We’re living in an era of uncertainty. Employees don’t know what their jobs will look like in two years, or if their function will still exist at all. Employers seem equally unsure. The labor market is coming off the sugar rush of post-pandemic hiring sprees, followed by waves of layoffs, and now very little net growth. According to Bureau of Labor Statistics data, private employers added roughly 7.1 million net jobs in 2021 and roughly 460,000 in 2025, a decline of about 93%.
In the first quarter of 2026 the net gain was 88,000. At that pace, 2026 would come in about 23% below 2025. Employers and employees are both waiting to see what the next cycle will look like. Are we headed toward the next industrial revolution, or toward a future where the skills people spent careers building are rendered obsolete?
One early indicator of what companies may look like in an AI era comes from the employers building AI themselves. If you work at a company with the most access to AI tools and talent, how does that change the roles, skills, and makeup of the workforce? To find out, Findem and Aptitude Research compared the workforces of five AI-native companies (OpenAI, NVIDIA, Anthropic, Databricks, and Perplexity) with five big tech companies (Meta, Apple, Amazon, Google, and Microsoft). Will all employers evolve into a big tech crab with an extra claw made of AI engineers?
AI-native companies are betting on experience
One finding that stood out was years of experience. You might expect young, fast-growing companies to hire younger talent. Early-career workers can move between tools and workflows easily because they aren’t ingrained in the SaaS tools of yesteryear. That isn’t what we see. AI-native companies hire heavily from big tech, with Intel, Google, Meta, Apple, and AWS among their top sources. On average, AI-native employees have 7% less experience than their counterparts at big tech companies. The difference is in the middle of the career, not at the entry level.
To some degree this makes sense. The tasks early talent has taken on so far are the ones most easily automated by AI. The work of a junior consultant can now be done in Claude Design. No more comments on slide decks that say “please fix.” Claude can “please fix” on a faster timeline, and it has all of your brand’s hex codes memorized.
What does this mean for early talent? More new graduates are starting companies. Findem data shows the share of graduates in founder roles rose from 1.0% of the class of 2021 to 1.4% of the class of 2025. That’s a 40% increase, though on a small base. Freelance work moved less, from 0.6% to 0.7%. But how many are building companies that last, and how many are filling a gap on their resume until they find a role? According to Startup Savant, the median age of founders who have built $1 billion companies is about 34. These aren’t the hoodie-adorned, fresh-faced elite college dropouts building the next social media app, or the blood-testing scams of the early aughts.
The entry-level career path is changing
The entry-level job market is tightening too. In research with SAP SuccessFactors, Findem data showed hiring for the 10 roles that employ the most early talent fell 35% from 2024 to 2025. The share of new graduates who started a role in their graduation year fell from 54% for the class of 2021 to 44% for the class of 2025. Hiring is slow across the whole economy. Gross job gains were 5.5% of private-sector employment in the first quarter of 2026, the lowest rate since mid-2020, according to the Bureau of Labor Statistics. The BLS hires rate was 3.3% in August 2026, and layoffs held at 1.0%.
Employers can slow early talent hiring quietly. Mass layoffs are noisy and hurt the morale and productivity of the employees you keep. Fewer hires is the quieter route. In the US, this hasn’t yet shown up as more NEETs. The Federal Reserve Bank of St. Louis puts the share of 18 to 24 year olds who are neither working nor in school at 16%, essentially unchanged through December 2025. Elsewhere the trend is up. The International Labour Organization found the global NEET share rose slightly to 20% in 2025, and in the UK, 13% of 16 to 24 year olds were NEET in April to June 2026.
Growth is reshaping career paths and leadership priorities
In the roles that drive engineering, product, and sales, AI company workers have slightly more experience than their big tech peers. Software engineers average 11.4 years against 10.4, account executives 14.9 against 13.8, and product managers 14.6 against 14.2. One potential tradeoff for AI companies is promotion velocity. Over the past two years, big tech promoted software engineers, account executives, and product managers 1 to 12 percentage points more often than AI companies did. The widest gap is in product, at 29% versus 17%. AI companies may be so focused on growth, building, and hiring that they haven’t stopped to outline a career ladder.
There’s also compensation. When weighted against the value of equity, how much do you care about the difference between software engineer and senior software engineer? Title gains matter most when you’re on the job market. While you’re still vesting and the share price is climbing, the opportunity cost of leaving could be high.
Leaders hired over the past year suggest AI companies are building toward enterprise-grade products. 18% of director-level and above hires focused on cloud and infrastructure (engineering, systems, security, and applied research). The AI advantage is shifting from novelty to delivering a tool on infrastructure that is efficient, durable, secure, and reliable enough for enterprise customers. Another 16% went to the revenue engine (enterprise sales, marketing, GTM strategy, and customer success).
Once you’ve built a product enterprises want and made it enterprise-ready, you need people who can sell to, market to, and manage those customers. The next two largest areas, at 10% each, are product leadership (senior product management and product marketing) and ecosystem and strategic entrenchment (strategic initiatives, startup ecosystem, and business development). These companies aren’t finished growing. They’re hiring leaders to take them into new markets and protect the ground they’ve gained.
CAIO hiring shows a related signal. In Findem and Aptitude Research data on US CAIOs at companies with more than 200 employees, the map follows the major tech hubs with one exception. Washington DC ranks third by metro, with 45 CAIOs, ahead of Los Angeles and Seattle. A CAIO based in Washington DC may signal a company preparing to have serious and expensive boots on the ground where the high-stakes policy conversations happen, among people who still aren’t sure what cookies are.
What this means for HR and talent acquisition
What does this mean for HR and TA? The labor market feels sleepy in some sectors and on fire in others. Practitioners can take some comfort here. Even AI employers happen to recognize the value HR and TA deliver. Part of that may be the volume of hiring AI companies need to do. But across AI-native companies, Findem data shows talent and HR headcount still scales with company size, at between 1.7% and 6.2% of total headcount.
Anthropic’s recent workforce report found code commits by technical employees rose 5.1 times from December 2025 to mid-2026. If one person generates 5.1 times more code, you may need a smaller technical workforce in the future. But AI companies are in a hiring boom, with average employee tenure of 3.5 years against 5 at big tech. Growth like that needs a skilled, experienced HR and TA team. That makes familiarity with AI tools increasingly important. At AI-native companies, 11% of talent and HR people came from a software engineering, IT, or other technical role. As the tools evolve, so does the profile of the recruiter or leader who can keep pace.
Bio:
Madeline Andrews is the Director of Insights at Findem, where she leads research on the workforce, labor market dynamics and the growing impact of AI on hiring and organizational strategy. Drawing on Findem’s proprietary talent data, she produces original analysis and thought leadership with partners including Aptitude Research, McKinsey & Company, SAP SuccessFactors and AltoPartners.
Her work explores the forces reshaping how organizations hire, develop and retain talent, spanning workforce trends, labor economics, early talent, leadership pipelines and the evolving role of AI in the enterprise.
With a background in strategy and research, Madeline specializes in translating complex labor market and talent data into accessible insights for business leaders and broader audiences.
