led the company鈥檚 Series A, which was raised in just three weeks and included participation from repeat backers and . The round also included an investment from new strategic backer , a real estate and technology investment firm. It brings New York-based Trayd鈥檚 total funding to $17 million.

Co-founder and CEO grew up in a New York construction family, watching her father navigate razor-thin margins and complex compliance requirements.

鈥淚 saw firsthand the operational strain that comes with juggling union rules, multistate labor laws, and endless manual back-office processes,鈥� she recalls.

The experience inspired her to team up with , the company鈥檚 CTO, who spent 10 years as 鈥檚 web platform lead, to start Trayd in 2021.

Specialty trade service

For the unacquainted, specialty trade contractors are businesses that place skilled workers on job sites to perform the actual physical building work. These contractors include concrete crews, electricians, plumbers, ironworkers, painters and fireproofing. They’re distinct from general contractors, who manage and coordinate projects overall but don’t typically perform the hands-on trade work themselves.

Trayd automates payroll, HR, compliance and labor cost tracking for such contractors. Among the benefits it touts are providing real-time visibility into the costs of labor, equipment and materials.

The startup aims to substantially cut the time specialty trade contractors spend on its weekly payroll and compliance process.

鈥淲hat used to take 14 hours of manual work can now be done in under 30 minutes,鈥� Berger told 附近上门 News.

Trayd is working to fill what it believes is a unique gap in the market. While there are significantly more specialty trade contractors than general contractors, the majority of construction technology has been built for the latter, Berger believes.

The startup鈥檚 closest competitors are legacy payroll providers like and , along with newer companies like and .

鈥淭he difference is that most of these systems weren鈥檛 built for the complexity of specialty trades,鈥� Berger explains. 鈥淭rayd was.鈥�

Streamlining payroll

In construction, compensation is uniquely complex, Berger said. A single worker might earn four different pay rates in a single day depending on the specific trade task, the project scope and the jurisdiction.

鈥淕eneric鈥� payroll platforms cannot handle this constant rate variability, contends Berger. For example, payroll admins might receive stacks of paper timesheets or phoned-in hours from various job sites. Then they have to manually key all of that field data into Excel spreadsheets and calculate the pay rates by hand, factoring in union rules, prevailing wage requirements and state-by-state taxes.

They might then have to cross-check the spreadsheet math and manually double-enter the finalized numbers into a generic payroll system, and then again into their accounting software.

Trayd, according to Berger, dramatically reduces the time to perform all those tasks by capturing the time data directly from the field and automatically calculating the correct variable pay rates, union deductions, and multistate taxes.

鈥淯nlike salaried workforces, construction workers can earn multiple different rates in a single day depending on the trade, the project, and whether the work falls under prevailing wage, state or union requirements,鈥� she said. 鈥淭rayd was designed from day one to handle that complexity.鈥�

National expansion

The product seems to be resonating in the industry. Trayd has grown revenue over 600% year over year and moves tens of millions of payroll dollars each week, according to Berger. Several hundred contractors use Trayd weekly. , and are among its customers.

The startup operates on a SaaS model, with pricing tied to the number of workers processed through payroll.

Trayd started in New York and the broader Northeast, where union density and regulatory complexity are highest. It is now expanding nationally. Presently, it has about two dozen employees.

Before Trayd, Berger co-founded , a consumer social platform that is now defunct.

She acknowledges that being female founders in a male-dominated industry has not been easy.

鈥淎s women building in construction 鈥� where we’re outnumbered 9 to 1 鈥� the default assumption is that we’re too far removed or don’t have access to truly understand the problems on the ground. In the early years especially, there’s a ‘prove it twice’ dynamic. Without the benefit of the doubt, we had to earn credibility through repetition 鈥�- every meeting, every deal, every product decision. We’ve had to work twice as hard to be taken seriously,鈥� she told 附近上门 News. 鈥淏ut that pressure becomes an advantage. You show up more prepared, you listen more closely, and you build conviction faster. Over time, that compounds into a better product and deeper, more trusted customer relationships.鈥�

, general partner听at White Star Capital, said his firm was first impressed by Trayd鈥檚 founding team, describing Berger and Kessler as 鈥渁 rare combination.鈥�

鈥淎nna鈥檚 background and family ties to the space allow her to understand the unique pain points contractors face from the inside,鈥� he wrote via email. 鈥淐ara brings the technical depth to build mission-critical systems without sacrificing product simplicity.鈥�

Beyond the caliber of the founders, White Star also believes that Trayd stands out because 鈥渋t is truly a better product for its customers.鈥�

鈥淥n a technical level, we were very impressed by how thoughtfully the product has been built,鈥� Lee added. 鈥淲e see that as a real advantage, because by structuring data cleanly at the system level, Trayd is better positioned to scale reliably and to become a strong foundation for AI in the construction industry over time.鈥�

Venture investment in property technology startups has rebounded in recent years after plunging from the pandemic peak. In 2025, startups in the sector pulled in approximately $10.5 billion in seed- through growth-stage financing globally, per 附近上门 . That鈥檚 up about 17% from $9 billion in 2024, with much of the recent investment going to startups that promise greater ROI through the use of automation or AI.

Related 附近上门 query:

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The investor is vocal about his belief that AI is a massive threat to entry-level jobs, or what he views as a shifting social contract in the modern workforce.

He would know about AI鈥檚 impact. Choi has led investments in several AI-first companies.

Known for a founder-first conviction, he鈥檚 also backed enterprise software and fintech infrastructure companies.

Prior to joining Khosla in 2024, Choi was a partner at , where he led and managed high-profile growth investments in companies such as , , (which was acquired by ), and . Before Accel, he worked at , backing companies such as (acquired by ), , and (acquired by ).

I recently spoke with Choi to hear more about why he thinks entry-level jobs could be disappearing, why he鈥檚 flipped his investing philosophy, and how he鈥檚 gone from growth-stage investing to being stage-agnostic.

This interview was edited for brevity and clarity.

附近上门 News: You鈥檝e had a prolific run the past couple of years, leading deals in , , , , and others. How are you managing that volume?

Choi: It has been an intense stretch. About seven deals happened just last year, which was an insane year by any standard. This year has been a bit calmer as I focus on settling in with the companies I鈥檝e invested in.

You鈥檙e currently researching the disappearance of entry-level jobs. As a parent, that sounds a bit scary to me. What are you seeing?

AI is a massive conundrum. I鈥檓 seeing it in my own workflow. I use the models to get up to speed on technical capabilities 鈥� asking about Clickhouse鈥檚 indexing methodology versus 鈥檚 in voice mode while I鈥檓 driving. It鈥檚 pulling from research papers and documentation faster than any human could. I describe it as feeling like I have an “Ironman suit” on.

The problem is that if I can do the work of a junior associate myself, almost instantly, those roles vanish. We鈥檙e facing a world where the base-level work we used to rely on young folks for is now table stakes.

If the “on-the-job” training era is over, where does that leave students and universities?

The burden of the first three years of 鈥渓earning how to be a professional鈥� has to shift to the universities. I look at the traditional U.S. model of general education requirements and think, 鈥淲hy are we doing this?鈥� We did that in high school. Universities should be places where you use AI to actually build things and apply knowledge to the real world.

If you’re a computer science major today, you need to graduate looking and delivering like a third- or fourth-year engineer. The bar has been raised for everyone. While schools like and are leaning into an 鈥淎I-first鈥� curriculum, many elite institutions are still silent, trying to figure out how to adapt.

Khosla is known for being contrarian. How does that translate to the growth stage in such a competitive market?

I鈥檝e actually evolved to be stage-agnostic. While people still put me in the 鈥済rowth鈥� bucket, I鈥檓 doing much more seed and Series A. In this era, the metrics a company has today don’t guarantee where they’ll be in two years because the rate of change is so high.

I鈥檝e flipped my philosophy: it used to be 80% metrics and 20% founders. Now, it鈥檚 90% founders. The only constant is how special the founding team is and how quickly they can adapt. If code is being created 10x faster, a company might face 50 years of change in a single decade. You have to back the people who can handle that stress.

You鈥檝e predicted “mass carnage” for some software companies. Who survives the transition to an AI-native world?

We are moving from trading on revenue multiples to trading on free cash flow and PE multiples. That鈥檚 a painful transition. The market now needs to believe that a company is AI-native 鈥� that its revenue is moving toward inference- and usage-based models rather than just old-school seat licenses.

I expect carnage for lightweight, horizontal applications and mid-market companies that can’t attract applied AI talent. I have a ton of respect for founders like those at or who are 鈥渂urning the boats鈥� to reinvent their entire businesses. It鈥檚 incredibly difficult, but in this market you either reinvent or you get replaced by someone building natively from day one.

With your background in fintech infrastructure, where do you see the next 鈥渦nconventional鈥� opportunity in financial services that most growth investors are currently overlooking?

It’s now become somewhat consensus, but I still believe it’s fairly unconventional that systems of record can be ripped out, whether it be in financial services or in other categories.

For example, we recently invested in , which is seeking to replace and the core accounting system, which is the last system of record I would have thought might be under threat. We’re seeing that with AI, startups can build migration paths that didn’t exist before, and also the depth and breadth of incumbent platforms in a fraction of the time.

often talks about 鈥渃hallenging the conventional wisdom鈥� of founders. Can you share an example of a time you had to steer a growth-stage founder away from a 鈥渟afe鈥� path toward a much larger, albeit riskier, vision?

In general, there are many times when a founder is thinking through a very risky but potentially game-changing product addition or acquisition. While I view part of our job as investors and board members as helping identify and manage potential risks, the most important thing we can do is give them the courage to take risks that are transformational to the business and the category they are in.

You鈥檝e noted that talent density is the most important variable for success. In a market where AI is automating routine work, how has your criteria for what defines an 鈥渆lite鈥� executive hire changed?

Perhaps somewhat ironically, one of the main differences in criteria is whether this exec has “IC’d” (individual contributor鈥檇) themselves and can do most of the work required out of the gate with their own two hands plus AI.

Related 附近上门 query鈥�

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By and

This story starts with a loss.

A client didn鈥檛 renew, so we reduced headcount. Operationally, it made sense, but the decision surfaced something bigger than a single role or contract: a growing tension inside modern leadership that鈥檚 becoming harder to ignore.

Our company spans a creative services business and an AI-powered software platform. That combination has made one trend obvious: The modern economy rewards software, automation and what investor and entrepreneur calls 鈥� the 鈥渇orce multiplier for your work.鈥�

Years ago, Ravikant bemoaned that labor 鈥� once the original form of leverage 鈥� had become 鈥渙vervalued,鈥� and that capital, code and now AI would define the next era. 鈥淵ou want the minimum amount of labor that allows you to use the other forms of leverage,鈥� he wrote.

, perhaps the 鈥渇ace鈥� of AI writ large, is famous for espousing the as the ultimate form of leverage, claiming that it will create 鈥�,鈥� and that 鈥淸a]lmost everyone will want more AI working on their behalf.鈥�

Investor enthusiasm around , and 鈥� companies with extraordinary output and extremely small teams 鈥� suggests he may be right. Humanoid robots, though still early, extend this trajectory even further.

Behind their enthusiasm, however, lies something the most fervent AI supporters seem reluctant to name. In this brave new world of AI, optimal business models continue to reward leverage, software and automation, of course, but they now also appear to reward something new: having materially fewer people.

As a consequence, we keep returning to a central question: What happens when the efficient thing and the decent thing diverge, even temporarily?

The economic pressure is clear, but the human pressure is just as real. We all seem convinced that we鈥檒l remake our workflows with AI, but what of our culture 鈥� the intangible connective tissue beneath it all? It鈥檚 no coincidence that before AI, the best companies often had the best cultures. In fact, not only do we believe this dynamic will continue as we adapt to AI, we think it will become even more important.

The future of work 鈥� perhaps, even, the future period 鈥� will invariably depend on how humans and machines collaborate, but it will also depend upon the culture we create that holds that collaboration together.

The frameworks, norms and practices that will eventually govern human-AI partnerships are critical, but they remain undefined. And we鈥檙e all going to have to build that culture largely from scratch.

is the co-founder of the world鈥檚 newest creative AI marketing tool, . He鈥檚 also the co-founder of , an advertising agency that leverages data to make hits for , , , and many other marquee brands. Over the past two decades he has led cross-functional teams and developed multidiscipline communications and creative strategies for both for-profit and nonprofit organizations. Himmelsbach is a MBA graduate from 鈥檚

is head of business development at WestComms. He strongly believes that high-quality communication will only continue to appreciate in value and supports clients working in AI, crypto and frontier technologies. Pinson still keeps a regular hand-written journal, loves wine and earned a degree in economics and philosophy at in California.

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For white-collar workers, AI looks like a pink slip. But for the millions of blue-collar individuals who struggle to land a job, not because they lack ability, but because of the lack of an effective hiring infrastructure, AI is emerging as the green light they鈥檝e been looking for.

The inability to connect the demand for blue-collar labor, given the substantial supply of skilled talent, is not only an embarrassing systemic failure, but it鈥檚 also a huge blow to the American economy. A of just one part of the blue-collar workforce 鈥斕齧anufacturing 鈥� by and projects that about 2.1 million U.S. manufacturing jobs will be unfilled by 2030, with the gap potentially costing $1 trillion in that year alone. We (if any at all) across industries that face similar shortages of skilled talent.

That is, unless something changes.

Blue-collar hiring infrastructure is broken

It starts and ends with hiring. The applicant-tracking and recruitment-management systems that most companies rely on are designed to uplift top candidates who meet the norms in white-collar industries, leading to a critical divide. As research demonstrates, these tools that they are trained to recognize.

This design is especially problematic given the realities of America鈥檚 blue-collar workforce. Foreign-born workers are overrepresented in these roles: the construction , for example, employs the largest percentage of immigrants of any industry.

U.S. r茅sum茅 conventions, from formatting to phrasing, are unfamiliar to many immigrants, and automated r茅sum茅 systems often down-rank these applications based on immaterial factors rather than work experience or relevant skills.

Credentials, critical across numerous blue-collar jobs, are another obstacle: Licenses and certificates earned abroad often map poorly to domestic job codes, even when the underlying skills are equivalent. For example, a 20-year veteran electrician certified in Nicaragua starts at the same place as a novice in the U.S.

For those who don鈥檛 speak or write English fluently, if at all, these issues are compounded. A key reason: automated systems are trained to reject applications that contain typos, incomplete phrases or grammatical errors. With all these issues combined, it鈥檚 easy to imagine why so many qualified candidates don鈥檛 bother to apply for jobs at all.

How AI is clearing the gutters of hiring

AI succeeds where these legacy hiring infrastructure systems have failed: nuance. Machine learning platforms can circumvent the obstacles that prevent immigrants from landing jobs at scale. Natural language processing allows the systems to interpret nontraditional r茅sum茅s, conduct interviews in multiple languages, and verify credentials automatically.

A welder without a formal r茅sum茅 can be matched to an employer based on verified training records earned in another country. A warehouse worker with limited English can be assessed by their abilities, not their syntax on a resume. This reality would have massive, positive implications for blue-collar employment numbers and the American economy. Better still, it鈥檚 possible.

Further, when these candidates are hired, the business case doesn鈥檛 stop. Employers that have brought refugees into shop-floor roles report meaningfully higher retention in manufacturing, logistics and blue-collar industries, which traditionally experience high turnover.

Put simply, hiring systems that prioritize skills, credentials and language inclusivity don鈥檛 just expand candidate reach, they drive lasting productivity and growth.

The competitive edge

For businesses, the payoff of implementing AI to improve or overhaul blue-collar hiring practices goes beyond altruism. It鈥檚 good business. AI-driven hiring platforms can shrink vacancy times, lower onboarding costs and expand labor pools 鈥� advantages that matter for companies individually, and for the American economy at large.

The challenge for executives and policymakers isn鈥檛 to slow AI down, but to deploy it wisely. Used correctly, these tools can rebuild the connective tissue of the labor market, helping millions of workers find the jobs that need them most.

The workers are out there. The jobs are waiting. The system is broken 鈥� but not beyond repair.

AI won鈥檛 take every job. Not even close. And for many, the technology will actually do the opposite: unlock one.

is the CEO of , an AI-powered hiring platform solving blue-collar America鈥檚 $1 trillion workforce crisis. He was previously the CMO of , a top-five globally ranked coding boot camp that has trained thousands of software engineers.

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Venture capital has become a mechanism for extracting executives from trillion-dollar companies and paying them whatever it takes to build in an AI-native world.

We’re not funding companies anymore 鈥� we’re buying access to the few hundred people who’ve built AI systems inside , and .

was founded by (ex- chief scientist), (ex- AI lead), and (former researcher at OpenAI). The company operates with roughly 20 employees. So far, it鈥檚 raised $3 billion at a $32 billion valuation, without a product or any revenue. What they do have is three executives from trillion-dollar companies who understand how to build superintelligence. That alone commands $1 billion in capital per team member.

This has become the playbook.

agreed to pay to use its models and hire co-founder as CEO of Microsoft AI, along with most of Inflection’s 70-person team. CEO reportedly offered up to over six years. struck a nonexclusive licensing deal with and hired its CEO, co-founder and select R&D staff. Meanwhile, OpenAI CEO has publicly stated that Meta offered top OpenAI talent signing bonuses.

The traditional venture formula is inverting. It used to be simple: raise $20 million, spend 95% on growth and headcount, allocate 5% to executive comp. Now the majority of capital flows toward recruiting a handful of executives who understand how to operate in an AI-native environment.

But most founders can’t compete in $20 million bidding wars. And so there’s an asymmetric play, and it’s 10x cheaper.

Why executive judgment is the new scarce resource

In an agentic era, AI systems write code, process data, handle customer service and automate operations. The scarcest resource has become the judgment of executives who know how to orchestrate these systems effectively.

Think about what this means in practice. A decade ago, $100 million might have hired 200 engineers. Today, that same capital might fund five FAANG executives at $10 million each, with the remaining $50 million allocated to compute, AI tooling and a skeleton crew of 20 to 30 people overseeing autonomous agents.

Executives from frontier AI companies command massive premiums because they possess knowledge that doesn’t exist elsewhere. They navigate what’s possible with current AI capabilities, understand the economics of model training and inference costs, and can anticipate regulatory frameworks before they’re codified.

What this means for capital formation

This shift creates a new power dynamic. Founders who can attract marquee executives unlock fundraising rounds that would be impossible based on traction or revenue alone. VCs evaluate deals increasingly on “who’s building this” rather than traditional metrics like customer acquisition cost or gross margins.

The clearest signal: , with 19 employees, was acquired by for 鈥� $11.6 million per employee. In an agentic world, team size has become irrelevant.

How to compete with the asymmetric playbook

Most founders reading this can’t offer $10 million to $20 million equity packages to marquee executives.

But there’s a counterintuitive strategy emerging: Instead of competing directly for executives who’ve built AI systems at frontier labs, target the operators who’ve integrated them at scale inside Fortune 500s. A chief technology officer who deployed LLMs across 50,000 employees at or understands enterprise AI adoption patterns that most OpenAI researchers don’t.

Here’s the asymmetric approach we’re seeing work:

1. Hire the “translator” executives, not the “builder” executives. A former VP of engineering from who integrated AI into enterprise workflows is more valuable for a B2B AI startup than a research scientist from DeepMind. They’re 10x cheaper and often more relevant to your actual go-to-market challenges.

2. Offer board seats, not just equity. The most compelling pitch to executives earning $800,000 at FAANG companies isn’t just equity 鈥� it’s offering: (a) a board seat they’d never get at a big company; (b) meaningful ownership in a high-growth company; and (c) the chance to compress 10 years of career advancement into two to three years. The value proposition isn’t “get rich” 鈥� it’s autonomy, impact and an accelerated path to becoming a recognized operator in AI.

3. Build technical credibility through advisory networks, not executive hires. Instead of hiring one $5 million executive, allocate $500,000 across 10 advisers from Google, Meta and Microsoft who can provide technical validation during enterprise sales cycles.

4. Target executives in “golden handcuff” situations. The best candidates aren’t those getting $100 million offers 鈥� they’re the overlooked VPs at trillion-dollar companies who’ve built AI systems but are stuck behind org politics. They have the expertise, they’re ready to leave, and they’ll join for $2 million or $3 million equity packages if you can articulate a clear path to relevance.

The companies winning without massive war chests aren’t trying to out-recruit Anthropic for research talent. They’re targeting enterprise operators who understand how AI systems actually get deployed at scale 鈥� and building credibility networks instead of expensive org charts.

The real tradeoff

We’re witnessing the formation of a technical aristocracy. A few thousand individuals now command compensation packages previously reserved for successful founders, as wealth transfers from broad-based tech employment to an elite operator class. Venture capital has fundamentally transformed from a growth capital fund into a talent acquisition fund.

The AI gold rush will eventually end, but the economic structure it’s creating is permanent. In the agentic era, you don’t raise capital to hire engineers 鈥� you raise it to hire executives who know how to orchestrate AI agents that do the actual work.

The question isn’t whether the rules have changed. They have. The question is which version of the new game you’re playing: competing for $20 million executives from frontier AI labs, or building asymmetrically with $2 million enterprise operators and credibility networks. Both paths work. Only one is accessible to most founders.

is founder and CEO of , which eliminates the security review bottleneck that stalls enterprise deals for companies such as , and . Born in rural Nepal, he built a profitable company with a 24/7 security operations command center in Kathmandu, proving that world-class execution doesn’t require Silicon Valley overhead. He writes about capital formation and the economics of AI-era operations.

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No founder plans for the day they get fired from their own company.

You plan for funding rounds, product launches and exits, but not for the boardroom moment when everyone raises their hand, and you realize your journey inside the company is over.

It happened to me. I called that board meeting. I set the vote. We had to choose who would stay, me or my co-founder. The vote didn鈥檛 go my way.

In movies, this is where the music swells and the credits roll. after . after . In real life, there鈥檚 no cinematic pause. No final scene. Just the quiet realization that everything you built now belongs to someone else.

What follows isn鈥檛 drama, either. It鈥檚 disorientation. And like most founders, I had no idea how to handle it.

Don鈥檛 fill the silence too fast

When it ended, I filled my calendar with aimless meetings. Five or six a day. Not because they had any real purpose, but because it felt strange not to be doing business. For more than 10 years, I鈥檇 never had a day when I didn鈥檛 have to think about work. A startup teaches you to fix things fast.

When you鈥檙e out, though, there鈥檚 nothing left to fix. Only yourself. Getting pushed out isn鈥檛 like missing a quarterly target. It鈥檚 like losing the story you鈥檝e been telling yourself for years.

The hardest part is that you don鈥檛 know who to blame.

Investors? They were doing their job. Yourself? Every decision made sense in context. So the frustration lands on the person closest to you. Your co-founder. It鈥檚 not about logic. I would say it is more of a defense mechanism. It鈥檚 how the mind tries to make sense of loss.

Learn to see the pattern

For months, I kept asking: What did we do wrong? It took me a couple of years to see the pattern.

Later, working inside a venture fund helped me see the truth. I saw the same story play out again and again. Founders repeating the same emotional arc, as follows:

• Expectation of an M&A deal;
• Long wait for the deal;
• The deal collapses;
• The startup stalls;
• Expectations diverge; and then
• Resentment between co-founders

Every time, the same sequence. And when the dream fades, blame fills the gap.

The pattern itself is that the anger toward a co-founder is often a projection of disappointment from a failed deal. If that energy isn鈥檛 processed consciously, it finds its own way out, usually as anger. You can鈥檛 really be mad at yourself; you did everything right. The other side acted in their own interest. So it lands on the person next to you, your co-founder and your team, and for them, it鈥檚 you.

And that鈥檚 where I have a bit of a claim toward investors because they often see this dynamic coming and could at least warn founders about it.

Once I recognized the pattern, I stopped seeing my story as a failure. It was part of a cycle almost every founder goes through, only most don鈥檛 talk about it.

Trade strategy for emotional tools

Traditional business tools didn鈥檛 help. OKRs, planning sessions, strategy off-sites, none of it worked on the inner collapse that comes when your identity and your company split apart.

This led me to begin studying . It gave me the language to understand how situations like this actually work, their cycles, causes and effects, and how to think about them with the right awareness and perspective. One part of building startups isn鈥檛 about pivots or fundraising. It鈥檚 realizing how much of yourself you鈥檝e tied to the story you鈥檙e telling the world.

The point is to first get conscious of your anger, and then let it out.

Acceptance comes in stages

Acceptance doesn鈥檛 show up all at once. It arrives in pieces.

For me, the first piece came when I watched another founder go through the same breakdown and recognized every stage.

The second came when my first startup was acquired. Not at the valuation I鈥檇 dreamed of, but enough to accept that it continued without me. The third came with my current company, , which is built from calm, not from fear.

I no longer measure success by control, but by clarity.

What I鈥檇 tell a founder in that room

Here鈥檚 what I鈥檇 share now with another entrepreneur who finds themselves in the same situation.

• You鈥檙e losing a story, not your worth. Give yourself space to grieve it.
• Don鈥檛 let anger choose a target. Name the pattern instead.
• Find mirrors. Other founders are walking through the same steps.
• Business tools have limits. Emotional tools matter here.
• Acceptance comes in stages. You鈥檒l recognize them when they arrive.

Founders are trained to manage everything except their own psychology. But startups are way more than capital and code. They run on the emotional architecture of the people who build them. And when that structure breaks, rebuilding it is the most important startup you鈥檒l ever work on.

听 is a seasoned entrepreneur with more than 10 years of experience in IT and technologies, as well as scaling businesses internationally. As a product manager at , he led a team that grew the product鈥檚 user base from 500,000 to 1.2 million and secured its entry into the international market. Subsequently, he co-founded , which he scaled to 45 countries and eventually exited, after it was acquired by in 2018. In 2021, Filippenko launched , an AI-powered platform connecting part-time professionals with flexible roles.

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After the 2024-25 job cuts at , and other tech companies, the second wave of tech layoffs is rewriting the startup labor market.

Skilled professionals are suddenly available, creating both opportunity and pressure for founders and workers alike. Startups now compete for talent that once seemed untouchable, while employees face longer job hunts and rethink how and where they work.

Higher expectations, more side gigs

With talent flooding the market, candidates are demanding more flexibility and clearer growth paths, even as many accept contract work or lower pay to stay employed. The typical job search now stretches six to seven months, even longer for those needing visas or relocation. That uncertainty has fueled a surge in freelancing and side projects.

reports that now have a side gig, with more than half of them having started in the past two years. While many professionals didn鈥檛 plan to freelance, they turned to it because they had no other choice. For some, it has proved liberating, with compared with corporate roles, according to our internal data.

Despite all the buzz in the media and even on , overemployment 鈥� the trend of holding two jobs 鈥� remains a niche phenomenon, affecting according to the . The more common pattern is a mix of contract work and short-term projects, which gives startups a chance to hire A-level talent for fractional roles they couldn鈥檛 have afforded before.

Smaller, sharper teams

Payroll is every startup鈥檚 biggest cost, and founders are trimming teams while raising output per employee. The examples are striking. reports about with a staff of only 11.

has reached roughly with 15-20 people. Data from shows that the average seed-stage team in the consumer and fintech since 2022.

This lean approach is spreading beyond early-stage ventures. Around say they are open to hiring freelancers during peak workloads; more than 28% already integrate them into daily operations. As this makes clear, smaller core teams, supplemented by trusted project-based workers, can move faster and spend less.

Opportunity on both sides

For workers, the takeaway is that startups may now be the safer bet. Mid-sized firms that once promised stability are cutting jobs, while startups are candid about their risks and can reward performance with equity or future roles. A short contract can become a long-term stake.

On the other hand, for founders, today鈥檚 market is a chance to recruit top engineers, designers and operators at terms that were impossible two years ago. It also demands a new mindset involving compensation flexibility, project-based roles and hiring processes built for speed.

All in all, the second wave of layoffs has changed expectations and shifted supply and demand in the job market. Workers are blending traditional jobs with side gigs, and startups are proving that small, focused teams can out-execute much larger competitors.

On both sides, adaptability is now the ultimate advantage; companies that remain nimble will win.

, founder and CEO of , is an entrepreneur with more than 20 years of experience, and a freelance economy pioneer who aims to transform how companies engage with contractors. In 2014, Shynkarenko launched his first HR tech company, , a fintech payroll company for freelancers, which showed $10 million-plus in revenue for 2022 and 2023. In early 2024, responding to the growing demand for specialized solutions for long-term interaction with contractors, Solar Staff, as a global company, pivoted to Mellow ($1 million MRR).

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

This week offered a change of pace on the giant round front as it was a biotech company, rather than an AI startup, at the top of the ranks. , a developer of obesity therapeutics, led with a $600 million Series B. Other sizable financings went to companies offering fractional aircraft ownership, fintech services and hair loss treatments.

1. , $600M, biotech: Waltham, Massachusetts-based Kailera Therapeutics, which focuses on treatments for obesity, announced a $600 million Series B financing led by . The company plans to initiate Phase 3 trials by year end for an injectable therapy to treat obesity.

2. , $350M, aviation: Bond, a company offering fractional ownership for its fleet of private aircraft, $350 million in debt and equity funding. The financing consisted of $320 million in debt and equity from funds and accounts managed by along with $30 million in equity investment from founding partners.

3. (tied) , $300M, payroll and compliance: HR and payroll platform Deel picked up $300 million in fresh funding. , , and led the financing. The round set a $17.3 billion valuation for the 6-year-old company, which it recently surpassed $1 billion in annual recurring revenue.

3. (tied) , $300M, business software: Vantaca, a provider of software for homeowners associations and management companies, said it secured a growth investment of more than $300 million led by . The financing set a $1.25 billion valuation for the Wilmington, North Carolina-based company.

5. , $254M, biopharma: Kardigan, a startup focused on developing cardiovascular drugs, closed on $254 million in a Series B backed by , , and . The round brings total funding to date to more than $554 million, per .

6. , $165M, fintech: Upgrade, a provider of consumer loans, credit cards and online accounts, pulled in $165 million in a Series G financing led by . Launched in 2017, San Francisco-based Upgrade has raised more than $750 million in venture funding to date, per .

7. , $150M, dermatology, hair regrowth: New Haven, Connecticut-based VeraDermics, a startup developing therapeutics for dermatologic conditions, raised $150 million in a Series C round led by . Funding will go toward ongoing trials for an oral therapeutic designed for hair regrowth.

8. , $120M, hair loss treatment: Pelage Pharmaceuticals closed a $120 million Series B round co-led by and 鈥檚 . The Los Angeles startup is focused on a topical small molecule designed to reactivate dormant hair follicle stem cells for men and women experiencing hair loss.

9. , $78M, therapeutics: Pittsburgh-based Peptilogics, a developer of surgical therapeutics to treat and prevent serious medical device infections, raised $78 million in a Series B2 financing. , and led the round.

10. , $77M, telehealth: Telehealth platform MD Integrations landed $77 million in growth financing from and . The New York-based company works with digital health brands to provide access to a network of doctors for patient consultations.

Methodology

We tracked the largest announced rounds in the 附近上门 database that were raised by U.S.-based companies for the period of Oct. 11-17. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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As of mid-September, HR software startups globally have raised a collective $1.9 billion, per 附近上门 鈥� just under the $2 billion raised by such startups in all of 2024. U.S.-based human resources software startups have raised a combined $1.2 billion, up from the $1.1 billion raised in all of 2024.

It鈥檚 important to note that while funding may be on an uptick this year, investment is still among the lowest it鈥檚 been in several years. The $2 billion raised in 2024 was not only far less than the $10.5 billion peak in 2021 鈥� when startup investment overall was inflated 鈥� but also lower than all the years prior since 2018.

Fewer, bigger deals

Notably, deal count in 2025 is proportionally lower, both globally and in the U.S, signaling larger round sizes for HR-related startups.

The dollar total raised so far this year was across 236 deals. That compares to $2 billion across 419 deals in all of 2024. In the U.S., the total raised was across 95 deals, versus $1.1 billion raised across 168 transactions all of last year.

Indeed, a few huge deals have taken place in 2025. San Francisco-based HR tech startup in May raised at a $16.8 billion valuation. At that time, the 9-year-old company also announced it was conducting a $200 million tender offer to provide current and former employees with some liquidity. Its backers include ,,,, 1听and , among others.

Also in May, Utah-based secured $165 million in , propelling it to unicorn status. Investors include , and . The 14-year-old company has built an employee recognition software and rewards platform.

In July, , a 7-year-old San Francisco-based startup that has built an 鈥渁ll-in-one鈥� AI-powered platform designed to help businesses more efficiently manage the recruitment process, announced a $50 million Series D.

Ashby told 附近上门 News that it more than doubled its customer base over the past year 鈥� from about 1,300 to more than 2,700 鈥� and saw its ARR jump by 135%. Those customers are a high-profile bunch, including the likes of startups , , , and , as well as enterprises such as and .

As the workforce has become more global 鈥� especially since the COVID pandemic led to more remote and hybrid work 鈥� the competition for hiring and retaining employees has intensified. On top of that, managing an existing workforce is more complex than ever. It鈥檚 no surprise, then, that HR software has become the digital infrastructure to help companies become more competitive and manage all the complexities. Add to that the increased popularity and usage of artificial intelligence to automate business processes, and it鈥檚 no wonder more companies in the space are raising capital.

, general partner at and an Ashby investor, told 附近上门 News earlier this year: 鈥淭he world is entering a new infrastructure cycle. Every system that companies rely on 鈥� CRM, ERP, finance, security, and yes, hiring 鈥� is being rebuilt with AI at the core,鈥� he said.

M&A dealmaking ticks up too

M&A activity for HR-related startups has also been robust, mirroring an overall surge in mergers and acquisitions among venture-backed companies.

Earlier this week, HR software giant听听announced plans to听听, which describes itself as an AI company building the next generation of enterprise knowledge tools, for $1.1 billion.

Founded in 2016, Stockholm, Sweden-based Sana had raised nearly $140 million in funding, per 附近上门 , from investors such as ,,, and.

The deal announcement followed one a few weeks earlier from Workday, which said it also plans to acquire , an AI-powered hiring startup that helps employers capture and screen candidates, improve conversions and answer candidate questions. The 9-year-old Scottsdale, Arizona-based company had in funding, according to 附近上门, from investors including , , , and .

In April, , parent company of , and , it would buy , a provider of HR and benefits solutions for small and mid-market businesses.

And as more venture dollars continue to flow into the space, expect to see more M&A activity that follows.

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That was one core finding from our latest analysis around funding for companies developing tools for employers and jobseekers.

A deeper dive into the numbers shows startups targeting frontline workers in industries such as healthcare, construction and retail comprised one of the larger investment themes. This translates into both deal flow and big checks. To illustrate, we used 附近上门 to put together a list of 14 recently funded听1 companies in this area.

Healthcare in the lead

Investment recipients include a mix of industry-specific and more general purpose offerings. But among sector-focused startups, healthcare stood as a favored area.

This makes sense given the sector鈥檚 longstanding status as a growth industry. Even amid the rather bleak U.S. August , healthcare was one of the few areas that saw measurable gains. It seems the is not going away.

Two of the largest funding recipients on our list 鈥� and 鈥� are tackling this predominantly in-person profession with different aims. McLean, Virginia-based ShiftMed has raised over $315 million to date for a platform helping employers bring local per diem nurses on board and tackle understaffing with AI-enabled scheduling tools.

New York-based Nomad Health, on the other hand, offers a platform for travel nurses to find jobs. The decade-old company has raised over $240 million in funding to date.

Construction, industrial and customer-facing businesses

Developers of employer tools for other industries are also generating enthusiasm with investors.

In the construction space, for instance, San Francisco-based is looking to build up its user base with $23 million in Series A funding this spring led by and . The 4-year-old company offers a single platform for contractors to manage payroll, HR, expenses and field operations.

In some ways, the startup fits in the category that Bessemer partner , who led the firm鈥檚 investment in Miter, calls 鈥渂usiness in a box鈥� companies. These are essentially platforms that business owners can use to handle administrative tasks and free up time to concentrate on their core offerings.

鈥淪o much of that is language-based work,鈥� Bennett said, regarding the peripheral demands of running a business, and so can see remarkable efficiency gains with the rise of AI-enabled language models.

Several others have raised rounds for tools targeting the manufacturing space, including San Francisco-based and Paris-based . In the hospitality industry, meanwhile, Paris-based raised a fresh fund this spring for a platform targeting hospitality industry employers.

Where the jobs are

For frontline work-related startups, it helps that this category encompasses most jobs. While remote work may seem widespread, nearly three-fourths of employed Americans still on a typical workday. Typically, frontline jobs are either difficult, impractical or flat-out impossible to carry out remotely.

Moreover, these are commonly sectors like healthcare, building and home services, that have shown to be more resilient of late, even amid a weakening job market. For employers, there鈥檚 apparently considerable appeal in tools that automate some of the drudgework and administrative work, to focus on their primary mission.

And for investors there鈥檚 also potential for some good returns. The most recent big exit offering validation for this thesis is , the software platform for home services providers that went public in December. Shares got a boost last week following a strong earnings report, pushing its market cap to around $11 billion. Seems you can do alright on public markets, too, as a software company focused on the frontline workforce.

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