Eben Upton sees trouble ahead. The founder and CEO of Raspberry Pi delivered a pointed warning this week. Overplaying what AI systems can achieve may steer young people away from technology careers. That shift, he argues, would deepen an existing gap in engineering talent and damage economic prospects.
“Some people are very inclined to overestimate what these [AI] tools can do,” Upton told the BBC in a recent Big Boss Interview podcast. The comments, reported across outlets including TechRadar, strike at the heart of current industry debates. Tech layoffs have topped 100,000 so far in 2026. Many companies point to artificial intelligence as justification. Yet Upton pushes back. He believes the narrative around replacement distorts decisions. It makes the talent shortage worse, not better.
His concern carries weight. Raspberry Pi launched in 2012 precisely to reverse a slide in computing skills among British youth. Mobile phones and games consoles had pulled attention away from programming. The low-cost computer succeeded beyond expectations. It became the UK’s best-selling computer brand. The company listed on the London Stock Exchange in 2024. Success stories like this built momentum. Organizations worked hard to draw students toward engineering paths. Now Upton fears that progress could slip away.
The Pipeline Problem
Entry-level roles once offered hands-on experience. Junior coders debugged systems. They learned by doing. AI chatbots now handle many of those tasks. The result looks efficient in the short term. But it creates a vacuum. Without that foundational layer, who advances to senior positions? The talent pipeline thins. Retirement and turnover leave gaps that prove hard to fill.
Upton frames this as a feedback loop. Young people read headlines promising AI will replace programmers. They choose different GCSE subjects. They pursue other degrees. “You read in the paper: ‘What guidance should you give your child about what GCSEs to choose in the context of an AI future?’ We have no data to inform a rational decision on that,” he said. “The answer is: wait five years, wait 10 years, and then maybe we might know something.”
Parents face pressure. Students weigh options. The enthusiasm for tools like ChatGPT feels genuine. These systems produce code, draft reports, analyze data. Impressive outputs fuel speculation. Yet Upton cautions against the leap. Overestimating capabilities risks undoing years of outreach. “It’s possible to get caught up in this,” he added. “This is the risk of damage right in this moment of incredible enthusiasm for what are genuinely incredible tools.”
Recent data backs his broader point. UK engineering employers report serious recruitment struggles. A 2025 IET survey found 76% face difficulty filling key roles. Automation, cybersecurity, data engineering and software skills top the list. Demand for AI specialists outpaces supply across finance, healthcare and enterprise sectors. Yet generalist tech hiring remains flat. The market rewards depth. It punishes the absence of practical experience.
And here’s where the distortion bites hardest. If AI handles routine work, newcomers miss the repetition needed to build judgment. They skip the struggle that forges expertise. Similar warnings emerged this week from investment manager Tom Slater of Baillie Gifford. He told audiences that aggressive AI adoption could hollow out the next generation of workers. Short-term productivity gains might yield long-term skill erosion. Companies still need humans who understand systems at a fundamental level.
Upton doesn’t dismiss AI. He calls the tools incredible. Raspberry Pi itself benefits from advances in computing. But he draws a line. Claims that AI will destroy vast numbers of computing roles lack grounding, he says. The technology reshapes tasks. It rarely eliminates the need for oversight, creativity or domain knowledge. “Overestimating chatbots’ ability to replace people could undo a lot of the good work that’s been done, not just by Raspberry Pi, but by a lot of other organisations” in steering people toward tech careers.
The economic stakes feel immediate. “Absolutely. We need a supply of engineers,” Upton replied when asked about growth impacts. Britain already wrestles with high energy costs. Among the highest in the G7, they raise operational expenses. They influence decisions on where to build factories or run data centers. Manufacturing suffers. Labor costs rise to cover living expenses. Without enough skilled engineers, innovation stalls. Productivity suffers. The advantage built through decades of STEM promotion fades.
TechRadar noted the self-feeding problem clearly. Junior positions shrink. Senior replacements grow scarce. The cycle accelerates if students opt out now. Harvey Nash research from early 2026 projects continued strong demand for data architects, cybersecurity experts and platform engineers. IT spending rises. Specialist skills drive the market. Generalist roles lag. This mismatch rewards those who gain real experience. It penalizes those scared off by hype.
But the message spreads fast. Headlines amplify fears of mass displacement. Students encounter them early. Career advisers repeat them. The absence of solid data makes matters worse. No one knows exactly how roles will evolve over the next decade. Predictions vary wildly. Some forecast net job creation in new AI-related fields. Others see contraction. Upton’s call for patience lands as pragmatic. Five or ten years of observation would yield better guidance than speculation today.
His track record adds credibility. Raspberry Pi didn’t just sell hardware. It sparked a movement. Millions of devices reached schools, hobbyists and developers. Programming became accessible. The foundation supported later careers. That model worked. Reversing it through misplaced AI optimism would carry costs far beyond any single company.
Industry leaders watch closely. Recent analyses from Harvey Nash and the IET confirm persistent shortages in digital capabilities. Fifty-four percent of UK firms report trouble filling entry-level digital positions. Many would pay premiums for proven talent. The pattern holds. Demand exists. Supply lags. AI could widen that divide if it discourages entrants rather than augmenting them.
Upton’s intervention arrives at a charged moment. Tech giants continue restructuring. Layoff trackers show no slowdown. AI features prominently in earnings calls as efficiency driver. Yet efficiency alone doesn’t build the next cohort of experts. That requires people who master fundamentals first. They learn through application. They iterate. They fail and improve. Tools accelerate some steps. They don’t replace the process.
So the caution feels timely. Distorted choices today shape workforce realities tomorrow. Overhype risks self-inflicted wounds. The talent shortage doesn’t fix itself. It deepens without deliberate effort to attract and train new generations. Raspberry Pi showed one path forward. Upton now highlights a potential detour. Industry insiders would do well to heed it. The tools matter. The people using them matter more.