Michelle Goldberg appeared on the The Opinions podcast recently and laid out a simple case for believing that this myth may be repurposed in American politics in the near future. America has the strongest military in the world, so it doesn’t make any sense that we can’t simply dominate Iran and the Strait of Hormuz. If the outcome of the War in Iran is a cratering economy, a military stalemate or defeat, and international humiliation that ultimately results in a sequence of crushing political defeats for Republicans in 2026 and 2028, then some future American authoritarian may believe their best route to power is to claim that Trump, the last great Republican President, was hypnotized by Netanyahu, the Jews, and domestic criminals all of whom ultimately stabbed him in the back. In this hypothetical, NATO will have abandoned the US despite the fact that NATO is a defensive alliance and the War in Iran is a war of choice. The solution will be to crack down even harder than before. This is the sort of rhetoric that has taken root on the far right. The similarities are clear. We don’t want to have to learn this lesson the hard way all over again.

Many Americans are now suffering and will continue to suffer from Trump’s disastrous policies, so the question is will Trump supporters rationalize their way into believing Trump was stabbed in the back? Or will they admit that Trumpism failed because it was just corruption and greed wrapped in nationalism and religiosity and they were duped into supporting it by one of the most successful con artists of all time? Or will we find a way to move on that allows them to save face and believe they weren’t duped?¹

This is why Democrats absolutely must have a positive vision for America. They have to find some way to bring some significant portion of Trump supporters back into their coalition. It’s not enough to run against Trump. We have to have a vision that actually addresses the material challenges people are facing today not because it’s the right thing to do (though it is) but because recent authoritarian power grabs make it even easier for a future authoritarian to leverage myths like the Stab-in-the-Back myth. To take one example, the K-shaped economy has to be meaningfully addressed or it will be used as evidence to justify a future authoritarian movement. What is the Democratic proposal? This was essentially what happened with immigration. It’s not enough to point at the absolute depravity of ICE. We also have to develop effective alternatives that work and are clearly shown to be working to common, every day Americans.

If people cannot see tangible success, they will support empty promises based on myths and con artistry. We will not be saved by long term investments in things like K-12 education or scientific research (though we definitely need them). We will not be saved by a revival of morality or basic human decency. If anything, the belief that the American electorate was too moral or decent to support a man like Trump is how we got here in the first place.

I’m deeply concerned that Michelle is right. Someone will find a way to lay the blame of Trump’s failures on the Democrats. A lot of the problems during the Carter administration were a result of Nixon’s economic policies, but they were nevertheless great leverage for Regan’s campaign in 1980. If something like this pattern repeats in 2028 and 2032 it will be because Democrats ran against Trump rather than towards a better future. People will rationalize anything if they do not have their bread and circuses.

If you ask me, “AGI” stopped being a useful term as early as 2022. The original ChatGPT, released that November, was not human level in most ways… but it was clearly “general” in some meaningful way that had not been true of AI systems before the advent of large language models. Rather than ditch “AGI” in 2022, though, instead we just coined a new term, “general-purpose artificial intelligence,” which is obviously a totally different thing from artificial general intelligence.

In 2026, it’s time to move on.

Well, it’s been time to move on for a long time now. My first course on artificial intelligence at NC State, around 2007 or so, started with detailing the A* algorithm, which simply wouldn’t qualify as AI these days. I remember thinking at the time, “Is this really what everyone was talking about as AI?” Reader, it was. And 20 years from now, we’re going to look at LLMs and wonder how people ever thought this was supposed to be AI.

The problem is that explaining this to people, whether they have a technical background or not, is very hard. In 2007, I was already pretty technically savvy. I had a BS in Computer Engineering and I had spent years programming device drivers for the Linux kernel. It’s hard to be more technical than that. But I still thought there was something about AI that I just didn’t get. It turns out that there was: I didn’t get that the term was just a bunch of artifice. Since then, I’ve been talking with people about how the term does more to muddy understanding that to clarify things.

I haven’t been all that successful, but I have found a piece that I think does it better than I have: Emily Tucker’s Artifice and Intelligence. It’s worth reading in full because it’s great. Her four key takeaways are worth summarizing here:

1. Be as specific as possible about what the technology in question is and how it works. Words like “intelligence” or “learning” aren’t technically specific, and they mask what’s really going on under the hood.
2. Identify any obstacles to our own understanding of a technology that result from failures of corporate or government transparency. Accepting the premise that technology works without auditing or real-world accountability is a mistake.
3. Name the corporations responsible for creating and spreading the technological product. Letting an organization take either the blame or the credit for something that’s really done by a third-party tech company is always a mistake.
4. Attribute agency to the human actors building and using the technology, never to the technology itself. Technology is not ethically neutral; it’s designed by people who make ethical choices.

Emily’s piece was posted in March of 2022, and in the years since I’ve seen other writers come to the same basic set of conclusions on a pretty steady pace. I’m not intending to malign any of these folks. It’s never too late to get to the right answer. If anything, I was late in getting to this in 2007. And I could still be wrong.¹ It’s not recognizing that you’re wrong that matters; it’s what we do next. I still think Emily’s takeaways are the best, most succinct articulation that I’ve seen on this. These are things that are still pretty rare to see people doing in practice.

Let’s start with some context. Ben’s nominally discussing the Axios supply chain hack. Basically, the maintainer for a npm package with hundreds of millions of downloads had their GitHub credentials compromised. The attacker then added a malicious dependency that was essentially a remote access trojan. Huge deal. All over the security news. If you’re interested, Microsoft has a good post with more details. Also, it’s worth noting, as Ben does, that these supply chain attacks have been becoming more serious for some time now.

Ok, with that out of the way here’s Ben’s take, which focuses on the role that AI may play in similar incidents, emphasis mine:

What I also believe, however, is that (1) vibe-coding is going to lead to a lot of security issues in the near term, but (2) AI is going to lead to fewer security issues in the long-term. The fact of the matter is security is nearly impossible. There are so many potential or already-existent bugs in basically all software, for one, and even the most thoughtful security implementations end up having vulnerabilities that no one expected. And, on top of that, there is the fact that humans — like AI at times, to be fair! — are lazy and seek convenience, and security is almost always at odds with convenience. The axios incident demonstrates all of these issues.

AI is the answer to all of this. The truth is that all code needs to be examined for bugs, not just new code; AI is going to provide a way to examine everything that has ever been released (and yes, in the short-term, this is going to manifest as a host of new exploitable vulnerabilities). AI can also examine an entire dependency tree, which almost no human will. AI can navigate an extremely inconvenient but highly secure workflow, and can stress test every aspect of that flow — repeatedly — in a way no human can.

Ben’s making an understandable mistake. Put simply, security is more of a human problem than it is a technical problem. It’s not about the code.¹ It’s about the people, and it’s an adversarial challenge with AI on both sides. Because of this, my predictions are rather different.

Vibe-coding will lead to more security issues in the near term, but it won’t be as impactful as many think it will. Vibe-coding isn’t replacing professional software so much as creating a solution to problems that were previously too small to justify spending time on. Sure, some of these programs will result in security incidents, but we’re not going to see many high-profile incidents that are a result of bad vibe-coding. Part of the security risk calculation is the value of the assets being protected, and I don’t think vibe-coding will be the default approach as the value of the artifacts involved increases. I think Ben over-estimates the effect of vibe-coding on security, at least to the extent that his prediction is measurable.

Let’s skip the emphasized bit for a moment and focus on something that I think Ben got right for the wrong reasons. Security is impossible because people simply do not understand how computers work not because creating perfect, flawless code is impossible. Education is so ineffective that the security community has effectively given up on educating the general public as a primary solution. Now, the goal is to make it impossible for the average computer user to fuck things up through their own ignorance. This is why passkeys are better than passwords. People are the problem. Getting better at the coding part may help, but it’s not addressing the actual problem.

Ok, time for the big difference of opinion: AI will lead to more security issues in the long-term, and there are at least three reasons to believe this to be true. First, most security incidents involve a person doing something dumb or being confused or being tricked. Again, people are the problem, and this is the common denominator, not a coding failure. As AI gets better at tricking people, we will see more security incidents. The Axios hack is a good example in that the maintainer admits the failure was a social engineering failure that likely would have resulted in compromise regardless of his particular approach to credentials and authentication at the time.

Second, as AI gets better, we will simply have more software, which means more security incidents. Imagine AI didn’t take off or that it wasn’t good at coding. In that world, we still had a ton of software that we wanted to create but couldn’t because we were limited in the number of trained software engineers. If AI makes all our software engineers even 10% more efficient in creating software, then we’re going to have more software to fill this latent demand. It remains to be seen just how much more efficient AI can make software engineers, but the better it is, the more software we will have. And the more security incidents we will have.

Third, even in the best case scenario, AI is going to be available to both attackers and defenders. Normal people aren’t going to be interested in the restrictions that would be necessary to disrupt this dynamic. That would require air gaps, moving back to fully paper-based processes for everything, or something else equally extreme.

If AI is available to both parties, then it would be surprising if it favored defense in an information security context. So far, the only thing that actually works in favor of defense is the mathematics behind encryption. But even there, the problem is the human being. Furthermore, we have reason to believe that AI may be much better at attacking than defending. Imagine a script kiddie with functional knowledge of every kind of hack and the ability to work 24/7 without so much as a bathroom break. Remember that attackers only have to be successful once, but defenders have to be successful every time they are attacked.

All of this is speculative, predict-the-future stuff, and it’s worth being a bit humble here. I disagree with Ben, but this isn’t a trivial, easily predicted thing. We could also both be wrong. AI may prove to be more disruptive than anything else, making a lot of the things we may want or need to measure to see who was more correct either irrelevant when we’re thinking about security. We may already be seeing examples of this in the ways that AI has changed warfare through the use of drone technologies. Although I would like to think that’s an example that proves my point because it makes current approaches to ensuring security much less effective, the reality is that we don’t know how this dynamic will ultimately affect the long term picture yet.

Sharing things that make you happy is, I think, an incredibly important part of life, but I’ve found it hard to do in the format of a blog post. (And harder in social media.) There’s a well-known negativity bias in the press. As the saying goes, “if it bleeds, it leads.” Academic research shows that we read and share negative news more readily than we do the good things we come across.

Anyhow, I’m not sure I have solved this problem, which is why I’ve not really advertised that I’m blogging again. Maybe I will eventually, but for now, I just want to see if I liked the way it changed how I was thinking about the things that I’m reading.

All of this is rather recent, so it was rather serendipitous to see the latest Vlogbrothers video:

I’m really looking forward to this, having become an unabashedly huge John Green fan since discovering him through The Broccoli Tree video. I’ve read all of his books, followed his various podcasts, become a fan of his brother Hank, read all of Hank’s books, subscribed to their coffee service, and just generally felt better about this part of my life as a fan than I have about almost anything else I follow. I know it’s crazy, but absolutely everything just clicked into place with them almost instantly. I literally cannot believe I hadn’t heard about them before. I just wasn’t into YouTube much, so I didn’t know about the Vlogbrothers. I had never read YA books, and I hadn’t even considered them. I still don’t really read them outside of the Green brothers.

But I was a big fan of Tracy Kidder, who died last week at 80 years old. I read his Soul of a New Machine as a grad student, became a fan of his writing, and was blown away by his Mountains Beyond Mountains. So, when I learned about the work that the Green brothers are doing with Partners in Health, I just couldn’t help myself from becoming completely bought in to their stuff.

(Add to all of this the functional death of American Christianity at the hands of Donald Trump… Well, let’s just say that it’s easy to justify paying attention to people who take morality seriously.)

I could wax poetic about them all day. I won’t, today at least. I’ll stop simply by saying that I’ve already preordered Hollywood, Ending, and I’m looking forward to reading it. I hope you find something today that makes you as happy as this news made me.

If China finally eclipses the United States as the world’s preeminent scientific superpower, there won’t be an official announcement. Neither will there necessarily be a dramatic Promethean demonstration, a bomb flash in the desert, a satellite beeping overhead, a moon landing. It will be a quiet moment, observed by a small, specialized subset of scientists who have forsaken the study of the stars, animals, and plants in favor of a more navel-gazing subject: the practice of science itself.

Worth reading in full.

My quick thoughts as someone who spent more time than most in academia doing research:

Measuring research quality is extraordinarily hard. Just counting published papers doesn’t do much more than encourage practicing scientists to aim for the least publishable unit. There are very, very few measures that I trust here, but the big one is employment data. Countries that employ and support STEM PhDs will have better research outcomes in the long run.

The STEM disciplines have been absolutely dominated by international students for the last 25 years. The only realistic path for continued American dominance in science is to radically change our current approach to immigration, both legal and illegal. This means opening up the H-1B program, finding some sort of reasonable compromise about the various categories of people affected by our historic approaches to illegal immigration, and making a cultural U-turn on just generally hating immigrants and minorities. No smart immigrant wants to live in a country that hates immigrants.

Systemic reform for research funding in the US could help quite a lot here as well. The grant proposal system has real problems that could be addressed if we were willing to try new approaches, but that’s sort of old news. The bigger problem is that it is impossible to trust that a future Trump-like administration won’t make similarly drastic cuts to research funding. So many research projects were killed in medias res that any serious scientist pondering a non-trivial, multi-grant line of research would have to consider options outside of the US. And countries eager to build research programs, including China, could easily make a better offer.

Systemic reforms for research positions in the federal government could also help quite a lot. The US government was once one of the absolute best places to work as a STEM-oriented researcher, and it was the only place to work for whole disciplines of research. That’s a much, much harder case to make now. As Ross points out in the article, tens of thousands of PhDs have left government service, voluntarily or not. The academic community will not forget this sort of employment shock.

The financialization of everything will continue to affect the STEM disciplines, especially basic research. People who can get a PhD in STEM disciplines and want to have some sort of job security or financial security are attracted to positions as quants in the many financialized industries. America is also the place to be for that sort of thing. We can argue about whether or not the financialization of everything is a good idea. In general, I don’t think it is. But the reality is that it’s a real competitor in the market to hire STEM PhD talent. If you’re asking questions about markets, ads, or crypto, then you’re not doing much in the way of basic STEM research.

If you’re running a company into the ground and you are going to have to do something drastic that may have negative repercussions, then why not reframe those actions as a result of a rapidly changing technology that has revolutionized your business? What do you have to lose? I think the best example in this space is Jack Dorsey’s massive, 40% layoffs at Block.

If you’re trying to understand why the new college graduates are having trouble getting a job, then why not gin up a story about AI overlords destroying our future? This sells. This gets clicks. But a boring story about the pervasive disruption resulting from tariffs killing supply chains and from the uncertainty of an economy run by a geriatric toddler who’s bankrupted six businesses won’t. Seventy-seven million people don’t want to hear how their support of a racist with a penchant for sexual assault wasn’t actually justifiable based on his ability to run an economy. Many of these people are small business owners who have taken it on the chin because of Trump’s policies and they still don’t want to recognize the truth. Talking about economic uncertainty and tariffs is a boring story that’s tedious to research and requires a basic understanding of macro economics to understand, whereas the AI story only requires having seen several popular movies in the last decade or so.

If you’ve just bombed a school and killed over 175 girls under the age of 12, then why not blame a new-fangled AI-based targeting system? After all, killing children is horrific and everyone knows new technology breaks all the time. Why go out of your way to admit that you’ve made a catastrophic mistake when there is such a good excuse available?

Of course, not every example is obvious. The data centers used to power AI do actually pose real environmental challenges. But those challenges aren’t fundamentally new, and AI cannot justifiably take the blame for decades of poor environmental policy. Deepfakes aren’t created without a human prompting the AI, but AI safety systems can make generating deepfakes much more difficult. These nuances often end up as finger-pointing exercises in the inevitable lawsuits that follow.

Even with those less-clear scenarios, AI is great at taking the blame.

The most basic question people have about this is simple: How big of a deal is it? And the answer here is hard to state. It’s an important milestone, but I think it’s hard to claim much more than that. This doesn’t immediately change the world of computing, let alone the non-computing world. It’s just another critical step on the long path towards realizing the potential of quantum physics for computing. We’re still decades away from practical quantum computers that “anyone” can access.

That said, this still makes me happy in a fundamental way. It’s amazing that computers are possible, that we can program them to do so many useful things for us. They are so common now that it’s easy to look past the small miracles of the physics and math that make computers what they are, whether classical or quantum. And this is one of the few moments when even non-computing professionals might get a taste of that magic.

Here’s one example from CNET of how that might work, which includes an opportunity to correct Richard Feynman:

Feynman made this last point back in 1981. “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical,” he said in a speech at MIT. You can approximate nature with a simulation on a classical computer, but Feynman wanted a quantum computer that offers the real thing, a computer that “will do exactly the same as nature,” Feynman said.

First, the insight here is exactly what I hope people take from the Google quantum computing story. There’s a better model of nature than what classical computers can do, so if we’re going to improve, then we have to go quantum. Google’s breakthrough is a necessary step in that journey.

Second, the correction! Feynman is wrong to claim that quantum computing is “exactly” what nature would do. Quantum physics are demonstrably more accurate than classical physics, but they are both still models. We don’t have answers to all the questions quantum physics raises about how nature works, so it’s silly to suggest that this is “exactly” how nature works. It’s still, after all, just a model of nature that seems to fit really well. I think, ultimately, Feynman would have known this, and I mostly blame Stephen Shankland, the author of the CNET piece, for positioning Feynman’s quote in this way. Of all the philosophical mistakes I’ve seen scientists make, this is a rather minor one that is probably more misspeaking than an actual mistake. But still, it’s fun to think that I’m correcting Feynman, even if he was ultimately misrepresented in the first place.

So, there’s a lot to unpack here, and I’m not sure that a blow-by-blow analysis will really enlighten anyone. Instead of that, I’m going to respond to a comment posted on the video and then revisit a question Brady asked during the video. First, the comment from YouTube user calvarycome:

I think we need to distinguish clearly between the messy creative process of coming up with scientific theories, a process which depends a lot on social factors and chance, versus the more structured and systematic process of testing them, which doesn't. The latter is what we normally mean by 'the scientific method'. The discovery of graphene is no exception. No one would have believed that graphene existed without a systematic followup to that messy cellotape business.

So this is one of the more upvoted comments on the video, and almost all of the actual replies to this comment are positive, saying things like, “I think you’re right on the money.” I’m sorry to report that, in fact, calvarycome has missed the point entirely.

It’s understandable. It seems clear that calvarycome is a scientist, and the realization that The Scientific Method itself is a fallible, human enterprise is not obvious or straightforward, especially for scientists. Most scientists have loved science since they were children, and their view of the scientific method as an inevitable, infallible march to capital “T” truth is an important part of why they love science. Professional scientists have, as Upton Sinclair would point out, their salary and livelihood pushing them to not bother with this understanding. Thus, in a strangely ironic way, most scientists have also never seriously studied the philosophy of science or thought deeply about why and how The Scientific Method really works.¹

So what, specifically, is calvarycome getting wrong? They suggest that there’s a “messy creative process” for creating scientific theories that “depends a lot on social factors and chance.” This is a false separation. There is no part of The Scientific Method that is not influenced by social factors and chance.

Showing that The Scientific Method is influenced by social factors and chance is most easily accomplished by examining scientific scandals. The Schön scandal is a solid example of this. Jan Hendrik Schön was an award winning scientist credited with numerous breakthroughs in semiconductor physics before it became obvious that his results were unreproducible and fraudulent. Researchers have faked studies on acceptance of gay marriage, the possibility of an AIDS vaccine, and done all manner of unethical, untruthful things in the name of science. Retraction Watch is a great place to follow if you’re interested in seeing some modern examples of this sort of problem.

To which, you might say (or calvarycome might say), so what? They were caught. Their work couldn’t be replicated, and replication is part of the scientific method.

First, how much replication is “enough” for truth? In the video, Professor Moriarty talks about how we don’t really know what that 101st replication would produce. And in the context of scientific fraud, we would have to replicate every finding however many times you decide is “enough” before we can declare we’ve caught all the frauds. Of course, we may have a pretty damn good guess given the previous 100 experiments, but we cannot know in an important sense what the result will be. That is, any answer to the question of how many replications is enough to be certain we have finally reached capital “T” truth relies on an inductive argument: that the previous experiments can be “generalized” into truth. From a logical perspective, this is not certainty. It is probability, even if that probability is very nearly certain.

Second, how precise do our measurements have to be? Accurate measurement of our observations is crucial to conducting science, but when do we know we’ve gotten an accurate enough measurement to have arrived at capital “T” truth? This is why Professor Moriarty talks about precision in the video. Newtonian mechanics might seem like the one and only truth if you didn’t have access to some pretty spectacular equipment. What scientific “truths” do we currently hold that only appear like truth because we don’t have the ability to measure or observe some important, latent piece of reality? From a scientific fraud standpoint, we can point to things like P-hacking or other abuses of statistical measures to demonstrate a “finding” that may actually be meaningless or actively wrong. In some sciences, this sort of statistical abuse is so widespread that half or more of the published results in some fields are not replicable. This is what scientists refer to as a replication crisis, and it’s something I’ve complained about previously.

Third, how much “true” knowledge is rejected by the scientific community? The graphene example that calvarycome uses to bolster their argument is telling in that it was rejected twice by major journals prior to winning the Nobel Prize. What if the authors had just given up? How long would it have been before someone else attempted to publish something similar? How do we even know that someone would have attempted to publish something similar? All the “science” that John Snow employed in creating his map of the 1854 Broad Street cholera outbreak didn’t matter all that much. The community at the time rejected his theory and replaced the handle on the pump after the outbreak ended. How do you even measure this sort of “lost” science that is proposed, reviewed by The Scientific Method, rejected, dropped, and then never (or at least not yet) revisited?

In short, the existence of known scientific fraudsters or bad-but-published methodological investigations raises the simple question: Are there others who haven’t been found yet? If so, who? If not, how do you know? What science can we trust? Rejected “good” science raises the question of whether we’re generating all true knowledge about the world by using The Scientific Method. So, here we are right back in the messy world of humans, not just in the “creative process” used to generate hypotheses, but throughout The Scientific Method.²

So, ending on this note would be rather sad, and luckily, we don’t have to end on this note. We can trust science, and I want to talk about how in relation to Brady’s question during the video at around the 10:30 mark. Brady says, and I’m paraphrasing, “It sounds like you’re just stoking the fires of climate change deniers who are saying that you can’t trust The Scientific Method. And you’re just saying, ‘Well, yeah, that’s right.’” That is, how can we trust science if we know and accept that The Scientific Method is not flawlessly leading us to actual truth?

The modern world is ridiculously complex. No one will ever be able to survive in it without trusting other people, even with their own lives. I trust the engineers who built my office every minute I spend inside it. I trust the teachers at my daughter’s school to teach her well when she goes there. I trust accountants, lawyers, civil servants, and scientists with matters great and small every day. This notion of trust was made explicit in the information security realm by Ken Thompson’s Turing Award Lecture, titled “Reflections on Trusting Trust.” Essentially, his argument is that ultimately we have to trust the people who built the software we use, not the software itself. Otherwise, we would have to personally write every line of code on the systems we use, from the assembler, to the compiler, to the operating system, and on up to the cat video player itself. So when a climate denier says that they don’t trust the scientists who claim that climate change poses a threat to civilization, they are not methodologically wrong because the exercise is ultimately an exercise of trust.

Still, I believe climate change deniers are factually wrong. Why? Well, it’s not as though they have somehow accessed capital “T” truth where the scientists haven’t. Their belief structures ultimately rely on trust as well. Who are they trusting? Oil and gas companies? Opinion pieces from political think thanks funded by oil and gas companies? Their own guts? Where are they getting their information? It is not as though we somehow “knew” the climate wasn’t being dramatically and negatively affected by human actions and that scientists are “disproving” this either. All knowledge depends on trust, so what you think of as “true” ultimately depends on who you trust, which is a fundamentally human process. This is why philosophers define knowledge as “true, justified belief.” It requires justification, and The Scientific Method has a great track record. Personally, I’ll trust the people who have presented reams and reams of compelling scientific evidence that climate change is real, vaccinations work, and so on. But I won’t make this mistake of positioning this knowledge as somehow beyond criticism because it was produced by The Scientific Method.³

To end this post with another comment from the video, Ashley Johnston says:

I suggest that it is more about differentiating the scientist in his role as scientist and his role as messy human. To state it clearly, ‘A man who does science is always a scientist and everything he does is science’, this is absurd and frankly dangerous. With this method of understanding the idea of science is lost and it just tracks the practices of a community of so called ‘scientists’, whatever they may end up doing. Which would assuredly end up being the opposite of science until they had vamipred out the last of the value in its reputation.

This demonstrates a much more accurate understanding of how science really operates than calvarycome’s earlier comment. The Scientific Method is simply a mechanism that produces trustable results, not infallible results. The long, successful history of results generated from The Scientific Method bolsters that trust. You don’t have to look far to see that The Scientific Method has materially improved your life. But we could still be wrong, maybe even about important things. If we stop questioning knowledge simply because someone labels it as “science” or because it came from a “scientist” then we have failed fundamentally at understanding the point of The Scientific Method.

Of course, the Internet has lived up to that hype in some ways and failed to do so in other ways. Reality has a way of making everything we dream about a bit less Technicolor. We couldn’t foresee the rise of deepfakes or fake news, not to mention the digital divide. But every once in a while I come across something that reminds of the early promise of the Internet, and I would like to share it in the interests of sharing simple pleasures.

Standard Ebooks was created with the goal of providing high-quality ebooks from public domain sources. Imagine Project Gutenberg, but with the polish and careful craftsmanship usually associated with purchased products. And almost everything about Standard Ebooks is fully open and available. They dedicate all their materials–including their cover art–to the public domain.

Again, I am reminded that this sort of open accessibility is both rare and precious. The web itself has become so complex that “view source,” which was one of the key drivers of the early Internet (and also its hype), has been under attack for years. One might say the same thing about the notion of a public domain for previously copyrighted materials too.

But for now, Standard Ebooks is chugging along, doing great things. Search for your favorite author, and see what’s there. They have Austen, Dickens, Wells, Twain, Doyle, and more. Take a look at their catalog, and find that classic you keep meaning to read.

The last few years have been pretty terrible, both as an objective statement of the facts of American life but also as a personal lived experience. I thought these years would be great as I worked towards them. When I look objectively at my life, I think I should be happy if not thrilled by the last few years. My wife and I are finally homeowners, albeit in our mid-30s. We have three beautiful kids now, all in the last few years. I got my “dream job” as a tenure track assistant professor. These aren’t bittersweet achievements. They are genuinely amazing milestones. And yet, I find myself more often than not thinking about how needlessly terrible life is right now. The rage, anger, and incompetence of the Trump administration. The continuing idiocy of tech companies failing miserably at privacy, security, or even simple empathy and ethics. The hideousness of a Christianity so broken and warped that there’s apparently not even enough decent Christian residue left to rebel openly. I could go into a litany of more personal disappointments, including rifts with old friends and family, but let’s just not. The point is that I have this odd feeling. I suppose it’s not technically another moment of cognitive dissonance, but it rhymes with that. I should be happy, but I’m just not. What should I take from this moment, too?

Upon discovering that the John Greens I thought were separate were actually one and the same, I took another look at the author. Was there something I missed? Something I should re-examine? I wandered over to the author’s website and started browsing. Moments later, I was watching his video titled The Broccoli Tree: A Parable, embedded below.

You should watch it now because I’m going to talk about it.

Done? I don’t want to ruin anything.

Ok, let’s continue.

Seeing this now, I think it’s among the truest videos on YouTube, even knowing that sappy YA author John Green wrote it. The Broccoli Tree: A Parable would remain genuinely great even if the whole story was made up because the point of a parable is to demonstrate a deeper moral lesson in stark terms.

At one point in the parable, Green says, “To share something is to risk losing it. […] The truth is, if we hoard and hide what we love, we can still lose it. Only then, we’re alone in the loss.” When I heard that, I immediately understood something I had spent months wondering about. Namely, why did Tyler Trent’s family help him travel all over the place in the months leading up to his death?

If you haven’t heard about Tyler Trent, he was a 20 year old Purdue student diagnosed with an aggressive bone cancer. His story was the focus of a College Game Day story leading up to the football game where Purdue hosted then undefeated Ohio State. These stories are sadly common. Some sports team has a big game. Media outlets have time to fill, so they start digging for a story. Eventually, they find a fan suffering from some disease or misfortune, and they share the story of how it happened. It’s easy to mock these moments, much as it is easy to mock YA fiction as somehow sappy or unimportant. These stories often feel forced or artificial, also like YA fiction.

But Purdue is my team, so I watched the story. I tried to go into it with an open mind, and not bring the disaster baggage of the last few years or the pointless cliché of so many similar sports stories into my perspective. And the story is basically what you would expect had you brought all that disaster baggage and cliché with you anyhow. But it was also a real story, with a real person living it.

Then Purdue won the game, and Tyler Trent’s story didn’t disappear. He became a fixture at Purdue games, even traveling in late December to their bowl game. Sportswriters kept writing about him and reaching out to him. Maybe this was because he wanted to be a sportswriter and they saw a little of themselves in him. I don’t know. I was once a 20 year old kid from Indiana who went to Purdue, but I still only saw the cliché in the story. It just didn’t feel real to me, and one of the questions that keep bouncing around in my head was, “Why did his family help him travel to all those away games?” He was basically on his deathbed. It was clearly painful and expensive and logistically challenging and certainly not something a parent would plan to do.

Green says, in The Broccoli Tree, “You loved something. You shared it. Many people loved it too. And then one or a few people decided to cut it down. Given enough time, such people will always cut down such trees.” Many people loved the Tyler Trent story because it wasn’t a cliché to them. They related to it because it was their life. I was privileged to view it as a cliché simply because nothing that bad had ever happened to me, even in the dumpster fire of the last few years. I know why Tyler’s family took him to those games and shared him with ESPN and other media outlets, even knowing that those outlets were fundamentally seeking to fill airtime.

When asked why Jesus used parables he said, “Because seeing they do not see, and hearing they do not hear, nor do they understand.” I think I may have misunderstood John Green’s novels, literally without reading them. Of course, we all must evaluate and decide on things–even big, important things–with limited information and time. Careers. Finances. Relationships. Elections. We’re going to make mistakes, sometimes awful, horrible ones. But we keep trying anyway. The purpose of parables, including The Broccoli Tree, isn’t to ensure we will forever afterwards make perfect decisions. It is to help us understand why and how we should work to make good decisions in an imperfect world.

One of the mistakes I’ve made the last few years was too often expecting that things would be good rather than searching for the good things that were there. I didn’t used to think this way, and this shift in my thinking was too subtle for me to notice immediately. But it was an important, dangerous shift. This year, I’m going to correct for this by sharing something I think is as close to unambiguously good as possible each month. I recognize that this act alone won’t fix the Trump administration, give tech companies the empathy or ethical backbone they need, reveal all the wolves wearing sheep’s clothing, or solve any number of professional disappointments. But it will force me to look for good things more often, rather than simply expect them to be there. I hope this will get me closer to the mindset I need to be happy.

This month, I’ve shared The Broccoli Tree: A Parable because the purpose of that parable is that recognizing, identifying, and sharing the things we love is actually how we love those things best. No part of that is easy. I’m sure it was profoundly difficult for Tyler Trent’s family to share him with us. I don’t expect finding something unambiguously good to be easy for me either, but I’ll share something else next month. It will take time. It will feel like too much effort for a blog that no one reads. But the reality is that I need to do it for me. I need eyes that see and ears that hear the good things in life, and in this world we must actively cultivate those things.

And I’m going to read one of John Green’s books.