Alphabet is preparing to expand its direct investments in artificial intelligence startups, buoyed by massive gains from earlier bets such as SpaceX. CEO Sundar Pichai said the company sees a growing number of opportunities to deploy capital as AI reshapes the technology landscape. His comments come as Alphabet’s 2015 investment in SpaceX could be worth around $100 billion, depending on future valuation milestones tied to a potential IPO.

Speaking in a conversation published Tuesday, Pichai pointed to SpaceX and Anthropic as examples of how early investments can scale alongside major technology shifts. Alphabet initially invested $900 million in SpaceX at a $12 billion valuation. Following a merger between SpaceX and xAI earlier this year, the combined entity has been valued as high as $1.25 trillion, with reports suggesting a future IPO could target $1.75 trillion. If Alphabet has maintained its stake, the return would rank among the most successful venture-style investments in the company’s history.

The company is now adapting its investment strategy to match the scale of the AI boom. Rather than relying solely on its venture arms GV and CapitalG, Alphabet is increasingly deploying capital directly from its balance sheet. This approach mirrors moves by other major technology firms, including Nvidia, Microsoft, and Amazon, as AI startups require significantly larger funding rounds than traditional venture deals.

Anthropic illustrates this shift. Alphabet invested $300 million in the AI startup in 2023, followed by an additional $2 billion later that year. Its total investment now exceeds $3 billion, with a reported ownership stake of about 14%. Over the same period, Anthropic’s valuation has surged to roughly $380 billion, reflecting rapid growth in demand for generative AI systems. The partnership also has strategic value, as Anthropic relies on Google’s cloud infrastructure and tensor processing units to run its models.

From Venture Bets to Strategic Capital

Pichai’s comments suggest Alphabet is moving beyond passive venture investing toward a more strategic model tied closely to its core business. Large AI investments can drive demand for its cloud services, custom chips, and infrastructure, creating a feedback loop between capital deployment and product growth.

This shift also reflects lessons from past investments. Pichai noted that Alphabet could have invested more heavily in its own autonomous vehicle unit, Waymo, at earlier stages. Waymo has since raised significant external funding, including a $16 billion round this year that valued the company at $126 billion.

A New Era of Mega Investments

The scale of current AI funding rounds is reshaping how tech giants allocate capital. Companies are increasingly making multi-billion-dollar investments to secure strategic partnerships and infrastructure demand, rather than pursuing smaller, diversified venture portfolios.

Alphabet’s experience with companies like Stripe, where early investments have grown significantly in value, reinforces the potential upside of this approach. But the AI era is raising the stakes, with fewer deals and much larger check sizes.

As competition intensifies, Alphabet’s willingness to invest aggressively could determine its position in the next phase of AI development, where capital, infrastructure, and partnerships are becoming as critical as the technology itself.

Intel said Tuesday it will participate in Elon Musk’s “Terafab” initiative, a project focused on expanding semiconductor manufacturing and generating massive computing power for artificial intelligence and robotics. While the company did not disclose specific responsibilities, it confirmed the partnership in a post on X, highlighting its role in designing, fabricating, and packaging high-performance chips at scale. Intel shares rose about 2% following the announcement.

The Terafab project brings together several of Musk’s companies, including SpaceX, xAI, and Tesla, in an effort to rethink how advanced chips are produced. The initiative aims to deliver as much as 1 terawatt per year of compute capacity, a scale that reflects the rapidly increasing demands of AI systems. Intel’s contribution appears centered on its core strength in semiconductor manufacturing, particularly its ability to integrate chip design, fabrication, and advanced packaging technologies.

Although details remain limited, Intel’s involvement signals a potential shift in how large-scale AI infrastructure is developed. The company recently hosted Musk and xAI team members at its headquarters, suggesting early-stage collaboration and alignment. A photo shared by Intel showed Musk alongside CEO Lip-Bu Tan, reinforcing the strategic nature of the partnership.

Terafab’s ambition is notable even within the context of today’s AI boom. Producing 1 terawatt of compute annually would require vast manufacturing capacity and energy resources, far exceeding current deployments by most AI firms. The project appears designed to support Musk’s expanding ecosystem, including AI models developed by xAI, autonomous systems at Tesla, and data-intensive operations at SpaceX.

A New Model for AI Infrastructure

The collaboration reflects a broader trend toward tighter integration between chipmakers and AI developers. Instead of relying solely on external suppliers, companies are increasingly forming partnerships to secure dedicated compute capacity and optimize hardware for specific workloads. Intel’s manufacturing capabilities could complement Musk’s vertically integrated approach across hardware, software, and data.

At the same time, the move positions Intel more directly in competition with other semiconductor leaders benefiting from the AI surge. Nvidia has dominated the market for AI chips, while companies like AMD and Broadcom are expanding their roles through custom silicon and infrastructure partnerships. By aligning with Terafab, Intel may be seeking to strengthen its relevance in next-generation AI systems.

Scaling Beyond Traditional Limits

The Terafab initiative also highlights the industrial scale that AI development is reaching. Meeting the project’s compute targets would require advances not only in chip design but also in fabrication processes, supply chains, and energy efficiency. Intel emphasized that its ability to produce and package chips at scale will be central to achieving these goals.

For Musk’s companies, the project could provide greater control over critical infrastructure, reducing reliance on third-party suppliers and enabling faster iteration of AI systems. For the semiconductor industry, it points to a future where partnerships between chipmakers and AI firms become essential to meeting the growing demand for compute.

Broadcom is expanding its footprint in artificial intelligence infrastructure through new agreements with Google and Anthropic, underscoring the growing demand for compute power behind generative AI systems. The company said it will develop future versions of Google’s AI chips while also supporting a major expansion of Anthropic’s access to computing capacity. The updates, disclosed in a regulatory filing, pushed Broadcom shares up about 3% in extended trading.

At the center of the announcement is Broadcom’s continued work on Google’s tensor processing units, or TPUs, custom chips designed to train and run AI models at scale. While the companies have collaborated for years, the latest agreement signals a deeper alignment as competition intensifies among chipmakers and cloud providers. Custom silicon is becoming increasingly important as AI companies look for alternatives to general-purpose graphics processing units.

Broadcom is also scaling its relationship with Anthropic, one of the fastest-growing AI startups. The expanded deal will provide the company with access to roughly 3.5 gigawatts of compute capacity, primarily powered by Google’s TPU infrastructure. That marks a sharp increase from earlier deployments. Broadcom CEO Hock Tan recently said the company had already begun supplying around 1 gigawatt of compute to Anthropic, with demand expected to exceed 3 gigawatts by 2027.

Anthropic’s rapid growth helps explain the scale of the investment. The company said its annualized revenue has surpassed $30 billion, up from about $9 billion at the end of last year. It now counts more than 1,000 enterprise customers spending over $1 million annually, a figure that has doubled in just two months. Its Claude chatbot also saw a surge in popularity earlier this year, briefly becoming the most downloaded free app in Apple’s U.S. App Store.

The broader opportunity for Broadcom could be substantial. Analysts at Mizuho estimate the company may generate $21 billion in AI-related revenue from Anthropic in 2026, potentially doubling to $42 billion in 2027. While Broadcom did not disclose financial terms, the projections highlight how central large AI customers are becoming to semiconductor revenue growth.

A Shift Beyond GPUs

The deals also reflect a wider shift in how AI infrastructure is built. For years, companies like Anthropic and OpenAI have relied heavily on Nvidia GPUs accessed through cloud providers such as Amazon, Google, and Microsoft. That model is now evolving.

Broadcom is working with multiple AI developers, including OpenAI, on custom silicon tailored to specific workloads. At the same time, OpenAI has committed to using large volumes of AMD GPUs, signaling a diversification of suppliers. This mix of custom chips and alternative hardware suggests the AI ecosystem is moving toward more specialized and distributed infrastructure strategies.

Scaling the AI Backbone

The expansion of compute capacity into the gigawatt range highlights the industrial scale of modern AI. Training and deploying advanced models now requires vast energy, data center space, and specialized hardware. Much of Anthropic’s new infrastructure is expected to be located in the United States, reflecting both capacity needs and strategic considerations around data and supply chains.

For Broadcom, the partnerships reinforce its transition from a traditional semiconductor supplier into a key enabler of AI platforms. For the industry, they illustrate how the race to build and control AI infrastructure is becoming as critical as the development of the models themselves.