From the Fraser Institute

By Michael Zwaagstra

At the dawn of a new schoolyear, the issue of artificial intelligence (AI) looms large. But innovations have always been a part of classroom instruction.

For example, calculators changed the face of math class forever. Kind of.

Before the invention of calculators, all math equations were done manually. Calculators changed things by making it possible to solve complex equations in seconds and often without thinking much about the problem. All you had to do was punch in the correct numbers and—presto—the answer magically popped up on the screen.

Naturally, this led to some debate among teachers. Some thought there was no longer a need for students to memorize math facts including multiplication tables, while others argued that learning basic skills was still important, regardless of whether calculators were available or not.

With the benefit of several decades of hindsight, the evidence is clear that students still should learn basic math facts. While calculators make it possible to solve equations quickly, students who don’t know, by memory, the order of operations, or basic math facts such as multiplication tables, struggle to solve complex equations.

That’s because people have only a limited amount of working memory available at any given time. By committing basic math facts to their long-term memories, students can free up space in their working memories to tackle challenging math questions. In short, it would be a huge mistake to allow students to get away with not mastering important math skills.

Fast-forward to the present challenge of AI. Just as calculators made it easier to solve math equations, AI programs such as ChatGPT can perform research, correct grammar, and even write essays for students in a matter of seconds.

This leads to an obvious question: What should schools do about students using AI? Some schools have tried to ban AI entirely while others embrace it as a regular tool just like a pencil or a pen. Simply put, AI creates even more ethical questions and instructional challenges for teachers than calculators ever did when they were first introduced in classrooms.

Rather than bury our collective heads in the sand, we should tackle the problem of AI head on.

One of the most important things we can do is identify which activities are immune to AI’s influence. Frankly, this is why in-person tests and exams are more important than ever. If tests are written with pen and paper under a teacher’s supervision, students will not be able to use AI to formulate answers. Thus, rather than abolish tests and exams, with the advent of AI programs, we must embrace formal tests and exams even more than before. And we must use them more regularly.

As for regular assignments, schools should have students complete as much of their work in class as possible. For assignments that must be completed at home, teachers should design questions that are as “AI-proof” as possible. For example, asking students to answer specific questions about something discussed in class is much better than having them write a generic essay on a famous person’s life.

Teachers will need to redesign assignments so that they cannot be easily completed by AI. Students are naturally inclined to follow the path of least resistance. So it’s important for teachers to make it hard for them to get AI to do their homework. That way, most students will conclude it’s better to do the assignment themselves rather than have AI do it for them.

Finally, it makes good sense to allow students to use AI as a tool on some assignments. Since AI is already being used by many professionals to make their jobs easier, it’s a good idea to teach students appropriate ways to use AI. The key is to ensure that students know the difference between using AI as a resource and using it to cheat on an assignment.

AI is here to stay, but that doesn’t mean schools should let this new technology take over the classroom. The key is to keep AI in its proper place.

Michael Zwaagstra

Senior Fellow, Fraser Institute

  David Clinton

Based mostly on their 2024 budget, the federal government has promised $2.4 billion in support of artificial intelligence (A.I.) innovation and research. Given the potential importance of the A.I. sector and the universal expectation that modern governments should support private business development, this doesn’t sound all that crazy.

But does this particular implementation of that role actually make sense? After all, the global A.I. industry is currently suffering existential convulsions, with hundreds of billions of dollars worth of sector dominance regularly shifting back and forth between the big corporate players. And I’m not sure any major provider has yet built a demonstrably profitable model. Is Canada in a realistic position to compete on this playing field and, if we are, should we really want to?

First of all, it’s worth examining the planned spending itself.

• $2 billion over five years was committed to the Canadian Sovereign A.I. Compute Strategy, which targets public and private infrastructure for increasing A.I. compute capacity, including public supercomputing facilities.
• $200 million has been earmarked for the Regional Artificial Intelligence Initiative (RAII) via Regional Development Agencies intended to boost A.I. startups.
• $100 million to boost productivity is going to the National Research Council Canada’s A.I. Assist Program
• The Canadian A.I. Safety Institute will receive $50 million

In their goals, the $300 million going to those RAII and NRC programs don’t seem substantially different from existing industry support programs like SR&ED. So there’s really nothing much to say about them.

And I wish the poor folk at the Canadian A.I. Safety Institute the best of luck. Their goals might (or might not) be laudable, but I personally don’t see any chance they’ll be successful. Once A.I. models come on line, it’s only a matter of time before users will figure out how to make them do whatever they want.

But I’m really interested in that $2 billion for infrastructure and compute capacity. The first red flag here has to be our access to sufficient power generation.

Canada currently generates more electrical power than we need, but that’s changing fast. To increase capacity to meet government EV mandates, decarbonization goals, and population growth could require doubling our capacity. And that’s before we try to bring A.I. super computers online. Just for context, Amazon, Microsoft, Google, and Oracle all have plans to build their own nuclear reactors to power their data centers. These things require an enormous amount of power.

I’m not sure I see a path to success here. Plowing money into A.I. compute infrastructure while promoting zero emissions policies that’ll ensure your infrastructure can never be powered isn’t smart.

However, the larger problem here may be the current state of the A.I. industry itself. All the frantic scrambling we’re seeing among investors and governments desperate to buy into the current gold rush is mostly focused on the astronomical investment returns that are possible.

There’s nothing wrong with that in principle. But “astronomical investment returns” are also possible by betting on extreme long shots at the race track or shorting equity positions in the Big Five Canadian banks. Not every “possible” investment is appropriate for government policymakers.

Right now the big players (OpenAI, Anthropic, etc.) are struggling to turn a profit. Sure, they regularly manage to build new models that drop the cost of an inference token by ten times. But those new models consume ten or a hundred times more tokens responding to each request. And flat-rate monthly customers regularly increase the volume and complexity of their requests. At this point, there’s apparently no easy way out of this trap.

Since business customers and power users – the most profitable parts of the market – insist on using only the newest and most powerful models while resisting pay-as-you-go contracts, profit margins aren’t scaling. Reportedly, OpenAI is betting on commoditizing its chat services and making its money from advertising. But it’s also working to drive Anthropic and the others out of business by competing head-to-head for the enterprise API business with low prices.

In other words, this is a highly volatile and competitive industry where it’s nearly impossible to visualize what success might even look like with confidence.

Is A.I. potentially world-changing? Yes it is. Could building A.I. compute infrastructure make some investors wildly wealthy? Yes it could. But is it the kind of gamble that’s suitable for public funds?

Perhaps not.