If you only have six months of experience in an industry, you usually aren’t equipped to poach your employer鈥檚 clients and start a competing business. Of course, you may believe otherwise if you plan to use your employer鈥檚 proprietary information to train an AI agent to do the heavy lifting for you.

That is exactly what the defendant attempted in I litigated on behalf of a client in the U.S. District Court for the Northern District of California. In that case, the defendant, a former senior employee at the marketing agency, didn鈥檛 just copy client lists to solicit accounts. Instead, he , attempting a shortcut to create a competing business without spending years developing the skills and expertise typically needed to provide such services.

AI is creating new ways to misappropriate trade secrets, well beyond the classic theft story of an employee downloading files onto a thumb drive. This case highlights a new reality for founders, exposing unprecedented risks related to AI and trade secrets, employee use of personal devices, and the heightened need for companies to protect themselves in the age of agentic AI.

Key lessons for founders

AI contamination cannot be undone

The defense in this case argued that because their client placed the stolen documents in a “quarantine folder” and no clients had been poached yet, no harm had been done.

The court appropriately recognized that the 鈥渜uarantine folder鈥� was grossly insufficient, granting our temporary restraining order and preliminary injunction while recognizing the competing AI agent as an “existential threat.”

An agreement not to touch just the stolen documents themselves is essentially irrelevant once AI gets involved: If a trade secret is used to develop a machine learning model or used to train an AI agent with recursive capabilities, it contaminates the model and the impact can be difficult to undo. Even if raw source files are completely scrubbed of any direct evidence of stolen materials, defendants will struggle to prove to courts that the AI isn’t operating a certain way precisely because it once had access to that proprietary information.

Personal devices can pose big risks聽

While trade secret misappropriation has typically been malicious, AI now also creates significant risks of inadvertent violations. Startups frequently scale with employees working on personal devices under standard Confidential Information and Invention Assignment Agreements. Running desktop AI agents on these devices can pose a serious risk.

Unlike standard web interfaces, a desktop AI agent can scan files, create folders and ingest directory paths across a local hard drive, often without requiring special permission or alerting the user.

Right now, thousands of employees may be inadvertently violating confidentiality agreements by running consumer AI agents on personal laptops that house their employers’ IP. If settings allow model training from this data, courts may eventually find these trade secrets to have lost legal protection due to dissemination outside the company.

Standard IT audits won’t catch AI data theft

This recent case serves as a warning for how attorneys and founders must approach trade secret investigations. Traditional digital discovery is no longer sufficient because AI tools make it effortless to disperse, alter and recreate data across nonobvious folder paths.

Actions that previously seemed like overkill 鈥� such as full forensic imaging of a personal device 鈥� are now the bare minimum. Furthermore, AI dramatically reduces the friction of reverse-engineering software and tech products, potentially removing telltale signs that code or data was copied.

Proving theft in AI contexts requires intensive, highly technical expert analysis and carries far more uncertainty than traditional IP litigation.

3 practical steps companies should take

Founders should take three legal and operational steps to proactively protect their business:

1. Implement strict agentic AI policies: Mandate tight controls and settings that isolate outside AI from company documents. Web-based tools like Claude with strict data privacy policies are vastly safer than open source or desktop apps that crawl local drives. Some AI agents should not be permitted to run at all within an environment containing trade secrets.
2. Eliminate remote access from unmanaged devices: Shift to a closed cloud environment where data can be viewed and manipulated on a secure server, but files cannot be downloaded locally to a personal desktop. Removing local file downloads eliminates the risk of desktop AI agents indexing proprietary data.
3. Rewrite your Confidential Information and Invention Assignment Agreements for the AI era: Standard confidentiality agreements need explicit clauses addressing AI use, model training inputs, and a precise definition of data misappropriation through machine learning.

If you suspect an employee has walked away with data or misused it in an AI tool, your first step should be immediate: cut off all system access and retain counsel. Don鈥檛 wait until you start losing clients or experiencing financial losses. Feeding trade secrets into AI presents an existential threat from day one.

AI gives individuals the unprecedented ability to compete in spaces where they lack experience, making proprietary data more vulnerable than ever. Modernizing security, policy and legal strategies to address today鈥檚 AI threats will help startups and established companies avoid catastrophic exposure.

is a partner at , where he focuses on complex commercial litigation, including disputes related to trade secret and IP misappropriation, corporate governance and breach of fiduciary duties, breach of contract, and other complex business disputes. He specializes in preventing and resolving all manner of disputes among and between corporate founders, directors, officers and shareholders, LLC members, and other current and former business partners.

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Today鈥檚 venture capital ecosystem looks markedly different from even just five years ago.

Most significantly, a small number of AI companies are now attracting the vast majority of available venture capital. In Q1 2026, just four companies collectively raised $188 billion, which alone accounted for 65% of total global venture investment, per 小蓝视频色情网页版 data.

Meanwhile, in that same quarter, just 20% of global VC funding went to non-AI firms. meaning that checks are being made out to fewer companies all around.

So what happens to startups when the VC route is all but closed?

I built a company into a multibillion-dollar business without any venture funding. Here鈥檚 a few lessons I picked up along the way:

Prioritize customers and revenue, not fundraising

Many founders unknowingly treat fundraising as a measure of success. But while it鈥檚 useful, funding doesn鈥檛 result in success on its own.

Achieving strong levels of customer trust is also helpful when it comes to inspiring investor confidence. High retention rates, repeat purchases and sustained engagement drive the聽 financial fundamentals on which strong valuations are built. In B2B SaaS, for instance, companies with net revenue retention above 110% . Additionally, customers provide a feedback loop that helps create the discipline every company needs, and each user is a vote of confidence that compounds over time.

Going to market: Take time on pricing strategy

Too many early-stage companies set prices too low because they fear that higher prices will repel customers. In my experience, however, underpricing more often signals uncertainty rather than good value. Customers want quality products and services, and they expect that the quality will be reflected in the price.

Incredibly, , and firms more commonly regret underpricing than overpricing. So at the outset, it’s important for startups to inform themselves what price their product or service can actually sustain, and then raise quality if it鈥檚 too low.

Equally, in services markets where prices are more established around a norm, superior quality can be an important differentiator. At the same time, however, companies must continually resist the temptation to chase customers with ever-cheaper prices, which can start a race to the bottom.

Hire the best talent

Too many founders hire for speed rather than quality. Take the time needed to raise the bar with talent. Hire the right people, and they鈥檒l help maximize every last drop of value.

Many years ago, I decided to focus on just a small fraction of our applicants and . When we found the best developers, it meant we could compete on delivery quality rather than just price. Keeping costs low is important, but skimping on talent is not a winning strategy.

Use process automation efficiencies to drive growth

One of our best decisions was to at an early stage. Not only do these systems screen developer applications, but they can also build, match and deploy top-quality teams for each client project.

This advance allowed us to cut costs associated with evaluating applications, accelerate lead times, and improve the quality of our teamwork. In parallel, so that growth wouldn’t require proportional increases in overhead.

Whether it鈥檚 in recruitment, sales or operations, process automation can create some of the biggest drivers of growth and help bring about conditions needed for significant expansion to occur.

Operate as if cash is running out tomorrow

Companies thrive in challenging funding environments by operating with discipline. That means obsessing over unit economics, prioritizing activities that directly drive revenue, questioning every cost, and eliminating anything that isn’t pulling its weight.

Not only does this aid growth, but it also makes a business more resilient in hard times. Financial constraints drive leaders to create efficiencies by automating workflows, streamlining processes and inventing innovative work models.

Years before COVID-19 forced others to follow our lead, our company streamlined its operations and boosted scalability by developing a remote-working model. When the pandemic made remote work necessary, the fact that we already had the systems and workflows in place gave us a competitive advantage over other firms, and this in turn led to significant growth.

Long term: Stay married to the problem, not the solution

The past decade convinced many founders that venture capital was the default path to building a meaningful company. But while funding cycles rise and fall, the fundamental principles of good business don’t. Infusions of capital can accelerate growth, but they cannot create a great company from scratch.

On the longest timescale, the most successful founders are the ones committed to solving problems rather than just selling a product and chasing funding. In a world short on funding, growth simply can鈥檛 happen without it.

is the founding GP of , and chairman of , one of the largest bootstrapped nearshoring companies worldwide. Driven by his entrepreneurial spirit, he launched BDev Ventures in 2021 to help B2B companies accelerate their growth through a proprietary lead-generation platform. He also invests in and serves on the boards of biotechnology and e-learning companies. In recognition of his career path, he won distinctions such as an EY Entrepreneur Of The Year 2024 Bay Area Award.

Related reading:

• Q1 2026 Shatters Venture Funding Records As AI Boom Pushes Startup Investment To $300B聽

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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鈥檛 name it. Don鈥檛 have to. You鈥檝e read the press release 鈥� “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 鈥渨e鈥檝e built the future of work鈥� quite like raising half a billion dollars to hire more people to do the work.

The business model, decoded

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

1. Sell the customer an “AI platform.”
2. Discover the platform doesn鈥檛 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 鈥淎RR go up.鈥�
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 鈥� it鈥檚 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 鈥渙perating leverage鈥� with a completely straight face.

It is not operating leverage. It鈥檚 a staffing agency that learned to say 鈥渁gentic.鈥�

The tell

Ask the one question the pitch deck conveniently never answers: 鈥淚f you pulled the embedded engineer out tomorrow, does the customer still get what they paid for?鈥�

If the honest answer is 鈥渨ell, no, but the ARR is real鈥� 鈥� that鈥檚 not product-market fit. That鈥檚 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鈥檛 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, 鈥淎I software company.鈥� 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鈥檚 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鈥檛 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鈥檚 a fine bet in a hype cycle. It鈥檚 a terrible one in a market that鈥檚 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.鹿

So no, your AI isn鈥檛 autonomous. But your business model might be 鈥� 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鈥檝e 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鈥檝e learned the best workplaces are the ones where questioning things is the norm. Here鈥檚 how to build a team that questions orders as much as it obeys them.

AI by design

鈥淎I by design鈥� doesn鈥檛 only mean building the right autonomous systems. It鈥檚 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鈥檛 control. So surrounding yourself with people you trust to make decisions when you鈥檙e 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鈥檝e found a candidate, and a six-month roadmap is outdated by the time you鈥檝e finished drawing it up.

The has stated that as AI reshapes the workplace. Hiring for a fixed set of skills won鈥檛 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鈥檚 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鈥檒l 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鈥檛 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鈥檛 working. Allow people to pursue new ideas and reinvent things. Your employees are likely to spot potential problems before you do when they鈥檙e 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 鈥� including 鈥檚 8,000-job reduction 鈥� the highest monthly count since March 2023, when layoffs reached 36,602.

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

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

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

Companies cutting

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

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

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

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

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

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

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

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

An AI focus

, of , says AI is affecting jobs in two ways. Some work, including coding, can now be done with fewer people. 鈥淭here are jobs that are literally being replaced by artificial intelligence,鈥� he told 小蓝视频色情网页版 News.

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

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

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

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

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

Methodology

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

Related reading:

• The 小蓝视频色情网页版 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 鈥渓eapfrog鈥� leaders promoted from below the C-suite.聽

But a separate 10-year study called the suggests that the fastest climbers 卤 鈥渟printers鈥� who reached the CEO seat well ahead of the 24-year average 鈥� didn鈥檛 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.聽

In fact, what set them apart wasn鈥檛 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鈥檛 appear in the CEO-pathway research, likely because none of the major studies breaks the role out as a separate category. It鈥檚 a relatively newer function, and it tends to get folded into general management or engineering in career datasets.聽

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

I鈥檝e 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.聽

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

Don鈥檛 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鈥檚 chief product officer before taking the top job there in 2023.聽

Yes, the scope of the job differs, but that鈥檚 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鈥檚 remit is naturally narrower: one product, one roadmap, one team to rally.聽

But scope isn鈥檛 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鈥檛 report to you to deliver anyway. Learn to do that for a product, and you鈥檝e already learned to do it for a business, just on a bigger scale.

That mindset isn鈥檛 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.聽

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 鈥減roduct manager鈥� on their CV spend more time managing processes and stakeholders than owning outcomes and building amazing products. And it鈥檚 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.聽

The best advice I can give to aspiring executives and entrepreneurs today – whether they鈥檙e 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鈥檓 focusing on EBITDA. Strip away the layers and remain outcome-oriented.聽

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.聽

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 小蓝视频色情网页版 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 小蓝视频色情网页版 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聽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 小蓝视频色情网页版 . 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.

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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.

小蓝视频色情网页版 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.

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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 鈥斅爉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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