
AI-generated summary
Four years into the defense tech boom that began after Russia's invasion of Ukraine, venture capital has traditionally avoided defense startups due to moral concerns and lengthy government contracts. However, recent conflicts have depleted U.S. missile supplies, creating urgency for rapid, affordable production.
Four years into the defense tech boom that arguably began when Russia invaded Ukraine, the storied Silicon Valley venture firm Benchmark is placing its first bet on a pure defense startup.
The VC is leading a $25 million seed round into Furientis, a startup on a mission to mass-produce interceptor missiles. Just one year old, the company—whose name means “of fury” in Latin—has already secured a funded Pentagon contract to build mid-range interceptors.
Furientis’ seed round follows a $5 million pre-seed that was announced in May. The startup is now valued at $125 million, according to a person familiar with the company.
Until recently, venture capitalists largely steered clear of defense startups. Investments in the sector were viewed as morally controversial, and the process of securing government contracts was too lengthy and uncertain to justify venture funding.
But that sentiment changed rapidly after conflicts in Ukraine and Iran began to deplete the U.S. military’s missile supply. Startups that prove they can build equipment fast and affordably now stand a real chance of landing a Pentagon contract on an expedited timeline.
“What they accomplished with just a pre-seed round was nothing short of remarkable,” Benchmark general partner Chetan Puttagunta told TechCrunch. “They manufactured prototypes with just $5 million. They had already successfully done launches by the time we invested.”
That efficiency is in stark contrast to the slow-moving, high-cost development models used by traditional defense primes. The startup’s velocity is driven by its co-founders, who bring years of aerospace and defense engineering experience. Brody Franzen served as deputy chief engineer at Virgin Galactic before joining Castelion, a hypersonic missile startup valued at $13 billion, while Aris Simsarian previously led rocket engine testing at Virgin Orbit.
The duo launched Furientis after realizing the U.S. military is facing a severe shortage of “exquisite” missiles, which can cost around $3 million each, to counter inexpensive adversary threats, Franzen told TechCrunch.
According to Franzen, the U.S. Navy receives a mere 300 to 500 interceptor missiles annually, compared to China, which claims a production rate of 3,000 anti-ship cruise missiles a month.
“We’re being outproduced by a factor of 100 plus,” he said. “That is why we started the company.”
Furientis is not aiming to match the performance of best-in-class missile interceptors; instead, it is designing and manufacturing its systems so they can be assembled quickly from readily available components. As a result, each unit costs a fraction of legacy interceptors, a compelling value proposition for a Pentagon desperate to scale inventory within strict budget limits.
Unlike traditional defense primes such as Raytheon and Lockheed Martin, which are bogged down by long design cycles, Furientis has been testing its missiles in the field on a bi-weekly basis. The startup’s ultimate goal is to manufacture 1,000 systems a year in each factory. But for now, the young company is working out of its facility in Los Angeles and taking its prototypes for trial launches to White Sands, New Mexico.
Given the need, Furientis is not the only company working on helping the government restock its stockpiles. Franzen said that larger defense startups like Anduril, Castelion, and Shield AI are also racing to build mass-produced interceptors, while traditional primes scramble to modernize their own manufacturing cycles.
Benchmark’s Puttagunta is not fazed by the seemingly crowded space. “I think there’s opportunity for lots of lots more companies in this sector,” he said.
AI outlook — possibilities, not facts
Furientis will scale production to 1,000 interceptor systems annually per factory within the next 2-3 years.
Possible · Within years
More venture capital firms will follow Benchmark's lead in investing in defense startups focused on rapid, low-cost production.
Likely · Within months

TechCrunch will host its Founder Summit on November 4 at Boston’s SoWa Power Station. The event features expert-led sessions on fundraising, leadership evolution, and AI-native product development for startup founders.

French lab Mistral AI has unveiled Mistral Large 4, a one-trillion-parameter multimodal model. While currently accessible via a guardrail endpoint, the firm plans to release open weights in three weeks, aiming to compete with both American and Chinese AI models.

Starting October 9th, Google is restructuring its Gemini subscription plans. Free users will be limited to the Flash Lite model, while the $4.99/month AI Plus tier loses Gemini Pro access. Advanced models now require higher-priced Pro or Ultra subscriptions.

Meta is testing a new full-screen, video-first Facebook interface in India that launches directly into Reels. The test aims to increase engagement with short-form video, allowing users to opt out and return to the traditional feed if preferred.
Google may expand its 'Call for Me' AI feature beyond business calls to allow users to send personal messages via automated calls, such as telling a friend they will be late or asking about dinner plans, based on an APK teardown by Android Authority that revealed a 'Gemini Calling' screen and granular app permissions for controlling Gemini's access to phone functions.

OpenAI will roll out an invisible text watermark for ChatGPT and Codex in the European Union to comply with the EU AI Act's transparency rules, which took effect on August 2. The watermark, called textGrain, shapes word choices to create a detectable pattern without affecting model performance. It will be available to eligible users in the EU over the coming weeks and to API developers worldwide starting today, though off by default. OpenAI notes the watermark can be reduced by editing and does not prove human authorship if missing.