The difference between participation
and commitment is illustrated at breakfast.
The chicken participates.
The pig is committed.
Everyone thinks that students somehow become great writers at university, and some do, but many don’t. The reasons are fairly simple.
Firstly, coming from highschool, students are often not well prepared to write. This is an amalgam of age, a failure to teach proper writing skills, and likely the use of AI to “help” write things. Just as importantly though is a lack of reading skills. Students often don’t care that much about reading, and I’m not just talking about academic fluff. Reading Pride and Prejudice is not for everyone, including most teenage boys. I had to suffer through novels like The Great Gatsby, The Old Man and the Sea, and The Merry-Go-Round in the Sea (a coming of age novel set in Australia during and after WWII). At the time I found them boring, and avoided reading them as much as I could. I likely didn’t have the fortitude or any interest in reading them, and I can guarantee I wasn’t the only one. I doubt much has changed in highschools in the intervening years. Today I would understand the themes behind The Merry-Go-Round in the Sea, but that’s because I have experienced life, and perhaps see the nostalgia of childhood from the perspective of someone viewing it as a past experience.
Now reading at an early age promotes the subconsciously absorption of proper writing mechanics, vocabulary, and structure. It expands vocabulary and language use, exposes different styles and voices and critically shows how characters develop, fostering active learning. I don’t think you have to use the same tired old books, I think just about any book is good as long as it engages the reader. Even graphic novels work. Making students read books they perceive to be boring or irrelevant does nothing to promote reading, in fact it may do just the opposite. Perhaps The Hobbit would have been a better choice for having 16 year old boys read. As much as an interest in what we read develops as we gain life experiences, so too does our ability to write. I doubt many people were interested in writing when I was in high school in the 1980s. I enjoyed creative writing to a point, which was getting an assignment done. Looking back this was because I also had very little context, and few life experiences to base any creative writing on. It’s hard to write a poem about war when you haven’t experienced it.
So we can hardly expect students to come to university as seasoned writers. Most leave university as mediocre writers if they are lucky, and that’s just in the humanities. STEM students are rarely provided any opportunity to learn real writing skills (Sorry, scientific writing is not the same). Many people don’t find themselves successful writers until much later in life. Before I retired I taught a first year course on the history of food a couple of times. I had some very interesting short essay questions which I thought would be well written. I was sadly mistaken, but what I learned was something I should have already known − that first year students don’t have the worldly experiences to write well (but then again in STEM nobody is expected to write well, just answer questions and analyze things). However good writing assignments provide a means for them to explore their abilities. It may take two years, it may take ten, or forty, but providing them with different reading and writing experiences will help them become better writers. The rest is up to them.
On March 5, the computing community lost another pioneer, Sir Tony Hoare at age 92. Sir Charles Antony Richard Hoare was born on January 11, 1934 in Colombo, Ceylon (now Sri Lanka). More commonly known as Tony Hoare or C.A.R. Hoare, he made foundational contributions to computer science in the fields of programming languages, algorithm design, operating systems, and formal verification. To some he is best known for the Quicksort sorting algorithm (1961) an archetypal example of both a divide-and-conquer algorithm, and a recursive algorithm. His many achievements include a seminal work, his 1980 Turing Award lecture “The Emperor’s Old Clothes“, warning against the use of needless complexity in software. It should be required reading for all computer science students.
Canada renegotiated a trade deal with China recently. Trying to fix a bunch of issues with tariffs and Canadian agricultural produce, leveraged with the import of 49,000 Chinese-made EV’s. Those 49,000 cars would make up 3% of the vehicles sold in Canada. Of course the auto-industry in Ontario is annoyed because it will hurt the industry. So how big is 3%, and will it really make a difference?
3% of anything is quite insignificant. If I looked at the back of a container of hot chocolate and it told be that 1 tablespoon of the powder provided 1% of daily protein, I would say that’s extremely insignificant. I would have to drink hot chocolate made with 100 tablespoons of the chocolate powder to get my daily requirement of protein. But people get scared about numbers because they perhaps don’t understand them. So let’s look at the stats (all for 2024, because complete data for 2025 isn’t yet available):
So when looking at the stats, it seems like there is about a 625,000 difference between new vehicles sold and vehicles produced. But that’s not the full story, because Canada exports a good majority of the cars it produces (C$41.4B worth). While the US is currently the number one market (C$38.9B), others include China (C$328M), United Arab Emirates (C$276M), and Netherlands (C$141M). So if you export vehicles, to make up the short-fall you have to import some. In fact from a monetary value, Canada imports more than it exports. And where do these imports come from? The majority come from the United States (C$20.3B), Mexico (C$7.98B), Japan (C$7.29B), South Korea (C$6.17B), and Germany (C$3.62B).
It’s hard to find an exact number of cars actually exported, because most places can’t even agree on the exact value of cars exported, although the Department of Finance Canada suggests 1.1M vehicles were exported in 2024. That really doesn’t leave a lot of cars for Canada. Let’s do the math: 1.294M−1.1M = 194,000 vehicles for domestic use (roughly, again without exact figures, its hard to give exact numbers. That math gives us roughly 10% of the vehicles sold in Canada in 2024, so the figures aren’t too far off.
But you know one figure nobody seems to talk about? The number of zero-emission EV’s produced in Canada. Why? Because it’s abysmally low. In fact to date (Feb.2026) only some 40,000 have been built. So in order to make up the shortfall, you have to import most of them. Canada currently manufactures a bunch of low-emission cars, here is a list (Feb.2025) with base prices (without the additional things they like to charge for cars included):
You’ll notice however, that six are hybrids, and only one is (or rather was) a ZEV (that got the chop due to uber-low sales). So if there are currently no zero-emission EV’s being produced, that would imply, and they all have to be imported? Right? So all of the 110 EV models available to Canadians are imported. So really, what’s another one or two? Here’s the reality − 3% is nothing. This is especially relevant because Canada only keeps around 12% of the cars it produces for the domestic market. But that’s not a number that anyone ever talks about, because it’s an unsavoury statistic. The Canadian auto market relies heavily on imports, so who cares if another company takes 3% of the sales market?
It might be more meaningful if Canada manufactured any ZEV’s, but as yet we don’t, just hybrids. And even if we exported no cars, and only used what we produced in Canada, we would still be short some 625,000 cars on a yearly basis. This is why you often have to dig a little deeper, because companies and governments like to use numbers to their own benefit.
Here are the statistics to take away from this discussion:
There is probably a lot more to say on the topic, but to get the complete picture you have to look at all the data. As to worrying about US car makers moving their production south? Well Japanese companies already account for just over 60% of auto assembly employment in Canada as of 2024. If Canada wants to build ZEV’s, maybe it’s time to look towards other international partners, and why not China who produces some 70% of worlds EV’s.
There are some lotteries that allow the choice of $1000 per week for life, or $1,000,000 up front. Let’s look at the math behind it. Now in Canada, prize money is not taxable, so either way there is no tax on the principal amount (once you start earning money on the principal, that money is taxable). One person recently in the news was 20 years old, so well take “life” to mean 60 years on top of that. So is there a better option?
The first option is to take $1M up front. $1M is not really enough to retire on at age 20, so let’s look at some options (you would still need a job, with either option).
If someone were buying a large item, like a house this may be an ideal option. It might leave you with next to no mortgage, which is nice, in fact it might actually save money over the lifespan of the mortgage. For arguments sake, if a house costs $1M, and you need a 20% down-payment regardless, then the mortgage would be for $800,000. At 4% over 25 years, the interest would be around $462K. Not a trivial savings if you pay cash for the house − it’s basically saving $4,208 per month in payments. (Note this is a very simplistic calculation, just to prove a point. Because a fixed rate of 4% for 25 years is unrealistic in any situation, and fixed-rate terms are typically 5 years in Canada).
The alternative of course is to invest the $1M. Now obviously there are a myriad of options here, from risky stock type investments, to more conservative term deposits. It’s hard to look at scenarios here because there are so many options, including whether someone wants a return every year, or just wants the investments to continue growing. Let’s just look at the scenario of a GIC (Guaranteed Investment Certificate), which is fairly low risk (but the money can’t be touched for the term of the investment). Of course any investment gains will be taxed… at what rate really depends on what other income a person has. Then there is also inflation. All investments are compounded annually (for simplicity). In addition it would be approximately $30K a year to add to income come tax time.
5 years at 3% = $1,159,274
10 years at 3% = $1,343,916
25 years at 3% = $2,093,777
50 years at 3% = $4,383,906
Of course it’s hard to leave an investment for 10 years without touching it, let alone 50. Of course if you left it for 25 years you would still be $630,000 ahead of the mortgage-free scenario (i.e. you paid the mortgage for 25 years and this is payback). If we factor in inflation of 2% for the 25-year scenario, then the effects of inflation on the principal and interest would mean the total future value is $1,276,222 (Bank of Canada calculator). See, making money from money is tricky. And you still have to pay the taxes on money earned.
Now it is possible to put some money in a TFSA, which is tax-free, but at 20 that’s probably a max of $20,500 if you turned 18 in 2023. Some will say “invest it in the stock market where you can earn 10% and live off the earnings”. Never listen to this sort of advise. The high returns of the stock market come with high risk, and higher taxes on earnings. 10% seems nice, but the wrong strategy can see $1M turn into $200K overnight. I could give a scenario here, but it’s not as simple as compound interest. Needless to say that there is no such thing as ‘fast money’.
The second option is to take $1000 per week for life. That’s $52,000 per year, which is about equivalent to a salary of $70,000 (in Ontario). Over 60 years that’s $3.12M. Like I said, you will likely still need a job, unless you are living off-grid somewhere. That’s tax free money. You could live off it, or pay the mortgage yearly (it just about covers the $800K mortgage), or use part of it and invest part. The good thing is that after 25 years the mortgage is paid, and the remaining 35 years (in a best case scenario) are all gravy. In all likelihood it would allow someone to retire early (because with the mortgage paid, any extra money someone earns can be invested). For example if we put the maximum (current) TFSA contribution $7000, and assume we already have $20,500 then after 35 years at 3% annual growth, the TFSA would be worth $493,615 − tax free.
But let’s not forget inflation. In 25 years the $1000 a week may only be worth $400-$600 in today’s dollars, i.e. you will still get $1000 per week, but it’s purchasing power will be greatly reduced. But if you are young, what $52,000 a year offers is a sense of freedom. This is also a scenario for a 20 year old. Winning the money as a 40 or 50 year old would of course change which option is chosen.
You see there are no easy choices to be made − there is no right or wrong answer, because no-one has a crystal ball to see what the future holds. Every scenario has its pros and cons. But needless to say, if you do win any sort of money, seek the help of a reputable financial advisor from a reputable institution.
Statistics are interesting because they are often used to sway arguments, or entice people. This discussion looks at the impact of normal statistics without an underlying context. Take the example of a cookware company that uses statistics in its advertisements (very few other companies provide statistics of any sort). Some of the statistics are shown below.
These numbers may seem significant, but put in context, they are not. For example in the first statement, “100 Michelin-recognized restaurants use the cookware”. Worldwide there are approximately 18,713 Michelin-recognized (not starred) restaurants. So 103/18713 = 0.55%. One would argue that this really isn’t a significant number. If we were to look at only Michelin-starred restaurants, some 3700 worldwide, the number would increase to 103/3700 = 2.78%. The number looks a lot better if we only consider North America, where the number is 1941, so 103/1941 = 5.3% which is 10 times as significant, but still not that much.
For the second statistic, that it is “used by over 2,000 global restaurants”, let’s look at a number of scenarios. This is hard to quantify, because how do you define a “global restaurant”. It has to be different from a run-of-the-mill restaurant, because there could be 15-25 million of those. A better way to look at is to consider the number of professional chefs who run restaurants. Let’s consider just Canada first, where there are 62,200 chefs (2023). This would give us 2000/62200 = 3.2%. If we look at the USA, this number climbs to 286,000, and 0.7%. Neither is earth-shattering, and if we include worldwide restaurants the number just plummets.
The last statistic, 90,000 pans, is quite meaningless. It’s just a reflect of how many pans have been sold, and can’t really be compared to other companies because they don’t publish their data. Some vague figures suggest the Le Creuset foundry in France produces 25,000 items a day. Restaurants use either stainless steel or aluminum pans, and to put the number into context, there are circa 97,000 restaurants in Canada alone (of course the 90,000 pans were not suggested to come from restaurants exclusively).
There is nothing to say that the numbers used in the advertisements are whatsoever incorrect, but they do provide a level of ambiguity because they aren’t given in any frame of reference. Numbers only mean something if put into context.
There was a time when most people considered the future of computer science to be unlimited. A degree in computing was a ‘golden ticket’. But the labour market may have other plans. A recent study by the Federal Reserve Bank of New York highlights a 6.1% unemployment rate for computer science. To put that into context, it’s on par with chemistry, but not as bad as fine arts (7.0%), computer engineering (7.5%), or physics (7.8%). History majors, often lauded by STEM for their “unemployable” degrees are at 4.6%, geography 3.3%, biology 3.0%, and accounting 1.9%. The flip side of the study shows that CS is still in the top three early career median wage at $80K. So remuneration is still okay, but the amount of jobs are fewer, and in reality those jobs will go to the best computer science students. Which makes sense. Things may be a little different in Canada, where there is still a talent shortage.
But the pigeons are slowly coming home to roost. CS may have become a victim of its own success. Maybe because of AI-automation replacing the lower tier of jobs for CS graduates, you know, the ones that likely involve some programming, for people who likely didn’t do too well in their CS degree. AI can do their jobs, it was always just a matter of time before it happened. There is still a talent shortage in Canada, so I wouldn’t say this is anything near the end of computer science, but from here on in graduates will need exceptional skills, and experience, in order to get the good jobs.
How will this affect computer science? Well CS has long been a cash cow for universities. Let’s face it, it’s a STEM that doesn’t need a lot in the way of resources − no large labs beyond a room of computers, and even then most students have their own. There is no need for a chemistry or biology lab, or an engineering workshop. With the knowledge that there are plenty of jobs, schools filled CS degrees up, sometimes with students that were not at all suited to the degree. They often suffer from a lack of real interest, poor problem solving skills, and an inability to program (yet somehow they get degrees). Class sizes have ballooned resulting in a reduced student experience. CS degrees have become a promise of a high paying job for all, or at least that’s how it was marketed.
If you’re doing a computer science degree, as much as I don’t like AI, I imagine your best path forward is a degree that is AI-centric. But again, these are not degrees for people who have the talent to move the industry forward. The days of coding jobs for mediocre CS grads is over. Of course if you want a real AI-resistant job that pays well, there is healthcare, and the skilled trades, especially things like welding (I kid you not).
Apple just released the latest iPhone, the 17. It’s boring. Well let me put that statement into context, it’s no more boring than the 16, or the 15. I have a 14, and frankly I don’t know what Apple is doing anymore. It was once a company that came up with leading edge, exceptional tech. Now each new release is just blah, underwhelming even. I don’t know why people bother buying it. More AI? Yeah, nobody needs that. More cores? Yeah, no.
I mean to be completely honest most tech companies aren’t producing anything exciting anymore. It’s the same with camera companies, oh another 60MP camera, whoop-de-do. Nothing exciting here, move along… more megapixels is somewhat meaningless today. Like the 48-megapixel camera lenses are a selling point. When is anyone ever going to use 48-megapixel photos on a smartphone? Great to upload to social media − not! The sensor hasn’t changed size, they just crammed more photosites in. The main camera is has a 1/1.28 sensor, meaning it’s approximately 7.5×10mm in size. It relies on a lot of processing to make things look nice. But even with 48MP I doubt future phones will need to go any higher, I mean why? Great for pixel-peeping I guess.
Perhaps the one thing I see with this phone is some emphasis on improved battery life, but you need it given the high end process for the cameras etc. 27 hours of local video playback supposedly, something tom’s guide demonstrates is fairly on the mark. Overall good marks on the battery, tech that has been somewhat complacent over the years.
The rest of the iPhone is basically the same sort of things we’ve seen the past 10 years, perhaps incrementally improved. Don’t get me wrong, I like the iPhone, but I think that technology in general has become so ubiquitous that there is no longer any reason to upgrade a phone too soon. Ask yourself what functions you use on a smartphone? Texting? Video calls? Maps? (and let’s face it, it’s likely Google Maps) Camera? When was the last time you uploaded a truthfully useful app? In fact if you deleted all the apps you never use, you wouldn’t have many left.
Are we now creating tech for tech’s sake? Do we need new phones every year? Likely not. What we need is some leap into the next evolution of communications tech, something I doubt we will see anytime soon. Perhaps a ‘neural iPhone’ that can connect directly to the brain so you can take pictures ‘through-the-eye’?
In the modern age it seems like everyone is dealing with some form of mental health issue. Maybe this has always been the case, and we are just realizing it now. Or perhaps it is because once we moved away from ‘survival mode’ a while back (not that far from living memory though), our minds have somehow tried to counterbalance the void. It’s really hard to know. What is certain is that there is far too much information being thrown at us on a daily basis, and it’s hard to imagine that the mind is actually capable of dealing with it all. In the 1970’s there were a few core sources of information, or news: newspapers, TV news, and magazines. If you didn’t actively go looking for news, you could avoid it. Nowadays that’s not true anymore, unless you live an off-line life somewhere in the countryside.
The problem is one of too many data streams. All day long we are attacked by information from every direction, with an increasing amount of miss-information thrown into the melee. It’s hard to believe the human brain can actually handle the daily deluge, and as a result I think it probably triage’s information, as much as it can to avoid an overload. This may be even more problematic for children who are thrown in front of a tablet at age 6 months, and become overstimulated with information at a time when they should not be.
There was a time, not that long ago, when humans had to deal more with daily life and survival than anything else. Their concern was likely the safety and well being of the community around them. While they may have read about events in other places in newspapers, it was often days old, and didn’t really affect their daily lives. Over the years the lag between events and news shortened and with the mainstreaming of TV in the 1950s and 60s changed to almost real-time, albeit usually summarized at the evening news broadcast. Fast-forward to 2025 and we watch events as they happen. There are many places in the world where they still spend every day in survival mode, so have no real time for news from other places.
Not a lot of news is positive in the overall scope of things. Most is negative in some way, and the best way of dealing with negative information is just not to absorb it in any form. Yes, things happen all around the world, but for most people it is impossible to do anything, so filling our minds with additional information does nothing for the well being of our mental health. It would almost be better if we didn’t absorb so much of the world’s happenings on a daily basis.
To be informed, but not overloaded.
I never thought we would see this day because computer science was suppose to be “the” degree to get. It seemed like a discipline with endless possibilities. But alas what we now have the perfect example of technology, i.e. AI, turning on its creator. Not in any sort of dystopian or bad way, but simply by taking their future jobs. The industry has slowed down over the past few years, and that is really for two reasons. Firstly, technology has somewhat plateaued. Look at the iPhone − what exactly is awe-inspiring about it anymore? Not particularly much of anything. I would upgrade to a new iPhone why? More AI? Please, I like to think.
More importantly though, the industry has become a victim of itself, and by that I mean AI. While AI may be ‘okay’ at answering people’s queries (or rather just finding the answer from a uber-vast database), it is surprisingly good at coding. That means it is ideally suited to replacing the people who created it, or at least the lower rungs of the ladder. The reality is that mediocre computer science graduates are going to find it harder to find meaningful jobs. The days of lucrative jobs in CS for everyone are over, mainly because AI can likely write more efficient code in a more timely manner than a human can. It’s no different to when combine harvesters displaced human power for harvesting grain − machines were just faster and more efficient.
So where does this leave us? Well, it’s a grim time for programmers, well specifically entry-level programmers. This in all likelihood means the number of students enrolled in CS courses will decline over time. College-level courses might be wiped out completely, and some university-level degrees will take a hit. But some of those students likely were on the mediocre side of things anyways (the type that use AI to do their assignments), and so losing them won’t be a big issue. Some have no natural talent for CS, they just do it because they perceive a large salary. But it likely won’t impact people who are problem-solvers. AI might be able to do some of this, but it too has its limitations − at the moment it’s intelligence comes largely from the thoughts of humans.
There will always be jobs for good software-engineers, and people who can think outside of the box. AI can assist with optimization and analysis of software systems, but ultimately, human expertise and insights are still needed to guide development, inspire creativity, and make critical decisions. While AI can mimic creativity it struggles to produce truly original and innovative solutions to problems. Human CS specialists bring a blend of creativity, emotional intelligence, and cultural understanding to the software design process. And honestly reducing the size of CS programs will only benefit those who might want a better learning environment. It may not be great for CS departments, but that is just how things go sometimes.
P.S. Frankly I taught enough students over the years to see the gradual influx of mediocrity when it came to things like programming. Some people just don’t have the skill set to solve problems and implement them in any sort of efficient manner. These are the people who ultimately turn towards AI to solve their problems for them, so effectively the machine is doing their work and they have no intrinsic value as programmers. Good, talented students will always have a bright future in CS.
P.P.S. Will the AI bubble burst? This article from The Telegraph is sombre reading. Apparently the vast majority of AI investments are yielding zero returns.