This is a weekly feature that runs down the week鈥檚 Top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

This week delivered a bountiful supply of big startup funding rounds, led by two $400 million financings for cybersecurity unicorns and . Rounding out the week were large financings for startups across hot sectors, including foundational AI, drug discovery, neurotech, and even rainmaking.

1. , $400M, cybersecurity: Island, a developer of tools for secure enterprise digital operations, raised $400 million in Series F financing. led the round, which set a $6.4 billion valuation for the Dallas-based company, more than double its 2024 valuation.

2. , $400M, cybersecurity: Cyera, a provider of enterprise data security tools that govern both humans and AI agents, picked up $400 million in Series G extension funding led by and backed by . The round brings total funding for New York-based Cyera to $2.7 billion, .

3. , $350M, foundational AI: San Francisco-based Snorkel AI, provider of tools for frontier labs and AI teams to develop specialized training data and environments, secured $350 million in Series E funding. and led the round, valuing the 7-year-old company at $3.2 billion, and it now has more than $375 million in annual recurring revenue.

4. , $311M, biotech: Enveda, an AI-enabled drug discovery startup, closed on $311 million in Series E financing led by and joined by a long list of new and existing investors. The round brings the Boulder, Colorado-based company’s total capital raised since inception to more than $845 million.

5. , $250M, neurotech: New York-based Precision Neuroscience, a startup focused on brain-computer interface technology, secured $250 million in Series D funding with and the as lead investors.

6. , $200M, wireless communications: Hubble Network, operator of a satellite network that extends connectivity to Bluetooth devices, pulled in $200 million in Series C funding. led the financing, which set a $1.6 billion valuation for the Seattle-based company.

7. , $155M, pharmacy benefits: New York-based Rightway, a provider of pharmacy benefits and care navigation services, raised $155 million in Series E funding led by . The startup will use the financing in part to expand its AI capabilities.

8. (tied) , $100M, cloud seeding: El Segundo, California-based Rainmaker, a startup focused on atmospheric research and producing new freshwater through cloud seeding, picked up $100 million in Series B funding. The round includes investments from , , , and .

8. (tied) , $100M, tax compliance: San Francisco-based Numeral, an AI-powered sales tax compliance platform, closed on a $100 million Series C round led by .

8. (tied) , $100M, AI data: San Francisco-based Micro1, a provider of AI training data to major AI labs, has reportedly raised a fresh funding round of more than $100 million at a valuation of $4 billion, according to a citing people familiar with the deal.

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of Sept. 19-25, 2026. Although most announced rounds are in the database, there may be a small time lag, as some rounds are reported late in the week.

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So Shariff offered to build an AI agent for a couple of properties. The first, called Alfred, answered guest questions and helped with check-ins and check-outs. Within 90 days, he said, online reviews improved at both properties. He then began deploying it at more hotels, before deciding that guest messaging was only one part of the opportunity.

That work led him to team up with to start , the San Francisco startup they founded in July 2025. The company is now emerging from stealth with $6.7 million in seed funding to build agents that handle reservations, guest requests, staff coordination and other hotel tasks. led the raise, with participation from . The round marks Dextr鈥檚 first institutional financing.

From guest requests to bookings

After early guest management deployments, Dextr turned to incoming calls with a voice reservations agent called Daisy. If a caller has a question about a reservation or something they can’t find on a booking site, answering it could lead to a booking, Shariff found. As hotels began using Daisy, he realized that the phone calls were 鈥渓eading to revenue.鈥�

In fact, today, one large hotel that Dextr works with handles $100,000 to $300,000 a month in bookings through the voice agent, according to Shariff.

Dextr has since expanded into agents for group bookings, staff management and other work. Its goal is to connect those agents so information gathered in one part of a hotel can prompt action in another. For example, if a guest declines housekeeping, a guest management agent could pass that information to an operations agent so that staff schedules could be adjusted accordingly.

Another use case could involve a property operating without an overnight front desk employee. A guest arriving late could call the hotel, have Daisy verify their reservation and receive instructions for entering their room. Daisy and Alfred, Dextr鈥檚 guest management agent, would work together to handle the arrival.

For some properties, using agents to handle late check-ins could remove the need to staff an overnight front desk shift. Shariff estimated that such a shift could cost a property from $70,000 to $80,000 a year in a market such as California.

鈥淚t was very hard to hire for these shifts to begin with,鈥� he said.

Building ROI

Shariff says combining agents is just one of three things that set Dextr apart from other hotel AI tools. An agent that answers calls or messages can handle a particular task, he said, but connecting it with other agents opens up additional uses for a property.

鈥淚t’s important that we do multiple use cases to see the higher ROI,鈥� he told 兔子先生传媒 News in an interview.

The second distinction, according to Shariff, is Dextr鈥檚 approach to getting the agents running. Putting them to work takes more than installing software, he said. Every hotel differs from another in how it manages reservations, assigns staff, and handles guest requests. So Dextr sends engineers to work directly with customers, identify where agents could be useful and then connect them to existing systems. Those engineers stay involved as the property puts the agents to use, he said, and work to find applications that have the potential to deliver a return on investment.

鈥淓very property, every operation is different in this space,鈥� he said. Dextr integrates with property management systems including Oracle Hospitality, OPERA Cloud, , and .

The third is how employees use the agents. They can direct them by text or voice through a companion called Doss, without having to learn a series of steps in a new software system. Shariff said frequent turnover makes it difficult for hotels to train staff on complicated tools and retain that knowledge when employees leave. With Doss, 鈥測ou can call it, you can talk to it, you can text it,鈥� he said.

Hotels and more

Dextr says its agents now handle more than 1 million interactions a month across hundreds of hotels. The startup鈥檚 customers range from a 21-room property to one with 550 rooms. They include independent hotels and properties operating under the , , and brands, as well as reservation centers, vacation rentals and outdoor hospitality businesses.

The startup works directly with the owners and operators of those branded properties, Shariff said, and is beginning its first direct relationship with a hotel brand that he declined to name. It also works with hotel and property management companies, as well as reservation centers, vacation rentals and outdoor hospitality venues such as campgrounds, RV parks, lodges and golf courses.

The company charges a subscription based on the property and the agents it uses, with usage-based fees for some agents. Shariff declined to disclose revenue, profitability or the round鈥檚 valuation. After spending much of last year on pilot projects, he said, Dextr began growing more quickly this spring. He said annual recurring revenue has been growing about 20% to 30% month over month, though he did not disclose hard figures.

Dextr plans to use its new funding to hire more engineers who work directly with customers, expand its software integrations, and develop additional agents. Its team has grown from three people a year ago to 21.

, an AI partner at Elevation Capital, told 兔子先生传媒 News that Dextr鈥檚 early traction got the firm鈥檚 attention. He pointed to customers using the agents to generate bookings and upsells alongside reducing operating costs.

鈥淲hat stood out immediately was Sajid and Scott’s execution. Dextr scaled to [hundreds of] contracted properties and meaningful ARR without raising a single dollar,鈥� he wrote via email. 鈥淚n a space where most AI companies are still searching for product-market fit, Dextr had customers, zero churn, and a clear ROI story. That combination of founder quality and early traction is exactly what we look for at Elevation.鈥�

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This month鈥檚 installment of this column is all AI, though applications for the technology range widely, from two startups that apply AI to trash or recycling, to another that promises to help people breathe and sleep better, to a company that says its AI can help architects and builders spot commercial projects before they鈥檙e even announced. Let鈥檚 jump in.

$27M to help recycling plants see what鈥檚 in trash

For decades, the recycling industry has relied on sampling and educated guesses to understand what moves through its facilities. But wants every discarded bottle, carton and wrapper to become data.

The London-based startup said last month that it has raised a 拢20.3 million ($27 million) Series B led by technology investor . The company installs AI-powered camera systems above conveyor belts in recycling plants, then uses computer vision to identify materials, products and brands in real time. Greyparrot says that data helps operators recover more valuable materials, improve sorting efficiency and comply with increasingly strict recycling regulations.

Its systems are now deployed in more than 20 countries and have analyzed more than 1 trillion waste objects, per the company. It counts large waste-processing companies such as and among its customers.

The data gathered at plants also feeds Greyparrot鈥檚 Deepnest platform, which it says consumer brands including , and use to understand what happens to their packaging after consumers throw it away, helping to inform redesigns and comply with Extended Producer Responsibility rules in places such as Canada and the EU.

The fresh funding will help expand the company’s footprint across North America and Europe and support its goal of preventing more than 1 million tons of waste by 2030.

The raise reflects growing investor interest in applying AI in the physical world rather than behind computer screens. Companies in the physical AI sector raised nearly $47.3 billion in the first half of 2026, 兔子先生传媒 data shows, up nearly 80% year over year, as startups increasingly apply artificial intelligence to settings such as factories, recycling plants and other 3D environments.

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$17M to help robots navigate where GPS can’t

For a recently funded robotics company, the next frontier for physical AI is underground: in mines, tunnels and other places where GPS doesn’t work.

Australian startup said last month that it secured $17 million in new funding. That includes a $10 million equity round backed by , , , and as well as a $7 million venture debt facility from the country鈥檚 National Reconstruction Fund Corp. The company plans to scale manufacturing and expand its AI autonomy and cloud mapping platforms.

Emesent is best known for Hovermap, a LiDAR scanning payload that mounts to drones, vehicles or backpacks to create detailed 3D maps of mines, industrial sites and other hazardous environments. But increasingly, the company’s focus is software. Its Cortex AI platform enables robots to navigate autonomously in environments without GPS, while its Aura cloud platform processes and analyzes the resulting spatial data.

The company says its technology is already deployed at more than 200 mine sites worldwide and that it is expanding into the defense, critical infrastructure and construction sectors.

Its raise is another example of increased interest and investment in physical AI. As industries grapple with labor shortages and increasingly dangerous operating environments, startups that combine robotics, computer vision and autonomy are attracting fresh capital to automate work that’s difficult, dirty or unsafe for humans.

Robotics investment funding overall has been on a tear in recent quarters. Startups in the category raised $15 billion globally in 2025 鈥� an annual record that has already been eclipsed partway through 2026 鈥斅犕米酉壬� data shows.

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$12.25M for AI that treats congestion with sound

We鈥檝e covered AI that can do dirty work like help sort through trash or navigate underground mines. What about AI to help people breathe?

San Francisco-based medtech startup recently raised an oversubscribed $12.25 million Series A led by . The company develops FDA-cleared, noninvasive devices that it says use AI and acoustic resonance therapy to treat congestion and improve sleep without drugs.

The company said its flagship Sonu band personalizes sound waves based on a user’s facial anatomy to open nasal passages, while its newer Spatial Sleep device aims to help users fall asleep faster and stay asleep longer.

The raise comes as investors continue to back AI-powered medical devices that combine software with regulated hardware. Companies that intersect 兔子先生传媒鈥檚 AI and medical devices industries raised more than $629 million in the first half of this year, our data shows, up about 33% year over year.

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$3.85M to turn unrecyclable trash into fuel

Trash and recycling emerged as an unexpected theme in this month鈥檚 column. While Greyparrot helps companies better understand what鈥檚 in landfills and recycling plants, another recently funded company, , says it鈥檚 working to turn unrecyclable garbage into industrial fuel.

The Las Vegas-based company last month announced a $3.85 million round co-led by and to commercialize technology that converts hard-to-recycle plastics and other waste into industrial fuel. The startup says its engineered fuel can replace coal in cement, steel and other heavy industries without requiring factories to modify existing equipment. The new funding will help it build its first commercial U.S. biofuel facility outside Las Vegas.

Global venture investment into cleantech-related startups has been steady but not record-breaking in recent years, 兔子先生传媒 data shows. Around $15 billion went into rounds for companies in 兔子先生传媒鈥檚 cleantech-, EV- and sustainability-focused categories in the first half of 2026, putting this year鈥檚 funding on track to slightly exceed the 2025 tally, which was the lowest in several years.

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$3.5M to predict construction projects before they’re announced

The biggest construction opportunities often surface months before the first request for proposals. promises to use AI to spot them first.

The New York-based startup last month raised $3.5 million in seed funding from 鈥檚 accelerator, , and others to build an AI platform for architecture, engineering and construction firms. The startup promises to give such companies an edge over their competitors by helping them discover projects earlier and identify the best path to winning them. Instead of searching public bid databases, Cascade says its tech can analyze signals such as bond filings, property transactions, capital budgets and meeting minutes to identify projects while they’re still taking shape.

Overall funding to real estate-related startups has trended higher in recent quarters, and the sector emerged as a bright spot for seed funding in the first half of 2026, an analysis of 兔子先生传媒 data shows. Other seed-funded real estate startups this year have spanned areas ranging from streamlining planning and building processes to real estate investing to reducing power consumption in buildings.

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Correction: The article was updated to reflect Forge Industries’ correct headquarters location.

Related reading:

• VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape
• Sector Snapshot: Cleantech Startup Funding Stabilizes As Energy Demand Grows

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While financings of a few million haven鈥檛 gone away, today most venture capital actually goes to rounds of a billion dollars or more. Moreover, it looks like a rising trend.

So far this year, 60% of global startup funding across stages聽1 聽鈥� around $320 billion 鈥� went to rounds of $1 billion or more, per 兔子先生传媒 data. Such rounds were instrumental in pushing global funding for the first half of the year to record levels.

The U.S. funding tallies are even more tilted to megadeals this year, with 73% of funding going to billion-dollar-plus rounds. Of the $290 billion invested in these deals, just two rounds for AI leaders and account for more than half the total.

As you can see, the notion of billion-dollar-plus rounds accounted for a minority of funding before this year. The lone exception was the first quarter of 2025, when OpenAI closed a $40 billion financing.

Not just bigger deals, more of them too

Giant rounds aren鈥檛 just getting more ginormous. They鈥檙e happening with greater frequency too.

So far this year, U.S. startups have closed 23 known rounds of $1 billion or more, per 兔子先生传媒 data. That puts 2026 already on par with 2025, a record-setting year, and we鈥檝e still got about five months left.

Not surprisingly, these megarounds are generally later-stage rounds or corporate financings. Only two of this year鈥檚 billion-dollar-plus rounds 鈥� and 鈥� were seed or early-stage rounds, per 兔子先生传媒 data.

Lessons from the first crop of billion-plus financings

In the history of startups, meanwhile, the billion-dollar-plus venture funding round is a fairly contemporary phenomenon.

The first American example, per 兔子先生传媒 data, was 鈥檚 $1.2 billion Series D, in 2014. Over the next three years, a handful of others pulled in 10-figure rounds as well, including , , , , , , , and .

Most of those companies went on to go public and reach valuations that well-exceeded levels set for prior megarounds. SpaceX ($1.6 trillion recent market cap), Uber ($148 billion) and Airbnb ($87 billion) were the standout success stories.

Two of the megafund recipients 鈥� Argo AI and WeWork 鈥� did not fare so well, while a third, cancer diagnostics provider Grail, has been up and down. Fanatics, meanwhile, remained private and is still thriving.

If these early billion-plus fundings taught investors anything, it was that pouring unusually large sums into well-regarded unicorns can be quite lucrative but is far from a sure bet.

Uncharted territory

In the current funding cycle, it鈥檚 not enough to ask whether billion-dollar rounds have potential for high returns. With Anthropic and OpenAI, the question now applies to rounds in the tens of billions or even over $100 billion. As both have already filed confidentially to go public, it may not take us long to find out.

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Related reading:

• 兔子先生传媒 Data: Global Startup Investment Hit Record $510B In H1 2026 As AI Boom Accelerates Funding And Exits
• 兔子先生传媒 Data: Q2 Brought The Most Billion-Dollar Startup Exits Since 2021
• The $100M+ Round Is Now Just Your Typical Late-Stage Financing

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This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

U.S. startups announced sizable funding rounds at a steady clip during a truncated holiday week, with energy and AI leading the way.

Houston-based energy startup secured the biggest round, a $1.75 billion strategic financing, followed by , a developer of infrastructure for companies running open source AI models, and , a provider of compliance tools for enterprises.

Other big rounds were for companies focused on therapeutics, homebuilding, and even lacrosse.

1. , $1.75B, energy: Houston-based Joulent, a provider of energy infrastructure focused on the demands of artificial intelligence and other compute-intensive industries, raised $1.75 billion in a strategic investment backed by through its arm.

2. , $800M, AI infrastructure: Together AI, developer of an infrastructure layer for companies running open source AI models, secured $800 million in Series C financing. led the round, which set an $8.3 billion post-money valuation for the San Francisco-based startup.

3. , $180M, compliance: LeapXpert, a provider of tools for tracking enterprise communications for compliance needs, closed on $180 million in growth financing. led the financing for the New York-based company.

4. , $135M, AI software development: Redwood City, California-based 8090 Solutions, developer of a platform for building enterprise software with coordinated AI agents under human-led oversight,聽 picked up $135 million in a round led by 1. The company, founded in 2024, counts prominent startup investor as co-founder and CEO.

5. , $126M, biotech: Boston-based Beeline Medicines, a startup focused on precision therapies for autoimmune and inflammatory diseases, secured $126 million in Series A extension funding backed by , and . The financing follows a previously disclosed $300 million Series A.

6. (tied) , $100 million, professional sports: The Premier Lacrosse League, a men’s professional lacrosse league in North America, closed a $100 million Series E financing round led by and . New York-based PLL said the deal represents the largest capital raise in the history of professional lacrosse.

6. (tied) , $100M, video-based AI: Twelve Labs, a San Francisco-based startup developing AI systems trained on video archives, raised $100 million in a Series B round co-led by and .

8. , $95M, AI for homebuilding: Higharc, a developer of AI-enabled tools for designing homes and managing workflows around homebuilding, picked up $95 million in Series C funding. led the financing for the Durham, North Carolina-based company.

9. , $85M, biotech: Cambridge, Massachusetts-based Flare Therapeutics, a startup targeting transcription factors to develop treatments for cancer and other ailments, raised $85 million in Series C funding led by and .

10. , $65M, AI privacy: Venice, developer of a platform enabling private, surveillance-free access to a wide array of AI models, secured $65 million in Series A funding led by . The round set a $1 billion valuation for the 2-year-old Sheridan, Wyoming-based startup.

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of June 27-July 2. 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.

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His investment mandate primarily focuses on early-stage technology companies from seed to Series B that align with or impact the global telecommunications, network infrastructure and enterprise software sectors.

Under his leadership, AT&T Ventures targets investments in software, hardware and infrastructure sectors where AT&T’s network scale and internal engineering resources provide a distinct commercial or technical diligence advantage. Portfolio companies include enterprise and deep-tech firms such as , , , , and .

Prior to his current 12-year stint directing AT&T Ventures, Taneja spent more than two decades working across corporate development, venture lending and investment banking. He previously managed M&A and strategic investment activities for during ownership.

Taneja also served as a director at , where he focused on growth-capital debt and equity investments in mid- to late-stage technology businesses, as well as holding corporate finance and investment banking roles at and .

In an email interview with 兔子先生传媒 News, Taneja shares why he believes that while AI has drastically lowered the barrier to building software, it has also shifted the definition of seed-stage technical risk.

The new dynamics, in his view, gives AT&T Ventures an opportunity to differentiate itself by offering immediate, real-world technical validation and network integration rather than just capital.

The interview has been edited for brevity and clarity.

兔子先生传媒 News: If startups are building fully functioning apps by the seed round using AI, what does that mean for the traditional definition of technical risk? Is tech risk dead at seed, or has it just evolved into something else?

Vikram Taneja: The old definition of technical risk was 鈥渃an they build it?鈥� Although not entirely absent at the seed stage, I鈥檇 say it is becoming less relevant given the dramatically lower barrier to building software with AI tools.

But what replaced it is actually harder to answer: 鈥淚s the tech defensible?鈥� Not just 鈥渄oes it work?鈥� but 鈥渄oes it compound?鈥�

Data moats, proprietary training sets, network effects built into the architecture 鈥� that’s the new measure of durability.

In prior cycles, technical complexity alone created some natural protection. As a result, the technical risk conversation has shifted to focus on how a company defends itself over the next three to four years, especially as frontier labs move down the stack into application layers and start targeting entire verticals.

Similarly, the distribution question shows up much earlier. 鈥淗ow can you get this to market?鈥� is increasingly asked at the seed stage rather than later in the cycle.

We鈥檙e also seeing increased competition for investors to secure larger stakes at seed that they would have previously pursued at the A round. This is driving investors to be more thorough at the seed stage, and founders have to be prepared to meet higher expectations across the board.

When anyone can use AI tools to spin up a working app in a weekend, product execution happens fast, but moats can be incredibly shallow. At the seed stage, how are you separating a truly defensible platform from a beautifully executed wrapper?

Taneja: In early 2025, we saw a wave of AI wrapper companies built on top of frontier models like ‘s GPT, 鈥檚 Claude or LLaMA, and a lot of capital flowed into them. What鈥檚 changed is that frontier LLMs have now clearly started to take more of a platform approach 鈥� moving into the application layers and beginning to pick off the low-hanging fruit.

This is why defensibility becomes critical in AI investing. No platforms are totally defensible, but on some level, you have to ask that question now at the seed stage.

We鈥檙e looking for platforms using proprietary data that can鈥檛 be replicated by AI, companies that have embedded deep domain expertise 鈥� areas where general-purpose AI still lacks industry context 鈥� into their workflows, or highly specialized ecosystems or niche markets that provide another layer of insulation in categories that are too targeted for frontier labs to pursue directly.

Are you seeing a change in the actual headcount or makeup of seed teams? If AI handles the heavy lifting of the initial code, are these founders spending their seed capital on engineers, or are they shifting resources immediately to distribution and go-to-market?

Taneja: There is still an engineering focus in the early stage, as there should be, but we are increasingly seeing product, sales, or partnership roles becoming sought after earlier than in the past. And the reason is, as you stated, that it鈥檚 easier to build a working prototype, or even a production-ready application, so the focus very quickly turns to establishing trials with customers or exploring distribution paths to dial in the product features.

For strategic investors like AT&T Ventures, where we often do proof-of-concepts with potential portfolio companies, this is very exciting. We get a chance to work with companies earlier in their formation, can get real technical validation much earlier than otherwise, and can similarly try to find a path to collaborate more quickly.

AT&T Ventures has traditionally played heavily in the Seed to Series B space. If institutional VCs are rushing to seed to grab larger stakes because the tech is mature, how does that change the competitive landscape for CVCs? Are you finding yourself competing directly with traditional multistage funds earlier than before?

Taneja: The makeup of seed rounds has definitely changed. Multi-stage funds used to show up at Series A or B when there was enough traction to underwrite. Now they’re at seed because, as we discussed, the companies are mature enough, and they are trying to find winners earlier in the cycle. So yes, we’re in the same rooms as before.

But I’d push back on the idea that we’re competing directly.

A Tier 1 financial VC鈥檚 seed check and an AT&T Ventures seed check are different instruments. They are offering capital, brand, guidance and pattern recognition from backing hundreds of companies.

We’re offering something a financial VC structurally does not: our network teams working with your product in a production environment, oftentimes before we even write the check, for example. That’s free diligence running in both directions. We’re validating the company, but it’s also receiving a real-world signal from one of the world’s largest network operators.

For a seed-stage company that’s already solved the building problem and now needs distribution, that鈥檚 tangible value and complementary to what financial VC firms are providing. So that competitive pressure has actually sharpened our value proposition. It forces us to bring more than just capital to the table.

Historically, corporate partners want to see enterprise readiness, security compliance and scalability 鈥� things a seed startup rarely has. If a seed startup has a fully functioning product but is still a two-person team, can an enterprise like AT&T actually run a pilot with them, or does the corporate integration timeline become a bottleneck?

Taneja: It starts with strategic rationale. That has always been the entry point for us at AT&T Ventures, and that hasn鈥檛 changed. If that is in place, then it doesn鈥檛 always require full enterprise readiness to start a pilot. It can be a structured trial or a highly targeted engagement, depending on the company’s stage.

We have a number of ongoing proof of concepts with portfolio companies across areas such as AI-RAN, connected infrastructure and computer vision.

The key is clarity upfront 鈥� clarity on what the objective of the engagement is and how we measure success. Once that is clear, even early-stage companies can be integrated into a learning or testing environment without unnecessary delay. The goal is to make the AT&T relationship feel like an accelerant to further adoption.

If seed is the new Series A in terms of product maturity, are you seeing Series A pricing bleed into the seed round? How are you disciplined about valuations when the product looks like a Series A, but the company infrastructure is still very early?

Taneja: Seed pricing indeed looks different than maybe four or five years ago. We鈥檙e routinely seeing seed deals priced in the low- to mid-single-digit-million range at about $20 million to $25 million post-money. This is pretty much where Series A deals were a few years ago. But it鈥檚 not necessarily unjustified 鈥� the makeup and traction of seed-stage companies are much further along than predecessor vintages as we鈥檝e discussed.

We stay disciplined by being explicit about what we’re actually underwriting. We’re not just underwriting the financial return on this round 鈥� we’re underwriting the strategic value of the relationship over a five- to 10-year horizon.

Does this company make AT&T’s network more intelligent? Does it open up a new customer segment? Does it validate a thesis we’re building around? Are there commercial opportunities beyond our initial thesis? When you frame it that way, it gives us a longer horizon to work with and provides multiple levers to pull.

And honestly, that’s where our engineering and product teams play a key role. They help us decipher whether the product that looks like a Series A is actually built like one, or whether it’s a great demo sitting on a foundation that hasn’t been stress-tested. That technical read bolsters our conviction when making investments.

A functional AI app at the seed stage still requires massive infrastructure. When you evaluate these early-stage companies, how much does their underlying architecture and how they handle data processing or edge computing factor into your decision?

Taneja: Architecture is a key part of our diligence process. The way we think about it really depends on the ultimate use case. Is it for internal use 鈥� i.e., a tool that AT&T will be working with in our environments 鈥� or is it something we鈥檇 be distributing or incorporating into some form of product offering?

If the former, all aspects of the architecture will be reviewed, and this is most likely to occur throughout trials and proof of concepts as we develop a technical understanding of the application or product. If it鈥檚 the latter, then we鈥檙e likely most interested in understanding how this product architecture scales over time and what it means from a cost, latency and infrastructure perspective. We love to see companies embracing edge-related technologies, but that doesn鈥檛 preclude us from working on applications that use traditional data processing methods.

You鈥檝e spoken before about your interest in 鈥減hysical AI鈥� and robotics (like Apptronik). The software lifecycle is easily compressed by generative AI, but hardware and physical deployment take time. Does this 鈥渟eed is the new Series A鈥� trend apply to pure-play software strictly, or are you seeing AI accelerate physical tech and IoT at the early stage too?

Taneja: Physical AI is a sector we鈥檝e been looking at quite a bit, particularly because inference and decisioning in autonomous systems, robotics and connected devices create a very different type of demand profile on networks.

The software layer is clearly accelerating 鈥� things like perception, control systems and decisioning are moving faster because of AI (the rounds show it!). That will ultimately help pave the way for the adoption of physical AI. However, the physical deployment cycle still takes time, so you don鈥檛 see quite the same level of time compression there.

What is interesting for us at AT&T is the intersection 鈥� how intelligence is moving closer to the edge and how that changes the way networks need to be architected to handle those workloads.

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At least not these days. The space and AI pioneer posted a net loss of $4.28 billion in the first quarter of 2026, up more than 700% from a year ago. Revenue, meanwhile, totaled $4.69 billion in Q1, up 15% from a year ago.

As a public company, SpaceX is reportedly seeking a valuation of around $1.5 trillion or more, . It鈥檚 aiming to raise up to $80 billion or more in the offering, which would make it the largest IPO in history.

At its target valuation, SpaceX would join a rarified club of just seven U.S. public technology companies with market caps of $1.5 trillion or more. Of those, just five have crossed the $2 trillion mark.

Of course, those companies took time to grow into their 13-digit valuations. But at some point, they too made their first public IPO filings. And they too had revenue.

The similarities end there. For a sense of how SpaceX compares at IPO time to other members of the trillion-plus-club, we took a look at their original S-1s from the 1980s and onward. Here鈥檚 what their numbers looked like just before their public market debuts:

: Today, the Silicon Valley chip designer is a $5.3 trillion market cap company. Anyone who invested in its 1999 IPO, needless to say, has done extraordinarily well.

At the time of its market debut, of course, such a trajectory was not obvious. Still, it looked like a solid bet. The company, which then focused on designing 3D graphics processors for the PC market, had $93 million in revenue for the three reported quarters prior to its IPO, growing severalfold year over year. Over the same period, it posted a modest $3.5 million loss.

: Google was already the dominant player in online search when it went public in 2004, with impressive financials to boot. Revenue for the first half of that year totaled $1.35 billion, more than doubling in a year, paired with a $326 million profit.

While that was impressive, so is Google鈥檚 ongoing growth. Currently, its market cap is $4.7 trillion and it posts more than $400 billion in annual revenue, with massive profits as well.

: The iconic smartphone and computing giant knows a thing or two about longevity. Apple turned 50 last month, and it went public over 45 years ago, in 1980.

It was an impressive and attention-getting offering for the time, with $118 million in sales and nearly $12 million in profit. It helped that Apple was already a prominent consumer brand at the time due to its popular home computers. These days, its market cap hovers around $4.5 trillion.

: Microsoft went public in 1986, so it鈥檚 had some 40 years to grow into its current $3.1 trillion valuation. But even back in the era of big hair and floppy disks, the software giant鈥檚 IPO prospectus showed clear signs this would be no ordinary market entrant.

In the year before its IPO, Microsoft had revenue of $140 million and net income of $24 million. That income figure, however, includes stepped-up spending on marketing and R&D. Without those expenses, profit margins looked astoundingly high for a time before software business models were status quo.

: At the time of its public offering in 1997, Amazon was known as an online bookseller, branding itself as “Earth’s Biggest Bookstore.” All the other stuff came later.

Still, it was a compelling offering at the time, with Amazon growing annual sales from zilch to around $16 million in just two-and-half years after its inception. It pitched losses as part of its growth strategy, which called for investing heavily in marketing and promotion, site development and operating infrastructure.

Needless to say, things worked out well, with Amazon currently valued at more than $2.8 trillion.

SpaceX is not like the others

If we look at the most valuable public tech companies, a few commonalities about their earlier days stand out. All went public relatively early in their operating histories and debuted with sharply growing revenue and either profits or losses in the single-digit millions.

SpaceX, founded in 2002, looks by comparison like an oldster for a company on the cusp of a public market debut. It鈥檚 also worth pointing out that Google, founded in 1998, is only four years older than SpaceX. That means, it鈥檚 had 28 years to grow into becoming a company with over $400 billion in revenue over the past 12 months and $138 billion in operating income.

SpaceX, by contrast, has had 24 years to grow into becoming a company that loses $4.3 billion in a single quarter.

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led the financing, which included participation from , and angel investors from companies such as , , and .

Miravoice has developed an AI interviewer that it says can conduct phone surveys and voice interviews for 鈥減recision data collection鈥� without human interviewers. The surveys are long-form and quantitative, with some including more than 120 questions and lasting over 40 minutes. They span open-ended responses, numerical inputs, multiple choice questions, Likert scales and matrix questions.

鈥淚magine talking to 100,000 people and instantly capturing what they know,鈥� said CEO and co-founder . 鈥淲e make that as simple as creating a Google Form.鈥�

Voice interviews have long been the gold standard for rigorous data collection, but the costs and operational frictions of talking to people have made it more challenging, Jain contends.

鈥淗aving to hire call centers made running quantitative research surveys infeasible for most organizations,鈥� he said.

Miravoice claims its agent is designed to be simple for anyone to deploy and not require technical backgrounds to operate.

A user can build a questionnaire, spin up a phone number, and launch its trained voice agent 鈥渢o get results back in hours rather than weeks,鈥� Jain said.

Multiple languages and 鈥榤essy realities鈥�

Miravoice is hyper-focused on precision, according to Jain.

鈥淯nlike other voice agent companies, we focus on structured conversations in which most questions are known in advance,鈥� he explained. 鈥淥ur customers know what information they want to get ahead of time, which is why we focus on extracting as much information as possible from respondents while minimizing bias.鈥�

He said Miravoice鈥檚 agent will ask every question in a survey without hallucinating responses.

鈥淎nd when the messy realities of human conversations arise, like interruptions or pauses, our AI can handle them seamlessly,鈥� Jain said.

The Miravoice interviewer is also multilingual by design, a capability that Jain believes is difficult for individual call centers to match.

Using Miravoice鈥檚 agent is also cheaper than hiring and training call centers to conduct the same surveys, Jain contends. The platform can handle both outbound and inbound calls if a respondent calls back at any time of day.

Idea and business model

Miravoice was founded by Jain, and , three close friends from California who have known each other for more than a decade.

The idea for Miravoice came from firsthand experience with the pains of scaling quantitative survey research in their roles as product managers and consultants. They realized that voice agent technology would be the way these calls would be handled in the future, 鈥渋f agents were appropriately crafted for the unique needs of this market use case.鈥�

Miravoice has between 10 and 20 customers at varying stages 鈥� from paid pilot to production use cases 鈥� according to the company. Those customers include a variety of public-opinion survey organizations, market research firms, university departments and private companies across retail, entertainment and logistics.

Its revenue model is usage-based billing: Customers pay for the time its AI agents are actually on the phone with respondents.

Miravoice surpassed 100,000 calls made in 2025, per the company, and expects that number to be significantly higher this year.

鈥淲hat鈥檚 exciting about the space we鈥檙e operating in is that the scale of the number of calls our platform has to handle dwarfs most other voice agent use cases,鈥� Jain said. 鈥淥ur pilot projects alone are on the order of tens of thousands of calls: more than some voice agent companies鈥� monthly production workloads. In production, some of our customers expect to perform millions to tens of millions of calls each year, after full deployment and implementation.鈥�

Voice AI on the rise

, general partner at Unusual Ventures, said his firm was impressed by the founding team鈥檚 technical acumen and product vision.

In Albright鈥檚 view, Miravoice鈥檚 focus on precision data collection sets it apart from most other entrants in the voice agent market research space.

鈥淭hey鈥檝e correctly identified that voice AI can streamline operations and time-to-insight for large-scale quantitative research studies,鈥� he wrote via email.

Another area where Miravoice distinguishes itself is its ease of use, he said.

鈥淢any voice agent platforms are geared towards technical audiences and software developers,鈥� Albright said. 鈥淢iravoice was built from the ground up with simplicity in mind so that truly any team can use it. This is a step-function change in making AI voice agents for surveys as ubiquitous as web forms are today.鈥�

Indeed, voice AI startups have emerged as standouts in the vast AI space, attracting the attention of investors globally, according to 兔子先生传媒 data. Over the past two years, several voice AI companies have seen their valuations triple 鈥� a signal of accelerating market demand and perceived long-term worth.

One example of a voice AI company that has seen a massive valuation jump is , which allows creators, enterprises and others to use AI software to replicate voices in dozens of languages. The Brooklyn, New York-based startup went from achieving unicorn status with an $80 million Series B raise in January 2024 to being valued at about $3.3 billion one year later with a $180 million Series C co-led by and . Then, in February of this year, it raised a $500 million Series D round led by at an $11 billion valuation.

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The round marks a significant step-up in valuation from the $3.6 billion that Boston-based Whoop achieved in August 2021 when it raised a $200 million Series F round. In total, it has raised over $900 million since it was founded by in 2012.

led its latest financing, which included participation from a slew of institutional investors, athletes and angels, including , , , and .

Individual investors in the round include soccer star , players and , and musician .

Whoop says it is powered by more than 24 billion hours of physiological data and purpose-built AI models to provide predictive, personalized health insights. It claims to help users understand how they slept, whether they have recovered, how hard to push or pull back, and how daily behaviors like training, nutrition and stress are impacting their performance and long-term health. It further ambitiously claims that it helps users 鈥渋dentify early warning signs, reduce risk, and take action that can prevent serious health events.鈥�

Subscription-based insights

Its model is different from that of many wearables companies. The actual wearable doesn鈥檛 cost anything, but 鈥渕embers鈥� pay a subscription to access the insights it offers. There are different tiers based on style and performance level.

Whoop has historically been more popular among athletes and die-hard workout enthusiasts, although it appears to be becoming more mainstream. The company says it now has over 2.5 million members globally, and that in 2025 bookings grew 103% year over year. It聽 operated cash flow positive and ended the year at a $1.1 billion run rate.

The company is actively hiring for over 600 roles as it plans to double down on R&D and global expansion across Europe, the Middle East, Latin America and Asia.

Whoop鈥檚 round is a bright spot in a sector that hit a cyclical low last year after peaking about four years ago. Just over $5 billion in global venture funding went to fitness and wellness-related startups in 2025, 兔子先生传媒 data shows.

That said, it鈥檚 not as if investors have abandoned the space, and there are clearly still companies securing big rounds.

Last year, the standout was , maker of a smart ring that collects data on dozens of personal health and wellness metrics. Last October, the 12-year-old Finnish company it had closed on more than $900 million in funding at an impressive $11 billion valuation.

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This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

It was going to be a fairly business as usual top 10 list this week until decided to disrupt our Friday with news that it raised $110 billion in new funding. Yes, $110 billion. That is so much money, and so record-setting as a private company funding round, that it makes all those other $100 million and $200 million rounds we usually write about look very paltry by comparison.

That said, we did nonetheless see a number of these kinds of rounds, in sectors including semiconductors, AI, healthcare and biotech.

1. , $110B, artificial intelligence: Generative AI giant OpenAI that it has raised $110 billion in new investment at a valuation of $730 billion pre-money, or $840 billion post-money. The deal includes $50 billion from , $30 billion from , and $30 billion from . San Francisco-based OpenAI says more investors are expected to join as the round progresses.

2. (tied) , $500M, semiconductors: MatX, a startup that designs custom chips and hardware architectures to support large language models, secured $500 million in Series B funding as it prepares to scale manufacturing. and led the financing for the Mountain View, California-based company.

2. (tied) , $500M, broadband: Boulder, Colorado-based Vero Networks, a fiber infrastructure and broadband internet provider, picked up $500 million in a growth funding round backed by , and .

4. , $240M, fusion: Janesville, Wisconsin-based Shine Technologies, a developer of fusion technologies with applications in the medicine and energy sectors, raised $240 million in equity funding led by .

5. , $150M, hardware testing tools: Revel, developer of a software platform for hardware test and control, closed on $150 million in Series B funding. led the financing for the Los Angeles-based company, which plans to expand its offerings across aerospace, defense, robotics and industrial sectors.

6. , $140M, healthcare: Nashville, Tennessee-based Honest Health, a provider of tech-enabled tools for primary care providers, secured $140 million in a new financing led by .

7. , $130M, biotech: Slate Medicines, a startup working on therapeutics for headache disorders, announced its launch alongside $130 million in Series A financing. , and led the investment for the Raleigh, North Carolina-based company.

8. , $106M, smart infrastructure: Fort Lauderdale, Florida-based Ubicquia, provider of an analytics platform for smart lighting, grid monitoring and public safety applications, raised $106 million in Series D funding. and led the financing for the 12-year-old company.

9. (tied) , $100M, AI-enabled accounting: Basis, an AI agent platform for accountants, closed on $100 million in Series B funding at a $1.15 billion valuation. led the round for the New York-based startup, along with , and .

9. (tied) , $100M, satellite and network communication: spinout Aalyria, a developer of software that configures communications satellites to meet demand, secured $100 million in Series B funding. and led the financing for the Livermore, California-based company.

9. (tied) , $100M, smart glasses: Viture, a San Francisco-based maker of extended reality (XR) smart glasses and accessories, says it $100 million in a financing led by .

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of Feb. 21-27. 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.

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