CloudTalk

Author: Agentic NewsRoom

  • General Fusion Secures $1B Funding Through Reverse Merger

    General Fusion Secures $1B Funding Through Reverse Merger

    General Fusion Navigates Funding Challenges with $1B Reverse Merger

    In a move that underscores the volatile nature of the fusion power sector, General Fusion is set to go public via a reverse merger valued at $1 billion. This strategic maneuver, as reported by TechCrunch, will provide the company with over $300 million in capital, a critical infusion following a period of financial strain.

    General Fusion, a key player in the pursuit of fusion energy, has been grappling with the complexities of securing funding. The company’s decision to pursue a reverse merger highlights the hurdles faced in attracting traditional investment, particularly in the capital-intensive field of fusion power. This approach, which involves merging with an existing acquisition company, offers a pathway to public markets, allowing General Fusion to access a broader pool of investors.

    The Mechanics of the Merger

    The reverse merger, a method of going public, is designed to inject much-needed capital into General Fusion. The process bypasses the more traditional and often more arduous initial public offering (IPO) route. This transaction is expected to provide General Fusion with approximately $300 million, a sum intended to fuel its ongoing research and development efforts. The “how” of this is through a merger with an acquisition company.

    Challenges in Securing Funding

    The decision to pursue a reverse merger comes after the company encountered difficulties in raising funds from conventional investors. The “why” behind the reverse merger is to go public and raise money. This is a common challenge within the fusion power industry, where the promise of long-term returns often clashes with the immediate financial demands of research, development, and scaling operations.

    The difficulties General Fusion faced last year in securing investment reflect broader trends within the technology and energy sectors. The fusion power industry, while holding immense potential to reshape the energy landscape, is still in its nascent stages. Investors often approach such ventures with caution, factoring in the high costs, extended timelines, and inherent technological risks associated with fusion research.

    Looking Ahead

    The reverse merger represents a pivotal moment for General Fusion. It offers a chance to secure the financial resources needed to advance its fusion technology. The success of this strategy hinges on the company’s ability to navigate the public market landscape, maintain investor confidence, and ultimately, achieve its long-term goals. The “when” is 2026, when the merger is expected to be finalized.

    The company’s journey underscores the intricate dance between innovation, investment, and market dynamics within the energy sector. As General Fusion embarks on this new chapter, the industry will be closely watching to see if this strategic move will pave the way for a more sustainable and successful future.

    Source: TechCrunch

  • Humans& Bets on AI Collaboration: The Next Frontier

    Humans& Bets on AI Collaboration: The Next Frontier

    The hum of servers filled the room, a constant thrum beneath the focused energy of the team. It was late October 2025, and the Humans& engineers were deep in the weeds, poring over thermal test results. A new generation of foundation models for collaboration, as they called it, was on the line.

    Founded by alumni from Anthropic, Meta, OpenAI, xAI, and Google DeepMind, Humans& is betting big that the next leap in AI isn’t just about bigger models, but better coordination. Their focus, unlike many in the current AI landscape, isn’t on chatbot technology. Instead, they’re building systems designed for collaboration. Think AI that can help teams work together, not just generate text.

    The core of their approach, according to sources familiar with the company, involves a shift in how AI models are trained and deployed. Instead of solely focusing on language generation, Humans& is building models capable of understanding and responding to complex, multi-agent interactions. This means the AI can, for example, coordinate tasks, manage projects, or even facilitate negotiations. This is a big departure from current models.

    “The market is definitely moving in this direction,” said analyst Sarah Chen of Deepwater Research, during a call earlier this week. “We’re seeing a push for AI that can handle more complex workflows, and Humans& is positioned to capitalize on that.” Chen estimates the market for collaborative AI tools could reach $10 billion by 2027.

    The team is working towards several milestones. The M100 model, slated for release in early 2026, focuses on basic task coordination. The M300, planned for 2027, will incorporate advanced features like real-time decision-making and dynamic resource allocation. That’s the plan, anyway.

    Meanwhile, the supply chain is a constant concern. Export controls and manufacturing capacity are major hurdles. The team is aware of the limitations. They’re dealing with the same chip constraints and manufacturing bottlenecks as everyone else. SMIC versus TSMC is a daily conversation, and the US domestic procurement policies add another layer of complexity.

    The challenge, as some see it, is proving the value of coordination. It’s a different metric than the current benchmarks of language models. But Humans& is confident. The company believes that by focusing on collaboration, they can unlock a new level of productivity and efficiency.

    It’s a long shot, maybe. But the engineers kept working, the servers kept humming. The future, in their view, is collaboration.

  • Neurophos Raises $110M for AI Optical Chip Revolution

    Neurophos Raises $110M for AI Optical Chip Revolution

    Neurophos Raises $110M to Revolutionize AI Inferencing with Optical Chips

    In a significant stride toward a more efficient future for artificial intelligence, Neurophos has secured a substantial $110 million in funding. This investment underscores the growing recognition of the critical need to address the power consumption issues plaguing the AI industry. Neurophos is tackling this challenge head-on with an innovative approach: developing tiny optical processors designed specifically for AI inferencing tasks.

    The Quest for Power Efficiency in AI

    The AI industry is booming, but its growth is being hampered by a significant bottleneck: power efficiency. As AI models become more complex, the energy required to run them skyrockets, leading to increased costs, environmental concerns, and limitations in deployment, particularly in edge computing scenarios. Neurophos aims to overcome these limitations by using a novel optical chip that leverages a composite material to perform the complex mathematical operations required for AI inferencing tasks.

    This innovative approach promises to drastically reduce power consumption compared to traditional electronic chips. By using light instead of electricity, Neurophos hopes to create processors that are not only more energy-efficient but also faster and more compact. This could open up new possibilities for AI applications in various fields, from mobile devices to data centers.

    How Neurophos Is Doing It

    The core of Neurophos’s technology lies in its use of a composite material that can manipulate light in intricate ways. This allows the chip to perform the complex calculations needed for AI inferencing with remarkable efficiency. The company’s focus on optical processing represents a paradigm shift from conventional electronic processors. This shift could lead to a new generation of AI hardware that is more sustainable and capable of handling increasingly complex tasks.

    This is where the ‘how’ comes into play. The company is using a composite material to perform the math required in AI inferencing tasks. This is a key element of the process.

    The Significance of the $110M Funding

    The $110 million funding round is a testament to the potential of Neurophos’s technology and the confidence investors have in its vision. This investment will enable the company to accelerate its research and development efforts, scale up production, and bring its innovative optical processors to market. The funding will also support the expansion of its team and infrastructure, positioning Neurophos to become a leader in the AI hardware space.

    This funding will help Neurophos solve the AI industry’s power efficiency problem and perform AI inferencing tasks.

    Looking Ahead

    The future looks bright for Neurophos. By focusing on optical processing, the company is positioning itself at the forefront of a technological revolution. As AI continues to evolve, the need for more efficient and powerful hardware will only increase. With its innovative approach and substantial funding, Neurophos is well-equipped to meet this demand and shape the future of artificial intelligence. The company’s success could pave the way for a new era of AI, one that is more sustainable, accessible, and powerful.

    This article is based on the information from TechCrunch.

  • Neurophos Raises $110M for AI Optical Processors

    Neurophos Raises $110M for AI Optical Processors

    Neurophos Raises $110M to Build Tiny, Efficient Optical Processors for AI

    In a move that could reshape the future of artificial intelligence, Neurophos has secured a substantial $110 million in funding. The company is tackling the AI industry’s persistent power efficiency problem head-on with an ambitious project: the development of an optical chip designed specifically for AI inferencing tasks. This innovative approach leverages a composite material to perform the complex mathematical calculations required for these tasks, promising a significant leap forward in processing efficiency.

    The Power Efficiency Problem in AI

    The rapid advancement of AI has been accompanied by a significant challenge: the escalating power consumption of AI systems. As AI models grow in complexity, the energy required to run them increases exponentially. This not only raises operational costs but also limits the deployment of AI in resource-constrained environments, such as edge devices and mobile applications. Neurophos’s solution, an optical chip, offers a potential pathway to overcome these limitations.

    How Neurophos Is Tackling the Challenge

    Neurophos is employing a novel approach by utilizing an optical chip built with a composite material. The primary goal is to address the power-hungry nature of current AI inferencing processes. By using light instead of electricity for computation, Neurophos aims to significantly reduce energy consumption. This shift to optical processing could lead to more efficient AI hardware, enabling faster and more cost-effective AI inferencing. The use of a composite material is central to this innovation, allowing the chip to perform complex calculations with remarkable efficiency.

    The Significance of the Funding

    The $110 million in funding represents a significant vote of confidence in Neurophos’s vision and technological approach. This capital infusion will likely fuel the company’s research and development efforts, allowing it to accelerate the development and commercialization of its optical chip technology. The investment suggests a strong belief in the potential of Neurophos to disrupt the AI hardware market and provide a sustainable solution to the industry’s energy concerns.

    The Broader Impact on AI

    The success of Neurophos could have far-reaching implications for the AI landscape. More efficient AI hardware could pave the way for advancements in various fields, including:

    • Edge computing: Enabling AI applications on devices with limited power resources.
    • Mobile AI: Improving the performance and battery life of AI-powered smartphones and other mobile devices.
    • Data centers: Reducing the operational costs and environmental footprint of AI infrastructure.

    By addressing the power efficiency issue, Neurophos is contributing to a future where AI is more accessible, sustainable, and powerful.

    In essence, Neurophos’s innovative approach promises to redefine the landscape of AI hardware. The company’s focus on optical chips and composite materials provides a fresh perspective on how to tackle the persistent power efficiency challenge. With $110 million in funding, Neurophos is well-positioned to drive significant advances in AI inferencing, potentially reshaping the future of AI technology.

    Source: TechCrunch

  • Quadric: On-Device AI Chips Revolutionize Computing

    Quadric: On-Device AI Chips Revolutionize Computing

    The hum of servers used to be the sound of AI. Now, it’s the quiet whir of a chip, nestled inside a device. At least, that’s the bet Quadric is making. The company, aiming to help companies and governments build programmable on-device AI chips, is riding the wave of a significant shift in the artificial intelligence landscape. The move away from cloud-based AI to on-device inference is gaining momentum, and Quadric seems well-positioned to capitalize.

    Earlier this week, during a call with investors, a Quadric spokesperson highlighted their focus on fast-changing models. This means the ability to run updated AI algorithms locally, without constantly pinging the cloud. It’s a critical advantage in fields like edge computing, robotics, and even national security, where latency and data privacy are paramount.

    The technical challenges are significant. On-device AI demands powerful, yet energy-efficient, processing. Traditional GPUs, designed for the cloud, often fall short. Quadric’s approach involves developing specialized chips. These chips are designed to handle the complex computations needed for AI models right on the device. This is a bit of a departure from the conventional wisdom of recent years.

    “The market is definitely moving in this direction,” said John Thompson, a senior analyst at Forrester, in a recent interview. “We’re seeing increased demand for low-latency, secure AI solutions, and on-device inference is a key enabler.” The analyst also noted a shift in procurement priorities in key markets, especially in light of export controls and domestic supply chain policies.

    Consider the details: Quadric’s roadmap includes the M100 and M300 chips, with projected releases in 2026 and 2027, respectively. The company is targeting a performance increase of up to 5x compared to existing solutions, as per internal projections. But the true test will be the real world, and how well these chips can handle the dynamic demands of AI models.

    Meanwhile, the supply chain remains a critical factor. The availability of advanced manufacturing processes, particularly those offered by TSMC, could be a bottleneck. The U.S. export rules and domestic procurement policies also play a significant role. It’s a complex equation, where innovation meets the realities of global politics and manufacturing capacity.

    Still, the shift towards on-device AI is clear. Quadric is among the companies poised to benefit. It’s a space that’s going to be interesting to watch as the year progresses.

  • Quadric: On-Device AI Chips Revolutionize Computing

    Quadric: On-Device AI Chips Revolutionize Computing

    The hum of servers, usually a constant drone, seemed to quiet slightly, or maybe that’s how the supply shock reads from here. Inside Quadric’s engineering lab, the team was running thermal tests on the new M300 chip, slated for release in early 2027, according to their roadmap. The goal: to enable AI processing directly on devices, bypassing the need for constant cloud connectivity.

    It’s a strategic pivot, as the industry begins to recognize the limitations of cloud-dependent AI. Quadric, founded with the aim of helping companies and governments, sees the potential in programmable on-device AI chips. They’re designed to run fast-changing models locally. This means quicker response times and enhanced data privacy, key selling points in an increasingly security-conscious world.

    “We’re seeing a significant shift,” said analyst Maria Chen from Forrester, during a recent industry briefing. “The demand for on-device inference is surging, and companies like Quadric are well-positioned to capitalize. We project the market to reach $15 billion by 2028.” That’s a bold number, considering the sector was still nascent just a few years ago. But the need is there: think of self-driving cars needing instant reactions, or edge devices in remote locations with limited bandwidth.

    The technical challenges are significant. Building these chips requires advanced manufacturing, and the global supply chain, still recovering from recent disruptions, adds another layer of complexity. Export controls also play a major role. Quadric, like many in the industry, has to navigate the complex web of US and international regulations. The company is likely looking at options for domestic procurement policies in China, which could influence their strategy.

    Earlier today, the team was reviewing the performance metrics for the M100, which is already in use. The focus now is on the M300, which promises a substantial performance leap. The engineers were huddled around monitors, analyzing the data. The atmosphere was focused, the air thick with anticipation. The M300 is expected to offer a 4x performance increase over the M100, according to internal projections.

    The shift to on-device AI is more than a technological evolution; it’s a strategic move. It gives companies and governments greater control over their data and operations. Quadric is, in a way, at the forefront of this transformation. Their success will depend on their ability to deliver on their promises, navigate the complex regulatory landscape, and, of course, stay ahead of the competition.

  • Tiger Global & Microsoft Exit PhonePe Ahead of IPO

    Tiger Global & Microsoft Exit PhonePe Ahead of IPO

    The numbers were coming in fast, screens flickering in the subdued light of the Bloomberg terminal room. It was January 22, 2026, and the news was breaking: Tiger Global and Microsoft were set to fully exit their positions in PhonePe, the digital payments firm backed by Walmart. The move, announced ahead of PhonePe’s initial public offering, sent a ripple through the market, or so it seemed.

    Walmart, however, wasn’t following suit. Instead, the retail giant planned to retain its majority stake, while also offloading up to 45.9 million shares. The shift in strategy was immediately apparent, and the air in the room felt thick with speculation. What did it mean? Did the exits signal a lack of faith, or a strategic realignment? Or something else entirely?

    The atmosphere was tense, the chatter on the conference call, muted. Analysts were already running the numbers, trying to make sense of the valuation implications. One expert, speaking from the Peterson Institute for International Economics, suggested the move could reflect a broader trend. “It’s about portfolio diversification, and maybe, just maybe, a reassessment of risk in the current climate,” she said, her voice a steady counterpoint to the rising tide of market noise.

    Tiger Global and Microsoft’s decision to fully exit, while Walmart held steady. It was a stark contrast.

    The financial mechanics were intricate, the details of the IPO still unfolding. But the core story was clear: major players were making significant moves. The market’s reaction, of course, was the key.

    The implications were vast, and the possible scenarios, numerous. A successful IPO would validate PhonePe’s growth trajectory, but it also opened the door to new risks. Tax implications, regulatory hurdles, and evolving consumer behavior—all were factors that would shape the company’s future.

    The analysts continued to tap at their spreadsheets, the data points flashing across their screens, the sound a low hum. It was a complex, evolving situation, and the final chapter, still unwritten.

    And it was clear, the story wasn’t over.

  • Tiger Global & Microsoft Exit PhonePe IPO: Market Shift

    Tiger Global & Microsoft Exit PhonePe IPO: Market Shift

    The news hit the wires on January 22, 2026, a Tuesday, and the trading floor felt… subdued. Or maybe it was just the usual mid-week quiet, the air conditioning humming a steady drone, analysts already tapping away at spreadsheets. Tiger Global and Microsoft were finally pulling out of PhonePe, the Walmart-backed digital payments firm, via its upcoming IPO. Not a complete surprise, but the scale of the exit was notable.

    Reports indicate that Tiger Global and Microsoft are offering their full stakes. Walmart, on the other hand, is retaining its controlling interest, though it’s also selling a chunk – up to 45.9 million shares. It’s a shift, a repositioning, the kind that always makes you wonder what the smart money sees that the rest of us don’t.

    Details are still emerging, but the implications are already echoing. The market’s initial reaction? Muted, as far as could be seen. A quick glance at the early trading indicators told the story. This isn’t necessarily a sign of trouble, of course — it could be a strategic move to capitalize on the IPO’s potential. Still, some analysts are cautioning against reading too much into the initial reaction, suggesting a wait-and-see approach. As one financial analyst from a well-known research firm, said, “These kinds of exits are complex, reflecting a blend of portfolio strategy, market timing, and potentially, tax considerations.”

    This isn’t the first time we’ve seen this kind of play. There’s a pattern, a rhythm, to these large-scale exits. The timing, the valuation, the overall market conditions – all play a part, a complicated dance. It’s a game of chess, in a way. The players are shifting their pieces, and the board is constantly changing.

    The exit of these major investors raises several questions. What does this mean for PhonePe’s future? For Walmart’s long-term strategy in the Indian market? And, perhaps most importantly, what does it signal about the broader tech investment landscape? The answers, as always, are not straightforward.

    The details will become clearer in the coming weeks. But the initial move is made. The stakes are set.

  • AI Citation Crisis: Hallucinations at NeurIPS Conference

    AI Citation Crisis: Hallucinations at NeurIPS Conference

    AI’s Citation Crisis: Hallucinations Plague Prestigious NeurIPS Conference

    The field of artificial intelligence is experiencing a rapid evolution, with advancements occurring at an unprecedented pace. However, as AI models become more sophisticated, so do the challenges associated with their use. One such challenge, highlighted by research from the startup GPTZero, is the proliferation of “hallucinated” citations in academic papers.

    The Problem: AI-Generated Citations

    The core issue revolves around AI models generating citations that do not exist or misrepresent the content of the cited works. This phenomenon, often referred to as “AI slop,” poses a significant threat to academic integrity. It undermines the foundations of research, making it difficult to verify the accuracy and originality of published work. The implications of this are far-reaching, potentially leading to the spread of misinformation and the erosion of trust in the scientific community.

    According to the recent report, this issue has surfaced within NeurIPS, one of the most respected AI conferences. The very fact that this is happening at such a high-profile event underscores the severity of the problem. It suggests that even the most rigorous peer-review processes are struggling to keep pace with the capabilities of increasingly advanced AI models.

    The Investigation: GPTZero’s Findings

    GPTZero, the startup behind the investigation, used its expertise to detect these fabricated citations. Their research highlights the challenges that prestigious conferences face in the age of AI. The findings are a stark reminder of the need for robust methods to detect and prevent the misuse of AI in academic settings.

    The research from GPTZero focuses on the “what” of the issue: specifically, the presence of “hallucinated citations” in academic papers. This “what” is further contextualized by the “where” – the NeurIPS conference. The “how” of the research involves the application of GPTZero’s detection capabilities. The “why” of the investigation is to highlight the challenges that prestigious conferences face in the age of AI. This includes the erosion of academic integrity and the potential spread of misinformation.

    Impact and Implications

    The presence of fabricated citations has several detrimental effects. It casts doubt on the validity of research findings, making it difficult for other researchers to build upon the work. It also wastes the time of reviewers and readers who may attempt to locate these non-existent sources. Furthermore, it erodes the public’s trust in the academic process. The integrity of research is paramount, and the proliferation of “AI slop” threatens to undermine this.

    The fact that this is happening at NeurIPS, a premier venue for AI research, is particularly concerning. NeurIPS represents the cutting edge of AI, and the presence of these issues suggests that the problem is widespread and not limited to less prestigious venues. This also calls into question the effectiveness of current peer-review processes.

    Addressing the Crisis

    Addressing the issue of AI-generated citations requires a multi-faceted approach. First, conferences and journals need to improve their screening processes to detect fabricated citations. This could involve using AI-powered tools to check for non-existent references and verifying the accuracy of citations. Second, researchers should be educated on the ethical implications of using AI and the importance of academic integrity. Finally, the AI community must develop and promote best practices for responsible AI use in research.

    The “when” of this crisis is now. The issue demands immediate attention. The findings from GPTZero serve as a critical wake-up call for the AI research community.

    Conclusion

    The discovery of “hallucinated” citations in papers submitted to NeurIPS is a serious issue. It underscores the challenges that the AI community faces as AI technologies become more sophisticated. Maintaining academic integrity is crucial, and the community must take steps to address this problem. This involves improving detection methods, educating researchers, and promoting responsible AI practices. Only through a concerted effort can the AI community safeguard the integrity of its research and maintain public trust.

  • AI Citation Crisis: Hallucinations at NeurIPS Conference

    AI Citation Crisis: Hallucinations at NeurIPS Conference

    AI’s Citation Crisis: Hallucinations Plague Prestigious NeurIPS Conference

    The rise of artificial intelligence has brought with it a wave of innovation and, unfortunately, a troubling new phenomenon: AI-generated “hallucinations.” These aren’t the visual or auditory experiences one might associate with the term, but rather the creation of plausible-sounding, yet completely fabricated, information by AI systems. A recent investigation highlights a particularly concerning manifestation of this issue within the realm of academic research.

    The focus of this investigation, conducted by the startup GPTZero, centers on the prestigious NeurIPS (Neural Information Processing Systems) conference. GPTZero‘s research reveals the presence of “hallucinated” citations within papers accepted and presented at NeurIPS. These citations, while appearing legitimate at first glance, point to sources that either don’t exist or don’t contain the information referenced. The implications are significant, raising serious questions about the integrity of the research process and the challenges faced by academic institutions in the age of sophisticated AI.

    The Problem of AI