Tag: AI funding

Prince Mario-Max Schaumburg-Lippe: Metaview Raises $60M to Automate Hiring With AI Agents

Recruiting is an $800 billion industry that still runs, in large part, on manual processes, fragmented tools and human guesswork. Metaview, the London-founded startup building what it calls an Agentic Recruiting Platform, just raised $60 million to automate it. The Series C, led by Insight Partners with participation from GV, Intrepid Growth Partners, Seedcamp, Vertex Ventures US, Plural and Garuda Ventures, was announced October 1 and brings the company’s total funding to $110 million.

The round is a bet on a simple observation: AI made applying for jobs trivially easy, and the system broke under the volume. Applications per recruiter have jumped 412%, and 53% of job seekers say they were ghosted by an employer in the past year. The old machinery can’t keep up. Metaview’s argument is that the fix isn’t better applicant tracking, it’s agents that do the work.

From interview notes to autonomous coworkers

Metaview started in 2018 as an interview-intelligence tool, capturing the conversations where hiring decisions actually form. The company has now processed more than 6 million interviews, and that corpus of conversation data feeds a suite of connected AI agents that operate across the whole hiring workflow: sourcing candidates before they apply, evaluating inbound applications against role briefs, running structured screening conversations, and converting human interviews into structured, reusable notes.

The headline product is Fillmore, an autonomous recruiting coworker that Metaview is taking to general availability with the new funding. Fillmore sources candidates, writes personalized outreach, manages follow-ups and books screening calls. A dedicated AI screening agent is in development alongside it. The crucial design choice: final hiring decisions stay with people. The agents do the work; humans make the call.

Metaview says the platform now serves more than 7,000 companies, from startups to the Fortune 100, including names like Deel, Affirm, Navan and Replit. In one example cited by CEO and co-founder Siadhal Magos, AI sourced, researched and contacted 52 candidates and booked five screening calls, and the eventual hire moved from first contact to signed offer in 30 days. The company claims customer time-to-hire reductions of more than 75% in some cases, though those are company-reported figures.

Where the $60 million goes

The raise funds three specific priorities. First, Fillmore’s general availability plus new specialist agents. Second, people: Metaview plans to grow from 80 employees to around 250 by the end of 2027, and open a New York office alongside existing hubs in London and San Francisco. Third, 10x Recruiting, the company’s training community for talent professionals, which teaches what Metaview calls “talent engineering”, the discipline of building and running AI-powered hiring systems.

That second priority is the tell. Tripling headcount in 15 months is a growth plan, not a maintenance plan. Insight Partners doesn’t lead $60 million rounds for companies that are going to coast. Ryan Hinkle, the Insight managing director on the deal, said Metaview brings “more structure and intelligence across each stage of hiring, from sourcing and screening through to the final decision,” and that the result is “better hiring decisions at scale, without losing the judgment that good hiring requires.”

The round comes just a year after Metaview’s previous financing, and the company has been stacking capabilities in the meantime. In August it acquired Reval, a California-based AI-native recruiting company, with both founders joining to accelerate Fillmore’s development. The platform integrates with more than 62 tools, including Greenhouse, Ashby, Lever, Workday, Zoom, Google Meet and Microsoft Teams, and carries SOC 2 Type II, GDPR and CCPA compliance.

Why recruiting is next

Siadhal Magos’ framing line for the announcement deserves quoting: “The best engineering teams have already moved from writing code by hand to orchestrating AI agents that do the work. Recruiting is next.” Software engineering was the first white-collar workflow to get the agentic treatment, and the pattern is now repeating across every department that runs on repetitive knowledge work.

We’ve seen the same movie in finance, where AI-native platforms like DualEntry are rebuilding ERP from the ground up, automating the repetitive tasks that used to define back-office jobs. Recruiting is arguably a better fit for agents than finance, because so much of the work is communication: outreach, scheduling, follow-ups, summaries. That’s exactly what agents are good at, and exactly what burns out human recruiters.

The ghosting statistic is the moral of the story. When 53% of candidates get ignored, the system isn’t just slow, it’s failing the people it’s supposed to serve. Agents that follow up reliably, screen consistently and keep candidates informed could make hiring better for both sides, not just cheaper for employers. The optimistic case for recruiting AI is that it fixes the experience, not just the spreadsheet.

The part that should stay human

To Metaview’s credit, the company draws a bright line: agents prepare, people decide. That matters more than any feature list. Hiring is one of the highest-stakes decisions an organization makes, and it’s also one of the most vulnerable to encoded bias. An agent that screens candidates needs to be auditable, and the human in the loop can’t be decorative.

The compliance posture suggests Metaview takes this seriously. SOC 2 Type II, GDPR and CCPA compliance, structured and reusable interview notes that create a paper trail for decisions, integrations with the HR systems where the audit data already lives. This is the unglamorous infrastructure that makes AI in hiring defensible instead of just fast. Speed without accountability in recruiting is how you get lawsuits.

The competitive picture

Metaview isn’t alone in this space, and the $60 million is partly a moat-building exercise. The recruiting software market spent years as a graveyard of applicant-tracking systems that organized data after humans created it. The agentic wave is changing the category’s definition: software that participates in producing hiring decisions, not just recording them.

What Metaview has that newcomers don’t is the data corpus. Six million processed interviews is a training and fine-tuning asset that compounds. The agents get better at evaluating candidates because they’ve seen more hiring conversations than any competitor. That’s the classic data moat, and it’s why the company’s origin as an interview-intelligence tool turned out to be a strategic head start rather than a pivot away from it.

The conversational layer is converging too. As AI voice stacks get fast enough for natural screening calls, the distance between a text-based recruiting agent and a voice-based one shrinks. Fillmore books screening calls today; the version that conducts them is clearly on the roadmap, even if nobody’s saying so out loud yet.

What it means for job seekers and hiring teams

If you’re hiring, the message is that the tooling is about to get dramatically better at the parts of recruiting nobody enjoys: sourcing, screening, scheduling, note-taking. The teams that adopt agentic recruiting early will move faster on candidates.

If you’re job seeking, the honest advice is to assume your first screen may be with an agent, and to treat it accordingly: clear, structured answers, specific examples, no rambling. The upside is that the ghosting era may finally be ending, and that’s worth celebrating.

Recruiting was always going to be rebuilt with AI at the core. Metaview just got $60 million to finish the job.

Prince Mario-Max Schaumburg-Lippe: FieldAI Eyes $10B Valuation in $700M Robotics Round

The hottest money in AI right now isn’t going to chatbots. It’s going to robots. FieldAI, the Irvine startup building what it calls a universal general-purpose brain for robots, has signed a term sheet for a $700 million financing round at a $10 billion valuation, according to a Business Insider report published October 2.

Five times. That’s the multiple. FieldAI was worth roughly $2 billion barely a year ago. The new round, which hasn’t formally closed and whose lead investor remains undisclosed, would quintuple that number and put the 2023-founded company in the top tier of private robotics firms, alongside Physical Intelligence at around $11 billion and Skild AI above $14 billion.

What FieldAI actually builds

Here’s the contrarian part: FieldAI makes no physical robots at all. No humanoids, no arms, no wheels. The company sells software, foundation models for robots that let machines navigate and work autonomously in messy, unpredictable environments. One stack powers humanoids, robot dogs, drones, industrial rovers and wheeled vehicles, turning sensor data into continuously updated digital twins of the environment so robots can operate without prior maps, GPS or predefined paths.

That “no maps” detail is the technical pitch. Traditional robot navigation leans on pre-mapped environments, which works fine in a warehouse and falls apart on a construction site where the layout changes daily. FieldAI’s models are designed to account for uncertainty and risk on the fly, adjusting behavior to avoid collisions and navigation mistakes as conditions shift. In March, the company partnered with Boston Dynamics to support the Spot quadruped for industrial inspection tasks, putting its software on one of the most deployed mobile robots in the world.

The customer list is where the story gets its legs. FieldAI says revenue plus signed customer contracts has crossed $135 million across more than 30 customers in construction, data centers, energy and defense, up at least $35 million since June. Construction firms, data center operators and inspection contractors are paying for robot autonomy that works outside the lab. That’s the difference between a demo and a business.

Why investors are paying up

The 5x valuation jump reflects how fast investor appetite has swung from chatbots toward machines that act in the physical world. Robotics startups have drawn a wave of capital this year as foundation models proved good enough to control hardware without constant human oversight, and FieldAI sits at the center of the software layer: the brain, not the body.

CEO Ali Agha brings a resume that helps explain the conviction. He spent seven years at NASA’s Jet Propulsion Laboratory leading autonomy work, including the DARPA Subterranean Challenge, where his team won the urban circuit in 2020. Robots that navigate caves and collapsed tunnels without GPS are a decent audition for robots that navigate construction sites. The company has also been hiring engineers from Google DeepMind, Tesla, Nvidia and Boston Dynamics as the competition widens.

The investor roster doesn’t hurt either. Prior backers include Jeff Bezos’ family office, Laurene Powell Jobs’ Emerson Collective, Khosla Ventures, Nvidia’s NVentures fund and Intel Capital. When that crowd writes follow-on checks, it’s a signal the diligence is real, even if the lead on this round hasn’t been named yet.

The physical AI gold rush

FieldAI’s round is the latest and largest marker in what has become 2026’s defining funding theme: physical AI. The logic runs like this. Language models conquered the digital world; the next frontier is models that operate in the physical one, and whoever owns the software layer under the humanoid and industrial-robot boom owns a platform position.

The numbers tell the story of the frenzy. Robotics trackers have logged over 150 stories in the last 90 days. The comps keep ratcheting upward: NEURA Robotics raised up to $1.4 billion in June at about $7 billion in Europe, Genesis AI was reported raising $500 million at around $3 billion in July, and now FieldAI at $10 billion with a term sheet signed. Whether these valuations reflect fundamentals or FOMO depends on who you ask, but the direction of the money is unmistakable.

There’s a practical side to the boom that gets less attention than the valuations. Training robot brains takes serious GPU capacity and serious data, which is why companies like Sharon AI are borrowing hundreds of millions against their GPUs to build AI factories. And serving the resulting models efficiently is its own industry now, with new inference platforms bringing open-model serving to production scale. FieldAI’s software has to live somewhere, and the infrastructure to run it is being built in parallel.

The honest caveats

Let’s be clear about what’s known and what isn’t. The round hasn’t closed. The lead investor hasn’t been disclosed. The $135 million figure combines recognized revenue with signed contracts, and the report doesn’t break out the split, so treat it as pipeline strength rather than run rate. At $10 billion, FieldAI needs deployments that convert pilots into large recurring contracts. Paper valuations don’t torque motors, as one industry observer memorably put it.

There’s also the integration question. A single software brain that pilots quadrupeds, humanoids, drones and rovers across construction, energy and defense is a massive engineering promise. The environments are different, the sensor suites are different, the failure modes are different. FieldAI’s bet is that foundation-model scale generalizes across all of it. That’s the thesis investors are paying $10 billion for, and it’s still a thesis.

Why this one might be different

What separates FieldAI from most physical-AI pitches is the revenue number, however blended. A lot of robot-brain startups sell a future. FieldAI sells a present: $35 million in new revenue and contracts since June, 30-plus paying customers, a Boston Dynamics partnership, and deployments on real industrial sites. CEO Ali Agha told Business Insider the company has seen “very, very fast growth in the last several months,” and the customer count backs up the claim.

The defense angle deserves a mention too. Construction, energy and defense contractors all show up on FieldAI’s customer list, and dual-use robotics is having a moment as governments look for autonomous systems that work in contested environments. The company doesn’t lead with this, but the investor base, including In-Q-Tel’s peers in the broader ecosystem, suggests it’s part of the thesis.

What to watch next

Three things will tell you whether the $10 billion tag holds. First, who leads the round when it closes, and whether the terms match the reported number. Second, whether FieldAI starts disclosing named customers beyond the anonymized counts, because enterprise logos are the currency of credibility at this scale. Third, the conversion story: pilots to production contracts, contracts to recognized revenue.

The broader trend to watch is the platform battle underneath. FieldAI, Physical Intelligence, Skild AI and a handful of others are all racing to become the operating system layer under the humanoid era. Only one or two will get there, but the winner gets to tax an entire industry’s worth of machines. That’s the $10 billion bet in a sentence.

Robots that work in the real world, not the demo hall, are the whole game. FieldAI just got priced like it’s winning. Now it has to prove it.

Prince Mario-Max Schaumburg-Lippe: Flow Engineering Lands $50M to Give AI Agents CAD Tools

AI rewrote how software gets built. Code practically writes itself now, and iteration cycles at the best companies have collapsed from weeks to hours. Hardware, meanwhile, has been watching from the sidelines, still doing things the slow way: months of coordination, manual verification, engineers chasing changes across a dozen disconnected tools.

Flow Engineering wants to end that asymmetry. The San Francisco startup announced on September 30 that it has raised $50 million in Series B funding at a $750 million valuation, and its pitch is simple: bring software-like iteration speeds to hardware development. The round was co-led by Antonio Gracias of Valor Equity Partners and Gavin Baker of Atreides Management, with Sequoia Capital, which led the Series A, participating alongside Human Capital, Evantic, SV Angel, Odyssey, and EQT. The individual checks are a story in themselves: Hugging Face co-founder Thomas Wolf, Mercedes-Benz CIO Jonas von Malottki, Formula 1 champion Nico Rosberg, and Roelof Botha, who invested personally and joined the board.

The problem is plumbing, not intelligence

Here’s what makes Flow interesting, and it’s not the AI hype. The bottleneck in hardware development isn’t that engineers lack smart tools. It’s that a single design change ripples across mechanical, electrical, and software systems, and the data about those systems lives in disconnected tools: requirements in one place, CAD drawings in another, simulation results somewhere else, test data in a fourth.

An AI agent can’t reason across a design it can’t see. That’s the architectural insight. Flow’s platform connects requirements, CAD, simulation, code, and test data into one living system of record, and then lets AI agents continuously analyze engineering changes, identify downstream impacts, and verify that requirements and test coverage still hold, in seconds, across the tools teams already use.

Think of it like version control for physical things. Software got fast when Git gave every change a history, a branch, and a review process. Hardware never got that layer. Flow is building it: review, branching, and evaluation capabilities for engineering data, plus an AI harness that lets frontier models work securely with sensitive design information.

The customer list is the proof

Flow is three years old and already names customers that read like a who’s-who of ambitious hardware: Anduril, Rivian, Joby Aviation, Stoke Space, Intuitive Machines, Pacific Fusion, Astranis, Radiant Industries, plus General Motors PPU and RV Tech, the Rivian-Volkswagen joint venture.

That’s not a pilot list. Those are companies building rockets, electric aircraft, autonomous defense systems, and next-generation vehicles, putting Flow’s agents to work in live hardware programs. When the people designing spacecraft trust your platform with their iteration cycles, you’ve cleared a bar that slideware can’t fake.

The use of funds tells you where this goes next. Flow plans to build out the AI harness for secure work with sensitive engineering data, expand review and evaluation capabilities, pursue FedRAMP authorization, and grow its engineering and sales teams. FedRAMP is the tell: that’s the certification for selling to the US federal government, and it signals serious ambitions in defense and regulated industries. The goal, in the company’s words, is to reduce hardware iteration cycles from months to days. Ambitious? Sure. But the trajectory from “weeks to hours” in software suggests the direction is right.

Why hardware speed matters to everyone

It’s easy to file this under enterprise software and move on. Don’t. The speed of hardware iteration is the speed of the physical world getting better.

Every month shaved off a design cycle is a month sooner that a better battery, a safer aircraft, a cheaper rocket, or a more efficient grid component reaches the real world. Software ate the world by getting fast. The physical world has been waiting for its turn, held back not by physics but by process. If AI agents can take over the verification drudgery, the coordination overhead, the endless impact analysis that eats engineering calendars, then human engineers get to do the part they’re actually good at: the creative leaps.

There’s a deeper point about where AI creates value. The last two years were about AI writing and talking. The next phase is AI doing: working with tools, checking its own work, operating inside real workflows. Flow’s bet is that the highest-leverage place for that shift is the most complex, most coordination-heavy work humans do, which is building physical systems. The unglamorous plumbing, requirements traceability, change propagation, turns out to be the unlock.

What to watch

The FedRAMP timeline. Getting authorized for federal work is slow and expensive, but it opens the biggest hardware customer on earth. Watch whether Flow lands defense contracts in the next year. That’s the real validation.

Whether “months to days” holds up. The company’s stated goal is bold, and one analyst has already noted the announcement measures adoption rather than proven output. Fair. The customer list is impressive, but the industry will want case studies with numbers: this program shipped X weeks faster.

The competitive response. The big CAD and PLM incumbents aren’t standing still. The question is whether a startup built AI-native from day one can outmaneuver decades of entrenched tooling. History says the native player usually wins the new paradigm, but incumbents have the distribution.

The talent signal. When Thomas Wolf, the Hugging Face co-founder, writes a personal check into a hardware company, pay attention. The smartest people in AI are following the agents into the physical world. That’s where the next decade of interesting problems lives.

The takeaway

Software got its AI revolution first because software was already digital, already versioned, already fast. Hardware is harder: atoms don’t branch and merge. Flow Engineering just raised $50 million on the thesis that they can, or at least that AI agents can make it feel that way.

Faster hardware cycles mean everything physical improves sooner: the planes, the cars, the robots, the power grid. That’s a future worth building quickly. And the investors, from Sequoia to a Formula 1 champion, are betting that the company connecting CAD files to AI agents is the one holding the stopwatch.

If you’re in New York and want to see ambitious engineering up close in the meantime, the city’s 2026 holiday tree and lights celebrations are worth saving the dates for. And for a lower-tech but equally impressive feat of design, NYC’s best breakfast sandwiches remain undefeated.

Prince Mario-Max Schaumburg-Lippe: ElevenLabs Hits $22B Valuation, Launches Voice Model v4

Everyone spent the last two years arguing about which chatbot would win. Meanwhile, ElevenLabs went and proved the interface that actually matters is the one people have been using since Alexander Graham Bell: the phone call.

On September 30, the London-based AI voice company completed a $300 million employee tender offer that values it at $22 billion, twice the $11 billion valuation it carried after its $500 million Series D in February. That’s a doubling in seven months. The tender was co-led by Wellington and T. Rowe Price, with participation from existing backers Andreessen Horowitz and Lightspeed and new investors including EQT and Goldman Sachs.

And the same week, the company launched Eleven v4 and v4 Turbo, new text-to-speech models that natively speak, listen, and translate across more than 90 languages used by over 5.5 billion people, at sub-100-millisecond latency. Valuation doubling plus a flagship model launch in one week: that’s a company announcing it intends to own the category.

The number that explains the valuation

Forget the $22 billion for a second. The number that does the explaining is 15 million.

ElevenLabs says its voice agents now handle more than 15 million conversations every week, three times the level in February. And these aren’t demos. The agents are doing real enterprise work: processing refunds, renewing insurance policies, booking appointments. The client list includes Stripe, Deutsche Telekom, DoorDash’s SevenRooms, the insurer Admiral, and the sovereign governments of Ukraine and Greece.

That’s the tell. Chatbots got the headlines, but voice agents got the jobs. There’s a reason: for all the talk about conversational AI, the overwhelming majority of customer interactions at real businesses still happen by voice. The call center is the largest interface in commerce, and it runs on humans having the same conversations thousands of times a day. AI that can hold those conversations naturally, in 90-plus languages, with human-sounding expression, doesn’t need a market to be invented. The market is a phone line.

“We’re already seeing rapid adoption of expressive voice agents by enterprises and governments, who are deploying them in service of consumers and citizens,” said co-founder and CEO Mati Staniszewski. Founded in 2022 by Staniszewski and CTO Piotr Dabkowski, the company has gone from text-to-speech startup to one of Europe’s most valuable startups in four years. The velocity is the story.

Why voice is winning the agent race

There’s a thesis hiding in this valuation, and it’s worth spelling out: chat was the training wheels; voice is the vehicle.

Text chatbots had to teach users a new behavior. Voice agents meet users where they already are. Nobody needs onboarding to have a phone conversation. The elderly customer renewing an insurance policy, the traveler rebooking a flight, the citizen calling a government service line: they all already know how to talk. The AI just has to be good enough at listening and responding that the caller doesn’t notice the difference.

That last part is where the v4 models matter. Sub-100-millisecond latency is the threshold where conversation stops feeling like a walkie-talkie exchange and starts feeling like a person. Expressive speech, the pauses, the emphasis, the warmth, is what makes callers stay on the line instead of mashing zero for a human. ElevenLabs built its name on voice quality back when it was just a text-to-speech tool; now that quality is the moat around the agent business.

The multi-language angle is underrated too. Ninety languages spoken by 5.5 billion people means a single deployment can serve a global customer base without the traditional call-center model of staffing language queues. For governments and multinationals, that’s transformative. Ukraine and Greece using AI voice agents for citizen services is the kind of deployment that would have sounded like science fiction five years ago.

The tender offer tells its own story

One detail worth pausing on: this wasn’t a fundraise. A tender offer lets employees and existing shareholders sell stock to investors, providing liquidity without necessarily raising new capital for the company. The company didn’t need the money. Its people got paid.

That’s a retention weapon in the AI talent war. At $22 billion, with Goldman Sachs and T. Rowe Price buying in, ElevenLabs employees just got a very tangible reason to stay. In a market where top voice-AI researchers can name their price, keeping the team that built the thing is as important as any model release. The v4 launch the same week makes the message complete: we’re winning, we’re shipping, and we’re taking care of our people.

It’s also a signal about where smart money thinks the agent economy is going. The biggest AI investments of 2026 have flowed to agent developers as businesses race to automate customer service and back-office work. Voice is where that automation meets the customer directly. A $22 billion bet says the phone call is not a legacy channel to be replaced. It’s the channel to be upgraded.

What this means for the rest of us

A few practical readouts.

Expect to talk to AI more, and notice it less. Fifteen million conversations a week is still a rounding error against global call volume, but the growth rate is the thing. At 3x in seven months, the crossover point where a meaningful share of routine calls are AI-handled is closer than most people think. The good news: done well, it means no more hold music for a refund.

Voice quality is now a competitive dimension. If you’re building anything customer-facing with AI, the voice matters as much as the brain. The companies winning in this space compete on latency and expressiveness, not just accuracy. Users forgive a slightly wrong answer delivered warmly faster than a correct one delivered like a robot.

The “AI takes jobs” framing misses the point here. The calls being automated are the ones nobody wanted to staff: repetitive, high-volume, emotionally draining. The humans move up to the exceptions, the edge cases, the moments that actually need judgment. That’s been the pattern with every automation wave, and voice AI looks like it’s following the script.

Watch the government angle. Sovereign deployments in Ukraine and Greece are the leading edge of AI in public services. Multilingual, always-available, consistent: it’s a strong pitch for citizen services. Expect more governments to follow, and expect the procurement debates to be lively.

The bigger picture

The chatbot era taught the industry that people will talk to AI. The voice era is teaching it something more valuable: people will talk to AI the way they talk to people, about the boring stuff that keeps businesses running. Refunds. Renewals. Appointments. Fifteen million times a week.

ElevenLabs doubled its valuation in seven months because it found the biggest, most familiar interface in the world and made AI fluent in it. The phone call survived the internet, the smartphone, and the chatbot. Now it’s getting an upgrade.

If all this talk of conversation has you craving the real, unscripted kind, here’s what’s on around New York this week, from jazz nights to night markets. And if you’re the type who does their best thinking over something spicy, the city’s hottest chicken spots are ready when you are.

Prince Mario-Max Schaumburg-Lippe: Armadin Raises $255.5M at $2.5B for Agentic AI Security

There is a particular kind of founder who only needs a name. Kevin Mandia is one of them. The man who built Mandiant into the firm governments called when things went badly, and who sold it to Google, has a new company. And investors just handed it a quarter of a billion dollars to teach AI to think like a hacker, in defense of the good guys.

Armadin announced on October 1 that it has raised $255.5 million in Series B funding at a valuation of more than $2.5 billion. The round was co-led by Andreessen Horowitz and Accel, with new money from Bain Capital Ventures and Redpoint, plus a deep bench of returning backers: Google Ventures, Kleiner Perkins, Menlo Ventures, In-Q-Tel, 8VC, and Ballistic Ventures. Total funding now sits at $445 million, for a company that only emerged from stealth seven months ago.

The core idea: fight AI with AI

Let’s be honest about the problem first, because it’s the kind of thing that usually gets framed with doom. Frontier AI models have compressed the time between a vulnerability being disclosed and a working exploit appearing. What used to take attackers weeks can now take hours. The old defenses, a penetration test twice a year and a scanner that spits out a list of findings, were built for a slower world.

Armadin’s answer is an inversion that feels obvious once you hear it: deploy an autonomous swarm of specialized AI agents that reason like a skilled adversary. Not a scanner that flags individual issues in isolation, but agents that chain individually low-severity weaknesses into full, validated attack paths. The company’s description of a kill chain is worth quoting in plain terms: it can run from unauthenticated remote code execution at the perimeter, through lateral movement inside the network, to full cloud compromise. The point is the chain, not the links. Scanners score each finding on its own and miss how they connect. Attackers don’t.

This is the part that should make security teams sit up. Armadin says security teams get to see the exact attack paths an adversary would use in production, with the blast radius of each mapped out, and can cut those paths before anyone exploits them. In other words: you get to watch the heist in rehearsal and lock the doors it would have used.

Why this raise matters right now

Two things make the timing notable. First, seven months after emerging from stealth, the company says it’s already running agentic attack campaigns in production for Fortune 500 enterprises and government customers. That is a very fast path from stealth to production, and it suggests the demand side is urgent. Enterprises aren’t buying a vision here; they’re buying capacity.

Second, the money flooding into AI-native defense is becoming one of the defining investment themes of 2026. Investors have been pouring capital into early-stage startups building protections against AI-driven cyberattacks all year. A $255.5 million Series B at a $2.5B-plus valuation is among the largest raises the category has ever seen, and the investor list reads like a vote of confidence in both the founder and the thesis.

Mandia’s pedigree is doing real work here. He has sold a security company to Google before, and he has been on the receiving end of the nastiest incident-response calls in the industry. When he says periodic testing can’t keep pace anymore, it lands differently than when a first-time founder says it. The track record is the pitch.

The “good-guy red team” model

Zoom out and Armadin represents a structural shift in how security gets bought. The traditional model is expertise-as-a-service: hire a red team for a few weeks, get a report, fix what you can, repeat next year. It’s episodic, expensive, and the attackers don’t take semesters off.

The agentic model is expertise-as-software: the adversary simulation never stops. The swarm keeps probing, keeps chaining findings, keeps updating the map of how an attacker would actually get in. For a Fortune 500 company with cloud estates that change daily, continuous is the only honest answer. Your infrastructure doesn’t pause between pen tests. Why should your testing?

There is a nuance worth holding onto. These systems are powerful, and power in security tooling always raises the dual-use question. But the framing here is firmly defensive: the agents find the paths, the security team closes them. The company exists to make the defense faster than the offense. In a year when AI safety has been a constant drumbeat, a well-capitalized defense-first company is good news for everyone who isn’t an attacker.

What to watch next

Three things will determine whether this raise is remembered as a landmark or just a big number.

Proof of production value. The Fortune 500 claim is the one to watch. If Armadin can show that continuous agentic testing measurably shrinks the window of exposure, competitors will have to match the model, and the whole pen-testing industry reorganizes around it.

The talent magnet effect. $445 million in total funding, a Mandia-led company, and a mission that reads like a spy novel: this is a recruiting machine. In a security talent market that has been brutally tight for years, that matters. The best defenders are going where the hardest problems are.

Pricing the defense premium. Right now, agentic security is enterprise-only by economics. The question is how fast the model gets cheap enough for the mid-market companies that are actually the softest targets. The sooner that happens, the bigger the dent in the attack economy.

The takeaway

Strip away the funding theatrics and the story is simple. The same AI advances that made attacks faster are now being aimed at defense, by one of the most credible security founders alive, with a quarter-billion dollars of fresh fuel. The attackers have had the momentum. This is the market voting, loudly, that the defenders are catching up.

If you’re in New York this week and security talk over dinner sounds fun (it is, trust me), there’s a full lineup of things to do across the city to pair with the conversation. And if the funding news has you dreaming of your own security startup, fuel up properly first: NYC’s best breakfast sandwiches are a fine place to sketch a pitch deck.

Prince Mario-Max Schaumburg-Lippe: General Intuition Raises $220M to Teach AI the Real World

The next frontier of AI isn’t a smarter chatbot. It’s AI that can see, move, and act: pick up a box, navigate a warehouse, climb a set of stairs. And on September 30, one of the biggest bets on that future got a lot bigger: New York–based General Intuition raised $220 million at a $6.2 billion valuation.

The backers are a who’s who of venture capital: Valor Equity Partners, Atreides Management, 776, Point72 Ventures, Khosla Ventures, and General Catalyst. One of the largest physical-AI raises of the month, and a clear signal that serious money is following the agentic-AI wave into the real world.

The capital is earmarked for GPU cluster acquisitions, accelerated foundation-model training, expanded machine learning and reinforcement learning research teams in New York, and commercial infrastructure spanning both virtual gaming systems and physical robotics platforms.

Why Games Are the Gym for Robots

The company’s core idea is elegant. Games were the original training ground for modern AI: think DeepMind learning Atari, AlphaGo conquering Go. General Intuition is betting that millions of hours of gameplay telemetry is the bridge to robots that function in messy reality.

Here’s the clever bit. Instead of hand-labeling the physical world, an expensive, slow, painstaking process, learn intent from players who already demonstrate it. Every game session is a human showing, moment by moment, what they meant to do: navigate this space, grab that object, avoid that obstacle.

General Intuition’s tech is a multi-modal “action foundation model” trained on massive proprietary datasets of multi-angle gameplay video combined with player input telemetry and real-time execution matrices. The model learns spatial navigation, physics interactions, and operational intent, the same skills a robot needs, and drives both autonomous agents in simulations and humanoid robots in the real world.

In other words: the training data is hiding in play. Humans already generate exquisitely detailed demonstrations of physical intent every time they game. General Intuition is just harvesting it.

The Embodied AI Wave Is Building

This raise doesn’t exist in isolation. Physical AI, the industry term for AI that acts in the physical world, is having its moment. Humanoid robots like Agility’s Digit are getting stronger, safer, and more capable. Driverless trucks are on public roads. Robotaxi fleets are scaling fast.

Each of those machines needs a brain that understands physics, not just language. A chatbot can be wrong and it’s a joke; a 200-pound humanoid can be wrong and it’s a lawsuit. The bar for “good enough” in physical AI is brutally higher than in text, which is why the training approach matters so much.

General Intuition’s angle, learning from demonstrated intent at massive scale, sidesteps the biggest bottleneck in robotics: labeled real-world data is scarce and expensive. Gameplay telemetry is abundant and rich. If the transfer from virtual to physical works, it’s a shortcut around years of slow data collection.

What $6.2 Billion Says About the Moment

Valuations this size say investors believe embodied AI is following the same arc as language AI: a period of expensive foundational work, then a sudden unlock. The GPU clusters, the expanded research teams, the commercial infrastructure across gaming and robotics: this is a company building the full stack, not a demo.

The New York angle is nice too. The company is expanding its ML and reinforcement learning research teams in the city, planting a flag for physical AI on the East Coast in an industry that tends to default to the Bay Area.

Where the Robots Go First

The commercial infrastructure piece of the raise deserves attention. General Intuition isn’t just training models — it’s building the deployment pipeline across virtual gaming systems and physical robotics platforms. The near-term beachhead is likely the warehouse: structured enough to be tractable, labor-hungry enough to pay for automation. Longer term, the same action models that navigate a game level could navigate a disaster site or a factory floor.

That’s the bet the investors are making with $220 million: that “action” becomes a foundation-model category the way language did, and that the company holding the best action model holds a position worth far more than $6.2 billion. It’s early. But every major AI platform started with someone training an expensive model on data nobody else thought to collect.

The Optimist’s View

Picture where this leads. Robots that learn movement the way humans do, by watching and doing at scale, could take on the dull, dirty, and dangerous work that’s hard to staff: warehouse shifts, disaster cleanup, elder care assistance. The path from a game controller to a helpful humanoid is longer than a press release makes it sound, but the direction is right, and $220 million is a serious down payment.

General Intuition’s bet is simple and, in retrospect, may look obvious: the internet taught AI to think; play will teach it to move. The funding announced today suggests a lot of very smart investors agree.

Prince Mario-Max Schaumburg-Lippe: EliseAI Hits $4B Valuation With $350M AI Raise

While the AI industry argues about whether we’re in a bubble, one company just posted the kind of numbers that end arguments.

EliseAI announced Tuesday that it raised $350 million in a round led by Andreessen Horowitz and Bessemer Venture Partners, with participation from the Ontario Teachers’ Pension Plan, Sapphire Ventures, and Navitas Capital. The valuation: $4 billion. That’s nearly double the $2.2 billion valuation from its Series E round in 2025.

But the number that actually matters came one sentence later. The company surpassed $200 million in annual recurring revenue in June — and it has doubled revenue year over year for the fifth consecutive year. Five doublings. In a row.

What EliseAI actually does

Forget chatbots. EliseAI sells automation to the two most paperwork-burdened industries in America: housing and healthcare.

For property managers, its platform automates leasing, maintenance requests, and lease renewals — the endless churn of tenant emails, showing schedules, work orders, and follow-ups. For healthcare, it works with physician groups on patient intake, scheduling, insurance checks, referrals, and follow-up coordination. If you’ve ever sat on hold with a doctor’s office trying to reschedule, you’ve experienced the exact problem EliseAI is selling the fix for.

The new funds go toward expanding engineering, deployment, and sales, and toward establishing San Francisco as a second engineering hub alongside its New York headquarters.

The timing lines up with the demand data. Bank of America Institute reported that AI spending growth among mid-sized firms peaked in August, concentrated specifically in healthcare and education admin automation. EliseAI isn’t chasing a trend. The trend is chasing EliseAI.

The money is in paperwork

Here’s the thesis that keeps winning in enterprise AI: pick a painful workflow, own it end to end, charge real money for it.

Consumer AI gets the headlines — the demos, the viral launches, the existential debates. But the revenue is in the unglamorous stuff: the leasing office drowning in maintenance tickets, the medical practice where front-desk staff spend their days on insurance verification calls. Nobody posts about those workflows on social media. Everyone pays to fix them.

EliseAI’s approach is the opposite of the general-purpose assistant. It doesn’t try to be useful at everything. It buries itself in one domain — property management, medical intake — until it knows the forms, the edge cases, the compliance requirements better than the humans currently doing the work. That’s what “vertical AI” means in practice: narrow scope, deep competence, and a product that slots into an existing operation instead of asking the customer to reinvent one.

The fifth consecutive revenue doubling is the detail the “AI is all hype” crowd can’t easily wave away. Hype doesn’t double revenue five times. Contracts do. The company’s customers are paying — and renewing — because the automation works well enough to justify the invoice. That’s the oldest signal in business, and it still works.

Why vertical keeps beating horizontal

Look at the broader enterprise AI market and a pattern emerges. The companies winning real contracts are the ones that arrive with domain expertise baked in. Healthcare AI has to know how insurance verification actually works, including the maddening variations between payers. Proptech AI has to know what a lease renewal workflow looks like at 2 a.m. when a tenant’s heater dies.

A general model can’t learn that from the internet. It has to be built, workflow by workflow, customer by customer. That’s slower and less exciting than launching a consumer app — which is exactly why the moats are deeper. Once EliseAI’s system handles a property manager’s entire leasing pipeline, switching vendors means ripping out the nervous system of the business. That’s a very sticky $200 million in ARR.

Contrast that with the horizontal players fighting over who has the best general chatbot. Lower switching costs, murkier pricing power, constant feature races. Useful? Sure. But the enterprise buyers with actual budgets are voting for the company that eliminates their phone tag.

The physical-world connection

There’s a wider thread worth pulling. The most durable AI businesses of this cycle are the ones touching the physical world — the scheduling, the maintenance, the intake desks, the front lines where digital systems meet real operations. We see the same pattern in autonomy: driverless trucks moving onto public roads, robot fleets scaling in Texas, robots taking on warehouse work. The AI that matters economically isn’t the AI that writes poetry. It’s the AI that handles the work nobody wants to do.

EliseAI understood that earlier than most. Five doublings later, the market is catching up.

The takeaway

Bubbles are characterized by rising valuations and flat revenue. EliseAI has rising valuations and revenue that has doubled five years running. Andreessen Horowitz and Bessemer don’t write $350 million checks for vibes; they write them for $200 million in recurring revenue with a growth curve that looks like a staircase.

The lesson for founders is unfashionable but clear: the biggest AI opportunity isn’t the flashiest model. It’s the most annoying paperwork. Find the industry where skilled humans are still doing robotic work, and build the robot.