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Prime Minister Carney announces Team Canada Strong – a nationwide plan to recruit up to 100,000 skilled trades workers

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April 29, 2026

Gatineau, Québec

The global trade landscape is rapidly changing. In response, Canada’s new government is focused on what we can control: building a stronger, more resilient, more independent Canadian economy. At the core of this strategy is an ambitious plan to build nation-building projects, more affordable homes, new defence industries, and stronger local infrastructure.

To build the next port, bridge, mine, or data centre, Canada needs more welders, crane operators, and electricians – a strong skilled trades workforce. Many young Canadians want to join the trades, but are discouraged by the long wait times, lack of opportunities, and high cost of training.

Canada’s new government is changing that with a new flagship measure, Team Canada Strong. Today, Prime Minister Carney outlined this new $6 billionnationwide effort to recruit, train, and hire 80,000 to 100,000 new Red Seal trades workers in the next five years. Announced as part of the Spring Economic Update, this measure will:

Recruit:

We will deploy $2 billion to support young Canadians with paid, job-ready placements that lead directly into registered apprenticeships.

This investment will also support the launch of the Build Canada Apprenticeship Serviceto provide up to $10,000 for an apprentice’s first-year salary, match apprentices to job opportunities, and offer direct navigation and support to help employers hire, train, and retain apprentices.

Train:

We will boost and modernise apprenticeship training to expedite Red Seal certification with $331 million in funding over five years, starting in 2026-27, and $18 million ongoing.

We will digitise the Red Seal Program, introducing online exams, digital logbooks, and secure credentials to reduce certification timelines, including by creating a single national registered apprenticeship number.

We will expand the Union Training and Innovation Program to enable union-run training centres to upgrade facilities, expand capacity, and invest in modern equipment.

Hire:

We will provide $3.4 billion over five years, starting in 2026-27, and $468 million ongoing to address the challenges that can stop apprentices from completing their training and moving into permanent jobs.

We will offer a one-time $5,000 apprenticeship completion bonusto those that obtain certification in a Red Seal trade.

With the Apprenticeship Training Grant, we will provide a $400 weekly top-up while apprentices attend mandatory in-class technical training.

This represents a total payment of up to $16,000 per apprentice, paid in addition to Employment Insurance.

Team Canada Strong will deliver:

End-to-end support: Supporting young Canadians every step of the way, from first interest to first job to Red Seal certification.

Faster results: Goal of cutting the time it takes to get certified by 50%.

A multi-channel approach: We will train more workers, faster, working with our partners – other orders of government, Indigenous partners, unions, businesses, and the Canadian Armed Forces (CAF).

The scale to deliver: An ambitious $6 billion investment in Team Canada Strong.

In addition, we will expand Canada’s skilled trades training capacity through the CAF with $250 million in funding. We will enhance the Cadets and Junior Canadian Rangers programs to provide hands-on training and early exposure to the trades. We will launch the Reserve Trades Experience Pilot Program, offering fully funded trades training for Canadians who commit to serve in the Primary Reserve.

With rapid-scale, high-quality training and end-to-end supports, Team Canada Strong will transform the skilled trades, bring more apprentices into the workforce, and ensure young Canadians are ready to build the homes, energy projects, ports, arenas, and defence industries we need. We are creating clear, paid pathways for young Canadians into good careers, strengthening Canada’s future workforce, and ensuring we can build Canada strong for all.

Quotes

“Canada is building big – in ports, mines, railways, and millions more homes. It’s going to be a great time to be in the trades. Team Canada Strong is a nationwide effort that will get more young people into the trades and on the job, so we can build Canada strong for all.”

The Rt. Hon. Mark Carney, Prime Minister of Canada

“By launching Team Canada Strong, we’re opening new doors for young Canadians to serve their country and build meaningful careers. This initiative delivers real, paid pathways into the skilled trades – combining training with hands-on experience. It gives employers more opportunities to hire and mentor the next generation. It rewards achievement with a $5,000 bonus upon Red Seal certification. And above all, it invites young people to play a direct role in building a stronger Canada.”

The Hon. François-Philippe Champagne, Minister of Finance and National Revenue

Quick facts

By 2033, Canada will need more than 1.4 million new trades workers to build homes, expand transit, and develop energy infrastructure across the country.

The Team Canada Strong program will provide young Canadians with paid, entry-level, trades-related work experience that leads to an apprenticeship.

Canada’s new government is building Canada strong for all. Earlier this week, we also launched the Canada Strong Fund to allow Canadians to invest directly in the country’s growth and share in the returns.

The Globe and Mail's editorial board ran a piece in March titled "AI can be a crutch, or a springboard." To illustrate the crutch half, they offered this: someone asked AI to explain a passage from Dune that warns against delegating thinking to machines. Instead of reading the book.

That anecdote is doing more work than the studies the editorial cites. But the studies are real.

Researchers at MIT published a paper in June 2025 titled "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" (Kosmyna et al., arXiv 2506.08872). The study tracked brain activity across three groups: people writing with ChatGPT, people using search engines, and people working unaided. The LLM group showed the weakest neural connectivity. Over four months, "LLM users consistently underperformed at neural, linguistic, and behavioral levels." The most striking finding: LLM users struggled to accurately quote their own work. They couldn't recall what they had just written.

The Globe cites this and similar research to make a point about dependency. The implicit argument: hand enough of your thinking to a machine and you stop doing it yourself.

That finding is probably accurate for the way most people use these tools.

The question is whether that's the only way they can be used.

The Globe's own title contains the counter-argument. Crutch or springboard. They wrote both words. They just didn't develop the second one.

Ethan Mollick, a professor at Wharton who has been writing about AI use since the tools became widely available, argued in 2023 that the real challenge AI poses to education isn't that students will stop thinking, it's that the old structures assumed thinking was hard enough to enforce. ("The Homework Apocalypse," oneusefulthing.org, July 2023.) When AI can do the surface-level cognitive work, the only tasks left worth assigning are the ones that require actual judgment. The tool, in that framing, doesn't reduce the demand for thinking. It raises the floor under it.

Nate B. Jones, who writes and consults on what it actually takes to work well with AI, has made a sharper version of this argument. His position: using AI effectively requires more cognitive skill, not less. Specifically, it requires the ability to translate ambiguous intent into a precise, edge-case-aware specification that an AI can execute correctly. It requires detecting errors in output that is fluent and confident-sounding but wrong. It requires recognizing when an AI has drifted from your intent, or is confirming a premise it should be challenging. These are not passive skills. They are harder versions of the same thinking the MIT study found LLM users weren't doing.

The difference between the group that lost neural connectivity and the group that doesn't isn't the tool. It's what they decided to do with it.

Here's my own evidence.

In the past year I built a working web application. Python backend. JavaScript frontend. Deployed on two hosting platforms. Payment processing. User authentication. A full data model.

I do not know how to code.

Every product decision was mine. Every architectural call. Every tradeoff judgment. I defined what the system needed to do, why, and what done looked like. I reviewed every significant change before it was accepted. When something broke, I identified where the breakdown was and directed the fix. The implementation was handled by AI. The thinking was mine.

This mode (call it AI-directed building) is the opposite of the Dune reader. The quality of what gets produced is entirely a function of how clearly you can think, how precisely you can specify, and how critically you can evaluate what comes back. There is no shortcut in that. A vague brief to an AI doesn't produce a confused output. It produces a confident, fluent, wrong one. The discipline that prevents that is yours to supply.

Non-coders building functional software with AI is common enough now that it isn't a story. What's less visible is the specificity of judgment underneath the ones that actually work.

The practices that force more thinking rather than less are not complicated, but they require a decision to use the tool differently.

When I've formed a position on something, I give the AI full context and ask it to make the strongest possible case against me. Ask for the hardest opposing argument it can construct. Then I read it. Sometimes it changes nothing. Sometimes it surfaces something I had dismissed without fully examining. The AI doesn't form my view. It stress-tests one I've already formed.

When I'm uncertain between options, I don't ask which is better. I ask: here are two approaches, here is my constraint, now what does each cost me, and what does each require me to give up? I make the call. The AI laid out the shape of the decision. The judgment was mine.

The uncomfortable part of thinking is still yours in this mode. The tool makes the work more rigorous, not easier.

The MIT researchers and the Globe editorial are almost certainly right about the majority of current use. Passive use produces passive outcomes. That's not a controversial claim.

The crutch half and the springboard half use the same interface. The difference is whether the person in front of it decided to think.

What are you doing with it that forces more thinking rather than less? Are you using it to skip a step, or to take a harder one? Genuinely asking.