Congratulations to the enterprise AI company that just raised another nine-figure round to solve a problem software is supposed to solve by itself: getting humans to use the product.

You know the one. Won’t name it. Don’t have to. You’ve read the press release — “autonomous,” “agentic,” “category-defining” in paragraph one, and then, a few paragraphs down, where the money is actually going: hiring an army of engineers to sit inside customer offices and manually make the “autonomous” thing behave.

Nothing says “we’ve built the future of work” quite like raising half a billion dollars to hire more people to do the work.

The business model, decoded

Here’s how it works, in case the deck was too artfully vague:

1. Sell the customer an “AI platform.”
2. Discover the platform doesn’t actually do the thing on its own.
3. Send in the forward deployed engineers: sharp young people with CS degrees who now spend six months hand-building the workflow the website implied already existed.
4. Bill the customer for “software.”
5. Book it as ARR.
6. Raise a bigger round because “ARR go up.”
7. Repeat, forever, with an increasingly large and increasingly expensive engineering org standing between the product and the promise it made.

This is not a flywheel. This is a payroll with a pitch deck.

And the industry has stopped even pretending otherwise — it’s fundraising on it, loudly.

pledged valued at $10 billion, with another $4 billion from ,, , and . The vehicle, DeployCo, has been with more than $4 billion in investment.

Meanwhile, job postings for “forward deployed engineer” are up somewhere between 800% and 1,000% year over year. Lightcast data reported by from January to August 2026. data carried by put listings up more than 800% from January to September 2025, and data from 643 in April 2025 to 5,330 in April 2026. Total comp is pushing past $300,000 in the U.S. list at $280,000 to $320,000 base pay and OpenAI at $185,000 to $300,000 base, plus equity. Fortune reported some Anthropic roles reaching $400,000 in total compensation.

Somewhere, a VC is calling this “operating leverage” with a completely straight face.

It is not operating leverage. It’s a staffing agency that learned to say “agentic.”

The tell

Ask the one question the pitch deck conveniently never answers: “If you pulled the embedded engineer out tomorrow, does the customer still get what they paid for?”

If the honest answer is “well, no, but the ARR is real” — that’s not product-market fit. That’s people-market fit, and people-market fit has a brutally unglamorous ceiling: You can only hire humans so fast, and you can never hire them cheap enough to earn a software multiple. Every new logo doesn’t get more profitable to serve. It gets another headcount request.

Two companies, same $20 million ARR. One needs 10 engineers to keep the lights on. The other needs 100. Both slides say, “AI software company.” The difference shows up in gross margin, cost to onboard the next customer, and whether that cost falls as the company scales.

The bill always comes due

Here’s the thing about people-market fit: it feels great right up until someone asks the unfashionable questions: What does it actually cost, fully loaded, to onboard the next customer? Does that number go down as the company scales, or does it just grow the org chart? What happens to gross margin if the first year growth slows and the headcount can’t hide behind hypergrowth anymore?

Companies funding their FDE armies with fresh nine-figure rounds are betting nobody asks these questions until after the round closes and the confetti is swept up. That’s a fine bet in a hype cycle. It’s a terrible one in a market that’s about to start reading the fine print.

Because eventually, someone always reads the fine print. A CFO builds a cost-per-deployment model. A CIO benchmarks time-to-value against three vendors instead of trusting one demo. An investor finally does the unglamorous work of separating software ARR from human-powered ARR on the same balance sheet.

The distinction buyers and investors can test is on the numbers. Software ARR should get cheaper to deliver as it scales. Human-powered ARR does not. OpenAI guaranteed the private-equity backers of its deployment vehicle an annual 17.5% return over five years, which is a different structure from a growth thesis.¹

So no, your AI isn’t autonomous. But your business model might be — autonomously transferring the cost of a services company onto a software valuation, for as long as the music plays.

Enjoy the round. Hope the org chart was worth it.

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

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This tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence and is updated at least bi-weekly. We’ve included both startups and publicly traded, tech-heavy companies. We’ve also included companies based elsewhere that have a sizable team in the United States, such as , even when it’s unclear how much of the U.S. workforce has been affected by layoffs.

Layoff and workforce figures are best estimates based on reporting. We source the layoffs from media reports, our own reporting, social media posts and , a crowdsourced database of tech layoffs.

We recently updated our layoffs tracker to reflect the most recent round of layoffs each company has conducted. This allows us to quickly and more accurately track layoff trends, which is why you might notice some changes in our most recent numbers.

If an employee headcount cannot be confirmed to our standards, we note it as “unclear.”

How would you handle a junior employee questioning the way you’ve always done something? Might you roll your eyes and dismiss their comments as naivety? Or would you indulge their curiosity?

As AI has made knowledge accessible on a scale never seen before, companies are becoming leaner and operating faster. What once took a large team months to do can now be done by a handful of people in a few days.

It is undeniable that autonomous systems are changing the way we work. In the small and dynamic teams of the future, choosing the right humans to direct your AI systems will become make or break for a company.

As a six-time founder, I’ve learned the best workplaces are the ones where questioning things is the norm. Here’s how to build a team that questions orders as much as it obeys them.

AI by design

“AI by design” doesn’t only mean building the right autonomous systems. It’s also about creating autonomous humans. You need to trust the human in the loop as much as you do the technology. The team around you ultimately shapes the company in ways you can’t control. So surrounding yourself with people you trust to make decisions when you’re not in the room is critical to success.

The nature of job roles is changing. A job description can be totally different from the one advertised by the time you’ve found a candidate, and a six-month roadmap is outdated by the time you’ve finished drawing it up.

The has stated that as AI reshapes the workplace. Hiring for a fixed set of skills won’t move the needle. The people worth investing in are those who are able to learn what comes next.

Hire for curiosity

Curiosity is the best trait you can hire for. When I look at a CV, it’s not just experience or academic credentials that draw me in. I look for what people have built on the side. What have they made on their own initiative?

Hire for curiosity, and you’ll create an environment where people are willing to throw new ideas around and challenge outdated ways of doing things. If your team is worried about saying the wrong thing, they won’t end up saying the right thing either.

In a world where technology is turning everything on its head, the companies that will thrive are the ones with curious people who are able to adapt to change quickly.

Hand over real responsibility

Employees with autonomy operate faster. They make better decisions because company vision is not abstract to people who are given real responsibility to drive it. Being involved in strategic direction stitches an employee into the fabric of the company and keeps them engaged.

Trust your employees to know what isn’t working. Allow people to pursue new ideas and reinvent things. Your employees are likely to spot potential problems before you do when they’re closer to the product. Make sure your team has the autonomy to try to fix them before they take hold.

Boredom is a big business risk

As companies become smaller, employee retention is more important than ever. Losing your best people is costlier when you have fewer of them to begin with.

The effects of a bored employee surface quickly in a small team. Boredom makes talented people a little bit slower, and a lot less likely to try the difficult thing.

The challenge for modern leaders is keeping your team engaged and interested. A bored employee is more likely to be swayed by a flattering recruiter.

Make sure to invest in development, keep your team learning, and always encourage curiosity. Give people autonomy and responsibility from the beginning, and boredom has nowhere to take hold. The mentality of risk and the drive to challenge something outdated is what you need to succeed in the changing work environment.

Dare to be a beginner, and teach your team to do the same. That junior employee questioning how you’ve always done things could be the most valuable person in the room.

is co-founder and CEO of Stockholm-based , an agent-driven, game-based learning platform. He has experience building, exiting and scaling digital companies across 70 countries. Uniplay is his sixth company to date.

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������Ƶ AI-focused startups raised $23 billion in the first half of 2026, up 130% year over year and accounting for 55% of the region’s venture funding, according to a joint ������Ƶ and HumanX . That investment surge is sharpening a question at the heart of the sovereign AI debate: Where should countries concentrate their resources to capture the technology’s economic value and retain control over their data?

But sovereignty does not require owning every layer of the AI stack, according to two executives I spoke with onstage at the conference: founder and CEO and , ’s chief product and technology officer.

Their perspectives from Europe and the United Arab Emirates, respectively, pointed to opportunities in specialized chips and applications and underscored how purchasing decisions, government mandates and access to computing power can determine whether AI ambitions translate into growth.

Five-layered cake

, CEO and president of chip giant , has detailed a “” to deliver AI that includes  energy, chips, infrastructure, models and applications.

For Del Maffeo, whose company develops chips for inference, the opportunity lies in the semiconductor layer, as more AI processing moves beyond centralized data centers and onto devices.

“Artificial intelligence will expand from cloud computing, from centralized data centers, to devices closer to us in the physical world,” he said. “To enable this, you need specific chips which can run efficiently, at a lower cost, to connect these networks that today are running in the cloud. We are here to solve this problem.”

Five-year-old Axelera AI has two generations of chips it sells to around 600 customers. The company next plans to expand to products for decentralized cloud computing.

AI transformation

Abu Dhabi-based AI71 partners with large organizations and government agencies to help them benefit from AI transformations.

The United Arab Emirates has the largest compute per capita in the world and has a three-month mandate for every government agency to set up agentic processes for its citizens, according to Ghissassi. Energy is abundant and cheap. Over the next two years, the goal is to reach half of every citizen’s interactions with the government with an AI agent, he said.

Competing at the model layer does not make sense, said Ghissassi. He believes there will be two, three or four companies that can afford to be in the race, given the amount of capital required and the fact that it commoditizes very quickly.

The application layer is the one that is really important, for sovereignty where you want to own your data, said Ghissassi, who previously led product development at . “You want to make sure that it stays with you, be it that you’re a government or an enterprise. If you’re giving away your trade secrets and know-how, nobody stops whoever is being a provider to you today, from replacing you.”

In the UAE, “what helps is the mandate, the pace at which things happen, the availability of compute, both in terms of sovereign clouds, or on-prem, or global clouds, and then the amount of capital that is being invested to help transform these companies to benefit from these technologies,” Ghissassi said.

By contrast, Europe struggles as it is a net importer of energy. It has strengths in the semiconductor industry but lacks all the real compute for AI, in neural networks and frontier labs.

“We should not be obsessed with controlling the entire stack,” said Del Maffeo. In Europe we have to create value instead of just paying for a service, he added. “Creating value means creating a wealthy economy.”

Europe’s strengths include its large pools of research talent and its population of 440 million people who can benefit from AI. However, the culture of large businesses purchasing from startups is less established, meaning it doesn’t have the same flywheel for growth seen in the U.S., said Del Maffeo.

“What worries me is that we are a little bit lagging behind, and therefore we are missing this value creation, and this will weaken the economies of Europe,” he said.

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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 — including ’s 8,000-job reduction — 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’s total, to AI.

“There’s been little evidence that AI is actually replacing the work of the human employees let go,” Lee said of this year’s 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’ve 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.

“Big companies [have] made up about 87% of everyone laid off in 2026, which is similar to last year, when they made up 85%,” 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, ’s 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  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. “There are jobs that are literally being replaced by artificial intelligence,” he told ������Ƶ News.

But companies are also changing their priorities. They’re putting more money into AI and cutting teams working on other parts of the business. “They’re letting people go from one area of their organization while they might even be hiring in an area that is focused on AI,” Challenger said. That’s 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’s 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’t afford before. That could mean new jobs outside tech, though it’s 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  report.

Methodology

Layoffs figures are from The ������Ƶ 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 — both privately and publicly traded — 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’s methodology section.

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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 ������Ƶ 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.

������Ƶ 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’t 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’ve invested in what you might call the SpaceX-mafia and hard-tech sectors — 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’ve 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’t 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’m not looking to invest in science. I don’t 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’ 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’s 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 — 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’t 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’s another example of SpaceX alumni applying the playbook and technologies they learned at SpaceX to a much broader commercial industry.

You’ve 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’re missing out on hardware but don’t necessarily understand it. I’ve met beauty investors who now say they’re 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 — from China Lake to San Diego — 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’re being thrown for a loop.

Ironically, Bay Area VCs have been among those leaning most heavily into this. But it’s happening everywhere. I’m in Washington, D.C., now, and one of the first investors in , the hypersonic missile company, was in Virginia — 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’t know how to distinguish a strong opportunity from fear of missing out or something merely cosmetic.

So investors’ lack of experience in the space isn’t 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’re 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 “SaaS apocalypse,” which I don’t think is real — although I sometimes question ’s 1 stock price for fun.

It creates what venture does best: tourists and FOMO. LPs ask why their managers aren’t investing in the same companies and how they can participate, raise more money and show that they aren’t missing out. That’s how I’ve seen investors unfamiliar with these sectors enter the market.

Some of the largest Silicon Valley firms … missed this dynamism wave. Now they’re 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 — 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’s 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’t based near Washington, D.C.?

Espahbodi: It’s 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 ’s 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’re also seeing portfolio companies backed by influential investors win government contracts worth as much as $1 billion at a time. That’s 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’t organize themselves entirely around the government.

My catchphrase is that I want everyone to be commercially focused but mission-aware. I don’t 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’t building and recruiting there, you’re 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’re part of the ecosystem and geography. They’re spending time in the same bars and restaurants, and their children attend the same schools. They’re 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’t think startups should locate there solely to be close to the customer. They should be there for the talent they need to build.

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

But she didn’t 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.

“Working as a doctor is great, but you’re only working with one patient at a time,” she said in an interview with ������Ƶ News. With software, “you have millions of users using your products.”

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 — including 1 in 2 U.S. nurses — 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 “the best engineer I’ve ever worked with,” has led Incredible Health’s engineering and data teams from the beginning.

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

“At the end of the day, when it comes to creating software companies, it’s about solving problems,” she said. “It’s about identifying the markets, understanding the problems customers are facing and figuring out ways to solve them.”

A mismatch in healthcare hiring

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

Doctors in Abuzeid’s family and circle of friends frequently complained about understaffing. At the same time, nurses in Portlock’s 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.

“We started to dig into it more, and we were like, ‘This doesn’t make any sense,’” she recalls.

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

Incredible Health’s 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’s 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’s 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.

“I think it was the vision and the mission and the team,” she said. “At that point, you’re really investing in the founders.”

Each of Incredible Health’s 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.

“I don’t want to be the first,” she said. “I’m not here to overcome someone’s bias. That’s not a good use of my time.”

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.

“It’s really important to be ambitious and to be very clear about your vision and what you’re trying to achieve,” 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’s 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 résumés 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’s initial mission of removing hiring delays in the healthcare industry. They also reflect the founders’ complementary roles. Portlock continues to oversee engineering, data and technical architecture, while Abuzeid’s 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.

“At the end of the day, when it comes to creating software companies,” she said, “it’s about solving problems.”

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We spoke with to talk through the firm’s evolution and how it is meeting this AI moment. She is a partner at the firm who relocated to Silicon Valley from Mexico City.

When the firm was founded in 2005, Brazil didn’t yet have a startup ecosystem — there was no network of founders, no LPs backing VC firms based there, and no follow-on investors. That has changed drastically in the couple of decades since, with the country emerging as LatAm’s startup powerhouse and a place where U.S. investors and tech giants are increasingly courting business.

Brazil is an exceptionally digitally savvy market. It is the for and the third-largest . Its real-time payment rail , created by the , was rolled out in 2020 and is used by more than 90% of adults in the country.

Latin America has also become an important market for U.S. AI labs and technology companies. Monashees recently announced a partnership with called the in which the two companies co-invest up to $2 million in AI-native and deep tech pre-seed or seed-stage startups in Brazil. Its first summit is planned for later this year in San Francisco, where it will gather Latin American startup founders building businesses with AI.

Monashees makes around eight to 10 new investments per year and is finalizing the deployment of its $370 million fund into roughly 35 companies.

The firm opened an office in Mexico City in 2022 and in September 2025 set up an office in San Francisco.

The interview has been edited for brevity and clarity.

Gené Teare: To set this up, tell me about Monashees.

Fabiola �ϳܾ��Գ���ñ�Dz�: Monashees is the pioneer of venture capital in Latin America. It started 20 years ago, in 2005, with the premise that what had happened in Silicon Valley with tech could also happen in LatAm — that many of the structural problems could be solved with tech.

and , Monashees’ co-founders, were crazy enough to believe this could happen, so that’s when they started Monashees. Just to give you a little context, back in the day there was nothing. It really took time for this flywheel to get started, because if you don’t have funding, you don’t have great talent.

Finally, after five years, they managed to crack that. In 2010, you started to have the first wave of tech companies in the region, and Monashees started positioning Brazil on the global tech map.

As a second phase, the team realized that what was happening in the Brazilian ecosystem was also starting to happen in other countries in the region. Great teams were starting to build great companies. That’s when Monashees decided to expand across Latin America and back these teams. That’s when we led ’s seed round, one of the flagship companies of Latin America.

The third wave, which is what we’re focused on right now, is Global LatAm: backing Latin American founders who are building global businesses, regardless of whether they are building in Latin America or globally.

That has also been the rationale for opening an office in San Francisco. In the context of AI, you have many Latin American founders starting businesses from here because you have to be close to the labs and the talent.

We’re early-stage investors. We invest at pre-seed, seed and Series A. Seed and Series A are our sweet spot, and we are lead investors. We’re also generalists. We’re not sector-specific, we’re mostly sector-agnostic.

I see this trend when I talk to a lot of ������Ƶ VCs with earlier-stage investors, establishing a U.S. presence. It seems fairly recent, and it seems to be driven by this AI wave. Do you think it’s the VCs coming here and the founders following, or are the founders coming first and the VCs realizing they need more of a presence here?

�ϳܾ��Գ���ñ�Dz�: I think initially it was mostly founders. Now, it’s a little bit of both; they’re feeding each other.

The reason we started the office in San Francisco is that the pace at which AI evolves is unseen, even compared with other technology paradigms in the past. If you’re not here, it’s very difficult to keep pace and stay up to speed with where the AI frontier is going. You even have a gap with Wall Street, so imagine the gap with Latin America.

If you want to build an AI-native company as a Latin American founder, part of that is coming to San Francisco and Silicon Valley. San Francisco is now the magnet for all of this. It’s highly dense and concentrated. You have to be here to absorb the tools and understand what other people are doing.

I also think it’s super important because founders realize that Silicon Valley is the champions league. In Latin America, you do have great talent, but you don’t know what great looks like if you’ve never worked here.

The reason we opened an office here is to bridge that gap: to help founders be here, see what is happening at the frontier, and understand what the best companies are doing so they can replicate that back in LatAm.

The talent in the region is now sophisticated enough. It has been a 20-year process to get to a point where you have great, ambitious founders who believe they can build global businesses.

You already have success stories like from or from . Founders realize they can build globally.

In the context of AI, many of these global companies have to be based here because you have access to AI talent, but also to funding. Being close to all the Silicon Valley funds is also crucial for them.

I do think several founders are coming here to build, but that also creates some issues. One important thing to note is that many of these founders are building for the Latin American market, where your revenue is in Brazilian reais or Mexican pesos. In terms of headcount, you need to be very careful that you don’t have a U.S. cost basis when your revenue is in Brazilian reais or Mexican pesos.

These very early-stage startups also cannot compete with the big labs here that are paying a lot of money for talent. Right now in San Francisco, finding AI talent is really difficult and it’s very expensive.

I think it’s more about coming here, learning and bringing back the best practices. That’s where the real arbitrage opportunity comes from. You have amazing talent in LatAm, and you can teach them. Now, in the context of AI, you have much better ways to do that and you can operate with a smaller headcount.

That’s the rationale for founders coming here and for us being here as a bridge. We help our portfolio companies stay close to AI innovation, but we also get access to Latin American founders who are building from the U.S.

We hired a researcher for the Monashees team. Andrés [Campero] has a Ph.D. from in AI. He’s one of the disciples of , who is a very renowned researcher. The rationale for having Andrés, who is Mexican, on the team is to help us connect with the research diaspora here in San Francisco. It’s very different to talk about business than it is to talk to researchers.

This is important because most researchers from Latin America don’t stay in Latin America. They come to the U.S. and work at the different universities here. It’s important for us to be connected to where most of the innovation is happening. Andrés also helps us identify the best companies emerging in the region from a technology standpoint.

Of those, how many are coming to the U.S. at the earlier stages? What proportion do you expect to come here?

�ϳܾ��Գ���ñ�Dz�: Some of the companies we’re seeing start in LatAm and then expand to the U.S.

We have a portfolio company called . It’s AI-native, and it develops preventive-maintenance software. The company started in Brazil.

Its customers were global businesses, and those customers started pulling the company into the U.S. Its product was much better than what was available here. The company is now headquartered in Atlanta, so you could say it’s a U.S. company now.

Most of its revenue comes from the U.S. I think examples like that — companies born in LatAm that expand globally — will tend to be around 30% of the portfolio.

Companies we invest in from the U.S., where most of the revenue will be U.S.-based, will probably be around 20%, because we continue to be a LatAm-focused fund. But we’re also going to see more LatAm-born companies coming here.

You mentioned the focus on Latin America, and talent is obviously very difficult to find here in the U.S. right now. For the companies that are based here, do you see them setting up offices in LatAm to attract talent? Are most of them using a hybrid model, or are some completely U.S.-based?

�ϳܾ��Գ���ñ�Dz�: It depends on the stage they’re at. Later-stage companies — think Series C or Series D — tend to have most of their technology teams in Brazil, Argentina or elsewhere in LatAm.

Another example is . It’s headquartered in Salt Lake City, but most of its technology team is in Brazil, in a smaller city called João Pessoa.

The company is building very sophisticated AI infrastructure. It was able to do that because it was very good at hiring a senior team that could teach and transfer knowledge to the local team.

We’re seeing more of that. You start with senior people, senior researchers or senior data scientists in the U.S., while much of the junior team is in LatAm.

Now, with AI, you can have fewer junior people. But you also have talent in Latin America that is strong enough to act as the senior engineers.

What are the standout companies in the Monashees portfolio that you would highlight?

�ϳܾ��Գ���ñ�Dz�: I’ve shared a couple. One is Tractian, the preventive-maintenance software company. The company has been growing. It’s a success story for us because it started in Brazil, and it has proprietary technology. It combines software and hardware, and it owns the patents for its hardware.

Today, it is really conquering the U.S. market. It’s a perfect example of an AI-native company born in Brazil, where the AI lab lives in Brazil, but the company is competing at the global level.

We also have Music.AI, which is in a fun industry. If you’re an amateur musician, its platform allows you to play whatever song you want and play with the instruments in the background. You can play the drums, the flute or whatever you want while having the other instruments behind you. The company has both a B2B and a B2C business. It has more than 50 million users or downloads and is growing very fast. It won iPad App of the Year two years ago. It’s another success story. It is based in Salt Lake City, but has its technology team in Brazil.

We recently invested in a company called . It’s an accelerator, so it’s similar in some ways to what we’re doing with Google. Shiva is trying to capture this new wave of entrepreneurs who might not have pursued entrepreneurship if Shiva and AI didn’t exist. , the founder, is a second-time founder. He was one of the early co-founders of one of our portfolio companies, which later went public. You can think of Shiva as the of Latin America in the sense that it is very community-driven. It is also trying to capture these solopreneurs: companies started by just one person that can go global from day one and have revenue from day one.

I think it’s a super-interesting and very different investment. It speaks to how we’re always trying to keep pace with how the ecosystem is going to evolve, because this ecosystem is also likely to be disrupted by AI.

I can also tell you about some of the companies in the portfolio that are focused specifically on Latin America.

We have a company called . It’s an HR platform. It’s very specific to the Brazilian ecosystem because regulation requires employers to provide certain benefits to employees. Flash managed to build a technology product around that, and now the company is expanding into a full HR platform. It’s one of the flagships of Fund IX. It’s growing very fast, and it has become a flagship company in Latin America. Fintech is one of the largest and most important markets in Latin America.

Another company was actually the first investment I made at Monashees. It’s a payment-orchestration platform called . The company is at the Series B stage. We invested at seed back in the day. It gives an e-commerce company a single integration through which it can manage all of its payment methods. If you’re a multinational company — think about — and you want to enter Brazil, Colombia and Peru, you have to deal with so many payment methods. With Yuno, you have just a single integration. In the context of AI, Yuno has developed a very strong agentic platform that helps with fraud and conversion. Fraud in LatAm is a big issue, and the platform helps companies manage fraud and increase conversion across these marketplaces.

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A clean sheet rewrite of a legacy product certainly improves customer experience, but it can take years from starting gun until the last customer is migrated and the old code is fully decommissioned.

is an HR software business, owned by my investment firm, with just that challenge. In December, when joined as CEO, the company had developed a plan to rewrite from scratch one of its oldest software products. The timeline was 18 months, with a 30% surge in engineering headcount to power through the project. But Jeff and his new CTO did it better, faster and smarter.

Jeff came to the board in February with a radical alternative: redesigning the engineering team organization and individual job specs, literally changing what people do all day to best put to work the power of off-the-shelf AI tooling.

HireRoad’s new approach would complete the development in 16 weeks, not 18 months, and the customer base migration and legacy decommission would be completed in calendar year 2026. In my 30-year career as a software investor, I had never seen any organization achieve such a task at anything like that velocity. The board debated and made the leap, killing the old plan and taking this frontier bet.

The rebuild was done in 15 weeks, a week ahead of schedule, and as of this writing, the first 34 customers have been migrated to and are live on the new platform, with glowing feedback. The pacing to complete the migrations and decommissioning is on track. The kicker is that Jeff and team completed this with a smaller team, freeing up the 30% headcount surge to work on other HireRoad developments.

AI-Sprinkle vs. AI-native

In 2024 and 2025, we at felt proud of ourselves and quite cutting-edge for providing the engineering teams across our software portfolio with access to AI tools such as Copilot and Claude Code. We saw productivity gains of 10%, then 20%, now more like 30%.

But somehow, our companies were all getting stuck at those 30ish percent gains.

How to reach 3x gains? The realization was that providing AI tool access alone was, candidly, not AI-enabled but rather AI-sprinkled. The breakthrough came when leaders went beyond the AI-sprinkle and instead adopted AI-native daily practices.

Let’s pause for a moment on terminology here. The phrase “AI-native” is thrown around a lot just now. In our usage, AI-native describes what you do all day, not when your company was founded. Anyone can learn to work in an AI-native fashion, and it means directionally using AI tooling first and humans to orchestrate, coordinate and communicate.

AI-native work is not just doing the same thing faster; it means doing different things with more delegation and quicker learning loops, and I will share some specific examples as we go.

Startups will call the move to so-called AI-native organizational practices obvious. They are right, but they are not burdened by an existing organization or established products and customer bases. They get to build AI-native practices into their organization from the start. In contrast, private equity portfolio companies have to remodel.

Our experience is that the AI-sprinkle — or, giving an AI layer to an otherwise unchanged organization — provides mere percentage gains to productivity. We have to redesign the organization around the power of the tools to get multiples on productivity.

A 30% productivity gain feels good, but it is the trap of the current moment in AI. And the path from 30% to 3x is uncomfortable. It runs through changing how teams are structured and what people actually do all day. In this way, delivering on the promise of AI is a business change, not a technology change.

I had the great good fortune to take a course in strategy at business school from and Andy Grove. Burgelman is a professor whose 12-year study, , delivered the definitive business text on , which Grove famously ran through its own era of technological revolution in the chip industry. His intellectual framework applies exactly to the current moment of technological revolution.

Evolutionary vs. revolutionary

Burgelman’s framework is that there are two kinds of strategic behavior, which he called induced and autonomous. Induced strategies fit the company’s existing structure and trajectory, like an AI layer inserted into an existing process. They are evolutionary moves, continuously advancing and improving on the current direction of travel. Autonomous strategies are those arising from outside the current business plan, like rewriting the job definitions and changing the team structure and work patterns of your product and engineering teams around the power of AI tooling.

Autonomous strategies are revolutionary moves. With AI, 30% gains are to be had from AI-sprinkle on the induced-strategy evolutionary path. The 3x gains require AI-native autonomous strategies, meaning revolution.

An oft-repeated analogy is how electricity transformed manufacturing. Replacing the steam engine powering a mill with an electrical motor delivered very little productivity gain.

Productivity skyrocketed only when the manufacturing plant itself was redesigned, distributing small electric motors throughout the factory in a horizontal layout, delivering what a single steam engine never could. What interests me most about this story is why it took decades before the factories were redesigned. Why couldn’t those organizations make the revolutionary leap more quickly? That is where the Burgelman/Grove case study is so helpful.

Burgelman points out that revolutionary ideas are very often squelched by institutional inertia and the cultural power of the evolutionary path. To be realized, revolutionary strategies need full buy-in from the CEO and Board.

The retelling of Grove’s revolutionary moment is here very apt. As told in Grove’s seminal business book “,” he and Intel co-founder were sitting together struggling with a strategic question. Intel’s primary business at that time was memory chips, a business where Japanese competitors were assaulting them in a brutal price war, pushing Intel to the brink. Intel also had a smaller, growing business line in microprocessors, the CPUs inside personal computers.

After a long pause, head in hand I imagine, Grove looked up at Moore and said, “If we got kicked out and the board brought in a new CEO, what do you think he would do?” And Moore said without hesitation, “He would get us out of memories.” Grove replied, in effect, why shouldn’t you and I take a walk around the building just now, and come back in the door, and do it ourselves?

That is just what they did, and the great run of “Intel Inside” as the leading CPU maker was launched. The uprooting of your proven daily practices and time-tested organizational design, to an AI-native way of working and team design, is a difficult revolutionary act. It may feel just as uncomfortable, just as heroic, as that fateful Grove-Moore conversation.

So, what did HireRoad do to affect the 30% to 3x revolution? The new technology leadership trained the team on a new hour-by-hour how to spend your day, built around the power of the AI tooling. The new sales leadership worked with the engineers to put the rapidly produced prototypes in the hands of clients, shortening the user feedback loop. When users identified bugs, the system logged them, wrote code to fix them, and presented the solution to a “human in the loop” for final judgment and publication. Customer support was engaged to develop and communicate a high confidence transition plan for users.

Overall, the HireRoad team became smaller and more senior, with resources freed to work on other initiatives, and to roll out these practices across other HireRoad product lines.

Management innovation and private equity

AI-native organizations are the third major management innovation of my private equity career. The first management innovation was the removal of bloated cost structures and tight linkage of executive compensation to equity outcomes in the 1980s, and the second was the conversion of on-premise licensed software to subscription model SaaS in the 2010s.

Those investors who mastered and first put those techniques into practice created vast fortunes for their capital partners. The starting gun has just been fired on the third wave. The organization changes to implement AI are a business change, not a technology change. While the ideas and practices can arise from anywhere in the organization, companies will not participate until this revolutionary change is endorsed by the CEO and board.

There are some 10,000 privately held software companies in the U.S. today, depending on exactly how you count. Leaders of those businesses know, explicitly or perhaps just through gut feel of the shifting sands, that doing the same thing in the same way in the age of AI is a losing strategy. You won’t lose all at once. You will be slowly starved as competitors move at 3x your pace around you. Certainly, your prospects to be a leader will close.

You have the customers, the distribution and the knowledge of the problem you are solving, all legs up on the startups. The nature of the organizational change you need to make is known, or knowable.

When considering this moment, shared by all of us who work with existing software organizations, think about the decades between the initial one-big-motor electrification of factories and the 1920s many-small-motors factory redesign which delivered the huge productivity gains. These changes don’t just happen on their own, and this time around, we won’t have the luxury of a lengthy transition. When considering your own revolutionary strategic move, run the Grove thought experiment. Walking outside around your building, ask yourself, “If I were fired, what moves would the newly hired CEO make today, to win with this company in the age of AI?” I suspect the nature of your answer will not be to sprinkle more LLM access across your unchanged organization. Rather, ideas will occur to you on how to change your team structures and what people do all day to better serve your customers through the incredible AI tooling now at your disposal.

Are those the moves you are making today?

co-founded in 2014 and is managing partner. He has served on numerous private and public technology company boards, and currently is a director of , , , , and . Previously, he was a partner and member of the investment committee at . He also worked at and . Morse serves on the board of directors of and as member of the advisory board for the HMTF Center for Private Equity Finance at . He attended , graduating summa cum laude with a BSE, and , where he earned his MBA and was an Arjay Miller Scholar. Morse lives in Austin.

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Venture firms that set up shop in the Texas capital city included , , and 1, among others. famously moved ’s headquarters to Austin in 2021, while also purchasing a house and establishing a residence there.

But as more employees returned to in-office work, Austin slowly seemed to fall out of favor with the tech community, some of whom said it had been overhyped as a startup hub.

There were reports of tech workers who had moved to the city during the pandemic and , saying they were going back to places like the Bay Area. Musk back to California in 2023.

Funding tops pandemic peak

Undeterred by the “tourists,” the startup and venture community in Austin kept plugging away. And those efforts are reflected in a surge in funding to startups headquartered there last year, with 2025 posting an all-time high for Austin venture investment, ������Ƶ data shows.

Investment into Austin-based startups spiked 64.8% to $7.19 billion in 2025 as more investors poured money into companies based in the region, according to ������Ƶ . That’s compared with the $4.37 billion raised by Austin-area startups in 2024 and tops even the $6.1 billion raised in 2021, at the height of the venture funding frenzy.

Notably, deal counts actually decreased from 312 in 2024 to 272 year over year, signaling an increase in later-stage deals. Indeed, the data corroborates that with $4 billion of the total raised in 2025 classified as late-stage rounds.

Last year’s totals were also more than double — 130% higher — than the $3.1 billion raised in 2023. That money was raised across 403 deals, signaling much smaller round sizes at the time and a more mature market.

A tech scene decades in the making

, managing partner of , doesn’t believe that the Austin funding performance in 2025 was anomalous.

Rather, he calls it “the payoff from decades of compounding.”

“Talent density in venture categories such as software, fintech, health tech, defense and  robotics has reached a critical mass, driven by waves of Bay Area relocations, both full HQ moves and satellite offices, that brought technical, product and operational talent into the market,” Flager said.

That talent eventually left to build new companies, he said, and the cycle repeated.

“On the capital side, the stack has matured across all stages, from pre-seed through growth, with local firms that have now cycled through multiple funds and understand the market deeply,” Flager said. “Layer in a business-friendly regulatory environment, a relatively lower cost of living, as well as a lower effective tax rate, and Austin becomes an attractive place to start and scale a company.”

Former Austin Mayor saw so much potential in the city’s startup scene that he began a career in venture investing after his tenure ended in early 2023. (He now works for New York-based ).

Part of the city’s success as a startup hub stems from its reputation as a haven for mavericks and risk-takers, Adler has said.

“Most cities in the world, you try something, you fail; it’s hard to have access to the capital the second time,” he told co-founder in a in 2022. “In Austin, the civic folk heroes are the people that tried something and it didn’t quite work out and they worked on it until it did.”

, founder of , a solo GP venture firm based in nearby San Antonio, said that it feels like Texas and the Austin metro area specifically are becoming more attractive to manufacturing- and engineering-heavy businesses.

“Some of that may be thanks to Tesla, and some of it may simply reflect the physical advantages of the state,” he told ������Ƶ News. “Either way, this [surge in financing] feels less like hype returning and more like capital concentrating around a narrower set of serious, technically differentiated companies.”

Deal sizes grow

That diversity among funded startups is reflected in last year’s investment totals for Austin, which were boosted by several large, late-stage deals across a broad range of industries.

The largest was a $1 billion Series C round for energy provider in October. New York-based led that financing, which valued the 2-year-old company at $4 billion.

Looking back, February in particular was a busy month for venture funding. That month alone saw the second-, third- and fourth-largest rounds in Austin for the year. They included:

• A February Series C round in which autonomous surface vessels maker raised $600 million at a $4 billion valuation. led the round for the defense tech startup.
• Also in February, , which provides endpoint management, security and monitoring, raised $500 million in Series C extensions at a $5 billion valuation — more than doubling its value from just 12 months prior. The funding came in separate tranches led by and ’s , with participation from other investors.
• Robotics company in February raised $415 million in Series A financing led by  and accelerator (A $520 million extension to that Series A was raised in February 2026, taking the total round to over $935 million.)

The findings correspond with Flager’s observations.

“A good chunk of the capital raised in Austin was driven by several large deals. Similar to what we saw across the U.S. in 2025, venture funding in Austin was more concentrated than it has been in the past,” he told ������Ƶ News. “Roughly 38% of the capital deployed went to the top five venture financings in Austin. I believe the top 10 deals nationally accounted for more than 40% of the capital raised last year. We’ll see if this trend continues into 2026 and beyond. The start of the year suggests it will.”

, founding partner of , agrees, noting that from a dollars perspective, the surge in financings was driven by a handful of outsized capital-intensive deals in newer categories such as defense and deep tech.

“These companies require a combination of technology, land for manufacturing facilities, and talent for manufacturing tasks. Austin has unique skillsets for that,” he said. “It has a density of three things: talent in deep tech with , and many others moving to Texas in light of favorable business conditions with expertise in these industries; expansive land around Central Texas that is inexpensive, especially compared to California; and lower cost manufacturing-related labor especially given the surge in manufacturing jobs such as at Tesla in recent times.”

Burgeoning industries

Once upon a time, Austin was better known as home to software and CPG companies. And while those types of companies certainly still exist, a number of other industries are growing increasingly robust, as the local investors have pointed out.

As with many top tech markets, Flager said Austin has long been strong for application and infrastructure software, which is currently being challenged by AI. In his view, that talent has migrated to building “quality” vertical agentic software and AI-native businesses.

“We are seeing these companies grow quickly and build scale, while using less capital — which is exciting,” he added. “The domain experts who built and scaled application software companies here over the last two decades are spinning out to build the next generation of native AI businesses.”

The market overall is also broadening in interesting ways. Defense and autonomy have emerged as breakout categories, with Austin becoming one of the stronger markets in the country for dual-use and autonomous systems companies, noted Flager.

“The combination of software and hardware skills now in Texas, along with a business-friendly regulatory environment, has allowed Austin to take a leadership position in these important and developing markets,” he said. “Energy tech is also a natural fit given Texas’ grid scale and the surging power demands of AI infrastructure.”

Finally, robotics and advanced manufacturing are also gaining momentum, driven by deep engineering talent and the ability to scale manufacturing near Austin cost-effectively, allowing engineers, executives and other factory employees to coexist and collaborate in close proximity.

Srinivasan noted that his firm is seeing strong activity in vertical AI companies, or companies that serve vertical markets with AI that is tuned on specialized proprietary vertical data, often targeting the services and labor expenditures by their customers.

“These companies deliver ‘Services as Software’ with close to software gross margins and pricing models that are based more on usage and outcomes as opposed to the traditional seat-based models,” he said.

Srinivasan also expects the city to continue to see large funding deals in defense and deep tech, given the combination of local strengths and robust global demand for such products.

Continued momentum

Investors and companies continue to be drawn to Austin. In late December, San Francisco-based venture firm in the city. One of the firm’s founders, , also announced that he had personally moved to Austin. The firm’s other founder, , had lived and worked in the city since 2022.

In late March of this year, Musk to build two semiconductor factories totaling 100 million square feet in Austin to supply advanced chips for and Tesla. The venture, known as Terafab, aims to manufacture 1 trillion watts of computing power per year, he said. Media outlets valued the initiative at nearly

Also this week, Barcelona-based AI health tech startup announced it will open an office and hire in Austin.

CEO told ������Ƶ News that with the company’s New York office already established, the next step was not just expansion, “but choosing the right place to build.”

“And we chose Austin for one reason above all: talent,” he said. “As an AI health tech company, our success depends on attracting exceptional people across engineering, data and life sciences. Austin has rapidly become one of the most competitive talent markets. The city is one of the fastest-growing in the United States. This brings together deep tech expertise, entrepreneurial energy and a growing concentration of healthcare innovation. Ideal for our goal of building an R&D hub. “

Coelho also points out that Biorce has witnessed a “trend” of people moving from the Bay Area to Austin, noting that “the quality of life has gained notoriety.”

“But for us, this isn’t about following a trend,” he added. “It’s about building where the best people are — and where they want to be.”

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