Splashing Out 27 Billion Yuan, OpenAI Establishes New Company to Accelerate AI Deployment

marsbitОпубліковано о 2026-05-12Востаннє оновлено о 2026-05-12

Анотація

On May 11th, OpenAI announced the formation of a new company, "OpenAI Deployment Company," with an initial investment of over $4 billion (approximately 27.2 billion RMB). This venture aims to help businesses build and deploy AI solutions. OpenAI is also acquiring the AI consulting firm Toromo to rapidly scale the deployment company's capabilities. This new entity, majority-owned by OpenAI, brings together 19 investment, consulting, and system integration partners, led by TPG with co-lead founding partners including Advent International, Bain Capital, and Brookfield. OpenAI's Chief Revenue Officer, Denise Dresser, stated that while AI is becoming increasingly capable, the current challenge lies in integrating these systems into core business infrastructure and workflows. The deployment company is designed to bridge this gap and translate AI capabilities into operational impact. This move comes as OpenAI emphasizes the next competitive phase will depend on the efficiency of deploying AI in real business scenarios. The company reports over 1 million businesses already use its products and APIs. OpenAI is significantly increasing its investments in computing power, with co-founder Greg Brockman stating the company expects to spend $50 billion on compute this year, a dramatic increase from $3 million in 2017. The announcement follows OpenAI's recent completion of a record $122 billion funding round in late March, led by Amazon, Nvidia, and SoftBank, valuing the company at $852 ...

On May 11th local time, OpenAI announced the establishment of a new company called 'OpenAI Deployment Company,' with an initial investment exceeding $4 billion (approximately 27.2 billion yuan), aiming to help enterprises build and deploy AI. Simultaneously, OpenAI will also acquire the AI consulting firm Toromo to rapidly expand the scale of the deployment company.

The deployment company is controlled by OpenAI and brings together 19 investment, consulting institutions, and system integrators. OpenAI stated that this is a long-term collaborative project led by TPG, with Advent International, Bain Capital, and Brookfield serving as joint lead founding partners. Goldman Sachs, SoftBank, and others are also founding partners. Among them, Brookfield announced on May 11th that it had agreed to invest $500 million in OpenAI Deployment Company.

According to the introduction, the AI consulting company Toromo, which OpenAI plans to acquire, will bring about 150 AI engineers and 'deployment experts' to the deployment company. Toromo was founded in 2023 and formed an alliance with OpenAI, with clients including Mattel, Red Bull, Tesco, and Virgin Atlantic.

OpenAI Chief Revenue Officer Denise Dresser believes that AI is becoming increasingly capable of performing tasks within organizations, but the current challenge is how to help enterprises integrate these systems into the infrastructure and workflows that support their businesses. Therefore, the purpose of the deployment company is to help organizations bridge this gap, turning AI capabilities into real operational impact.

OpenAI stated that over the past few years, over 1 million enterprises have adopted OpenAI products and APIs. The next stage of competition will depend on the efficiency of AI deployment in actual business scenarios and the support capabilities of OpenAI and its ecosystem partners.

OpenAI is increasing its investment in computing power and the AI field. OpenAI co-founder and president Greg Brockman said on May 5th local time that the company is expected to invest $50 billion in computing power this year. Brockman noted that as OpenAI develops more advanced AI models and provides services to a broader user base, its computing costs have surged from about $30 million in 2017 to hundreds of billions of dollars this year. Informed sources stated in February this year that OpenAI aims to achieve a total computing expenditure of around $600 billion by 2030.

At the end of March, OpenAI announced the completion of $122 billion in financing, the largest single financing round in Silicon Valley corporate history, with a post-investment valuation of $852 billion. This round was led by Amazon, Nvidia, and SoftBank, with Microsoft continuing its participation. SoftBank co-led the round with a16z, D. E. Shaw Ventures, MGX, TPG, and T. Rowe Price Associates, Inc. Additionally, institutions such as ARK Invest managed by Cathie Wood, Blackstone, Sequoia, Temasek, and Thrive Capital widely participated.

Previously, OpenAI had revealed that some strategic investors had committed $110 billion as the basis for this financing round, including $50 billion from Amazon, $30 billion from Nvidia, and $30 billion from SoftBank.

OpenAI is also intensifying the research and development of large models. At the end of March, OpenAI announced it would cease using its video generation tool Sora. It is reported that OpenAI will refocus team efforts on developing advanced robotics and AI models capable of interacting with the physical world. On April 14th, OpenAI announced allowing select users access to a new model more adept at discovering software security vulnerabilities, with fewer restrictions on user probing methods for such tasks. On April 20th, reports indicated that OpenAI is set to release a completely new image model in the coming weeks, with significantly enhanced capabilities in generating complex images and charts.

Separately, according to previous remarks by OpenAI founder Sam Altman, OpenAI may go public in 2027. Reports suggest that OpenAI's IPO (Initial Public Offering) valuation could be as high as approximately $1 trillion, with a potential filing for listing with regulators as early as the second half of 2026.

This article is from 'Jiemian News,' reporter: Hou Ruining

Пов'язані питання

QWhat is the name of OpenAI's new company and what is its initial investment amount?

AThe new company is named 'OpenAI Deployment Company', with an initial investment exceeding $4 billion (approximately 27.2 billion RMB).

QWhich consulting firm is OpenAI acquiring to scale its deployment company, and what does it bring?

AOpenAI is acquiring the AI consulting firm Toromo, which will bring about 150 AI engineers and 'deployment specialists' to the deployment company.

QAccording to OpenAI's Chief Revenue Officer, what is the current challenge for AI adoption in organizations?

AThe current challenge is how to help enterprises integrate AI systems into the infrastructure and workflows that support their business, bridging the gap between AI capability and real operational impact.

QHow much does OpenAI plan to spend on computing power this year, and what is its long-term target by 2030?

AOpenAI plans to spend $50 billion on computing power this year, with a long-term target of approximately $600 billion in total compute expenditure by 2030.

QWhat was the amount of OpenAI's latest funding round and its post-money valuation?

AOpenAI announced the completion of a $122 billion funding round in late March, which is the highest in Silicon Valley history, with a post-money valuation of $852 billion.

Пов'язані матеріали

Jensen Huang: Vera Rubin Full Mass Production, AI Agent a Key Focus, Challenging Intel to Target the Next-Generation AI PC Gateway

NVIDIA CEO Jensen Huang delivered the keynote speech at GTC Taipei 2026, announcing several major product launches and strategic directions. The company's Vera Rubin architecture is now in full-scale production, with OpenAI, Anthropic, and SpaceX among the first customers. NVIDIA highlighted AI Agent as a key future focus, introducing the Vera CPU designed for AI agents and the Vera BlueField-4 STX for secure, chip-level AI storage processing. A significant move involves challenging Intel in the PC market. NVIDIA, in collaboration with MediaTek, is developing the RTX SPARK PC chip (manufactured by TSMC) for Windows systems, set to launch this fall for laptops and desktops. This signals NVIDIA's push into the next-generation AI PC arena, aiming to provide a vertically integrated core computing platform for the entire Windows ecosystem, similar to Apple's approach. Other announcements include the new Nemotron 3 Ultra AI model and the NVIDIA DSX platform, described as a complete "playbook" for building AI factories, allowing performance simulation and validation before physical deployment. In automotive, the DRIVE Hyperion platform was positioned as a global robotaxi platform, with major Chinese automakers like BYD, Geely, Zeekr, Xiaomi, and Pony.ai already adopting or developing autonomous driving solutions based on it. The Alpamayo 2 super open inference model for robotaxis was also introduced. For robotics, NVIDIA unveiled the Isaac GR00T humanoid robot reference platform for academic research and a large open-source agent tools and skills suite for Physical AI. The company plans to collaborate with global humanoid robot manufacturers, including China's Unitree, whose H2 Plus robot served as the reference hardware for the GR00T platform demonstration.

marsbit24 хв тому

Jensen Huang: Vera Rubin Full Mass Production, AI Agent a Key Focus, Challenging Intel to Target the Next-Generation AI PC Gateway

marsbit24 хв тому

Running MoE on Mobile Phones? Meta Proposes MobileMoE, Speeding Up iPhone 16 Pro by 3.8x

Meta's MobileMoE, a mobile-optimized Mixture-of-Experts (MoE) language model architecture, enables efficient on-device large language model (LLM) inference for the first time on commercial smartphones. Designed for decoder-only Transformers, it replaces dense feed-forward layers with MoE layers. Key design choices include 8 experts with granularity g=8, top-4 routing, and a shared expert. The model undergoes a four-stage training process: pre-training, intermediate training, supervised fine-tuning, and quantization-aware training. Results show MobileMoE models, with similar memory footprint, achieve equal or higher average accuracy across 14 foundational benchmarks while using only 1/2 to 1/4 of the FLOPs compared to dense baselines. After INT4 quantization, they remain competitive. Notably, on an iPhone 16 Pro, MobileMoE-S demonstrates significant speedups: up to 3.8x faster in the prompt phase and 2.2-3.4x faster in per-token generation compared to a dense counterpart, with lower peak memory usage. While MobileMoE establishes a new Pareto frontier for on-device LLMs in accuracy-compute trade-offs, particularly excelling in code and math tasks, it currently lags behind models like Qwen3.5 2B in advanced instruction following and knowledge reasoning. Future work includes improving post-training techniques, exploring NPU deployment, and managing the runtime memory sensitivity of MoE models to varying inputs.

marsbit28 хв тому

Running MoE on Mobile Phones? Meta Proposes MobileMoE, Speeding Up iPhone 16 Pro by 3.8x

marsbit28 хв тому

Bitcoin's Weak Rebound Fails to Mask Adjustment Trend, HYPE's Top Signal Warns of Short-Term Risks | Invited Analysis

**Title:** Bitcoin's Weak Rebound Fails to Mask Downtrend; HYPE Top Signal Alerts of Short-Term Risks | Exclusive Analysis **Abstract:** This weekly market analysis examines the current technical structures of Bitcoin and HYPE, outlining key trading strategies. Bitcoin's daily chart shows it has broken below the median line of its primary ascending channel, indicating structural weakness. It is currently experiencing a weak rebound within a short-term descending channel, targeting resistance at $75,000-$76,000. Failure to break above this zone could lead to a resumption of the downtrend, testing support at $69,500-$70,500. Trading strategies include positioning for a rebound rejection (Plan A) or a breakdown below key support (Plan B) with controlled short positions. For HYPE, the 4-hour chart reveals a potential seven-wave advance from the May 14 low, now showing signs of exhaustion. A bearish divergence (momentum weakening) has been observed, coupled with a top signal from the proprietary "Spread Trading Model" at potential endpoint 47. The key this week is to monitor if a confirmed top forms here, especially upon a breach of the $62.5-$64.57 support area. If broken, a larger corrective move towards $54-$56.30 is anticipated. The short-term strategy for HYPE focuses on cautious long entries only upon confirmed stabilization within the support zone. The report also details a successful short BTC trade from the previous week, yielding a ~5.07% profit, executed based on model signals and price action. Strict risk management rules, including dynamic stop-loss adjustments, are emphasized.

marsbit45 хв тому

Bitcoin's Weak Rebound Fails to Mask Adjustment Trend, HYPE's Top Signal Warns of Short-Term Risks | Invited Analysis

marsbit45 хв тому

Торгівля

Спот
Ф'ючерси
活动图片