Gen Z turn to trade jobs, ditch white-collar careers amid AI uncertainty, poor corporate wages
It's trick of the trade.
Gen Z is turning to traditional trade jobs amid fears AI will soon replace many white-collar careers, a new survey has uncovered.
Resume Builder polled more than 1,400 Gen Z adults between the ages of 18 and 28, finding that 42% of Zoomers are currently working in or pursuing a blue-collar or skilled trade job, such as plumbing, welding or electrical work, including 37% of those with a bachelor's degree.
Advertisement
Almost a third of respondents said such jobs offer better long-term prospects, while a quarter said the roles are less likely to be taken over by AI.
'More Gen Z college graduates are turning to trade careers and for good reason,' Resume Builder's Chief Career Advisor Stacie Haller declared. 'Trade jobs offer hands-on work that's difficult to automate. Additionally, many grads find their degrees don't lead to careers in their field, prompting them to explore more practical, in-demand alternatives.'
Advertisement
Indeed, almost one in five Zoomers (19%) who are currently working in a trade said they were unable to find a job in the field that they had studied for.
Of those who were able to land a white-collar role, 16% eventually quit and turned to a trade job because it potentially offered more money.
It's a stunning inversion from decades' past, where a job requiring a college degree typically offered far better pay than blue-collar work.
3 Of those who were able to land a white-collar role, 16% eventually quit and turned to a trade job because it potentially offered more money.
alfa27 – stock.adobe.com
Advertisement
Resume Builder also found that trade jobs were particularly enticing for Gen Z as the cost of obtaining a college degree continues to rise.
Many surveyed Zoomers said they didn't want to be burdened by paying back burdensome college loans.
The average cost of college in the United States has more than doubled over the past 24 years to $38,270 per student per year, according to the Education Data Initiative.
Advertisement
3 A college degree and a white-collar job no longer guarantees economic security, particularly with the looming layoff threats caused by AI.
Jadon Bester/peopleimages.com – stock.adobe.com
The findings come less than a year after The Wall Street Journal reported that Gen Z is becoming 'the toolbelt generation.'
Trades are flourishing as college enrollment shrinks, per the report, which found that 'the number of students enrolled in vocational-focused community colleges rose 16% last year to its highest level … since 2018.'
Kids studying construction trades rose 23% during the five-year period, while those training for HVAC and vehicle repair careers increased 7%.
An Associated Press article from 2023 also reported on the trend, similarly saying pricey college tuition was turning Zoomers off higher education.
'If I would have gone to college after school, I would be dead broke,' one young man working at a Ford plant told the Associated Press in a story about young people skipping college in favor of the skilled trades. The youngster is making $24 an hour at age 19, with no student debt.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Yahoo
32 minutes ago
- Yahoo
OpenAI's Altman sees 2026 as a turning point for AI in business
STORY: :: June 2, 2025 :: San Francisco, California :: Sam Altman says 2026 will be a big year for AI solving problems and making discoveries :: Sam Altman, CEO, OpenAI 'I think we'll be at the point next year where you can not only use the system to sort of automate some business processes or fill these new products and services, but you can really say, I have this hugely important problem in my business. I will throw tons of compute at it if you can solve it. And the models will be able to go figure out things that teams of people on their own can't do." 'I would bet next year that in some limited cases, at least in some small ways, we start to see agents that can help us discover new knowledge or can figure out solutions to business problems that are kind of very nontrivial. Right now, it's very much in the category of, okay, if you got something like repetitive cognitive work, we can automate it at a kind of a low level on a short time horizon." 'So what an enterprise will be able to do, we talked about this a little bit, but just like give it your hardest problem if you're a chip design company, say go design me a better chip than I could have possibly had before. If you're a biotech company trying to cure some disease, so just go work on this for me. Like that's not so far away.' Speaking alongside Conviction founder Sarah Guo and Snowflake CEO Sridhar Ramaswamy, Altman said companies prepared to harness the full potential of AI will experience a 'step change' as models evolve from automating routine tasks to tackling non-trivial challenges. 'I would bet next year that, at least in some small ways, we start to see agents that can help us discover new knowledge,' Altman said, adding that future systems may significantly accelerate scientific discovery. Se produjo un error al recuperar la información Inicia sesión para acceder a tu portafolio Se produjo un error al recuperar la información Se produjo un error al recuperar la información Se produjo un error al recuperar la información Se produjo un error al recuperar la información
Entrepreneur
35 minutes ago
- Entrepreneur
5 Metrics Every Business Should Track to Maximise AI Investments
As the European AI landscape evolves, so too must the standards to measure success. Opinions expressed by Entrepreneur contributors are their own. You're reading Entrepreneur Europe, an international franchise of Entrepreneur Media. The Office of National Statistics (ONS) updates its shopping basket every year to reflect how consumers spend, adding new items like VR headsets or yoga mats as habits evolve. Businesses need to do the same with performance metrics. Artificial intelligence is now a central force in driving growth, yet many companies still measure success using outdated KPIs. With nearly half (42%) of European businesses now regularly using artificial intelligence (AI) — a 27% increase in just one year — the urgency is clear: if you don't measure what matters, you can't manage it. To truly maximise AI investments, C-suite leaders must update their own shopping baskets and rethink the benchmarks used to judge value. Here are five metrics that every business should be tracking to ensure AI success. 1. Data quality Even the most advanced AI models produce untrustworthy results if they're trained on inaccurate or irrelevant information. At best, this shortcoming is a temporary inconvenience that drains money and time. At worst, entrusting unsatisfactory data to AI systems leads to costly mistakes in user-level applications — all of which can damage an organisation's reputation and profit. With the success of AI hinging on high-quality data, it's important to perform regular data audits focused on improving accuracy. Routine reviews like this are a way to patrol data pipelines, checking that they're free of inconsistencies that could otherwise undermine AI outputs. 2. Data coverage Clean data is one priority; complete data is another. The AI models without access to every dataset are more vulnerable to blind spots, causing limitations in their ability to detect trends and identify key opportunities. For instance, insurers that automate their risk assessment processes with AI typically ingest data from operational logs, market patterns and even independent sources like weather forecasts. Accidentally neglecting just one of these could result in the misinterpretation of costly payout claims. To counter similar risks, conducting regular assessments of your data landscape to uncover overlooked data points. Eliminating visibility gaps allows businesses to paint a full picture of their digital environment, ensuring all data channels are readily available for AI usage. 3. Operational efficiency gains The clearest way to measure the success of a new initiative is to see how much time or money it saves compared to the previous approach. Put simply: a factory that installs a faster conveyor belt should see an increase in productivity. AI is no exception to that logic. From accelerating loan approvals to automating data entry, the long-term objective of AI in any industry is to reduce turnaround times and cut costs. Failure to gauge operational impact makes it difficult to justify ongoing investment. As such, it's sensible to measure process durations before and after AI integration — a benchmarking method DHL deployed to recognise that its AI-powered robots had delivered a 40% increase in sorting capacity, quantifying their investment's active contribution to business KPIs. 4. Adoption rate across teams Just because a solution successfully goes live, it doesn't mean adoption is fully guaranteed. Really, true value comes when AI is embedded into workflows across the whole company — not just the IT department. Some teams will immediately embrace the AI tools presented to them, whereas others need more support. To assess where training or change management might be necessary, it's helpful to track departmental usage data and run regular employee feedback surveys. This approach works for high-performing organisations, who are more likely to bring employees with them on their AI journey by providing extensive AI training. In this context, understanding digital behaviour is the starting point for extracting more engagement from AI. 5. Return on investment (ROI) Naturally, businesses leaders need to understand the financial return they're getting back from investment. However, the ROI generated from AI initiatives is often complex, involving both tangible and intangible benefits. Take the Berlin-based online retailer Zalando, which recently shared that it uses generative AI to produce digital imagery at a rapid rate. Not only has that directly reduced costs by 90%, but the faster turnaround in editorial campaigns also indirectly boosted the company's competitiveness in the fast fashion market. Every possible performance metric must be considered when curating a digital strategy. That's why it's important to develop a well-rounded ROI framework for AI — factoring in both the direct and indirect consequences of any planned change. Measure what matters, scale what works AI is already demonstrating its ability to reshape organisations, but the reality is that many still struggle to prove its concrete value. Without establishing the right criteria for success, businesses will lack accountability and struggle to align tech performance with financial gains. To maximise ROI on AI, you must clarify the standards that you wish your digital growth to be founded on. This will unlock the insights needed to safely course-correct, scale success, and build long-term trust in your AI strategy. As the AI landscape evolves, so too must the standards to measure success. Just like the ONS shopping basket reflects changing habits, businesses must ensure performance metrics reflect the realities of AI-driven operations. By focusing on data quality, coverage, efficiency, adoption, and ROI, leaders can ensure AI investments aren't just tracked but transformed into long-term value.
Business Insider
an hour ago
- Business Insider
Windsurf's CEO says big teams can kill new ideas
When it comes to building new products, a bigger team isn't always better. It might be what kills the idea altogether, said Windsurf's CEO, Varun Mohan. Mohan said on an episode of the "Twenty Minute VC" podcast published Monday that having as many as 10 people working on an unproven idea can backfire fast. "Everyone has opinions. Everyone has ideas. And nobody's ideas are wrong — because no one has proven anything," he said. "It's very hard to get alignment. It's very hard for people to work in one direction without causing communication issues," he added. Windsurf, founded in 2021 as Codeium, created an AI-powered coding development tool and has been riding the vibe coding wave. According to PitchBook, it has raised $243 million in VC funding. Mohan recommends keeping early-stage product teams lean — ideally just three to four people. It could be a couple of engineers and a designer — or if it's a pure systems problem, just engineers, he added. A small, "opinionated" group moving fast to prove an idea is "actually really good," he said. Even a "crappy" version of a great idea is "already amazing," he said. "Once you've proven the crappy version has legs, you can then go and resource more people on the project to actually go out and and pursue it more deeply." Windsurf isn't the only startup espousing small teams. Some of AI's biggest names have built upon tiny teams, such as Anysphere, the maker of coding copilot Cursor. The advent of AI has also enabled startups to do more with less, prompting some founders to maintain extremely lean teams. "We're going to see 10-person companies with billion-dollar valuations pretty soon," OpenAI's CEO, Sam Altman, said in February 2024. No budgets set for new ideas Mohan also said that Windsurf doesn't set specific budgets for these early-stage projects. "We're in an unconstrained market," he said. Technology that could accelerate software development "is so valuable" to customers that the company doesn't approach new ideas with fixed budget constraints, he added. Instead, Windsurf evaluates progress over time and decides whether to keep going, hit pause, or shift resources elsewhere. "That's not a democratic process," Mohan added. "It's a little bit of a top-down process."
