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While OpenAI Shattered Records, Robotics and Semiconductor Startups Quietly Added The Most New Unicorns In February
AI frontier labs continued to lead The Crunchbase Unicorn Board last month in terms of dollars spent and valuations, but it was hardware — robotics and semiconductors — that added the largest number of new billion-dollar companies in February. A total of 27 companies joined the Unicorn Board last month, including six robotics companies and four semiconductor-related startups. Healthcare minted three new unicorns, while foundation AI, cloud services, aerospace and financial services each accounted for two companies that joined. The U.S. once again dominated, with 19 companies joining the board. China tallied four new unicorns, the U.K. contributed two, and India and Germany each added one new unicorn. Soaring valuations Overall unicorn values soared in February as OpenAI raised $110 billion at a value of $840 billion, making it the most highly valued private company of all time. Its closest rival, Anthropic, raised $30 billion at a valuation of $380 billion, making it the fourth-largest valued company on the list. Waymo, the autonomous driving technology company, was valued at $126 billion, positioning it among the top 10 most highly valued private companies. February’s new unicorns Here are February’s newly minted unicorns. Robotics Semiconductor Healthcare Cloud services Foundational AI Aerospace Financial services E-commerce Coding Defense Forecasting Sales & marketing Web3 Related Crunchbase unicorn lists: Related reading: Methodology The Crunchbase Unicorn Board is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on Crunchbase data. New companies are added to the Unicorn Board as they reach the $1 billion valuation mark as part of a funding round. The unicorn board does not reflect internal company valuations — such as those set via a 409a process for employee stock options — as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter. Funding to unicorn companies includes all private financings to companies that are tagged as unicorns, as well as those that have since graduated to The Exited Unicorn Board. Exits analyzed here only include the first time a company exits. Please note that all funding values are given in U.S. dollars unless otherwise noted. Crunchbase converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to Crunchbase long after the event was announced, foreign currency transactions are converted at the historic spot price. Illustration: Dom Guzman
From Hype To Outcomes: How VCs Recalibrate Around Agentic AI
By Harsha Kapre For much of the past year, the conversation around agentic AI was dominated by ambition. Founders and investors alike talked about autonomous systems that could reason, act and operate with minimal human involvement. As we move into 2026, that narrative is shifting away from what agents might do someday toward what they can reliably deliver today. Harsha Kapre, head of Snowflake Ventures This shift is evident in findings from Snowflake’s Startup 2026: AI Agents Mean Business report, which quotes from conversations with eight AI-focused VC investors who discuss what they see in the market today and in the year ahead. Their perspectives reflect a broader recalibration underway across the venture ecosystem. The experimentation era is giving way to one of more intentional adoption. AI is increasingly treated not as a standalone feature, but as an operating layer — embedded in workflows, governed by policy and evaluated on outcomes rather than ambition. In practice, that means agents are finding traction in narrow, well-defined use cases. Fully autonomous agents remain rare in production, particularly for complex or high-risk workflows. What is working are agents deployed in data-rich domains like software development, customer support, sales operations and internal analytics. In these environments, human-in-the-loop designs are not a compromise; they are often the reason agents can be trusted and adopted at scale. What investors look for This shift has changed how startups are evaluated. As agentic tooling becomes easier to build, impressive demos have lost much of their signaling power. What matters now is evidence of usage: customers running agents in production, measurable productivity gains and early revenue momentum. Founders need to clearly articulate how their agents improve existing workflows and why that value persists over time. Without that clarity, even technically strong products struggle to stand out. Capital dynamics are also shaping the market. Investment continues to concentrate around a small group of foundational models and infrastructure providers. Rather than crowding out startups, many investors see this as an enabling layer. Well-capitalized platforms absorb the cost of training and inference, allowing startups to focus on application-level value. Looking ahead, 2026 is shaping up to be less about sweeping claims of autonomy and more about execution. Enterprises want agentic solutions that fit into existing operating models, meet governance requirements and deliver quantifiable business impact. For VCs, the hype cycle has done its job. The next phase will reward startups that turn agentic AI into focused, outcome-driven businesses and can prove it. Harsha Kapre is the head of Snowflake Ventures where he focuses on investments to drive innovation and unlock new value on top of the Snowflake platform. A seasoned product management leader, he originally joined Snowflake in 2017 as a senior product manager, a role in which he played a pivotal role in the company’s partner ecosystem expansion. Prior to Snowflake, he spent 18 years at IBM with various roles across master data management and data platforms. Kapre earned his bachelor’s degree in electrical engineering and computer science from the University of California, Berkeley. Illustration: Dom Guzman
Khosla’s Ethan Choi On AI, Founder-First Investing And The Fate Of Entry-Level Jobs
As a partner at Khosla Ventures, Ethan Choi isn’t shy about speaking his mind. The investor is vocal about his belief that AI is a massive threat to entry-level jobs, or what he views as a shifting social contract in the modern workforce. He would know about AI’s impact. Choi has led investments in several AI-first companies. Known for a founder-first conviction, he’s also backed enterprise software and fintech infrastructure companies. Prior to joining Khosla in 2024, Choi was a partner at Accel, where he led and managed high-profile growth investments in companies such as 1Password, Klaviyo, Pismo (which was acquired by Visa), Nuvemshop and commercetools. Before Accel, he worked at Spectrum Equity, backing companies such as Lynda.com (acquired by LinkedIn), Headspace, Lucid Software and PicMonkey (acquired by Shutterstock). I recently spoke with Choi to hear more about why he thinks entry-level jobs could be disappearing, why he’s flipped his investing philosophy, and how he’s gone from growth-stage investing to being stage-agnostic. This interview was edited for brevity and clarity. Ethan Choi, partner at Khosla Ventures. (Courtesy photo) Crunchbase News: You’ve had a prolific run the past couple of years, leading deals in Ramp, ClickHouse, Vercel, Glean, Bridge and others. How are you managing that volume? Choi: It has been an intense stretch. About seven deals happened just last year, which was an insane year by any standard. This year has been a bit calmer as I focus on settling in with the companies I’ve invested in. You’re currently researching the disappearance of entry-level jobs. As a parent, that sounds a bit scary to me. What are you seeing? AI is a massive conundrum. I’m seeing it in my own workflow. I use the models to get up to speed on technical capabilities — asking about Clickhouse’s indexing methodology versus Snowflake’s in voice mode while I’m driving. It’s pulling from research papers and documentation faster than any human could. I describe it as feeling like I have an “Ironman suit” on. The problem is that if I can do the work of a junior associate myself, almost instantly, those roles vanish. We’re facing a world where the base-level work we used to rely on young folks for is now table stakes. If the “on-the-job” training era is over, where does that leave students and universities? The burden of the first three years of “learning how to be a professional” has to shift to the universities. I look at the traditional U.S. model of general education requirements and think, “Why are we doing this?” We did that in high school. Universities should be places where you use AI to actually build things and apply knowledge to the real world. If you’re a computer science major today, you need to graduate looking and delivering like a third- or fourth-year engineer. The bar has been raised for everyone. While schools like Vanderbilt and Arizona State University are leaning into an “AI-first” curriculum, many elite institutions are still silent, trying to figure out how to adapt. Khosla is known for being contrarian. How does that translate to the growth stage in such a competitive market? I’ve actually evolved to be stage-agnostic. While people still put me in the “growth” bucket, I’m doing much more seed and Series A. In this era, the metrics a company has today don’t guarantee where they’ll be in two years because the rate of change is so high. I’ve flipped my philosophy: it used to be 80% metrics and 20% founders. Now, it’s 90% founders. The only constant is how special the founding team is and how quickly they can adapt. If code is being created 10x faster, a company might face 50 years of change in a single decade. You have to back the people who can handle that stress. You’ve predicted “mass carnage” for some software companies. Who survives the transition to an AI-native world? We are moving from trading on revenue multiples to trading on free cash flow and PE multiples. That’s a painful transition. The market now needs to believe that a company is AI-native — that its revenue is moving toward inference- and usage-based models rather than just old-school seat licenses. I expect carnage for lightweight, horizontal applications and mid-market companies that can’t attract applied AI talent. I have a ton of respect for founders like those at Intercom or Airtable who are “burning the boats” to reinvent their entire businesses. It’s incredibly difficult, but in this market you either reinvent or you get replaced by someone building natively from day one. With your background in fintech infrastructure, where do you see the next “unconventional” opportunity in financial services that most growth investors are currently overlooking? It’s now become somewhat consensus, but I still believe it’s fairly unconventional that systems of record can be ripped out, whether it be in financial services or in other categories. For example, we recently invested in DualEntry, which is seeking to replace NetSuite and the core accounting system, which is the last system of record I would have thought might be under threat. We’re seeing that with AI, startups can build migration paths that didn’t exist before, and also the depth and breadth of incumbent platforms in a fraction of the time. Vinod Khosla often talks about “challenging the conventional wisdom” of founders. Can you share an example of a time you had to steer a growth-stage founder away from a “safe” path toward a much larger, albeit riskier, vision? In general, there are many times when a founder is thinking through a very risky but potentially game-changing product addition or acquisition. While I view part of our job as investors and board members as helping identify and manage potential risks, the most important thing we can do is give them the courage to take risks that are transformational to the business and the category they are in. You’ve noted that talent density is the most important variable for success. In a market where AI is automating routine work, how has your criteria for what defines an “elite” executive hire changed? Perhaps somewhat ironically, one of the main differences in criteria is whether this exec has “IC’d” (individual contributor’d) themselves and can do most of the work required out of the gate with their own two hands plus AI. Related Crunchbase query” Related reading: Illustration: Dom Guzman
Turing Winner LeCun’s New ‘World Model’ AI Lab Raises $1B In Europe’s Largest Seed Round Ever
Advanced Machine Intelligence, a startup co-founded by computer science pioneer and former Meta AI chief Yann LeCun, said Tuesday that it has raised $1.03 billion to develop “world models,” or AI designed to learn from and interact with the physical world. The funding for Paris-based AMI represents the largest seed round ever for a European startup and one of the region’s largest fundings for an AI startup overall, per Crunchbase data. Bezos Expeditions, Cathay Innovation, Greycroft, Hiro Capital and HV Capital led the funding, which reportedly values AMI at $3.5 billion. AMI differs from the popular generative AI startups in that it aims to develop world models, or artificial intelligence that interacts with and learns from three-dimensional reality. “My prediction is that ‘world models’ will be the next buzzword,” AMI Labs CEO Alexandre LeBrun told TechCrunch after the funding. “In six months, every company will call itself a world model to raise funding.” His co-founder LeCun is considered one of the pioneers of the large language model approach to AI. In 2018, LeCun was one of the computer scientists who received the industry’s prestigious A.M. Turing Award for his work on neural networks and learning algorithms. Their new startup, however, is premised on the idea that the two-dimensional approach used by LLMs is, by definition, limiting. To be truly useful, AI must be able to understand and interact with 3D reality, AMI’s leaders argue. “Generative architecture trained by self-supervised learning mimic intelligence; they don’t genuinely understand the world. Predicting tokens, though powerful, works best for discrete and low-dimensional tasks like information retrieval, summarization, coding, and mathematics,” AMI CEO LeBrun wrote on LinkedIn. “However, factories, hospitals, and robots operating in open environments demand AI that grasps reality. And reality is not tokenized: it’s continuous, noisy and high-dimensional. Despite their immense power, I do not believe that generative architectures are the path to achieving this true understanding.” AMI’s first partnership is with Nabla, a healthcare AI startup also headed by LeBrun. Funding to world-model AI While the bulk of AI startup funding thus far has gone to LLM-based generative AI giants, investors appear to be turning their attention to funding more companies like AMI that seek to bring artificial intelligence into the physical world. Late last month, Fei-Fei Li’s San Francisco-based World Labs, another startup founded by an AI pioneer to work on foundation models for real-world AI, raised $1 billion in fresh funding. Fewer large deals for Europe’s AI sector Global venture funding hit an all-time monthly record in February as OpenAI announced a $110 billion funding round — by far the largest-ever investment in a private company. As global startup funding has increased in recent quarters, so has concentration into the AI sector and the dominant players, most of which are based in the U.S. Europe, by comparison, has seen only modest gains in its venture funding growth and just a handful of billion-dollar-plus deals for AI companies, including $2 billion for Paris-based Mistral AI last year and $2 billion for Nscale earlier this week. Related Crunchbase query: Related reading: Illustration: Dom Guzman
Swedish Legal Tech Startup Legora Triples Valuation To $5.55B With $550M Series D Led By Accel
Legora, an AI platform built for lawyers, has raised $550 million in a Series D funding round, valuing the Swedish company at $5.55 billion. The valuation is a big jump from the $1.8 billion Legora achieved just last October, when it raised a $150 million Series C round. The company has now raised a total of $816 million since its inception. Sigge Labor, president, and Max Junestrand, CEO, co-founders of Legora. (Courtesy photo) Founded in 2023, Stockholm-based Legora says it plans to use the new capital to accelerate its expansion across the U.S. It set up shop in New York one year ago and is now opening new offices in Houston and Chicago. It has over 800 customers across 50 markets. Accel led the round, which also included participation from existing backers Benchmark, Bessemer Venture Partners, General Catalyst, Iconiq Capital, Redpoint Ventures, and Y Combinator, as well as a slew of new investors, including Bain Capital, Menlo Ventures and Salesforce Ventures 1. Venture funding for legal tech startups reached a record high in 2025, driven by investor enthusiasm for AI’s potential to automate the legal profession. Per Crunchbase data, companies in the legal and legal technology sectors raised $4.08 billion in seed- through growth-stage funding in 2025. That’s an impressive 77.4% increase from the $2.3 billion raised by legal tech startups in 2024. Other startups in the industry that have closed on sizable fundings over the past year include: “Over the past year, the pace of adoption in the U.S. has exceeded our expectations, as leading firms and in-house teams move decisively from experimentation to embedding AI across their organisations,” Max Junestrand, CEO and co-founder of Legora, said in a press release. “This funding enables us to accelerate our U.S. growth — investing in talent and infrastructure, strengthening our presence in key markets, and ensuring we can support customers on the ground as they integrate AI into their core workflows.” The company expects to open more offices in the U.S. this year. Related Crunchbase query: Related reading: Illustration: Dom Guzman
Exclusive: Rebar Lands $14M To Help HVAC Suppliers Generate Quotes Faster With AI
Rebar, a startup building an AI operating system for commercial HVAC suppliers, has raised $14 million in a Series A funding round, it tells Crunchbase News exclusively. Prudence led the financing for the New York-based company. Zero Infinity Partners, Founder Collective, Villain Capital and Optimist Ventures also participated in the round. Founded in October 2024, Rebar says it uses artificial intelligence to help commercial HVAC suppliers generate quotes on average 60% to 70% faster than traditional methods. Its proprietary computer vision models analyze construction blueprints and identify, categorize and count all HVAC equipment to produce a bill of materials and generate a quote, according to the company. Rebar’s focus on the HVAC industry is intentional. Evan Brown and Andrew Schwartz, co-founders of Rebar. (Courtesy photo) “Most companies either concentrate on other trades or try to cover all of them at once, which ultimately means they serve none particularly well,” CEO and co-founder Evan Brown told Crunchbase News. “Rebar takes the opposite approach. Our AI is trained on millions of HVAC blueprints and mirrors the workflows real estimators use when building a quote.” Rebar doubled its annual recurring revenue in the first six weeks of 2026, according to Brown, thanks to dozens of new customers using the platform. It operates on a usage-based subscription model. “If you’re able to increase your volume and build more proposals, the opportunity to win projects goes higher,” Brown explained. “On average, the companies that are using us [would] win around 5% to 10% of the proposals that they created, and those proposals could take up to a week to create before using Rebar.” He noted that, while it’s still early and construction contracts often take years to finalize, initial conversations with customers suggest win rates may be increasing by roughly 2x to 3x using the new tool. That same process can take minutes, according to Brown. Today, Rebar has 40 clients, and notably, seven of them are investors in the startup. While Rebar’s first product is AI quoting for HVAC suppliers, Brown said the startup plans to expand its product capabilities to plumbing and electrical equipment suppliers through additional agents. Firsthand industry perspective Brown’s interest in the HVAC industry stems from summers working with his uncle, the owner of a large HVAC commercial distributor. He then ended up working as an estimator and sales engineer at Johnson Barrow/DMG Corp., where he partnered with contractors, engineers and manufacturers to design, sell and support customized HVAC systems. “I spent over five years in the space focused on designing and selling HVAC equipment and doing the manual process that we’re solving at Rebar many times,” he recalled. “I’ve looked and built inventory lists off of a plan set by printing out blueprints, highlighters and rulers. I’ve used PDF editors. I’ve really used everything, and I never thought I would end up in tech, nor start a tech company.” When that company was acquired and became part of Ambient Enterprises, Brown had the opportunity to explore the application of AI in day-to-day operations. Eventually, he decided to branch out and founded Rebar along with Andrew Schwartz. What investors are looking for Jordan Viniar, a partner on Prudence’s investment team, told Crunchbase News that Brown’s education in mechanical engineering combined with his experience working in the HVAC industry give him a “unique,” firsthand perspective. Prudence is always looking for companies that leverage unique technology to automate historically manual processes, according to Viniar, often in industries where needs have never been addressed by purpose-built software. “Rebar fits the bill perfectly — HVAC, plumbing, and electrical are large but historically overlooked industries with dozens of manual workflows, none of which could be automated without AI,” he wrote via email. “For the first time, Rebar is delivering tools to their customers that, in many cases, are compressing the time to complete manual tasks by over 90%, leading to greater efficiency, lower costs, and more revenue.” Venture investment in real estate-related startups has rebounded in recent years after plunging from the pandemic peak. In 2025, startups in the sector pulled in approximately $10.5 billion in seed- through growth-stage financing globally, per Crunchbase data. That’s up about 17% from $9 billion in 2024, with much of the recent investment going to startups that promise greater ROI through the use of automation or AI. Related Crunchbase query: Related reading: Illustration: Dom Guzman
The Series B Pipeline Looks Refreshingly Diversified
Before pulling data, one usually has a preconceived idea of what the results will show. In this case, looking at recent U.S. Series B investments, my assumption was that big rounds would be dominated by a few buzzy AI sectors. The reality, however, looks far more diversified. Startups securing the largest rounds run the gamut from biotech to robotics to security and more. Obviously AI is the leading theme, but the pipeline of funded companies is anything but cookie-cutter. Overall funding levels also look fairly healthy, with annual Series B funding moving steadily higher after hitting a low in 2023. This year is off to a strong start as well, as charted below. Round counts are also holding up at a steady level, an encouraging indicator for those worried about capital concentration thinning the ranks of funded companies. It may be happening at late stage, but Series B is not so dramatically affected. Investor favorites Even so, a good chunk of Series B investment did go to a handful of favored startups. Looking at rounds from the past six months, the largest was a $2 billion Nvidia-led financing for Reflection AI, a developer of open foundation models founded in 2024 by former Google DeepMind researchers. Another standout was Kailera Therapeutics, which is developing oral treatments for obesity. The then year-old company raised $600 million in October. Physical Intelligence, an AI robotics startup, also raised $600 million in a November Series B led by Google’s CapitalG. For a bigger-picture view, we used Crunchbase data to put together a list of 10 of the largest Series B recipients of the past six months. Round sizes grow bigger Another trend we’re seeing is that average round sizes are getting larger. So far in 2026, for instance, the average Series B is $68 million, which appears to be the highest on record. As you can see charted below, the average size of a Series B round has been inching higher for a few years. It’s not as pronounced as what we’re seeing at later-stage, which continues to set fresh records for deal size. But still, investors are putting more capital into their largest deals. Meanwhile, smaller Series B rounds are scarcer. From 2020 through 2023, for instance, there were typically about 150 rounds of between $1 million and $10 million each year. Last year, there were only 44 such rounds. Still plenty of variety While Series B investors may be consolidating their bets somewhat, they’re doing so across a wide range of sectors and technologies. Per Crunchbase data, more than a quarter of Series B funding over the past six months has gone to healthcare and biotech startups. About 15% has gone to robotics and hardware-related investments. Roughly half of Series B investment for the past six months also went to companies in AI-related categories across multiple industries. In addition, a majority of investment went to software-focused companies. At this stage and typical size, one can assume investors are no longer making risky bets on unproven upstarts. To get to Series B requires some impressive technological edge, early traction, or both. And for most, they’re just getting started. Related Crunchbase queries and lists: Related reading: Illustration: Dom Guzman
The Week’s 10 Biggest Funding Rounds: Space Tech, AI Infrastructure Lead Fundraises
Want to keep track of the largest startup funding deals in 2025 with our curated list of $100 million-plus venture deals to U.S.-based companies? Check out The Crunchbase Megadeals Board. This is a weekly feature that runs down the week’s top 10 announced funding rounds in the U.S. Check out last week’s biggest funding deal roundup here. The first week of March was a relatively brisk period for large startup funding rounds, led by three deals of $500 million or more in the space tech and AI infrastructure sectors. In addition, we saw some good-sized deals around healthcare, neuroscience and enterprise software. 1. Sierra Space, $550M, space tech: Sierra Space, a space and defense tech company that designs and manufactures satellites, spacecraft and space subsystems, secured $550 million in equity funding led by LuminArx Capital Management. The financing sets an $8 billion valuation for the 5-year-old, Louisville, Colorado-based company. 2. (tied) Ayar Labs, $500M, AI infrastructure: Ayar Labs, a producer of co-packaged optics for use in AI infrastructure, landed $500 million in Series E funding led by Neuberger Berman. The financing sets a $3.75 billion valuation for the 11-year-old, San Jose, California-based company. 2. (tied) Vast, $500M, space tech: Long Beach, California-based Vast, a startup developing next-generation space stations, announced it has raised $500 million in fresh funding. The financing includes $300 million in Series A equity and $200 million in debt, with Balerion Space Ventures as lead investor. 4. Findhelp, $250M, care platform: Findhelp, developer of a platform to coordinate care across health systems, governments, benefits providers and other entities, secured $250 million in investment from TPG’s The Rise Fund. Founded in 2010, Austin-based Findhelp describes its mission as connecting people to help and support systems. 5. Science Corp., $230M, neurotech: Alameda, California-based Science Corp., a biotech startup focused on brain-computer interface technologies, announced it has closed on a $230 million Series C fundraise. Lightspeed Venture Partners, Khosla Ventures, Y Combinator, IQT and Quiet Capital were among the investors participating in the syndicated round. 6. Cart.com, $180M, e-commerce: Cart.com, provider of an e-commerce platform and logistics services for brands to sell across digital channels, picked up $180 million in growth equity investment. Springcoast Partners led the financing for the Houston-based company. 7. Grow Therapy, $150M, mental health care: Grow Therapy, a New York-based platform for providing mental health care, raised $150 million in Series D funding led by TCV and Goldman Sachs Growth Equity. 8. Cognito Therapeutics, $105M, neuroscience: Cambridge, Massachusetts-based Cognito Therapeutics, a developer of therapies for neurodegenerative diseases, secured $105 million in Series C funding. Morningside, IAG Capital Partners and Starbloom Capital led the financing. 9. Nominal, $80M, engineering software: Nominal, a self-described provider of tools for engineers to test and operate critical technology, picked up $80 million in new funding. Founders Fund led the financing, which set a $1 billion valuation for the Austin-based company. 10. Sage, $65M, health software: New York-based Sage, provider of a software platform for senior living and skilled nursing, raised $65 million in Series C funding led by Goldman Sachs Alternatives. Methodology We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the period of Feb. 28-March 6. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week. Illustration: Dom Guzman