Companies spanning construction and robotics have raised about $1.6 billion this year through Oct. 29 —��more than 4x the $389 million recorded in all of 2025 — according to Crunchbase data. The surge was driven by three companies that raised the majority of the funding.

The broad trend: For contractors struggling to hire, the appeal of construction-oriented tech offerings is straightforward: They need to get work done and there aren’t enough skilled workers to do it. The AI-driven data center building boom is only adding to that competition for workers. Labor shortages remain the biggest cause of project delays, according to by the and .

Some of the technology is already at work on job sites.

San Francisco-based , this year’s largest funding recipient in the sector, said in August that excavators equipped with its autonomous systems were operating without human operators on commercial projects in Texas and Nevada.

The numbers: With roughly two months left in the year, funding has already surpassed the previous five years combined.

Companies spanning construction and robotics raised about $1.6 billion through Oct. 29, 3x the previous high of $506 million in 2021, according to our dataset that goes back to 2018. This year’s total is also more than 3x the 2021 high of $506 million.

So far, the funding surge reflects much bigger bets rather than more deals. The sector has recorded 30 rounds this year, compared with 28 in all of 2025. Bedrock alone accounts for $870 million across two deals — more than half of this year’s total. This underscores just how much a few large financings are impacting the recovery.

Austin-based , which builds homes using 3D printing, also raised two rounds in 2026. It has raised nearly $800 million since its 2017 inception. A company spokesperson told Crunchbase News that the company has taken its technology from an experiment to “an in-the-field system that can build faster, with fewer people, less waste, greater design freedom and lower cost.”

To date, the company has built more than 270 homes and structures across five states and two countries. It says that it’s on pace to grow revenue by 4x this year.

“Early on, the question was whether a robotic construction system could build a good home,” the spokesperson wrote via email. “Today, the question is whether a company can deliver at volume, across geographies and project types, and improve its economics with each build. That’s only answerable with time in the field.”

Notable deals

Bedrock Robotics was the largest funding recipient in 2026, impressively raising $870 million across two rounds. The 2-year-old company, which converts existing heavy construction equipment into autonomous machines, announced a $600 million Series C in October led by and. That round, which valued Bedrock at $4.1 billion, followed a $270 million Series B in February led by CapitalG and.

Austin-based construction technology company Icon also raised two rounds, bringing its listed 2026 funding to $265 million. Its largest financing was a $200 million Series D in October led by and, following a $65 million Series C in March.

Another $200 million round went to, based in Zurich, Switzerland, which develops technology to retrofit construction machinery for autonomous earthmoving and excavation. led the company’s Series A in August, which valued the startup at $1 billion post-money.

Beyond those large financings, investors backed companies automating homebuilding and specific construction tasks.

• , based in Andover, Massachusetts, uses AI, robotics and automation to build homes. It raised $40 million in an August venture round led by.
• Amsterdam, Netherlands-based, developer of robots for on-site masonry and other building workflows, secured a $32 million Series B in July led by.
• Meanwhile Las Vegas-based raised a $30 million Series B in May led by. Its autonomous systems serve construction, exhibitions, industrial fit-out and facility operations.
• Belmont, California-based, which develops AI-powered robotic systems for infrastructure construction, followed with a $26 million Series A in July led by.

Sizable seed rounds also made the list.

London-based, which uses AI and robotics to design, manufacture and assemble buildings, raised $25 million in April led by., based in San Francisco, developing a general-purpose construction robot, secured $20 million in October, led by and.

Exits

Acquisitions have dominated exit activity in construction robotics and related jobsite automation this year.

One of the largest disclosed deals was Carpinteria, California-based’s acquisition of, a San Francisco-based platform that combines imagery from drones, ground robots and cameras to track construction progress and site conditions. Procore announced the approximately and closed it in September.

Other buyers have focused on automating the work itself. In January, subsidiary developed by, a San Francisco-based startup known for robotic drywall finishing.

Heavy-equipment autonomy also attracted buyers. In March, of Palo Alto, California-based , whose technology lets operators remotely oversee heavy machinery.

The following month, San Francisco-based confirmed that, ’s physical AI company, had completed its acquisition for an undisclosed price. Pronto’s autonomous haulage technology now reportedly underpins Atoms’ mining division.

Public offerings have been less prominent, with no known IPOs this year by companies focused specifically on construction robotics. The broader construction technology market did produce a sizable debut: Columbia, Missouri-based, an equipment rental and connected-jobsite technology company, raised approximately $747 million in gross proceeds in its January IPO, selling 30.5 million shares at $24.50 each.

For all the variety in the companies attracting funding, this year’s surge has so far largely rested on three: Bedrock, Icon and Gravis, which together accounted for more than $1.3 billion, or about 83%, of the roughly $1.6 billion raised. Investors are placing unusually large bets on a small group of companies, even as acquisitions give others a route into larger businesses already serving construction customers.

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

This week featured a robust lineup of large rounds, led by manufacturing startup , which closed on $2.1 billion, and predictions marketplace , which picked up another $1 billion. Other sectors in which startups drew significant rounds included construction automation, biotech, batteries and enterprise AI.

1. , $2.1B, manufacturing: Re:Build, a manufacturing startup that partners with industrial companies and innovators from the product design phase to large-scale production, secured $2.1 billion in new funding. and led the financing for the 6-year-old, Framingham, Massachusetts-based company.

2. , $1B, predictions marketplace: Predictions marketplace Kalshi announced a $1 billion Series G financing led by and . The round set a $40 billion post-money valuation for New York-based Kalshi, which says its business’ annualized trading volume now exceeds $1 trillion.

3. , $600M, construction tech: San Francisco-based Bedrock Robotics,�� a developer of autonomous systems for heavy equipment used in construction, raised $600 million in Series C funding led by and . The round brings Bedrock’s total funding to nearly $1 billion and sets a post-money valuation of $4.1 billion.

4. , $350M, biotech: Dren Bio, a developer of antibody therapeutics for cancer and autoimmune diseases, closed on $350 million in a new round led by and . The funding will support advanced trials for the San Carlos, California-based company’s blood cancer therapies.

5. , $169M, battery tech: Woodinville, Washington-based Group 14 Technologies, a manufacturer and supplier of advanced silicon battery materials, picked up $169 million in Series D extension funding. The company also announced plans to expand into Europe with a production facility in Sagunto, Spain.

6. (tied) , $150M, defense tech: Firehawk Aerospace, a developer of propulsion technology, said it secured over $150 million in Series D funding at a valuation of more than $1.4 billion. and led the financing for the 7-year-old, Dallas-based company.

6. (tied) , $150M, enterprise AI: Serval, a startup focused on coding agents for enterprise automation, raised $150 million in Series C funding at a $3 billion valuation. led the round for the 2-year-old, San Francisco-based company.

8. (tied) , $100M, AI infrastructure: AI cloud provider General Compute picked up $100 million in Series A funding led by and . The company is building out its infrastructure using inference chips from rivals , and .

8. (tied) , $100M, autonomous factories: Emeryville, California-based Foundry Robotics, a startup focused on building and operating autonomous factories, closed on $100 million in a Series A funding round led by .

8(tied) , $100M, data analytics: Hex Technologies, a startup focused on building an AI-native platform for data analytics, secured $100 million in new financing. and led the round, which set a $1.2 billion valuation for the San Francisco-based company.

Methodology

We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the period of Oct. 24-30, 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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A landlord in California has 21 days after a resident moves out to return the security deposit or send an itemized list of deductions. Miss it in bad faith, and the penalty can reach twice the deposit. That deadline doesn’t care whether the letter was supposed to come from a person or a piece of software. Either way, someone will answer for it.

I think about this a lot. At my company, , we now run 16 AI agents across more than 3 million rental units. When we built them, the technical questions turned out to be the easier ones. The harder question, for each agent, was actually who it reports to.

Most companies are about to face that same question, and few are ready for it. survey found that 23% of companies use agentic AI at least moderately today, and 74% expect to within two years.

, meanwhile, has that more than 40% of agentic AI projects will be canceled by the end of 2027.

Those numbers make a case for running agents the way you would run staff in a regulated job, with written rules, a defined scope, a manager and a regular review.

Here is what has worked for us, and what I would advise any decision-maker to focus on when it comes to scaling.

Start with work that already has a rulebook

The best early candidates for agents are tasks where the rules already exist somewhere outside anyone’s head: a statute, a contract clause, an insurance requirement in a vendor agreement.

Take vendor certificates of insurance. Across the industry, property teams spend roughly three hours per property every week chasing those documents by email. The work is tedious, however at the same time, it is completely specified.

Assignments like these give decision-makers two advantages. First, the agent has something objective to check its output against, and second, your team can audit the result without debating judgment calls.

‘s found that the small group of companies seeing significant bottom-line returns from AI, about 6% of respondents, were nearly 3x as likely to have fundamentally redesigned their workflows.

In my experience, that redesign starts with writing the rule down. If a new hire couldn’t follow your description of a decision on the person’s first day, an agent won’t follow it either.

Set autonomy by workflow

Tech execs I speak with often ask whether an AI system is autonomous, as if that were a fixed property of the product. In practice, the same agent should run with different levels of freedom depending on what it touches.

At Get Covered, we configure each agent on different levels. For instance, a reminder to a vendor whose certificate expires next month can go out automatically. A response to a resident who has requested an emotional support animal never does.

A useful exercise for any leadership team is to list every workflow an agent will touch and ask one question about each: If this action is wrong, how long until we find out, and what will it have cost by then? Quick detection and low cost earn an agent more room.

Give every agent an owner with a name

Earlier in my career I learned that every automated credit decision eventually had to be explained to a customer, a regulator, or both. The model did it was never an acceptable answer.

That still holds, except agents now act more often and across more systems than any underwriting model did.

So every agent at our company has a person ultimately responsible, the same way a team or a budget does. That person reviews its error log, decides when to widen or narrow its autonomy, and answers for it.

Ownership also heads off a quieter problem. When several teams launch agents independently, nobody sees where those agents contradict one other.

Where the constraint actually sits

In today’s marketplace, it’s inevitable that the models will keep improving. A company’s clarity about who decides what will not improve on its own. Gartner’s cancellation forecast reflects that: Most agent projects that fail will do so because nobody defined the value or controlled the risk, and both of those are management problems.

For decision-makers heading into 2027, my advice is to treat each agent the way you would treat a new employee in a regulated role. Write down its rules, limit its scope, and assign it a manager who checks its work and discusses its performance.

Companies that do this will be able to give agents more responsibility over time, because they will have the evidence to justify it. Businesses that skip it will spend 2027 explaining why their software did something nobody approved.

is chief operating officer at , an insurance technology company that provides cost-effective property coverage with just a few clicks. He has more than 25 years of executive leadership experience in the financial and insurance industries.

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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, Crunchbase data shows.

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£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 £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 — 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, Crunchbase 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, Crunchbase 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 — and often very expensive — 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, Crunchbase 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’s 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’s 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’s 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 Crunchbase 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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This is a weekly feature that runs down the week’s Top 10 announced funding rounds in the U.S. Check out last week’s biggest funding deal roundup here.

Startup investors aren’t exactly hitting pause, but they did scale back on giant rounds this past week, with just two startup financings in the multiple hundreds of millions. The biggest round went to AI infrastructure provider , which picked up $750 million, followed by construction automation unicorn , which secured $200 million. Other sectors that attracted larger rounds this past week included biotech, insurance and AI.

1. , $750M, AI infrastructure: Modal Labs, a provider of cloud infrastructure for AI inference and other applications, secured $750 million in a Series D funding round led by at a $15.75 billion valuation. The New York-headquartered company said it recently exceeded an annual revenue run rate of $1 billion.

2. , $200M, construction tech: Austin-based Icon, a developer of robotic construction systems known for its 3D-printed homes, closed on $200 million in Series D funding led by�� and . The company says it is on pace for 4x revenue growth in 2026.

3. , $135M, RNA therapies: Boston-based Soufflé Therapeutics, a developer of RNA therapies, raised $135 million in Series B funding led by as part of a large syndicate of investors.

4. , $103M, gene therapies: Menlo Park, California-based Estuary Biotherapeutics, a developer of genetic medicines for patients with serious kidney diseases, launched with $103 million in financing. The funding includes $93 million in a Series A round led by as well as $10 million in previously undisclosed seed capital.

5. , $100M, AI tools: Labelbox, a developer of technology to train and evaluate AI models and to help deploy agents that improve with experience, picked up $100 million in Series E funding. led the financing for the 8-year-old, San Francisco-based company.

6. , $90M, health AI: Redwood City, California-based Suki, a provider of AI tools that turn clinical conversations into intelligence for documentation, coding and other healthcare workflows, announced a $90 million Series E financing from backers including and .

7. , $82M, caregiver platform: Abby Care, a platform for family members to become certified, paid caregivers and deliver care at home, closed on $82 million in Series C funding, per a . led the financing for the 5-year-old, San Francisco-based startup.

8. , $70M, insurance: Stand, a provider of insurance for homes in areas at higher risk of natural disasters, secured $70 million in Series C funding at a $1 billion valuation. led the financing for the 3-year-old, San Francisco-based company.

9. , $65M, semiconductor systems: Mueon Corp., a semiconductor systems company building a modular platform for AI and hyperscale computing, completed a $65 million Series A funding round. led the financing for the Portland, Oregon-based company.

10. , $60M, AI spend management: New York-based North.cloud, provider of an infrastructure spend intelligence and automation platform, raised $60 million in a Series B round led by .

Methodology

We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the period of Oct. 10-16, 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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Twenty-one of last month’s new unicorns are U.S. headquartered. Six new unicorns joined the board from China and three from the U.K., while Canada, Saudi Arabia, Japan, Israel, Italy, Finland, Germany and Bulgaria each added one new unicorn.

Through September, 311 companies have joined the board so far this year, putting 2026 on track to be the second-highest year for new unicorn creation, following only the venture peak year of 2021, when counts topped 600.

Several companies already on the board stand out for significant valuation increases via new raises last month. They include San Francisco-based , developer of AI for software engineering, which saw its value climb to $48 billion, up from $26 billion only four months earlier. Data center provider was valued at $31 billion last month, up from $10 billion a year ago. And Paris-based frontier lab was valued at $24.4 billion in its more recent funding, up from $13.7 billion a year ago.

New unicorns in September

Here are September’s 38 new unicorn companies:

Semiconductors

• , an Austin-based semiconductor manufacturing startup, closed a $500 million Series A at a $3.7 billion valuation, led by . The 3-year-old company builds compact chip factories and develops their manufacturing equipment internally, aiming to make semiconductor production more accessible.
• , an Irvine, California-based semiconductor company, raised a $275 million Series C, valuing it above $3 billion and bringing total funding to $415 million. , and led the round. The 2-year-old company develops digital signal processing chips for high-speed optical connectivity in AI infrastructure.
• , a Hangzhou-based AI chip developer, announced a $149 million Series C. Its IoT inference chips combine memory and computing to reduce power consumption and data movement. The 5-year-old company was valued at $1 billion.
• , a Beijing-based AI chip company, raised $298 million at a valuation of $2.2 billion. The 4-year-old company develops AI computing chips using the open RISC-V architecture.

• , a San Jose, California-based developer of specialized AI chips and software, raised a $150 million Series C at a $1.45 billion valuation, co-led by and , bringing total funding to $500 million. The proceeds for the 8-year-old company will support its software platform and next-generation hardware for robots, vehicles and drones.

Financial services

• , a Texas-based prediction-market platform, announced a strategic partnership with , which will take an equity stake following OG’s separation from . The less than 1-year-old company was valued at $5 billion. The investment amount was not disclosed; the partnership will route Robinhood event-contract trading through OG’s regulated exchange and clearing infrastructure.
• Saudi Arabia-based , a digital payments company, raised a $329.5 million Series A at a $1.85 billion valuation. Investors included , and . The 3-year-old company has processed more than $117 billion in 2 years.
• , a Nevada-based financial software company, raised a $250 million Series E at a $1.43 billion valuation led by founder and chairman . The 8-year-old company provides an AI-based investment management platform for asset and wealth managers.
• , a Miami-based remittance platform for Latino immigrants operating through , secured $200 million in financing: an $87 million Series C equity round led by valued at $1.4 billion and $113 million in debt from ’s Customer Value Fund. The 5-year-old company plans to expand to lending and add to its Latin American reach.

Space technology

• Munich-based space company raised a $450 million Series C, co-led by , and at a valuation of $2 billion. The financing for the 5-year-old company will advance its reusable Nyx spacecraft for an International Space Station mission and support development of its Storm rocket engine.
• , a Seattle-based satellite connectivity company, raised a $200 million Series C at a $1.6 billion valuation, led by , bringing total funding to approximately $300 million. The 5-year-old company enables standard Bluetooth devices to connect directly to satellites for tracking and sensor-data applications.
• Oxfordshire, U.K-based , a satellite company, secured $346 million, co-led by and, valued at $1 billion. The 11-year-old company manufactures satellites and provides infrastructure, connectivity, Earth observation and intelligence services.
• Bulgaria-based , a satellite company, raised $205 million in a round co-led by and , reaching a $1 billion valuation. The 11-year-old company has raised financing that will expand standardized spacecraft production, including new manufacturing capacity in the U.S. and Europe.

Biotechnology and medical devices

• , a Framingham, Massachusetts-based developer of treatments for Alzheimer’s disease and other neurodegenerative disorders, raised around $79 million. The �� 13-year-old company was valued at $1.9 billion. Investors were not disclosed.
• Shanghai-based , an AI drug discovery company, raised $290 million at a $1.5 billion valuation following its spinoff from . , , and co-led the round. Anew develops tools for biomolecular prediction and drug discovery; ByteDance retained a 56% stake.
• , a New York-based brain-computer interface developer, closed a $250 million Series D, bringing total funding to $430 million and reaching a valuation of approximately $1 billion. and led the round. The 5-year-old company is building a brain–computer interface that is minimally invasive and removable.
• Toronto-based , a biotechnology company, raised $153 million at a $1 billion valuation led by the , with participation from and other investors. The 3-year-old company uses AI to identify abandoned drug candidates that could succeed under different treatment approaches. The financing will expand clinical development and support additional drug acquisitions.

AI and internet infrastructure

• , a San Francisco-based AI cloud infrastructure company, raised a $300 million Series B at a $3 billion valuation, led by . The 3-year-old company distributes AI inference workloads across different types of chips to improve performance and efficiency. It announced in March that its customers include one of the leading frontier labs.
• Helsinki-based , an AI cloud company, raised a $189 million Series B led by . The financing valued the 6-year-old company above $1 billion with total funding over $450 million. The company manages physical data centers along with its cloud platform.
• London-based , an internet infrastructure company supporting content delivery for large streaming, gaming and software companies, raised $570 million in its first external financing led by . The 15-year-old company was valued at $1.9 billion.

AI training

• , a San Francisco-based AI training-data company, raised more than $100 million led by at a $4 billion valuation, an eightfold increase from its valuation in September 2025. The 4-year-old company recruits and vets specialists who create training and evaluation data for AI developers. that participants included two frontier AI laboratories and two co-founders.
• , a San Francisco-based provider of expert-created AI training data, reached a $3.2 billion valuation in September, earlier, according to . The funding amount and lead investor were not publicly disclosed. The 1-year-old company’s datasets capture professional reasoning in fields such as software engineering, law and finance. AfterQuery was part of the Winter 2025 batch.

Robotics and autonomous driving

• , the Shanghai-based operator of Hello’s robotaxi business, raised about $100 million at a valuation close to $3 billion. fund led the round. The financing to the 1-year-old company will support a 10,000-GPU computing cluster, AI model development and commercialization of Level 4 autonomous driving.
• , a Sunnyvale, California-based robotics perception company, raised a $165 million Series C at a $1.6 billion valuation, led by . Founded by former and engineers, the 5-year-old company combines custom chips, sensors and spatial software to help robots perceive their surroundings. The financing will accelerate deployments across logistics, manufacturing and inspection applications.

Health insurance

• , a San Francisco-based health benefits company, announced a $200 million Series C at a $2.7 billion valuation. led the financing. It also includes $400 million in secondary financing for existing investors. The 7-year-old company provides a benefits platform using AI to make healthcare more affordable for small and midsize employers. The company services 5,000 businesses.
• , a San Francisco-based health benefits platform, raised a $108 million Series C at a $1 billion valuation. Investors included , , and . The 5-year-old company helps employers provide individualized health budgets for employees to select their own medical plans.

Energy and EV infrastructure

• China-based , a battery-swapping and charging company, agreed to a strategic transaction that gives a 30% stake. Geely will contribute its Yiyi battery-swapping operation and a $95 million investment. The 9-year-old company was valued around $2.4 billion.
• , an Austin-based developer of off-grid power systems for AI data centers, raised a $120 million Series A at a $1 billion valuation, led by . The less than 1-year-old company builds modular systems for renewable energy and battery systems as data-center operators seek access to electricity.

AI applications

• raised $150 million led by , at a valuation just over $2 billion. The 3-year-old company, headquartered in London with offices in Los Angeles and Mumbai, built an enterprise audiovisual content platform for media, advertising, sports and healthcare customers. The financing will support research, commercial expansion and a larger Silicon Valley presence.
• , a New York-based AI ranking and personalization company, raised a $90 million Series B at a $1.44 billion valuation, led by . The 2-year-old company’s software helps enterprises respond to customer behavior in real time, improving the relevance of recommendations.

Cybersecurity

• Rome-based , a cybersecurity company protecting connected devices and autonomous machines, raised $270 million at a $1.7 billion valuation, led by . The financing to the 8-year-old company will support U.S. and Asia-Pacific expansion and development of AI security models.
• Tel Aviv-based , a consumer cybersecurity company, raised $40 million at a $1.1 billion valuation, bringing total funding to $167 million. co-founder joined existing investors including and , among others. The 8-year-old company protects consumers against phishing, scams, malicious websites and AI-generated fraud.

AI models

• Beijing-based , a language-model startup, raised $120 million reaching a valuation of $1.4 billion. Investors include , , and . The less than 1-year-old company raised $400 million across three rounds so far this year, before releasing its first public model.
• , a San Francisco-based developer of open-weight foundation models, announced a $150 million Series B led by , and , valuing the company above $1 billion. The financing will support the 3-year-old company’s Trinity models and products for customizing and operating them.

Manufacturing and construction software

• , an AI company for manufacturing with dual headquarters in Tokyo and Chicago, raised a $114 million Series D at a $1.2 billion valuation, bringing total funding to $234 million. Investors included and . The 9-year-old company’s platform organizes design and procurement and production; the financing will deepen its technology and accelerate North American expansion.
• , a Georgia-based construction software company, secured a growth investment of $250 million led by , valuing it above $1 billion. The 17-year-old company manages construction projects, with the investment supporting its AI capability build out and commercial growth.

Defense technology

• , a Washington, D.C.-based defense company, emerged from stealth with $250 million raised across three rounds at a valuation above $1 billion. The 2-year-old company develops long-range cruise missiles with manufacturing in Texas, Germany and Israel. Backers who have led across three rounds include , , , 1, and .

Food

• , the New York-based parent of consumer food brands David Protein and HallPass, raised a $250 million Series B at a $2.25 billion valuation. and co-led the round. The financing to the 2-year-old company will expand retail distribution, develop new products and support additional brands.

Related Crunchbase unicorn lists:

• (1,901)
• (666)
• (321)
• (198)
• (119)
• (102)
• (998)
• (547)
• (258)
• (39)
• (489)

Related reading:

• Crunchbase Data: Q3 2026 Posted A Record Count Of Billion-Dollar Rounds As The Global AI Race Heats Up
• 29 Companies Joined The ������Ƶ In August, Led By AI Software And Semiconductors

Methodology

The Crunchbase ������Ƶ is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on Crunchbase data. New companies are as they reach the $1 billion valuation mark as part of a funding round.

The unicorn board does not reflect internal company valuations — such as those set via a 409a process for employee stock options — as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter.

Funding to unicorn companies includes all private financings to companies that are tagged as unicorns, as well as those that have since graduated to .

Exits analyzed here only include the first time a company exits.

Please note that all funding values are given in U.S. dollars unless otherwise noted. Crunchbase 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 Crunchbase long after the event was announced, foreign currency transactions are converted at the historic spot price.

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Founded in 2016, Los Angeles-based Fifth Wall has grown into one of the largest investment firms focused on real estate technology, with roughly $3 billion in commitments and capital under management. Its portfolio includes , , and .

Notable exits include , which went public in 2024, and flexible workspace provider , which real estate giant of in January 2025 at a valuation of more than $800 million.

Major real estate and hospitality companies — including CBRE, , and — are among its investors, giving the startups it backs a direct connection to potential customers.

Wallace, who serves as CEO and chief investment officer, brings experience on both sides of that relationship. He worked in real estate finance at and before co-founding workforce analytics startup , which was acquired by in 2014, and ridesharing company .

As AI makes certain products easier to replicate, Wallace sees growing potential in startups that use technology to own and operate real estate themselves.

In a recent interview with Crunchbase News, we discussed that shift, why proptech funding totals may overlook some of the sector’s biggest investments, what it all means for founders, and the housing problems he believes still need solving.

This interview has been lightly edited for brevity and clarity.

Crunchbase News: Our recent proptech funding analysis showed capital on pace to roughly match or slightly exceed last year, but across fewer deals. What does that tell you about the market?

Brendan Wallace: My sense is that statistic might belie the actual amount of funding flowing in, because what was previously characterized as proptech in the narrowest sense is probably representing a smaller share of where proptech investors are deploying capital.

There are increasingly horizontal solutions that are just as good, if not better, than many of the vertical solutions we’re seeing. We recently made a very big investment out of our growth fund into , the visualization software company. Most people reflexively think of that as being for movies or rendering scenes. But we saw real estate agents and commercial leasing brokers using it to represent spaces.

For an unbuilt home, you could feed it blueprints, have it build an entire home, put your family inside and walk through it in a full scene. That had surpassed the industry-specific solutions for space visualization that would have been narrowly defined as proptech.

Another category is investment flowing into full-stack businesses: real estate companies themselves. We just invested $135 million into , and I would be surprised if that fell under the definition of proptech.

Those are probably the two categories that might not be captured: horizontal software, with Higgsfield as an example, and businesses that reimagine physical space based on autonomy, AI or robotics. They’re actually building a physical thing. They’re building a real estate company, directly or indirectly.

How much is AI driving that shift toward software that serves multiple industries?

Wallace: It’s a big part of it. Without something profoundly better that you can’t replicate on your own, there’s less justification for buying outside software. There still is a justification, to be clear. There’s just less of one.

No real estate company can build the kind of visualization software Higgsfield has. I don’t believe they ever will. But a tenant engagement app they might have previously purchased and rolled out to their assets, they can now replicate over a long weekend.

The imperative to buy these third-party solutions has been reduced, and more value has been placed on the best horizontal solutions, whether that’s a foundation model or a specific use case like sound, audio or visual.

I don’t know the statistic, but it would be interesting to know how much real estate companies are spending on foundation models versus proptech applications. If I had to guess, we’re probably close to, or at, an inflection point where it’s passing it.

No one would characterize as a proptech company, but it might be taking a significant amount of the upstream proptech dollars, and an even larger share downstream once you go through the application layer.

Has AI reset the bar for what makes a venture-scale proptech software company? What does a startup need beyond adding AI to existing workflows?

Wallace: We’re still figuring that out, but I think it has to be something that can’t be replicated. Anything where the intellectual property moat is not extremely defensible is very challenged, and I think it will be for some time.

Simple application point solutions are becoming less financeable by venture investors because the capacity of one of the large labs to build that as an interface, or of a company to build it on its own, has caught up so dramatically.

The flip side is that bespoke AI development labs are now building solutions for the real estate industry. We backed one called , which is kind of like a for real estate. Antares works with a number of our strategic limited partners. They go in-house, identify a problem they think can be solved with an AI-based solution, build the intellectual property specifically for the organization, and leave it with them.

It’s more of a fee-for-development model than an intellectual property licensing model. You’re seeing that across industries, and you’re absolutely seeing it in real estate.

In some sectors, founders argue that domain expertise gives specialized AI companies an advantage over broad models. How durable do you think that advantage is?

Wallace: I’m a little more optimistic about foundation models’ and general intelligence’s capacity to adapt rapidly to specific use cases.

I wouldn’t argue that specific legal expertise, for example, is not valuable. But I think that moat collapses over time. You might be on a treadmill.

Think about what ChatGPT looked like in 2023 versus what Claude looks like today. At a basic level, it’s the same thing, but they’re totally different things. The sophistication, memory, capacity to be personalized, and ability to build off natural language instructions are unlike anything I think anyone could have anticipated in 2023.

What will that look like in 2029? I think what you’re saying is true today. It’s less true than it was, and it might become even less true in three years.

Is construction technology still an area of focus for Fifth Wall?

Wallace: It is, but it’s less of a focus today. When we look at our performance, it has generally favored two things: pure software businesses, whether marketplaces, consumer software or enterprise software, and full-stack businesses.

Anytime you get into physically doing things, the risk profile changes pretty profoundly. I don’t think it should be surprising that construction tech broadly is probably the biggest graveyard in all of venture. Maybe there’s a bigger category, but if there is, it’s not obvious to me.

Why do you think construction tech continues to attract significant funding?

Wallace: Because it’s still a massive market and a huge problem. If someone can bring greater efficiency to construction, you’re talking about one of the largest markets on earth. It’s such a massive prize that the largest funds are going to focus on it.

For founders raising seed or Series A funding, what opportunities would you encourage them to pursue that might not have been fundable a few years ago?

Wallace: You can actually build new real estate companies. That hasn’t really been conceptualized as being within a venture mandate, but it’s absolutely something we’d like to see.

For example, we have a better approach to buying multifamily assets, stripping out costs, and running more of the processes, operations and management with AI. That’s something I think is eminently fundable today by Fifth Wall and many venture funds. I don’t think that would have been the case five years ago.

I’d encourage founders to look at building full-stack, capital-R real estate businesses.

The second opportunity is around data. Real estate is characterized by extremely opaque data in a way that’s different from most large financial markets. Nothing that’s happened in proptech in the past 10 years has fundamentally changed that.

Where can generative AI provide signal through that opaque data to make capital allocation decisions? Real estate is generally a cash-flowing asset. We don’t necessarily think of it as comparable to bonds or stocks, but it is, and it should be.

Can you derive signal by looking at more data or different data, or use generative AI to predict what kind of real estate will perform better? The quantitative model that has existed for securities for a very long time now has the potential to affect real estate, because we have computational capacity that wasn’t previously possible.

Are there opportunities you’re interested in that have less to do with AI?

Wallace: We’re intrigued by the intersection of land, real estate and the financial instruments around them. To make most of what we describe as real estate, you usually have to build something on land. Land is an input to almost all real estate, but many real estate owners don’t want to own it or take on that risk profile.

Is there a way to decouple having to own land from owning and operating a building? That’s less about AI and more about financial engineering. There are new financial solutions emerging in that category that interest us.

How could AI, robotics and autonomy change the kinds of real estate businesses founders build?

Wallace: I’m interested in entrepreneurs who can conceptualize real estate through a sociological lens: New catalysts are changing how humans use space compared with the past.

That’s happened for a long time. E-commerce built the modern industrial real estate industry. Cloud computing, the internet and now AI built the data center industry. Cell towers were born out of mobile phones and smartphone technology. Genome sequencing helped create the lab-space real estate asset class.

I think we’re on the precipice of three new catalysts that will spawn entirely new real estate asset classes: AI, robotics and autonomy. In many ways, they’re entangled.

Do you expect fewer dedicated proptech venture firms? What would that mean for founders?

Wallace: I do think you’re going to see fewer vertical funds, in the narrowest sense of where we started this conversation. Dedicated venture capital funds investing in consumer and enterprise software focused on an industry —– I think you’ll see less of that, and funding will probably continue to go down in that category.

The inverse is venture capital funding real estate businesses themselves. I think that’s about to explode.

If you were starting a company in this sector today, what problem would you most want to solve?

Wallace: Housing drives everything. Housing costs drive our economy, and the U.S. is grappling with a very real housing affordability crisis. It’s unclear how to solve it.

There are multiple approaches. One is building homes better, faster and cheaper — that’s construction tech. Another is financing homes at a lower cost. How do you bring efficiency to the housing finance market?

That’s a place where we’ve made a lot of money and invested a lot, but I would argue it’s still unsolved. Getting a mortgage is still painful, archaic, slow and too expensive.

Related to that is making regulation more efficient to improve housing affordability. That’s not something an entrepreneur can solve alone, but there’s potential to bring efficiency to how cities deal with this problem.

There’s an intersection of regulatory technology, policy work and consulting. How do we make it so more people can afford homes in the cities they live in? That problem isn’t going away. Despite everything that’s happened, housing has gotten more expensive, not less.

Related Crunchbase queries:

Related reading:

• Sector Snapshot: Proptech Funding Holds Up, But Investors Are Placing Different Bets

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

This week’s list of the largest U.S. startup funding rounds was pretty much all about AI. That includes the biggest financing — a $1 billion round for , a developer of AI assistants for everyday tasks — as well as most of the rest of the Top 10. Other large fundraisers include , which is deploying AI for the home rental sector, and , an AI cybersecurity startup.

1. , $1B, AI assistants: San Francisco-based Instinct, a startup building a personal AI assistant for everyday tasks, secured $1 billion in a Series C round. , and backed the financing, which set a $10 billion valuation for the year-old company.

2. , $350M, AI for housing: EliseAI, a startup deploying AI for the home rental industry, completed a $350 million funding round at a $4 billion valuation. and led the financing for the New York-based company, which is also expanding into healthcare.

3. , $255.5M, cybersecurity: Palo Alto, California-based Armadin, an AI-native cybersecurity company focused on autonomous security, raised $255.5 million in Series B funding. and led the round, which set a valuation of more than $2.5 billion for the year-old startup.

4. , $250M, project management:�� Kahua, a platform for enterprises to manage complex capital projects, picked up $250 million in growth funding from . The financing set a valuation of over $1 billion for the Alpharetta, Georgia-based company.

5. , $223M, AI infrastructure: Mountain View, California-based GMI Cloud, a provider of GPU cloud infrastructure used by AI teams to help train and run models, closed on $223 million in Series B funding as well as $445 million in debt financing. led the round, with participation from .

6. , $220M, foundational AI: General Intuition,a developer of AI models trained on videos and virtual worlds to develop skills for physical environments, secured $220 million in fresh funding. and were lead backers in the round, which set a $6.2 billion valuation for the New York-based company.

7. (tied) , $150M, physical AI: SiMa.ai, developer of a software platform for scaling physical AI applications, picked up $150 million in Series C financing at a $1.45 billion valuation. and led the round for the San Jose-based company.

7. (tied) , $150M, agentic app development: Supabase, provider of an AI-enabled open source Postgres platform for app developers, $150 million in new funding led by . The San Francisco-based startup also said it is acquiring database architecture startup .

9. , $145M, AI infrastructure: CScale, a developer of optical interconnect technology for AI data centers, emerged from stealth and says it secured $145 million in Series C funding.�� , , and led the round for the Palo Alto, California-based startup.

10. , $120M, real estate: Austin-based Homeward, a startup that helps homeowners buy before selling their existing homes or get cash offers for their properties, closed on $120 million in Series D equity funding led by . The 8-year-old company also secured $330 million in debt financing.

Methodology

We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the period of Sept. 26-Oct. 2, 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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, an alternative investment firm specializing in asset-backed credit, led the round, which also included participation from , , , and as well as , , , and .

Homeward has now raised $360 million in equity since its 2018 inception. The company declined to disclose the valuation at which it raised the latest round, saying it was similar to its valuation at the time of its in 2021, when it was reported to be “just north of $800 million.”

Austin-based Homeward has also secured a $330 million asset-backed debt facility to fund more home transactions. The equity funding will support expansion of its financing products and investment in its technology platform.

Its recent funding comes amid an uptick in startup investment to the proptech category this year. So far in 2026, global real estate-related startups have pulled in about $12.7 billion in seed- through growth-stage investment, per Crunchbase , putting this year on pace to top last year’s funding, which totaled $12.3 billion. Investment in the sector still pales compared to the peak years however: In 2019, the second-highest year on record after the 2021 venture funding spike, real estate-related startups raised $24 billion, Crunchbase data shows.

Cashing out before a sale

Homeward works through real estate agents to help clients access the equity tied up in their homes and navigate the timing of buying and selling. Its Buy Before You Sell program provides short-term financing so homeowners can purchase their next house before selling their current one, along with a guaranteed backup offer for the existing property.

For homeowners seeking a faster sale, its Sell Before You List program offers a cash purchase and a closing in a matter of weeks. Homeward then fixes up the property and resells it on the open market, returning the profit to the original homeowner while charging a program fee.

“We realized that there’s an opportunity to help homeowners sell their home fast without sacrificing all of their home equity like they would have to if they sold to an investor,” founder and CEO told Crunchbase News in an interview.

The financing has helped Homeward roll out its cash-offer program across the 48 contiguous states, Heyl said. The company also plans to make its Buy Before You Sell program available nationwide by year-end.

The expansion follows a pivot that Heyl credits with helping Homeward more than quadruple revenue since 2021, even as U.S. home sales fell roughly 30%, according to his estimates.

“Our ability to really exponentially grow over the last four years or so was a huge thanks to the pivot,” Heyl said, pointing to its Sell Before You List product, which is aimed at “the home sellers that are still transacting.”

Over the years, Homeward says it has partnered with more than 25,000 real estate agents and facilitated over $4 billion in transactions.

A pivot as homeowners stayed put

From 2019 through 2022, Homeward focused exclusively on helping homeowners buy their next house before selling their existing one. The offering appealed to agents and consumers competing in a tight housing market, and the company grew quickly, Heyl said.

But rapidly rising interest rates made that customer base harder to reach. Homeowners who might otherwise have moved into a larger or smaller house stayed put as moving costs rose.

“It became more expensive, definitely a lot more expensive, to move up, but even for a lot of people, more expensive to move down,” Heyl said.

Homeward responded by broadening the types of sellers it could serve. While fewer people were buying and selling at the same time, some homeowners still needed to sell without buying another property. And with homes taking longer to sell and prices less predictable, Heyl saw a growing demand for cash offers.

In early 2023, Homeward launched Sell Before You List. The distinction, in Heyl’s view, is that Homeward offers a service to sellers rather than seeking to capture the upside from buying and reselling their homes.

“Once we fix the house up and sell it for its full price on the open market, we send that profit back to the original homeowner,” he said.

Homeward also reworked its original Buy Before You Sell offering, lowering its cost and simplifying it for a market in which homes no longer routinely sell over a weekend with multiple offers. That business has contributed significantly to growth over the past year or so as more homeowners have begun making moves again, Heyl said.

“Most homeowners that are trying to move up or move down still plan to use the majority of their home equity to make that next purchase,” he said.

Homeward also offers Buy With Cash, which lets buyers make cash-backed offers and refinance into a traditional mortgage after closing.

Direct-to-consumer marketing without the cost

The company’s distribution strategy centers on real estate agents. Homeward has partnered with more than 25,000 agents and facilitated more than $4 billion in real estate transactions, according to Heyl.

Rather than pursuing individual consumers, it seeks ongoing relationships with agents, teams and brokerages that can repeatedly bring clients to its programs.

“We don’t spend a dime going direct to consumer,” he said. “We don’t advertise. We don’t market.”

Some agents incorporate Homeward’s offerings into their own marketing under their own brands, also known as white-labeling. Others turn to the company when a client’s existing home or access to equity keeps them from making a new purchase.

“Sometimes it’s just unblocking the transaction,” Heyl said.

That growth in a difficult market helped attract Saluda Grade, which is backing Homeward for the first time. , who runs the firm’s growth equity fund, said the team’s understanding of the problems facing buyers, sellers and agents stood out when compared with other “similarly situated” operators it had met.

“They really understood the core issues they were addressing,” Stepp said. “It was evident in their financial performance and their growth.”

Geographic expansion was another draw, he said, with the capital raise creating an opportunity to bring Homeward’s products to more markets.

“We recognize the product-market fit, and how useful this product set really is to eliminate some of the friction in the home transaction process for consumers,” Stepp told Crunchbase News in an interview, “and how useful of a tool it is for agents to be able to offer this.”

Using AI to speed up underwriting

As many other startups, Homeward is incorporating AI into its workflows. For example, the company is using AI to reduce the manual work involved in processing transactions and underwriting properties.

Large language models extract information from documents and help underwriters review property videos, photos and inspection reports. It does this by pulling out relevant details about roofs, heating and cooling systems, and a home’s condition, according to Heyl.

“AI has been huge for streamlining operations and underwriting,” he said.

For Heyl, the broader opportunity is to continue addressing the financing and timing problems that complicate home purchases and sales, even as the housing market changes.

“There’s been a major pullback, but it hasn’t changed the opportunity that exists to solve problems for buyers and sellers,” he said.

The company makes money in several ways. Buy Before You Sell carries a 1% program fee and a monthly interest cost. Sell Before You List carries a single program fee, with no monthly charge. Heyl did not specify the fee for that offering.

Homeward also generates revenue through its in-house mortgage and title businesses. Heyl said providing more services within the transaction can simplify the experience for buyers and sellers while reducing the program fee Homeward needs to charge.

Related Crunchbase query:

Related reading:

• Sector Snapshot: Proptech Funding Holds Up, But Investors Are Placing Different Bets
• Sector Snapshot: Real Estate Tech Funding Sees Slight Rebound, But Still Far Lower Than Peak Years

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She later became a general partner at , where she led early institutional investments in AI chipmaker , and later invested at . Now she’s founder and managing partner of , a $52 million fund backing startups that use AI to do work in industries such as construction, industrials and insurance.

In an interview with Crunchbase News, Venkatachalam discusses why she looks beyond familiar founder profiles, what makes an AI company durable, and how an early investment in Groq shaped her approach.

This interview has been edited for clarity and brevity.

You invested at Khosla Ventures before starting Axiom. What did you take from that experience, and what did you want to do differently?

Venkatachalam: One thing I took was ’s open view of where great founders can come from. Silicon Valley has gravitated toward a fairly narrow idea of who can build the next great AI company: Someone with a computer science or machine learning background, or experience at . We’re looking for more nonobvious founders, particularly in nonobvious industries.

The other thing was how we think about risk. Instead of asking a company every conceivable diligence question, we focus on the risks that matter for its next set of milestones. Can this team do what it says it will do? And if it can, could the result be massive?

That means accepting that many bets won’t work out while aiming for the outliers. I think that’s what my investors are backing me to do: identify categories of the future, rather than participate in the categories everyone already recognizes.

At Axiom, the biggest difference is that we’ve built the firm around people who are actively working with AI. If you aren’t building, productizing, pricing or taking AI to market regularly, it’s very hard to keep up. Our team includes people doing exactly that in their other roles. They help keep our investment thinking current, and founders want to work with them because they’ve encountered many of the same challenges.

We also use AI throughout the firm. We’ve built what we call the Axiom Brain to help us make sense of market trends, identify interesting people and companies, and move faster on diligence and other work. For me, the value is the ability to act quickly.

You mentioned that some of those AI practitioners have other jobs. How does their role at Axiom work?

Venkatachalam: They have dedicated time to Axiom and work with us part time. They also receive carry in the fund. They’re partners in the work, rather than people whose names appear on an adviser list.

Their other jobs are central to the model. Some of the best angel investors are people who are still operating and building. I don’t need these people full time. In fact, they would be less valuable to Axiom if they left the work that keeps them close to the market.

Axiom says it invests in “AI for the real world.” What does that mean when you’re evaluating a startup?

Venkatachalam: Our view is that AI should benefit a much broader population than the early adopters who are already using it. We look at industries underserved by technology, where AI can produce an outcome rather than simply provide another software tool.

That may lead us to construction, industrials or insurance. Some of those companies involve hardware, sensors or robotics; others are entirely software-based. What connects them is that they’re doing work that matters to customers in real-world industries.

We generally don’t invest in products that look like conventional enterprise software tools. We want to see AI delivering a result.

You’ve described a shift from software people use to digital workers that perform jobs. Are customers actually paying for AI from labor budgets?

Venkatachalam: Yes, and that’s one of our investment criteria. Even when a portfolio company is at an alpha or design-partner stage, we do diligence to understand whether customers are willing to buy it that way. We’re often seeing contract values in the hundreds of thousands of dollars, rather than the much smaller contracts you might expect for a midmarket software tool.

We’ve seen that buying behavior play out across the majority of the portfolio companies we’ve invested in.

AI products are becoming faster to build and easier to imitate. What makes one durable enough to become a large company?

Venkatachalam: If you’re doing important work inside a customer’s business, and that work is worth a lot of money, you become difficult to replace. You’re handling what we call the last mile of the job.

In industrial settings, for example, delivering an outcome means integrating deeply with the systems customers use. You have to understand their data, train on it, learn the workflows that matter, and stand behind the result. That takes more than putting an interface on top of a model.

Those relationships and capabilities can become difficult for another startup to replicate. They also involve work that the large AI model companies may have little interest in doing themselves.

Before Axiom, you backed Groq when AI inference was far from an obvious investment category. What led you to it?

Venkatachalam: I came from both hardware and software. At one point, I became interested in why was building its own networking switches when it could buy them from existing suppliers. As I looked into that, I learned it was building its own chips, too.

That led me to , who had been involved in that work and had left to start Groq. I began learning why large technology companies were building chips to train models. Then Jonathan made the case that the much larger future market would be inference.

I’ll be honest: In 2016, I barely understood inference. But if you believed these models would spread, it made sense that people would build on top of them and need the infrastructure to support that. That insight drove my investment.

How did that experience shape what you look for now?

Venkatachalam: It taught me the value of being a little early and having some patience. You don’t have to be wildly contrarian, but you do have to see the opportunity before it becomes obvious to everyone else.

In a way, our thesis hasn’t changed. We’re still asking what will be built on top of AI infrastructure and models. We want to invest while the answer is emerging, before there’s a consensus.

What happens when one of those early bets doesn’t work out?

Venkatachalam: We plan for it. We have a $52 million fund and expect to make 35 investments. We fully expect about half of them to fail, whether that means a company shuts down or simply never reaches the growth trajectory we’re looking for.

The model depends on finding an exceptional outcome. We need one outstanding investment to return the fund. If you invest early enough and the company becomes very large, that can offset many bets that didn’t work.

That willingness to accept losses is part of making investments before an opportunity is obvious. It’s built into how we approach the fund.

Related Crunchbase query:

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