As tech companies race to secure the computing power needed to train and deploy AI systems, Meta is making its first AI infrastructure bet in India, striking a data center partnership with conglomerate Reliance Industries in a market that is rapidly emerging as a hub for AI infrastructure.

The partnership, announced on Wednesday, will see Meta collaborate with Reliance on a 168-megawatt AI-enabled data center in Jamnagar, Gujarat, expanding a relationship that has evolved from Meta’s multibillion-dollar investment in Reliance’s Jio Platforms to a $100 million joint venture launched last year to develop enterprise AI solutions for customers in India and overseas markets.

The deal comes as India cements its status as a natural destination for AI infrastructure investments, with tech giants seeking new geographies for data centers amid soaring demand for computing power to train and deploy AI models. Companies including Microsoft, Amazon, Google, OpenAI, and Uber have recently announced AI and cloud infrastructure investments in the country, which has rapidly expanded its data center footprint in recent years.

The rush into India extends beyond global technology firms. Earlier this week, Blackstone-backed AirTrunk announced plans to invest $30 billion to build 5 gigawatts of data center capacity in the country by 2030, while Indian conglomerates including Adani and Tata Consultancy Services have also unveiled major data center expansion plans aimed at supporting AI workloads.

New Delhi has sought to attract such investments through policy incentives, including tax exemptions through 2047 for foreign cloud providers on services sold overseas, so long as those workloads are run from Indian data centers.

India’s installed data center capacity has risen from about 375 megawatts in 2020 to around 1.5 gigawatts in 2025, according to government data. Industry estimates project that figure could grow more than fivefold to over 8 gigawatts by the end of the decade, driven by cloud adoption, AI workloads, and rising demand for local data processing.

The Meta-Reliance agreement marks the latest chapter in a relationship that has steadily deepened since Meta invested $5.7 billion in Jio Platforms in 2020. Since then, the companies have expanded their collaboration across digital services, enterprise AI, and now the infrastructure underpinning next-generation AI systems.

As part of the partnership, Meta is leasing capacity at Reliance’s new Jamnagar facility, which the companies said will be powered by renewable energy and cooled using desalinated seawater. Meta has committed to covering the entire cost of the energy and water required to support its operations there.

Reliance said the 168-megawatt facility will ready within two years and can be expanded over time. Further, the data center will also support Meta’s global infrastructure and AI computing requirements, plugging India more directly into the company’s worldwide network of AI facilities.

Under the agreement, Reliance said it would provide end-to-end services ranging from design and construction to renewable power, connectivity, and ongoing operations, a sign of the conglomerate’s ambitions to become a one-stop shop for AI infrastructure among global technology companies.

Separately, Meta said it had contracted nearly 1 gigawatt of new renewable energy capacity in India through agreements with CleanMax and Fourth Partner Energy, which will supplement the renewable power supporting the Jamnagar facility.

The companies did not disclose the value of the agreement, the type of AI workloads that will run from the facility, or whether Meta plans additional AI infrastructure investments in India.

Waymo has created a new computer model designed to more accurately answer a fundamental question: how does its autonomous driving software stack up against humans?

The Alphabet-owned robotaxi company, which developed the computer model of human driving capabilities in conjunction with TU Delft, published a research paper about it in Nature Communications on Wednesday.

Waymo said it expects the new model to be more accurate than the previous version it has used over the past several years. The new model was built using a framework called active inference — the theory that a driver is constantly imagining possible futures and taking actions to reach the safest, most predictable one.

Waymo said the new model will help it better understand how humans behave in crash scenarios that its robotaxis encounter.

“For decades, the automotive industry has used physical and virtual crash dummies to evaluate a car’s safety features, including its hardware and structural integrity,” Waymo wrote in a blog post on Wednesday. The new model, Waymo said, “evolves this concept, serving as a behavioral benchmark for autonomous driving systems able to realistically represent reasonable expectations on how a careful and competent human driver responds to traffic conflicts.”

A more accurate model of human driving behavior is table stakes for autonomous vehicle companies that need to understand and grade the performance of its robotaxis in crashes. And it comes at a critical juncture for Waymo, which is scaling to more cities and facing greater scrutiny from regulators and the public.

In January, when a Waymo robotaxi struck a child near a school in Santa Monica, California, the company relied on its previous computer model to claim that an attentive human driver would have made impact at around 14 miles per hour. The Waymo robotaxi hit the child at just 6 miles per hour, after decelerating from 17 miles per hour, and the company said she sustained minor injuries. (The crash is still under investigation by the National Highway Traffic Safety Administration and the National Transportation Safety Board.)

The biggest difference between this new model — which Waymo calls the Reference Driver — and its predecessor is that it is able to reproduce a human driver’s behavior in the run-up to a crash. Previously, Waymo’s models (and other industry models) focused on replicating “last-second, reactive” human maneuvers, according to the company.

The Reference Driver, meanwhile, can “simulate the internal ‘surprise’ a driver feels during a conflict, providing a more human-like benchmark for autonomous driving systems that was previously impossible to automate at scale,” Arkady Zgonnikov, an assistant professor at TU Delft, said in a statement.

Waymo says this new driver model can be adapted to model a “wide range of road user behaviors beyond collision avoidance,” and that it is better-equipped to be applied to “large test sets with thousands of scenarios.”

“The model can represent and evaluate numerous complex, real-world crashes in a virtual environment, identifying performance improvements with unprecedented speed and efficiency,” the company wrote.

Waymo wants others to collaborate on pushing the Reference Driver further, too. The company said Wednesday that it is making the research code for the model available under an academic, non-commercial license that allows it to be used for research, teaching, personal experimentation, and scientific publication.

Here’s one metric for tracking SpaceX’s IPO later this week: The company has changed the venture industry’s perspective on long-term, capital-intensive space so much that a talented founder with no space experience can fund a space data center company.

Orbital, a new firm that emerged in May from a16z’s startup accelerator program Speedrun with a $5 million seed round, is the latest company promising to do inference in space — just as soon as Starship is flying regularly. Other investors include Basis Set, Human Element, Wayfinder, Antler, Anti Fund, Ascent, Rubik, Zero Knowledge Ventures, LYVC, Feld Ventures, New Legacy, FNDR, UpHonest, and Asterisk.

Founder and CEO Euwyn Poon previously founded e-scooter company Spin in 2017 and sold it to Ford a year later, joining the automotive giant. When he was ready to start a new company, a16z’s Speedrun was eager to get on board, according to partner Andrew Chen, who told TechCrunch that Poon worked through several ideas before landing on space data centers.

You’re familiar with the pitch. There’s insatiable demand for AI compute, and deploying it is slow going on Earth. Why not head to space for limitless sunshine and limited environmental reviews? The main problem is the brutal economics of launching stuff into orbit, which currently leaves the business case unable to close.

Orbital, like many of it competitors, is betting on SpaceX figuring out its Starship rocket and offering it to commercial customers. “We will get to full scale when Starship comes online,” Poon explained. The price of the Falcon 9, the current state of the art, “makes this not economically feasible.”

For now, Poon and company — which includes about a dozen folks in Los Angeles, with experience at Amazon LEO, SpaceX, and Northrop Grumman — are working toward a demo flight that will see the company fly an Nvidia Blackwell chip on a partner’s satellite to test Orbital’s radiation shielding and thermal management tech. In 2028, the company hopes to launch its first data-processing spacecraft with Nvidia’s Space-1 Vera Rubin-class GPUs.

At that point, the company wants to start doing piece-wise inference work, which would allow it to generate revenue with each satellite launched. That’s a similar path to rival data center startup Starcloud, which already has a GPU in orbit and plans to launch several more to generate income until Starship enables them to deploy their full constellation.

Orbital’s goal is to deploy 10,000 satellites that provide a distributed gigawatt of computing power, with each satellite providing 100 kW of power. For comparison, Elon Musk said SpaceX expects its AI satellites to produce up to 150 kW, and Starcloud expects to field larger 200 kW-rated spacecraft to run chips.

Some companies are too impatient to wait for Starship. Cowboy Space Company, another space data center startup backed by a16z, recently decided to start building its own rockets. Jeff Bezos’ space company Blue Origin also announced plans to launch data centers into space using its New Glenn launch vehicle.

Poon is confident that the breadth of AI demand will allow many companies to succeed. “There’s so many lanes for companies in our space to pursue,” he told TechCrunch, before rattling off an array of choices that included companies pursuing different AI workloads, designs, and concepts of what a space data center looks like.

Chen said that Poon’s experience scaling up a company that deployed 250,000 scooters across 100 cities shows he can manage the tricky task of building an aerospace company. Over the long term, a project like this might take a decade and $5 billion or more, but Chen said venture firms are more comfortable with timelines like that.

“This kind of thing would have sounded crazy 10 years ago when we were all building mobile apps,” he said. “Starting it in 2026 just lets you tap into all the energy and excitement that’s happening in the capital markets.”

Poon found his way into the space data center business by a circuitous route. After leaving Ford, he bought a Nvidia A100 on a lark, co-locating it in a Santa Clara data center and serving open-weight models. That firsthand experience convinced him the value in delivering compute in the era of AI.

Now he’s just got to put a couple thousand GPUs in space.