The contemporary era of artificial intelligence (AI) features an unlikely early leader, a 47-year-old technology titan better known for ubiquitous, boring business software, Microsoft. It has created over USD 2 trillion in shareholder value, a truly astonishing feat, primarily due to a lucrative cloud partnership with OpenAI, the creator of ChatGPT, the chatbot that redefined the industry.
This strategy, where specialised firms cooperate and compete across the AI stack, was lauded as a nimble, genius business model. But let us be clear: this model was never about superior agility; it was about leveraging market dominance, and that dependence has now revealed its crippling limits.
Look at the evidence: on October 28, 2025, Microsoft was forced to loosen its grip on OpenAI, granting the lab “boundless promiscuity” in choosing cloud partners, waiving Microsoft’s prior right of first refusal—no permission required.
This shocking structural concession came despite a monumental, restructured deal that guaranteed Microsoft a massive 27% ownership stake in OpenAI’s for-profit entity, valued at approximately USD 135 billion, and entitled the tech giant to 20% of the startup’s revenue until 2032, or until an expert panel verifies the achievement of Artificial General Intelligence (AGI).
The hypocrisy is stunning. Microsoft secured a USD 250 billion commitment from OpenAI for Azure cloud services over the next few years, yet OpenAI is simultaneously forced to make massive infrastructure deals with rivals.
It is contracting with Oracle for data centre services, joining a USD 500 billion data centre project called “Stargate,” and securing compute resources from AMD for six gigawatts of GPUs. The truth is that the intense, multi-billion-dollar cost of running frontier AI models—costs OpenAI is desperately trying to alleviate—is simply too high for any single horizontal partner to bear, even one receiving a quarter of a trillion dollars.
This crippling reliance on external suppliers for core compute is not a triumphant strategy; it is a debilitating structural weakness, forcing the supposed AI king to scramble for capacity across rival clouds.
Google’s Silent Triumph
In stark contrast to Microsoft’s frantic horizontal scrambling, Google, the one company universally mocked as the AI laggard, went all in on deep vertical integration—a strategy once dismissed as slow and burdened by bureaucratic inertia.
Google is the only major technology company that has committed entirely to this end-to-end approach, designing its own Tensor Processing Units (TPUs) in-house, training frontier models through Google DeepMind, and deploying them across its massive ecosystem, including Search and YouTube.
While corporate inertia did cause a delay in releasing their chatbot, leading to initial embarrassment, that delay was architectural patience, allowing them to refine a system that is now economically devastating to every competitor.
Google’s vertical control over its hardware stack has provided a structural, defensible advantage, a true moat that no amount of market spending can easily breach. The proprietary TPU is not just faster; it is a superior financial weapon.
TPUs are reported to be 4 to 10 times more cost-effective than conventional GPUs in large-scale language model training scenarios, offering 1.2x to 1.7x better performance per dollar compared to NVIDIA A100 GPUs. Furthermore, due to optimised deep learning architecture and reduced cooling requirements, TPUs consume 30% to 50% less power, leading to vast cost savings for Google and achieving up to 2.7x better performance per dollar in some instances.
This architectural genius means the cost per AI query for Google is not five times that of traditional search, as the doom-mongers originally estimated, but only twice. This control over unit economics is everything—it means integrating AI into Search dilutes Google’s gross margin only marginally, from a robust 90% to a still-highly competitive 86%. This is the economic foundation of sustained power, a foundation Microsoft simply does not possess.
Why Verticality Always Wins
The market has now, finally, recognised this strategic gulf, turning its cold shoulder toward Alphabet into a warm embrace. This reversal resulted in a USD 1 trillion gain in market value over just four months, supported by strong financial performance that proves AI is additive, not cannibalistic, with Google Cloud sales growing at an annual rate of 35%, driven explicitly by generative AI solutions and infrastructure.
The most damning indictment of the horizontal model, however, comes directly from Google’s competitors. Anthropic, a prominent AI lab, has now become a major, deeply committed customer of Google’s proprietary infrastructure, announcing a massive expansion to utilise up to one million TPUs from Google Cloud, a deal worth tens of billions of dollars.
This move was driven explicitly by the superior “price-performance and efficiency” of the TPUs, confirming that Google’s vertically integrated approach has created an economic necessity that rivals cannot ignore, compelling them to become major customers for the infrastructure they were supposed to be bypassing.
Recognising this fundamental failure, Microsoft is now frantically pivoting to mirror Google’s strategy, establishing its own in-house chip-design studio and AI lab in a belated effort to control its destiny.
But attempting to copy a mature, decade-long architectural advantage is costly, slow, and strategically embarrassing. Microsoft’s second-generation Maia chip is already delayed, and its model-building efforts are considered “inchoate.”
The war for the AI stack is, for all intents and purposes, fundamentally decided, and the vertical approach, powered by proprietary silicon, has won—comprehensively—through superior unit economics.