It was the latest in a run of quarters with nearly $10 billion or more in funding. Funding to the robotics sector in Q1 through Q3 2026 is up around 185% year over year.

China and the US lead

The bulk of robotics funding this year has been in China, with $13.2 billion invested, and the U.S., with $12.1 billion. Germany, the third-largest recipient of robotics startup funding, trails with $1.7 billion invested.

The largest rounds in China went to Guangzhou-based , a builder of AI models and physical robots that raised $900 million, and Shenzhen鈥檚 , which is working to build intelligent robots for manufacturing, healthcare and services and raised a $736 million Series C round.

In the U.S., billion-dollar rounds have also been raised this year by companies including 鈥檚 Los Angeles-based 鈥斅爓hich includes its ghost kitchen business, and divisions for robotics in mining and transport 鈥斅燼nd Pittsburgh-based , which raised $1.4 billion for its robotics foundation model Skild Brain.

Another notable round this year went to Germany-based , which raised $1.4 billion for its cognitive robots, with AI and data infrastructure to build intelligence.

Building out the data and model layer

鈥淲e’re just seeing physical AI take off,鈥� , co-CEO of San Francisco-based , a provider of the data layer for physical AI, told SA国际传媒 News at its inaugural Long Horizon Physical AI Summit in San Francisco earlier this month.

The compute and storage capabilities are being developed to support these systems, he noted, but building for the physical world takes time, especially compared to large language models trained on the vast troves of text on the internet. 鈥淧hysical AI is very different because there is no internet of physical AI data, and so the data first has to be collected, and you have to convert it from sensor tokens to action tokens,鈥� he said.

Data sets are orders of magnitude larger for physical AI, requiring multimodal data combining vision, tactile, force and torque. 鈥淪toring all that data, curating all that data, enriching all that data, and validating that all the data works for these systems is a very expensive operation,鈥� said Hansen.

We are still in the pre-training phase of physical AI, but will soon be entering the post-training phase, he noted.

More winners

Around 75 robotics companies on the unicorn board have raised funding in the past three聽 years. Of these, more than half 鈥� 45 鈥斅爅oined the board just this year. Among the most highly valued robotics startups, many competing companies are building data and models, as well as varied vertical use cases in heavy industry, manufacturing, logistics and household and use cases among others.

Robotics funding is surging and new unicorns are emerging, but the sector鈥檚 direction remains uncertain as companies race toward further breakthroughs over the next year.

鈥淭here are a lot of companies that are raising capital, and I think a lot of them are going to win, just because there’s so many different types of applications to the physical world,鈥� said Hansen.

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Related reading:

• Sector Snapshot: Robotics Startups On Fire As Venture Funding Surges To Record Numbers In 2026
• Embodied AI Fuels Record Robotics Funding In China As IPO Momentum Builds
• VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape

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, founder and former CEO of the AI startup , raised roughly $27 million after telling investors the company had customers it never had and revenue it never actually earned. When an investor later asked for proof, she sent a forged bank statement and fake purchase orders.

Last month, Raz . She faces a maximum of 20 years in prison.

Founders raising capital must sell investors on what their companies could become. But Raz鈥檚 case illustrates the personal stakes of misrepresenting a business’ achievements, and investors are increasingly willing to take those claims to court.

Plaintiffs filed just in the first half of 2026, nearly matching the 16 filed in all of 2025, according to . The distinction between future potential and present-day facts matters when describing customers, revenue and traction.

Where 鈥榩uffery鈥� crosses the line into securities fraud

The issue isn鈥檛 whether a pitch sounds optimistic or ambitious, but whether its factual claims mislead investors. Knowingly misrepresenting metrics that matter to an investor鈥檚 decision can expose that founder to personal liability.

Before presenting a pitch deck, founders should scrutinize four key areas where those distinctions can often become blurred:

• LOIs and pilots: Labeling soft, nonbinding letters of intent or unpaid test runs as 鈥渃ommitted recurring revenue鈥� or 鈥渟igned enterprise contracts.鈥�
• Customer counts: Presenting free pilot users or informal trial accounts as active, paying customers to inflate user growth.
• Traction and churn: Omitting significant customer losses in a way that makes growth claims misleading, or presenting one-time project fees as recurring revenue.
• Product capabilities: Presenting human-assisted workflows as fully autonomous AI systems, or claiming proprietary technology the company does not own.

Legally, the question boils down to whether a misstatement or omission is material. Would a reasonable investor consider the information important when deciding to invest?

Let鈥檚 be clear: An inaccurate figure doesn鈥檛 automatically establish securities fraud. But false claims about customer contracts, recurring revenue or retention can expose founders to securities-fraud claims.

An ambitious forecast is one thing; a false claim about existing customers or revenue is another. The latter can expose founders personally, with no guarantee that D&O insurance will cover the fallout.

Where D&O coverage may stop

Founders facing investor claims typically look to directors and officers, or D&O, insurance for protection. But defending a fraud allegation isn鈥檛 the same as covering established misconduct. The policy鈥檚 wording and applicable law determine where protection ends.

As part of a June 2025 court-approved settlement with Joonko, Raz agreed to abandon claims against the company鈥檚 D&O policies. Still, that doesn鈥檛 clarify whether an insurer had previously funded her legal defense or denied coverage.

Three policy provisions deserve attention:

• The fraud exclusion: D&O policies commonly exclude losses arising from deliberate fraud, criminal conduct or improper personal profit. Allegations alone generally do not trigger those exclusions.
• Final adjudication: Insurers may advance defense costs while a case proceeds. A guilty plea or conviction can trigger an exclusion, but the policy wording sets the exact threshold, including whether appeals must be exhausted.
• Repayment provision: Some policies allow insurers to recover advanced defense costs if the claim is ultimately excluded. Founders should check whether repayment is required and under what circumstances.

D&O insurance can help founders during investor disputes, but it鈥檚 not a guaranteed backstop for established fraud. Founders must understand when coverage can end and whether they鈥檒l have to pay back the legal fees their insurer advanced.

Practical steps for founders before presenting metrics

Before sharing a pitch deck, create an easily accessible guide for your team that spells out your math. Specify what counts as a paying customer, how pilots and LOIs are classified, and how recurring revenue and churn are calculated. Use those definitions consistently.

Team with finance and legal advisers to connect material claims to supporting records, including executed contracts, invoices, accounting data and customer-status reports. Keep projections separate from current results, and document the assumptions behind the forecasts.

Don鈥檛 confuse ARR with cash collected; they do not belong in the same category. Show clearly how the company calculates recurring revenue and distinguishes it from one-time fees, pipeline opportunities and contracted commitments that haven鈥檛 gone live yet. Remember that ARR and contracted ARR are distinct metrics, not to be used interchangeably.

Lastly, date the figures and update material changes before investors rely on them for decision-making. If the team can鈥檛 explain how they calculate a number or substantiate a customer claim, resolve the discrepancy before presenting your deck.

Investors might buy into your vision, but regulators will look at your math.

is the market-facing and client leader at Founder Shield, the innovation arm of , with eight years invested in the boutique broker and more than a decade in the insurance industry. Before Founder Shield, he worked at on the FINPRO team focusing on management liability in the large private and public space. A graduate of 鈥檚 risk management and insurance program, Jeziorski has focused his entire career helping clients navigate through an ever-changing risk environment.

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But that鈥檚 not all. As investment hit unprecedented levels in 2026, Golden State companies alone raised more than all U.S. startups in any prior year on record. Most of that stems from a handful of megarounds for San Francisco-based AI pioneers.

And keep in mind: This year still has a couple months left. Yet California companies have already pulled in an unparalleled $385 billion in seed- through growth-stage rounds. That鈥檚 more than double last year鈥檚 tally.

California鈥檚 share of U.S. startup funding also has been trending higher for the past three years. Much of this is due to San Francisco AI unicorns, but we鈥檙e also seeing strength in the broader Silicon Valley region as well as Southern California.

Where the money went

Flabbergasting financings for San Francisco-based and account for more than half of this year鈥檚 California statewide tally. But even without those, California companies 1 still secured a disproportionate share of the largest U.S. equity funding rounds.

To illustrate, we used SA国际传媒 data to put together a list of the 10 next-largest California funding rounds this year.

Some of these are familiar names and involve fairly mature companies. For instance , which launched inside in 2009 and spun out as an independent company 10 years ago, picked up $16 billion in February.

Another old-timer is , which picked up $5 billion in August. It鈥檚 now 13 years old and has raised over $30 billion in debt and equity funding to date.

Still, we鈥檙e also seeing younger companies secure megarounds. , the physical AI startup co-founded and led by and launched last year, pulled in $12 billion in new funding this spring. And AI model startup landed $5 billion in a -led round this summer.

Early-stage funding

That brings us to our next point. California鈥檚 startup funding windfall isn鈥檛 only about late-stage and pre-IPO companies bagging enormous rounds. Early- and seed-stage companies are also attracting big checks from investors.

Per SA国际传媒 data, California companies have secured nearly $62 billion in seed- and early-stage financings so far in 2026. That鈥檚 roughly 57% of all U.S. funding at these stages.

It鈥檚 also far above prior year tallies, as charted below, and appears to be a record funding level.

Don鈥檛 bet against California

If there鈥檚 a lesson to be learned in these numbers, it鈥檚 one that savvy startup investors ought to have learned a long time ago: Don鈥檛 bet against California.

Whatever the year, whatever the technology cycle, California, and Silicon Valley in particular, have a knack for being home to the leading companies of the era.

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

It was 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 SA国际传媒 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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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, SA国际传媒 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, SA国际传媒 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, SA国际传媒 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, SA国际传媒 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 SA国际传媒 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.

Related SA国际传媒 query:

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 SA国际传媒 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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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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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.

SA国际传媒 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.

SA国际传媒 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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• 鈥楴obody Wanted to Give A Former Principal Money鈥�: How An Educator Built An Edtech AI Startup With $63M From VCs

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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 SA国际传媒 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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All told, fintech startups raised $10.6 billion globally in the third quarter of 2026, down 28.4% from $14.9 billion in Q2 2026 and 21.4% from $13.5 billion in Q3 2025.

The pullback comes as investors draw sharper distinctions between companies attracting capital and those struggling to raise, according to partner .

鈥淭he market remains bifurcated 鈥� AI vs. not and market leaders vs. not,鈥� she said.

founder similarly described capital concentrating in fewer, larger rounds, with infrastructure, payments and stablecoin rails attracting investor interest, supported by greater regulatory clarity.

Deal count drops

Notably, deal count in Q3 was the lowest in the quarters covered by our analysis, dating back to the first quarter of 2018. Just 687 fintech funding rounds were recorded globally in the third quarter, compared with 789 in the previous quarter and 995 in the same period a year earlier. That represents declines of 12.9% quarter over quarter and 31% year over year.

Investors are demanding stronger evidence of performance before writing checks, noted Green. For Series A and B rounds, 鈥渆vidence counts for more than narrative,鈥� she said.

In emerging categories, actual usage and momentum can matter more than revenue multiples. Elsewhere, investors want efficient growth, a credible path to profitability and stronger evidence of lasting customer demand before committing to a large Series B, Green said. More startups are also combining debt and equity to limit dilution.

Pullback in large rounds

The quarter-over-quarter funding decline was largely driven by a pullback in large rounds. Funding from rounds of at least $100 million fell 43.3%, from $9.07 billion in Q2 to $5.15 billion in Q3, accounting for approximately 93% of the overall dollar decline. Funding from rounds below $100 million declined just 5.1% sequentially.

Compared with Q3 2025, funding from rounds of at least $100 million decreased 7%, while funding from smaller rounds fell 31.4%. Large transactions remained more resilient than smaller-round funding year over year.

That divide also extends to pricing. Green said a small group of companies with clear momentum command aggressive valuations, while others face greater discipline.

Bock pointed to and as similarly sized businesses with widely different valuations, attributing Ramp鈥檚 premium to investor enthusiasm for its AI momentum and growth trajectory.

Global mega-rounds

The United States remained the largest market for fintech funding last quarter. All told, U.S. startups attracted $5.38 billion, or 50.5% of the $10.64 billion global total in Q3.

The next-largest markets were Singapore with $1.28 billion (12.1%); Saudi Arabia with $626.6 million (5.9%); India with $452 million (4.2%); Brazil with $352 million (3.3%); and the United Kingdom with $337.8 million (3.2%). Singapore鈥檚 total was heavily concentrated in 鈥檚 $1.2 billion round. Despite the U.S. being the largest market for fintech funding, the quarter鈥檚 five largest funding rounds went to companies based outside the United States, ironically.

• Ant International, a Singapore-based provider of digital payments and financial services for businesses, raised the largest round in Q3 2026, a $1.2 billion Series A at a listed $11.2 billion post-money valuation. The data does not identify a lead investor but lists and as participants.
• , a financial app provider based in Riyadh, Saudi Arabia, raised a $329.5 million Series A at a $1.85 billion post-money valuation. , and participated in the financing.
• Hong Kong-based , which provides financing to e-commerce businesses, secured a $300 million Series C led by at an estimated $1.36 billion post-money valuation.
• Jakarta, Indonesia-based , an online investing platform, raised a $270 million Series C led by at a $1.35 billion post-money valuation.
• Rounding out the top five, Riyadh-based , a digital financial services provider, raised $233 million in a Series F round led by at a $6.5 billion post-money valuation.

Late-stage fintech funding fell to $4.8 billion, down 40.4% quarter over quarter and 17.3% year over year, in Q3, per SA国际传媒 data.

One startup lifts early-stage funding

Early-stage fintech funding was comparatively resilient at $4.23 billion, up 11% quarter over quarter and down just 0.6% year over year. However, Ant International鈥檚 $1.2 billion Series A accounted for 28.4% of that total. Excluding that transaction from Q3 2026 alone, early-stage funding would have fallen 20.4% sequentially and 28.8% year over year.

Early-stage deal count also dropped, from 173 in Q2 to 165 in Q3, suggesting that the increase in funding dollars did not reflect a broad expansion in activity.

Fundraising expectations are rising as AI makes products faster to build and launch, Bock said. Early revenue carries less weight than sustained growth. While $1 million in annual recurring revenue once served as a common Series A benchmark, investors now want greater traction and stronger momentum, including revenue that has at least tripled over six months.

鈥淓fficiency still matters, but growth is back in focus,鈥� she said, adding that expectations of larger potential outcomes are pushing valuations higher.

Meanwhile, angel and seed funding totaled $1.12 billion, down 19.7% from Q2 and 35.2% from Q3 2025.

AI reshapes investor priorities

AI is primarily redirecting investment within fintech, although agents and payments are attracting some new capital, Green said. Companies without an obvious AI offering can still raise funding, but investors increasingly expect evidence that the technology improves their economics, speed or ability to reach customers.

鈥淭he product has to carry that story,鈥� she said.

Bock said the distinction between companies adding AI to existing processes and those building their products around it matters. The strongest AI products deliver substantial improvements that drive rapid adoption and customers鈥� willingness to pay.

鈥淲hen investors say a company is 鈥楢I,鈥� they usually mean its growth rate is high, so growth has become the proxy,鈥� she said.

QED鈥檚 recent investments span customer support, analytics, financial advice, banking infrastructure and fraud prevention. Bock sees a durable opportunity for startups that handle the specialized integrations and accuracy requirements financial services demands 鈥� work she argues frontier AI labs are unlikely to prioritize.

Green, meanwhile, said payments initiated by AI agents have moved 鈥渇rom 鈥榠f鈥� to 鈥榳hen鈥欌�� in her conversations, as major networks and processors introduced protocols to support them. She is also seeing more fintech companies seek ownership of regulated infrastructure rather than rely on outside providers.

But she sees potential overfunding in stablecoin infrastructure, where many startups are pursuing the same customers, and in tools that let AI agents complete purchases for buyers, where, in her view, startup formation has outpaced transaction volume.

Green sees more opportunity in the systems that make agent-driven commerce trustworthy, including fraud prevention, identity verification, agent permissions and tools that help merchants accept those purchases.

鈥淚 suspect the market is treating the rails as the moat,鈥� she said. In her view, payment infrastructure is likely to become commoditized, leaving more value with the companies that own customer relationships and earn their trust.

Bock鈥檚 broader thesis is that AI could reshape financial services by sharply reducing the costs of underwriting, compliance and customer service while enabling more personalized financial products. QED is particularly interested in advice and wealth management, underwriting, insurance claims, risk and compliance, and finance back-office operations, alongside stablecoins and tokenization.

鈥淭he financial services operating system will be rebuilt end-to-end,鈥� she said.

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Methodology

The data contained in this report comes directly from SA国际传媒, and is based on reported data. Data is as of Oct. 5, 2026.

Note that data lags are most pronounced at the earliest stages of venture activity, with seed funding amounts increasing significantly after the end of a quarter/year.

Please note that all funding values are given in U.S. dollars unless otherwise noted.

SA国际传媒 converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to SA国际传媒 long after the event was announced, foreign currency transactions are converted at the historic spot price.

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• Fintech Funding Surges 23% In H1 2026 As Investors Concentrate Their Bets On AI And Financial Infrastructure
• SA国际传媒 Data: Q3 2026 Posted A Record Count Of Billion-Dollar Rounds As The Global AI Race Heats Up
• LatAm Startup Funding Jumps 26% In Q3 As VCs Begin 鈥楻ediscovering鈥� The Region
• North America鈥檚 Startup Funding Falls In Q3 As AI Giants Eye The Public Markets

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