Birthright

4 min read
government law religion

There’s a fascinating detail in my family history in how its lines crossed once, then not again until a hundred years later.

My great-great-grandfathers of the Miller line (Allen Jr., on my dad’s side) and the Todd line (Thomas Sr., my mom’s side) both emigrated to Utah in 1854 after their families in native Scotland joined the Church of Jesus Christ of Latter-day Saints. They didn’t live far from each other over there, but there’s no evidence that the Millers knew the Todds around the time they joined the Church.

They almost certainly met, though, on a ship called the John M. Wood, which carried both families that year from Liverpool to New Orleans. Allen was just six and Thomas was twenty-three. I wish I could go back and see it. I like to imagine young Allen running around on the deck of the ship while Thomas played with his two young sons. Allen sadly lost his baby sister during that voyage, and she was buried at sea.

Both families travelled north to Kansas City to join the Garns wagon train headed for Utah. While in the camp, Allen’s father died of cholera. The “Widow Miller” (as she was called) with her three sons crossed the plains anyway, receiving support from others in the camp throughout the journey. Perhaps Thomas and his wife Margaret were among the fellow travelers who helped them. Even though he was just six, Allen took turns with his brother walking and then riding in the wagon. More than 1,000 miles lie between Kansas City and Salt Lake City.

Upon arriving in Utah, the Millers immediately went hundreds of miles south to help settle the (still) tiny town of Parowan and the Todds found home in less-distant Springville, the families never to cross paths again. But a century later two of their descendants, my dad and my mom, met as teenagers at the Peach City diner in Brigham City where they fell in love and eventually married. So here I am.

Becoming American

Allen’s life inspires me. Before he turned twelve and in the same year, he lost his mom and older brother. Allen and Ninian, the last of the immigrant Millers, were orphaned. A kind foster family took in the boys to their little dugout home until they were old enough to strike out on their own. Despite his desperate beginnings, Allen went on to become a successful merchant, cattle rancher, and mill owner in nearby Panguitch, and he and his wife ended up with eleven (!) children. A detail I love from their life history is that the Miller home is where the teenagers in town came to hang out.

Allen Miller, Jr. (If only I'd inherited his full head of hair!)

The second-youngest of those eleven children was my great-grandfather Joseph, “Grandpa Joe.” I never knew him because he passed away shortly before I was born. I’ve heard he was a pretty gruff guy. My mom remembers him punching a horse because it bit him.

When Joseph was born in 1892, he was born an American, despite his father being Scottish. The 14th Amendment had been ratified 24 years earlier, Constitutionally guaranteeing Joe’s citizenship. That in turn gave citizenship to my grandpa, which meant my father was also a US citizen. I consider myself greatly blessed to be American as well.

When Allen came to the US, he was a reviled Mormon. But his immigration wasn’t against the law, even if he and his family only had refuge in territorial Utah. By the 1870s, though, anti-immigration efforts against foreign-born Latter-day Saints ramped up, culminating in an 1891 ban on Mormons. Otherwise contrary to First Amendment religious protections, it was justified by their practice of polygamy, even if that had been officially discontinued by the Church the year before. The ban’s enforcement carried on into the 20th century.

Being American

I’m not the only American with a family story like mine. Indeed virtually all Americans, except the Native ones, have a story like mine. American ancestors were hated for being Irish or Italian or German or Russian. American ancestors also of course came into the US as slaves, the national sin for which the 14th Amendment became partial atonement.

What should “birthright” mean? Outside of US politics, the word invokes lineage, rights and privileges endowed by the spinning of a roulette wheel. You had no say about your parents, or theirs. To me there’s something powerful in the way the United States—a country formed to eschew centuries of peerage—redefines birthright by granting its precious gift of citizenship to any and all born here, not just to the ones whose parents were already the lucky winners.

I hear the people making arguments against birthright citizenship that this time is different, that granting citizenship for a birth on US soil is a reckless mistake. But has there ever been an argument against the Constitution that didn’t claim that this time is different? I wonder if “this time is different” would persuade them to reinterpret the Second Amendment, or the Fourth, or the First…

All of this is to say that I’m grateful to be a citizen of the United States. I’m grateful for my inspiring ancestors. But I’m especially grateful this week for our shared, national heritage, descended from a world-changing document and its principles, not from any one ancestry. I’m grateful that our country justly preserved the best meaning ever conceived for the word birthright.

Some common misconceptions about LLMs

6 min read
ai

I was talking to my mother-in-law this week about how she downloaded ChatGPT to her phone two weeks ago. It’s her first experience with an LLM. We talked about all the ways she’s started using it, but also the common things that people misunderstand about how they work. It got me thinking that this would be worth writing about.

Send this article to someone who is just starting out with ChatGPT, Claude, or Gemini. I hope it’s helpful.

—

If you’ve been sent this article, it should mostly explain why AI has done stupid or confusing things. It will also help you think better about what AI can do reliably. It’s not a deep-dive, but just covers a handful of common misconceptions.

Quick note: when I say LLM, it’s short for Large Language Model, which is what Claude, ChatGPT, and Gemini all are. I’ll alternate between “LLM”, “model”, and “AI”, but I mean the same thing.

AIs are much more human-like and much less computer-like than most people think.

It’s natural when you’re using a computer program to assume it has all the predictability and precision that computers have. When you type on your keyboard, the letters you press appear on the screen. Unless something has broken, that happens just as expected every time.

AIs are a lot less computer-like than this. It’s not their fault; it’s primarily because they operate on human language. LLMs are created by having a very large computer program learn patterns in all different kinds of human language, including everything from French, English, and Chinese to computer code and math equations. They get very good, but not perfect, at predicting what to say next based on those patterns.

Language makes AIs surprisingly similar to people, because language contains a bunch of our tendencies, perspectives, and ways of reasoning. This is why LLMs can actually even have preferences, which is not the sort of thing you expect a computer program to have. Designers of LLMs wrestle with this problem in the development process, which is when they’re essentially teaching the models rather than programming them. This is why it’s sometimes described as growing rather than building an AI.

You will tend to get much better results if you lean into the humanness of LLMs. Treat AI the way you’d treat a smart intern instead of a computer program. You’d give the intern specific, clear instructions instead of assuming they know something unique to you. You’d ask them to try a task first so you can give them feedback. You’d assume they couldn’t read your mind. And you’d be (hopefully) nice to them. All of these things—even the being-nice part—have been shown to get better results from LLMs.

AIs do not remember.

With LLMs, every conversation starts fresh, as though you were talking to a person with memory-loss. Think 50 First Dates or some other movie about a person with amnesia. This is the unavoidable byproduct of how they operate, not an intentional design.

ChatGPT, Claude, and Gemini do have (relatively weak) ways to get their models to “remember” things by injecting what they hope are relevant details at the start of every new conversation (in a way that’s hidden from your view). These details are gleaned from past conversations. Sometimes it works and often it doesn’t. This also explains why ChatGPT might weirdly throw in a fact it knows about you but that isn’t really relevant to the current topic, and why it has no recollection of a conversation you had a couple of months ago.

The amnesia problem is why there’s so much work being done by people to create memory-style systems that LLMs can call on to help. These mostly operate by giving the AI a way to take notes and, in a later conversation, check its notes. Right now, though, there are no widely used, consumer-friendly ways to give AI better memory.

So with each new conversation, you have to give the LLM the information it needs. You can do that by just typing it in, or adding a document, or using the built-in things like projects, connectors, and skills. If you don’t know what those are, just ask ChatGPT or Claude to teach you how they work.

AIs don’t learn new things.

This next misconception is related to the memory problem. AIs have a way of being both incredibly smart and incredibly dumb, but why they’re dumb might feel mysterious.

What AI “knows” comes from two, and only two, places:

  • What the LLM learned during training by the company who made it.
  • What it was told in the current conversation.

That’s it. What this means is that LLMs don’t really learn anything from you. They don’t get to know you over time, except for what the software might save as a memory. They don’t learn your habits. In fact, they don’t even know who you are beyond what they’re told about you or by you during the conversation. If it feels like ChatGPT or Claude knows you intimately, most of that intimacy is either trapped in a single, long conversation you’re having or it’s imaginary and might be just a general vibe thanks to the model’s training to be friendly to users.

For the same reason, LLMs also don’t know recent events. All models have a “knowledge cut-off date.” This is the date at which the latest training data was collected. So a model with a January 2026 cutoff date doesn’t know that the US attacked Iran in February, or that Jessie Buckley won Best Actress for Hamnet, or that the US men’s team just qualified for the knock-out round in the World Cup.

The only way they can know recent facts in a new conversation is if someone or something tells them so. During the chat with you, the most common way they learn new things is by searching the web. All of the major AI companies basically give their LLMs a version of Google. This means the correct answer is only as good as the search results fed to the model. And again, because of the amnesia problem, if you start a new conversation and ask again about 2026 Oscar winners then they will need to search again for the same information.

AI drinks all the water.

It doesn’t. There are serious concerns to weigh, current and future, in a world with AI. ChatGPT taking all the drinking water is not one of them.

AI today can do far more than most people realize.

Large language models are surprisingly simple machines. They only produce one thing: text. But that makes a lot of things possible.

Text is how we give commands to computers. Even when you point and click, there was text (computer code) that made it work. To make AI more powerful, LLM products like ChatGPT and Claude have been trained to output text that takes the shape of software commands. This is how they make a chart for you, edit a document, check your calendar, or read your email. Basically everything done on a computer can be done by an LLM using text commands that triggers software.

Without these software tools, LLMs are nothing but chatbots. But with tools, they’re frankly amazing. (Note that Gemini is far more limited in the kinds of tools it has than you’ll get with ChatGPT and Claude.) To make the most of the available tools, do the following things:

  • Download the ChatGPT or Claude applications to your computer instead of using the web browser versions. With the software actually on your computer, they can get a lot more done.
  • Try Codex (ChatGPT) or Cowork (Claude). These are tools that make those two LLMs far more powerful. Just ask the AIs how to use these tools and for examples of what you can do with them.
  • Explore Connectors. These are ways for your LLM to talk to other software products you use, like Gmail or Canva. When you tell ChatGPT or Claude to draft an email for you, and then you discover the draft sitting ready to be sent, it feels pretty magical.
  • Pay for a subscription. Much of what I’ve described above is only available if you pay for at least the $20/month Pro plans. Try it for a month and see if it’s worth the money.

AI can teach you how to use it.

If any of this felt over your head, or if you’re not sure what to do next, just ask AI to teach you. If the explanation is too complicated, tell it to explain more simply. I’ve learned more in the last year than I have in any previous year of my life. (Including four years of grad school!) Claude and ChatGPT have been the teachers.

Of course, people can be helpful teachers, too. The best place to learn from people about AI is currently YouTube. So I’ll leave you with this post about my favorite AI YouTube channels.

Jesus Christ and Latter-day Saints

1 min read
religion government

All the discourse on social media over the last week+ has been expectedly pointless after the Dept of Defense listed The Church of Jesus Christ of Latter-day Saints as a non-Christian denomination for chaplaincy purposes.

If you'd like a thorough, expert treatment of what Latter-day Saints believe about Jesus Christ, I can't recommend this one highly enough. It will dispel common misconceptions but also won't sugarcoat differences that matter to Nicene Christianity.

To say that Latter-day Saints worship an utterly different Jesus is, at once, rashly uncharitable, and yet precisely true. On the one hand, it cannot be said that LDS theology teaches a Jesus who is totally foreign to the Christ of Christianity and completely devoid of any similarities whatsoever ... On the other hand, LDS theology irreversibly departs from traditional Christianity ... These differences are very serious and overshadow every shared conviction between Christianity and Mormonism about the nature of the Son of God. So, who is Jesus Christ according to Mormonism?

Who Is Jesus Christ According to Mormonism? | Kyle Beshears

A Fed, but for AI

1 min read
ai government

I'd looked forward to spending more time with Anthropic's Fable model today, but alas the government had other plans. As usual, Zvi has the most thoughtful, thorough commentary on things.

I am however taking the position that the implementation method chosen by the government, with no warning, was deeply terrible, even given our options with our current very terrible level of relevant state capacity, and reflects some combination of at least one of either malice or a deep misunderstanding by decision makers of how jailbreaks and cyber security work.

It makes me wonder if the answer is something like a Federal Reserve, but for AI. Certainly AI safety is a serious enough issue for government oversight, and too serious an issue for reactionary incompetence.

I know it's not a perfect analogy, but at the very least something with political independence, clear measures, and experts who actually understand what they're overseeing would do immense good. The Fed is one of America's best national government institutions that we have right now. It feels like a good watermark for AI regulation.

“What the University Is Now For”

1 min read

The threat that AI poses to the future of higher education would be less imminent if it weren’t for a list of other ways that universities have become worse for students and families. These include:

  • A cost of attending that’s grown faster than inflation for decades.
  • The concentration of donations to the most elite institutions where the marginal benefit is the lowest.
  • The underfunding and politicization of state universities by legislatures.
  • Degree requirements detached from the needs of the labor market and the students entering it.

Dr. Shehu notes much of the same in this provocative article. She has a unique vantage point with her background in education and AI. Higher ed has survived attacks on its legitimacy in the past, but AI is something new that poses a stronger threat.

As much as we want to think this moment is unique, we stand at a similar inflection. The question is the same. The pressures are different. The hedging that has carried the American university through the last half century is no longer available, and the institution will have to answer, in language its students and their families can recognize as honest, what it is now for.

The article doesn’t—and I don’t either—make the case that higher ed is doomed, but it will absolutely need to change to better meet the needs of our students and communities.

The university has told one story to its trustees, its accreditors, and the public, which is the story of the holistic education, the formation of citizens, the cultivation of judgment, the well-rounded life of the mind. It has told a different story to its students and their families and the labor market, which is the story of the credential, the ticket, the signal, the return on investment. The two stories were never quite compatible. They were held in suspension by an institution wealthy enough, slow enough, and culturally trusted enough that no one had to choose.

What the University Is Now For - Amarda Shehu

Becoming Improbable

Your life’s goal should be to become the most improbable person you can be. Your path, your character, your life, should be the most unlikely, the most unexpected, the least predictable version you can make.

This really struck a chord with me. I’ve had a unique and improbable career and I’ve often found myself feeling less than others for it. (A professor without a PhD, a law student who didn’t want to be a lawyer, etc.) This piece by Kevin Kelly—one of the great improbables—helped me appreciate my improbability.

Your Most Improbable Life | Kevin Kelly

Chesler Park & Fiery Furnace

Chesler Park & Fiery Furnace

2 min read
photography

Katie and I knocked out two bucket list hikes in one trip, Chesler Park in Canyonlands and Fiery Furnace in Arches. Easily one of my favorite trips we’ve ever done. Here are some of the photos.

We stayed in a perfect little cabin on highway 211, called Marie's Place. Strongly recommend.

Chesler Park loop has two standout features in addition to the Needles: the Joint Trail (one of the most impressive and varied slots I've ever done) and the meadow surrounding the Chesler Park formation. Look for the little hiker on the trail in the bottom right photo to get a sense of scale.

Fiery Furnace in Arches National Park requires a permit to hike it, which you need to get on the website a week in advance, right at 8am. You have to watch a training video, in part so you learn how to not get lost down in there. My favorite part of this hike were all the secret features hiding just past the dead end signs.

“Academics Need to Wake Up on AI”

1 min read
ai education science

The third of a series by Alexander Kustov on AI attitudes among academics. I found the entire series to be quite persuasive. I suspect before long that AI use in academic writing will be common as long as its use is disclosed.

But the idea that AI use in research somehow pollutes it needs to go. Researchers are at least as likely to produce slop as AI is.

Meanwhile, academics routinely cite papers they haven’t read beyond the abstract. At least AI hallucination rates are tracked and improving. Human hallucination rates in academia are not tracked at all. We just call them “contributions to the literature.” And if you’re a peer reviewer, you don’t even have to hallucinate on your own: you just write “please cite me” and move on.

Also, another smart point about the “stochastic parrot” metaphor I wrote about this week.

One of the most influential slogans in the AI debate has always functioned as a thought-terminating cliche. As Cate Hall observed, it is a potent coinage: fun to say, conceptually efficient, and it has permanently colonized many people’s minds despite not being true of today’s models. A genuine linguistic work of art. It is also empirically false: every major frontier model since GPT-4 has been trained on non-textual input, and the original argument’s own logic requires text-only training to work.

Academics Need to Wake Up on AI, Part III

Humanity’s Best Invention

4 min read
ai tech

“Stochastic Parrot”

If you’ve followed the AI discourse even just a little bit, you’ve probably come across this dig at Large Language Models like ChatGPT and Claude. As accused, LLMs are just unintelligent “fill-in-the-blank” machines with a layer of randomness to boot, i.e. stochastic parrots. Or, as another common criticism goes, LLMs are just an “average of all human expression”. And who wants average?

At a technical level, this isn’t incorrect. Even the frontier LLMs today are doing what GPT version 1 did eight years ago. Given a length of text, they produce the next bit of text (a “token”) repeatedly until they’ve made a new passage. Each next token is chosen with some degree of randomness, with the likeliest tokens having been learned during the LLM’s training process. The training is done on a massive body of text, mostly scraped from the internet.

Of course, the difference between GPT v1 and v5.4 (the most current) is like the difference between a weed trimmer and a Formula One car, even though both of them just compress and ignite gasoline in a cylinder over and over. What the AI researchers and developers have been able to do with next-token prediction is turn LLMs into skilled software developers, patient tutors, and all other kinds of human-like products.

Despite these abilities, many people still dismiss what a modern LLM can do with the right software around it. And it isn’t just the technical feat. Critics who scoff at modern AI as a mere slot machine that spits out text, or as an “average of all human expression”, are missing what’s really going on. What’s being overlooked is how the power of LLMs lies in the much older technology they’re invoking: language itself.

Our best invention

I’m convinced that language is humanity’s best invention. It’s how we store information across space and time through stories, histories, lessons, and facts. We use it to bridge the gap between our brains, which are otherwise completely opaque to each other. It gives us a way to externalize ideas so everyone can participate in them, whether in the form of sound, code, math, or letters. Language is how people coordinate and cooperate, and also how they compete and fight.

What’s the average of all of that?

Perhaps the problem is that there’s so much language that’s average (including, perhaps, this article). We’re more often bored or annoyed than moved by the language we encounter each day. Notably, we were surrounded by slop before LLMs came along, inundated with advertising, social media, and political entertainment. But even those mediocrities prove the point that language is powerful, otherwise why bother producing so much of it?

When we think of technology in the context of language, it’s typically media that come to mind, like the printing press, the telegraph, radio, television, and the internet. But language itself makes a strong case for being a technology, too. It’s an invention that we’ve refined over thousands of years. It’s useful, learned, and does things for us that we couldn’t do otherwise. I think it serves us well to think of language this way.

And like any other technology, it’s the foundation for more advances. In the case of language, there’s not a single human accomplishment that doesn’t rely on it. Medicine, law, engineering, computing, art, and so on. It’s the core technology of education. We’re hard-pressed to come up with a more important invention than this.

Language brought to life

With AI, we’re feeling the sharp edges of our best invention. LLMs harness the power of language, but with speed and ceaseless energy and often without good judgment. AI psychosis, for example, is a thing because of how powerful language is. We’re all far more persuadable than we believe ourselves to be. It’s genuinely scary to think about what happens when persuasion is automated.

This is why I’m far more sympathetic to the people who are worried about AI than to the people who are dismissive of it. I don’t think it’s wrong to be scared by the prospect of not knowing if you’re talking to a person. The Turing test is far away in the rearview mirror at this point. People have always used language well, and poorly, and dangerously. To be nervous about what LLMs can do is just to be nervous about what language can do, but at scale.

Ironically, the original source of “stochastic parrots” is a paper from 2021 where the authors discuss the risks of large language models that have the capacity of language without the human ability to understand it. The term is meant to draw attention to the perils of the technology, not to pooh-pooh it.

For me, the most interesting detail is this one: LLMs aren’t just a medium, like books and the web. AI uses language to do things. Each token in the flow changes what comes next. The closest thing we’ve had to this is software code, but code doesn’t generate itself in anything like the same way. It’s as though we brought language to life.

Reasoning models are perhaps the best example of what I mean, altering the way they work by talking to themselves. They generate tokens for their own benefit, to help them “think” about what the user wants, what the next step ought to be, and what they might be getting wrong. This is, frankly, incredible. Reasoning models have, in turn, opened the door to agentic AI, personal assistants that are now taking shape in exciting ways. And all of this only works because language with all its complexity and nuance and density makes such things possible.

I’ll finish with this. As I was workshopping this article with Claude, it gave me this gem:

“Just text" is how humans declare war, fall in love, spread conspiracy theories, write constitutions, and teach children right from wrong.

Not bad, if a bit dramatic. Also, not at all wrong.

Apple at 50

4 min read
apple tech

Apple is 50. (And, hey, me too!) It’s kind of a stunning number really, when you consider how tumultuous the tech industry can be. What a 50 years, though.

It feels weird to pay tribute to a corporation, so instead I want to think of this post as appreciating the products and honoring the people at Apple whose work has made a dramatic difference in the lives of so many. My entire professional life has been intertwined with what these talented people have made.

As I was writing down these experiences, it occurred to me that there’s a theme in them. There’s value in not doing what everyone else is doing. Certainly that’s been at the heart of Apple’s continued success and its most important products. That’s been my experience, too.

Unlocking iMovie

iMovie led to one of the most fun experiences I’ve had. After grad school, I started a side project blogging about iMovie ’08, a much-resented but major update to the video editing software that Mac users relied on for things like home videos, class projects, and real estate walkthroughs. Everyone was so put off by the dramatic changes that they undervalued the new benefits, like nondestructive editing.

So I started blogging about it. I’d write posts on how to use the new software, about hidden ways of doing things and how the new features were actually really impressive. It turned out I was the only person on the internet doing this, instead of just criticizing iMovie like everyone else.

Long story short, my little blog—Unlocking iMovie—got the attention of both Randy Ubillos at Apple (basically the father of desktop video editing) and David Pogue who was then at the New York Times. From the blog, I ended up getting three trips to Apple to meet with Randy and the iMovie team and multiple published iMovie: The Missing Manualbooks I co-authored with David. (This is why David so kindly agreed to be on my podcast a little while back. Also, his book on Apple’s first 50 years is exceptionally good.)

This was also an interesting fork in the road, professionally speaking. I imagine an alternate reality where I leaned more into writing about Apple, which I think I would have really enjoyed. But around this time I was teaching at BYU as an adjunct professor and the prospect of getting hired as full-time professional faculty came up. That ended up being my path. I’m lucky to have found my calling, so I’m doing what I’m meant to do. But it’s fun to think about that other life.

EndPin, LLC

During my undergrad, I took a semester off to work full-time for a local company in their IT support group. I was the Mac specialist tasked with supporting the design team, the only Mac users in the whole company. It was a decent job and I briefly considered committing to it as a career path.

But I eventually realized it wasn’t for me, and decided to go to law school instead. Instead of just leaving, though, I came across the idea of starting a Mac-support consulting businesses that we named EndPin, LLC. (An endpin is the pointy stem used to hold up a cello. That was the instrument my wife studied for her degree at BYU.) At that time, there were little pockets of Mac users all over the place, in local design firms or in small groups within larger companies. So I quit, signed up my now former employer as my first client, and signed up a few other businesses. My coworkers thought it was crazy.

This turned out to be an excellent decision. I hired a couple of friends to help with the tech support work and ended up having a small business that paid for our groceries through four years of a JD and MPA. At one point, the author of Who Moved My Cheese, Spencer Johnson, was even a client.

One company over many years

My oldest brother, Peter, in the mid-80s got an Apple IIe for Christmas. That was my first experience with a personal computer. It was magnetic to my other brothers and me. Despite Peter’s best efforts to lock his bedroom and even lock the case holding the floppy disks, we regularly found our way in to play the handful of games he had like Choplifter and Lode Runner.

Since then, I’ve owned a lot of Apple stuff. Here are the notable ones that still feel magical to me:

  • PowerBook 1400cs. A laptop before everyone had laptops. My roommate tripped over the power cord one day and broke the power socket off the logic board. I found a used replacement board online for $600. Back then, repairs like this were possible.
  • iMac DV in lime green. I’m sad that young people today don’t have the chance to see a product like the iMac come to life. Truly groundbreaking.
  • Titanium PowerBook G4. This was a purchase for law school and I absolutely loved that computer. I don’t think a product has felt quite as cool to use as that one. Then my two-year-old son poured an entire glass of water into the keyboard and fried it. During law school finals. RIP.
  • iPod (3rd gen). My first iPod. Freaking awesome. What else to say?
  • iPhone. The original, which I bought after Apple dropped the price by $200. My wife accidentally dropped it on the pavement and broke the screen. The iPhone was in such demand internationally at the time that I sold it on eBay, broken screen and all, for more than the cost of a new iPhone 3G when that came out two months later.
  • iPad Pro, 11in. I remember a moment when I was using this and thought about how my younger self would have absolutely freaked out that such a product existed. The iPad is still my most used and most enjoyed Apple product.

Over the years, I’ve seen the company stumble and amaze, but it’s been a consistent part of my work and personal life. My career is a bit of an oddball, and maybe that’s part of the same core instinct to be different. I’m grateful to the intensely talented people who made all of these incredible products, doing things their own way.

Anthropic is winning against DoD

ai government law

As predicted, Anthropic has the obviously superior legal argument and Judge Lin has granted a preliminary injunction shutting down (for now) the Government's actions agains the company.

Here's an excellent and detailed overview from Zvi Mowshowitz. Quoting Zvi, who's quoting the judge:

At bottom, Anthropic has shown that these broad punitive measures were likely unlawful and that it is suffering irreparable harm from them. Numerous amici have also described wideranging harm to the public interest, including the chilling of open discussion about important topics in AI safety. The motion for a preliminary injunction is granted.

It's not going to get better for the Department of Defense, but they are stubborn so it's likely to make them look worse as the case progresses.