Matthew Morgan explaining why Bill Watterson despised the idea of merchandising Calvin and Hobbes.

It was still early days in the ten-year run of Calvin and Hobbes when the syndicate approached Watterson with its big ideas of Calvin sweatshirts, Spaceman Spiff bumper stickers, an animated Calvin and Hobbes Saturday show, maybe a movie, and — worst of all — a Hobbes doll. Watterson really loathed the Hobbes doll. To make sense of how much it bothered him, we need to talk about the tiger in the room.

When Watterson created Hobbes, his focus was on the character more than the conceit of a teddy that comes to life. Watterson told Rich West for The Comics Journalthat “there’s something a little peculiar about [Hobbes] that’s, hopefully, not readily categorised”. But Watterson’s readers often wanted Hobbes categorised into either “real” or “imaginary”. So Watterson came up with a compelling non-answer to the question:

“Calvin sees Hobbes one way, and everyone else sees Hobbes another way. I show two versions of reality, and each makes complete sense to the participant who sees it. I think that’s how life works.”

You could say Hobbes is both imaginatively real and really imaginary, depending on your perspective. Hobbes can be either, which also means he’s both. Is Hobbes a tiger or a toy? Yes.

Watterson insisted that if he wasn’t going to settle the question of Hobbes, then he definitely wouldn’t let some toy manufacturer settle it by turning Hobbes “into a stuffed toy for real, and deprive the strip of an element of its magic”. He’d sound off wherever he could on how “licensing usually cheapens the original creation” by saturating a market with characters until readers are bored of seeing them; how a multi-paneled story with dynamic action cannot be respected by the vagaries of a coffee mug illustration; how subtlety is sacrificed for immediacy; how selling off “everything fun and magical” means “the strip’s world is diminished”.

He has a hundred lines like these, articulations of higher reasoning against merchandising, but just once, in the Tenth Anniversary Book, he drops the high-and-mighty in favour of I-the-mighty: “Calvin and Hobbes was designed to be a comic strip and that’s all I want it to be. It’s the one place where everything works the way I intend it to.”

Tom Bedor sharing a few examples of how, in the past, suppression of encryption by the U.S government didn’t work. In the same vein, it won’t work with suppression of AI models from China.

Open source software is very difficult to suppress

In reality, the argument about suppressing open source models is mostly beside the point. History tells us that suppression of open source software is extremely difficult, and attempting to do so only serves to weaken companies against international competitors. A brief history of encryption is illustrative:

Today, PGP is a commonplace tool anyone can use, and most devs are at least familiar with. But when Phil Zimmermann invented it in 1991, the U.S. government considered encryption to be military technology. A criminal investigation was opened against Zimmermann.

When Netscape created SSL, the U.S. government allowed it to only release a weakened version of it internationally. These controls backfired: it was much easier to acquire the weakened, “international” version, so even many Americans used it.

Export controls did not succeed in limiting encryption as the government wished. SSL, PGP, and similar tools were readily available throughout the world, and the controls disadvantaged Americans. Eventually, courts ruled that releasing encryption source code is protected speech, and the U.S. government relaxed encryption export controls.

But are the models from China really open source? This comment on Hacker News by petcat:

This is not “open source” AI.

Photoshop source code + OSI license = open source

Photoshop binary = open weight

Photoshop SAAS web app = closed model like GPT, Opus/Fable etc.

There is nothing “open source” about the Chinese models in question. All they’re doing is allowing you to run their binary yourself instead of through their API.

Linus Torvalds:

AI is a tool, just like other tools we use. And it’s clearly a useful one.

It may not have been that “clearly” even just a year ago, but it’s no longer in question today.

There are other questions around AI (like what the economy of it will actually look like in the end), but “is it useful” is no longer one of those questions. Anybody who doubts that clearly hasn’t actually used it.

Yes, it can also be a somewhat painful tool, both for maintainer workloads and just from a “it keeps finding embarrassing bugs” standpoint.

But the solution is not to put your head in the sand and sing “La La La, I can’t hear you” at the top of your voice like some people seem to do.

The solution is to make sure those LLM tools help maintainers instead of just causing them pain. There’s no question on that side.

We’re not forcing anybody to use it, but I will very loudly ignore people who try to argue against other people from using it.

And no, AI isn’t perfect. But Christ, anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time.

Because it’s not like natural intelligence is always all that great either.

Ben Landau-Taylor talking about how we need more organisers to continue creating events for the consumption of social fabric.

Lots of people have a sort of consumer attitude towards their communities, where they take everything for granted. I saw things this way when I was young. A social scene is an automatic feature of the world that appears on its own, like a wild blueberry bush. It starts sprouting parties and dinners and conferences and reading groups as naturally as the bush sprouts berries.

Surprisingly, it turns out things don’t actually work that way. In fact, events happen when someone puts in the legwork to organize them. And one of the most reliable laws of the universe is that, if something takes a little bit of legwork, then most people just won’t do it. A scene’s leaders are mostly the people who actually bother to put in the work.

The work of organizing is underappreciated by many people. But the other organizers are very attuned to this, and absolutely will notice who else is picking up the burdens.

I’ve come to believe that part of today’s problem of social alienation is a problem of too many free riders. Lots of people want to consume social fabric, but our social scripts telling people to produce social fabric have largely fallen by the wayside. I don’t know how to solve this undersupply at the scale of society. But you can solve it at the scale of your own community by just supplying it.

Satya Nadella talks about how:

In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.

He then goes on to share suggestions on how to confront the paradox.

In the cloud era, enterprises accumulated data. In the AI era, they accumulate learning. The trust boundary must evolve accordingly, from protecting information to protecting the mechanisms through which organizations learn, adapt, and compound intelligence. There are a few things every enterprise must do to ensure this:

• Control: Create your private evals, because evals define what “good” looks like inside the organization. Also, retain ownership of your organization’s memory, traces, feedbacks, decisions, and institutional context, and ability to use outputs of models from your own tasks and queries.
• Capability: Build your own proprietary learning environments within the tenant boundary to train or tune models, where models learn against real workflows without exposing the company’s knowledge.
• Choice: Ensure the orchestration layer is decoupled from any single model. Ask yourself: If any one model you are using is taken away, do you still have the ability to operate and optimize for your evals using other models? Does your company “veteran” capability remain with you even if a given “generalist” model is taken away?
• Cost: By decoupling the orchestration layer, you are also able to bring together context, models, and tasks in the most efficient and cost-effective way without sacrificing quality.
• Compound: Bring these four together and you create your own continuous learning loop (i.e. hill climbing machine) that will allow your AI investments to compound the value of your firm.

In other words, a company should be able to use a model without giving up the knowledge that makes it unique. That is the reverse information paradox we need to confront.

Sneha Rege writing for Freefincal explaining why DIY investing is a lonely journey.

One of the strangest things about managing your own money is that nobody ever tells you whether you’re doing a good job. Your other parts of life come with some feedback or other. You get exam results, performance ratings, promotions, or client appreciation.

But investing doesn’t work that way.

You increase your SIP every year. You stay invested during market corrections. You rebalance instead of reacting. You avoid chasing every new trend that appears on social media. Years go by, and then while sitting in traffic or making tea one evening, an uncomfortable thought quietly appears.

Am I doing enough?

[…]

Sometimes I wonder if DIY investors are not really looking for advice at all. Perhaps what we quietly seek is reassurance. Not stock tips or product recommendations, but another thoughtful person looking at our plan and saying, “It makes sense. Stay with it.”

After making financial decisions alone for years, certainty becomes less valuable than perspective.

Perhaps that is the hidden cost of DIY investing. Not the spreadsheets, not the research, and not even the market volatility. It is spending twenty years making invisible decisions and never knowing what would have happened if you had chosen differently.

Nobody can show you a parallel version of your life.

The SIP you increased instead of buying land. The equity allocation you stayed with instead of chasing silver. The correction you sat through instead of selling. The bond you wisely avoided. 

That alternate life remains invisible forever.

Siddhant Pusdekar talking about how lifelike biochemistry continued to unfold in sterilized soil for six years, pointing to a metabolic theory for how biology began.

For 15 years, Sébastien Fontaine has been trying to kill dirt. The biochemist, who runs a lab at the French National Institute for Agriculture, Food, and Environment, wanted to know how much carbon is released by soil — just dirt alone, completely devoid of life. His team sealed dirt into jars and blasted them with sterilizing gamma radiation. Then they waited for the carbon dioxide released by the soil — a sign of ongoing microbial respiration — to drop.

They waited, and waited, and waited some more: weeks, then months. Under a microscope, the irradiated soil showed no signs of life, but it continued to emit carbon dioxide. The soil wouldn’t stop breathing.

Fontaine’s lab repeated the experiments and produced the same results. Finally, convinced that they weren’t dealing with an artifact of the experimental setup, they set out to find the source of breath in dead soil.

Now, Fontaine and his colleagues have reported that their soil samples continued to consume oxygen and spew carbon dioxide for six years. In a 2025 paper in Science Advances, they proposed that a metabolic process that powers much of life is also possible outside living cells. Their experiments point to how it could work in dirt, absent the living proteins that would typically organize it. If they’re right, some biochemical reactions, such as those that release the energy of carbon-rich sugar molecules, may not be unique to living things. Such reactions — known as metabolism when performed by cells — could even predate life on Earth, Fontaine said.

The experiments show “what happens to biomolecules when they’re left to their own devices,” said Joseph Moran, an organic chemist at the University of Ottawa who was not involved with the research. They’re finding that the chemistry of life is not exclusive to life, he added. “It’s the chemistry of geology.”

Maciej Cieglowski’s wonderful, and at times hilarious, talk on super intelligence which he gave in 2016. Yes 2016. It’s a long read, but worth every second of your time.

On intelligence level:

Our intelligence level, cognitive speed, set of biases and so on is not predetermined, but an artifact of our evolutionary history. 

In particular, there’s no physical law that puts a cap on intelligence at the level of human beings.

A good way to think of this is by looking what happens when the natural world tries to maximize for speed.

If you encountered a cheetah in pre-industrial times (and survived the meeting), you might think it was impossible for anything to go faster.

But of course we know that there are all kinds of configurations of matter, like a motorcycle, that are faster than a cheetah and even look a little bit cooler. 

But there’s no direct evolutionary pathway to the motorcycle. Evolution had to first make human beings, who then build all kinds of useful stuff.

So analogously, there may be minds that are vastly smarter than our own, but which are just not accessible to evolution on Earth. It’s possible that we could build them, or invent the machines that can invent the machines that can build them.

There’s likely to be some natural limit on intelligence, but there’s no a priori reason to think that we’re anywhere near it. Maybe the smartest a mind can be is twice as smart as people, maybe it’s sixty thousand times as smart. 

That’s an empirical question that we don’t know how to answer.

On general intelligence:

The concept of “general intelligence” in AI is famously slippery. Depending on the context, it can mean human-like reasoning ability, or skill at AI design, or the ability to understand and model human behavior, or proficiency with language, or the capacity to make correct predictions about the future.

What I find particularly suspect is the idea that “intelligence” is like CPU speed, in that any sufficiently smart entity can emulate less intelligent beings (like its human creators) no matter how different their mental architecture.

With no way to define intelligence (except just pointing to ourselves), we don’t even know if it’s a quantity that can be maximized. For all we know, human-level intelligence could be a tradeoff. Maybe any entity significantly smarter than a human being would be crippled by existential despair, or spend all its time in Buddha-like contemplation. 

Or maybe it would become obsessed with the risk of hyperintelligence, and spend all its time blogging about that.

On AI cosplay:

The most harmful social effect of AI anxiety is something I call AI cosplay. People who are genuinely persuaded that AI is real and imminent begin behaving like their fantasy of what a hyperintelligent AI would do.

In his book, Bostrom lists six things an AI would have to master to take over the world:

• Intelligence Amplification
• Strategizing
• Social manipulation
• Hacking 
• Technology research 
• Economic productivity

If you look at AI believers in Silicon Valley, this is the quasi-sociopathic checklist they themselves seem to be working from. 

Sam Altman, the man who runs YCombinator, is my favorite example of this archetype. He seems entranced by the idea of reinventing the world from scratch, maximizing impact and personal productivity. He has assigned teams to work on reinventing cities, and is doing secret behind-the-scenes political work to swing the election. 

Such skull-and-dagger behavior by the tech elite is going to provoke a backlash by non-technical people who don’t like to be manipulated. You can’t tug on the levers of power indefinitely before it starts to annoy other people in your democratic society.

I’ve even seen people in the so-called rationalist community refer to people who they don’t think are effective as ‘Non Player Characters’, or NPCs, a term borrowed from video games. This is a horrible way to look at the world.

So I work in an industry where the self-professed rationalists are the craziest ones of all. It’s getting me down.

Aaron Levie talking to Casey Newton about what kind of jobs he expects in the future dominated by AI coding assistants.

Newton: It’s the sort of thing I love to hear. I would love to not live through a massive disruption where we see super high unemployment. I also can’t help but note we saw almost 46,000 tech layoffs announced in March alone, with AI sometimes cited as a potential cause. So would you put a number to it? On the software engineering front, do you think in three years we have about as many software engineers as we have today, or more? Or do you think there’s a bigger shift?

Levie: I think we’re going to have more. I don’t want to be unsympathetic to people who really will face these changes. But big picture: if you were a CS grad of the past two decades from a top 25-to-50 CS school, by and large you were trying to go to a tech company — in Silicon Valley or a couple other places. So most of the software talent in the world of this cohort ended up building software for consumers. We were building ad apps, ride-sharing, enterprise software (thank God). We’ve accumulated a lot of engineers on that kind of work, and some of those companies have over-hired.

Who’s the loser of that equation? Every other company on the planet, because they couldn’t compete with Google and Facebook and Microsoft for that top engineer. They couldn’t automate things in the life sciences process, or the supply chain, or automotive AI systems. I don’t know how much software you’ve used from companies that aren’t in the Valley, but if you log into your bank and you’re happy, you’re a totally rare person. If you look at most car console designs of any car that’s not from two companies, you can imagine how unusable these systems are. That correlates to the fact that those companies couldn’t overstaff with all the top engineers and designers.

Now what happens? All of a sudden, what was maybe a 30- or 50-engineer problem previously, Claude Code and Codex come in, and now it’s a 5- or 10-engineer problem. For the first time ever, those companies are able to take on work that wasn’t possible before. They can bring automation to all the systems and workflows they couldn’t have afforded or justified.

So in some cases of tech, you’ll see a temporary dislocation. At the exact same time, the thing you should be tracking is the number of engineering jobs opening up at traditionally non-Silicon Valley tech companies — small businesses, consulting firms, life sciences, manufacturing.

And as a force multiplier, you’re going to have a number of new types of engineering jobs where the job is entirely about how to deploy agents inside the firm to automate work. I did this for fun just to make sure I wasn’t full of shit. If you go to the Eli Lilly careers page, as one does, they have this job title called “lab automation software adviser.” That person is an engineer whose job it is to bring automation through AI to the lab process.

Think about how many hundreds of thousands or millions of jobs will look like that in the future. My job is to take the innovation coming from AI-land and apply it to this particular business process in my organization. You’re kind of like an FDE — a forward-deployed engineer — but for that company. Those will be the people who would have gone to Meta or Google five years ago. They’re going to now work in pharma, banking, manufacturing. And those are actually incredibly stimulating jobs. You’re not just building an app, you’re automating drug discovery.

For the sake of my career continuity, I really hope that these forward deployed engineers come from Indian IT industry.