Modal, an AI startup based in San Francisco, has successfully secured $355 million in a recent financing round, pushing its valuation to an impressive $4.65 billion, according to CEO Erik Bernhardsson.

This year has brought notable challenges for AI startups due to two major trends: a remarkable increase in AI coding activities and a tightening supply of computing resources. Modal Labs is strategically addressing both of these developments.

The company provides essential access to computing chips required for AI operations, known as inference. Additionally, Modal offers a development environment that enables programmers to try out AI-generated code prior to integrating it into their applications.

The Series C funding round was spearheaded by Redpoint Ventures and General Catalyst, the latter of which will have representation on the company's board. The latest valuation marks a significant rise from $1.1 billion recorded in the fall.

This increase reflects a substantial revenue growth at Modal, as reported by Bernhardsson on Tuesday. The startup's annualized revenue has surged to approximately $300 million, a significant jump from just $60 million a year prior, attributed to a growing number of companies incorporating AI into their products.

"Coding has been the driving force for the last six months," Bernhardsson remarked, noting that Modal’s clientele includes biotech firms, hedge funds, and two companies specializing in weather forecasting.

Concurrently, the rising costs and decreasing availability of computational power have emerged as challenges. Bernhardsson noted that the company expanded its search for compute providers, discovering many previously unknown options. Currently, Modal collaborates with 13 cloud service providers, up from five last year.

The Series C funding was executed in two phases. The initial set of investors came on board at a valuation of $2.5 billion, but as demand grew, more investors began to express interest, prompting a second funding round at the new $4.65 billion valuation. This latter group of investors includes notable firms like Accel and Menlo Ventures.

Exclusive: Modal Labs reaches a valuation of $4.65 billion amid the surge in AI coding.

Bright modern tech office flooded with warm sunlight through floor-to-ceiling windows. A huge Modal Labs logo is etched in frosted glass behind a sleek workstation where dual monitors show cascading lines of code that arc upward, morphing into a stylized rocket about to launch. Above the desk, subtle floating numerals “$4.65 B” appear in soft amber light, conveying explosive AI coding growth.

Exclusive: Modal Labs valued at $4.65 billion as AI coding takes off - Reuters

- AI startup Modal raised $355 million, reaching a valuation of $4.65 billion.  
- The company aids AI firms in accessing computing resources and testing AI code.  
- Modal's annualized revenue surged to $300 million amid rising demand for AI tools.

SAN FRANCISCO, May 21 (Reuters) - AI startup Modal raised $355 million in a new round of financing, valuing the company at $4.65 billion, CEO Erik Bernhardsson ​told Reuters.

AI startups have been whipped by two shifts this ‌year: the surge in AI coding and the increasing scarcity of computing power. Modal Labs is confronting both.

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The startup helps AI companies access the chips they need to ​run AI tools, called inference. It also has a sandbox product ​that allows developers to test their code, increasingly created through ⁠AI, before embedding it in their products.

The company's new round is ​led by Redpoint Ventures and General Catalyst, which will have a board seat. ​The Series C round values the company at $4.65 billion, the company said, up from $1.1 billion , opens new tab in the fall.

The valuation reflects Modal’s surge in revenue, which has spiked in the ​last six months as more companies build products with AI code, ​Bernhardsson said on Tuesday. The company’s annualized revenue is about $300 million, up from an annualized ‌rate ⁠of $60 million in September.

“Coding for the last six months has been driving everything,” Bernhardsson said, adding that Modal's customers include biotech companies, hedge funds and two weather-forecasting firms.

During that same time frame, however, computational resources have become ​more expensive and ​harder to find. ⁠Bernhardsson says the company cast a wider net and found compute providers that he had never heard of ​before. Modal works with 13 cloud companies, up from five ​last ⁠year.

Modal raised cash in the Series C round in two tranches. The first tranche of investors invested at a $2.5 billion valuation, the company said. But more ⁠investors ​started knocking on Bernhardsson's door, leading to the ​company raising a second tranche at the $4.65 billion valuation. Investors in the second tranche include ​Accel and Menlo Ventures.

Reporting by Deepa Seetharaman in San Francisco Editing by Rod Nickel

Our Standards: The Thomson Reuters Trust Principles. , opens new tab

Exclusive: Modal Labs valued at $4.65 billion as AI coding takes off

Technological revolutions are often hailed for their promise of increased efficiency, which traditionally leads to decreased demand and lower expenses. However, the economic landscape of artificial intelligence (AI) seems to defy this expectation. This phenomenon can be elucidated through a historical concept known as Jevons Paradox, which suggests that improvements in resource efficiency actually spawns increased consumption rather than reduced demand.

Coined by economist William Stanley Jevons in 1865 during his research on coal, Jevons Paradox reveals that enhanced efficiency diminishes consumption barriers, opens up new applications, and eventually raises demand levels. Microsoft’s CEO, Satya Nadella, has frequently referenced this concept to illustrate why advancements in AI are causing a surge in computing needs rather than alleviating them. As AI technologies become more affordable and effective, businesses are not merely optimizing their current usage; they are also discovering innovative applications for it.

Three key players in AI hardware, infrastructure, and enterprise software provide insights on how this evolving demand is reshaping the competitive terrain for startups and smaller enterprises.

As AI hardware continues to advance, faster and more economical solutions are dissolving previous limitations and transforming the economics of AI. For instance, Tensordyne, a company specializing in inference racks, reports a staggering 90% reduction in power consumption compared to its alternatives. This aligns perfectly with Jevons’ findings: reduced costs lead organizations to engage in more AI-related activities, rather than less. Co-founder Gilles Backhus points out that as AI tasks become more intricate, organizations leverage AI for multifaceted elements simultaneously, which in turn demands greater computational capacity.

To keep pace with rising demands, the need for affordable and efficient hardware is critical. Backhus emphasizes that overcoming Jevons Paradox requires significant reductions in AI operational costs while maintaining key priorities like speed and model performance. Tensordyne's Napier inference system shines in this context, providing enhanced throughput while consuming less energy and space, thus maintaining lower operational costs. The aspiration is to eliminate the dilemma of fast yet expensive versus slow yet cheap systems, allowing high-performance AI workloads to be run at a fraction of the current costs. This could radically alter the financial landscape for startups, making high-quality AI applications accessible and potentially igniting unprecedented growth.

In the realm of professional services, the impact of AI is evident as businesses seek creative applications for the technology. Companies like Orbital exemplify this shift. In industries such as law and real estate, where outputs have long been correlated to manpower, AI not only reduces time intervals; it enables tasks that were previously unfeasible or too costly. Orbital facilitates around 200,000 transactions annually across the U.S. and U.K., with CTO Andrew Thompson noting how developers are not necessarily working less; they are simply producing more significant outcomes.

The inherent creativity in software development adds another layer of intrigue regarding what this enhanced productivity entails. According to Thompson, the efficiency gained through AI can accelerate the product development cycle. The ultimate goal is a higher frequency of delivering new offerings to customers. By enhancing this discovery process, AI helps teams maximize their productivity.

Furthermore, the rise of the ‘10x engineer’ reflects how individuals who adeptly harness AI can achieve output levels that were once the domain of larger teams, thereby contributing to the increase in compensation within tech roles. Thompson attributes this wage growth to a new cycle of consumption, where increased utility drives further adoption of AI tools. He draws parallels to past tech experiences, such as the evolution of software capabilities alongside hardware improvements, noting how increased performance leads to richer functionalities and innovative offerings that were impossible before.

Additionally, infrastructure plays a pivotal role in supporting these AI-driven workflows. As more companies adopt AI, demand for computational resources escalates, perpetuating a cycle of growth. Verda, an AI infrastructure provider, has experienced rapid expansion, with revenue increasing twentyfold over two years and surpassing a $100 million annual run rate. This surge can be attributed to the synergistic development of improved hardware and sophisticated AI models. CTO Arturs Poli observes that the momentum of this growth is not merely rooted in better access to computing; rather, it reflects a harmonious progression between technological advancements and real-world applications, resulting in accelerated and unpredictable expansion in the AI sector.

The Impact of AI Economics on Startups and Small Enterprises

A bright, sunlit co-working studio: a solo adult entrepreneur in casual attire studies a laptop screen showing glowing financial charts overlaid with subtle circuit-board patterns. Above the desk, golden coin and digital token icons float gently, symbolizing AI-driven growth. A small coffee cup and potted succulent sit nearby on a minimalist wooden desk. Natural warm window light streams in, casting soft shadows on modern white brick walls for an optimistic, real-world feel.

How AI Economics Is Changing For Startups And Small Businesses - Forbes

- AI advances drive higher compute demand, contrary to traditional efficiency expectations.  
- New hardware innovations reduce costs, enabling businesses to leverage AI in novel ways.  
- Increased productivity from AI tools leads to significant salary growth in tech roles.  

Data centers; increasing usage as efficiency improves, getty

Every major technological revolution promises greater efficiency. Conventional wisdom says that should reduce demand and lower costs. Yet AI economics appears to be doing the opposite. A 160-year-old economic principle known as Jevons Paradox may explain why. As AI becomes cheaper, faster and more accessible, businesses aren’t using less of it; they’re finding entirely new ways to consume more.

Jevons Paradox, identified by William Stanley Jevons in 1865 while studying coal, holds that improving resource efficiency does not cut consumption; it lowers barriers, unlocks new use cases, and ultimately drives demand higher.

Microsoft CEO Satya Nadella has repeatedly pointed to Jevons Paradox to explain why advances in AI are driving soaring compute demand rather than reducing it. As models become faster, cheaper and more capable, businesses don’t simply use AI more efficiently; they find entirely new ways to deploy it.

Three companies at the forefront of AI hardware, infrastructure and enterprise software explain how this dynamic is reshaping the competitive landscape for startups and small businesses.

Efficiency That Unlocks New Demand In Hardware Faster, cheaper hardware is removing bottlenecks and transforming AI economics. Tensordyne builds inference racks using 90% less power than alternatives, an advance that embodies Jevons’ insight: lower costs mean organizations run more AI, not less. “Jevons Paradox is gaining momentum because the type of work AI is actually doing has become more complex and intensive,” says cofounder Gilles Backhus. “When ChatGPT launched a few years ago, it was often one input in, one output out. Now, a single developer's agent might be performing hundreds of tasks at a time, each requiring detailed reasoning that uses more compute.”

Customers Value Speed And Model Quality The shift to agentic systems has already boosted compute use per user and task, but hardware has struggled to keep pace, leaving many facing rising costs even as they rush to adopt AI. “To outrun Jevons Paradox, we need to make it much cheaper to run AI workloads, without compromising on what customers value: speed and model quality,” says Backhus. “Software will get more efficient, but the biggest needle-mover will be hardware: chips and racks.” The Tensordyne Napier inference system uses less energy, less space, and less money per token, keeping costs down while still running 13 times higher throughput than Nvidia. Systems, and the AI models served by them, are typically either fast and expensive, or slow and cheap. The company plans to eliminate that compromise, making it less capital-intensive to run high-performance AI workloads. “Startups have a limited supply of capital to burn, so each token is much more valuable,” says Backhus. “If chips and data centers are more than 10x cost-efficient, models will be cheaper to run, and the margins of financially constrained early-stage companies would transform overnight. It could be rocket fuel for growth.” Lower costs could democratize AI R&D, unlocking use cases currently too expensive to test. For founders, the core lesson lies in the underlying economics. “If a state-of-the-art customer support model currently costs $5 a session, but becomes $0.50 because the hardware is cheaper, then we will see ten or even a hundred times the number of companies adopting it,” says Backhus.

Productivity That Creates New Possibilities In Professional Services Lower-cost AI only matters if businesses can find new ways to use it. That’s exactly what companies like Orbital are seeing. In fields like law and real estate, where output has long been tied to hours worked, AI doesn’t just cut time; it enables work that was previously uneconomical or impossible. Orbital, a real estate AI platform supporting 200,000 annual transactions across the U.S. and U.K., sees this play out daily. “Developers designing these AI systems aren't working fewer hours either, usually the same or sometimes more; they're just getting far more done,” says CTO Andrew Thompson. “Human capital plus token capital is now producing more software per head.”

Software Is Inherently Creative The real question is what that extra capacity enables. “Definitely higher total output, but the more interesting point is what that output buys you,” says Thompson. “The single greatest predictor of a software team's success is the rate of shipping new things to customers. Software is inherently creative. Nothing is real until customers actually use a product.” He also points out that building software is a discovery process. If AI supercharges that discovery loop, you can ship more and arrive at solutions faster. “Those are the efficiency gains that people aspire for when levelling up their work with AI,” he says.

Salary Growth Is Spiking The ‘10x engineer’, once a rarity, is now achievable for those who master AI, letting small teams deliver what once required entire departments. “Fewer people producing the same or more output means productivity per person is up, and compensation is rising for the individuals who've leveraged AI effectively,” says Thompson. “I think that’s why salary growth is spiking inside tech for various roles.” MORE FOR YOU He anticipates the Jevons effect creating a virtuous cycle of adoption, where greater utility drives ever-higher consumption of AI tools, but with some nuance. “Back in the day, Intel kept producing CPUs faster, but Microsoft Word never sped up at the same rate,” he says. “That's because Word wasn't static, and as CPUs could handle more, Microsoft's developers built richer and richer functionality into Word that wasn't previously possible. The hardware gains were absorbed by new capability rather than speed.” Due diligence in commercial real estate is also being rewritten, with firms able to review every document for every property and produce a full set of due diligence reports as if every property had been manually reviewed. “This is a new offering that law firms can sell to clients, and work that simply didn't exist before because it wasn't economically viable,” says Thompson.

Accessibility That Fuels the Flywheel Of Infrastructure As more businesses adopt these AI-powered workflows, demand for compute doesn’t plateau; it accelerates. That’s where infrastructure providers such as Verda come in. Better hardware drives efficiency, and easier access to compute accelerates the Jevons effect further. The AI infrastructure provider has grown revenue twentyfold in two years, topping a $100 million annual run rate and raising $117 million in April. That growth stems not just from easier access, but a reinforcing cycle, where improved hardware and more capable models advance together. A big part of the growth story for Verda, and for the AI industry generally, is the models and their capabilities getting better, moving from almost-good-enough to genuinely production-ready. CTO Arturs Poli says: “Rather than thinking of rapid growth purely as a by-product of easier access to compute, I'd see it as hardware and models improving together: accelerators getting more capable, and the models running on them becoming more accurate. They go hand in hand, and it's a clear Jevons dynamic with very rapid, hard-to-predict growth.”

How AI Economics Is Changing For Startups And Small Businesses

This narrative combines insights from an interview with Lily Vittayarukskul, age 30, who is the co-founder and CEO of Waterlily. The following account has been refined for clarity and conciseness.

From a young age, I was captivated by the cosmos and had a deep curiosity about how the world operates. Science taught me valuable lessons: even the most daunting questions could be dismantled and understood, leading to potential solutions.

Growing up with limited resources, I worked in my parents' factory after school. This experience highlighted the diligent efforts families put in to attain stability and the discipline and creativity required to build something from scratch.

At just 11 years old, I participated in a NASA competition with a water rocket I built myself. Winning that competition awarded me a trip to the Kennedy Space Center, and later, I interned at NASA as a teenager. I initially envisioned a future in aerospace engineering.

That trajectory shifted dramatically when my aunt was diagnosed with cancer, altering the course of my family’s life nearly overnight.

Language barriers compounded our challenges, as my mother became the translator at medical appointments due to my aunt's limited English skills. As a family, we quickly discovered unexpected expenses and realized that caring for a loved one with a severe illness extended far beyond regular medical visits.

I was 16 during this difficult time, and rather than recalling specific medical diagnoses or conversations, I remember the silence. Exhausted, my family moved through a relentless cycle of work, doctor visits, and caregiving, rarely pausing to address our emotional struggles.

Witnessing my family unravel profoundly reshaped my understanding of impact.

My mother took on the role of my aunt’s primary caregiver while the rest of us contributed to the family business. I would often stay at the factory until late in the evening, working hard but oblivious to the deeper costs we were incurring.

Eventually, I came to realize the strains on our family relationships and finances had taken a toll far beyond the loss of my aunt, resulting in lingering tension and isolation.

Being part of a first-generation immigrant family taught me the importance of financial stability. My parents’ relentless hard work didn’t always guarantee security; instead, I grasped that creating economic value was essential. This lesson has influenced my decisions ever since.

After my aunt’s passing, I began to reframe my inquiries. Rather than focusing solely on engineering challenges, I started to question why families like mine had to face such trying times with inadequate support.

I recognized a gap in addressing issues related to long-term care.

With a passion for engineering still intact, I pivoted to leverage those skills in a more meaningful way.

I spent nearly ten years in product and engineering leadership roles at various startups, absorbing knowledge about healthcare, financial services, and the needs of underserved populations. I sought to comprehend the fragmented landscape of aging and long-term care planning.

Seeing no one addressing the problem in a way that resonated, I co-founded Waterlily.

The platform harnesses AI to assist individuals in estimating future long-term care needs, expenses, and potential funding sources based on health, financial status, and family dynamics. It offers tailored insights and funding strategies to help families navigate the complexities of aging. Since its inception, Waterlily has successfully raised $9.2 million.

The journey has proven to be far more challenging than I anticipated.

Healthcare and aging intersect multiple sectors, requiring us to navigate the intricacies of healthcare, insurance, and financial services concurrently, each operating under different incentives. Each week presents new complexities.

There were times I questioned our ability to overcome these challenges. Yet, I found motivation in a core question I pondered after my aunt's death: If we don't tackle this issue, who will?

After engaging with countless families, I discovered that my own experience was not as unique as I believed. Many families facing serious long-term care challenges similarly endured financial strains and relational distress. While details may differ, the emotional toll often appears strikingly similar.

The experience of building this company has transformed my perspective on my future.

Launching Waterlily has increased my financial literacy significantly, compelling me to gain proficiency in areas such as insurance, retirement planning, and healthcare. Understanding the aging process means comprehending how various systems and family decisions intertwine.

Moreover, my understanding of responsibility has evolved.

Previously, I defined success as the ability to solve intriguing technical problems. Now, I believe true success lies in creating solutions that empower individuals to regain control during tumultuous times in their lives.

If my aunt were here today, while she might not fixate on the technicalities of our platform, I’m certain she would ask two pivotal questions: Are you making a difference? Is this venture sustainable?

I hope she would be proud of the answers I would provide.

Waterlily also offers a complimentary tool on its website that generates personalized insights, drawing from an extensive database, enabling users to better understand what aging and long-term care may entail.

I aspired to create rockets until my aunt's health issues motivated me to launch an AI startup that has secured almost $10 million.

A warm, sunlit composite scene: on the left, a young entrepreneur gently holds his aunt’s hand at her hospital bedside; on the right, he stands in a bright AI lab filled with monitors displaying rocket schematics overlaid with neural-network patterns. A translucent rocket blueprint arches between the two halves, symbolizing how her illness sparked his vision. Behind him, a sleek sign reads “AI Startup – $10 M Raised,” all bathed in soft natural morning light for an uplifting, story-driven thumbnail.

I dreamt of building rockets until my aunt's illness inspired me to build an AI startup that's raised nearly $10 million - Business Insider

- Lily Vittayarukskul founded Waterlily to help families navigate long-term care challenges.  
- Her personal experience with caregiving shaped her focus on financial and emotional support.  
- Waterlily utilizes AI to provide personalized long-term care planning and funding strategies.

This as-told-to essay is based on a conversation with Lily Vittayarukskul, 30, cofounder and CEO of Waterlily. It has been edited for length and clarity.

When I was a kid, I was fascinated by the universe and wanted to understand how the world worked. Science taught me that even the biggest, most intimidating questions could be broken down, understood, and eventually solved.

I also grew up with limited resources, and working in my parents' factory after school taught me how hard families work to create stability, and how much discipline and resourcefulness it takes to build something from very little.

At 11, I entered a NASA competition with a homemade water rocket, won the competition, and got the chance to visit Kennedy Space Center. Later, I interned at NASA as a young teenager. For years, I thought aerospace engineering was my future.

Then my aunt was diagnosed with cancer, and the trajectory of my entire family changed almost overnight.

My aunt didn't speak much English, so my mom translated at doctor's appointments. My family quickly learned that there were expenses we hadn't expected. We also learned that caring for someone with a serious illness extended far beyond medical appointments.

I was 16 when she became sick. What I remember most wasn't a single diagnosis or conversation. It was the quiet.

Everyone in my family was exhausted, moving from work to doctor's appointments to caregiving without ever stopping to talk about what was happening. We were all operating on autopilot because there was simply too much to do.

Watching my family unravel changed my definition of impact

Courtesy of Waterlily

My mom became my aunt's primary caregiver. The rest of us worked at the family factory whenever we could. I went to my parents' factory after school, often staying until 10 p.m. or 11 p.m. before going home.

Only later did I realize the full cost.

Our family relationships had become strained. Financially, we had almost nothing left. There was blame, tension, and isolation that lingered long after my aunt died.

Growing up in a first-generation immigrant family, I'd always understood that money mattered. My parents worked incredibly hard, yet I learned early that hard work alone doesn't guarantee financial security. You have to create economic value. That lesson shaped almost every decision I've made since.

After my aunt died, I started asking a different question: Instead of wondering how I could build better rockets, I wondered why families like mine had to navigate one of life's hardest moments with so little support.

I realized no one was solving the problem I cared about most

Vittayarukskul cofounded her company to help families avoid what hers went through. Courtesy of Waterlily

I still loved engineering and building things, but I realized I could apply those skills somewhere that felt much more personal.

I spent nearly a decade leading product and engineering at early-stage companies while learning everything I could about healthcare, financial services, and underserved populations. I wanted to understand why planning for aging and long-term care was so fragmented.

No one seemed to be solving the problem the way I thought it needed to be solved. That's why I cofounded Waterlily.

Waterlily is an AI-powered long-term care planning platform that helps people estimate when they may need long-term care, how much it could cost, and how to pay for it based on their health, finances, and family situation. The platform provides personalized projections and funding strategies—including insurance, savings, and caregiving plans—to help families prepare for the financial and logistical challenges of aging. Since launching, the company has raised $9.2 million.

The work has been much harder than I imagined.

Healthcare doesn't exist in one industry. Neither does aging. We work across healthcare, insurance, and financial services simultaneously, each with different incentives and different ways of operating. Every week seems to introduce a new layer of complexity.

There were plenty of moments when I questioned whether we could figure it out. What kept me going, though, was the same question I asked myself after my aunt died. If we're not solving this problem, who will?

After speaking with thousands of families, one thing surprised me. I'd assumed my family's experience was unusually devastating. Instead, I found that many families who experience serious long-term care needs face financial strain and relationship breakdowns. The details vary, but the emotional toll often looks remarkably similar.

Building this company changed how I think about my own future

Starting Waterlily has changed me in ways I didn't expect.

I've become much more financially literate than I ever thought I'd need to be. Building Waterlily has required me to develop deep expertise across insurance, retirement planning, healthcare, and financial services. Understanding aging means understanding how all of those systems — and the decisions families make within them — fit together.

Understanding aging means understanding insurance, retirement accounts, savings, investing, and how all those pieces fit together.

It's also changed how I think about responsibility.

When I was younger, I thought success meant solving interesting technical problems. Now, I think success means building something that helps people regain a sense of control during moments when life feels completely uncertain.

If my aunt were alive today, I don't think she would care very much about the technical details of the platform. But I also think she'd ask me two questions:

Are you helping people? Is this business sustainable?

I'd like to think she'd be proud of both answers.

Waterlily offers a free tool on its site that provides personalized insights, based on more than half a billion data points, to better understand what aging and long-term care could look like.

I dreamt of building rockets until my aunt's illness inspired me to build an AI startup that's raised nearly $10 million

Microchip Technology, a prominent American semiconductor company, has officially agreed to acquire Hailo, an Israeli firm specializing in AI chip technology. Hailo focuses on developing Edge AI processors, computer vision chips, and AI software tailored for intelligent edge devices. This acquisition comes on the heels of a challenging period for Hailo, characterized by severe financial difficulties, emergency loans, substantial workforce layoffs, and the failure of a merger with a special purpose acquisition company (SPAC) due to its market valuation dropping beneath $500 million. While the financial details of the deal remain undisclosed, industry insiders believe the price reflects a significant reduction from Hailo's former valuations.

This acquisition represents a bittersweet conclusion for Hailo, which was established in 2017 and managed to secure around $340 million in funding from notable investors such as the Zisapel family, OurCrowd, Alfred Akirov, and Delek Automotive, at one point achieving a valuation exceeding $1 billion. Among its major backers, Delek Automotive provided crisis funding to keep Hailo afloat and has indicated plans to write off much of its investment in forthcoming financial disclosures, signifying a looming accounting loss despite the acquisition.

For Microchip, a leader in various semiconductor fields including microcontrollers, embedded processors, and connectivity, this acquisition is a strategic move to bolster its position in the burgeoning Edge AI market. Edge AI facilitates the execution of AI tasks directly on devices like robots, drones, industrial automation systems, and smart cameras, optimizing performance by minimizing latency and reducing both bandwidth and energy consumption.

Hailo's product lineup features the Hailo-8, Hailo-10, and Hailo-15 families of AI processors. These processors cater to traditional computer vision tasks and more sophisticated AI processing needs, supporting advanced models such as transformers and large language models while incorporating functionalities for image signal processing (ISP), digital signal processing (DSP), and video encoding.

The acquisition also grants Microchip access to Hailo's existing customer base of over 100 active clients, an engaged developer community of over 10,000 users, and a thriving ecosystem surrounding Raspberry Pi, which has facilitated the adoption of Hailo’s Edge AI technology among those creating AI applications.

Mark Reiten, Senior Corporate Vice President at Microchip, remarked that this acquisition propels the company's growth into high-performance Edge AI processing. He stated, "Hailo's AI acceleration and advanced vision processing, combined with our own embedded processing and technology solutions, will enable us to assist customers in developing more efficient and sophisticated intelligent edge systems."

Orr Danon, CEO and co-founder of Hailo, expressed optimism regarding the acquisition, emphasizing that joining forces with Microchip would empower Hailo to scale its Edge AI technology on a larger scale. He noted, "Partnering with Microchip offers Hailo the chance to leverage the extensive reach and capabilities of a global leader in embedded systems, facilitating the delivery of advanced vision processing and AI acceleration to a broader spectrum of edge applications."

Additionally, this acquisition is in line with Microchip's comprehensive AI strategy. Previously, the company enhanced its AI offerings through the acquisition of Neuronix AI Labs, which added neural network optimization for AI workloads on FPGA and SoC devices. With the inclusion of Hailo, Microchip significantly expands its AI hardware and software solutions.

Microchip buys Hailo following the significant decline of the Israeli AI chip startup from a $1 billion valuation.

A dark, moody room with low, dramatic lighting focusing on a cracked golden AI chip stamped “Hailo” shattering into fragments across a sleek black table. Behind it, a large, glowing Microchip logo dominates the wall, casting deep red highlights. To one side, a small Israeli flag stands folded on the table, symbolizing Hailo’s origins and its dramatic fall before the acquisition.

Microchip acquires Hailo after Israeli AI chip startup's dramatic fall from $1 billion valuation - calcalistech.com

- Microchip Technology acquires Israeli AI chipmaker Hailo to enhance Edge AI capabilities.  
- Hailo faced severe financial issues and workforce reductions before the acquisition deal.  
- The acquisition expands Microchip's AI portfolio, adding over 100 active customers and developer community.  

American semiconductor giant Microchip Technology has signed a definitive agreement to acquire Israeli AI chipmaker Hailo, which develops Edge AI processors, computer vision chips and AI software for intelligent edge devices. The acquisition follows a particularly difficult period for the Israeli company, which struggled with severe liquidity challenges, took on emergency loans, carried out significant workforce reductions, and saw its planned merger with a special purpose acquisition company (SPAC) collapse as its valuation fell below $500 million. The purchase price was not disclosed, but industry estimates suggest it is only a fraction of Hailo's previous valuations.

The deal marks a bittersweet ending for the Israeli startup. Founded in 2017, Hailo raised approximately $340 million from investors including the Zisapel family, OurCrowd, Alfred Akirov and Delek Automotive, reaching a valuation of more than $1 billion. Delek Automotive, one of Hailo's largest investors, provided emergency financing to keep the company operating and has already announced it will write off most of its investment in its upcoming financial statements, meaning the acquisition will not prevent a significant accounting loss.

1 View gallery Or Danon. ( Photo: David Garb )

For Microchip, a global leader in microcontrollers, embedded processors, FPGAs, connectivity, analog chips and secure embedded control solutions, the acquisition significantly expands its capabilities in one of the fastest-growing segments of the semiconductor market: artificial intelligence at the edge. Rather than processing AI workloads in cloud data centers, Edge AI enables applications to run directly on devices such as robots, drones, industrial automation systems and smart cameras, reducing latency while lowering bandwidth and power requirements.

Hailo's portfolio includes the Hailo-8, Hailo-10 and Hailo-15 families of AI processors, which support both traditional computer vision applications and more advanced AI workloads, including transformer models and large language and vision-language models. The chips also integrate advanced image signal processing (ISP), digital signal processing (DSP), H.264 and H.265 video encoding, and AI-powered video stream processing capabilities.

The acquisition also gives Microchip more than 100 active customers, an established developer community of more than 10,000 users, and Hailo's growing Raspberry Pi ecosystem, which has helped drive adoption of its Edge AI technology among developers building AI applications.

"The acquisition of Hailo accelerates Microchip's expansion into high-performance edge AI processing," said Mark Reiten, Senior Corporate Vice President at Microchip. "Hailo's AI acceleration, advanced vision processing and software ecosystem directly complement Microchip's embedded processing, FPGA, connectivity, security, power and analog portfolio. Together, we can help customers build more capable intelligent edge systems with the right balance of performance, power efficiency, reliability and system cost."

Hailo CEO and co-founder Orr Danon said the acquisition would provide the company with the scale needed to commercialize its technology more broadly.

"Joining Microchip would give Hailo the opportunity to scale our accelerated Edge AI technology through a global embedded systems leader," Danon said. "Microchip's customer reach, channel scale and broad technology portfolio would create a strong platform for bringing advanced vision processing and AI acceleration to a broader range of intelligent edge applications."

The acquisition also continues Microchip's broader AI strategy. The company previously strengthened its AI capabilities through the acquisition of Neuronix AI Labs, adding neural network optimization technology for AI and machine learning workloads running on FPGAs and system-on-chip devices. Hailo adds dedicated Edge AI processors, vision system-on-chip products and software tools that significantly broaden Microchip's AI portfolio.

Microchip acquires Hailo after Israeli AI chip startup's dramatic fall from $1 billio

In Silicon Valley, a contentious discussion is ongoing regarding the rise of Chinese-developed artificial intelligence tools, especially open-weight AI systems that, in some comparisons, may rival or exceed the capabilities of leading American models. Recently, my colleague Hugo Lowell from WIRED addressed the ongoing deliberations within the Trump administration about tackling these Chinese technologies. Among AI firms in the region, the topic is proving to be even more polarizing.

A primary concern for stakeholders in both Washington, D.C., and Silicon Valley revolves around the technique known as distillation. This method involves training a less sophisticated AI using outputs from a more advanced system. In June, Anthropic accused Chinese tech titan Alibaba of stealing its intellectual property via distillation attacks. More recently, the White House raised suspicions that Moonshot AI, based in Beijing, had created its Kimi K3 model by distilling Anthropic’s Fable 5 model.

Another significant worry is the rapid emergence and dissemination of China’s AI models. Open-weight AI models make their foundational elements publicly accessible, allowing users to customize them according to their requirements. However, they lack the safeguards that have helped Anthropic establish its reputation. Yasir Atalan, deputy director and data fellow at the Center for International and Strategic Studies, emphasizes that the main advantage of these open-weight AI models is their swift adoption. They can be easily shared through platforms like Hugging Face, GitHub, and various cloud services. For established companies like Anthropic, which have cultivated a brand around safety and premium proprietary models, there is a strong incentive to advocate for regulation.

Yet, many smaller startups in Silicon Valley—distinct from the massive firms like OpenAI and Anthropic—oppose any government restrictions on these AI models. On Wednesday, a coalition of over 200 startups known as the Little Tech Association sent a letter to President Trump's science advisor, Michael Kratsios, and U.S. Commerce Secretary Howard Lutnick, urging against an outright ban on open-weight AI models. This group, which includes notable startup accelerator YCombinator, supports some regulatory measures but contends that restricting access to overseas AI models would undermine U.S. startups and lead to monopolistic practices among larger companies.

Veteran tech investor Bill Gurley, a partner at Benchmark Capital, has made a case for allowing “the free market to function.” In a detailed blog post that chronicles the history of open-source software, he argues that open-weight models prevent vendor lock-in, stimulate genuine academic research, and are essential for startups with limited capital resources.

“Every AI startup, every solo developer, and every small team creating products based on AI infrastructure relies on having access to high-quality models at reasonable prices,” Gurley asserts.

Chamath Palihapitiya, a co-host of the All-In podcast, shared on X that relying on the U.S. Government to protect the business models of certain companies by invoking fears about China is misguided. He argued that it primarily benefits a handful of investors in OpenAI and Anthropic at the expense of the broader community. His podcast co-host, venture capitalist Jason Calacanis, chimed in with a sarcastic remark suggesting reliance on governmental protection, highlighted by a plethora of laughing emojis.

At first glance, this perspective from some of Silicon Valley’s more aggressive capitalists may seem paradoxical—why allow the technology of a foreign competitor to thrive in the U.S.? It’s akin to holding a slight edge in an AI World Cup match while the audience cheers for the opposing team to gain a favorable opportunity.

Silicon Valley Is Deeply Divided on Chinese AI

A dimly lit Silicon Valley boardroom where two groups of sharply dressed executives sit at opposite ends of a glossy black table, tension visible in their posture. In the center lies a sleek AI processor module bathed in warm amber light, deep shadows filling the corners. Along one wall, a single Chinese flag hangs half-illuminated, while scattered monitors display scrolling neural-network code in rich crimson.

Silicon Valley Is Completely Divided Over Chinese AI - WIRED

- Debate in Silicon Valley intensifies over Chinese-made AI tools competing with U.S. models.  
- Concerns arise about IP theft via distillation techniques used by Chinese tech giants.  
- Over 200 startups lobby against restrictions, arguing for access to affordable, competitive AI models.  

A huge debate is brewing in Silicon Valley over the proliferation of Chinese-made artificial intelligence tools, particularly “open-weight” AI systems that, by some measures, can compete with or even outperform some of the best US models. My WIRED colleague Hugo Lowell has written about the Trump administration’s internal debate on how to handle these Chinese models. Among AI companies in the Valley, the issue is proving even more divisive.

A top concern in both DC and the Valley relates to distillation, in which a less capable AI model is trained on the outputs of a more powerful one. In June, Anthropic accused the Chinese tech giant Alibaba of illicitly stealing its IP through distillation attacks. Then, earlier this week, the White House said that it believes the Beijing-based Moonshot AI had developed its Kimi K3 model by distilling Anthropic’s Fable 5 model.

Another big concern is how quickly China’s models are appearing and spreading. An open-weight AI model has its core components made public, so that it can be fine-tuned to suit a user’s needs. But they don’t have the kinds of guardrails on which Anthropic has been building its reputation. Yasir Atalan, deputy director and data fellow at the Center for International and Strategic Studies, points out that the main benefit of open-weight AI models is their speed of diffusion. They can spread especially easily “through Hugging Face, GitHub, cloud providers, local deployments, and third-party inference platforms,” he writes. If you’re Anthropic, and you’ve built a cult around safety and charge for access to your big expensive proprietary models, you have every reason to want to regulate this.

But some Silicon Valley startups—not the trillion-dollar ones like OpenAI and Anthropic—really don’t want the US government to put restrictions on these AI models. On Wednesday a group of over 200 startups called the Little Tech Association sent a letter to Michael Kratsios, science adviser to President Donald Trump, and US Commerce Secretary Howard Lutnick lobbying against an outright ban of open-weight AI models. The group, which includes famed startup incubator YCombinator, has argued in favor of certain safeguards but says that denying Americans access to AI models abroad would weaken US startups and create a monopoly among the AI giants.

Bill Gurley, the legendary tech investor and longtime partner at Benchmark Capital, has publicly argued in favor of letting “the free market work.” In a lengthy blog that offers a nice little history of open-source software, Gurley writes that open-weight models avoid lock-in, encourage true academic research, and are critical for capital-constrained startups.

“Every AI startup, every solo developer, every two-person team building a product on top of AI infrastructure depends on having access to good models at affordable prices,” Gurley says.

Chamath Palihapitiya, one of the All-In podcast hosts, wrote on X that “tricking the US Government to protect frontier labs’ business model by using a China boogeyman is a mistake…It is protecting the equity of 5,000 people who are investors in OAI and Ant at the sale of everyone else. This would be a terribly stupid decision.” His cohost and fellow VC Jason Calacanis piled on. “Daddy Trump protect us!!!!” he wrote on X, with an alarming number of crying-laughing emoji.

This stance from some of Silicon Valley’s most ruthless capitalists might at first seem counterintuitive. Why let a foreign adversary’s technology flourish in the US? It’s as if the US is up just 1-0 in the AI World Cup, a slightly uncomfortable lead, and the crowd is chanting for the opposing team to get a free kick.

Silicon Valley Is Completely Divided Over Chinese AI

AI technology is making significant strides in healthcare, with various startups pushing towards integrating AI systems that could potentially replace doctors entirely. While the majority of clinical AI firms aim to enhance healthcare efficiency by reducing physician burnout and aiding in decision-making, a select group is venturing into more disruptive territory, advocating for AI to take over certain medical responsibilities. Here’s a closer look at three pioneering companies in this space.

Doctronic, a New York-based innovator, presents a unique platform that acts as a personal AI doctor, making clinical decisions autonomously while adhering to HIPAA regulations. After securing $40 million in a Series B funding round in March—led by Abstract and Lightspeed Venture Partners—the company’s total funding now stands at $65 million. Doctronic operates in Utah, where it has gained approval to renew prescriptions for patients with chronic illnesses without requiring a physician's oversight. Notably, the AI has successfully authorized prescription renewals independently 72% of the time for 192 different medications addressing issues such as hypertension, diabetes, and depression. When human intervention was necessary, the AI appropriately escalated the case 28% of the time, with independent physician reviewers agreeing with this judgment in 69% of instances. Encouragingly, no major safety issues have been reported, though concerns regarding accountability and security vulnerabilities remain under review by regulatory bodies.

Another emerging player, Certuma, founded by entrepreneur Martin Varsavsky, aims to tackle frequent health issues that typically crowd urgent care facilities. With $10 million in seed funding led by 8VC, Certuma is striving to become the first AI doctor with FDA approval. Its focus includes 25 low-risk, high-prevalence conditions like urinary tract infections and strep throat, leveraging well-established clinical pathways. The company is following a dual strategy: a rigorous route through the U.S. FDA while simultaneously pursuing quicker regulatory approval in Argentina, where it has already launched a service providing diagnosis and prescription recommendations via a consumer-friendly chatbot. The long-term vision includes enhancing the AI with autonomous robotic booths for conducting imaging and blood draws.

Ada Health, based in Berlin, is developing an AI health companion designed to assist users in evaluating their symptoms and determining the next steps without necessarily connecting them to a doctor. With $242 million in funding, highlighted by a substantial $120 million Series B round from investors like Inteligo Bank and Farallon Capital, Ada Health claims to support 13 million users who have completed over 32 million symptom assessments. A peer-reviewed study published in BMJ recognized Ada's tool for its superior coverage, accuracy, and safety among various symptom-checking applications, positioning it more as an "AI front door" than a direct replacement for medical professionals, effectively reducing the number of patient visits to physicians.

Contrasting these startups, established AI companies are more focused on augmenting healthcare practices rather than supplanting physicians. For instance, Anthropic introduced Claude for Healthcare in 2026 as a resource to assist medical professionals rather than replace them. This aligns with the majority of large healthcare systems, where the objective is to use AI to streamline tasks like documentation and triage, allowing physicians to dedicate more time to patient care decisions. However, the landscape is changing. Recently, Revere Health initiated workforce reductions involving medical coding and administrative roles—approximately 7% of its staff—while maintaining its physician workforce. This shift indicates a potential trend where AI could gradually reshape roles in the medical field, provoking unease among physicians. A recent poll indicated that 58% of doctors fear that AI might either diminish their roles or lead to outright obsolescence in the future.

Three AI Firms Aiming to Supplant Medical Professionals

A dimly lit hospital operating room at night, bathed in deep shadows and pulsing red accent lights. In the center, an empty stainless-steel operating table sits under a harsh spotlight. Surrounding it are three towering corporate monoliths—sleek metal pillars etched with glowing, stylized AI logos—each poised with a multi-jointed robotic arm above the table. At the doorway, a lone doctor in scrubs stands silhouetted, hesitating, as wall-mounted monitors flicker patient vitals now controlled by machines.

Here Are 3 AI Companies Betting They Can Replace Doctors - Forbes

- A few startups aim to replace doctors entirely, diverging from AI's typical supportive role.  
- Doctronic can autonomously renew prescriptions in Utah, raising regulatory and safety concerns.  
- Ada Health's AI companion helps users assess symptoms without needing a doctor visit.  

AI tools are increasingly present in the exam room — but a handful of startups want AI to replace doctors. getty

Most clinical AI companies today focus on deploying products for functions focused on making doctors faster such as reducing their burnout or helping them make more informed decisions. For example, AI scribes like Abridge and Cleo reduce documentation time and medical-focused AIs like OpenEvidence help doctors make more informed decisions.

But a smaller group of startups is pushing a different narrative: they’re not building copilots. They’re trying to replace physicians. Here are three of those companies.

Doctronic: the AI doctor with prescribing power Doctronic, a New York-based startup, offers a HIPAA-compliant platform it describes as a free, 24/7 personal AI doctor that can make clinical decisions on its own. The company raised $40 million in a Series B round in March, co-led by Abstract and Lightspeed Venture Partners, bringing its total funding to $65 million. Doctronic is working in the state of Utah which is allowing it to legally renew prescriptions for patients managing chronic conditions without a doctor approving the prescription. It's the first time a state has authorized an AI platform to handle routine prescribing on its own. Six months in, the data give show that the AI approved renewals without any human involvement 72% of the time for 192 drugs treating conditions like hypertension, diabetes and depression. In the other 28% of cases, the system escalated to a human physician on its own, typically because it needed an additional step like needing labs or it flagged a complication. Independent physician reviewers agreed the escalation was the right call 69% of the time. MORE FOR YOU Where the AI did grant a renewal without escalating, physicians agreed with its decision 91% of the time. Yet in 3% of cases, reviewing physicians disagreed with the AI’s call entirely. As of this writing, Utah’s Office of AI Policy says no serious safety incidents have been reported. Still, the Utah Medical Licensing Board and the American Medical Association have both raised accountability concerns, and researchers have flagged security vulnerabilities in the pilot that regulators are still working through.

Certuma: an AI replacement for urgent care Certuma was founded by serial entrepreneur Martin Varsavsky and is targeting common ailments that typically fill up urgent care waiting rooms. The Austin-based company has raised$10 million in seed funding, led by Joe Lonsdale’s 8VC, at a $60 million valuation. Certuma is trying to be the first AI doctor with formal FDA approval. Its initial target list is 25 low-risk, high-frequency conditions, including UTIs, strep throat and sinusitis. They were chosen specifically because the clinical decision trees are well established and low-variance. Certuma is also running a two-track strategy: a slower, higher-bar path through the FDA in the U.S., and a faster international track in Varsavsky’s native Argentina. There, the company has already worked with Argentinian regulators, aided by a deregulation-friendly government, to offer diagnosis and prescribing advice through a consumer-facing chatbot. It’s a longer-term vision is to pair the AI with autonomous robotic booths where patients could get imaging or blood draws done.

Ada Health: AI triage without a waiting room Ada Health has taken a related approach, building an AI health companion that helps people understand their symptoms and decide what to do next — often without ever routing them to a doctor. The Berlin-based company has raised $242 million. Their most significant financing was a $120 million Series B round backed by investors including Inteligo Bank, Farallon Capital, and Red River West. Ada Health says says it has 13 million users worldwide who have completed more than 32 million symptom assessments. Ada points to a peer-reviewed BMJ study it led comparing eight symptom-checker apps on coverage, accuracy and safety where its own tool came out on top. It’s less "AI doctor" than "AI front door," but the effect is the same: fewer visits that require an actual physician.

Bigger AI players are building copilots, not replacements It’s worth contrasting all of this with what the major AI companies are doing. Anthropic launched Claude for Healthcare in January 2026, at the J.P. Morgan Healthcare Conference, framed as a tool to support physicians. That’s also been the position of most large health systems where the goal is for AI to handle documentation, triage support and administrative drudgery, freeing doctors to spend more time on the judgment calls only they can make. But even that line is getting harder to hold at the edges. Revere Health has already made some of the first AI-linked workforce cuts in U.S. healthcare with cuts to medical coding, billing and documentation staff, not physicians. Revere eliminated roughly 200 jobs, about 7% of its workforce. But watching AI quietly hollow out the roles around physicians, even while doctors themselves stay untouched, may be a preview of where the pressure could head next. And physicians themselves are uneasy: in a recent Sermo poll, 58% said they believe AI will either diminish their role or make doctors obsolete outright.

Here Are 3 AI Companies Betting They Can Replace Doctors

Etched, an AI chip startup established by three Harvard dropouts in 2022, has successfully completed a Series C funding round, securing $300 million and achieving a valuation of $10.3 billion, as reported by co-founder and COO Robert Wachen to TechCrunch.

This funding round, led by Sequoia, included participation from prominent investors such as Andreessen Horowitz, SK Hynix, Jane Street, and Diffusion Capital, along with earlier supporters. Notable backers include influential figures like Peter Thiel, Andrej Karpathy, Dylan Field, and Amjad Masad, among others.

In December, just seven months prior, Etched was valued at $5 billion after raising $500 million, reflecting a remarkable growth trajectory. This valuation marks a record for a Sequoia-led Series C. Last month, the company announced the successful production of its proprietary chips and the initiation of testing its initial complete systems with clients while also reporting $1 billion in booked orders.

When Etched emerged, the concept of creating a chip tailored for AI models based on transformer technology was met with skepticism. The founders continue to tackle misconceptions about their products, which encompass full systems beyond merely chips. Wachen clarifies that their systems can run any AI model, including Mixture of Experts frameworks like DeepSeek and Qwen, as well as non-transformer architectures such as Mamba that utilize a state-space model. The innovative idea of etching components of AI models directly into silicon to enhance performance is now gaining traction, with companies like Google reportedly exploring similar concepts.

Currently, Etched's standout achievement is the creation of two new components designed from the ground up to accelerate inference—the process that occurs once a user submits a prompt. Wachen describes inference as occurring in two stages: "prefill" and "decode." The prefill stage involves comprehending the prompt and its context, requiring significant computational resources, while the decode phase generates the response tokens, which is less intensive but demands substantial memory capacity.

To enhance prefill performance, Etched has developed a chip that operates "dramatically" quicker, facilitated by its ability to run at a much lower voltage than existing AI chips—what they refer to as low-voltage inference. This voltage reduction helps minimize heat output, allowing for increased transistor density.

For the decode phase, the startup has introduced a novel memory type and interconnect technology termed cluster-scale memory, which enables multiple chips to connect and utilize a shared memory pool with minimal latency. This innovation promises both high-speed capabilities and reduced costs.

The founders of Etched—Wachen, CEO Gavin Uberti, and Chris Zhu—have faced considerable skepticism due to the lack of widespread access to their systems. Access has primarily been limited to early customers and investors, a strategy that helped attract notable investors by showcasing private demos in their office.

Wachen notes that some of the most enthusiastic backers, including Andrej Karpathy from Anthropic and Geoffrey Hinton, have personally tested the hardware. However, the journey to mass production remains challenging. The three founders, who left Harvard, initially had no experience in fundraising or hiring talent.

Reflecting on their early days in the Bay Area after leaving school, Wachen recalls sleeping on a friend's floor without a proper bed. The team first set up their servers in the garage of an early employee. As they grew, so too did their facilities, now operating with a workforce of 400 and managing a 2 megawatt data center. Recently, they inaugurated an 80,000-square-foot facility with a 10 megawatt capacity in Milpitas, close to their primary location in San Jose.

Today, Wachen and his team collaborate with some of the largest players in the AI sector while reflecting on their journey. "It’s come a long way," he remarks. "When you genuinely believe something is achievable and remain committed to it over time, you can make it happen."

AI chip startup Etched silences doubters, achieving a $10.3B valuation from prominent investors.

A sleek, futuristic AI chip engraved with the Etched logo sits on a polished pedestal bathed in warm morning sunlight streaming through floor-to-ceiling windows. In the softly lit background, blurred silhouettes of well-dressed investors exchange congratulatory handshakes and smiles. A discreet digital display above them reads “$10.3 B Valuation” in elegant, gold-accented type.

AI chip startup Etched defies skeptics, hits $10.3B valuation from big-name investors - TechCrunch

- Etched raised $300 million in Series C funding, increasing its valuation to $10.3 billion.  
- The startup specializes in AI chips, creating faster, low-voltage inference and unique memory technology.  
- Originally launched by Harvard dropouts, Etched now employs 400 people in its growing facilities.  

Etched, the AI chip startup founded by three Harvard dropouts in 2022, has closed a $300 million Series C funding round at a $10.3 billion valuation, co-founder and COO Robert Wachen tells TechCrunch.

The round was led by Sequoia, with Andreessen Horowitz, SK Hynix, Jane Street, and Diffusion Capital also participating, along with other, earlier investors. Other backers of the company include names like Peter Thiel, Andrej Karpathy, Dylan Field, Amjad Masad, and more.

Etched was previously valued at $5 billion in December when it raised a $500 million round, meaning it has doubled its valuation in about seven months. The company says this is the highest valuation ever for a Sequoia-led Series C. Last month, Etched announced that it had successfully manufactured its homegrown chips, that its first full systems were being tested by clients, and it had already booked $1 billion worth of orders.

Etched launched at a time when the idea of building a chip specifically for AI models based on transformer technology (the architecture behind most modern AI systems, including ChatGPT and Claude) was considered wild if not wacky. The company is still battling the perception that its products — which are sold as full systems, not just chips — involve chips designed to run only specific LLMs.

That’s not the case, Wachen explains. The systems can run any AI model, including Mixture of Experts models like DeepSeek and Qwen — an architecture that splits tasks across specialized sub-models rather than relying on one large model — as well as non-transformer designs like Mamba, which is built on a different underlying architecture known as a state-space model. (Interestingly, the idea of etching parts of a specific AI model directly into silicon to boost performance isn’t considered far-fetched anymore. Google is reportedly pursuing the same concept with its Frozen v2 chip for Gemini.)

Still, Etched’s claim to fame today is that it designed two new components from scratch to speed up inference — the computing process that happens after a user submits a prompt.

“Inference is built in two stages,” Wachen says, “prefill and decode.” The “prefill phase” involves understanding the prompt, including context. It’s mathematically and compute-intensive. The “decode” phase generates the output tokens (the actual answer the user sees). It requires less computation but needs massive amounts of memory.

Etched created a prefill chip that operates “dramatically” faster, he promises, “by running at a much lower voltage than any other AI chip. We call this low-voltage inference.” Lower voltage generates less heat, which allows the chip to pack in more transistors.

For the decode process, Etched created a new type of memory and “interconnect technology that we call cluster-scale memory. It allows many chips to connect together and use a shared memory pool at a very, very fast, low latency,” he says. The result, Etched promises, is high speeds but lower costs.

Etched founders Rob Wachen (seated, middle) and Gavin Uberti (standing, right) with Sequoia partners Abishek Malani (far left) and Sonya Huang (middle). Image Credits:Etched

Because the startup was launched before most of the tech world (besides Nvidia) understood AI’s specialized compute needs, the founders, CEO Gavin Uberti, Wachen, and Chris Zhu, have faced plenty of skeptics who kept doubting even after the company announced that its first batch of silicon had been successfully manufactured by TSMC.

Much of that comes from how few people have had access to the systems. So far, access has been limited to investors and early customers. In fact, that’s how Etched landed its list of famous investors in the first place — by showing them private demos in its office.

“Andrej Karpathy from Anthropic, Noam Brown from OpenAI, Geoffrey Hinton, as well as all the investors in the funding round — these are all people who actually tried the hardware and are very excited about it,” Wachen says.

Still, it’s been a long, difficult road with more to go until the rack systems are mass produced and delivered. The trio famously dropped out of Harvard to launch Etched, not knowing then how to raise cash (much less the loads of it they would need) or how to hire.

“We had no idea how hard it was going to be,” Wachen says. “I think we still have to be humbled by what it will take to actually get to scale.”

Wachen remembers landing in the Bay Area after telling his parents he was leaving school to do a startup, with no office or apartment arranged. He slept on the floor of a friend’s unfurnished house.

“I remember staying in my friend’s house that they were about to sell, using a towel as a blanket,” he laughs. The founders eventually set up the servers they needed to run the chip-design tools in the garage of an early employee and “every time it needed to be rebooted, he would call his wife, and she would go and hit the reboot button.”

Today, there are 400 people bustling in an office, and Etched operates a 2 megawatt data center there. And it just opened a new 80,000 square-foot, 10MW facility in Milpitas, down the road from its main San Jose office.

“We’re running tokens in our lab today, working with some of the largest AI companies in the world,” he says.

Wachen also has a blanket now, and a mattress “and a pillow even. Multiple pillows,” he jokes.

More importantly, he and his co-founders never let the doubters stop them. “It’s come a long way. It’s a very, very different world. But I think, when you really think something’s possible, and you just work at it for a long time, you can do it.”

Note: This story was updated to include a reference to the new 10MW facility.

AI chip startup Etched defies skeptics, hits $10.3B valuation from big-name investors

HONG KONG -- Officials from the Trump administration have issued warnings of potential sanctions and other measures aimed at Chinese artificial intelligence startups, including Moonshot. The government accuses these companies of engaging in intellectual property theft from leading AI research laboratories in the United States.

US authorities warn of potential sanctions on Chinese AI startups following Moonshot launch

Dark government hearing room, low-key tungsten lighting casting long shadows. Silhouetted US officials sit behind a polished mahogany table, one raises a heavy gavel above a miniature sleek office tower model emblazoned with the Chinese flag. Behind them, a pull-down screen glows with bold text “Moonshot Release.”

US officials threaten to sanction Chinese AI startups after Moonshot release - Nikkei Asia

- Trump administration officials threaten sanctions against Chinese AI startups like Moonshot.  
- Accusations include theft of intellectual property from U.S. artificial intelligence labs.  
- Actions reflect ongoing tensions between the U.S. and China over technology competition.

HONG KONG -- Officials in the Trump administration are threatening sanctions and other actions against Chinese artificial intelligence startups including Moonshot, alleging they are stealing intellectual property from American frontier AI labs.

US officials threaten to sanction Chinese AI startups after Moonshot release

Corgi, a San Francisco-based AI insurance startup known for its round-the-clock operations and unique café concept, has successfully secured additional funding from investors for the third time in under three months, ultimately achieving a valuation of $4 billion—almost double what it was worth in late May, according to multiple sources who spoke with Forbes.

In the competitive arena of AI businesses, Corgi's fundraising activities have garnered significant attention. In early May, the company raised $160 million in a Series B round, backed by investors such as TCV and Kindred Ventures, which propelled it to unicorn status with a valuation of $1.3 billion. Just three weeks later, the startup announced a further extension of that funding round, adding another $106 million and boosting its valuation to $2.6 billion.

The latest funding round marks yet another extension of the Series B and is now officially closed, though details about the investors and the specific amount raised remain undisclosed. The company anticipates a significant increase in its revenue, projecting its run rate to surge from $45 million to $450 million by year's end.

Corgi’s founders, Nico Laqua and Emily Yuan, have expressed gratitude for the ongoing support from their community, emphasizing that any future fundraising communications would originate directly from them. "We truly appreciate the interest and backing as we strive to build something impactful," a spokesperson stated.

Corgi has emerged as a notable example within the bustling AI landscape of San Francisco. Established just two years ago, it leverages AI technology to generate insurance quotes, primarily catering to startups while enabling them to offer insurance products to their own clients. The company also employs AI to streamline claims management, reducing reliance on teams of human evaluators, many of whom are based overseas.

A distinctive feature of Corgi is its workplace culture. Employees share the responsibility of caring for a two-year-old corgi named Trudy, receiving reminders for tasks through an AI bot on Telegram. Additionally, the startup runs the Corgi Cafe, a 24/7 coffee shop accessible to the public. Laqua, the CEO, even takes his dedication to the company further by sleeping on a mattress in the office, a testament to Corgi’s commitment to a seven-day workweek.

Laqua emphasized this ethos on the 20VC podcast, stating that employees must be available every day, and those with traditional weekends off won't fit into the company culture. 

However, this 'hustle culture' has drawn criticism. Following the initial fundraising announcement in May, some founders publicly voiced their concerns about the aggressive fundraising strategies employed by Corgi. One user expressed frustration over receiving numerous unsolicited calls and messages within a short period.

Skeptics have labeled Corgi's work environment a mere gimmick, arguing that such extreme displays of dedication are not typically characteristic of highly successful businesses. Karri Saarinen, CEO of Linear, tweeted in response to the spotlight on Corgi, highlighting a trend in startup culture where founders feel pressured to publicly showcase their hard work and sacrifices, unlike leading companies.

Despite the skepticism, Laqua remains firm in his beliefs about the company's rigorous work policy, stating, “My only regret is that there aren't eight days in a week.”

AI Startup Corgi, known for its seven-day work week, secures more funding at a $4 billion valuation.

A bright, sunlit startup office: a confident corgi in a crisp white shirt and tie sits at a sleek desk, laptop open showing “$4B” in bold. Behind it, a glass wall bears a large Corgi logo and a wall calendar with all seven days circled in green. Neatly stacked folders labeled “Funding Round” rest nearby, hinting at another successful raise.

AI Startup Corgi Of Seven-Day Work Week Fame Raises Yet Again At $4 Billion Valuation - Forbes

- Corgi, an AI insurance startup, raised funds valuing it at $4 billion recently.  
- The company emphasizes a seven-day work policy, facing backlash for its intense culture.  
- Corgi uses AI for insurance quotes and claims, catering primarily to startups.  

AFP via Getty Images

Corgi, the AI insurance startup that works seven days a week and operates an all-night cafe in San Francisco, has raised money from investors yet again — for the third time in less than three months. The current fundraise values the company at $4 billion, almost twice its worth since late May, multiple sources familiar with the matter told Forbes.

Even in the frenzied world of AI startups, Corgi’s fundraising has been eyebrow-raising. In early May, the company announced a $160 million Series B from backers including TCV and Kindred Ventures, which vaulted the company to unicorn status at a $1.3 billion valuation. Just three weeks later, it announced an extension to that round from the same investors, raising $106 million and doubling its valuation to $2.6 billion.

The newest round is yet another extension of its Series B and is now closed, according to a person familiar with the matter. It’s unclear who participated in the new round or how much money the company raised. The company is projecting it will increase its revenue run rate tenfold by end of year, from $45 milion to $450 million.

Corgi founders Nico Laqua and Emily Yuan Corgi

"Any fundraising announcement would come directly from us when we’re ready to share it,” a Corgi spokesperson said. “We’re truly grateful for the continued interest and support from our community as we focus on building something important.”

Corgi has become something of a poster child for the insanity of San Francisco’s current AI boom. Founded just two years ago, it uses AI to generate insurance quotes for clients — mostly other startups, and allows startups to sell insurance to their own customers. It also uses AI to manage claims instead of sending them to teams of human evaluators that are often working overseas.

What really makes Corgi stand out, though, is its unusual workplace. Employees collectively own a two-year-old corgi named Trudy, who they feed, walk and bathe with the help of an AI bot on Telegram that reminds employees to carry out the tasks. The startup owns and operates the Corgi Cafe, a 24/7 coffee house that’s open to the public. And its founder CEO sleeps on a mattress in the office, thanks to the company’s policy to work seven days a week in person.

“If your days off happen to be Saturday and Sunday every week, then you will not have a place at Corgi,” CEO Nico Laqua said on the 20VC podcast in May.

The company’s ‘hustle culture’ has faced backlash. After Corgi announced its first fundraise of the year in May, startup founders took to X to complain about the startup’s aggressive tactics. “Tell me why I got bombarded with 6 calls and 5 text messages all from different people within a 1 minute time span,” one user posted .

Others have called Corgi’s work culture a viral gimmick rather than a sound business strategy. “There is a growing cliché in startup culture where founders and startups feel the need to perform intensity publicly,” Linear CEO Karri Saarinen posted to X, in response to the 20VC podcast detailing Corgi’s work policies. “How hard they work, how little they sleep, how many tokens they spend, how busy they are, how much personal sacrifice they make. You almost never see this from the most successful companies or people.”

Still, Laqua seems steadfast about the seven-day work policy. “My only regret is that there's not eight days in a week,” he told Forbes in May.

AI Startup Corgi Of Seven-Day Work Week Fame Raises Yet Again At $4 Billion Valuation

Arrakis, a nascent startup with operations in London and Paris, is emerging from the shadows after securing $38 million in venture capital. Founded just seven months ago, the company aims to develop an AI "operating system" tailored for industrial sectors such as aerospace, energy, logistics, and manufacturing.

The latest funding round consisted of a $30 million Series A led by Blossom Capital, with participation from firms like Accel, GFC, MainObject, and Rerail. Previously, Arrakis raised a $7.5 million seed round, with notable individual investors including Olivier Pomel, CEO of Datadog, and Olivier Godement, who oversees business products at OpenAI.

Following this funding, Arrakis is now valued at $140 million, as co-founder and CEO Rafael Quintanilla revealed to Fortune.

Quintanilla was formerly a vice president at Accel, where he dedicated significant time to understanding the landscape of defense and industrial resilience across the U.S., Europe, and the Middle East. His insights drove him to leave his position and embark on his own entrepreneurial journey.

"I discovered a considerable disparity between the innovations I encountered at Accel—investing in companies like Anthropic and Lovable in Europe—and the realities observed in the more traditional industrial sectors," he explained.

While much of the current AI focus has been on knowledge workers—those utilizing software at desks—Quintanilla emphasizes that substantial opportunities lie within the 70% of the workforce involved in physical production and logistics. "AI investment so far has largely been directed at the 30% of desk-based workers, but the real returns on investment are in the industrial operations," he noted.

Sonali de Rycker of Accel, who supported Arrakis’s seed funding, expressed confidence in Quintanilla as both a visionary and a determined founder. "Rafa possesses a unique mix of curiosity and relentless drive," she remarked, highlighting their strong working relationship from his time at Accel.

Arrakis faces fierce competition as consulting powerhouses like Accenture and Boston Consulting Group enter the industrial AI space, along with tech giants like Palantir and Prometheus—backed by Jeff Bezos—which are heavily investing in automating physical product engineering.

However, Quintanilla insists that Arrakis is positioning itself uniquely against these rivals. While Prometheus may create AI tools specifically for core aircraft engineering, Arrakis aims to offer a comprehensive AI solution for the broader operations of these companies. 

Regarding Palantir, Quintanilla respects their contributions but believes their technology is becoming outdated. "While Palantir is a commendable enterprise, it's a legacy company with an antiquated tech stack and a substantial price tag," he pointed out.

He further noted that traditional consulting firms, despite adapting to AI trends, often maintain a business model that promotes billing for consulting hours and outsourced labor. "Consultants typically solve strategic issues through human resources, while we prefer to facilitate solutions using software and human input as necessary," he clarified.

Quintanilla also expressed skepticism about the sweeping "process transformation" often peddled by consultants. "Such grand transformation pitches sound appealing but can lead to short-term stock boosts without real commitment," he observed, adding that many CEOs are currently fatigued by vendors who hesitate to adhere to tight timelines.

Instead, Arrakis promotes a more gradual approach. For example, in its work with a New York-listed shipping company—which Quintanilla had to keep unnamed due to confidentiality—Arrakis focused on enhancing cash-flow tracking from monthly to daily visibility. Their engineers restructured the existing spreadsheets, employing AI to automate data input while also capturing operator insights and corrections. "The strategy begins at headquarters to validate value before moving to field operations," he explained, noting that their fees often hinge on achieving specific performance targets.

Penetrating the conservative European industrial market presents its own challenges. Quintanilla shared that he has found significant traction in family-run businesses, which tend to focus on long-term initiatives and enable him to cultivate deeper, non-transactional relationships.

Designed to be model-agnostic, Arrakis appeals to executives wary of being tied to a single AI provider due to concerns over high costs. Quintanilla recounted a Swiss executive’s frustrations with shifting recommendations between models, emphasizing the need for guidance toward the most effective provider.

Typically, Arrakis begins with proprietary models from OpenAI or Anthropic but transitions clients to open-source alternatives, potentially including offerings from Mistral or even Chinese developers—wrapped in a comprehensive "fat harness"—which reportedly enhances quality while significantly reducing token costs.

Currently, Arrakis serves five clients and plans to expand its team from about 15 employees, with plans to establish additional offices in New York and the Middle East.

Exclusive: Startup Arrakis reveals itself after stealth mode with $38 million in funding to introduce AI to the industry.

A sunlit industrial loft where a heavy curtain is being pulled aside to reveal a row of sleek robotic arms assembling precision components beneath a large Arrakis logo banner. In the background, two engineers review blueprints on a tablet displaying “$38 M” in bold. Natural morning light streams through high windows, casting warm highlights across polished metal surfaces, symbolizing the startup’s emergence from stealth into industrial AI.

Exclusive: Startup Arrakis emerges from stealth with $38 million in funding to bring AI to industry - Fortune

- Arrakis, a startup focused on industrial AI, raised $38 million in venture capital funding.  
- The company aims to streamline operations across sectors like aerospace, energy, and manufacturing.  
- Arrakis plans to expand its team and operations while catering to family-controlled businesses.  

Arrakis, a seven-month old London- and Paris-based startup building what it calls an AI “operating system” for industrial companies, is emerging from stealth with $38 million in venture capital funding. It says its goal is to bring agentic AI to sectors such aerospace, energy, logistics, and manufacturing.



The company’s latest funding is a $30 million Series A led by Blossom Capital, with participation from venture capital firms Accel, GFC, MainObject, and Rerail. Accel led an earlier $7.5 million seed round, and individual backers include Datadog CEO Olivier Pomel and OpenAI’s head of business products, Olivier Godement.



The latest round values the company at $140 million post-money, cofounder and CEO Rafael Quintanilla told Fortune.



Quintanilla is a former vice president at Accel. While there, he spent the better part of a year crisscrossing the U.S., Europe, and the Middle East to develop the firm’s thesis on defense and industrial resilience. What he found convinced him to quit and become a founder himself.

“I realized that there was a huge gap between what I was seeing at Accel and in the Valley, with us investing in companies like Anthropic and Lovable in Europe,” he said, “and what I was seeing in the more industrial parts of the economy.”



He said that most AI has targeted so-called knowledge workers who complete their jobs using software, but that many more jobs in the economy involve the production and movement of physical goods. “Most AI investment to date has targeted the 30% of workers behind a desk. The real ROI lies in the 70% running industrial operations,” he said.

Sonali de Rycker, the Accel partner who backed Arrakis’s seed round, said she is betting on the founder as much as the market. “Rafa has a rare combination of curiosity, hustle and tireless drive,” she told Fortune. “After working closely with Rafa during his time at Accel, it’s an honour to be working with him again as an entrepreneur.”

But Arrakis is hardly alone in going after manufacturing and industrial firms. Consulting giants such as Accenture and Boston Consulting Group are racing into industrial AI, as is Palantir, and Jeff Bezos-backed Prometheus—now valued in the tens of billions of dollars—is pouring capital into automating the engineering of physical products. The frontier labs are circling too.

Quintanilla argues Arrakis is carving out a distinct niche from each of these competitors. If Prometheus worked with Airbus, he said, it would build AI for “the core engineering of building an aircraft.” He said Arrakis, by contrast, “want[s] to take care of everything around it… We want to be the AI layer for key operations of those companies.”

As for Palantir, Quintanilla said he respects the company but that its tech was not built to be AI-native. “Palantir is a fantastic company. I think half of my team currently comes from Palantir,” he said, noting that, among others, those hires include a former head of Palantir’s procurement and supply-chain team. “However, Palantir is a 20-year-old company that has a very hefty price point, that has a technology that is starting to become legacy.”

And Quintanilla says consulting firms, even as AI has begun to change their business model, still often have an incentive to charge for either consultant hours or outsourced human labor. “If you think about what consulting firms are, they solve strategy problems for companies with human as the key enabler,” he said. “We want to solve problems for companies with software and human as key enablers.”

He said he is skeptical of the sweeping “process transformation” that consultants often sell. “The big project transformation pitch sounds very sexy on paper, and can be great if you want to pump your stock in the short term,” he said, “but what I’m hearing in the boardrooms where I’m sitting is that there is a lot of fatigue from CEOs on having vendors that do not want to commit to short timeline[s].”

Instead, Arrakis starts small, he said. For one New York-listed shipping company—which he said he could not name due to non-disclosure agreements—the goal was to improve cash-flow visibility from monthly to daily. Arrakis’s engineers rebuilt the spreadsheet operators already used, having AI populate the data while the system “learns and starts to codify the knowledge of those operators” as they make corrections. The playbook, he said, “always starts with HQ, prove the value, move to field operations as soon as you get the pull to get there.” Arrakis usually charges about half of its fees for hitting a particular performance target, he said.



Landing conservative European industrial firms is its own challenge. Quintanilla offered what he called “an open secret”: his best traction has come from family-controlled businesses. “They think long term, they can push for top-down initiatives to be executed, and I can build non-transactional relationship[s] with those people.”

Arrakis is model-agnostic by design—a stance Quintanilla said resonates with executives worried about being locked in to a single AI model provider and wary of high token costs. He said one Swiss C-suite executive told him: “When we started this, everyone told us we had to be on Copilot. Then we went to OpenAI. Now it’s Anthropic. My head is going like this… I basically want someone who is able to route me to the best provider.”

He said the company typically starts building using proprietary models from OpenAI or Anthropic, and then shifts customers to open-source alternatives—such as those from Mistral, or, if the customer permits, Chinese vendors—wrapped in a “fat harness” that he claims delivers a two-to-four-fold quality improvement while cutting token costs by roughly 70%.

Arrakis currently has five customers and plans to triple its headcount from roughly 15, opening outposts in New York and the Middle East.

Exclusive: Startup Arrakis emerges from stealth with $38 million in funding to bring AI to industry

Cybersecurity startup Glow has recently transitioned out of stealth mode, securing $180 million in funding and achieving a valuation of $1.2 billion. The company aims to revolutionize endpoint security for an environment where employees and autonomous AI systems increasingly inhabit enterprise networks.

Established in 2025 by ex-executives from Meta, Snowflake, and Claroty, Glow's latest funding round was spearheaded by Sequoia Capital, along with Cyberstarts, Greenoaks, and Redpoint Ventures. Additional contributions came from Index Ventures, Swish Ventures, Lux Capital, and Holly Ventures.

Glow has successfully raised $180 million over three funding rounds within approximately a year. This includes an initial $20 million seed round led by Sequoia and Cyberstarts, a $60 million Series A led by Index Ventures with Greenoaks contributing, and the current $100 million Series B round.

The startup plans to allocate its funds towards enhancing its market presence in the U.S. and expanding Glow Labs, its research division. With a workforce of about 100 employees, including around 65 based in Israel, the company is active in both Israel and the U.S. It has secured enterprise clients across various sectors such as healthcare, retail, and financial services.

Glow is focused on addressing the challenges in the endpoint security market, which traditionally involves multiple specialized products to safeguard employee devices and corporate information. CEO and co-founder Roi Tiger emphasized that current security solutions do not adequately tackle the unique security threats posed by AI advancements.

As reported by Tiger, "The landscape for endpoints is cluttered with isolated solutions. Our comprehensive offering caters to the entire endpoint landscape rather than just focusing on AI threats, addressing a vast $40 billion market that includes major players like CrowdStrike and SentinelOne."

Glow contends that AI has not only introduced new security vulnerabilities but has also exacerbated existing ones. Data from the company indicates that the frequency of AI tool usage on corporate devices—whether sanctioned or otherwise—has surged from 15% to 45% within a year.

Tiger noted that cyber attackers now leverage sophisticated AI capabilities, enabling them to exploit vulnerabilities at an accelerated pace. He stated, "Prevention has always been the ideal approach in security, although it frequently fell short on an enterprise scale. AI changes that paradigm. Once we recognized AI's potential, we realized we could finally safeguard endpoints effectively."

The Glow platform utilizes advanced AI agents to continuously assess and map an organization’s endpoint ecosystem, identify risks, and autonomously enforce security protocols. The technology also evaluates which software should be authorized for use and which should be removed, thereby minimizing attack surfaces while maintaining seamless business operations.

Tiger articulated that the shift towards AI-driven security necessitates a reevaluation of corporate cyber defense strategies. "As cyber investments increase, attackers innovate to find new vulnerabilities, and everything culminates at the endpoint. By embracing AI proactively, a robust preventive strategy can extend to the endpoints," he explained.

Emily Heath, the company's chief operating officer, reiterated that current security tools are outdated for today's demands. "Having been in the CISO role for quite some time, I've observed that the available tools are not equipped for the challenges enterprises face now. It's no longer a question of whether enterprises will adopt AI, but whether security protocols can keep pace."

Glow's founding team includes Tiger, Omer Singer, and Ophir Arie. Tiger boasts nine years at Meta, where he served as vice president of engineering following the acquisition of his startup, Onavo. Singer, Glow’s CTO, previously headed cybersecurity strategy at Snowflake and has a military background from Unit 8200. Arie, the vice president of R&D, brings experience from Claroty and academic training in the elite Talpiot program.

The leadership roster also features Arnon Joseph, chief product officer with a significant tenure at Meta, and Heath, who has served as CISO at United Airlines and DocuSign, and played a role during Wiz's multi-billion dollar acquisition by Google.

Tiger expressed his aspirations for Glow, aiming to be a leader in the endpoint security sector. “My entrepreneurial journey is deeply personal,” he said. “Having witnessed Onavo contribute to Meta’s foundation, I realize I can create something new from scratch and achieve new heights.”

He acknowledged the evolving market needs, talent competition, and the urgency for rapid sales growth. Cyberstarts founder Gili Raanan emphasized that the capabilities of Glow's team were pivotal in the investment decision.

AI security company Glow unveils itself after stealth mode, securing $180 million at a $1.2 billion valuation.

A high-tech underground vault bathed in warm natural light streaming through a newly opened hatch above. The heavy vault door stands slightly ajar, emblazoned with a large glowing “Glow” logo. Inside, stacks of gold bars stamped “$180 M” sit beside a sleek plaque engraved “$1.2 B VALUATION.” Shadowy server racks line the walls, hinting at a powerful AI security startup emerging from stealth.

AI security startup Glow emerges from stealth with $180 million at $1.2 billion valuation - calcalistech.com

- Cybersecurity startup Glow raised $180 million and achieved a $1.2 billion valuation.  
- Glow's AI-driven platform aims to enhance endpoint security for modern corporate environments.  
- The company plans to expand operations in the U.S. and grow its research arm.  

Cybersecurity startup Glow has emerged from stealth with $180 million in funding at a $1.2 billion valuation, aiming to build a new approach to endpoint security designed for an era in which employees and autonomous AI systems increasingly operate inside enterprise networks.

The company, founded in 2025 by former executives from Meta, Snowflake and Claroty, said its latest financing round was led by Sequoia Capital, Cyberstarts, Greenoaks and Redpoint Ventures, with participation from Index Ventures, Swish Ventures, Lux Capital and Holly Ventures.

1 View gallery Glow founders ( Glow )

Glow has raised $180 million across three rounds in roughly one year, including a $20 million Seed round led by Sequoia and Cyberstarts, a $60 million Series A led by Index Ventures with participation from Greenoaks at a $400 million valuation, and the current $100 million Series B.

The company plans to use the funding to expand its go-to-market operations in the United States and grow Glow Labs, its research arm.

Glow currently employs approximately 100 people, including around 65 in Israel, and operates from Israel and the United States. The company said it has signed enterprise customers across industries including healthcare, retail and financial services.

Glow is targeting the endpoint security market, where companies have traditionally relied on multiple specialized products to protect employee devices, applications and corporate data.

CEO and co-founder Roi Tiger said existing solutions were not designed for the security challenges created by artificial intelligence.

“If you look at endpoints, there are a lot of point solutions. Each company solves something small there and does not offer a comprehensive solution,” Tiger told Calcalist. “We offer a broad solution for the entire endpoint market, not just AI. It is a $40 billion market with giant companies like CrowdStrike and SentinelOne.”

Glow’s core argument is that AI has fundamentally changed the endpoint. Corporate devices have become the place where employees introduce new AI tools, connect AI agents and integrate new software into daily workflows, often faster than security teams can evaluate them.

According to data cited by Glow, regular usage of AI tools on corporate devices, whether authorized or unauthorized, has increased from 15% to 45% in one year.

The company argues that AI has not only created new security risks but has also accelerated existing ones. Attackers now have access to increasingly advanced AI capabilities, allowing threats that organizations previously tolerated to be exploited much faster.

“Prevention was always the right answer in security. It just never worked at enterprise scale without blocking the business,” Tiger said. “AI solves that. The moment we saw what AI made possible, we knew the endpoint could finally be protected the way it always should have been.”

Glow’s platform uses specialized AI agents to continuously map an organization’s endpoint environment, analyze risks and automatically enforce security policies.

The company says its technology can determine which software should be allowed to run and which should be removed, reducing the attack surface without slowing business operations.

Tiger said the shift toward AI-based security represents a fundamental change in how companies should approach cyber defense.

“With all the investment in cyber, attackers find new entry routes, and everything eventually ends up at the endpoint,” he said. “If you understand how to use AI to deal with it before it happens and work proactively, you can provide a solution where prevention also reaches the endpoints.”

Emily Heath, Glow’s chief operating officer, said existing security tools were not built for the current environment.

“I sat in the CISO seat for a long time, and I can tell you the tools available to us were never built for what enterprises face today,” Heath said. “Every enterprise wants to move faster with AI. The question isn't whether they'll adopt it, it's whether security can keep up.”

Glow was founded by Tiger, Omer Singer and Ophir Arie.

Tiger previously spent nine years at Meta, most recently as vice president of engineering, after Meta acquired Onavo, the company he founded.

Singer, Glow’s CTO and co-founder, previously served as head of cybersecurity strategy at Snowflake and earlier worked in Unit 8200.

Arie, vice president of R&D and co-founder, previously served as vice president of R&D at Claroty and is a graduate of the Talpiot program and Unit 8200.

The leadership team also includes Arnon Joseph, chief product officer, who spent eight years at Meta as senior director of product and led Meta Israel’s product group, and Heath, who previously served as CISO at United Airlines and DocuSign, a board member at Wiz through its $32 billion acquisition by Google, and a partner at Cyberstarts.

Tiger said his ambition is to build one of the world’s leading endpoint security companies.

“Entrepreneurship comes from a personal place,” he said. “I saw at Onavo how this thing created a foundation for a giant like Meta, and that I can build something from scratch and bring it to something new that can reach other heights.”

“The market today is different, the capabilities needed are different and the competition for talent is different,” he added. “We want to reach significant sales quickly.”

Cyberstarts founder Gili Raanan said the company’s team was central to the investment decision.

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