Business model is the logic of a firm, and the engine room that determines how companies capture value in the marketplace. When new technologies emerge, history has shown that the winners are not always the inventors of the pure technology. Instead, victory goes to those who redesign business models, anchoring innovation on fresh pathways through which markets create and redistribute value.

Earlier today, I explained the rising Circular AI Partnership Model that OpenAI is pioneering. In this construct, investments, enterprise deployments, and AI capabilities reinforce one another in a tight, self-sustaining loop. OpenAI invested in Thrive Holdings, not merely as a capital move, but to embed AI systems into Thrive’s portfolio of companies, retrofitting them for higher efficiency and elevated performance. Thrive had already invested in OpenAI. Now OpenAI will power its enterprise operations. That is a circular loop, capital feeding AI, and AI feeding enterprise growth, which in turn feeds capital. A new business model is emerging!

A second example: Nvidia’s $2 billion investment in Synopsys. That partnership will redefine the scale and velocity of AI and computing engineering across one of the world’s most complex design industries. Jensen Huang, Nvidia’s CEO, captured the moment with characteristic clarity: “This is a huge deal… We’re about to revolutionize one of the most compute-intensive industries in the world.”

Nvidia on Monday revealed it has purchased $2 billion worth of Synopsys’ common stock, cementing a sweeping multiyear partnership aimed at transforming the speed and scale of computing and artificial intelligence engineering across one of the world’s most design-intensive industries.

The investment — executed at $414.79 per share — forms the financial backbone of a collaboration meant to accelerate compute-heavy applications, advance agentic AI engineering, expand cloud access, and drive joint go-to-market initiatives, according to both companies. The market reaction was immediate: Synopsys stock rose 4%, while Nvidia gained 1%.

Their ambition is bold: shrink the cycle between chip design through chip manufacturing and AI model optimization. Good People, this may be one of Nvidia’s most consequential strategic moves. Why? Because before an Nvidia chip ever ships, EDA companies like Cadence, Synopsys, and others, must design and validate it. If these companies do not accelerate, Nvidia cannot advance. Nvidia’s trajectory is bounded by its upstream bottlenecks!

So, by investing in Synopsys, Nvidia is not merely buying stock; it is upgrading its supply chain, compressing time to market, and strengthening the foundational hardware layer needed to power the AI age. This is how modern technology empires are built: own the compute, shape the tools, and accelerate the pipelines that forge your future.

In 2021, in Harvard Business Review, I asked “Is Your Startup Doing Everything It Can to Capture Value?”. In that piece, I emphasized that value must not just be created; value must be captured. Today, AI companies are rethinking how they capture value. And to do that, they are inventing a new genre of business models.

Welcome to the Circular AI Business Model, the architecture that will power the AI-driven economy.

OpenAI’s New Playbook: Turning AI Partnerships Into Enterprise Wealth

Bill Gates did not become wealthy simply because he co-invented Microsoft technologies; he became a legend because he pioneered a new business model, one where software could be sold and licensed at scale. At a time when IBM bundled software with hardware at no explicit cost, Gates rewrote the economics of computing. That business model, not just technology, created Microsoft wealth.

Elon Musk is celebrated not merely because he built an electric car company, but because he reimagined how cars themselves generate revenue. His breakthrough was not batteries; it was the business model of monetizing software inside a car. When other automakers must pay Tesla billions in carbon credits, and when drivers pay recurring fees for additional features, Musk transformed an automobile company into a software subscription machine. That is why he is the high priest of money.

Now, with OpenAI, a new business model is emerging, a circular AI partnership model with a new enterprise layer. Consider this: “OpenAI and Thrive Holdings are partnering to accelerate enterprise AI adoption. Thrive invests in, acquires, and builds businesses positioned for long-term, technology-driven transformation. The initial focus is accounting and IT services—functions with high-volume, rules-driven workflows where OpenAI’s platform can drive immediate gains.”

The move widens OpenAI’s ongoing push to diversify its revenue streams at a time when the company is still battling to turn its wildly popular ChatGPT product into a profitable business. By embedding itself directly inside operating businesses across the “real economy,” OpenAI is placing bets that its models can unlock new efficiencies, reshape traditional workflows, and ultimately return financial gains that could support its heavy compute needs.

Thrive Holdings focuses on buying, owning, and running companies that could benefit from new technologies like AI, with an early focus on accounting and IT services. As part of the deal, OpenAI will embed engineers, researchers, and product teams inside Thrive Holdings’ companies, aiming to speed up their AI adoption and cut operating costs.

This is profound. Thrive has invested in OpenAI. And now OpenAI will supply the AI engines and engineering teams to transform the companies Thrive acquires. As those companies improve productivity and profitability through AI, OpenAI benefits again.

This is a flywheel. A loop. A circular business architecture where capital, AI capabilities, and enterprise outcomes reinforce each other.

For founders building foundational AI systems, this is the emerging playbook:
Partner with investment funds whose portfolio companies can become systematic adopters of your AI infrastructure. Embed your teams, deploy your stack, deepen usage, and your revenue becomes structural, not episodic.

The future of AI will not just be about models. It will be about new business models.

Apple has announced the most significant change to its Artificial Intelligence (AI) group since the 2024 launch of Apple Intelligence, confirming that long-serving AI chief John Giannandrea is stepping down.

The move comes as the company faces increasing pressure and criticism for falling behind its major tech rivals in the AI race.

Giannandrea, who has held the position of Senior Vice President for Machine Learning and AI Strategy since joining in 2018 and reported directly to CEO Tim Cook, will transition to an advisor role before fully retiring next spring (2026).

The New AI Chief: Amar Subramanya

Apple has hired a high-profile industry veteran to take the reins, signaling a fresh focus on foundational research and talent acquisition. Amar Subramanya will join Apple as Vice President of AI. Subramanya brings a wealth of relevant experience, having most recently served as a Corporate Vice President of AI at Microsoft. Crucially, he spent 16 years at Google, where he was the head of engineering for Google’s Gemini Assistant before his brief stint at Microsoft. He also worked at Google’s DeepMind AI unit.

Subramanya will now report to software chief Craig Federighi, marking a key organizational change. Previously, Giannandrea reported directly to Tim Cook.

Apple confirmed that Subramanya will lead critical areas, including the development of Apple Foundation Models, machine learning research, and AI safety and evaluation. The remaining teams previously under Giannandrea will be shifted under COO Sabih Khan and services chief Eddy Cue for better organizational alignment.

The shake-up occurs during a period of intense scrutiny over Apple’s AI execution. The suite, intended to put Apple alongside leaders like OpenAI and Google, has received lukewarm reviews from users and critics since its launch. One of Apple Intelligence’s centerpiece features—a significantly improved and personalized Siri assistant—was delayed until 2026, signaling internal development struggles.

CEO Tim Cook acknowledged that Federighi “has been instrumental in driving our AI efforts, including overseeing our work to bring a more personalized Siri to users next year.”

Although Apple’s stock is up 16% in 2025, it has lagged behind many other big tech companies. Investors worry that Apple is spending “much less” on the necessary cloud infrastructure, data centers, and frontier models compared to rivals like Microsoft, Google, and Meta, as its strategy prefers to run AI on-device.

Adding competitive pressure is the emergence of new AI-driven hardware. Jony Ive, Apple’s legendary hardware designer, sold his startup io to OpenAI for $6.4 billion earlier this year. Ive and OpenAI CEO Sam Altman have since collaborated on AI-driven hardware prototypes, which they intend to reveal in two years or less, potentially challenging the iPhone’s dominance built since 2007.

To address its competitive gap, Apple has increased its spending on AI and struck a key deal with OpenAI to integrate ChatGPT into some of its products, including Siri. The hiring of Subramanya, a leader experienced in commercializing foundation models at the industry’s top competitors, is a decisive move to reset and accelerate Apple’s internal AI development strategy.

Inside Amar Subramanya’s Trailblazing Run from Google Gemini to Apple’s AI Hot Seat

Apple’s appointment of Amar Subramanya as its new Vice President of AI is not just a standard executive change; it is a high-stakes talent coup that brings a chief architect of two rival AI superpowers—Google and Microsoft—to the helm of a division criticized for falling behind.

Subramanya’s career trajectory is a direct map of the global AI race’s most competitive battlegrounds. His deep expertise in turning cutting-edge Machine Learning (ML) research into products that scale to billions of users is precisely what Apple, grappling with the delay of its next-generation Siri until 2026 and lukewarm reviews for Apple Intelligence, desperately needs.

Google: The Architect of Gemini

Subramanya spent 16 years at Google, rising through the ranks to a senior leadership position defined by the company’s highest-profile AI initiative. He served as the Head of Engineering for Google’s Gemini Assistant (the successor to the Google Assistant), where he gained critical, practical expertise in building and deploying advanced AI products.

The Gemini project represents Google’s definitive effort to integrate its foundational Large Language Models (LLMs) into a highly interactive, multimodal consumer application.

His tenure included time within Google’s DeepMind AI unit, one of the world’s most influential AI research groups. This exposure gives him invaluable insight into the theoretical and engineering requirements necessary to develop state-of-the-art foundation models—the very core technology Apple needs to master.

His work at Google was centered on scaling complex machine learning and natural language processing systems, establishing a reputation as a researcher-turned-builder capable of productizing theoretical AI advances.

Microsoft: The Target of the Talent War

Subramanya’s stopover at Microsoft was brief but highly significant, placing him at the heart of Redmond’s aggressive AI expansion. He joined Microsoft in mid-2025 as Corporate Vice President of AI, reporting directly to Mustafa Suleyman, the CEO of Microsoft AI and co-founder of DeepMind.

His hiring was part of an aggressive talent acquisition strategy by Microsoft, which reportedly poached over 24 engineers and researchers from Google/DeepMind in a concentrated effort to bolster its consumer AI products.

At Microsoft, Subramanya was focused on developing advanced foundation models to drive AI-powered products such as Microsoft Copilot, which currently relies heavily on OpenAI’s GPT models. His experience was expected to accelerate Microsoft’s capability to build proprietary models and reduce its external dependency.

Upon joining Microsoft, he publicly praised the culture as “refreshingly low ego yet bursting with ambition”—a comment widely interpreted as a contrast to the competitive internal dynamics at Google. This unique perspective on both companies’ corporate cultures makes him an ideal leader to address the internal execution and collaboration challenges that have plagued Apple’s AI team.