Positron AI raises $875 million to scale AI inference hardware
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Positron AI raises $875 million to scale AI inference hardware

Positron AI has successfully raised $875 million in a Series C funding round, valuing the company at $5 billion. The company's core business involves developing hardware that makes the artificial intelligence inference process more energy-efficient and cost-effective.

The funding was secured in two stages: first, a Series C round of $375 million was closed, followed by a Series C-1 round of up to $500 million. The main round was led by NEA, Atreides Management, and Valor Equity Partners, with co-leads from Andra Capital and SemiAnalysis Capital participating. The second tranche was led by Jim Clark, founder of Silicon Graphics and Netscape, with participation from several institutional and strategic investors.

The capital raised will allow Positron to significantly expand its growing business in inference and add several experienced technology investors to its board of directors. Forest Basket from NEA and Gavin Baker from Atreides Management will join the board. Thomas Germoluk and Dylan Patel will also become directors.

As AI workloads increasingly shift towards inference, infrastructure is necessary for the continuous operation of models by every AI assistant, agent, and helper. Positron focuses on solving memory and power issues arising from this growth. The company's systems are designed with an emphasis on bandwidth and memory capacity, rather than raw computational power.

The company's next-generation systems utilize standard LPDDR5X memory, which reduces dependence on constrained high-performance memory supply chains. Positron claims its systems can achieve over 90% of available memory bandwidth.

Furthermore, the company focuses on high performance in tokens per dollar and tokens per watt metrics. Positron's architecture supports both air-cooled and liquid-cooled data centers, giving customers flexibility in deploying systems across various rack densities.

Positron already has clients using the first version of the Atlas system. Over 50 Atlas racks have been deployed in Oracle Cloud Infrastructure, where Parasail uses this power for its own inference services. Jump Trading and i3d.net are also production clients of Atlas.

The new funding will be directed towards developing the next generation of silicon chips. The Asimov chip is scheduled for fabrication using TSMC's N3P process by the end of 2026; TSMC describes N3P as an improved 3nm process. Production of Asimov is slated for the second half of 2027. Each Asimov chip will support between 288 GB and 2304 GB of memory, meeting the demands of increasingly complex AI inference workloads.

The Titan system will integrate four to eight Asimov chips into a single system and is designed to support models exceeding 16 trillion parameters. Titan will also target context windows exceeding 10 million tokens and can scale to thousands of nodes for larger deployments. Positron also plans to build a data center engineering facility with a capacity of over 2 MW and an emulation platform to support development, testing, and manufacturing readiness.

Positron intends to use the funds to secure LPDDR5X supply commitments, as well as to increase manufacturing capacity and system integration. Go-to-market operations will expand in parallel with production, helping the company meet the growing demand for inference infrastructure.

CEO Mitesh Agrawal noted that the Atlas deployments provided valuable customer insights that influenced the design of Asimov and Titan. The company is currently in a demanding execution phase, requiring it to complete silicon development while simultaneously scaling production and customer adoption.

Positron's strategy is focused on the economic efficiency of AI model operation. Its memory-centric architecture aims to reduce both energy consumption and infrastructure costs. The $5 billion valuation reflects investor confidence in the inference market.

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UniPat raises $300 million with Alibaba support to expand AI testing and benchmarking
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UniPat raises $300 million with Alibaba support to expand AI testing and benchmarking

As artificial intelligence develops, the problem of testing has emerged. While many focus on creating impressive large language models to attract investors, there are only a small number of verified platforms to check the functionality of these complex systems after training is complete.

AI models are prone to hallucinations and can drift from the norm over time. To solve this significant operational problem, which constantly deters large corporate users, UniPat has raised $300 million. UniPat actively conducts stress tests on these complex systems to ensure a full level of trust.

UniPat differs from other AI companies; they do not just create new algorithms to demonstrate at expensive tech conferences. Instead, the startup specifically tests models based on real-world scenarios, rather than sterile laboratory conditions.

This can be compared to a rigorous training camp for artificial intelligence. Before a model gains access to real consumer data or begins performing automated financial operations, UniPat subjects it to intensive trials. This allows for the generation of high-quality system performance data that developers need to eliminate critical flaws, as what hasn't been thoroughly broken first cannot be fixed.

The tech giant Alibaba led this major funding round, causing a stir in the industry. The financial details of this round are quite impressive: the new influx of capital boosted the startup's valuation to an impressive $2.5 billion post-investment. It is also interesting that this specialized center is headed by a former company employee.

It is clear that Alibaba is interested in retaining its top talent. Significant funds continue to flow despite the caution of the broader venture capital market. Moreover, this specific deal ranks among the top three percent of all registered late-stage venture capital rounds.

The high cost of the testing platform is due to basic corporate economics and the need for competitive survival. Early investors included representatives from Sequoia China. This demonstrates that leading financial players are looking for infrastructure-related enterprises amid the current AI race. We have moved past the phase of simply admiring smart chatbots.

Now, large international corporations demand flawless analytics. They need solid proof that implementing multi-million dollar AI will not lead to public embarrassment. UniPat provides this necessary insurance policy. This is not just another routine technology investment. It is a calculated move. As global powers fiercely compete for dominance in artificial intelligence, the basic infrastructure for evaluating these tools becomes infinitely valuable.

Alibaba's massive bet signals a clear shift in market priorities. The future belongs not only to those who can build the largest model but also to those who can prove their model is the safest, fastest, and most reliable in real-world conditions.

Forus raises $150 million at a $3 billion valuation to expand its AI-based medical solutions network
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Forus raises $150 million at a $3 billion valuation to expand its AI-based medical solutions network

Forus has successfully raised $150 million in a Series C funding round. The round was led by the investment fund Bain Capital Ventures, with participation from existing investors who provided additional support. Other participants in the round included Thrive Capital, General Catalyst, Accel, Redpoint, BoxGroup, Pear VC, Vast Ventures, and SV Angel.

This funding values Forus at $3 billion, representing a threefold increase from its previous valuation. As a result of this round, the total amount of funds received by Forus exceeds $300 million. Bain Capital Ventures has supported the company since its seed round.

Forus is developing an artificial intelligence-based network designed to improve access to medicines. Its platform connects doctors, pharmacies, payers, and biopharmaceutical companies. The company plans to use the raised funds to scale its platform nationwide and increase investments in technology and talent.

Forus utilizes AI agents to manage the process after physicians prescribe medication. These agents navigate insurance requirements and financial assistance programs, as well as coordinate processes in pharmacies and supply chains. Each prescription is processed by a separate AI agent that analyzes patient information before determining the next necessary step, relying on clinical models and specialized sub-agents. The company's technology is trained on data from millions of past cases.

The primary goal of the platform is to reduce delays between prescribing treatment and receiving it. This should enable doctors to prescribe newer medications with greater confidence. It is noted that one in three patients prescribed expensive or complex drugs never receives the first dose.

Forus aims to eliminate these obstacles through workflow automation. Meanwhile, the service remains free for both providers and patients. Currently, the platform supports providers in all 50 US states and covers 85% of US zip codes. The company intends to move beyond its initial specialization and begin working in additional areas of medicine. More than a third of providers in the US have already implemented the platform in their primary area of specialization, and adoption is growing in several other areas.

The company plans to expand the scope of its AI agents so they cover most of the treatment journey, including processes beyond simple prescription fulfillment. Forus is also collaborating with pharmaceutical and biotechnology firms. The company currently engages with nine out of the fifteen largest global biopharmaceutical companies and supports a number of fast-growing biotech enterprises. These partnerships give Forus insight into the barriers affecting patients and doctors, allowing the company to improve access to care and link drug development with real-world treatment experience.

The new investment provides Forus with significant capital for the next phase of growth. CEO Sahir Jaggi stated the company's ambition to implement its platform in every physician's clinic in the US. Forus believes that the pace of medical advancement is constantly accelerating, yet patients can still face difficulties accessing new treatments. The company aims to close this gap through AI-driven coordination that connects multiple parties in the drug delivery process.

Investor Bain Capital Ventures views Forus as becoming an important infrastructure platform in healthcare. The latest funding also highlights the growing investor interest in healthcare automation. Companies using AI are increasingly focusing on overcoming administrative barriers in medical care. Moving forward, Forus will focus on expanding technology and workforce, as well as increasing coverage across medical specialties and care settings.

HiddenLayer raises $100 million to expand AI security platform
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HiddenLayer raises $100 million to expand AI security platform

HiddenLayer, a company specializing in artificial intelligence security, has successfully closed a Series B funding round, raising $100 million. The leadership of this round included Delta-v Capital, with participation from Ten Eleven Ventures, Morgan Stanley, M12, and Booz Allen Ventures. The fundraising occurred amid a significant surge in demand for AI security solutions.

Over the past year, HiddenLayer's annual recurring revenue has increased more than tenfold, and the company has also acquired over 50 new clients. These clients operate in sectors such as financial services, healthcare, government, technology, and defense industry.

The HiddenLayer platform is designed to support organizations implementing generative, predictive, and agentic AI systems. The system provides protection for AI applications throughout their entire lifecycle. The capital raised will be used to expand the enterprise platform and strengthen sales and distribution channels.

HiddenLayer is scaling its platform as enterprises begin to utilize autonomous AI agents. These systems are capable of making decisions independently and interacting with external tools. The company's Agentic Runtime Security feature allows for monitoring AI behavior during operation, enabling the detection of unauthorized actions, tool misuse, and manipulation.

Furthermore, the Agent Harness Security feature was introduced, which extends runtime protection to autonomous coding agents. These agents can write, test, and release software with minimal human involvement, creating additional risks for development teams in the corporate sector.

HiddenLayer believes that traditional security tools cannot handle these risks alone. AI systems can be threatened through poisoned models, malicious inputs, and prompt injections. Therefore, the company has focused its efforts on creating security specifically designed for AI environments. The platform integrates threat detection, attack simulation, supply chain security, and runtime security.

HiddenLayer's security capabilities are backed by an extensive AI research program. The company's researchers hold 39 granted patents and 65 pending patents related to AI threat analysis, model protection, and adversarial attack detection. The team has also developed a comprehensive Adversarial Prompt Engineering Taxonomy, continuing to identify vulnerabilities in the AI ecosystem. Research covers foundational models, AI tools, and auxiliary infrastructure.

HiddenLayer is also involved in industry initiatives concerning AI governance and security. Its researchers collaborate with organizations such as CISA, MITRE, NIST, OWASP, and OpenSSF. These joint efforts contribute to improving red teaming practices and overall AI security.

The new funding will serve as a foundation for the company's next phase of commercial growth. HiddenLayer recently appointed Mike Gesnaldo as Chief Revenue Officer. The company also plans to strengthen partnerships with sales channels and expand its international operations. Key expansion targets are Europe and the broader EMEA region.

HiddenLayer anticipates continued growth in enterprise demand for AI security. The company already serves clients in several highly regulated industries, including banking, insurance, pharmaceuticals, and government. One leading provider of frontier models also uses HiddenLayer technology, reportedly serving over 700 million users weekly.

The growth in HiddenLayer's client base reflects growing enterprise concern over AI security issues. The company's platform aims to help organizations adopt AI with greater confidence. The company forecasts that autonomous systems will become increasingly significant in business processes, which in turn will create additional demand for specialized AI security tools. HiddenLayer will now focus on scaling its platform in parallel with the rapid advancement of artificial intelligence, asserting that its research helps enterprises respond to emerging threats, with a particular focus on protecting AI systems during real deployment.

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