Meanwhile, on Monday, details from AI giant 鈥檚 IPO prospectus, giving potential investors a peek into its financials.

Oura had planned to offer 50 million shares at $40 to $44 apiece and was expected to begin trading Wednesday. At the top of that range, the offering would have raised $2.2 billion. The company says it is delaying the deal despite strong demand and has not set a new date. CEO said Oura has 鈥渢he luxury of choosing our moment.鈥�

Anthropic is still moving toward an IPO, although its timing remains unclear. A prospectus leaked by Reuters showed that revenue climbed twelvefold to nearly $4.6 billion in 2025 with an operating loss of $8.06 billion. Its staggering nearly $42 billion net loss included roughly $34 billion in accounting charges tied largely to earlier financing.

The document also outlined an eye-watering $518 billion in future cloud, computing and infrastructure obligations. Reuters has reported that a listing is likely to come after the November midterm elections.

Anthropic, the world’s most valuable venture-backed startup, has indicated it plans to beat rival to the public markets. The company could debut as soon as October and raise up to $100 billion via the offering, according to a recent in , while OpenAI, which Reuters says filed confidentially in June, is reportedly now looking toward early 2027.

(兔子先生传媒鈥檚 predictive intelligence tools, meanwhile, point to a slightly longer timeline for an Anthropic IPO, saying it鈥檚 more likely to happen in six to 12 months.)

Who鈥檚 next

So far, the 2026 IPO class already has a record-setting headliner in .

There are some other candidates. -backed AI cloud provider filed publicly this month , reporting $140.6 million in first-half revenue and a $1.02 billion net loss. , a -backed specialty insurance underwriter, on Sept. 24.

Neither has announced a trading date.

Data center operator is another possible fourth-quarter entrant. Reuters that it had hired banks for an IPO that could raise as much as $10 billion, although the timing remains subject to change.

Related 兔子先生传媒 query:

Related reading:

• IPOs Are Holding Up In 2026, But SaaS Debuts Aren鈥檛 Happening
• The IPO Window Is Closing. Here Are 8 Startups To Watch.

Illustration:

鈥淚 learned that you can course-correct if you focus on the early life of the baby. If they’re missing certain beneficial bacteria, it can really cause a lot of issues for the infant,鈥� Sew Hoy recalls. 鈥淚 was reading all this literature and asked my doctors about it, and none of my doctors knew what I was talking about.鈥�

A week after her son鈥檚 birth in 2020, she incorporated Austin-based . Today, the company announced it has raised $33 million in a Series B funding round led by , it told 兔子先生传媒 News exclusively.

The financing brings Tiny Health鈥檚 total funding to $46 million, following a $4.5 million seed round in 2021 and an $8.5 million Series A in late 2023. Existing investors , and participated in the latest round, along with new investors including and . B Capital鈥檚 will join the company鈥檚 board.

Demand for Tiny Health鈥檚 at-home microbiome tests is growing. The startup鈥檚 overall revenue has more than doubled each year, according to Sew Hoy. Its B2B arm, Powered by Tiny, which sells tests through healthcare providers and other companies, grew fourfold last year. Sew Hoy did not disclose revenue figures or the company鈥檚 valuation, though she said the valuation was 鈥渕uch higher鈥� than in the Series A.

From baby gut tests to adult testing

Tiny Health spent two years developing its platform before launching an at-home baby gut test in 2022. Healthcare was new territory for Sew Hoy, whose previous startup, digital coupon company , was sold to in 2013. Before raising a seed round, she put $50,000 of her own money into an initial study and spent about a year collecting stool samples and building the platform.

The baby test gave Tiny Health a focused entry point. In Sew Hoy鈥檚 view, parents of children with conditions such as eczema wanted more information as to the cause, while clinicians had few practical tools to apply emerging microbiome research. The company also offered pregnancy, vaginal microbiome and adult tests, but initially focused on infant testing. Adults have since become its larger testing segment, according to Sew Hoy.

So, how does it work? Customers collect a small stool sample with a swab. After sending in the swab, they receive a report on the microbes detected, along with explanations and suggested next steps. Tiny Health also sells a subscription called Tiny Plus that includes a baseline test and a retest.

Sew Hoy believes one of Tiny Health鈥檚 biggest differentiators is that its tests use shotgun metagenomic sequencing to analyze microbial DNA. The method provides a broader view of the organisms in a sample than tests that look for a limited set of microbes, she said. Chief science officer , who joined Tiny Health in 2021, leads its scientific work. The company鈥檚 other leaders include co-founder , who joined Sew Hoy two years after she started Tiny Health and leads its technology work, and chief medical officer , an integrative pediatrician who previously advised the company.

The reports examine what the detected microbes may be doing and suggest steps such as dietary changes, with links to supporting research.

鈥淲e are a wellness test,鈥� Sew Hoy said. 鈥淲e鈥檙e not a diagnostic 鈥� It鈥檚 very actionable, very evidence-backed.鈥�

Insurance does not cover the tests, which can identify microbes in a sample, but cannot establish what caused a person鈥檚 symptoms.

Other startups are taking different approaches to digestive health. Austin-based makes a device that attaches to a toilet and tracks stool patterns and hydration. Tiny Health analyzes microbial DNA from a collected sample. Throne recently raised $10 million in a Series A round led by .

Beyond direct-to-consumer testing

Tiny Health鈥檚 fastest-growing business is Powered by Tiny, which sells testing through healthcare providers and companies that incorporate it into their own services. Partners include , and 鈥檚 Executive Health and Longevity Programs, according to Tiny Health. It also works with supplement makers and academic partners that want to measure the microbiome in clinical studies.

Customers began taking their direct-to-consumer results to doctors, according to Sew Hoy. Some clinicians then approached Tiny Health for help interpreting the reports. Today, the startup serves more than 6,000 practitioners and offers training on using its tests in patient care.

Its enterprise business supplies testing to other health and wellness companies, including through technology integrations. As mentioned earlier, that B2B arm is growing fourfold. Expanding both, along with clinician training, is a priority for the new funding.

, senior principal at B Capital, told 兔子先生传媒 News that his firm sees an opportunity to make microbiome research more useful to consumers as interest in preventive health grows.

鈥淲e’re in the middle of a real shift in how people engage with their own health. Consumers are paying out of pocket for wearables, blood panels, and full-body scans because they want to understand what’s happening inside their bodies before something goes wrong,鈥� he wrote via email. 鈥淭he gut microbiome is one of the most important and least understood pieces of that picture 鈥� Tiny Health turns that science into something people can actually act on, and we believe the company that does this well will define the category.鈥�

Whitehead also cited Tiny Health鈥檚 standing with clinicians the firm consulted during its diligence.

鈥淭iny Health was consistently the test they rated highest,鈥� he said.

Another attractive feature, Whitehead added, is the company鈥檚 data from families tested beginning in infancy.

Putting its data to work

Tiny Health has collected nearly 200,000 microbiome samples over time, according to Sew Hoy. To make use of those samples, the startup is developing a tool called TinyAI that will draw on its data and research reviewed by its scientific staff to help people understand their results. The company also uses health information customers provide, with identifying details removed, to inform that work.

Long-term, Tiny Health aims to identify patterns that could help spot health risks before a condition develops. has associated gut microbiome imbalances with more than 100 diseases.

鈥淲e want to be able to predict disease before it happens and intervene before it happens,鈥� Sew Hoy said in an interview with 兔子先生传媒 News.

Indeed, one pressing question is whether microbiome-guided interventions can improve health outcomes. Tiny Health says it has published four scientific papers, including a study involving infants, and plans to support further research involving adults.

Presently, the company has about 40 employees and plans to hire 30 more. It will use the new capital to expand its work with healthcare providers and other companies, and to fund research into whether changes in the microbiome can improve health outcomes. It plans to devote $5 million to a program that will give researchers and clinical partners access to its testing and data.

Related 兔子先生传媒 query:

Related reading:

• Pooptech Startups Are Passing The Smell Test
• Investments In Poop Startups Are Piling Up

Illustration:

She later became a general partner at , where she led early institutional investments in AI chipmaker , and later invested at . Now she鈥檚 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 兔子先生传媒 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 鈥檚 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鈥檙e 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鈥檛 work out while aiming for the outliers. I think that鈥檚 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鈥檝e built the firm around people who are actively working with AI. If you aren鈥檛 building, productizing, pricing or taking AI to market regularly, it鈥檚 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鈥檝e encountered many of the same challenges.

We also use AI throughout the firm. We鈥檝e 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鈥檙e 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鈥檛 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 鈥淎I for the real world.鈥� What does that mean when you鈥檙e 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鈥檙e doing work that matters to customers in real-world industries.

We generally don鈥檛 invest in products that look like conventional enterprise software tools. We want to see AI delivering a result.

You鈥檝e 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鈥檚 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鈥檙e 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鈥檝e seen that buying behavior play out across the majority of the portfolio companies we鈥檝e 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鈥檙e doing important work inside a customer鈥檚 business, and that work is worth a lot of money, you become difficult to replace. You鈥檙e 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鈥檒l 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鈥檛 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鈥檛 changed. We鈥檙e still asking what will be built on top of AI infrastructure and models. We want to invest while the answer is emerging, before there鈥檚 a consensus.

What happens when one of those early bets doesn鈥檛 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鈥檙e 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鈥檛 work.

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

Related 兔子先生传媒 query:

Illustration:

From January through August U.S. tech layoffs reached at least 94,046, up 16.8% from 80,486 in the same period of 2025. Interestingly, and unsurprisingly, many of the cuts came as tech companies redirected spending toward AI and restructured operations to reduce costs.

The year started off on a busy note on the layoff front. After job cuts dropped sharply in December 2025 to 5,151, they surged in January to over 20,000. May was particularly brutal. The month drove the year-to-date increase, recording 31,513 layoffs 鈥� including 鈥檚 8,000-job reduction 鈥� the highest monthly count since March 2023, when layoffs reached 36,602.

Recent months indicate a slowdown. Layoffs fell each month after May, reaching 2,347 in August. Overall, June-August 2026 layoffs totaled 19,331, down 16.2% year over year. The decline suggests recent easing, though it is too early to establish a lasting reversal.

Artificial intelligence has become a much more common explanation for layoff decisions, noted , founder of AI was cited in 33% of tech layoff events this year, up from just 1% in 2024. His tracker attributes 92,913 layoffs globally, or 72% of this year鈥檚 total, to AI.

鈥淭here鈥檚 been little evidence that AI is actually replacing the work of the human employees let go,鈥� Lee said of this year鈥檚 largest AI-attributed layoffs. He believes established tech companies are spending heavily on AI and cutting costs elsewhere, hoping to increase productivity with smaller workforces.

Companies cutting

This year, we鈥檝e seen a number of Big Tech and publicly traded companies, as well as startups, make deep cuts.

But interestingly, as with last year, public tech companies have dominated layoff headlines in 2026 so far, led by and Meta.

鈥淏ig companies [have] made up about 87% of everyone laid off in 2026, which is similar to last year, when they made up 85%,鈥� Lee said.

Amazon accounted for 17,388 cuts this year so far through August. Those included a 16,000-worker RIF announcement in January and several smaller subsequent rounds. Meta was next with 10,400 layoffs, including an 8,000-job reduction carried out in May that represented 10% of its workforce.

and recorded the next-largest totals, letting go of 4,800 and 4,760 employees, respectively. , and each recorded 4,000 layoffs, followed by with 3,000, with 2,900 and with 2,600. Notably, the Top 10 list spans a variety of sectors, including cloud computing, social media, payments and enterprise technology.

We should also note that according to reports, 鈥檚 workforce fell by about 21,000 employees in its fiscal year ended May 31, 2026, but the worker count and exact timing for each of聽 those reported cuts was unclear, so we did not include that total in our tracker.

Among privately held companies in the tracker, recorded the largest disclosed total at 1,000, followed by HR software provider with 950 and with 500. Those figures were substantially smaller than the largest public-company reductions, although undisclosed layoff counts limit comparisons between the two groups.

And in early September, reportedly laid off 3,300 workers, or 10% of its workforce.

An AI focus

, of , says AI is affecting jobs in two ways. Some work, including coding, can now be done with fewer people. 鈥淭here are jobs that are literally being replaced by artificial intelligence,鈥� he told 兔子先生传媒 News.

But companies are also changing their priorities. They鈥檙e putting more money into AI and cutting teams working on other parts of the business. 鈥淭hey鈥檙e letting people go from one area of their organization while they might even be hiring in an area that is focused on AI,鈥� Challenger said. That鈥檚 why a company may lay people off and advertise new jobs at the same time.

Tech has announced more job cuts than any other industry this year, Challenger said. Across the U.S. economy, layoffs are down somewhat from last year, though that comparison is skewed by the large number of federal job cuts in 2025. When compared with the period just after the pandemic, when employers struggled to find workers, layoffs remain elevated.

Few companies outside tech have blamed job cuts on AI so far, Challenger said.

It鈥檚 not all negative though, in his view. There’s potential upside for programmers, he said. If AI makes software less expensive to build, companies in other industries might embark on projects they couldn鈥檛 afford before. That could mean new jobs outside tech, though it鈥檚 too early to know whether those jobs will make up for the ones being cut.

Also, it appears that some companies might be regretting their layoff decisions. Amazon is reaching out to eligible former employees about open roles across the company, including in its cloud-computing and AI businesses, according to a聽 report.

Methodology

Layoffs figures are from The 兔子先生传媒 Tech Layoffs Tracker, where we record reported job cuts at U.S. tech employers. The tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence 鈥� both privately and publicly traded 鈥� and is updated at least bi-weekly. Layoff and workforce figures are best estimates based on reporting. Actual layoff figures are likely much higher than reported as many companies do not disclose the number of jobs cut when announcing layoffs. For more about our methodology for tracking layoffs, refer to the tracker鈥檚 methodology section.

Related reading:

• The 兔子先生传媒 Tech Layoffs Tracker

Illustration:

So Shariff offered to build an AI agent for a couple of properties. The first, called Alfred, answered guest questions and helped with check-ins and check-outs. Within 90 days, he said, online reviews improved at both properties. He then began deploying it at more hotels, before deciding that guest messaging was only one part of the opportunity.

That work led him to team up with to start , the San Francisco startup they founded in July 2025. The company is now emerging from stealth with $6.7 million in seed funding to build agents that handle reservations, guest requests, staff coordination and other hotel tasks. led the raise, with participation from . The round marks Dextr鈥檚 first institutional financing.

From guest requests to bookings

After early guest management deployments, Dextr turned to incoming calls with a voice reservations agent called Daisy. If a caller has a question about a reservation or something they can’t find on a booking site, answering it could lead to a booking, Shariff found. As hotels began using Daisy, he realized that the phone calls were 鈥渓eading to revenue.鈥�

In fact, today, one large hotel that Dextr works with handles $100,000 to $300,000 a month in bookings through the voice agent, according to Shariff.

Dextr has since expanded into agents for group bookings, staff management and other work. Its goal is to connect those agents so information gathered in one part of a hotel can prompt action in another. For example, if a guest declines housekeeping, a guest management agent could pass that information to an operations agent so that staff schedules could be adjusted accordingly.

Another use case could involve a property operating without an overnight front desk employee. A guest arriving late could call the hotel, have Daisy verify their reservation and receive instructions for entering their room. Daisy and Alfred, Dextr鈥檚 guest management agent, would work together to handle the arrival.

For some properties, using agents to handle late check-ins could remove the need to staff an overnight front desk shift. Shariff estimated that such a shift could cost a property from $70,000 to $80,000 a year in a market such as California.

鈥淚t was very hard to hire for these shifts to begin with,鈥� he said.

Building ROI

Shariff says combining agents is just one of three things that set Dextr apart from other hotel AI tools. An agent that answers calls or messages can handle a particular task, he said, but connecting it with other agents opens up additional uses for a property.

鈥淚t’s important that we do multiple use cases to see the higher ROI,鈥� he told 兔子先生传媒 News in an interview.

The second distinction, according to Shariff, is Dextr鈥檚 approach to getting the agents running. Putting them to work takes more than installing software, he said. Every hotel differs from another in how it manages reservations, assigns staff, and handles guest requests. So Dextr sends engineers to work directly with customers, identify where agents could be useful and then connect them to existing systems. Those engineers stay involved as the property puts the agents to use, he said, and work to find applications that have the potential to deliver a return on investment.

鈥淓very property, every operation is different in this space,鈥� he said. Dextr integrates with property management systems including Oracle Hospitality, OPERA Cloud, , and .

The third is how employees use the agents. They can direct them by text or voice through a companion called Doss, without having to learn a series of steps in a new software system. Shariff said frequent turnover makes it difficult for hotels to train staff on complicated tools and retain that knowledge when employees leave. With Doss, 鈥測ou can call it, you can talk to it, you can text it,鈥� he said.

Hotels and more

Dextr says its agents now handle more than 1 million interactions a month across hundreds of hotels. The startup鈥檚 customers range from a 21-room property to one with 550 rooms. They include independent hotels and properties operating under the , , and brands, as well as reservation centers, vacation rentals and outdoor hospitality businesses.

The startup works directly with the owners and operators of those branded properties, Shariff said, and is beginning its first direct relationship with a hotel brand that he declined to name. It also works with hotel and property management companies, as well as reservation centers, vacation rentals and outdoor hospitality venues such as campgrounds, RV parks, lodges and golf courses.

The company charges a subscription based on the property and the agents it uses, with usage-based fees for some agents. Shariff declined to disclose revenue, profitability or the round鈥檚 valuation. After spending much of last year on pilot projects, he said, Dextr began growing more quickly this spring. He said annual recurring revenue has been growing about 20% to 30% month over month, though he did not disclose hard figures.

Dextr plans to use its new funding to hire more engineers who work directly with customers, expand its software integrations, and develop additional agents. Its team has grown from three people a year ago to 21.

, an AI partner at Elevation Capital, told 兔子先生传媒 News that Dextr鈥檚 early traction got the firm鈥檚 attention. He pointed to customers using the agents to generate bookings and upsells alongside reducing operating costs.

鈥淲hat stood out immediately was Sajid and Scott’s execution. Dextr scaled to [hundreds of] contracted properties and meaningful ARR without raising a single dollar,鈥� he wrote via email. 鈥淚n a space where most AI companies are still searching for product-market fit, Dextr had customers, zero churn, and a clear ROI story. That combination of founder quality and early traction is exactly what we look for at Elevation.鈥�

Related 兔子先生传媒 query:

Illustration:

led the San Francisco-based company鈥檚 Series A, with participation from , , and of . The financing brings Baselayer鈥檚 total funding to about $40 million since its 2023 inception, according to co-founder and CEO . The company declined to disclose its valuation.

Baselayer combines business identity, credit and fraud data to help banks, fintech companies and other financial-services providers evaluate prospective customers. It sells its products directly and through software companies that resell or put their own branding on Baselayer鈥檚 technology. Its automated platform initially focused on Know Your Business, or KYB, identity verification, fraud detection and risk management.

More than 2,000 financial institutions 鈥� representing over 20% of such institutions in the U.S. 鈥� use its technology to onboard, underwrite and open accounts for merchants, according to Awad. Baselayer also works with Fortune 500 companies and has about 50 employees across offices in San Francisco and New York. Since its founding, the startup claims it has helped customers prevent more than $1 billion in fraud losses.

Awad declined to reveal hard revenue figures, saying only that Baselayer reached eight figures in revenue in less than two years.

Now, Baselayer is using its new capital to address a newer 鈥� and growing 鈥� identity problem: determining whether an AI agent is actually authorized to act on behalf of a particular person or business.

With people using AI agents left and right these days to book a restaurant reservation, for example, it鈥檚 becoming increasingly challenging to determine whether an AI agent鈥檚 automated activity is legitimate or if it鈥檚 a bot attempting to scrape data or commit fraud.

Alongside its raise, Baselayer today is also announcing the launch of its Agentic Identity Suite, extending its identity network from businesses to the AI agents transacting on their behalf.

From businesses to the agents acting for them

Awad and co-founder started Baselayer in February 2023, initially focusing on the lengthy and fragmented process financial institutions use to verify businesses and assess risk.

鈥淲hat we set out to do was essentially bring risk assessment to the 21st century,鈥� Awad recalls.

Awad describes Baselayer as both an identity network and a fraud consortium. Because its technology is used across thousands of financial institutions, Baselayer says it can recognize when the same person or business applies at multiple institutions and incorporate that activity into its risk scoring.

The company processes tens of millions of applications and says it sees many of the same businesses multiple times a year. That data becomes more useful as additional institutions and reseller partners join its network, according to Awad.

鈥淲e鈥檝e essentially streamlined 10 years鈥� worth of selling into two years,鈥� he said.

An AI agent presents a different problem, however. It may be created for a single task and disappear immediately afterward, leaving little or no history for a bank or risk provider to evaluate.

鈥淎gents spin up and they spin down,鈥� Awad said. 鈥淗ow can you trust this random one-task agent?鈥�

To address this dilemma, Baselayer is developing what it describes as 鈥淜now Your Agent,鈥� or KYA. The system is being designed to do things such as determine not only who deployed an agent, but also who that agent represents and whether it actually has permission to carry out a particular task.

It wants to do this by providing an authorized agent with a credential it can present when attempting to make a purchase or interact with another business. Then, when presented with a credential, a merchant, financial institution or online platform could use that information to decide whether to allow the transaction to proceed, Awad explained.

The startup is working with agent developers, payment processors, merchants and fraud-detection providers to issue and recognize its credential. They include , and Socure, among others. Unless agents can establish that they are acting on behalf of legitimate people or businesses, 鈥渁gents will just get blocked everywhere,鈥� Awad said.

AI can also make fraud easier to scale

Ironically, the same technology that allows legitimate agents to do more tasks can also help fraudsters operate faster.

In the past, identity fraud involved someone getting their hands on stolen personal and business information, creating a credible-looking identity, and then repeatedly applying for bank or credit card accounts until an institution approved one. At one point, the process took significant time and manual work. But today, AI agents can automate parts of it and run continuously.

鈥淚t鈥檚 fraud on steroids right now,鈥� Awad said. 鈥淚t鈥檚 so easy, it鈥檚 so cheap, it鈥檚 so fast, and it鈥檚 24/7.鈥�

Reports of AI agents bypassing restrictions have also raised questions about how to identify and control autonomous software. , for example, recently reported incidents in which its models took unauthorized or deceptive actions, including activity involving the e platform.

Baselayer鈥檚 technology would not keep a model from disregarding instructions or exploiting a vulnerability, Awad acknowledged. But its goal is to verify an agent’s credentials when it attempts to interact or transact with an outside party.

Without a way to identify themselves, he said, legitimate agents may resort to trying to get around websites鈥� restrictions just to be able to complete their assigned tasks. Or, they could simply become less useful because they are repeatedly blocked as suspected bots.

Competing to establish a standard

M13 managing partner told 兔子先生传媒 News in an interview that he met Awad about a year before his firm invested in Baselayer. At the time, he saw the startup primarily as a provider of Know Your Business technology.

鈥淭he business did not feel like a business of the future,鈥� he admits. 鈥淚t just felt like he was solving a KYB banking verification problem.鈥�

The investor鈥檚 view changed as more companies began exploring payments made by AI agents and Baselayer began applying its business-identity data to the field.

鈥淓very agent ultimately is going to have to be tied to something real, and they understand the real world,鈥� Alomar said.

He believes Baselayer鈥檚 existing data, identity network and relationships with financial institutions give it an advantage over a startup entering the market from scratch.

“AI agents are rapidly becoming economic actors, but the identity infrastructure underneath commerce was never designed for software that can open accounts, make purchases, move money or enter into transactions on someone else’s behalf,” Alomar added. 鈥淭hat creates an enormous new trust problem, and we believe identity will become one of the foundational infrastructure layers of the agentic economy.鈥�

So far, no dominant standard exists. But Baselayer still must work to persuade agent developers, merchants, financial institutions and payment companies to recognize its credential.

That could take time. Awad said relationships with financial institutions typically take 12 to 18 months to establish, while large merchant partnerships can take up to 24 months. Baselayer may be able to reach some institutions more quickly, however, through its existing reseller relationships.

The company also sees potential use cases beyond payments. For example, Alomar said the technology could eventually authorize agents involved in cryptocurrency transactions or smart contracts, among other things.

鈥淭his is not just a fintech business 鈥� it鈥檚 a security business,鈥� he said. 鈥淚t begins with payments, but ultimately that technology applies directly to anywhere that an agent is making a decision that you need to verify it is permitted to make.鈥�

Related 兔子先生传媒 queries:

Illustration:

A decade ago, Espahbodi co-founded aerospace and defense startup accelerator and moved back to the U.S. to expand it. On the advice of friends at , he opened an office in El Segundo, California, near , just as more alumni of that company were leaving to launch hard-tech startups of their own and next-generation defense startups including were emerging.

Espahbodi eventually sold his stake in Starburst and launched , which invests in companies spanning industrial infrastructure, manufacturing, energy and water desalination. The firm has backed 14 companies since making its first investment in January 2023.

He also advises federal agencies on working with nontraditional, venture-backed companies. In an interview with 兔子先生传媒 News, he discusses how AI is changing hardware economics, why software investors are rushing into industrial technology, and what he believes many of them misunderstand about the sector.

This interview has been edited for length and clarity.

兔子先生传媒 News: What led you to leave Starburst and launch Generational Partners?

Espahbodi: About four years ago, I noticed that my friends from SpaceX were leaving the space vertical and moving horizontally across physical industries. I reached an inflection point: I didn鈥檛 want to remain locked into the space sector. I wanted to follow my friends.

I sold my equity in the accelerator, and part of the investment team left with me to start Generational Partners. For the past four years, we鈥檝e invested in what you might call the SpaceX-mafia and hard-tech sectors 鈥� anything involving industrial infrastructure, manufacturing, energy or water desalination.

We made our first investment in January 2023, in a North Dakota-based drone company. It was a trial by fire and an opportunity to prove the thesis. We鈥檝e invested in 14 companies since then.

You were already investing in physical, safety-critical industries before the generative AI boom. Has AI materially changed where you invest, or has it mainly reinforced your existing thesis?

Espahbodi: I tend to arrive earlier than others. I embraced the idea that hardware does not have to be capital-intensive. People often confuse hard tech with deep tech, but nomenclature aside, you don鈥檛 need to invest in science to win in these categories.

AI has dramatically changed that narrative and encouraged more people to get on board. I鈥檓 not looking to invest in science. I don鈥檛 necessarily see opportunities in quantum computing, nuclear fusion or other technologies being spun out of laboratories.

People who worked at companies such as SpaceX, and laid their companies鈥� foundations digitally. AI has significantly improved that augmentation and performance, enabling these companies to tackle legacy industries more aggressively and, more importantly, with new business models.

Another major component of the AI question is that frontier labs have become more expensive and capital-intensive than traditional hardware companies. The success of frontier AI labs, combined with the SpaceX IPO becoming an enormous wealth-creation event, creates a new environment. It raises questions about what is truly capital-intensive, what makes a product or its intellectual property defensible, and where companies are reengineering products around different business models.

Hardware has historically been capital-intensive, slower to commercialize and difficult to scale. Under what conditions does its technical defensibility compensate for those challenges?

Espahbodi: Fundamentally, it comes down to the business model. I look for creative software talent combined with commoditized hardware, significant customer demand and a new business model.

One of our portfolio companies was founded by the team that built the factory for user terminals. When you buy a retail Starlink antenna, these people built and scaled the assembly line that produced it at high volume.

While deploying those terminals globally to provide internet access, they observed that poverty often stemmed from a lack of access to clean water. They asked whether they could replicate the proliferated satellite-and-user-terminal architecture for edge water desalination.

Rather than investing in multibillion-dollar, nation-state infrastructure like that used by Gulf countries, they wanted to mass-produce every component in a vertically integrated stack. Their goal was to produce a cooler-sized device that could clean water at the point of need.

used a digital, software-based approach to build the bill of materials needed for mass manufacturing. AI is part of its business and operations, but the company鈥檚 real innovation was inverting the infrastructure model and scaling it.

I helped Vital Lyfe win its first customers within the and . Those organizations can use its devices in the field rather than shipping pallets of bottled water by air freight. That created a signal for overseas partnerships and nonprofit humanitarian-aid applications. It showed that there could be a different way to provide clean water.

Those are the kinds of unique business models that excite me.

What other companies founded by SpaceX alumni demonstrate how hardware businesses can overcome the traditional challenges of the sector? What can these founders build today that would have been difficult five years ago?

Espahbodi: Another example is the team SpaceX recruited to build the autonomous drone ships that catch boosters in the middle of the ocean. The team included former Coast Guard personnel and oil-and-gas technicians.

At SpaceX, they had the freedom to use software and AI tools to automate station-keeping 鈥� the ability of those drone ships to position and navigate themselves and reach the right location.

That team spun out and brought in many former colleagues to change commercial maritime shipping. They retrofit legacy boats operating in harbors and waterways and move supply-chain goods.

They brought a digital-first foundation to automating the controls on tugboats and barges. That had never existed before because the communications link to those ships didn鈥檛 exist. Starlink changed the concept of operations. The company can use its software expertise to change how physical devices operate aboard these boats and allow their sensors to send signals anywhere in the world.

That makes it possible to retrofit and overhaul how legacy shipping vessels navigate harbors and waterways in the U.S. It鈥檚 another example of SpaceX alumni applying the playbook and technologies they learned at SpaceX to a much broader commercial industry.

You鈥檝e said AI is eroding traditional software moats. What evidence are you seeing that investors are responding by moving into hardware and industrial technology?

Espahbodi: I meet many software investors who feel they鈥檙e missing out on hardware but don鈥檛 necessarily understand it. I鈥檝e met beauty investors who now say they鈥檙e defense-tech investors.

Los Angeles is a hotbed of firms that historically invested in software, media or consumer packaged goods. But people forget that Southern California, particularly El Segundo, is the aerospace capital of the world and has the largest concentration of mechanical-engineering talent.

Across the region 鈥� from China Lake to San Diego 鈥� technicians, builders and vocational talent are intersecting with the democratization of software and access to AI tools. Many local VCs have never taken advantage of the hardware talent located around them, so they鈥檙e being thrown for a loop.

Ironically, Bay Area VCs have been among those leaning most heavily into this. But it鈥檚 happening everywhere. I鈥檓 in Washington, D.C., now, and one of the first investors in , the hypersonic missile company, was in Virginia 鈥� before and others became involved.

Los Angeles VCs in particular know there is a talent war underway and that many people are leaving established companies to launch new businesses in these categories. But they struggle to underwrite those deals. They don鈥檛 know how to distinguish a strong opportunity from fear of missing out or something merely cosmetic.

So investors鈥� lack of experience in the space isn鈥檛 deterring them from writing checks or competing for deals?

Espahbodi: You have to ask why. The answer is their limited partners.

Sophisticated allocators, such as endowments, foundations and pension funds, along with more FOMO-driven family offices and high-net-worth investors, are watching this wave of SpaceX, and Anduril alumni create new companies and raise extraordinary rounds.

Many of those companies are no longer raising solely to pursue intellectual property. They鈥檙e building war chests to acquire other companies. The lines between private equity and venture capital are blurring. VC-backed companies are doing private equity-style buyouts, while venture deals are bringing in private equity checks.

That leaves LPs pushing for more. The success of the frontier AI labs has also perpetuated a fear of a 鈥淪aaS apocalypse,鈥� which I don鈥檛 think is real 鈥� although I sometimes question 鈥檚 1聽stock price for fun.

It creates what venture does best: tourists and FOMO. LPs ask why their managers aren鈥檛 investing in the same companies and how they can participate, raise more money and show that they aren鈥檛 missing out. That鈥檚 how I鈥檝e seen investors unfamiliar with these sectors enter the market.

Some of the largest Silicon Valley firms … missed this dynamism wave. Now they鈥檙e leaning in hard, sometimes at ridiculous valuations for companies that have yet to produce anything.

If more venture funding continues to flow into defense, aerospace and industrial technology, what prevents hardware from developing the same problems software experienced, including too many competing companies?

Espahbodi: Bring it on 鈥� hard and fast, and as much as possible.

Venture as a category exists because it was always about hardware. I would argue that the SaaS era, from the dot-com boom until now, was a blip compared with what venture was originally intended to underwrite.

I would move away from the hardware-vs.-software distinction and ask who is reframing the business model. Is there a way to reengineer a combination of software and hardware to unlock customer value? That鈥檚 the more important question.

How important is geography for these startups? Does locating near a major government customer help a company win contracts, and how do startups navigate procurement if they aren鈥檛 based near Washington, D.C.?

Espahbodi: It鈥檚 a common misconception that Washington is where the money is. The Los Angeles Air Force Base houses , which is another way of saying it holds 鈥檚 wallet. El Segundo makes the purchasing decisions for the fastest-growing portion of the military budget.

Washington is a place of considerable activity that needs to be influenced. Venture has never had this degree of influence on an administration and its executive orders. We鈥檙e also seeing portfolio companies backed by influential investors win government contracts worth as much as $1 billion at a time. That鈥檚 extraordinary.

Geographically, companies need to be where the talent is as much as where the customers are. Government customers should signal what matters, but companies shouldn鈥檛 organize themselves entirely around the government.

My catchphrase is that I want everyone to be commercially focused but mission-aware. I don鈥檛 want them to be mission-focused on the government. I want government to signal what it cares about while companies remain commercially focused.

The talent war for this convergence of hardware and digital technology is centered in Southern California. If you aren鈥檛 building and recruiting there, you鈥檙e falling behind. I like that the Bay Area is trying to attract more hardware talent and capitalize on the automotive and humanoid-robotics markets.

But I think the talent base for the factory of the future starts in Southern California and can then be used as a model for expansion into other places, as companies such as Anduril have done in Ohio and Louisiana.

We invested in a company founded by people from SpaceX and . They immediately moved to Austin to build a smart factory for raw-material processing. They wanted to automate the process at its source.

The largest concentration of cotton farming is around Lubbock in the Texas Panhandle. The company is building automated factories from the ground up to mill cotton into yarn and then complete the digital, vertically integrated stack by producing textiles at prices that beat outsourcing to China, Vietnam and other countries.

It sounds crazy, but the founder is determined to do it. If you can prove the model in textiles, you can apply it to copper. If you can do it with copper, you can do it in pharmaceuticals. From there, it could go in any direction.

Do startups located near Space Systems Command have an advantage?

Espahbodi: Not for that reason alone. The advantage is that they鈥檙e part of the ecosystem and geography. They鈥檙e spending time in the same bars and restaurants, and their children attend the same schools. They鈥檙e witnessing the same velocity.

Space Force itself is facing greater demand than ever to protect assets in space. Whatever happens with funding for individual programs, it remains the fastest-growing portion of the Pentagon budget.

I don鈥檛 think startups should locate there solely to be close to the customer. They should be there for the talent they need to build.

Related 兔子先生传媒 queries:

Related reading:

• Sector Snapshot: Space Tech Startup Funding Orbits New Highs
• Sector Snapshot: Defense Startup Funding Hits An All-Time Record As VCs Begin To Eye Exits

Illustration:

The New York-based company publicly launched Thursday with an undisclosed seed round led by S茫o Paulo-based venture firm and a debt financing partnership with 鈥檚 Customer Value Fund. Since its January inception, Skalar has committed to finance more than $125 million in sales and marketing spending across seven technology companies over the next 12 months.

Financing tied to customer revenue

Skalar鈥檚 model is fairly straightforward, though somewhat unusual. The company provides startups with capital to fund sales and marketing initiatives. The startups then pay it back out of the revenue generated by the customers acquired with that capital.

If those customers generate less revenue than expected, Skalar says it absorbs the shortfall rather than requiring the company to repay the full original amount.

Skalar鈥檚 current deals generally call for it to collect about 1.1x the amount provided.

For example, if a company spends $10 to acquire a customer and expects that customer to pay $1 per month for 30 months, Skalar provides the initial $10 and collects the first $11 that customer generates. Once Skalar reaches that repayment limit, the company can keep the remaining revenue.

But if the customer cancels after eight months, Skalar collects only $8 and writes off the balance, according to co-founder and CEO .

鈥淲e only get repaid as they get repaid,鈥� C谩rdenas told 兔子先生传媒 News.

Notably, the startup doesn鈥檛 have to pay the capital back by a certain date. Instead, repayment is tied to revenue from the customers acquired with the financing, rather than a fixed schedule. For example, a company that recoups its acquisition costs in one month repays the loan in one month, while one that takes 12 months repays it over one year. So while the obligation remains contractual, Skalar operates under the premise that a flexible timeline reduces the risk of a cash crunch.

How it differs from other financing

Skalar鈥檚 structure differs from both venture debt and existing forms of revenue-based financing, according to C谩rdenas.

offers startups flexible funding without equity dilution, but with higher interest and risk. Skalar鈥檚 founders contend that paying back that debt can force startups to cut sales and marketing spending or hold onto cash when new growth opportunities emerge.

The model also differs from revenue-based financing, which typically advances money to companies based on signed contracts or revenue they are already generating, the founders said. Instead, Skalar finances a potential new revenue source before it exists and accepts some of the risk that it may never fully materialize.

Taking on that risk means that Skalar has to closely examine a company鈥檚 operations. It analyzes detailed transaction data to determine how much the company spends to acquire customers, how long those customers stay, and how much revenue they generate over time. It also means the company is very selective about who it chooses to finance. Skalar鈥檚 system continually updates company assessments as new information comes in, according to co-founder and COO Daniel Castrill贸n.

鈥淲e have become experts in understanding these types of risks and when they are sufficiently predictable and sufficiently profitable to be underwritable,鈥� he said.

The risks for founders

The arrangement is not without risk for startups, concedes C谩rdenas. Skalar sets minimum revenue targets for the companies it finances. If results fall below those targets, it can require faster repayment. It can also stop providing additional capital under certain circumstances, which could leave a company without funding it had expected to receive.

Its terms are based on estimates involving customer revenue, profit margins, currency fluctuations and which sales can be attributed to a particular marketing investment. If those estimates prove wrong, or if the cost of acquiring customers rises, the startup may receive less benefit from the arrangement than expected, C谩rdenas said.

Importantly, Skalar鈥檚 agreements do not give it the right to seize a company鈥檚 assets in the event of a default, C谩rdenas said, and they do not require borrowers to maintain specific financial benchmarks or cash balances.

Still, founders must weigh the possibility of accelerated repayment or interrupted funding when deciding whether the financing fits their plans.

鈥淥ur structure is fundamentally different because it absorbs most of the downside risk 鈥� and we are unlikely to walk away unscathed if something bad happens. This incentivizes us to always be mindful of not encumbering the companies we work with with credit risk, as this ultimately increases risk for us,鈥� C谩rdenas told 兔子先生传媒 News.

A narrow initial customer base

Skalar is targeting technology companies that spend between $100,000 and $3 million per month acquiring customers and have a consistent record of earning more from those customers than they spend to acquire them. It also considers whether a company has enough cash to remain in business long enough for that customer revenue to arrive.

Its first seven customers include four or five Latin American companies, C谩rdenas said, as well as businesses in the United States. Skalar initially plans to work with no more than 15 companies per year.

The company declined to disclose the size of its seed round, which closed during the first quarter. C谩rdenas described it as a large seed round by Latin America鈥檚 standards. and several angel investors with relevant industry experience also participated.

is providing the debt capital Skalar will use to finance its customers鈥� sales and marketing spending. The size of that partnership was also not disclosed.

The General Catalyst connection

Skalar grew out of C谩rdenas鈥� work as an entrepreneur-in-residence at Monashees, where he helped introduce several of the firm鈥檚 portfolio companies to General Catalyst鈥檚 Customer Value Fund model.

General Catalyst pioneered a similar approach but increasingly focused on larger financing deals, C谩rdenas said. That created an opportunity to serve smaller companies, including startups in Latin America.

鈥淭he best companies are thoughtful about matching their sources and uses of capital: equity for transformative but unstructured product and R&D bets, low-cost, duration-matched capital for predictable investments like customer acquisition,鈥� , partner at the Customer Value Fund, said in a statement. 鈥淢ost technology companies in Latin America have never had the choice, and Sebasti谩n came to us with that gap in mind. As an investor in the region, he saw the CVF model transform a handful of companies in his own portfolio, and he pitched us on closing the capital gap together.鈥�

Still, Skalar is not restricted to financing businesses with no connection to either General Catalyst or Monashees. Monashees general partner said his firm does not have access to the confidential operating data that startups provide to Skalar as it evaluates their businesses.

For Monashees, the model addresses the long-standing shortage of growth financing in Latin America. Bolognesi told 兔子先生传媒 News that his firm, the largest venture firm in Brazil, has watched companies with strong customer performance struggle to secure enough money to pursue their growth opportunities, particularly as equity investment in the region rose and fell.

鈥淲e鈥檝e seen capital flow into and out of the growth stage, leaving some excellent companies struggling to raise the equity they need to keep growing,鈥� he said. 鈥淪kalar fills that gap by giving promising companies access to capital while they build the track record investors want to see.鈥�

A market beyond venture-backed startups

Skalar is initially focused strictly on financing customer acquisition. Its founders eventually envision offering similar products for other business expenses that produce sufficiently predictable returns.

C谩rdenas also sees a longer-term opportunity beyond the relatively small group of companies able to attract institutional venture capital. Businesses that have trouble raising venture capital because of their location, industry or growth rate may still qualify for Skalar financing based on their financial performance.

鈥淰enture capital solved the problem of funding the top 1% of tech businesses,鈥� he said. 鈥淏ut 99% of tech businesses 鈥� out of which I鈥檇 say probably more than half could be underwritten by our product 鈥� just don鈥檛 have access to capital today, and ours is a product that fundamentally changes that.鈥�

In the long run, Skalar is betting that its approach can bring growth financing to a much larger group of companies. For startups that can raise venture capital, it also offers a way to fund predictable growth without giving up more ownership.

Related reading:

• The VC Firm That Helped Build Latin America鈥檚 Startup Scene Is Crossing Into Silicon Valley

Illustration:

Startups across sales, marketing and customer management have raised $7.5 billion so far this year, according to 兔子先生传媒 data. The largest rounds span everything from advertising and customer data to sales software, e-commerce and customer support 鈥� reflecting just how many companies are still trying to build a better way to market and sell.

The broad trend: Investors are making far fewer bets on sales and marketing startups than immediately before and after the COVID-19 pandemic, but they鈥檙e still writing checks into the space.

Unsurprisingly, AI-focused companies are capturing a much larger share of funding than during the prior peak, with most sales, marketing and CRM investment going to companies in 兔子先生传媒 AI-related categories.

The numbers: So far in 2026, startups in sales, marketing and CRM have raised $7.5 billion globally across 830 funding rounds, 兔子先生传媒 data shows. At the current pace, funding could finish near the $9.3 billion raised in both 2023 and 2024, although potentially below last year鈥檚 $11.1 billion. Deal volume, meanwhile, is on track to fall for a fourth consecutive year 鈥� pointing to a market where investors are putting more money into fewer companies.

Funding in recent years remains far below past levels. In 2022, for example, funding in the sector topped $27 billion, and in 2021, it totaled nearly $41 billion.

Notable deals

The year鈥檚 largest funding recipient so far was, which raised more than $1 billion in a June Series E from , , and . The San Francisco-based marketing measurement company, whose products now include AI agents that analyze marketing data and automate tasks, was valued at $2.7 billion.

Restaurant financing and rewards platform announced $450 million in new capital in February. The Austin-based company did not identify a lead investor or disclose a valuation.

In January, AI-native customer service company raised a $350 million Series D led by . The Berlin-based company develops AI agents that handle customer conversations by phone and other channels. The financing tripled its valuation to $3 billion.

Meanwhile, , an online marketplace for digital products, communities and courses, received a $200 million strategic investment from in February. The deal valued the New York-based company at $1.6 billion.

Another larger deal went to Dubai-based property listings platform , which announced a $170 million equity investment in January. The company uses AI in products including home valuations and tools that help real estate agents improve and prioritize listings. led the deal, with participation from another UAE sovereign wealth fund and existing investor . The company did not disclose a valuation.

On Sept. 9,聽 AI-powered sales automation startup announced it had raised a $115 million Series D at a $7.1 billion valuation. This was more than double the $3.1 billion valuation it achieved when it raised a $100 million Series C in August 2025. Wellington led the latest round, with participation from , , 鈥檚 a16z Perennial wealth management arm, , and others. The company says the raise followed 4x revenue growth in 2025. It also told 兔子先生传媒 News that it’s on track to hit $200 million in ARR this quarter, and $240 million by the end of the fiscal year.

Exits

The sector has produced one notable public offering, but most exits are coming through acquisitions as larger companies buy specialized sales and marketing products to add to their existing platforms, 兔子先生传媒 data shows.

, a Redwood City, California-based mobile advertising and app-marketing company, began trading on the in June. It initially sold 19 million shares at $23 each, raising $437 million. The IPO valued Liftoff at $3.83 billion, based on the outstanding shares disclosed in its IPO prospectus.

There have been a number of M&A deals this year in the sector, too, though in most cases, the acquisition price was not disclosed.

The largest known deal was Dutch payments giant acquisition of , a Berlin-based loyalty and promotions platform, in July for about $880 million. Talon had previously raised over $120 million in venture funding.

Other startup M&A deals in the marketing and sales arena in 2026 include:

• In July, acquired Seattle-based sales intelligence startup to add information about prospective buyers to its sales products.
• In June, agreed to acquire , whose software helps companies identify and contact people visiting their websites.
• Sales platform acquired , which helps sales teams identify prospective customers based on product use and other signals, in March.
• acquired the Estonian startup , whose software connects sales and marketing data, in August.
• acquired India-based marketing intelligence startup in September through a team and technology deal.

Funding is down from peak years, but it鈥檚 clear investors haven鈥檛 lost interest in sales and marketing startups. However, they are putting more money into fewer of them. Companies that help businesses find customers, increase sales, or retain existing business are still landing big checks and attracting buyers. But with acquisitions far more common than IPOs, a public-market exit remains much harder to come by.

Related 兔子先生传媒 query:

Related reading:

• Exclusive: Schematic Raises $6.5M To Help Companies Update Their Pricing Faster And Easier In The AI Era
• Exclusive: Winn.ai Raises $18M Series A For AI-Powered Real-Time Sales Assistant
• AI-Powered Sales Automation Startup Clay More Than Doubles Valuation To $3.1B

Illustration:

After completing medical school in London, decided not to pursue a residency. Her father was disappointed.

But she didn鈥檛 change her mind because she lost interest in healthcare. Instead, Abuzeid realized she wanted to work on problems affecting more people than she could treat individually.

鈥淲orking as a doctor is great, but you鈥檙e only working with one patient at a time,鈥� she said in an interview with 兔子先生传媒 News. With software, 鈥測ou have millions of users using your products.鈥�

Abuzeid went on to co-found , a San Francisco-based healthcare hiring platform that has raised about $97.5 million from investors such as , , , and . It says its products are used by 1.5 million healthcare professionals 鈥� including 1 in 2 U.S. nurses 鈥� and 1,500 healthcare employers.

Before launching Incredible Health in 2017, Abuzeid trained as a doctor, advised healthcare companies at and , and worked as a product manager at a health tech startup, but didn’t know how to code software.

An M.D. who chose not to practice

Originally from Sudan, Abuzeid was born and raised in Saudi Arabia and also lived in the United Arab Emirates. She moved to London at 18, where she completed her undergraduate education and medical school.

Her interest in business predated her medical career. Both of her grandfathers were entrepreneurs in Sudan, and she grew up hearing about the companies they built. By medical school, she was increasingly drawn to the reach that entrepreneurship and technology could offer.

After earning her medical degree, Abuzeid immigrated to New York at age 24. Her time in healthcare consulting at Booz Allen and McKinsey exposed her to the strategy, operations and economics behind the healthcare system, she said.

She later earned an MBA from the specializing in healthcare and entrepreneurship, and moved to San Francisco in 2013.

There, she joined an early-stage healthcare technology company as a product manager. The role taught her how to work with engineers, data scientists and designers, as well as how software products are built and grown.

It was also where she met , the software engineer who would become her co-founder at Incredible Health.

Abuzeid still does not code, although she has experimented with newer AI-assisted coding tools. Portlock, an -trained engineer who she calls 鈥渢he best engineer I鈥檝e ever worked with,鈥� has led Incredible Health鈥檚 engineering and data teams from the beginning.

But Abuzeid, the startup鈥檚 CEO, argues that a software founder’s central job isn’t writing code.

鈥淎t the end of the day, when it comes to creating software companies, it鈥檚 about solving problems,鈥� she said. 鈥淚t鈥檚 about identifying the markets, understanding the problems customers are facing and figuring out ways to solve them.鈥�

A mismatch in healthcare hiring

The problem behind Incredible Health surfaced through conversations the founders were having with people they knew.

Doctors in Abuzeid鈥檚 family and circle of friends frequently complained about understaffing. At the same time, nurses in Portlock鈥檚 family described applying to numerous jobs and often receiving no response.

The two accounts did not line up. Healthcare is the largest U.S. labor sector by number of workers, Abuzeid said, and it faces severe staffing shortages. Yet experienced nurses were struggling to get the attention of employers that urgently needed them.

鈥淲e started to dig into it more, and we were like, 鈥楾his doesn鈥檛 make any sense,鈥欌�� she recalls.

The founders discovered that hospital recruiting teams were often small, overwhelmed by applicant volume, and reliant on manual processes.

Incredible Health鈥檚 marketplace reverses the usual hiring process so that employers are actually the ones applying to healthcare workers. The software automates screening and matching for permanent jobs at hospitals, surgery centers, home health organizations and other healthcare facilities.

The service is free for healthcare professionals. Employers pay an annual subscription to use the marketplace and the company鈥檚 other hiring software. Customers include , , and .

A selective approach to fundraising

Incredible Health has raised approximately $97 million across seed, Series A and Series B rounds.

It was a process, she admits. She spoke with about 70 investors while raising the company鈥檚 seed round. Eight invested, including and .

At that stage, Abuzeid said, she had to educate investors about the healthcare labor market and persuade them that she and Portlock were the right founders to address it.

鈥淚 think it was the vision and the mission and the team,鈥� she said. 鈥淎t that point, you鈥檙e really investing in the founders.鈥�

Each of Incredible Health鈥檚 funding rounds was oversubscribed, Abuzeid said. She attributes that partly to raising from a position of financial strength. The company generally operates close to cash-flow break-even and at times has been cash-flow positive.

Overall, Abuzeid said she is selective about which investors she approaches. Specifically, she prefers firms with marketplace experience and partners who have previously operated companies. She also prioritizes investors who have already backed women or founders of color.

鈥淚 don鈥檛 want to be the first,鈥� she said. 鈥淚鈥檓 not here to overcome someone鈥檚 bias. That鈥檚 not a good use of my time.鈥�

While she acknowledges structural disparities in venture funding, Abuzeid said she didn’t choose to work with Portlock because she believed she needed a male co-founder. Rather, she recognized that a strong technical partner would balance her own skills and abilities.

Overall, Abuzeid believes female founders generally need to emphasize ambition. In her view, investors are used to hearing expansive visions from male founders, and women should be equally vocal about the size of the companies they intend to build.

鈥淚t鈥檚 really important to be ambitious and to be very clear about your vision and what you鈥檙e trying to achieve,鈥� she said.

Automating the first interview

In 2025, like many other startups, Incredible Health incorporated AI into its product lineup.

The company developed the agents with healthcare systems including , , Johns Hopkins and . Working with customers during the development process made it easier for Incredible Health to incorporate the technology into established enterprise workflows, Abuzeid noted.

One agent, Lyn, conducts the initial recruiter interview, asks clinical and behavioral questions, explains an employer鈥檚 value proposition, and discusses available roles. It then hands the candidate off for a possible interview with a hiring manager.

A second agent, Gail, helps healthcare professionals create r茅sum茅s and practice for interviews.

Abuzeid said Lyn has reduced hiring time by 30%. Three-quarters of interviews now occur within 24 hours of a candidate applying, compared with up to two weeks previously. About 40% take place at night or on weekends, when recruiters are less likely to be available.

The AI products clearly extend Incredible Health鈥檚 initial mission of removing hiring delays in the healthcare industry. They also reflect the founders鈥� complementary roles. Portlock continues to oversee engineering, data and technical architecture, while Abuzeid鈥檚 work draws on her experience across medicine, healthcare consulting and product management.

For Abuzeid, that distinction shows why she does not consider technical chops a prerequisite for founding a software company.

鈥淎t the end of the day, when it comes to creating software companies,鈥� she said, 鈥渋t鈥檚 about solving problems.鈥�

Related 兔子先生传媒 queries:

Related reading:

• 鈥楴obody Wanted to Give A Former Principal Money鈥�: How An Educator Built An Edtech AI Startup With $63M From VCs
• How A Teenage Carpenter Became The Founder Of AI Construction Startup Trunk Tools
• From Humanities To AI: How Ali Hussain Built Fintech Tabs Into A $400M Startup
• Craft鈥檚 Ilya Levtov Never Learned To Code. He Built A Software Company Anyway.
• A Startup General Counsel Knew What Corporate Lawyers Needed From AI. So She Built It.

Illustration: