Proof by Controlled Detonation

Prove: $\forall \epsilon > 0, |x| < \epsilon \implies x = 0$.

---

Suppose $x \ne 0$, then $x > 0$ or $x < 0$.

Case 1 ($x >0$) Let $\epsilon = x.$ From $|x| < \epsilon$, we have $|x| < x \overset{{\forall x>0, |x|=x}}{\Longrightarrow}x < x$, a contradiction.

Case 2 ($x < 0$) Let $\epsilon = -x$ ($-x >0 \;\text{since}\; x<0$). $|x| < -x \overset{\forall x<0, |x|=-x}{\Longrightarrow} -x < -x$, another contradiction.

Thus $x \ne 0$ is impossible, so $x = 0$.

$\square$

Now let’s make it even more brutally simple:

$x \ne 0 \implies |x| > 0$.

Choose

$\epsilon = |x|.$

Then the hypothesis gives

|$x| < \epsilon = |x|$,

which is impossible.

Therefore $x= 0$.

$\blacksquare$

This is not a joke!

In fact, this little theorem tells us:

To conclude that two real numbers $a$ and $b$ are equal, it is sufficient to show that their distance is less than any positive number. That is,

$\forall \epsilon > 0, |a-b| < \epsilon \implies a-b = 0$.

For example, to show that the convergent sequence $a_n$ has a unique limit:

$(\lim\limits_{n\rightarrow \infty}a_n = a, \lim\limits_{n\rightarrow \infty} a_n = b) \implies a = b,$

we proceed as follows:

Since

$\lim\limits_{n \rightarrow \infty} a_n = a \implies \forall \epsilon >0, \exists N_1 \ni n >N_1, |a_n-a| < \frac{\epsilon}{2}\quad\quad$(1)

and

$\lim\limits_{n \rightarrow \infty} a_n = b \implies \forall \epsilon >0, \exists N_2 \ni n >N_2, |a_n-b| < \frac{\epsilon}{2},\quad\quad$(2)

letting $N = \max(N_1, N_2)$ gives

$\forall \epsilon >0, \exists N \ni n > N$, $|a-b| = |a-a_n + a_n-b| \le |a-a_n| + |a_n-b| = |a_n-a| + |a_n-b| < \frac{\epsilon}{2} + \frac{\epsilon}{2} = \epsilon$.

i.e.,

$\forall \epsilon >0, |a-b| <\epsilon$.

Therefore,

$a-b =0 \implies a = b.$

Quantum Computing: The World’s Fastest Solution to Nothing

Finally, someone snapped.

Sabine Hossenfelder, the German theoretical physicist and science communicator, recently posted a video about the supposed uses of quantum computing. Somewhere along the way, apparently after years of listening to the same promises being recycled with increasingly impressive fonts, she ran out of diplomatic language.

Her verdict?

If the Bullshit Index goes from 0 to 10, quantum computing gets an 11.

That is not a mathematical error.

That is a measurement overflow.

The title of her video was even more direct:

“Quantum Computing Failure Now Obvious.”

The video is less than seven minutes long. Within roughly 24 hours, it had passed 400,000 views, accumulated about 18,000 likes, and generated more than 2,200 comments.

For a subject involving quantum circuits—something that normally causes ordinary human beings to suddenly remember they have laundry to do—that is remarkable.

Why did it resonate so strongly?

The reason is actually quite simple.

The quantum computing industry has been promising the future for a very, very long time.

The public may not understand quantum mechanics.

But the public does understand something much simpler:

If you keep selling me the pie, eventually I would like to see the pie.

Preferably before 2047.

---

Hossenfelder did not begin with qubits, superposition, entanglement, or a colorful animation of a photon doing something mysterious.

She did something much more dangerous.

She opened the old files.

And suddenly the past came rushing back.

These weren’t random internet comments from some guy named QuantumWizard69.

They came from major technology companies, research organizations, and consulting firms.

Here is the general pattern:

Year	Source	Promise	What happened
2017	Google	Small quantum devices might produce commercial returns within five years	Didn’t happen
2018	IBM	NISQ computers were expected to bring commercial advantages soon; the “dawn of the commercial quantum era” was approaching	Didn’t happen
2021	IonQ	Quantum machine learning was expected to become the first broadly useful NISQ application	Didn’t happen
2023	Kipu Quantum	Industrial NISQ advantage was being pursued within 18–36 months	The 36 months are now up

And that last one is particularly awkward.

Because by August 2026, the maximum 36-month window has expired.

So what do we do now?

Obviously:

Move the window.

This is an extremely useful scientific technique.

You predict something will happen.

It doesn’t.

You extend the deadline.

It still doesn’t.

Extend it again.

At some point, the prediction becomes so far into the future that everyone involved is dead.

Problem solved.

---

Consulting firms got into the game too.

In 2019, Boston Consulting Group predicted that quantum computing would generate between $2 billion and $5 billion in value for end users by 2024.

That was described as a relatively modest estimate.

Hossenfelder’s response was devastating:

The actual value was even more modest.

Precisely zero.

Now, before anyone panics, there is a perfectly respectable explanation.

The prediction was too optimistic.

So the 2024 report moved the NISQ era further out, toward 2030.

You see how beautifully this works?

2024: Not yet.

2030: Still coming.

2035: Technical challenges remain.

2040: We are entering an exciting new phase.

2045: Commercial quantum advantage is just around the corner.

At this point, the phrase “just around the corner” deserves its own Nobel Prize.

The remarkable thing is that quantum computing seems to have discovered a new form of quantum state:

the permanently approaching future.

It is simultaneously coming and never arriving.

Very quantum.

---

Now we need to be fair.

Quantum mechanics did not fail.

Quantum computing as a field of basic research did not fail.

And nobody should claim that quantum computers will never be useful.

That is not the point.

The failure is something much narrower:

the NISQ commercial fantasy that has been repeatedly sold for the last decade.

The story went something like this:

Small noisy quantum computers are coming.

Quantum machine learning is coming.

Financial optimization is coming.

Drug discovery is coming.

Materials science is coming.

Commercial quantum computing is coming.

A new industrial revolution is coming.

The dawn is coming.

The dawn is coming.

The dawn is coming.

At this point, I have a small question:

Where the hell is the sun?

We’ve been standing on the porch since 2017.

---

The fundamental problem is that several very different things have been repeatedly mixed together.

A quantum computer can run a quantum circuit.

Fine.

A quantum computer can perform a task that is difficult for a classical computer to simulate.

Fine.

A quantum computer can demonstrate some form of computational advantage.

Fine.

But none of those statements automatically means:

“Congratulations, we have a billion-dollar business.”

There is a rather important missing step.

What useful problem did you solve?

And then:

Did you solve it better, faster, or cheaper than the best classical method?

And finally:

Does anybody actually care?

That last question is surprisingly powerful.

Because you can have an algorithm that is fantastically fast at solving a problem nobody has.

Congratulations.

You have invented the world’s fastest solution to nothing.

---

Today’s quantum computers can certainly do impressive things.

They can run quantum circuits of limited size.

They can perform sampling experiments that are difficult for classical computers to reproduce directly.

That is real engineering.

It is interesting.

It is important.

But:

Engineering achievement ≠ useful computational advantage.

And:

Computational advantage ≠ commercial value.

And:

Commercial value ≠ industrial revolution.

These are four different boxes.

Somehow, in quantum computing presentations, they occasionally become one very large box labeled:

REVOLUTION!!!

The problem is not that the technology is useless.

The problem is that the marketing department often arrives before the application does.

A genuine commercial quantum application would have to demonstrate something rather boring:

1. There is a real problem.
2. People care about solving it.
3. Classical computers don’t solve it adequately.
4. A quantum computer solves it substantially better.
5. The quantum hardware is actually usable.
6. The whole thing costs less than the value it creates.

That last part is where the magic trick tends to disappear.

---

Hossenfelder also points to two people whose opinions are particularly interesting.

The first is John Preskill, one of the world’s leading quantum computing researchers and the person who coined the term NISQ, or Noisy Intermediate-Scale Quantum.

And here is the funny part:

Preskill never promised that NISQ machines would necessarily have commercial value.

That may sound like a minor detail.

It isn’t.

Sometimes the most important thing a scientist says is what he doesn’t say.

Then there is Scott Aaronson.

Aaronson is hardly a quantum-computing skeptic.

Quite the opposite.

He is one of the strongest intellectual defenders of quantum computing.

And yet he has consistently been cautious about claims of near-term commercial advantage.

His position can basically be summarized as:

Quantum computing is scientifically fascinating.

As for the commercial revolution?

Let’s maybe not put that on the PowerPoint yet.

This distinction is important.

Because saying:

“This is one of the most fascinating areas of theoretical computer science.”

is a scientific statement.

Saying:

“This will transform drug discovery, finance, logistics, materials science, and civilization itself by 2027.”

is something else.

The distance between those two statements is approximately the width of the Grand Canyon of Hype.

---

The video raises another fascinating possibility: perhaps artificial intelligence will solve some of the problems that quantum computing has been promising to solve—without needing a quantum computer at all.

Demis Hassabis has asked a very interesting question:

Are there things in nature that classical computers fundamentally cannot model efficiently?

The standard quantum-computing argument is seductive:

Nature is quantum.

Therefore, if we want to simulate nature accurately, perhaps we ultimately need quantum computers.

Beautiful argument.

Elegant argument.

Unfortunately, there is a tiny problem.

Do we actually need to simulate all of nature?

Weather forecasting does not require calculating the quantum state of every molecule in the atmosphere.

AlphaFold did not calculate the wavefunction of every electron in every protein.

It learned useful patterns.

That distinction matters enormously.

Suppose we want to know the structure of a protein.

We don’t necessarily care about every microscopic event occurring inside the protein.

We care about the answer.

If a classical machine-learning model can reliably predict that answer, we may not care whether it has reproduced the entire quantum machinery underneath.

This is exactly what happens in many areas of science.

We don’t simulate every atom in a hurricane to predict where the hurricane is going.

We don’t solve every microscopic interaction in a cup of coffee to predict that the coffee will probably make you more awake.

And we certainly don’t need to calculate the quantum state of every neuron in your brain before predicting that you are going to regret checking your email.

The deeper point is this:

Microscopic complexity does not automatically imply macroscopic computational complexity.

A physical system can be enormously complicated underneath while still exhibiting stable, learnable patterns at the level we actually care about.

AI may be very good at exploiting exactly those patterns.

This does not prove that every quantum system can be efficiently simulated classically.

It doesn’t.

But it forces us to ask a much better question:

Do we need to reproduce nature, or do we merely need to predict nature?

Those are not the same problem.

And if prediction is enough, then a classical machine learning system may occasionally walk into a problem that the quantum industry has spent years explaining requires a quantum computer—and quietly say:

“I already got the answer.”

---

Hossenfelder ends with perhaps the best joke in the entire video:

“To date, the only profitable quantum application has been forecasting profitable quantum applications.”

That is funny.

Unfortunately, it is also uncomfortably plausible.

Think about it.

Predicting that quantum computing will make money can already generate money.

Consultants can sell reports.

Companies can raise money.

Startups can increase valuations.

Governments can announce strategic initiatives.

Universities can apply for grants.

Journalists can write headlines.

Investors can talk about the future.

Everybody gets to participate in the quantum economy.

There is just one tiny problem.

The quantum computer itself still has to show up.

And eventually someone has to ask:

How much irreplaceable value has quantum computing actually delivered to an end user?

That question has a wonderfully annoying property.

It cannot be answered with a market forecast.

---

Let me make this very clear.

I am not arguing that quantum computing is useless.

I am not arguing that quantum mechanics is wrong.

I am not arguing that quantum computers will never become important.

They may become extremely important.

Perhaps they will.

But:

“This could become extremely important”

is not the same sentence as:

“The commercial revolution has arrived.”

And it certainly isn’t the same as:

“Buy our quantum stock before you miss the boat.”

The first is science.

The second is speculation.

The third is where someone’s uncle starts a podcast.

What I object to is the repeated conversion of:

research progress → computational advantage → practical usefulness → commercial value → industrial revolution

as though these were five consecutive lines of the same proof.

They aren’t.

There are several missing lemmas.

And unlike in mathematics, you cannot simply write:

“The remaining details are left to the reader.”

---

For nearly a decade we have repeatedly heard about the dawn of the commercial quantum era.

But somehow the dawn keeps getting postponed.

Perhaps quantum computing has discovered a revolutionary new form of timekeeping:

the deadline is itself in superposition.

The commercial breakthrough is both here and not here.

The market is both enormous and nonexistent.

The application is both imminent and approximately ten years away.

And the moment you ask for a concrete example, the quantum state collapses into:

“Well, the technology is still at an early stage.”

Of course it is.

It has been an early stage for a remarkably long time.

At some point, “early stage” stops describing the technology and starts describing the business plan.

---

But quantum computing has never had a shortage of ways to keep us impressed.

When the applications are elusive, bring out the stopwatch.

And just when you think the stopwatch has run out of numbers, along comes Jiuzhang 4.

Its reported quantum advantage has been described in terms of a speedup of roughly:

100 million trillion trillion trillion trillion trillion times.

At this point, I have a question.

Faster than what?

A supercomputer?

Fine.

But at this scale, the comparison becomes almost philosophical.

If my computer takes a billion years to finish a calculation and yours takes a nanosecond, that’s impressive.

If yours is 100 million trillion trillion trillion trillion trillion times faster, I assume it finishes the calculation sometime around the Big Bang.

Actually, forget the Big Bang.

It probably finishes before the question is invented.

And then there is the tiny little detail that makes all these astronomical numbers slightly less astronomical:

It is extremely fast at one very specific problem.

Which raises the most primitive question in computing:

Can it do anything useful?

Because if you build a machine that is a million-trillion-trillion-trillion times faster than a supercomputer at solving a problem nobody needs solved, you haven’t created the future.

You’ve created:

The world’s fastest way to waste electricity.

At least my laptop has the decency to waste electricity slowly.

To a Daughter I Never Had — Letter 1

I never had you.

But if I had, there are a few things I would have wanted you to know.

I would have told you not to be afraid of not knowing.

The world will often make you feel that you should have an answer. People admire certainty. They want to know what you think, what you believe, what you are going to do next.

But you don’t always have to know.

Sometimes the most honest thing you can say is:

I don’t know.

Then listen.

Look.

Think.

Ask questions.

Take your time.

I would have told you not to be afraid of being wrong, either.

You will be wrong sometimes. I have been wrong more times than I care to remember.

Being wrong is not the tragedy.

Refusing to discover that you are wrong is.

I would have told you to be careful with people who are too certain. Not because certainty is always wrong, but because the world is larger than any one person’s explanation of it.

And when you disagree with someone, disagree honestly.

Try to understand what they are saying before you decide they are wrong.

I would have told you not to measure yourself by how quickly you can do something.

Some people are fast.

Some people are slow.

Speed is useful, but it is not the same thing as understanding.

Stay with difficult things.

Sometimes you have to sit with a question for a long time before it begins to open.

Sometimes you need to walk away and come back.

Sometimes the answer arrives when you have stopped looking for it.

I would have wanted you to keep something in your life that belongs only to you.

Something you do not have to turn into a career.

Something you do not have to be good at.

Something you do simply because you find it beautiful.

A walk.

A piece of music.

A garden.

A difficult question.

And perhaps, someday, mathematics.

Because mathematics taught me something about all of these things.

It taught me that a question can be more valuable than an answer.

It taught me that something can look obvious and still deserve to be examined.

It taught me that a few examples can suggest a truth without proving it.

And it taught me one of the things I value most:

You should be able to say not only what is true, but why.

That is where proof enters.

I might have shown you a simple proof.

Not because I wanted you to become a mathematician. You would not have had to become anything for me.

I would have shown you a proof because there is a particular kind of joy in discovering that something is true—and then discovering why it is true.

A formula can give you an answer.

A proof lets you see the answer being born.

You might have asked me why we had to prove something that seemed obvious.

I would have smiled.

Because, I would have told you, mathematics has a habit of asking us to look again at the things we think we already know.

And perhaps that is not such a bad habit for life, either.

I never got to share these things with you.

Some questions never asked.

Some conversations never begun.

But I can still imagine where we would have started.

And I think we would have started here.

2+3=Output: The Factory Model of Modern Academia

Behold—the “2+3” BS-to-PhD pipeline at XXX…XXX University—or, as it increasingly feels, Education™: now optimized for throughput, compliance, and quiet efficiency.
Welcome to the system.

Input: student, age ~18, mildly uncertain, statistically hopeful.
Output: PhD, age ~23, highly specialized, existentially ambiguous.

Processing time has been reduced. Reflection cycles deprecated. Exploration modules removed for efficiency.

The university no longer presents itself as a place of learning. That language is… outdated. It is now an industry node—a clean, well-lit facility where human curiosity is streamlined into measurable output. The brochures still say “discovery,” but the architecture says “production.”

You enter. You are assigned a track. You proceed.

There is no wandering here. Wandering introduces variance. Variance reduces efficiency. Efficiency is the objective.

The educators—once professors, mentors, inconveniently human guides—have been reassigned in function. They are now closer to system operators. Their role is not to inspire, but to maintain flow rate. Keep the pipeline moving. Prevent blockages. Ensure each unit reaches the next stage on schedule.

Questions are permitted, but only if they align with the system’s direction. Doubt is… inefficient. Changing your mind? That is a system error.

Somewhere along the line, education stopped being about forming a mind and became about shaping a product. Smooth edges. Standardized outputs. Predictable competencies. You are not encouraged to become unpredictable—that would make you difficult to process. Instead, you are optimized.

The pipeline hums.

Students move through it in tight formation, each one a nearly identical unit of ambition and exhaustion. They learn quickly—because they must. They specialize early—because they are told to. They produce—because that is what the system measures.

And yes, from the outside, it works beautifully. Degrees are awarded. Timelines are shortened. Metrics improve. The machine is efficient.

Inside, however, something quieter happens.

Curiosity is trimmed to fit deadlines. Depth is compressed into deliverables. Identity—once something explored slowly—is selected early and rarely revisited. The process does not ask who you might become. It asks only: what function will you serve?

The metaphor is no longer subtle. This is not a classroom. It is a factory line.

And the students—bright, capable, full of potential—begin to resemble something else. Not thinkers in formation, but workers in sequence. Repeating, producing, advancing. Not quite forced, not quite free. Just… moving.

The system does not need to coerce. It only needs to continue.

At XXX…XXX University, the “2+3” model is presented as the future. Faster. Leaner. More efficient.
And perhaps it is.
But in that future, the university is no longer a place where minds are cultivated.
It is a place where they are processed.

Let My People Go

There is a phrase from the Bible that has always struck me for its extraordinary simplicity:

“Let my people go.”

It is a demand for liberation.

I want to borrow those ancient words for a different kind of liberation.

By “my people,” I mean mathematics itself.

Let the mathematics go.

Let it out of the cage we have built around it.

For decades, we have been told that mathematics education must become more engaging, more relevant, more collaborative, more student-centered, more exploratory, more interdisciplinary, and more equitable.

There is nothing inherently wrong with any of these goals.

But somewhere along the way, I wonder whether we began treating mathematics itself as the problem.

Mathematics became something that needed to be reformed before students were allowed to encounter it.

And what if the problem is not mathematics?

What if the problem is that we have forgotten to let students actually do mathematics?

A child encounters a mathematical question.

She tries something.

It doesn’t work.

She tries again.

She notices a pattern.

She asks why.

She makes a conjecture.

She tests it.

And then comes the most important question:

Can you prove it?

That is mathematics.

Not because proof is the only thing mathematicians do.

But because proof transforms a suspicion into knowledge.

A pattern may suggest that something is true.

Examples may persuade us that something is true.

A calculator may confirm it for a thousand cases.

But mathematics asks for something more:

Why must it be true?

This is one of the great intellectual ideas of mathematics, and it should not be reserved for the final years of schooling.

A student should grow up knowing that mathematics is not merely a collection of procedures to execute.

It is a world of definitions, assumptions, conjectures, arguments, counterexamples, and proofs.

Consider a simple statement:

The sum of two odd numbers is even.

A student can calculate:

,

,

.

Wonderful.

But none of these calculations proves the statement.

They merely illustrate it.

The mathematician asks:

Why can this never fail?

And now we can write

,

which is even.

The calculation has become an argument.

The pattern has become a theorem.

This transformation—from seeing that something happens to knowing why it must happen—is one of the most beautiful things mathematics has to offer.

Yet students can spend years in mathematics classrooms without experiencing it.

They can become remarkably competent at manipulating symbols while having little idea what a mathematical proof actually is.

Then one day they are told:

“Prove that…”

And suddenly everything falls apart.

This is not necessarily because the students cannot reason.

Perhaps we simply never taught them what mathematical reasoning looks like.

Perhaps we gave them answers when we should have given them questions.

Perhaps we trained them to ask:

“What formula do I use?”

when mathematics was asking them to ask:

“Why is this true?”

I am not arguing against reform.

Good teaching matters.

Students matter.

Motivation matters.

Multiple representations matter.

Discovery matters.

Problem solving matters.

But these things should be servants of mathematics, not substitutes for it.

We should not be afraid of the mathematics.

We should not apologize for definitions.

We should not hide theorems behind endless activities.

We should not treat proof as an advanced specialty.

And we certainly should not confuse doing something mathematically flavored with doing mathematics.

Let the students encounter mathematics.

Let them encounter difficult ideas.

Let them struggle.

Let them make mistakes.

Let them discover patterns.

Let them argue.

Let them be wrong.

Let them find counterexamples.

Let them ask why.

And then let them prove.

Perhaps what mathematics education needs is not more educational machinery.

Perhaps we need to get out of the way.

Let them meet mathematics itself.

And let mathematics speak for itself.

What Did We Reform Mathematics Into?

I have a simple question about mathematics reform.

What did we reform mathematics into?

I don’t mean to ask whether every reform is bad.

Some changes have been useful. Teachers have learned things about how students learn. Technology has opened possibilities that didn’t exist before. Some old practices deserved to be reconsidered.

Fine.

That’s not the question.

The question is what happens when we keep adding things to mathematics education until, eventually, we have everything except mathematics.

We want students to collaborate.

Good.

We want them to communicate.

Good.

We want them to explain their thinking.

Excellent.

We want them to explore.

Fine.

We want them to make conjectures.

Wonderful.

We want multiple representations.

Sure.

We want authentic problems.

Absolutely.

We want students to reflect on their learning.

Okay.

We want them to reflect on their reflection.

I’m beginning to worry.

Then we want them to collaborate while reflecting on their representations of authentic problem-solving experiences.

At this point, someone should probably ask:

Where did the mathematics go?

⸻

Imagine walking into a modern mathematics classroom.

Students are sitting in groups.

They are talking.

They are moving sticky notes around.

Someone is drawing a diagram.

Someone else is explaining their thinking.

A third student is asking a probing question.

The teacher is circulating.

There is a rich mathematical discourse taking place.

Everyone is engaged.

Everyone is communicating.

Everyone is collaborating.

Everyone is reflecting.

Nobody has done any mathematics.

Well, perhaps that’s unfair.

Maybe they have.

Maybe one student has written

.

But before anyone is allowed to do anything with it, the class must first explore multiple representations of the expression.

So the students draw a square.

Then they color the regions.

Then they discuss the visual model.

Then they compare it with an algebraic representation.

Then they explain which representation they prefer.

Then they write a reflection:

Today I learned that there are many ways to represent an algebraic expression.

Excellent.

But I have a small question.

Do they know that

?

Or did we merely have a very successful meeting about it?

⸻

This is where things get interesting.

We criticize traditional mathematics education for being too procedural.

“Don’t just teach students how to do it. Teach them why.”

Fine.

I agree.

But then we sometimes replace the old procedure with a new one.

The old procedure was:

1. Learn the formula.
2. Use the formula.
3. Get the answer.

The new procedure can become:

1. Explore.
2. Collaborate.
3. Represent.
4. Discuss.
5. Share.
6. Explain.
7. Reflect.
8. Revisit.
9. Re-represent.
10. Reflect on the re-representation.
11. Complete the exit ticket.

Somewhere around step 9, the quadratic equation has quietly left the building.

Both approaches can become mechanical.

The old procedure produces an answer.

The new procedure produces a classroom activity.

Neither guarantees mathematics.

⸻

And here is the irony.

Mathematics already contains an extraordinary form of discovery.

We don’t have to manufacture it.

Take two circles.

Give the students

and

.

Let them subtract.

Let them simplify.

Let them substitute.

Eventually they arrive at an equation involving .

And suddenly the possibilities are sitting there:

.

There is the discovery.

No laminated activity cards required.

No sticky notes.

No “turn and talk.”

No colorful poster entitled MY MATHEMATICAL JOURNEY.

Just mathematics.

The equations themselves are doing something interesting.

The mathematics is generating the questions.

The algebra is revealing the structure.

The student is discovering something that has to be true.

That’s not a simulation of mathematical discovery.

That’s mathematical discovery.

⸻

Perhaps this is what bothers me most.

In our eagerness to make mathematics engaging, we sometimes seem embarrassed by mathematics itself.

As though an equation cannot possibly hold a student’s attention without assistance.

An equation needs a story.

A picture.

A game.

A real-world context.

A group activity.

A manipulative.

A digital animation.

A culturally responsive launch.

A collaborative investigation.

A reflection prompt.

And, preferably, an exit ticket.

At some point I expect someone to put

into a little paper bag and ask the students to guess what it is before revealing the equation.

But sometimes

is enough.

Sometimes the question

“What happens if I subtract these two equations?”

is enough.

And sometimes a student sitting alone with a piece of paper, making a mistake and figuring out why it is wrong, is doing something more mathematical than an entire classroom full of carefully designed activities.

⸻

There is another problem.

We have become very good at describing what students should do.

Students should collaborate.

Students should communicate.

Students should explore.

Students should reason.

Students should model.

Students should represent.

Students should reflect.

But what, exactly, should they know?

That question sometimes feels almost impolite.

“Of course they should know things,” we say.

Yes.

But which things?

Definitions?

Theorems?

Algebraic identities?

Geometric relationships?

Methods of calculation?

Proofs?

Counterexamples?

Special cases?

Theorems and their hypotheses?

Or is “knowing how to communicate mathematical thinking” now sufficient evidence that mathematical thinking has occurred?

I hope not.

A student can communicate beautifully and be beautifully wrong.

A group can collaborate efficiently on an incorrect argument.

A diagram can be gorgeous.

A presentation can be excellent.

A reflection can be profound.

And the mathematics can still be false.

Mathematics has this rather inconvenient feature:

It doesn’t care how good the presentation was.

⸻

Imagine a student proving that

.

The student has worked collaboratively.

The group has created three representations.

The students have discussed multiple strategies.

They have explained their reasoning.

They have reflected on their learning.

The teacher has asked excellent questions.

Everyone has demonstrated agency.

The classroom is buzzing with mathematical discourse.

And yet:

.

Mathematics remains stubbornly uncooperative.

It refuses to negotiate.

It does not hold a class meeting and reconsider.

It does not say, “I really appreciate your perspective.”

It simply says:

No.

And that “no” is one of the great beauties of mathematics.

⸻

I don’t want to reform mathematics into something more fashionable.

I don’t want to reform it into a social activity.

I don’t want to reform it into a collection of strategies.

I don’t want to reform it into “problem solving” detached from mathematical knowledge.

And I certainly don’t want to reform it into an endless conversation about how we think about mathematics without actually doing much mathematics.

I want mathematics.

The real thing.

The definitions.

The notation.

The calculations.

The patterns.

The mistakes.

The counterexamples.

The special cases.

The proofs.

The beautiful shortcuts.

The ugly calculations.

The moments when an equation suddenly makes sense.

The moment when two apparently unrelated ideas turn out to be the same idea.

And above all, that extraordinary moment when you discover something that has to be true.

Maybe mathematics education doesn’t need to be constantly reinvented.

Maybe we don’t need another acronym.

Maybe we don’t need another framework.

Maybe we don’t need another seven-step model of mathematical engagement.

Maybe we could simply get better at teaching mathematics.

That would be reform enough.

The Emperor Has No Proof

There is a strange feature of modern mathematics education.

We talk constantly about problem solving, discovery, reasoning, multiple representations, and mathematical thinking.

All good words.

But sometimes I wonder whether we have forgotten the mathematics itself.

I am not opposed to reform. Mathematics education should change when we discover better ways to teach mathematics. But there is a difference between improving mathematics education and replacing mathematics with a collection of educational slogans.

A student who asks “Why?” is doing mathematics.

A student who checks a boundary case is doing mathematics.

A student who refuses to accept a statement until the missing step has been justified is doing mathematics.

And none of this requires a special classroom activity.

Consider a very elementary question.

Two circles are given by $x^2 + y^2 = r_1^2$ and $(x-d)^2 + y^2 = r_2^2$.

How many points can they have in common?

We are accustomed to saying that two distinct circles can intersect in zero, one, or two points.

Fine.

But why?

Let’s actually do the mathematics.

Assume first that $d > 0$. Subtract the two equations: $(x-d)^2-x^2 = r_2^2 – r_1^2$.

Hence $-2dx + d^2 = r_2^2-r_1^2$,

so $x= \frac{r_2^2-r_1^2-d^2}{-2d}$.

There is only one possible value of $x$.

Now substitute that value into $x^2 + y^2 =r_1^2$.

We obtain $y^2 = r_1^2 – x^2$.

And now the answer is sitting right there.

If the right-hand side is negative, there are no real points.

If it is zero, there is one point.

If it is positive, there are two points.

So: $0$, $1$, or $2$.

Nothing fancy happened.

We did not need a colorful activity. We did not need to “explore multiple representations.” We did not need a real-world context.

We needed algebra.

And notice something else.

What happens when $d=0$?

We don’t simply divide by $d$ and carry on. We go back to the original equations: $x^2 +y^2 = r_1^2$,

Subtracting gives $r_1^2 = r_2^2$.

Thus, if $r_1 \ne r_2$, there are no common points. If $r_1 = r_2$, the two equations are identical, and there are infinitely many common points.

So the careless statement

“Two circles intersect in at most two points”

isn’t even true without qualification.

The mathematics tells us exactly where the qualification belongs.

This is what bothers me about some of the rhetoric surrounding math reform.

We have become so concerned with how students experience mathematics that sometimes we seem less concerned with whether students actually know mathematics.

There is a difference.

A student can be encouraged to “discover” something without ever being required to establish it.

A student can produce multiple representations without understanding why the representations are equivalent.

A student can explain a strategy without knowing whether the strategy is valid.

And a student can be praised for mathematical thinking while never being asked to prove the statement in front of them.

At some point, somebody has to ask:

Where is the proof?

That is not an old-fashioned question.

It is the question.

Mathematics has a peculiar feature that makes it different from many other subjects: we don’t get to vote on whether a statement is true.

We can conjecture.

We can experiment.

We can draw pictures.

We can look for patterns.

We can use technology.

All of those things can be useful.

But eventually mathematics asks us to cross a line—from “I think this is true” to “I know why this must be true.”

That transition is not an educational accessory.

It is mathematics.

And perhaps that is the reform I would like to see.

Not a return to mindless memorization.

Not endless lectures.

Not a rejection of calculators, computers, experimentation, or discovery.

Just this:

Put the mathematics back at the center.

Let students ask questions.

Let them struggle.

Let them discover.

But then make them finish the job.

Because when the curtain is pulled back, when the educational vocabulary falls away, when the activity sheet is put aside, there ought to be something underneath it.

There ought to be mathematics.

And if there isn’t—

the emperor has no proof.

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Exercise 1. Assumed $d > 0$. Starting from $y^2 = r_1^2 – x^2$ and $x = \frac{d^2+ r_1^2-r_2^2}{2d}$​, show algebraically that the two circles intersect at exactly two points if and only if $|r_1-r_2| < d < r_1+r_2$.

The Theorem Hidden Inside the Problem

In my previous post, Engineer vs. Mathematician, we started with a concrete question:

Given two circles, do they intersect at exactly two points?

The engineer solves this particular problem.

The mathematician looks deeper and searches for a general condition that answers every problem of this type.

The result is the theorem: $|r_1-r_2| < d < r_1 + r_2$,

where $r_1$​ and $r_2$​ are the radii of the circles, and $d$ is the distance between their centers.

Once we know this condition, any specific problem becomes a simple verification.

There is one exceptional case worth setting aside. If $d=0$ and $r_1=r_2$, the two circles coincide and therefore have infinitely many common points. Our question concerns circles with distinct centers, so from here on we assume $d > 0$.

But this raises a deeper question:

How could we discover this condition if we did not already know the theorem?

The answer begins with a simple principle:

Find every way the desired situation can fail. Then what remains must be the answer.

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Instead of asking:

When do the circles intersect at exactly two points?

ask:

What conditions make it impossible for the circles to intersect at exactly two points?

We can identify these conditions directly from the geometry.

1. One circle is enclosed inside the other

Suppose first that $r_2 > r_1$

Fig. 1

From Fig. 1, we have

$d+r_1 \le r_2 \implies r_1-r_2 \le -d$.

For $r_1 > r_2$ (see Fig. 2),

Fig. 2

$d+r_2 \le r_1 \implies r_1-r_2 \ge d$.

What about $r_1 = r_2$?

Since $d > 0$,

$d + r_1 > r_2$,

so one circle cannot enclose the other.

Thus, the only ways one circle can be enclosed inside the other are

$r_1 – r_2 \le -d$

or

$r_1-r_2 \ge d$.

Therefore, for one circle not to be enclosed inside the other, we must have

$r_1 – r_2 > -d$

and

$r_1-r_2 < d$.

Together, these give

$|r_1 – r_2| < d$.

2. The circles are separated

Fig. 3

Fig. 3 shows that

$d \ge r_1 + r_2$.

Therefore, for the circles to be able to intersect, we must have

$d< r_1 + r_2$.

We have now eliminated every configuration in which two intersections are impossible. The boundary cases—where $d=r_1+r_2$ or $d=|r_1-r_2|$—give tangency and therefore only one intersection. The remaining configurations are precisely those satisfying

$|r_1 – r_2| < d <r_1 +r_2$.

Thus the condition for two circles to intersect at exactly two points emerges not from applying a theorem, but from eliminating every configuration in which two intersections are impossible.

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The familiar approach begins with a theorem.

If the circles intersect at a point P, then the centers and the intersection point form a triangle with side lengths $r_1, r_2, d$.

The Triangle Inequality tells us that a non-degenerate triangle exists only when $|r_1 – r_2| < d < r_1+r_2$.

The result follows immediately.

But the theorem itself is not the starting point. It is the destination.

Without knowing the Triangle Inequality, we can still uncover the same truth by examining the structure of the problem:

• Too far apart: impossible.
• One inside the other: impossible.
• The remaining configurations: two intersections.

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A theorem is often presented as a finished product: $|r_1 – r_2| < d < r_1 + r_2$.

But behind every theorem is a path of discovery.

Sometimes we build the object we want and identify the conditions that make it possible.

Sometimes we examine every way it can fail and eliminate those possibilities.

Both paths reveal the same mathematical structure.

The theorem was not merely applied to the problem.

The theorem was hidden inside the problem, waiting to be discovered.

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A note for the next post: The argument above assumes that two distinct circles can have at most two intersection points. Why is that true? What prevents two circles from intersecting at three or more points? We will take up that question in the next post.

The Inequality Beneath the Inequality

This post is a revisit of the triangle inequality from a different perspective. In The Triangle Inequality Before the Triangle, we explored the triangle inequality before a triangle exists: given three lengths $a, b, c$, what conditions allow them to form a triangle?

Here, we start with an actual triangle and uncover the algebra beneath the familiar inequality.

Consider an arbitrary triangle $\Delta ABC$.

Without loss of generality, place

$A=(0,0),\qquad B=(c,0)$,

where

$c=AB>0$.

Let the third vertex be

$C=(x,y)$,

where

$y > 0$.

Then the side lengths are

$a=BC=\sqrt{(x-c)^2+y^2}$,

and

$b=AC=\sqrt{x^2+y^2}$.

We prove that

$a+b>c$.

Since

$y>0$,

we have

$x^2+y^2>x^2$,

and therefore

$b=\sqrt{x^2+y^2}>|x|$.

Similarly,

$(x-c)^2+y^2>(x-c)^2$,

so

$a=\sqrt{(x-c)^2+y^2}>|x-c|$.

Therefore,

$a+b>|x|+|x-c|$.

Now,

$|x-c|=|c-x|$,

so

$|x|+|x-c|=|x|+|c-x|$.

At this point, the geometry disappears. The remaining argument is purely algebraic.

We now use the algebraic inequality for absolute values:

$|u|+|v|\geq |u+v|$.

This is an inequality about real numbers. It does not involve triangles, distances, or geometry.

Applying it with

$u=x,\qquad v=c-x$,

we obtain

$|x|+|c-x| \geq |x+(c-x)|$.

Simplifying,

$x+(c-x)=c$,

so

$|x+(c-x)|=|c|$.

Because

$c>0$,

we have

$|c|=c$.

Hence,

$|x|+|c-x|\geq c$.

Therefore,

$a+b>|x|+|c-x|\geq c$,

and thus

$a+b>c$.

$\blacksquare$

The familiar triangle inequality $a+b>c$ is not an isolated geometric fact.

Beneath it lies a simpler algebraic inequality:

$|u| + |v| \ge |u+v|$.

The algebraic inequality lives on the number line, while the triangle inequality lives in the plane.

The geometry supplies the distances, but the algebra supplies the comparison.

A statement about triangles in the plane is revealed to be an application of an inequality about numbers on the real line.

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Exercise-1 The proof relies on the hidden inequality

$|u| + |v| \ge |u+v|$.

Prove this hidden inequality.

The Triangle After the Triangle Inequality

Theorem (Converse of the Triangle Inequality)

Given $a, b, c > 0$, if

$a+b > c, \quad a+c >b, \quad b+c >a$

then there exists a triangle whose side lengths are $a, b$ and $c$.

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Proof

Place

$A = (0, 0), \quad \quad B = (c, 0)$.

We seek a point

$P = (x, y)$

such that

$AP = b, \quad\quad BP = a$.

These conditions are equivalent to

$x^2+y^2 = b^2$,

and

$(x-c)^2 + y^2 = a^2$.

Subtracting the two equations gives

$x = \frac{b^2+c^2-a^2}{2c}$.

Substituting this into the first equation yields

$y^2 = b^2 -\left(\frac{b^2+c^2-a^2}{2c}\right)^2$.

Thus the existence of the desired point $P$ is equivalent to the existence of a real solution of the quadratic equation

$Y^2 – \left[b^2 -(\frac{b^2+c^2-a^2}{2c})^2\right] = 0$.

Its discriminant is

$\Delta = 4\cdot\left[b^2-(\frac{b^2+c^2-a^2}{2c})^2\right]$.

Multiplying the expression for $\Delta$ by $c^2$,

$c^2\Delta = 4b^2c^2 – (b^2+c^2-a^2)^2$.

A remarkable factorization gives

$c^2\Delta = (a+b+c)(a+b-c)(a+c-b)(b+c-a)$.

Since $a,b,c>0$, we have $a + b + c >0$. By hypothesis,

$a+b-c >0, \quad a+c-b > 0, \quad b+c-a >0$.

Hence every factor on the right-hand side is positive, so

$\Delta > 0$.

Therefore the quadratic equation has two distinct real solutions for $Y$. Hence there exists a point $P=(x, y)$ whose distances from $A$ and $B$ are $b$ and $a$, respectively.

$AP = b, \quad BP = a$.

Consequently,

$AB = c$,

so there exists a triangle with side lengths $a,b$ and $c$.

$\blacksquare$

The proof actually gives the triangle explicitly.

Since the discriminant is positive, the two solutions are

$y = \pm\sqrt{b^2-(\frac{b^2+c^2-a^2}{2c})^2}$,

and therefore the two possible vertices are

$P = \left(\frac{b^2+c^2-a^2}{2c}, \pm\sqrt{b^2-(\frac{b^2+c^2-a^2}{2c})^2}\right)$.

These are reflections of each other across the $x$-axis, producing the two possible triangles with side lengths $a, b$ and $c$.

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Exercise-1 Verify by hand that $4b^2c^2-(b^2+c^2-a^2)^2$ factors into $(a+b+c)(a+b-c)(a+c-b)(b+c-a)$ using the difference of square twice.