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 another interesting week for AI-related startup funding, with a number of large rounds and quirky companies. The largest financings went to , a developer of AI processors and systems, and AI lab . The quirkiest round, meanwhile, was undoubtedly a $200 million financing for , which wants to breed elite military dogs.

A number of big rounds in areas including biotech, space tech and physical AI rounded out the list.

1. , $1.5B, AI infrastructure: Santa Clara, California-based Tenstorrent, a developer of AI processors and systems, secured $1.5 billion in Series E funding. and led the round, which set a $6.2 billion valuation for the 10-year-old company.

2. , $1B, foundational AI: Flapping Airplanes, an AI lab with a focus on developing models that cost less to train and run, closed on $1 billion in Series A funding led by and . The round comes less than 10 months after the year-old San Francisco startup raised a $180 million seed financing.

3. , $325M, AI for critical infrastructure: ASI, a developer of AI technology for securing and operating critical infrastructure in areas such as aviation, transportation and defense, picked up $325 million in Series C financing. led the round, which set a $3 billion valuation for the Boston-based company.

4. , $250M, space tech: Xona, a startup developing a next-generation version of GPS, raised $250 million in Series D funding led by . The round sets a $2.4 billion valuation for the Burlingame, California-based company.

5. , $237M, biotech: Pathos AI, a startup applying AI to drug development, completed a $237 million Series E financing at a post-money valuation around $3 billion. led the financing for the New York-headquartered company.

6. , $215M, AI, robotics and life sciences: Warp Sciences, a startup applying AI and robotics to help develop new medicines faster, raised a $215M Series A round led by .

7. (tied) , $200M, online business marketplace: Whop, a platform that helps entrepreneurs build and run online businesses, secured $200 million in Series D funding led by and . The round set a $5.1 billion valuation for the New York-based company.

7. (tied) , $200M, biotech: South San Francisco, California-based drug developer聽 Star Therapeutics says it raised $200 million in new financing led by and . The investment follows 鈥檚 July acquisition of , a subsidiary of Star, in a deal valued at up to $2 billion.

7. (tied) , $200M, military dogs: Warhound, a startup that wants to breed working dogs for U.S. military forces and emergency responders, launched from stealth with $200 million in seed funding from a long list of backers including and 1.聽 The startup, co-founded by founder , plans to equip the dogs with Anduril technology.

10. , $160M, AI manufacturing software: Arda Industries, a developer of AI-powered software for manufacturing engineering, closed on $160 million in Series A funding at a $1.5 billion valuation, co-led by , and . The San Francisco-based startup also announced a previously undisclosed $72 million seed financing.

Methodology

We tracked the largest announced rounds in the 小蓝视频色情网页版 database that were raised by U.S.-based companies for the period of Oct. 17-23, 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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If you only have six months of experience in an industry, you usually aren’t equipped to poach your employer鈥檚 clients and start a competing business. Of course, you may believe otherwise if you plan to use your employer鈥檚 proprietary information to train an AI agent to do the heavy lifting for you.

That is exactly what the defendant attempted in I litigated on behalf of a client in the U.S. District Court for the Northern District of California. In that case, the defendant, a former senior employee at the marketing agency, didn鈥檛 just copy client lists to solicit accounts. Instead, he , attempting a shortcut to create a competing business without spending years developing the skills and expertise typically needed to provide such services.

AI is creating new ways to misappropriate trade secrets, well beyond the classic theft story of an employee downloading files onto a thumb drive. This case highlights a new reality for founders, exposing unprecedented risks related to AI and trade secrets, employee use of personal devices, and the heightened need for companies to protect themselves in the age of agentic AI.

Key lessons for founders

AI contamination cannot be undone

The defense in this case argued that because their client placed the stolen documents in a “quarantine folder” and no clients had been poached yet, no harm had been done.

The court appropriately recognized that the 鈥渜uarantine folder鈥� was grossly insufficient, granting our temporary restraining order and preliminary injunction while recognizing the competing AI agent as an “existential threat.”

An agreement not to touch just the stolen documents themselves is essentially irrelevant once AI gets involved: If a trade secret is used to develop a machine learning model or used to train an AI agent with recursive capabilities, it contaminates the model and the impact can be difficult to undo. Even if raw source files are completely scrubbed of any direct evidence of stolen materials, defendants will struggle to prove to courts that the AI isn’t operating a certain way precisely because it once had access to that proprietary information.

Personal devices can pose big risks聽

While trade secret misappropriation has typically been malicious, AI now also creates significant risks of inadvertent violations. Startups frequently scale with employees working on personal devices under standard Confidential Information and Invention Assignment Agreements. Running desktop AI agents on these devices can pose a serious risk.

Unlike standard web interfaces, a desktop AI agent can scan files, create folders and ingest directory paths across a local hard drive, often without requiring special permission or alerting the user.

Right now, thousands of employees may be inadvertently violating confidentiality agreements by running consumer AI agents on personal laptops that house their employers’ IP. If settings allow model training from this data, courts may eventually find these trade secrets to have lost legal protection due to dissemination outside the company.

Standard IT audits won’t catch AI data theft

This recent case serves as a warning for how attorneys and founders must approach trade secret investigations. Traditional digital discovery is no longer sufficient because AI tools make it effortless to disperse, alter and recreate data across nonobvious folder paths.

Actions that previously seemed like overkill 鈥� such as full forensic imaging of a personal device 鈥� are now the bare minimum. Furthermore, AI dramatically reduces the friction of reverse-engineering software and tech products, potentially removing telltale signs that code or data was copied.

Proving theft in AI contexts requires intensive, highly technical expert analysis and carries far more uncertainty than traditional IP litigation.

3 practical steps companies should take

Founders should take three legal and operational steps to proactively protect their business:

1. Implement strict agentic AI policies: Mandate tight controls and settings that isolate outside AI from company documents. Web-based tools like Claude with strict data privacy policies are vastly safer than open source or desktop apps that crawl local drives. Some AI agents should not be permitted to run at all within an environment containing trade secrets.
2. Eliminate remote access from unmanaged devices: Shift to a closed cloud environment where data can be viewed and manipulated on a secure server, but files cannot be downloaded locally to a personal desktop. Removing local file downloads eliminates the risk of desktop AI agents indexing proprietary data.
3. Rewrite your Confidential Information and Invention Assignment Agreements for the AI era: Standard confidentiality agreements need explicit clauses addressing AI use, model training inputs, and a precise definition of data misappropriation through machine learning.

If you suspect an employee has walked away with data or misused it in an AI tool, your first step should be immediate: cut off all system access and retain counsel. Don鈥檛 wait until you start losing clients or experiencing financial losses. Feeding trade secrets into AI presents an existential threat from day one.

AI gives individuals the unprecedented ability to compete in spaces where they lack experience, making proprietary data more vulnerable than ever. Modernizing security, policy and legal strategies to address today鈥檚 AI threats will help startups and established companies avoid catastrophic exposure.

is a partner at , where he focuses on complex commercial litigation, including disputes related to trade secret and IP misappropriation, corporate governance and breach of fiduciary duties, breach of contract, and other complex business disputes. He specializes in preventing and resolving all manner of disputes among and between corporate founders, directors, officers and shareholders, LLC members, and other current and former business partners.

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From teaching robots to do everyday household chores to using lasers to track satellites in space, this month’s crop of interesting startup deals spans some decidedly different corners of tech.

Other startups that caught our eye are working on making cement production less polluting, turning agricultural waste into a useful tool for carbon removal, and helping independent video game developers get their product in front of more players. Let’s take a closer look.

$30M to teach robots by watching humans

Before robots can fold laundry, load dishwashers or prepare meals, they need to learn how humans actually do those things. is betting there’s a big business in supplying the training material.

The Santa Clara, California-based startup this week announced a $30 million seed round led by, with participation from , , and . The company, founded by veterans of the robotics and autonomous vehicle industries, is building what it calls the data layer for physical AI.

Its approach is surprisingly low-tech in some respects: Pay people to wear cameras while they go about everyday tasks, from washing dishes to preparing food or cleaning hotel rooms. But turning all that footage into something useful for robotics models is more complicated. Maxinsights develops its own recording hardware and AI-powered software to collect, label and organize the data so robots can learn from it.

The startup says it has already captured more than 2 million hours of human activity, with 1.5 million hours ready for use in AI training. Its customers include robotics model developers and . It is also developing a sensor-equipped glove called MaxGlove that can capture information cameras can’t see, such as grip strength and the force needed to manipulate objects.

The funding comes amid a massive boom in physical AI investment. Startups in the sector raised $47.4 billion globally in the first half of 2026, nearly 4x the amount invested in the second half of 2025, 小蓝视频色情网页版 data shows.

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拢8.2M to track satellites with lasers

Space is getting crowded, and keeping track of everything flying around up there is becoming an increasingly complicated problem.

With that in mind, London-based emerged from stealth this month with 拢8.2 million (around $11 million) in pre-seed funding led by , with participation from investors including , and .

The company uses a technique called satellite laser ranging, which involves firing laser pulses from ground stations toward reflectors mounted on satellites. The company says that by measuring how long the light takes to return, its system can calculate satellite positions with millimeter-level accuracy.

While the technology has existed for decades, Foundational is working to make it cheaper and more scalable. Its system combines automated ground stations, satellite-mounted hardware and a software platform called Origin that provides positioning data to satellite operators. The company says its stations cost roughly 30x less than legacy systems.

The technology could prove increasingly valuable as thousands more satellites enter orbit, raising the risk of collisions with other spacecraft and debris. The estimates that around are orbiting Earth 鈥� large enough to cause serious damage to functioning satellites.

Foundational’s raise comes amid a record-setting year for space technology funding. Investors had already poured $20.3 billion into space- and satellite-related startups globally by late August, 小蓝视频色情网页版 data shows, easily surpassing previous annual records with several months still left in the year.

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$10M to make cement without so much carbon

Cement is the glue holding much of the modern world together. It’s also , per an estimate.

Calgary, Canada-based wants to change that. The startup last month said it raised $10 million in new funding led by, with participation from , and , to advance technology that could substantially reduce emissions from cement production.

Cement manufacturing traditionally requires heating limestone to extremely high temperatures, a process that releases significant amounts of carbon dioxide. Cura is developing an electrochemical alternative that uses electricity to separate carbon dioxide from limestone before it enters the kiln.

The company says its process could cut cement-related emissions by up to 85% while also reducing energy consumption. Crucially, the technology is designed to integrate with existing cement plants rather than requiring manufacturers to replace their entire production facilities.

The fresh capital will help fund a pilot plant in Alberta capable of producing 100 metric tons of material annually, as well as engineering work for a larger commercial demonstration facility with a planned capacity of 30,000 metric tons per year.

Cura is among a growing crop of startups targeting industrial processes that have proven particularly difficult to decarbonize. While venture funding for cleantech has fallen well below the peaks of 2021 and 2022, the sector has shown signs of stabilizing. Investors put $15 billion into cleantech-, EV- and sustainability-related startups in the first half of 2026, with second-quarter investment reaching its highest level since 2024, 小蓝视频色情网页版 data shows.

Startups like Cura working on cleaner cement technologies, in particular, have also seen investor interest in recent years.

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$10M to help mobile games find their players

Making a successful mobile game is one thing. Getting enough people to discover and play it is an entirely different 鈥� and often very expensive 鈥� challenge.

Cyprus-based is tackling that problem with an unusual investment model. The company, founded by co-founder , said this week that it had raised $10 million in new funding led by to help early-stage gaming studios turn promising titles into commercial successes.

Mika isn’t a traditional game developer. Instead, it invests in small studios and helps finance the expensive marketing campaigns needed to attract players.

The problem is something of a catch-22 for developers: They often need hundreds of thousands of dollars in advertising to prove that their games can attract and retain paying users. But the financing providers that fund those campaigns typically want several months of marketing performance data before they’ll provide capital.

Mika aims to bridge that gap by combining equity investments in game development with separate financing for early marketing campaigns. If those efforts demonstrate that a game can acquire players profitably, the studio may then qualify for larger financing arrangements.

The company says it now has five studios in its portfolio, including the developer of Legendale, a farming adventure game with more than 3 million players, and Punko, a tower defense game with more than 500,000 downloads.

The investment comes as gaming startup funding is showing signs of recovery. Companies in the sector had raised around $2 billion globally in 2026 as of late September, already exceeding the full-year 2025 total, 小蓝视频色情网页版 data shows.

Mika isn’t the only investor experimenting with alternative financing for game developers. earlier this year a $100 million fund offering indie studios financing in exchange for a share of game revenue, another sign that investors see opportunities beyond conventional equity funding in the gaming industry.

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$3M to turn coffee waste into carbon removal

Not that you need another reason to look forward to your steaming morning cup of coffee, but consider this: The leftovers from that daily ritual might eventually help remove carbon from the atmosphere.

Zurich-based said late last month that it had raised $3 million in funding co-led by PINC, the venture arm of food and beverage company , and . Cotierra says it鈥檚 developing technology that turns agricultural waste into biochar, a charcoal-like material that can store carbon for long periods while improving soil health.

itself isn’t new. What’s interesting about Cotierra’s approach is where and how it wants to make it. Rather than transporting bulky crop residues to large centralized facilities, the startup says it鈥檚 developing smaller, decentralized reactors that can be deployed close to where agricultural waste is generated. Its technology combines the reactors with software to monitor production, track the resulting carbon removal, and verify the environmental benefits.

The company has initially on Colombia鈥檚 coffee-growing regions, including Huila, the Coffee Triangle, Antioquia and Santander, where growers face increasing pressure to reduce emissions and adapt to climate change. Cotierra says its technology has already been tested on coffee farms and is now moving toward larger commercial deployments, with plans to expand into cocoa, cotton and citrus production.

It’s a potentially appealing business model for both farmers and food companies: Turn an otherwise low-value waste product into something that can improve soil conditions while also generating measurable carbon removal.

Its raise, however, comes against a difficult backdrop for agricultural technology investment. Global funding to agriculture and farming startups remains far below its 2021 peak, when companies in the sector raised $10.5 billion, according to a 小蓝视频色情网页版 News analysis earlier this year. Funding to the sector totaled just $2.8 billion through the third quarter of 2026, down even from the $3.5 billion raised in the same period last year.

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Related reading:

• VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape
• Sector Snapshot: Space Tech Startup Funding Orbits New Highs
• Sector Snapshot: Cleantech Startup Funding Stabilizes As Energy Demand Grows
• Gaming Startup Funding Levels Up A Bit In 2026
• Sector Snapshot: Agtech Startups Face A Drier Funding Climate

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and co-led the round, with participation from insurance industry veterans including , who will join Slingshot鈥檚 board. Rally is also a co-founder of the Minneapolis-based startup, as part of the firm鈥檚 broader approach. So far, it has co-founded roughly eight portfolio companies that have either launched or remain in stealth.

Slingshot, which is led by first-time founder and CEO , focuses on small insurance policies that consumers can buy as part of another purchase, such as coverage when booking a flight. Known as embedded insurance, the model puts coverage in front of customers when they might need it.

The startup鈥檚 software helps businesses offer those policies by connecting them with insurers that back the coverage. It uses AI to process and share data between the two sides, aiming to reduce the administrative work that can make inexpensive policies costly to offer.

Its first customer and design partner is , which serves more than 750,000 customers at an average premium of $25, according to Slingshot. Genteman told 小蓝视频色情网页版 News that Slingshot鈥檚 first program will launch this month.

For now, Slingshot earns revenue as a technology provider working with outside insurers. But the six-person company, which formally began operating July 1, is also pursuing an acquisition that would allow it to back policies itself.

鈥淲e’re not a carrier yet, but we are rapidly closing on our carrier,鈥� Genteman said in an interview.

Making the math work on a $25 policy

One product Slingshot is working on would let parents buy insurance when signing a child up for youth sports, Genteman said. A parent spending $2,000 on a volleyball league, for example, might be offered a $25 policy covering cancellation or an injury during the season.

Each policy includes paperwork and coordination with an insurer, and can bring potential claims. But when policies cost so little, managing them can quickly eat into the money they bring in, noted Genteman.

He believes that as vertical SaaS companies increasingly look beyond traditional subscription fees for growth, embedded insurance is 鈥渁 largely untapped opportunity鈥� to generate incremental revenue from transactions already happening on their platforms while delivering additional value to customers.

鈥淭he challenge is that the insurance and regulatory landscape is incredibly complex, and most SaaS companies don’t have the expertise, industry relationships or understanding of where to begin,鈥� he said.

Slingshot aims to solve this by providing a white-glove service for those vertical SaaS companies, helping them go from an insurance product idea to 鈥渁 fully operational offering.鈥�

He offered a hypothetical example: A program collecting $500,000 in premiums with a 10% margin would have just $50,000 in margin.

鈥淭hat’s not a viable program for the way incumbent carriers have set themselves up operationally,鈥� he said.

Slingshot鈥檚 goal is to replace the spreadsheets, emails and disconnected systems used to manage that work with software that connects policy distributors and insurers directly. The company says its AI automates data-heavy tasks while keeping a record of decisions that the businesses involved can review and check.

It charges a 5% technology fee to administer programs through its platform. Acquiring an insurer would let it provide both the software and the financial backing, according to Genteman.

A customer helped shape the business

The difficulties facing Slingshot鈥檚 first customer helped bring the startup together.

Rally had already helped start Vertical Insure, which uses software platforms to sell insurance to its customers. According to Genteman, that business had opportunities to introduce more products but struggled to find insurers whose technology and processes could keep up.

鈥淲e can design the products our customers need almost overnight, but the industry is highly regulated, and we need delegated authority just to write and distribute what we鈥檝e built,鈥� Vertical Insure co-founder and CEO said in a statement. 鈥淭he antiquated approval process on the carrier-side slows everything down.鈥�

Even after finding an insurer willing to back a product, connecting the two companies鈥� systems could create more delays, Genteman said.

Slingshot was built to address those problems, with Vertical Insure helping shape its product as a design partner. Noland is also a Slingshot co-founder.

The opportunity in vertical SaaS

Genteman sees a broader opening among vertical SaaS companies: businesses that sell software tailored to a particular industry.

As vertical SaaS companies look for ways to grow beyond subscription fees, insurance could give them another avenue to make money from purchases customers already make through their platforms, argues Genteman. They already know their customers and what they鈥檙e buying, putting them in a position to offer coverage that fits the purchase.

A babysitting platform, for example, could offer coverage to help parents pay for replacement childcare if a sitter cancels at the last minute. Software used by pet boarding facilities could offer coverage for unexpected vet bills if a pet is injured during a stay. An equipment rental platform could let customers add protection at checkout against accidental damage or theft.

Those are examples of what could be developed, Genteman said. Slingshot aims to help vertical SaaS companies take ideas like those and turn them into insurance they can sell, providing both the software and help navigating the insurance industry and its regulatory requirements.

鈥淚nsurance becomes another product that can be offered naturally within the existing customer experience,鈥� he said.

For , managing director at Rally Ventures, that opportunity is familiar. Before joining Rally, he ran , where insurance sold through its software became a key revenue source.

鈥淭hat experience gave me firsthand insight into the opportunity for software companies to generate revenue through embedded financial products,鈥� he wrote via email.

Kaufenberg sees similar potential across Rally鈥檚 portfolio, where most companies combine AI products with financial services. He said founders increasingly want insurance partners that can help them introduce coverage quickly.

Rally co-founded Slingshot to bring insurance backing, underwriting, reporting, claims handling and distribution together in one business.

鈥淭o get the carrier, the reinsurance, the underwriting platform, the data flow engine, the reporting capabilities, the claims handling, the embedded distribution engine, and every other part of the offering from a single vendor is unheard of,鈥� Kaufenberg said.

From insurance executive to founder

Before starting Slingshot, Genteman was chief actuarial officer at publicly traded , where he also led data. He joined in 2019 and helped build its actuarial and data team as the company expanded beyond earthquake insurance into other coverage types.

That work led him to explore whether newer technology could reduce the costs of running insurance programs. About a year ago, he began coding and building prototypes in his basement. He started talking with Rally toward the end of 2025 and left Palomar on July 1 to run Slingshot.

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When a company delays its IPO to show investors better numbers, that’s a strategy. When the NYC pension fund publicly asks you to delay it over AI safety concerns, your chief rival’s CEO calls his own IPO “ill-advised” and postpones it until at least next year, tech stocks are sliding on open-weight model fears, and your own prospectus warns of “existential risks to humanity” from the product you’re selling, that’s not a strategy.

That’s a market sending you a message.

‘s numbers are extraordinary. Its third-quarter 2026 revenue was $16.8 billion 鈥� up 45% quarter on quarter. That means the company鈥檚 annualized revenue run rate is roughly $67 billion.

What the S-1 actually says

Here are the numbers from Anthropic’s :

• $4.6 billion: Anthropic’s full-year 2025 revenue.
• $7.33 billion: What Anthropic spent on compute and infrastructure in 2025 alone 鈥� more than its total revenue.
• $42 billion: Anthropic鈥檚 net loss in 2025. The roadshow will immediately explain that $34 billion of it was a noncash accounting charge 鈥� an adjustment to the estimated value of financing instruments that could eventually convert into shares.
• $518 billion: How much Anthropic expects to spend, at least, over the course of a decade on AI infrastructure with six partners, including , and , according to the confidential IPO prospectus by . About 80% of the total is noncancelable or payable regardless of usage.
• $2 trillion: That鈥檚 the IPO valuation Anthropic is still targeting for November, a figure representing 435x its 2025 revenue.

One more number from the S-1 that the roadshow will bury: A quarter of Anthropic’s 2025 revenue came from two unnamed customers.

Three signals the market is sending

The IPO delay is the headline. It isn’t the only signal worth reading.

Signal one: NYC Comptroller Mark Levine. Last week, the supervisor of New York City’s pension funds 鈥� which hold roughly $7 million in Anthropic exposure today, but could hold dramatically more if Anthropic enters the Russell 3000 post-IPO 鈥� sent to Anthropic CEO urging the company to pause the IPO entirely.

The risks he cited are the same ones Anthropic’s own prospectus flags: increasingly autonomous AI systems that could behave in unexpected ways, create security problems, and in the document’s own language, pose “existential risks to humanity.”

Signal two: delays 鈥檚 IPO. OpenAI has said it will not go public in 2026. CEO Altman called an IPO at this moment given AI safety concerns, and while that framing is partly strategic 鈥� positioning OpenAI as the more responsible actor 鈥� it鈥檚 also a real signal when the CEO of the second-most valuable AI company in the world looks at current conditions and decides to wait.

Signal three: The chip selloff. Last week鈥檚 was driven partly by concern over AI-buildout costs. 鈥檚 helps explain the concern: Through 2030, it sees at most $3.6 trillion in revenue attributable to AI data centers, versus $8.5 trillion needed to cover planned investment, financing costs and a 12% return.

The open-weight problem

Here is the structural argument that no amount of Q3 revenue growth resolves: Anthropic’s $2 trillion valuation requires closed-model AI to maintain a durable pricing premium over open-weight alternatives. That premium is being systematically dismantled.

Open-weight AI is challenging the economics behind closed-model valuations. Enterprise adoption rose from 23% to 67% in 2026, with companies reporting 70%鈥�90% cost savings. R1 offers frontier-level performance on most enterprise benchmarks at 7 cents per million input tokens; independent analysis puts Anthropic鈥檚 comparable input and output pricing at roughly 18x and 23x higher, respectively.

DeepSeek鈥檚 weights are free to download, allowing enterprises to self-host without a model-provider API contract or per-token billing, while the capability gap with closed models reportedly closes within six months of a frontier release.

Against that backdrop, 鈥� the research firm that called Anthropic鈥檚 proposed offering 鈥渁n unprecedented test of investor gullibility鈥� 鈥� that justifying a $2 trillion valuation would require an immediate 10% after-tax operating margin sustained for a decade, extraordinary revenue growth, and $358 billion in annual after-tax operating profit by 2035.

Why this is worse than 1999

The dot-com bubble had a genuine excuse. Nobody knew what the internet would become. The AI IPO wave of 2026 does not have that excuse.

Worldwide spending on AI is , up 47% year over year, according to Gartner. Yet a July 2025 estimated that 95% of the organizations it examined were getting no return from generative AI. Meanwhile, said in its earlier this year that just 7% of surveyed leaders say they鈥檝e established ROI from AI.

This is the context in which Anthropic is asking investors to pay 435x 2025 revenue for a company with $518 billion in locked infrastructure commitments, 25% revenue concentration in two unnamed clients, and a pricing moat that open-weight models are closing in six-month cycles.

The delay from October to November doesn’t change any of those numbers. It adds one quarter of revenue data.

What actually survives this correction

Here is what no AI IPO roadshow will say out loud: The biggest enterprise AI winners will not necessarily own the most expensive model. They will own the architecture that turns any model into governed, auditable, measurable outcomes 鈥� through proprietary enterprise data, domain-specific fine-tuning, multiagent orchestration and controls that make autonomous execution trustworthy.

That value does not depend on keeping model capabilities scarce or securing a $2 trillion valuation. It compounds in production, where customers understand that the model is not the product. The deployment is.

The dot-com crash did not happen because the internet was bad. It happened because the price was wrong. AI investors face the same distinction: Real technology does not automatically justify its valuation. A strong quarter and a one-month delay do not close that gap. November simply gives Anthropic more time to explain the math.

, the co-founder and CEO of , is an entrepreneur and product leader with more than 20 years of experience across cybersecurity and AI. He has built three generative AI companies, including one that reached unicorn status. Gurtu is known for turning advanced AI into scalable, real-world platforms, and previously contributed to successful exits, including acquisitions by , and , and a public company, . He holds a master鈥檚 degree from the and CISSP certification.

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But that鈥檚 often not the case, an analysis of 小蓝视频色情网页版 data shows. This year, a number of larger acquisitions of private companies involved startups that haven鈥檛 disclosed a new investment for more than four years.

Several of these acquisitions are quite recent 鈥� and in some cases, quite large. The standout is 鈥檚 purchase earlier this month of , a platform for catering and workplace meals, for $2.3 billion in cash. The deal comes nearly five years after Boston-based ezCater raised its last reported venture funding round.

That鈥檚 not the only one. A number of companies whose names might feature on a playlist of 2021 boom-era unicorns have sold to acquirers in recent months, including one-time highfliers like , and . In many cases, companies are selling at valuations which, while not puny, are still far below their former peaks.

Who鈥檚 getting acquired

To illustrate the extent to which these aging startups are finding acquirers, we used 小蓝视频色情网页版 data to put together a list of 15 deals involving well-funded companies that haven鈥檛 raised equity funding for four or more years.

Post-peak runway

One big issue for older and middle-aged startups is that valuations hit a cyclical peak in 2021 and early 2022. Many companies that raised during that time had difficulty attracting fresh capital or were reluctant to close new rounds at reduced valuations. Even with the more recent AI boom, little went to this cohort.

Moreover, since the 2021 boom-era rounds were quite large, funded startups commonly had the option to cut operating costs and extend their runways with existing capital. As a result, quite a few companies that haven鈥檛 raised rounds in years are still chugging along, managing to survive and even grow without more venture funding.

EzCater is a case in point. In the year prior to its acquisition, the business generated more than $2.5 billion in gross bookings. It had an annual growth rate in the high teens, with average order sizes over $400.

That said, even that revenue and growth rate didn鈥檛 secure ezCater much of an acquisition premium from its last funding round. At $2.3 billion, Uber鈥檚 purchase price is 53% above the $1.5 billion pre-money valuation the catering startup secured in its last funding round in late 2021.

Acquisitions below peak venture valuation

In many cases, even good-sized acquisitions of well-funded startups are occurring at valuations far below former peaks.

Take Miro, a provider of online collaboration tools that agreed to sell to last month in a deal valued around $1.36 billion. That鈥檚 a huge step down from early 2022, when San Francisco-based Miro closed its last round at a $17.5 billion post-money valuation.

Or consider Bending Spoons鈥� prior purchase of data and workflow platform , for $1.28 billion in August. That鈥檚 barely over one-tenth the $11 billion valuation Airtable secured in its December 2021 Series F.

Fashion startup is another that lost its shimmer after securing a 2020 valuation in the hundreds of millions. It sold to fast-fashion leader this spring for $100 million.

In the health and wellness sector, meanwhile, Headspace also saw its valuation plunge. The meditation and mental health app sold to in a deal reportedly valued around $300 million. That鈥檚 well below its peak post-money valuation of around $2.6 billion.

A good thing?

While startup investors aren鈥檛 celebrating acquisitions made far under peak private valuations, they鈥檙e not the worst outcome. And for startups themselves, they鈥檙e also far from a catastrophic scenario.

Of course, every unicorn investor hopes for high-multiple returns. But for startups that go years without funding, the market seems to be telling us that鈥檚 not likely in the cards.

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You鈥檝e just moved into a new house. It鈥檚 unfurnished; the walls are bare. Ten years ago, you might have loaded up and started searching for furniture that you like, consulting interior design magazines and looking for offers on retailers’ websites. In 2026, however, there鈥檚 an entirely different option at your fingertips. You could take a photo of the empty room, upload the image to an AI agent, share your budget and style, and get a list of furniture that suits your preferences. You could even have the agent purchase on your behalf.

Instead of just selling to one customer base 鈥� humans 鈥� brands are now selling to two.

This second audience of AI agents makes decisions on different criteria than humans. Often, in fact, AI agents will make purchasing decisions before a human ever sees the product page. There鈥檚 a real chance that all the emotive copy marketers have so carefully crafted will get broken down and reconstituted by a machine. Just relying on the human touch isn鈥檛 enough anymore.

Leave humans in the equation

A word of caution, though: Marketers shouldn鈥檛 forget the human entirely in the rush to prepare for AI agents. It鈥檚 easy to get carried away with visions of an entirely agentic future. But at the end of the day, each agent is still catering to a human with their own preferences, and personalization will only get more granular over time. The trick is to optimize for both audiences simultaneously, not just one.

Newer startup founders have the advantage here. Established brands have to retrofit decades of copy, guidelines and approval processes to make themselves agent-ready. New brands, on the other hand, can build in copy and a brand voice that鈥檚 legible to humans and agents from the very start.

But what does this look like in practice?

To start, brands should work out a distinct voice 鈥� and write it down. In most early-stage companies, the brand voice solely exists in the founder鈥檚 head. This might work if the copy鈥檚 written by a handful of people who work with the founder, but it doesn鈥檛 work well when the writer 鈥� or reader 鈥� is a machine.

AI tools follow explicit rules and drift without them. I’ve spent years at my startup, , working out how to get AI agents to write better content, but it’s only now that we’re seeing agents on the other side of the process too 鈥� and that makes the rules matter even more.

What a brand sounds like matters, but what it doesn鈥檛 sound like matters just as much. Brands should note words never to use and the claims never to make. Often these instincts don鈥檛 get written down; they feel obvious to the team who already know them. But if you take the time to spell them out before you have a team 鈥� or a machine 鈥� producing your copy, you鈥檒l save yourself from running into issues further down the line.

Verify your brand

Marketers also need to realize that product descriptions aren’t just administrative: they鈥檙e now core brand assets. In an agent-mediated world, they may be the most-read brand copy you own, since agents read them when looking for a product to recommend or purchase. If two personalized AI agents look for furniture for two different people, they may surface the same chair for both, but with the description tweaked for each user. The source description needs to carry the brand intact.

Where possible, brands need to test channels for both readers. Email inboxes are the clearest current example of the 鈥渟econd reader.鈥� Both Gmail and now summarize messages before the recipient opens them and offer summaries above the body of the email 鈥� which may be read before the email itself.

So before sending, check what the AI summary says your email is about. Does the offer survive? Does it maintain what makes you unique? It鈥檚 not time-intensive, and it鈥檚 a small habit any marketing team can adopt today with no tooling.

Brands also need to ensure they鈥檙e measuring the outcome of their marketing 鈥� not just the volume. Marketers who measure the success of their materials only by the amount produced will find their data meaningless as AI makes content production easier and easier. Focus instead on business outcomes, conversions and revenue. These are the metrics that will show if your marketing team is really doing their job.

And finally, don鈥檛 lose sight of the human. No one knows how fast AI purchasing will scale. Humans are still predominantly the ones reading copy and making purchasing decisions. Even AI agents, the second audience, cater to their users鈥� preferences.

New founders and brands are best positioned to take advantage of the transition to a dual-audience economy. Though it鈥檚 hard to say exactly how, the retail landscape will look very different in five or 10 years. Building these decisions into your brand now is easy.

Retrofitting them later is expensive.

is chief product and growth officer at, an AI platform that helps brands generate and optimize bespoke messaging at scale.

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Acquisitions are reshaping the technology industry. Venture-backed AI companies acquired 195 AI startups this year through late September, already exceeding the total for all of 2025, 小蓝视频色情网页版 data shows.

For investors and founders, such acquisitions can represent major financial milestones. For employees holding equity compensation, a transaction can also change personal financial plans they had expected to follow for years.

A client whose employer was acquired in an all-cash transaction described to me the atmosphere inside the company during the weeks that followed: Employees repeatedly discussed the deal in office corridors, trying to understand what it meant for their equity awards, their jobs and their future. She compared the confusion to the atmosphere surrounding a major reorganization or a round of layoffs.

The transaction offered the prospect of substantial financial gains. Yet employees were struggling to establish what those gains would mean for them individually.

One acquisition, different outcomes

Employees at the same company can emerge from an acquisition with remarkably different financial outcomes.

Some own vested shares. Others hold restricted stock units, stock options or performance-based awards. Their rights depend on grant agreements, vesting conditions and transaction terms. Even two employees holding RSUs may face different payment schedules or employment requirements.

Depending on the agreements, outstanding awards may be settled in cash, accelerated, converted into replacement awards, or remain subject to future conditions. One employee might receive a substantial payment when the transaction closes. Another may have much of the expected value tied to continued employment.

The announced acquisition price provides a reference point. It rarely tells an individual employee everything they need to know about the money they will receive.

This helps explain the conversations my client described. Colleagues may compare the value of their equity without realizing that their contractual rights differ.

Wealth and employment uncertainty

Technology employees with substantial equity compensation often depend on their employer in several ways.

Their salaries, bonuses, career opportunities and investment portfolios may all be linked to the same company.

An all-cash acquisition can reduce their exposure to its share price once vested shares are converted into cash. Other forms of dependence may continue.

The acquiring company may reorganize teams, change responsibilities or introduce retention arrangements. Employees whose unvested awards remain conditional on employment may face difficult choices about staying.

Someone expecting a large payment can therefore feel financially stronger while becoming less certain about their career.

Before making plans

A major acquisition announcement can change personal ambitions long before the proceeds arrive.

Employees may begin considering early retirement, a new business, a career break or a major family purchase.

Those possibilities are worth exploring. But an announced acquisition may still require regulatory approvals and other closing conditions. The headline value of an equity award may differ substantially from the cash ultimately available to its holder.

Taxes, exercise costs where applicable, outstanding liabilities and payment schedules can all affect the outcome. So can the employee’s financial position outside the company.

A substantial payment might make a career change affordable without providing enough capital to fund permanent retirement. Existing pension savings, household spending and future earning capacity remain important.

In my work with technology executives, I have seen expectations about new wealth reshape personal plans before the underlying numbers have been established.

Employees facing a cash acquisition can begin with three questions: What rights do my specific awards provide? When will the proceeds actually become available? And what choices will the resulting after-tax capital support?

The answers may differ considerably among colleagues.

An exit without an exit decision

Founders who sell their companies often participate directly in negotiating the transaction. Employees holding equity compensation generally have much less influence over its timing.

They may have intended to retain their shares for years, sell gradually or remain employed until additional awards vested. A corporate acquisition can replace those plans with a timetable established by the transaction.

For some employees, the result will be substantial financial flexibility. For others, a significant part of their expected compensation may remain connected to future employment.

The period between announcement and closing can be especially difficult because the financial opportunity is visible while important details remain unresolved.

My client’s description of the office corridors captured that experience. The company had made a decision about its future. Its employees were left to work out what that decision meant for theirs.

The acquisition determines the terms of a corporate transaction. Each employee still has to establish how much financial freedom it creates.

is co-CEO and managing partner of , which works with technology executives, founders and families on integrated wealth planning, equity compensation and major liquidity events. His background includes more than two decades in the technology industry.

Disclosure: The views expressed are the author’s own. This article is for general informational purposes and does not constitute financial, investment, legal or tax advice. Details of the client experience have been presented without identifying information.

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The partner began his career as a high school math teacher with after studying astrophysics at . He later worked in college access and Yale admissions, and earned graduate degrees in business and education from before joining Reach. As a teacher, he was an early user of , an experience he describes as 鈥済ame-changing.鈥�

His investment firm, Reach Capital, has also evolved. Founded in 2015 after spinning out of , the San Francisco-based firm initially focused on technology for K-12 schools and districts. Its mandate has since expanded to learning, health and work, with investments in companies including ClassDojo, , and .

In August, Reach announced its fifth fund, a $265 million vehicle that brought its assets under management to nearly $1 billion. The firm, which has backed more than 180 companies, plans to invest in approximately 50 additional teams through the new fund, targeting pre-seed, seed and Series A rounds.

小蓝视频色情网页版 News spoke with Kim about how Reach鈥檚 education roots inform its broader strategy, what it saw in AI detection company , and why some of its best investments initially divided its own team. We also discussed how ownership stakes and secondary transactions have shaped the firm鈥檚 approach to returning capital.

This interview has been edited for length and clarity.

小蓝视频色情网页版 News: You started your career as a high school math teacher. How did that shape your view of education technology?

Kim: I taught ninth through 12th grade, mostly Algebra I and geometry. I was very early to ClassDojo. Back then, it was a very simple classroom management tool, and it was game-changing for me. It has since evolved into a sprawling suite, but I already knew technology could move the needle.

Reach began as an education investor. How did the firm鈥檚 focus evolve into learning, health and work?

Kim: Reach started in 2015, and our original focus was exclusively on learning and education technology 鈥� more specifically, technology sold into schools and districts.

Our founding partners were seeing the growth of devices in schools, one-to-one device ratios and broadband penetration. The belief was that you would start to see a proliferation of technology serving students, teachers and administrators and moving the needle on learning outcomes. That is what ended up playing out. Some of our early bets are now household names in edtech, such as , and ClassDojo.

Reach spun out of NewSchools Venture Fund, where three of our four founding partners had started doing for-profit seed investing before 2015. Over time, we branched out from K-12 into higher education, and then into consumer learning.

We also started getting pulled toward healthcare and work. Health and wellbeing affect a learner鈥檚 readiness for the classroom, so we were being pulled toward mental health, behavioral health and developmental health. On the work side, formal learning typically ends in college, but you still need the bridge to a job. Once you鈥檙e on the job, you need to continue learning, especially in the current environment.

A few years ago, we officially put a stake in the ground and said we invest across learning, health and work. The common thread is that these are the most important systems underlying human development and flourishing.

So far, the breakdown has been roughly 50% learning, 25% health and 25% work.

These sectors have different buyers, regulations and business models. What did investing in education teach you that translates to healthcare and work?

Kim: Education is highly regulated, and institutional buying is complex. Many stakeholders must provide input, and decisions often require board-level approval. We were familiar with and comfortable with regulated products and complex go-to-market motions.

Selling to a university or a K-12 district, in terms of the complexity and the need to intersect with regulation and policy, is quite comparable to selling to a health system, hospital or insurance carrier.

Cartwheel is a good example of where our experience gave us an advantage. It provides mental health teletherapy, but the customer is the school district and the care setting is in schools.

While we were relatively new to behavioral health when we made that investment, we were very familiar with selling to school districts and implementing an intervention within a school setting. That gave us an advantage both in winning the investment opportunity and supporting the company afterward.

Reach talks about 鈥渆levating human potential.鈥� What makes an investment fit that mandate?

Kim: We look for products and services that increase access to quality education, quality care or quality earnings, or some combination of those. We really have to see the mechanism for that happening.

It鈥檚 not good enough to be pitched a SaaS or AI product and have the founders say, 鈥淭his will make people more productive at work, and therefore maybe they will earn more or enjoy their jobs more.鈥� That鈥檚 plausible, but it isn鈥檛 a good enough theory of how the product actually increases access to quality earnings.

Several teammates, including myself, are former educators. We care about meaningful, needle-moving impact.

We address access from both the demand side and the supply side. On the demand side, we build products that create delightful, impactful experiences for consumers: , Stepful鈥檚 allied healthcare training, and Replit for what its founder calls 鈥渃itizen developers.鈥�

On the supply side, it鈥檚 helping schools, health systems and companies function more efficiently and effectively. , for example, helps logistics and hospitality companies hire hourly workers better and treat them better. We think about the problem from both sides of the market.

Reach backed GPTZero, which agreed to join . Was that an investment in solving a problem created by AI?

Kim: That wasn鈥檛 actually our investment thesis. In my opinion, they have the best AI detector on the market. But their thesis was that AI use in education should not be banned. It shouldn鈥檛 be punitive or become a cat-and-mouse game between the educator and the learner.

Since all of us as professionals use AI in our jobs, students should learn how to use it responsibly and thoughtfully. It was the first AI detector company I had seen with that positioning, as opposed to helping schools, districts and universities clamp down on AI usage.

That鈥檚 why I liked it. I don鈥檛 see it as a problem for students to use AI for certain purposes, such as brainstorming, organizing thoughts and editing. GPTZero helps educators work with students and teach them how to use AI responsibly.

Do you see other opportunities to build companies around the problems AI creates?

Kim: Yes, most notably in AI governance and evaluations.

Going back to regulated categories, you need to make sure AI is not irresponsibly using personally identifiable information in education or healthcare. Those categories have very strict regulations, and infrastructure companies are emerging to help ensure AI products interact with that data responsibly.

I do think there are opportunities for large, successful, profitable companies to be created in response to the problems AI creates.

What does GPTZero鈥檚 sale tell you about which AI products can become durable standalone companies and which belong within larger platforms?

Kim: I don鈥檛 know whether GPTZero is an example of either. They had been fielding acquisition offers from pretty early on. This particular opportunity arose earlier this year, and there was a compelling synergy story with Superhuman, formerly .

The founders and the board agreed that it made sense to combine the companies. But they could just as easily have remained standalone. There is another AI detection company called that鈥檚 quite good and is still a standalone, venture-backed company.

It鈥檚 not clear to me that this category can鈥檛 exist on its own. We have to be very case-by-case. We don鈥檛 know until we see the trajectory, financial sustainability and evolution of the market, whether it makes sense to explore a strategic acquisition or grow independently and perhaps one day become a publicly traded company.

Reach has backed more than 180 companies. What patterns have you noticed among those that exceeded expectations?

Kim: The reality of venture is that a very small share of investments yields the overwhelming majority of returns. Given the outlier nature of those successes, it鈥檚 hard to identify unifying principles that always predict a successful exit. If we could, we would only make those investments.

But there are a few things we think are somewhat common across our wins. One is a very smart, gritty, genuinely mission-driven founder or founding team, often with a strong personal connection to the problem.

Second is some sort of nonconsensus insight about the market. That often comes from experiencing the problem themselves, sometimes as practitioners in the industry 鈥� a former educator, for example.

Third is a defensible and scalable business model.

The nonconsensus insight is funny because it鈥檚 what makes these companies look weird and maybe uninvestable in the beginning. Even within our team, they鈥檙e often the most polarizing opportunities. You look at the votes, and a bunch of people vote no, but it gets just enough yeses to pass. Then, 10 years later, it鈥檚 one of our best investments.

People lose sight of this in the current environment, but the best opportunities are often the most nonconsensus. Those can be the most creative and interesting founders, and you can get in at reasonable valuations early and help them become consensus bets.

Reach says its first two funds have returned investors鈥� capital. What produced those returns?

Kim: We鈥檙e no exception to the power-law distribution in venture. Our first fund has been returned 2.2x by literally two investments. Our second fund has been returned 1.2x by one investment. These are cash distributions, by the way.

Especially in the second fund, there鈥檚 still remaining value, so we鈥檒l see how high that number gets. One fund-returning exit would be great; two or three would be amazing. That鈥檚 what we鈥檙e striving for.

How has that experience changed your approach to ownership and follow-on investing?

Kim: We鈥檝e had some great exits where, given how prominent the company is, you might be surprised that our share of the returns wasn鈥檛 that large. That鈥檚 purely an ownership calculation.

From our first fund to today, we鈥檝e meaningfully increased our initial ownership target. We now try to get as close to 15% ownership upfront as possible.

That means writing slightly bigger checks and reserving relatively less for follow-on investments and more for the initial check. It鈥檚 important for the exit math and distribution math.

What role do secondary transactions play in getting liquidity?

We鈥檝e become very proactive and conscientious about liquidity opportunities. Much of our distributions to investors come from secondary sales 鈥� selling our shares in a subsequent round, before an actual exit.

Whenever there鈥檚 an opportunity, we evaluate it. We consider how long we鈥檝e been in the company, how much we can get out, and how much upside we think remains.

As companies stay private longer, early-stage investors are achieving liquidity much more often through secondary transactions. I like to think we were early to that trend, but it鈥檚 certainly mainstream now.

Sometimes, when we see enough upside, we鈥檒l buy secondary shares. GPTZero was an example: After our Series A investment, we bought secondary shares, and I鈥檓 really glad we did because it worked out well. For us, we sell more often, but occasionally we buy.

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Today鈥檚 venture capital ecosystem looks markedly different from even just five years ago.

Most significantly, a small number of AI companies are now attracting the vast majority of available venture capital. In Q1 2026, just four companies collectively raised $188 billion, which alone accounted for 65% of total global venture investment, per 小蓝视频色情网页版 data.

Meanwhile, in that same quarter, just 20% of global VC funding went to non-AI firms. meaning that checks are being made out to fewer companies all around.

So what happens to startups when the VC route is all but closed?

I built a company into a multibillion-dollar business without any venture funding. Here鈥檚 a few lessons I picked up along the way:

Prioritize customers and revenue, not fundraising

Many founders unknowingly treat fundraising as a measure of success. But while it鈥檚 useful, funding doesn鈥檛 result in success on its own.

Achieving strong levels of customer trust is also helpful when it comes to inspiring investor confidence. High retention rates, repeat purchases and sustained engagement drive the聽 financial fundamentals on which strong valuations are built. In B2B SaaS, for instance, companies with net revenue retention above 110% . Additionally, customers provide a feedback loop that helps create the discipline every company needs, and each user is a vote of confidence that compounds over time.

Going to market: Take time on pricing strategy

Too many early-stage companies set prices too low because they fear that higher prices will repel customers. In my experience, however, underpricing more often signals uncertainty rather than good value. Customers want quality products and services, and they expect that the quality will be reflected in the price.

Incredibly, , and firms more commonly regret underpricing than overpricing. So at the outset, it’s important for startups to inform themselves what price their product or service can actually sustain, and then raise quality if it鈥檚 too low.

Equally, in services markets where prices are more established around a norm, superior quality can be an important differentiator. At the same time, however, companies must continually resist the temptation to chase customers with ever-cheaper prices, which can start a race to the bottom.

Hire the best talent

Too many founders hire for speed rather than quality. Take the time needed to raise the bar with talent. Hire the right people, and they鈥檒l help maximize every last drop of value.

Many years ago, I decided to focus on just a small fraction of our applicants and . When we found the best developers, it meant we could compete on delivery quality rather than just price. Keeping costs low is important, but skimping on talent is not a winning strategy.

Use process automation efficiencies to drive growth

One of our best decisions was to at an early stage. Not only do these systems screen developer applications, but they can also build, match and deploy top-quality teams for each client project.

This advance allowed us to cut costs associated with evaluating applications, accelerate lead times, and improve the quality of our teamwork. In parallel, so that growth wouldn’t require proportional increases in overhead.

Whether it鈥檚 in recruitment, sales or operations, process automation can create some of the biggest drivers of growth and help bring about conditions needed for significant expansion to occur.

Operate as if cash is running out tomorrow

Companies thrive in challenging funding environments by operating with discipline. That means obsessing over unit economics, prioritizing activities that directly drive revenue, questioning every cost, and eliminating anything that isn’t pulling its weight.

Not only does this aid growth, but it also makes a business more resilient in hard times. Financial constraints drive leaders to create efficiencies by automating workflows, streamlining processes and inventing innovative work models.

Years before COVID-19 forced others to follow our lead, our company streamlined its operations and boosted scalability by developing a remote-working model. When the pandemic made remote work necessary, the fact that we already had the systems and workflows in place gave us a competitive advantage over other firms, and this in turn led to significant growth.

Long term: Stay married to the problem, not the solution

The past decade convinced many founders that venture capital was the default path to building a meaningful company. But while funding cycles rise and fall, the fundamental principles of good business don’t. Infusions of capital can accelerate growth, but they cannot create a great company from scratch.

On the longest timescale, the most successful founders are the ones committed to solving problems rather than just selling a product and chasing funding. In a world short on funding, growth simply can鈥檛 happen without it.

is the founding GP of , and chairman of , one of the largest bootstrapped nearshoring companies worldwide. Driven by his entrepreneurial spirit, he launched BDev Ventures in 2021 to help B2B companies accelerate their growth through a proprietary lead-generation platform. He also invests in and serves on the boards of biotechnology and e-learning companies. In recognition of his career path, he won distinctions such as an EY Entrepreneur Of The Year 2024 Bay Area Award.

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