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.Âč

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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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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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 ofÌę 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 ÂÜÀòÊÓÆ” 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 aÌę report.

Methodology

Layoffs figures are from The ÂÜÀòÊÓÆ” 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.

Related reading:

• The ÂÜÀòÊÓÆ” Tech Layoffs Tracker

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The road to the top rarely runs in a straight line, but there are less circuitous routes to becoming CEO. According to analyzing every CEO succession in the S&P 500 since 2000, there are four feeder roles: COOs, divisional CEOs, CFOs, and “leapfrog” leaders promoted from below the C-suite.Ìę

But a separate 10-year study called the suggests that the fastest climbers ± “sprinters” who reached the CEO seat well ahead of the 24-year average — didn’t get there by climbing the corporate ladder to the top. They got there through bold, often unconventional career moves, such as taking on a failing division or building something from scratch.Ìę

In fact, what set them apart wasn’t pedigree but building a specific skillset that made them CEO material: decisiveness, reliability, adaptability, and the ability to engage people around a plan.

Product management doesn’t appear in the CEO-pathway research, likely because none of the major studies breaks the role out as a separate category. It’s a relatively newer function, and it tends to get folded into general management or engineering in career datasets.Ìę

But once you look at what the job demands — ownership of a tangible outcome, obsession with what customers value, the willingness to make tough decisions — it maps directly onto the traits the CEO Genome Project found in its sprinters.

I’ve spent my career leading product and technology at companies including , , and . Those roles landed me my first CEO position without having to fill the typical CEO-starter pack jobs because I was able to articulate my skill set to the board.Ìę

In short, product management is about making a product successful; being CEO is about making a business successful. The ingredients are the same.Ìę

Don’t take my word for it. joined in 2004, leading product management for the Google Toolbar, years before he became CEO. spent eight years as YouTube’s chief product officer before taking the top job there in 2023.Ìę

Yes, the scope of the job differs, but that’s true of every promotion, especially for the hardest job on offer. A CEO carries the full weight of the business: financial performance, legal and regulatory exposure, the board, and the market. A PM’s remit is naturally narrower: one product, one roadmap, one team to rally.Ìę

But scope isn’t the same as skillset. The job gets bigger, but the muscles you exercise remain the same: setting a vision under uncertainty, prioritizing ruthlessly, making calls with incomplete information, and getting people who don’t report to you to deliver anyway. Learn to do that for a product, and you’ve already learned to do it for a business, just on a bigger scale.

That mindset isn’t new to start-ups and scale-ups either, where a PM is often the closest thing to a mini-CEO, making calls across product, growth, and operations simply because no dedicated function exists yet to do it for them.Ìę

Part of the reason I think the PM-to-CEO pathway is so often overlooked is that the function is judged by its worst practitioners. Plenty of people with “product manager” on their CV spend more time managing processes and stakeholders than owning outcomes and building amazing products. And it’s that version of the job that shapes how PMs get perceived, and why few are inspired to make the leap. The PMs who have done the job – by taking ownership of the outcome rather than the process – are building something that truly resembles the job description of a CEO.Ìę

The best advice I can give to aspiring executives and entrepreneurs today – whether they’re PMs or not – is to choose a metric that they want to be accountable for. In my previous role, I focused on monthly active users; now I’m focusing on EBITDA. Strip away the layers and remain outcome-oriented.Ìę

The CEO pathway research keeps looking for the right sequence of job titles, but that model is increasingly outdated in the modern-day work environment. Rather than focusing on traditional CEO pathways, aspiring executives should focus less on glitzy job titles to add to their CVs and more on the concrete skills they can gain. Product management, done properly, offers the perfect crash course.Ìę

is the CEO of , a London-based technology company building a global payments infrastructure to facilitate money transfers. He previously led product and technology for companies including , , and .

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led the company’s 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’s 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.

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

The experience inspired her to team up with , the company’s CTO, who spent 10 years as ’s 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.

“What used to take 14 hours of manual work can now be done in under 30 minutes,” Berger told ÂÜÀòÊÓÆ” 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’s closest competitors are legacy payroll providers like and , along with newer companies like and .

“The difference is that most of these systems weren’t built for the complexity of specialty trades,” Berger explains. “Trayd was.”

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.

“Generic” 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.

“Unlike 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,” she said. “Trayd was designed from day one to handle that complexity.”

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.

“As women building in construction — where we’re outnumbered 9 to 1 — 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 —- every meeting, every deal, every product decision. We’ve had to work twice as hard to be taken seriously,” she told ÂÜÀòÊÓÆ” News. “But 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.”

, general partnerÌęat White Star Capital, said his firm was first impressed by Trayd’s founding team, describing Berger and Kessler as “a rare combination.”

“Anna’s background and family ties to the space allow her to understand the unique pain points contractors face from the inside,” he wrote via email. “Cara brings the technical depth to build mission-critical systems without sacrificing product simplicity.”

Beyond the caliber of the founders, White Star also believes that Trayd stands out because “it is truly a better product for its customers.”

“On a technical level, we were very impressed by how thoughtfully the product has been built,” Lee added. “We 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.”

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 ÂÜÀòÊÓÆ” . That’s 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.

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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’s impact. Choi has led investments in several AI-first companies.

Known for a founder-first conviction, he’s 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’s flipped his investing philosophy, and how he’s gone from growth-stage investing to being stage-agnostic.

This interview was edited for brevity and clarity.

ÂÜÀòÊÓÆ” News: You’ve 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’ve invested in.

You’re 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’m seeing it in my own workflow. I use the models to get up to speed on technical capabilities — asking about Clickhouse’s indexing methodology versus ’s in voice mode while I’m driving. It’s 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’re 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 “learning how to be a professional” has to shift to the universities. I look at the traditional U.S. model of general education requirements and think, “Why are we doing this?” 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 “AI-first” 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’ve actually evolved to be stage-agnostic. While people still put me in the “growth” bucket, I’m 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’ve flipped my philosophy: it used to be 80% metrics and 20% founders. Now, it’s 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’ve 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’s a painful transition. The market now needs to believe that a company is AI-native — 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 “burning the boats” to reinvent their entire businesses. It’s 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 “unconventional” 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 “challenging the conventional wisdom” of founders. Can you share an example of a time you had to steer a growth-stage founder away from a “safe” 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’ve 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 “elite” executive hire changed?

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

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By and

This story starts with a loss.

A client didn’t 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’s 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 — the “force multiplier for your work.”

Years ago, Ravikant bemoaned that labor — once the original form of leverage — had become “overvalued,” and that capital, code and now AI would define the next era. “You want the minimum amount of labor that allows you to use the other forms of leverage,” he wrote.

, perhaps the “face” of AI writ large, is famous for espousing the as the ultimate form of leverage, claiming that it will create “,” and that “[a]lmost everyone will want more AI working on their behalf.”

Investor enthusiasm around , and — companies with extraordinary output and extremely small teams — 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’ll remake our workflows with AI, but what of our culture — the intangible connective tissue beneath it all? It’s 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 — perhaps, even, the future period — 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’re all going to have to build that culture largely from scratch.

is the co-founder of the world’s newest creative AI marketing tool, . He’s 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 ’s

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’ve 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’s also a huge blow to the American economy. A of just one part of the blue-collar workforce —Ìęmanufacturing — 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’s 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. résumé conventions, from formatting to phrasing, are unfamiliar to many immigrants, and automated résumé 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’t 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’s easy to imagine why so many qualified candidates don’t 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 résumés, conduct interviews in multiple languages, and verify credentials automatically.

A welder without a formal rĂ©sumĂ© 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’s possible.

Further, when these candidates are hired, the business case doesn’t 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’t 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’s good business. AI-driven hiring platforms can shrink vacancy times, lower onboarding costs and expand labor pools — advantages that matter for companies individually, and for the American economy at large.

The challenge for executives and policymakers isn’t 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 — but not beyond repair.

AI won’t 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’s $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 — 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’s 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 — $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 — 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” — 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 — 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 — 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 — 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’t go my way.

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

What follows isn’t drama, either. It’s disorientation. And like most founders, I had no idea how to handle it.

Don’t 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’d never had a day when I didn’t have to think about work. A startup teaches you to fix things fast.

When you’re out, though, there’s nothing left to fix. Only yourself. Getting pushed out isn’t like missing a quarterly target. It’s like losing the story you’ve been telling yourself for years.

The hardest part is that you don’t 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’s not about logic. I would say it is more of a defense mechanism. It’s 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’t processed consciously, it finds its own way out, usually as anger. You can’t 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’s you.

And that’s 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’t talk about it.

Trade strategy for emotional tools

Traditional business tools didn’t 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’t about pivots or fundraising. It’s realizing how much of yourself you’ve tied to the story you’re telling the world.

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

Acceptance comes in stages

Acceptance doesn’t 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’d 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’d tell a founder in that room

Here’s what I’d share now with another entrepreneur who finds themselves in the same situation.

• You’re losing a story, not your worth. Give yourself space to grieve it.
• Don’t 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’ll 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’ll ever work on.

Ìę 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’s 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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