The more I read about articles like these, I grow even more convinced that the education industry as a whole, regardless of the country or stature, has kind of lost their plot.
Sometimes I even wonder if this is the outcome of sheer laziness, fear or both.
When I speak to professors, teachers and students - It's also surprising that "top tier" institutions are fighting AI harder (outside extremely specific courses like Harvard’s flagship CS50, MBA courses at Wharton (UPenn) and MIT) while "bottom tier" institutions are completely embracing it and rebuilding their curriculum around it. One CS professor at a "bottom tier" CSU mentioned to me that he's actively going completely "open book using AI" - students are allowed to use anything they want from Claude to Codex to OpenCode to finish assignments but the assignments have now changed from "blurt out quicksort" to "let's sort N natural numbers in a cache efficient way using least amount of resources". I would hire the latter over the former anytime. I am tired of interviewing candidates who can shit out quicksort before I can even finish my sentence but stare at me dumbfounded when I ask them to sort people's name serialized in unicode.
In my opinion, the bulk of traditional education has been a mix of memorization of facts, knowing inference rules, and applying inference rules to those facts, coupled with recall.
Before the age of LLMs, only very expensive-to-build rule-based expert systems were able to replicate that functionality. Humans were just simply cheaper and way more reliable.
In 2026, Frontier models are exceedingly, across the board, across industries, breaking records and challenging those notions on cost, capability and sophistication.
Trying to replicate how education used to operate pre-frontier LLMs is like forcing people to farm by hand in the age of automated tractors that have LIDAR, Vision, RTK on board.
I'm not discounting other elements of learning like collaborative debate, clinical/lab work, Socratic reasoning, emotional intelligence and the development of a professional network - but I argue these skills are not limited to a school or university setting. Infact, a lot of this is distorted in a school or university setting compared to the real world.
I have been filing my own taxes, including complexities like equity, real estate and business income for over a decade now, so it's not just a simple 1040 and 540. Reading through IRS documentation, talking to EAs and CPAs to fill in ambiguities and gaps was pretty expensive in terms of time and money.
Both Claude Opus and GPT 5.5 now, as of 2026, answer all my tax questions correctly. Those thousands of dollars in time and money I had spent has been replaced by a single $20 subscription. Unless tax codes drastically change every year, that $20 is a one time cost.
If I were to begin my tax journey in 2026, I would never had to spend those dollars: dozens of tax professionals are out of a job - all their education is for nothing.
It's entirely irrelevant whether they passed their exams by carving answers out on granite, taking their exams in a jail cell on an island proctored by Catholic Nuns under the watchful guard of automatic machine guns manned by T-1000s, I just don't need them anymore. For my usecase, these humans and their credentials provide 0 additional value over a one-time $20 expense, no matter how complicated it was for them to get their credential and what complex interpretive dance they had to do to impress the people awarding their grades.
All this "reject AI, do it by hand" is just insanity at worst and laziness at best.
That said, I'm personally unsure what the future of education in the age of tractors is like but removing weeds, planting seeds, watering them, all by hand is certainly not it.
It's my understanding that most countries - developed or developing - are bottlenecked on educating their masses due to lack of qualified teachers.
That's a bandwidth problem. Having those teachers listen to oral arguments from a handful of students is not solving the core bandwidth problem. All these shenanigans is doing a disservice to the public.
> In my opinion, the bulk of traditional education has been a mix of memorization of facts, knowing inference rules, and applying inference rules to those facts, coupled with recall.
These are important skills to develop, even in a world with LLMs.
> All this "reject AI, do it by hand" is just insanity at worst and laziness at best.
If a 3rd grader complained about having to learn multiplication because calculators exist, would you agree with them?
Why do we make kids learn how to do math if they can pull out their phone and open the calculator app? Because learning how to learn is important and there is value in understanding how the answer is produced, even if you have a machine that can produce it for you.
Universities can teach a separate class on how to use LLMs. The existing classes should be focused on teaching their existing subject matter, not becoming an extension of a how-to-LLM class
> Why do we make kids learn how to do math if they can pull out their phone and open the calculator app? Because learning how to learn is important and there is value in understanding how the answer is produced, even if you have a machine that can produce it for you.
I actually appreciate this question very much because this question is extremely pertinent to our discussion. Before I continue my response, I want to turn around and ask you:
1. What's your definition of "learn how to do math"?
i. Would it be sufficient if they proved they understood what addition, subtraction, multiplication and division was? Or do they need to be able to correctly calculate what 4592 * 314 is? What if they could show you using diagrams of squares and rectangles *why* (a + b)** 2 expands to the equation it does but refused to chart out results for various values of a and b?
ii. Would you fail a student who could reliably chart out results for (a + b)** 2 given various values of a and b, but failed to explain that using diagrams of squares and rectangles?
iii. Would you fail a student who could reliably add 1 + 3 or 7 - 5 or 7 * 5 but fail to compute 4592 * 314?
iv. Is a student who could reliably add 1 + 3 or 7 - 5 or 7 * 5 but fail to compute 4592 * 314 at the age of 5 superior or inferior to a student who is capable of doing the same at age 7?
2. If an exam is composed of tables of long division and your score on the test boils down to your ability to how many of those long divisions you can complete in 45 minutes, does the person who scores the highest have a superior understanding of division than the slowest student who suffers from mental fatigue due to an underlying, undiagnoised health condition?
3. When you say "understand how the answer is produced", what level of abstraction is acceptable to be not considered as cheating? Do they need to understand how the calculator physically computes the floating-point math, or is pushing the button enough? If pushing the button is cheating, why isn't using a base-10 shortcut algorithm also cheating? What if the button pusher explained to you precisely how IEEE 754 worked, how a digital calculator works and then refused to do long division citing it was a complete waste of their time and yours? Would you refuse to let them pass the class or fail them unless they yielded to your demands and complied with your specific definition of learning?
Is the math class also doubling as ability to pass compliance and behavioral standards or is it purely a test of mathematical ability?
4. If a student uses an LLM to generate the boilerplate code for a script, but can perfectly explain the architecture, debug the logic, and scale the deployment, have they failed to 'learn how to learn' just because they didn't manually type the syntax?
Educational resources and classroom time is a zero sum game. We cannot afford to educate everyone if they all require 1:1 coaching from a qualified human in a room that has limited space. Time in a day is zero sum as well. Every minute someone spends on doing the 135th long division is a minute they're not spending thinking if solving long division problems is a meaningful differentor in their long term success - whether it's the long division that will make them wealthy, happy and successful or something else entirely?
> i. Would it be sufficient if they proved they understood what addition, subtraction, multiplication and division was? Or do they need to be able to correctly calculate what 4592 * 314 is? What if they could show you using diagrams of squares and rectangles why (a + b)* 2 expands to the equation it does but refused to chart out results for various values of a and b?
I’m not an educator by profession but I’ve done a lot of training and mentoring.
There is no replacement for actually doing the work. People who study something but don’t go through the exercises feel they understand topics better than they do. It’s only when they are put in a position where they have to apply it that the cracks in their understanding are revealed.
This would 100% result in students who thought they could explain multiplication but only had a surface level idea. The knowledge would also be fleeting because actually doing the thing makes the knowledge stick more than just observing and understanding the thing.
So yes, you still need to have the students do the thing.
Replace math with writing and it will be more obvious. It’s easy to look at someone’s writing and critique it. It’s much harder to write well without practicing.
Depends on what skills you want the students to have.
In e.g., philosophy, allowing students to write their assessments with the help of LLMs changes the students’ depth of understanding, as well as their ability to synthesize, formalize, and drum up their own arguments and objections.
These are all useful skills, even in a post-LLM world.
> allowing students to write their assessments with the help of LLMs changes the students’ depth of understanding, as well as their ability to synthesize, formalize, and drum up their own arguments and objections
indeed but LLMs improve these capabilities. Let me explain:
Most educators behave as if using an LLM robs the student of their ability to think. That has not and never been my experience.
You can just tell an LLM to debate, find out inconsistencies and issues in your writing, and challenge you, and it will very well do that!
LLMs for me, cheaply replace teachers who can barely be accessed during office hours because they are so busy doing research or overwhelmed with grading coursework.
In a typical educational setup, I might have half an hour at best with a teacher who looks kindly upon me, and at worst, five minutes with a teacher who doesn't like being around me. An LLM has no emotion, but it has knowledge, it has inference rules, and it has the ability to push back, debate, and find out inconsistencies, bugs and gaps in my thinking.
Behavior that is exactly what I need consistently, in a good teacher.
A frontier LLM is a good teacher who doesn't play games, has no emotion, infinite patience and always available for $20/mo.
Two points:
There are two kinds of engineers in Finland. Some received a more theoretical education at research universities. Most went to more applied institutions that focus more on practical skills. Regardless of what employers say, they generally prefer the theoretical engineers from research universities. Because the higher status of those universities attracts more talented individuals, because theoretical understanding tends to stay valid longer than practical skills, and because it's easier to learn practical skills at work.
The kids who start their studies today are supposed to graduate in 2030, and they will probably retire around 2080. Focusing too much on the skills their early employers might want in the 2030s would be a huge misallocation of resources.
> because theoretical understanding tends to stay valid longer than practical skills, and because it's easier to learn practical skills at work
depends. If you're building a datacenter and need welders to weld two very specific set of metals together, allocating a PhD in metallurgy to do that welding, I argue would be a huge misallocation of resources.
Now your argument seems to be whether the PhD in metallurgy can pivot to doing something else entirely compared to someone who has been narrowly trained to only weld two very specific set of metals together.
If I interpret your take on the matter, your point seems to be that the former is "intellectually superior and more flexible" than the latter.
I dont take that position - I find most humans to be extremely capable. Just because one human was able to grasp the intricacies of metallurgy doesn't necessarily imply they have more capability than someone who didn't.
The fact that employers in Finland can be so very picky says more about the productivity of Finland than the employee's capabilities. At peak productivity, Finland would just not be hiring anyone who could breathe but import labor because there's not enough people in Finland.
I had two main points.
First, employers favor applicants from prestigious schools over those from less prestigious schools, because prestigious schools are more selective. That seems to be at least as true in the US as in Finland.
Second, theoretical education is more useful in the long term, because it's a better foundation for learning new skills. It can be a disadvantage when you are trying to get your first job, as you have fewer practical skills potential employers would need right now. Which is why, from an individual perspective, theoretical education only makes sense in higher-tier institutions, where the prestige can offset the initial disadvantage.
I heard your points - you communicated them very well. I failed to communicate that the first, while true, isn't going to matter in a few years time and the second is not true.
I will repeat what I said - if an employer arbitrarily restricts their talent pool to legacy credentials, they are optimizing for signaling rather than raw productivity.
By 2026, there is overwhelming anecdote and evidence that credentials and where you went to school have very little to do with demonstrated performance. (See citations 1-3)
Hence, it's mostly theatrics.
It seems like you're arguing that it's safer to attend prestigious schools because that increases your chances of being employed. I argue that in the age of LLMs, where the cost of learning is very low and speed of building prototypes (and hence learning and improvement) is rapid, it's easier than ever to start your own business.
> First, employers favor applicants from prestigious schools over those from less prestigious schools, because prestigious schools are more selective. That seems to be at least as true in the US as in Finland
This represents a trailing-indicator mindset typical of "MNC".
Yes, legacy corporations still filter by prestige because they want to signal to others they hire only the best, but that's because in extremely large orgs like theirs, it's impossible for a single person's contributions (outside the C-Suite) to have an outsized effect on that org, so they resort to proxy metrics, like credentails, for signalling.
In real life though, credentials have an extremely weak correlation with impact (see citations).
What this translates for an end user, is that the end user doesn't care about the credentials of the person who built the product. I explain that below.
On the frontiers of excellence - startup founders will tell you that hiring people from prestigious schools is a liability over those who might only have a high school degree because the prestigious school graduate is entitled and is looking to build a resume so they can jump ship in 2 years instead of the barely high school degree student who's hungry to prove they are worth investing in.
The selectivity of prestigious schools is gamed heavily in the U.S where you have literally floors of "prep schools" full of trainers telling you which piano lessons to take, which tennis tournaments to play at, which charities to volunteer for and which SAT question types to learn well. Getting into Harvard is less about ability and more about checking off boxes on a checklist.
Notice that absolutely none of this actually has to do with what the student actually wants to do in life and everything to do with impressing the gatekeepers to admissions.
However, make no mistake, the end customer rarely cares about the credentials.
When you watch Netflix or play the XBox, I bet you don't look up the credentials of the production team or the engineers who built it - you enjoy the content and vote with your dollars - and that's real life.
HN is full of people who don't have anything past a high-school degree but they were obsessed with solving one problem well and now they are multi-millionaires and billionaires because they ended up having an outsized impact. There are far more in that bucket who are comfortable but not multi-millionaires (so you never hear about them).
The money is not the end result but proof of impact and this proves my point.
Let's even discard that, citing survivorship bias. Let's reduce my argument to one fact: engineering/code doesn't care about the author's pedigree - all that matters is whether the system compiles, works correctly and scales under load and a prestigious university certainly doesn't have exclusive rights to those skills.
> theoretical education only makes sense in higher-tier institutions, where the prestige can offset the initial disadvantage
That doesn't make any sense. An institution will hire as per their needs: higher-tier, lower-tier, mid-tier all have their unique needs.
The reason why higher-tier institutions can afford to hire advanced degree graduates is because lower-tier, mid-tier simply cannot afford that baggage: highly credentialed individuals often won't work on tasks they consider "beneath them", the actual "plumbing" of engineering, which makes them useless to lean, high-velocity teams - which is precisely the point the founders at start ups make.
I think you're conflating multiple issues together altough you're not alone in the population that strongly advocates for education as a proxy for ability. They have nothing to do with each other and have a lose correlation.
Here's a thought experiment:
- Kid A (Rural Finland): Has internet access, a Claude Code subscription, and a singular obsession. They spend their time learning how to write a provably correct, memory-safe OS kernel. Through sheer, unhindered iteration and thousands of failures, they master thread management, hardware concurrency, durable scheduling, and modern CPU architecture.
- Kid B (New York City): Attends a prestigious prep school. Their entire mental bandwidth is consumed by a generalized conveyor belt: memorizing 100 years of Eupropean and U.S. history, computing the molality of an acid drop, identifying stereocenters in organic chemistry, and writing essays on what Socrates said to Glaucon. They barely touch operating systems because there is no time left after juggling DBMS trivia, analog design, and quantum physics to satisfy a rubric.
Which person would you listen to or hire when it comes to building an OS?
Now your argument might then pivot to "but what if instead of building an OS, I wanted someone to do algoritmic trading on Wall Street? Ha! I got you there! Kid A knows nothing about that, they only know how to write a kernel!"
Kid A doesn't care. Their singular obsession is OSes and they are world class at it. Their brains aren't clouded by molality and stereocenters - an OS kernel certainly doesn't care about them. If in the future, we had a CPU that worked off organic chemistry, maybe he will learn about it or maybe not and there will be Kid C in the unlikely case Kid A failed to build a trading engine from scratch, fail 1000 times, and learn the math on the fly just like they did for the kernel.
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[1] Google's Internal Data (Laszlo Bock): In 2013, Google's SVP of People Operations publicly revealed that GPAs, test scores, and university prestige had zero correlation with employee success. They explicitly began hiring more candidates with no college degrees because work-sample tests were far better predictors of impact.
[2] The Dale & Krueger Study (NBER): A famous National Bureau of Economic Research study found that students who were accepted to Ivy League schools but chose to attend less prestigious state schools earned just as much money later in life. It proved that the ambition of the student drives success, not the prestige of the institution.
[3] The Schmidt & Hunter Meta-Analysis: The gold standard in industrial psychology (examining 85 years of hiring data) found that "years of education" has a dismal 0.10 correlation with job performance, whereas "work sample tests" and "cognitive ability" are highly predictive.
The actual Finnish kid who wrote a somewhat successful OS kernel was born in Helsinki, to an upper middle class family with a communist father. He chose to study in the most prestigious university in the country, largely due to his family ties. Because the CS program was theoretical, it didn't take that much of his time. He could enjoy student life and also pursue his other interests. The OS kernel emerged from the latter.
That's what education in a research university is like, when you actually take advantage of it. You don't have to focus on whatever seems useful and practical in the short term, you don't have to compete against your peers, and you don't have to overwork yourself. Instead, you have a socially acceptable opportunity to study whatever interests you, and you hopefully gain the attitude that you can learn whatever you need to learn.