Indian artificial intelligence startup Brahma AI has raised $150 million from private equity firm Multiples Alternate Asset Management at a $2 billion valuation, turning the company into one of India’s more closely watched bets on the commercialization of generative AI for audiovisual content.

The funding round, announced Wednesday, gives Brahma AI substantial capital to expand an enterprise-focused platform that combines artificial intelligence with technologies developed for high-end film and television production.

Brahma AI is owned by Indian media and entertainment company Prime Focus through its UK-based subsidiary DNEG, the visual effects and animation studio behind major productions including the Dune franchise and The Odyssey. Prime Focus said it will retain a 66% stake in Brahma AI through DNEG following the investment.

The funding could also become larger. Brahma AI has received an additional $100 million in investor demand and is considering increasing the size of the round to accommodate some or all of that interest, Prime Focus said.

The investment is notable because it puts a significant private-market valuation on a company operating at the intersection of two industries undergoing rapid technological change: enterprise software and media production.

Rather than positioning itself as another general-purpose AI model developer, Brahma AI is attempting to commercialize the specialized technology developed for Hollywood visual effects, animation and digital humans and make it available to businesses across multiple industries.

“Our ambition is much bigger: to build the AI-native technology platform through which the world’s leading enterprises manage, understand, create and transform their audiovisual assets,” Prabhu Narasimhan, founder and chief executive of Brahma AI, said in the company’s statement.

The company’s strategy is built around the idea that audiovisual data will become an important enterprise asset as businesses generate larger volumes of video, audio and other digital content.

Brahma AI says it is developing tools that can help companies create, manage and transform those assets rather than simply generate synthetic images or videos. That strategy places the company closer to an enterprise infrastructure and workflow provider than a conventional consumer-facing generative AI application.

Its current focus spans four sectors: media and entertainment, sports, healthcare and advertising. Its global anchor customers include Warner Bros., the NBA and Mayo Clinic.

The company is also preparing to expand into interactive digital humans, a technology that could have applications ranging from entertainment and advertising to customer service, training and healthcare.

“We are close to launching interactive digital humans,” Narasimhan said, describing technology designed to replicate the likeness and persona of real individuals to make digital interactions more closely resemble face-to-face encounters.

The technology raises the commercial value of Brahma’s platform while also placing greater emphasis on issues such as identity rights, consent, and the management of digital replicas. For companies working with recognizable individuals, the ability to reproduce a person’s appearance and persona could create new forms of digital content, but it also requires controls around who can authorize and deploy those representations.

Brahma AI is also seeking to avoid being tied to a single underlying AI model. Narasimhan said the company’s technology stack will be model-agnostic, potentially allowing enterprises to use Brahma’s applications and workflows as the underlying AI model ecosystem continues to change.

That could be an important part of its enterprise proposition. The generative AI market is evolving quickly, with model providers competing on price, capability, and specialized performance. An enterprise platform that sits above those models could theoretically retain value even as individual models become obsolete or interchangeable.

Brahma’s origins give it another potential advantage.

In February last year, the company acquired UK-based Metaphysic, a generative AI media company specializing in real-time synthetic content. Metaphysic was included in Time magazine’s list of the 100 most influential companies in 2023.

Brahma is now attempting to combine Metaphysic’s generative AI capabilities with DNEG’s visual-effects expertise and Prime Focus’ broader media technology operations.

Narasimhan said the objective was to combine technology developed across DNEG, Metaphysic and Prime Focus into an AI-native platform aimed at the world’s largest enterprises. That combination is central to the company’s $2 billion valuation. Investors are not simply backing another AI software startup. They are effectively betting that Brahma can turn specialized production technology developed for blockbuster entertainment into a scalable enterprise platform.

The investment also highlights a broader shift in India’s AI market.

India has traditionally been recognized for its large technology-services industry and its role as a major source of engineering and software talent. Increasingly, Indian companies are attempting to build proprietary AI products and platforms that can compete for enterprise spending globally.

Brahma’s connection to DNEG provides it with an unusual route into that market. DNEG has spent years building technology for some of the world’s largest film productions, where visual effects require sophisticated computer graphics, simulation, rendering, compositing, and increasingly machine-learning capabilities.

The challenge is converting those capabilities into repeatable enterprise products.

The $150 million investment gives Brahma considerable financial resources to pursue that transition, but the $2 billion valuation also raises the bar for execution. The company will need to demonstrate that its technology can move beyond high-value media productions into repeatable, scalable enterprise applications across healthcare, sports, advertising, and other industries.

Its decision to remain model-agnostic could help with that expansion, particularly as enterprises become more reluctant to commit their AI strategies to a single model provider. But it also means Brahma will need to establish a differentiated layer of technology and workflow that customers consider valuable enough to pay for independently of the underlying models.

Multiples sees the company’s combination of technology and industry expertise as a central part of the opportunity.

“Brahma AI is built on a unique heritage of Hollywood-grade technology and enterprise innovation,” said Renuka Ramnath, founder, managing director and chief executive of Multiples Alternate Asset Management.

For Prime Focus, the investment provides a way to monetize technology developed across its media and visual-effects businesses while retaining majority ownership of the AI company.

For Multiples, the deal provides exposure to an AI market increasingly moving beyond foundation models toward specialized applications and enterprise infrastructure.

And for Brahma AI, the immediate task is to turn its Hollywood pedigree and growing customer base into a technology platform capable of serving a much larger corporate market. The additional $100 million of investor interest suggests that demand for the round is strong, but the more important test will be whether Brahma can translate that investor enthusiasm into recurring enterprise revenue and a scalable AI business.

At a $2 billion valuation, the company is being priced not merely for its existing audiovisual technology, but for the possibility that AI-generated and AI-managed media becomes a major enterprise software category.

Amazon is preparing a major expansion of its quick-commerce business in India, with plans to invest as much as $3 billion through 2030 as the US e-commerce giant tries to close a substantial gap with local rivals that have reshaped how Indian consumers buy everyday goods.

The planned investment would represent Amazon’s largest commitment yet to India’s rapidly expanding quick-commerce market. Two people with direct knowledge of the company’s plans told Reuters that Amazon intends to invest $1 billion by the end of 2027, followed by another $2 billion through 2030.

Amazon declined to comment on the planned investment figures. The company said, however, that its quick-commerce operation has generated more than $1 billion in annualized gross sales over the past three months, describing it as the fastest-growing business in the history of Amazon India.

India’s quick-commerce market is currently worth about $19 billion and is projected to more than double to $41 billion by 2030, according to Datum Intelligence. That growth has created a market in which Amazon- and Walmart-backed Flipkart, despite their dominance in conventional Indian e-commerce, have been playing catch-up with companies that built their businesses around rapid delivery.

Eternal’s Blinkit, Swiggy and Zepto together control about 77% of the market and operate more than 4,500 stores, according to Datum data. Flipkart has more than 1,000 stores and an estimated 11% market share, while Amazon has about 6.2%.

Amazon’s planned spending therefore has a major infrastructure component. The company is expected to expand its network of small neighborhood warehouses, known as dark stores, from which Amazon Now orders can be assembled and dispatched quickly.

One source said Amazon is targeting about 1,300 stores by April next year, compared with roughly 750 currently.

The model requires a fundamentally different logistics architecture from Amazon’s traditional e-commerce operation. Rather than relying primarily on large fulfilment centers serving broad geographic areas, quick commerce depends on a dense network of smaller facilities located close to customers.

Amazon’s investment is expected to go beyond simply adding stores. The company plans to strengthen inventory-management software, use AI to forecast demand, and broaden the selection of products available through Amazon Now.

The focus, at least initially, will remain on frequently purchased essentials.

“The focus will be daily essentials. If the order is unlikely to be repeated, Amazon does not plan to stock it right now in quick commerce,” one source said.

That approach helps explain why Amazon is not currently prioritizing products such as iPhones through Amazon Now, even though some competitors have expanded quick-commerce offerings into expensive electronics.

The economics of that strategy remain a central challenge.

Quick-commerce operators must maintain inventory in numerous small facilities while employing delivery riders to complete orders within extremely short periods. Average grocery orders are relatively small, making it difficult to cover the cost of the delivery infrastructure from groceries alone.

Bernstein warned in a July note that grocery products by themselves may not generate sufficient economics for the sector because of low average order values, while non-grocery products can offer higher prices and margins.

Amazon’s response appears to be an attempt to improve the underlying economics before aggressively expanding the product range. One source said the company wanted its model to be operationally sound, including providing cold-storage rooms at each store rather than relying simply on refrigerators.

The approach could increase upfront costs, but it also highlights the operational complexity of competing in a market where speed has become a central selling point.

Satish Meena, founder of Datum Intelligence, said Amazon faces a difficult task in challenging established players that have built strong customer relationships around rapid delivery.

“It took some time for Amazon to commit. There appears to be a realization that this is a model they have to invest in,” Meena said. “They are doing discounts, which can help lure current Amazon customers to quick commerce.”

Amazon has already begun using incentives to encourage existing customers to try the service. Amazon Now is offering selected customers 20% cashback on initial orders above 499 rupees and free delivery on eligible orders above 99 rupees.

The company’s advantage is the large customer base already using its conventional shopping platform. Rather than having to build consumer awareness from scratch, Amazon can potentially move existing customers into faster delivery through the main Amazon app.

The investment also comes with regulatory and operational risks.

India has tightened scrutiny of the quick-commerce sector as the rapid growth of delivery services has raised concerns about rider safety. The government ordered companies in January to stop promoting services as “10-minute” deliveries, adding pressure to an industry whose marketing has often centered on extreme speed.

Amazon also operates under India’s restrictions on foreign e-commerce companies. Its regulatory exposure includes a 2024 antitrust case in which India’s competition watchdog found that the company had favored certain sellers. Amazon has denied the allegations.

The competitive structure makes the timing of Amazon’s investment particularly significant. Blinkit, Swiggy and Zepto have already established large networks and accumulated considerable operational experience, while Flipkart has also expanded aggressively.

Amazon is therefore not entering an undeveloped market. It is attempting to build sufficient density and customer usage in a sector where rivals have already spent years establishing neighborhood-level logistics networks.

The opportunity is that India’s quick-commerce market is still expanding rapidly. If the overall market reaches the projected $41 billion by 2030, Amazon would not necessarily need to displace existing leaders to build a substantial business. Its existing e-commerce customer base could provide a large pool of potential users as the company expands its network.

The $3 billion commitment also signals a broader change in Amazon’s approach to India. The company has already identified the country as a major growth market across e-commerce and cloud services, and quick commerce gives it another route into the increasingly digital consumption habits of India’s urban population.

IonQ shares climbed on Wednesday after the quantum computing company announced what it described as the industry’s first demonstration of an end-to-end, real-time quantum error decoder, a development that could address one of the major technical obstacles to building commercially useful fault-tolerant quantum computers.

IonQ stock rose more than 5% in morning trading after gaining more than 10% in premarket trading. Other quantum-computing stocks also initially advanced on the announcement, although most surrendered their early gains after the market opened.

The breakthrough centers on the ability to detect, correct, and decode errors continuously while a quantum computer is operating. IonQ said its system demonstrated that a single conventional computer processor can perform the decoding in real time rather than relying on large computing resources that can struggle to keep pace with the quantum system generating the errors.

Quantum computers are inherently vulnerable to errors because quantum states are extremely sensitive to environmental disturbances and imperfections in the underlying hardware. Error correction is therefore considered essential to scaling quantum machines from experimental systems into computers capable of reliably performing commercially important workloads.

The challenge is not simply correcting individual errors. As quantum systems grow, the volume of information required to identify and correct errors can increase rapidly. If classical processors cannot decode that information quickly enough, the error-correction system itself can become a bottleneck, creating delays and undermining the potential performance advantage of the quantum computer.

IonQ’s demonstration is significant because it addresses that problem at the level of a continuously operating system.

“Successfully validating real-time decoding across hundreds of logical qubits and over millions of logical operations is an important milestone. Moreover, the fact that our decoder runs on a single CPU provides a practical path to commercial-scale fault-tolerant quantum computing,” said Nicolas Delfosse, quantum research lead at IonQ.

The reference to logical qubits is important. Physical qubits are the basic hardware elements of quantum computers, but they are highly susceptible to errors. Quantum error correction combines multiple physical qubits to create more reliable logical qubits. The ability to maintain those logical qubits while carrying out millions of operations is a critical step toward fault-tolerant computing.

IonQ said its test demonstrated real-time decoding across hundreds of logical qubits and more than millions of logical operations. The company argues that running the decoder on a single CPU provides a more practical architecture for scaling than systems that require large amounts of classical computing power dedicated solely to error correction.

The development could have implications beyond IonQ’s own machines because the industry is attempting to solve the same fundamental problem: how to scale quantum processors without allowing the computational cost of error correction to overwhelm the system.

“Empirical evidence like this supports our vision for fault tolerance where time-to-solution, cost-to-solution, and energy-to-solution are always our North Star,” said John Gamble, vice president at IonQ Architecture.

The announcement also adds another dimension to IonQ’s broader effort to position itself within the expanding quantum-computing ecosystem. The company has partnerships with Amazon Web Services and Nvidia, two of the largest companies involved in the development of AI computing infrastructure, as well as pharmaceutical company AstraZeneca.

Quantum computing is increasingly being explored for applications where conventional computers struggle, including drug discovery, materials science, optimization, and complex simulations. Pharmaceutical companies in particular have been examining whether quantum systems could eventually accelerate parts of the drug-development process.

IonQ has also expanded beyond its core quantum hardware business. The company recently acquired semiconductor manufacturer SkyWater Technology and subsequently raised its full-year revenue guidance. The acquisition gives IonQ a greater connection to semiconductor manufacturing and could become important as the company attempts to control more of the hardware stack required for scaling quantum systems.

The market reaction shows how sensitive quantum-computing stocks remain to technical milestones. IonQ and its peers have attracted substantial investor interest as expectations for commercially viable quantum machines have increased, but the sector remains heavily dependent on advances that can convert laboratory demonstrations into reliable, scalable systems.

However, there is investor concern about the chances of the underlying architecture scaling economically. Error correction sits at the heart of that challenge because a useful fault-tolerant machine is expected to require large numbers of physical qubits to produce a smaller number of reliable logical qubits.

IonQ’s latest demonstration does not by itself establish that commercial-scale fault-tolerant quantum computing has been achieved. But if the company’s results can be reproduced and scaled, reducing the classical computing burden associated with error correction could remove one of the constraints standing between today’s experimental quantum machines and larger systems designed for practical workloads.