing Units (TPUs), specialized semiconductors optimized exclusively for training and inferring AI models. While OpenAI relies on external chips from Nvidia, subject to limited access and higher costs, Google can manufacture and optimize its proprietary TPUs in-house. This results in cost efficiencies at scale that OpenAI cannot achieve.

The latest generation of Google's Cloud TPUs, such as the TPU v5e, offers up to 2.5 times the throughput per dollar compared to the TPU v4. A single TPU v5e chip delivers up to 393 trillion integer operations per second. A full TPU v5e pod offers 100 quadrillion integer operations per second—or 100 petaflops—sufficient for even the most complex model predictions. For future scaling, Google has already announced the TPU Ironwood, which can combine an incredible 9,216 chips into a single pod, with inter-chip connectivity of 1.2 terabytes per second.

This infrastructure isn't merely cosmetic. It has concrete economic implications. Training costs for large language models have grown exponentially with their complexity and size. A GPT-3-like model cost $4.6 million to train in 2020. By 2022, the cost had fallen to $450,000—a 70 percent annual decrease. Gemini Ultra, one of the most complex models Google has ever trained, reportedly required around $191.4 million in training costs. These sums are considerably more difficult for OpenAI to bear without relying on external investors. Google, on the other hand, can finance these investments from its core business and has no incentive to prioritize short-term profits.

The true masterpiece of Google's strategy, however, lies not in the infrastructure alone, but in the fact that this infrastructure is directly connected to its distribution channels. Google has deeply integrated Gemini into its most dominant products. Every time a user turns on an Android device, opens Google Workspace, uses Gmail, or performs a Google search, they potentially come into contact with Gemini. This is a distribution advantage that no pure software company can replicate.

The numbers speak for themselves. Google's internal tracking shows that Gemini's daily usage has increased by over 50 percent since Q2 2025. The app has now reached 450 million monthly active users and boasts approximately 35 million daily active users. This is not only growth comparable to OpenAI's explosive growth rates in ChatGPT's early months, but it's being driven by entirely different factors. While ChatGPT grows primarily through word-of-mouth and active user choice, Gemini is growing through native integration across billions of devices.

Particularly noteworthy is the integration of Gemini into Google Workspace, Google's suite of productivity applications and a direct competitor to Microsoft 365. Over 46 percent of US companies have already integrated Gemini into their productivity workflows. This is a tremendous lever, as enterprise productivity applications are inherently "sticky"—switching to competing systems is expensive and time-consuming for companies with established processes. Google is leveraging this component of its user base to spread AI features previously found only in dedicated chatbot applications.

Gemini's multimodal capabilities—its ability to seamlessly process text, images, video, and audio—enable use cases that go beyond what ChatGPT currently offers commercially. An employee can send an email to Gemini with an attached document and screenshot, requesting a specific analysis. The system can understand all three modalities simultaneously, integrate them into the context of the request, and deliver a precise response. This is virtually impossible with purely text-based systems.

The OpenAI problem: A company that becomes a victim of its own success

OpenAI's previous dominance in the AI ​​market was a phenomenon of surprise and first-mover advantage. ChatGPT was launched with enormous technical momentum and even greater marketing hype. The application was free and accessible, leading to exponential adoption. Between the end of 2022 and mid-2024, ChatGPT was clearly at the center of the AI ​​conversation, and OpenAI benefited enormously from this market position.

However, a turning point has recently emerged. The release of ChatGPT 5 in August 2025 was perceived as disappointing by many AI enthusiasts and professionals. While the benchmarks remained impressive and the model showed improvements in some specific domains, the expected revolutionary leap was lacking. Many users reported that practical performance even fell short of its predecessor, or that the model produced more detached-sounding responses in real-world applications.

A specific problem with GPT-5 was Openai's attempt to optimize resource utilization by removing the ability for users to choose a specific model for a given task. Instead, the system automatically decides which internal model to use. From a server utilization perspective, this might be rational, but from a user perspective, it's a step backward. Experienced users who previously manually selected the highest-performing model for specific tasks now report having to make more frequent corrections and retry attempts to achieve the same results as before. Paradoxically, this leads to a higher overall load on Openai's servers, not a lower one.

This is a classic example of how a company under pressure makes decisions that save costs in the short term but undermine user satisfaction and loyalty in the long run. Various AI community moderators have reported that user complaints about the reliability and declining returns of AI models have increased by 30 percent since the fourth quarter of last year. This isn't feedback from a company in a growth phase, but from one that has begun to optimize.

OpenAI's branding problem also remains unresolved. ChatGPT is still the "Kleenex" of the AI ​​chatbot market—the first name that comes to mind when people talk about this technology. ChatGPT has roughly 700 to 800 million weekly active users, and about 160 to 190 million people use the platform daily. By comparison, Gemini has 450 million monthly active users and about 35 million daily active users.

At first glance, it might seem that OpenAI has a comfortable lead here. However, this interpretation is clouded by an important detail: ChatGPT's weekly engagement is about five times higher than Gemini's, but Gemini is experiencing faster growth in monthly metrics. This suggests that while some heavy users are dependent on ChatGPT, the base of casual users is migrating to Gemini—partly due to better integration and the fact that Gemini is present without users having to actively open a dedicated application.

Furthermore, Google's branding problem is addressed by Gemini 3.0. Google isn't busy defending an existing product; it's building a new one. The release of a quantitatively superior model could create a moment of renewed attention. If Gemini 3 demonstrates substantial improvements in both benchmarks and practical use cases, particularly in areas relevant to professionals, it could shift perceptions.

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Market dynamics: Where ChatGPT falters and Google wins

Empirical data already shows a shift in market share. According to a report by the company Higher Visibility, Google's market share for general information searches fell from 73 percent in February 2025 to 66.9 percent in August 2025. This is a decline of over six percentage points in just six months. At the same time, ChatGPT's use for information gathering increased from 4.1 percent to 12.5 percent – ​​almost tripling.

This might initially be interpreted as a sign of complete OpenAI dominance. However, a closer look reveals a more complex picture. Particularly among younger users, a fragmented search behavior is evident, with different platforms being combined for various tasks. 35 percent of respondents stated that they have changed their search behavior, switching between Google, AI chatbots, TikTok, Instagram, and other platforms depending on the context and query.

What's particularly surprising is that even in local searches, traditionally Google's strength, AI usage has doubled. This suggests that AI tools are increasingly being used not only for complex research but also for everyday search queries.

The key to understanding these dynamics lies in the way AI is used. While ChatGPT is actively sought out by users as a separate platform, Gemini is increasingly being integrated into users' normal workflows without requiring a conscious decision. A Google Workspace user reviewing their email and seeing a summary of a long thread generated by Gemini is using AI without actively choosing it. This "ambient intelligence" model could be more significant in the long run than the raw user numbers of dedicated chatbot applications.

Furthermore, the use of AI tools for e-commerce and product search is an area where Google has historically been dominant and where AI integration is becoming particularly relevant. Nearly half of all AI users intend to use ChatGPT and similar tools in the future to specifically research products and services. This figure is even higher among younger target groups and higher earners. Google, which has already deeply integrated its advertising and e-commerce business into its search results, can build Gemini's capabilities directly into this critical commercial infrastructure. This would allow Google to define the future of purchasing decision architecture.

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Competitive infrastructures: Why GPU scarcity is becoming a declining problem

Another factor working against OpenAI is the long-term availability of computing resources. Nvidia GPUs, long the tool of choice for AI training, are expensive and available in limited quantities. OpenAI has to compete for these resources, while Google controls its own TPUs. Although GPU availability has improved in recent months, this strategic dependency remains a long-term risk for OpenAI.

Of particular importance is the fact that Google's infrastructure has been optimized for different types of AI workloads. While general-purpose supercomputers can be used for any task, specialized architectures are more efficient for specific tasks. Google's TPUs, with their matrix multiplication units for dense computations and sparse cores for sparse data, are a good example of this. This results in lower operating costs for Gemini compared to ChatGPT over the model's lifetime.

The scalability of TPU infrastructure is also remarkable. Google's so-called TPU pods connect thousands of chips with specialized high-speed connectivity. The upcoming Ironwood model can bring together 9,216 chips in a single pod, with inter-chip connectivity of 1.2 terabytes per second. For even more massive models, Google uses Jupiter, its fifth-generation data center network, to connect multiple pods. This enables training runs spread across tens of thousands of chips—a scale that external partners struggle to achieve.

The monetization trap: How Google profits while OpenAI struggles with revenue models

A frequently overlooked element of this dynamic is how Google and OpenAI monetize their AI investments. OpenAI relies on direct subscriptions and API usage. ChatGPT Plus costs $20 per month, and API usage is billed on a per-use basis. This is a classic Software-as-a-Service model. It's profitable, but it's also limited by the willingness to pay and the demand from individual users and developers.

Google, however, has a different model. First, Google offers Gemini functionality for free in many of its existing services. This isn't altruistic; it's strategic. By making Gemini available for free in Google Workspace, Gmail, and other products, Google increases the value of these services for enterprise subscribers, thereby increasing the prices Google can charge for these products. This is an unbundling approach in reverse—rather than selling AI as a separate product, Google integrates it into existing products and raises the premium for the entire suite.

Furthermore, Google is monetizing AI through improvements to its traditional core businesses. AI in search enhances “AI Mode,” a mode in which search provides more precise answers while simultaneously presenting users with more commercial queries. Phipps Schindler, Google’s Chief Business Officer, has stated that AI Mode “helps people shop conversationally” and “drives already incremental commercial queries.” This means that AI improvements directly translate into higher advertising revenue—Google’s primary source of income.

This monetization strategy is more sustainable in the long run than OpenAI's approach. If OpenAI has to rely on API revenue and premium subscriptions, its AI offering will always face the risk of users switching to free or cheaper alternatives. Google, on the other hand, increases the appeal of products that are already deeply embedded in the workflows of billions of people. A user switching would not only mean abandoning ChatGPT, but also Gmail, Drive, Workspace, or another established Google application.

The question of technological innovation: Will the differences be relevant?

A critical issue facing the industry is whether marginal improvements in technical models can actually shift market share, especially given ChatGPT's already dominant position. History in technology shows that technological superiority does not always translate into commercial dominance. Betamax was technically superior to VHS, yet still lost out. The best search engine in 1990 was not Google, but AltaVista.

However, there is a crucial difference. ChatGPT's advantage stems primarily from familiarity and brand image, not from technical superiority. If Gemini 3.0 demonstrates substantial improvements in critical, commercially relevant domains such as code generation, image generation, and multimodal reasoning, it could signal a turning point. Professional users, especially developers and enterprise users, are price-sensitive to genuine technical differences. A developer who can generate faster and more reliably with Gemini 3 will seriously consider migrating once their ChatGPT subscription expires.

Furthermore, Google's strategy is not aimed at having a single model displace ChatGPT in sheer popularity. Instead, Google aims to make Gemini useful in a wide variety of contexts – in search, email management, document creation, and app development. This is a strategy of gradual displacement, not direct confrontation.

An example of this is Google's new ML Kit GenAI Prompt API for Android. This allows developers to integrate specialized AI features directly into their applications running on the on-device Gemini Nano model. The crucial point is that this processing takes place locally on the device – user data never leaves the phone. This is a huge advantage for applications in regulated industries such as financial services, healthcare, and law, where data privacy is not just a preference, but a legal requirement.

A real-world example: The parcel delivery company Kakao integrated Gemini's on-device capabilities to automatically extract details from unstructured text messages. This reduced order completion time by 24 percent and increased the user clone conversion rate by 45 percent. This isn't a technical micro-improvement; it's a business transformation. When such use cases multiply, it can reshape the market.

Scenarios for the next 18 months: From weak to transformative

The next 18 months will be critical for the dynamics of the AI ​​market. Several plausible scenarios exist:

The first scenario is a failure of Gemini 3, where the model, while technically sound, is not substantially better than Gemini 2.5. In this case, Google would lose its catch-up momentum and would have to focus on incremental improvements through integration. OpenAI would retain its market leadership, and the industry would enter a state of relative stability, with ChatGPT and Gemini sharing the market, similar to how Microsoft and Google did in the search market.

The second scenario is that Gemini 3 represents a significant improvement, but only for specific tasks. This could lead to market fragmentation, with different users employing different models for different tasks. A developer might use Gemini for coding, while an author might prefer ChatGPT for long-form writing. This would actually benefit both companies, as it expands the market.

The third scenario is that Gemini 3 is a transformative model that surpasses ChatGPT in several key dimensions. This could lead to an accelerated migration from ChatGPT to Gemini, especially among professional users. OpenAI would then need to take aggressive countermeasures, either by announcing GPT 6 or through strategic partnerships.

The fourth scenario, which is probably the most realistic, is that Gemini 3 demonstrates proven technical performance, but that Google's real competitive advantage lies not in pure model performance, but in its ability to embed AI in ecosystems where millions of people already work. In this case, Gemini would gradually gain market share, not through direct competition with ChatGPT, but by creating use cases that ChatGPT simply cannot achieve, as it is only a dedicated application.

The broader context: Why OpenAI is under pressure, even if it's not obvious

It's tempting to focus on user numbers and conclude that OpenAI is comfortably in the lead. However, this overlooks several structural pressure points on OpenAI:

• First, OpenAI is under pressure to continuously release new models to meet high expectations. This leads to hype cycles where each new version is announced with enormous fanfare, only to be followed by disappointment. This erodes trust.
• Secondly, OpenAI's business model relies on continuous API revenue and subscriptions. This means the company constantly has to justify to users why they should pay. Google doesn't need to do this; Google makes money from search and advertising, not directly from AI.
• Thirdly: OpenAI lacks true ecosystem integration. It exists where users consciously choose to leave. Once a better alternative is available, the barrier to switching is low.
• Fourth: OpenAI has no control over the infrastructure. It depends on Nvidia for GPUs, Microsoft for cloud infrastructure, and other partners for distribution. This gives OpenAI less control over quality, cost, and timing than Google.

Google is positioning itself for dominance, not competition.

Google's strategy with Gemini 3.0 isn't aimed at beating OpenAI in a head-to-head competition as an AI chatbot. Instead, it aims to embed AI so deeply into Google's existing ecosystems that the traditional notion of "AI chatbots" as a separate category erodes. In five years, the difference between Gemini and ChatGPT might not primarily lie in raw performance, but in context and proximity—Gemini will be available everywhere, while ChatGPT will remain a specialized tool for users who actively seek it out.

This is not a victory for quality over marketing, or innovation over established market position. Rather, it is a structural victory of ecosystem integration over isolated product performance. Google will not necessarily win with a better AI model. It will win with a better platform for showcasing and distributing that model.

The release of Gemini 3.0 before the end of the year will be a key indicator of this process. Should the model demonstrate the expected performance improvements, particularly in areas like code generation and multimodal reasoning, it could mark the beginning of a significant reassessment of AI market dynamics. OpenAI won't disappear overnight; it will remain a relevant force for specialized applications. But its days of unchallenged dominance may be numbered.

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