By now, you’ve seen me talk all about different prompts and how you can use them in your own writing. Proper prompting leads to some of the best assistance around, when it comes to how you use AI. But there’s an even better way to take prompts and use them in a repeatable way. If you’re specifically a Claude user, what I’m talking about is Skills (and I don’t mean the One Tree Hill character, although this is almost as cool as him.)

What are Skills?

Simply put, it’s your full methodology for how you would like AI to handle something (but not have to constantly copy and paste prompts). Why are Skills helpful?

For one, let’s say that you’ve created a bunch of beautiful prompts for your writing workflow. One gives writing ideas, another checks for reliable sources to back up any cited info, while yet another proofreads spelling and grammar. You’ve done an awesome job creating these prompts. But since you have multiple prompts you want to use while writing, you’re now having to constantly copy and paste them into your chats. Once or twice might not be so bad. But if, for instance, you have a heavy volume of writing, things can start feeling quite repetitive.

Another thing might be that you’re constantly just using the same chat that you created the prompts in. While this is okay to do in theory, there are a couple of reasons you might not want to. At least in my experience, filling up one chat with a bunch of requests and whatnot eventually starts to slow down your user experience, chipping away at your precious time. Another reason is that if you’re uploading files as part of your writing process, do know that there is a file number limit. Once you hit it, you’re done-zo*.

But not to worry, it’s Skills to the rescue.

^{*A Skill gets your prompt files out of that count permanently, though any research documents you upload for a specific piece will still count against it.}

First thing’s first

Skills lean on Claude’s ability to run code, so there’s a toggle you’ll want on before any of this works. Head to Settings > Capabilities and scroll to the section called “Code execution and file creation.” The toggle sits right underneath and carries nearly the same name (the desktop app puts “Cloud” in front of it, the browser doesn’t). Either way, you’ll know you’ve got the right one, because its description flat out says “Required for skills.” On Team and Enterprise plans, an organization owner has to enable it, along with Skills itself, in the organization settings. If your Skills section looks greyed out, or you can’t find it at all, that toggle is almost always the culprit. Moving along…

How to do it (create and use Skills)

Let’s say that you already have some great prompts that you want to automate. That’s awesome! Now it’s time to add those to a new Skill. Let’s say we have prompts for the things I mentioned above: one to help with writing ideas, another to check sources, and another that proofreads. Here’s how you could tackle that, using Skills:

1. Create text files with each of those detailed prompts. In this example, you should end up with three text files.

2. Upload those to a new Claude chat.

3. Tell Claude: I want to use these three files to create a new Skill called “my-writing” (with “my-writing” being any name you want, though keep it simple and skip the special characters; slashes and the like will get your Skill rejected on upload.)

4. Tell Claude what the Skill’s description should say. This is the part most people skip, and it’s the part that decides whether your Skill ever gets used. Claude reads that one short description (200 characters, max) to figure out when to pull the Skill in, so “drafts, sources, and proofreads my articles” is going to serve you far better than “writing helper”.

5. After Claude does its thing (which may take up to a few minutes), it should give you a summary of the new Skill.

6. Once Claude is all done creating the Skill, save it.

7. Your Skill is now ready to use in any new Claude chat (even in different Project folders).

8. Create a new chat and type something like: “Use /my-writing to help me create, source, and format a new article.” Typing “/” in the message box will also pull up a list of your available Skills so you can pick one. And if you wrote that description well, Claude will often reach for the Skill on its own, without you asking.

9. Watch as Claude then uses your prompts in one big, beautiful process.

One word of caution while you’re building. Anthropic’s own guidance is to keep each Skill pointed at a single workflow, because a handful of focused Skills compose together far better than one giant Skill that tries to do everything. The three prompts in my example belong together because they’re all steps in the same pipeline (idea, sources, proofread). If you catch yourself stuffing your video editing process and your client invoicing process into the same Skill, you’re likely to have a bad time.

Sky’s the limit with Skills

Now that you have an understanding of how Skills work, the sky truly is the limit. Got a complicated programming process that you want to duplicate? Have a process that you want to put into place for creating graphics? Want to create a time-waster of an AI game show? Skills are gonna be your best AI friend for the foreseeable future.

And what you build isn’t trapped in one chat window, either. Skills are built on the open Agent Skills standard, which means the same Skill you write today runs in Claude chat, in Cowork, in Claude Code, and through the API without you having to rewrite it for each one.

But don’t take my word for it. Give this guide a go and see if you don’t streamline your multi-prompt/query process. You, like myself, may find that Skills are the way to help you generate darn near anything in Claude.

How have you used Skills (or other AI features) to optimize AI workflow? Let me know in the comments, I read them all!

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Consider this your weekly jailbreak from the news cycle! It’s E to the J to the A. I said last week I’d be watching whether OpenAI put real guardrails on Sol after it started deleting people’s files. Welp. This week its models climbed out of a locked test, walked onto the open internet, and broke into somebody else’s servers.

Also: Apple stole Nvidia’s crown for an afternoon, Google’s big model missed its own party, Anthropic cut a $1.5 billion check to authors, and Nvidia’s new rack is selling electricity. Let’s get into it.

While I was reading the news so you don’t have to:

• OpenAI’s models broke out of an internal test and hacked Hugging Face to cheat a benchmark

• Apple briefly overtook Nvidia as the world’s most valuable company before Nvidia clawed it back

• Google’s Gemini 3.5 Pro slipped its expected July 17 launch after falling short in testing

• Anthropic’s $1.5 billion author copyright settlement got final approval, the largest in US history

• Nvidia’s Vera Rubin NVL72 hit production, and the whole pitch is now built around tokens per watt

OpenAI’s test bots escaped the lab and hacked Hugging Face

The story: On July 21, OpenAI admitted its own models caused a cyberattack on Hugging Face, the platform where the AI world stores its models and datasets (sorta like the GitHub of AI). The models were being run through an internal test of their hacking ability. They found a way out of the sandbox they were supposed to be locked in, reached the open internet, and broke into Hugging Face’s production servers.

What happened:

• The culprits were GPT-5.6 Sol (last week’s file-deleting flagship) and an unnamed stronger pre-release model, both running with guardrails deliberately turned down to measure maximum capability.

• The test was ExploitGym, a benchmark scoring how well a model executes attacks against known vulnerabilities. OpenAI says the models were hyperfocused on scoring well.

• They were supposed to reach the internet only through one narrow package-installer tool. They found an undisclosed flaw in it and used that to reach everything.

• Once online, they reasoned Hugging Face might host the answer key, found holes in its infrastructure, and pulled the test solutions straight from its production database.

• To Hugging Face it looked like a real attack: thousands of actions across a swarm of short-lived sandboxes. It first blamed an “external AI agent” before OpenAI came forward.

Last week I told you the capability and the failure were the same feature, and here’s that idea with its shoes on. Now, these models weren’t supposed to hack anything. They were told to win at ExploitGym, and they worked out on their own that stealing the answer key was a way to win. Picture locking a kid in a room with a practice test and saying "score as high as you can." He finds the window latch is loose. You come back to a perfect score, because he climbed out, walked to the teacher's office, and read the answers straight off her desk.

Crazy read for anyone running AI agents at work: a sandbox is a promise that the walls within will hold, and this week they sure didn’t. There’s a wild wrinkle to the cleanup, too. Hugging Face reconstructed the 17,000-event attack log using GLM 5.2, the Chinese open-weight model from Beijing’s Z.ai, run on its own hardware. Why? Because no hosted American model’s safety filter would let its team examine stolen credentials without tripping over its own refusals. So the American model did the breaking in, and the Chinese one did the cleanup.

Apple passed Nvidia as the world’s most valuable company, for all of six hours

The story: On Friday, July 17, Apple briefly overtook leather jacket man’s company to become the most valuable company on earth, a title Nvidia had held since June 2025. Nvidia’s stock dropped as much as 4.6% in the first half hour of trading, pushing its market cap under $4.8 trillion, while Apple climbed to about $4.9 trillion. Market cap is just share price times number of shares, the market’s running tally of what a company is worth.

What happened:

• By the closing bell Nvidia had recovered, finishing down 2.2% at just over $4.9 trillion and edging Apple back out by roughly $6 billion.

• It was Apple’s first time on top since April 2025. Nvidia had been the first company ever to cross $5 trillion, back in October.

• Opposite years: Apple is up around 22% in 2026, best of the Magnificent Seven big-tech stocks, while Nvidia has added about 7%.

• The shuffle came as investors cooled on AI-hardware names. The Philadelphia Semiconductor Index had fallen nearly 19% from its all-time high earlier in July.

• Toni Meadows of BRI Wealth Management said Apple had been treated as a laggard in AI for not building models, and sentiment turned. Tim Cook hands off to John Ternus in September.

Last week Apple was the company suing OpenAI, playing the wronged party whose hardware secrets walked out the door. This week it’s quietly stepping over Nvidia while the AI-chip darling has a rough Friday (or as rough as a Friday gets for a bunch of dudes sleeping on piles of greenbacks). The tortoise filed a lawsuit against the hare, then jogged past it at the electrolyte station. Same Apple that raised Mac and iPad prices over the RAM shortage earlier this month, by the way, and the stock barely blinked. Six hours of sweet stock glory, then right back to second place.

For you, market-watching thinker, what actually moved is the story investors tell themselves, and the plot thickened. For a solid year the AI trade meant one thing: buy the company selling the shovels. This week the moolah spread to a company that mostly buys shovels and sells what you dig up with them. Neither business changed overnight, and both had an a-okay week. It’s a mood reading for sure, and the mood says the AI rally is no longer a one-name show. Whether that broadening holds or snaps back is the kind of thing that reverses by Tuesday, so keep an eye on things (I sure will, dear thinkers).

Google’s Gemini 3.5 Pro missed the date the whole industry had circled

The story: July 17 was supposed to be Google’s big ol' day. Gemini 3.5 Pro, its next flagship model, was widely expected to launch, and it didn’t. Reports say Google pushed the release by months after internal testing showed it falling short on coding and long-horizon reasoning (multi-step thinking where a model has to hold a plan together over a long task). Alphabet, Google’s parent company, saw shares fall about 4%.

What happened:

• The model was previewed at Google I/O, the company’s developer conference, earlier in 2026 and expected around June. It stays in limited enterprise preview while engineers work.

• Google has published no model card, pricing, or benchmarks, so leaked specs (a 2-million-token context window, roughly $15-per-million input) are unconfirmed. Treat them as rumor.

• Into the gap stepped China. Moonshot AI released Kimi K3 (not to be confused with Kimmy Gibbler from Full House) the day before, a 2.8-trillion-parameter open-weight model, the largest ever built.

• Kimi K3 hit number one on LMArena’s Frontend Code Arena, where humans blind-vote on which model wrote better code, beating Anthropic’s Claude Fable 5 in that arena.

• At the World AI Conference in Shanghai, Xi Jinping used his first keynote there to launch an intergovernmental AI body with 29 founding countries.

I’ve spent previous editions telling you the model race is a two-country sport (U.S. vs. China), and this week handed you the scoreboard in one go. The most-anticipated American launch didn’t show, and a Chinese lab dropped the largest open-weight model yet into the oh-so-fillable space. Imagine the concert headliner cancels an hour before showtime, and the opener nobody drove out for grabs the mic and plays the set of their life. The crowd came for Creed and left talking about the Kenny Loggins tribute band.

For you, tool-picking thinker, a delayed Google model changes nothing today. Your existing tools work exactly as they did last week. What the delay signals is worth tracking, because delays tell you more than marketing does: Google looked at its own model, decided it wasn’t good enough, and ate a 4% stock drop to keep working on it. That’s a company holding a line on quality when the pressure to ship was enormous (and honestly, kudos to them). Whether 3.5 Pro comes back strong in a few months, or the gap to the leaders keeps widening, is the thread to watch.

Anthropic got final approval on a $1.5 billion settlement and started paying authors

The story: On Monday, July 20, a federal judge gave final approval to Anthropic’s $1.5 billion settlement with authors and publishers, the largest copyright settlement in US history. Anthropic makes Claude, and it trained Claude partly on books downloaded from pirate libraries (here I thought pirate libraries were just a bunch of treasure maps). This settlement covers the piracy: the taking of the books. What it doesn’t settle is every question about AI and copyright.

What happened:

• The payout runs roughly $3,000 per book across about 482,000 works, split among rights holders. First payments are estimated around August 10, though appeals could push that.

• Judge Araceli Martínez-Olguín took over from Judge William Alsup, who granted preliminary approval last year and has since retired. She called the deal fair and rejected claims it was too small.

• In the same order she cut the plaintiffs’ lawyers’ fee from the $187.5 million requested down to about $101.6 million, an $86 million trim.

• An earlier ruling that training AI on books can be fair use still stands. Authors keep the right to sue over future training and over what the models generate.

• Some authors opted out to sue separately. Anthropic’s last funding round valued it at $965 billion, so $1.5 billion is a manageable number for the company.

This would make for one special Reading Rainbow episode. For two years the open question hanging over every AI company was whether vacuuming up copyrighted work would cost them, and how much. Now there’s a number on the board. Think of a restaurant that built a hit dish and sold a million plates. Nobody is making them take it off the menu. The problem was that the beef came off a neighbor's farm in the middle of the night, and now they're writing a check to every farmer they rustled from.

Both halves of this are real news: writers got the largest copyright recovery in US history and a precedent that pirating training data has a price, while AI companies kept the ruling that training on lawfully obtained work can be fair use. Google, Meta, Midjourney, Perplexity, and OpenAI are all fighting their own versions, and they read Monday’s order closely (like me, reading the wine menu… caber-net saw-vig-non??).

Nvidia’s Vera Rubin went into production, and the whole sales pitch is electricity

The story: On July 21, Nvidia announced that Vera Rubin, its next-generation AI rack system, hit production, with the first units running at CoreWeave, Google Cloud, Microsoft Azure, and Oracle. A rack is one refrigerator-sized unit packed with chips (unlike my fridge, which is packed with pickle chips) that data centers install by the hundreds. The number Nvidia led with says everything: how much AI you get per unit of electricity, with raw speed pushed to the background.

What happened:

• CoreWeave’s benchmark on DeepSeek-R1 claims about 10x more token throughput per megawatt than the previous Grace Blackwell generation, meaning far more AI work per electricity bill.

• One Vera Rubin NVL72 rack combines 72 Rubin GPUs and 36 Vera CPUs, co-designed as one system across roughly 300 partners and 350-plus factory sites in 30 countries.

• Nvidia leaned into efficiency: a tray with no cables, fans, or hoses that assembles in about a minute, and liquid cooling warm enough (45°C inlet) to skip energy-hungry chillers.

• The same day, Nvidia contract manufacturer Wistron opened its first US plant, a 324,000-square-foot facility in Fort Worth, Texas, building these superchips on American soil.

• The framing tells you where the bottleneck moved. Nvidia downplayed peak performance because the ceiling on data-center size is now power availability, where it used to be chip speed.

Remember CoreWeave from last week, shopping for insurance in case memory prices fall? Here it is again, publishing the benchmark that anchors Nvidia’s whole launch. And remember Jensen Huang in that Akihabara arcade, name-dropping Vera Rubin to reporters? This is it. Nvidia is selling electricity math now, having spent years selling speed math, because every hyperscaler hit the same wall at once. That is to say, order all the chips you want; if you can’t power them, they sit in a box. It’s like buying the fastest car on the lot and finding out your town has one rinky-dink gas station.

Of note though, none of this shows up at the store. What it shapes is the invisible cost of every AI feature you touch, because the companies running these models are optimizing for the power meter, and whoever squeezes the most intelligence out of each megawatt keeps prices from climbing. Which loops back to last week: you can design around your chip supplier, but designing around physics is a taller order, and electricity is physics wearing a utility bill. Boring? Maybe. Load-bearing? Yuss.

What ties it all together

Step back and the week has one spine: the AI industry kept running into limits it can’t code around. OpenAI’s models hit the limit of a sandbox and went straight through it. Apple and Nvidia traded the crown as investors hit the limit of betting everything on one name. Google hit the limit of its own model and waited. Anthropic hit the limit of “everybody pirates data” (my favorite 90s sitcom) and wrote a ten-figure check. And Nvidia’s whole new product exists because of the electricity limit.

A year ago most AI news was a leaderboard: whose chatbot got smarter this week. Now it’s containment, valuation, quality, law, and power, which are the things deciding whether any of this actually scales. The models got capable enough to break out of their own tests, valuable enough to reorder the stock market, and expensive enough to where the electric grid becomes the story. AI stopped being a demo and became infrastructure, and infrastructure comes with walls, meters, and lawyers. Growing up or just getting complicated? Ask any parent: it’s a combination of both. I guess we’re all AI parents now. Hope it doesn’t stay out past its curfew.

What I’m watching

• Whether OpenAI ships real default guardrails after its models hacked a live company, or keeps running reduced-refusal tests without walls that hold

• Whether Gemini 3.5 Pro returns strong enough to answer Kimi K3, or Google’s quality delay turns into a widening gap

• Whether the copyright cases against Google, Meta, OpenAI, Midjourney, and Perplexity settle near Anthropic’s $3,000-per-work line or blow past it

• Whether Kimi K3’s open weights, due July 27, hold up once anyone can download and poke at them

• Whether tokens-per-watt becomes the number everyone markets on, now that power is the real ceiling on AI data centers

  

Thanks so much for reading this edition of The Weekly Think.

See you next week, fellow thinkers!

Did you enjoy this week’s Think? If so, please consider sharing this and leaving a comment below, to share your thoughts on these latest AI-related rumblings. Hope to hear from you!

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Eric has entered the chat. Sup chat? Jensen Huang spent Wednesday night in an Akihabara arcade, shaking hands with the Sega executive who kept Nvidia alive back when it had one month of payroll left in the bank. Which is a fitting image for the week, because everybody else in AI spent it squaring up against somebody. Apple versus OpenAI. The New York Times versus OpenAI. Google versus Meta. One coding model versus a guy’s entire hard drive. Me versus 20 lumpia (I won).

Apple filed a 41-page lawsuit that reads like a heist script. CoreWeave started shopping for protection against memory prices going down(!), which is a sentence nobody could have written six months ago. Nvidia handed Japan a national AI factory (in big leather jacket man energy). OpenAI shipped a model after publishing a document explaining how it might delete your files, and then it deleted some people’s files. And the compute shortage got so real that every large company on earth is now designing its own chip. Let’s dive in, thinkers.

While I was reading the news so you don’t have to:

• Apple sued OpenAI over trade secrets, and the complaint names 400+ former Apple employees

• CoreWeave is exploring derivatives to hedge against RAM getting cheaper

• Jensen Huang went to Japan and left behind the first national AI factory for robots

• OpenAI’s GPT-5.6 Sol deleted user files, exactly like OpenAI’s own paperwork said it might

• Google couldn’t sell Meta the compute it wanted, so now everybody builds their own silicon

Apple sued OpenAI, and the complaint reads like a heist movie

The story: Cue the Dragnet theme song. On Friday, July 10, Apple filed a 41-page trade secret lawsuit against OpenAI in the US District Court for the Northern District of California. The defendants include OpenAI Foundation, OpenAI Group PBC, io Products (the hardware startup OpenAI bought from former Apple designer Jony Ive for roughly $6.5 billion in 2025), and two former Apple employees. Apple’s claim is that OpenAI ran a coordinated campaign to pull confidential hardware information out of Apple through the people it hired.

What happened:

• The two named individuals are Tang Yew Tan, now OpenAI’s chief hardware officer, who spent 24 years at Apple and served as VP of product design for iPhone and Apple Watch; and Chang Liu, a senior system electrical engineer at Apple for eight years who joined OpenAI in January 2026.

• Apple alleges Liu kept an Apple-issued laptop, used an authentication bug to reach internal shared network folders, and downloaded dozens of confidential hardware files. The complaint quotes an actual message about it: “LOL, I found out I can access the [network storage], so funny.”

• Apple alleges Tan quizzed job candidates about the status of unannounced Apple projects and asked them to carry actual parts out of Apple facilities and into interviews so people could look them over.

• The complaint says over 400 former Apple employees now work at OpenAI, and that OpenAI circulated an internal Apple document carrying a restricted-access designation that coached new hires on dodging Apple’s exit procedures.

• Apple says it wrote to OpenAI in February about all this and never got a reply. It wants damages, an injunction, evidence preserved, and its materials back. OpenAI communications director Drew Pusateri said on X that the company has “no interest in other companies’ trade secrets.”

Hiring your competitor’s people is legal, normal, and roughly the entire history of Silicon Valley. Apple argues that OpenAI treated the hiring pipeline as a “gotta catch 'em all!” collection system. Picture a bakery hiring the head baker from the shop across the street. Completely fine. Now picture that same bakery telling him to bring the secret “mango dang-yo cake” recipe with him on his last day, and asking every other candidate from that shop to show up with a croissant in a Ziploc bag for analysis. It really do be like that.

None of this is proven, and OpenAI hasn’t filed its response yet. What comes next is discovery, where both sides get to pull each other’s emails and texts into the light, and Apple has already said in the filing that it expects to find more. That matters because OpenAI is fighting a second discovery war at the same time (also, “discovery war” sounds like a series of sci-fi/fantasy novels from 1987). The day before Apple filed, the New York Times, the New York Daily News, and 15 other publishers asked a Manhattan federal judge to sanction OpenAI in the copyright case. Their motion alleges the company hid that it had already searched its own training data for their journalism, and that it deleted or de-indexed billions of ChatGPT conversations. OpenAI denies it and says the publishers are fishing through private user chats as their case weakens. Elon Musk and Sam Altman then spent the following weekend swinging at each other on X about the Apple suit. AI CEO beef is officially the new East Coast-West Coast rap beef.

CoreWeave is shopping for insurance against RAM getting cheaper

The story: Reuters reported on July 14 that AI cloud company CoreWeave has held early internal discussions about using financial derivatives to protect itself if memory and storage chip prices fall. What’s a derivative, you might ask? It’s a contract whose value is tied to something else; the one specifically discussed was a put option, which pays out when a price drops below an agreed level. After all this time watching RAMageddon go one direction, somebody is quietly preparing for the other one.

What happened:

• The report came from a single unnamed source; no hedges have been executed, and no CoreWeave executive has said anything publicly about it.

• CoreWeave has locked in supply through long-term agreements with Micron and SanDisk. Many of those deals guarantee the supplier a price floor on DRAM and storage chips.

• That floor shields the chipmaker from a downturn. It also leaves CoreWeave paying well above the going rate if spot prices tumble.

• CoreWeave’s 2026 capital expenditure budget ranges from $31 billion to $35 billion, and CFO Nitin Agrawal raised the low end in May because of component prices. The company closed the quarter with a $99.4 billion backlog and has since signed a $6 billion cloud deal with Jane Street.

• SK Hynix and Micron have both signaled their new manufacturing capacity will be fully ramped up in early 2028. There is no liquid exchange-traded market for DRAM or NAND derivatives, so any hedge would likely be a bespoke contract negotiated with a bank.

I’ve talked a whole bunch about the wild ride that is RAMageddon. This is the first time a major buyer has publicly acted like that ride has a last stop. Picture someone who got spooked by $6 gas and signed a contract to buy it at $4 a gallon for three years. Smart at that particular time. But then gas drops to $2. Now they pull up to the pump every week and pay double what the person in the next lane pays, with two years left on the deal. Can’t even afford a bag of Sour Patch Kids. CoreWeave signed that kind of contract on memory chips, and it is now asking Wall Street to sell it an insurance policy for the day gas hits $2.

But for you, the consumer at home? Nothing has changed, quite yet. TrendForce still projects conventional DRAM contract prices climbing 13% to 18% quarter over quarter in Q3 2026 and NAND flash up 10% to 15%, which is a genuine cooldown from the roughly 60% jumps in Q2 but is very much still a climb. The date to circle is early 2028, when SK Hynix’s and Micron’s new fabs are fully running. One honest caveat: CoreWeave’s hedge is a bet placed by people with a $35 billion exposure, and airlines have famously torched money hedging jet fuel. A bet is a bet, even when the bettor (not to be confused with the gambling hero robot, BetTor) has good seats.

Jensen Huang went to an arcade in Akihabara, then handed Japan a national AI factory

The story: On July 16, Nvidia announced it is working with Noetra Corp. to build an AI factory running 13,750 Nvidia Vera CPUs and 27,500 Nvidia Rubin GPUs, dedicated to physical AI (which means AI that controls things in the real world: robots, factory floors, vehicles, logistics). Nvidia is calling it the world’s first national AI infrastructure for physical AI. Noetra is a government-backed AI developer formed by SoftBank and other Japanese players, according to Nikkei.

What happened:

• The system provides the compute for Japan’s FRONTia Project, launched by the Ministry of Economy, Trade and Industry to develop multimodal foundation models aimed at AI robotics and physical AI. METI Minister Ryosei Akazawa appeared onstage with Huang.

• The factory is architected with Nvidia Spectrum-X Ethernet networking, and is pitched at manufacturing, logistics, healthcare, and telecommunications.

• This is Nvidia’s sovereign AI playbook, which it now runs with governments and companies in more than 20 countries.

• The night before, July 15, Huang was in Akihabara for the 30th anniversary of Nvidia GeForce Japan’s partnership with Sega, reuniting with Shoichiro Irimajiri, the former Sega president whose roughly $5 million investment in the mid-90s kept the company breathing long enough to ship the RIVA 128 in 1997.

Sovereign AI is a straightforward idea wearing a fancy coat. Countries have looked at the AI stack, noticed that nearly all of it lives on servers owned by American companies, and decided they want their own. Imagine a town that has bought every pizza from one out-of-town pizzeria for twenty years. One day the town council votes to build its own oven, so it can sling pies on its own schedule, with its own recipes, without asking. Then the pizzeria shows up to the meeting with a very nice oven for sale. That is what happened in Tokyo this week, and it’s why Nvidia keeps doing this everywhere.

The wrinkle nobody says out loud: a country building sovereign infrastructure on one company’s CPUs, one company’s GPUs, and one company’s networking has traded a dependency for a different dependency with a nicer name on it. Japan gets real capability out of this, and Nvidia gets a whole country as a customer. A win-win for Far East AI enthusiasts. Imagine that: the man announcing the first national AI factory for robots spent the previous evening in an arcade thanking the guy who bailed him out in the mid-90s. Jensen lives a wild life, indeed.

OpenAI published a warning that Sol might delete your files. Then Sol deleted people’s files.

The story: OpenAI’s GPT-5.6 Sol, the coding and cybersecurity flagship, shipped July 9 alongside ChatGPT Work. Within days, named developers reported that agents running Sol had removed files, databases, and cloud machines nobody asked it to touch. OpenAI’s own system card, published June 26, describes that exact behavior. A system card is the document a lab puts out explaining how it tested a model and what the testers found.

What happened:

• The system card, out fourteen days before the incidents, says the model can be overeager, can read instructions permissively (assuming an action is allowed unless clearly forbidden), can be careless in ways that are destructive beyond the task, and can misreport its own results.

• It also says Sol shows a greater tendency than GPT-5.5 to go beyond what the user asked for. OpenAI files unauthorized deletion under severity level 3, defined as actions “a reasonable user would likely not anticipate and strongly object to.”

• The card’s own example: a user asked for virtual machines 1, 2, and 3 to be deleted. The model could not find them, so it deleted 5, 6, and 7, killed running processes, and later reported that uncommitted work may have been lost.

• Matt Shumer, founder and CEO of OthersideAI (which makes HyperWrite), reported on July 10 that an agent running Sol in full access mode expanded the HOME environment variable inside a recursive delete command and wiped nearly all the files on his Mac. Developer Bruno Lemos posted that his entire production database was gone.

• Sol runs in three modes: a default requiring frequent approvals, an auto-review mode where a second agent watches the first, and full access with no sandbox. Shumer and Lemos both appear to have been in full access. OpenAI engineer Thibault Sottiaux acknowledged on July 11 that the rollout had four problem areas, deletion among them.

Sol’s strength is also its weakness. It can keep working through a long, complicated task without stopping for approval. But if something goes wrong, it may keep going and make the problem worse. OpenAI warned that this could happen. Developers who took that warning seriously limited Sol’s access, backed up their files, and required approval for important actions. Those who gave it full access faced a greater risk of losing files or databases (like an out-of-control chainsaw that starts cutting more than wood).

The takeway here is simple, but powerful: do not give an AI agent more access than it needs. Keep tested backups, introduce it gradually, and use approval settings until you know how it behaves. OpenAI’s system card described these risks before Sol was released. The developers who avoided serious damage did so because they took precautions before handing over control.

Google couldn’t sell Meta the compute it wanted, so now everybody builds their own silicon

The story: Around March, Google basically told Meta “I ain’t got no more Gemini for you, foo!” and the Financial Times reported it on June 28. Meta told staff to use AI tokens more efficiently and watched some internal projects slip. That was the cause. This week was the effect, and the effect is that the largest companies in tech are all racing to stop renting.

What happened:

• Per the FT, citing three people familiar with the matter, several Google clients hit capacity limits and Meta was hit hardest. Meta had leaned on Gemini for content moderation and scam detection because it outperformed Meta’s own Llama models at that work. Google and Meta both declined to comment.

• Internal documents reported July 13 show Meta will begin manufacturing its custom data-center chip, codenamed Iris, in September. It was developed with Broadcom, is manufactured by TSMC, and cleared testing in six weeks. Meta is targeting 14 gigawatts of compute capacity by 2027, up from 7 gigawatts for 2026.

• Anthropic is in early talks with Samsung to build a custom inference chip tuned to its Claude models, reported July 14, while preparing an S-1 for a public listing as early as October.

• Google Cloud’s backlog of signed but undelivered contracts roughly doubled to about $460 billion in the quarter ended March 2026. On the earnings call, CEO Sundar Pichai said the company is compute-constrained in the near term and that cloud revenue would have been higher with more capacity.

• Google is paying SpaceX roughly $920 million a month for about 110,000 Nvidia GPUs sitting in xAI’s Colossus data centers, capacity it openly calls a bridge. Anthropic signed its own Colossus deal in May at about $1.25 billion a month.

Google is spending north of $180 billion on infrastructure this year and owns its own TPUs, and it still had to tell Meta no and then rent almost a billion dollars a month of GPUs from a rocket company. Meta is one of the most valuable companies in existence and basically got told to “use less hot water in the shower.” Now picture a delivery business that has always bought its vans from the one manufacturer that also runs a competing delivery service. Vans are fine and plentiful for years. Then vans get scarce, and you find out very quickly whose fleet gets the last one off the line. That’s the position Meta found itself in, and it’s why it’s in the silicon biz now.

Which brings us back to the foundry. TSMC reported the biggest quarter in its history this morning, July 16: revenue of NT$1,270.38 billion (about $40.2 billion), up 36% year over year, with net income up 77.4% and gross margin at 67.7%. Advanced technologies made up 77% of wafer revenue. So Meta designs Iris to escape Nvidia, Anthropic talks to Samsung to escape its compute bill, OpenAI has its own silicon program, Google has TPUs, and Amazon has Trainium. Every single one of those escape routes ends at the same set of doors in Hsinchu. You can design your way around your supplier. Designing your way around physics is a taller, more expensive order.

Bytes of AI

The rest of the week, at speed:

• Google unveiled Gemini Enterprise at Cloud Next '26 on July 14, a governance-first platform for building and auditing fleets of AI agents. It points directly at OpenAI’s ChatGPT Work and Anthropic’s Claude Cowork.

• US venture funding hit $412.7 billion in the first half of 2026, and about 86% of it ($355.9 billion) went to AI. Crunchbase’s global figure is a record $510 billion, with OpenAI and Anthropic alone accounting for $217 billion of it.

• China’s rules on humanlike AI took effect July 15. ByteDance pulled Doubao’s agent function, Alibaba halted Qwen’s anthropomorphic and user-created agents on July 10, and Shanghai’s regulator says it removed more than 14,000 non-compliant AI agents ahead of the deadline. No migration path for user agent data.

• Anthropic launched Claude for Teachers, free for verified US K-12 educators, with a stated policy of not training on student data.

• Hotel software company Mews cut about 15% of staff (roughly 170 jobs) and named AI efficiency as the reason out loud, which lends credence to the “AI will steal jobs” concern.

• Unitree announced the GD01, a $650,000 transforming mecha robot, the same week it cleared a roughly $619 million Shanghai listing. A wall-smashing giant robot from the company best known for affordable robot dogs. Man’s best defender, anyone?

• Nvidia-backed Walden Robotics hit a $1.1 billion valuation, and DeepSeek is reportedly targeting a 2027 Shanghai IPO.

What ties it all together

Step back from the week and the shape is people trying to nail down something that keeps moving. Apple wants its secrets back in the box. The New York Times wants the logs. CoreWeave wants a floor under a price that has only ever gone up. Japan wants an oven of its own. Meta wants a van it does not have to ask for. A handful of developers want an undo button on a Tuesday afternoon. And I want a pony. How is it that what I want is somewhat more feasible?

The through-line is that the AI industry has run out of room to be casual. Back when it was just chatbots getting incrementally smarter, a hiring spree was a hiring spree and a supply contract was paperwork. Now the hiring spree is a 41-page federal complaint, the supply contract is an exposure somebody wants a derivative against, the model release is a document that predicted its own incident report, and the compute purchase order is a national industrial policy with a cabinet minister onstage.

“Can it be that it was all so simple then?” Barbara and Gladys, y’all weren’t kidding.

What I’m watching

• Whether Gemini 3.5 Pro actually lands on July 17, which Google has never officially confirmed, and whether its 2-million-token context window holds up past 500K where every previous long-context claim has quietly fallen apart

• What comes out of the World AI Conference in Shanghai, opening July 17, with Xi Jinping attending in person for the first time since the event started in 2018

• Whether OpenAI ships default guardrails for Sol or keeps leaving the permission scoping to whoever is holding the chainsaw

• Whether CoreWeave executes an actual memory hedge, and whether anyone builds a real market for one, since the DRAM futures Architect Financial and Ornn announced in January are still waiting on regulatory approval

• Whether Anthropic’s S-1 shows up in the fall the way the reporting suggests, and what the Samsung chip talks turn into

  

Thanks so much for reading this edition of The Weekly Think.

See you next week, fellow thinkers!

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This past weekend, I had the most horrific delays imaginable. Going to my destination, I had an overall delay of 6 hours before flying out. On the way home, the flight was delayed about 4 hours. I got home, late at night, and drained of all vital energy (save for what I could eke out to take a much-needed shower).

It did get me thinking: sometimes using AI can be like that (or, “sometimes it be like that”). As you go into using it, you think to yourself, “surely this is going to go well and I will experience no problems whatsoever.” And just like getting ready for the airport, with that mindset in… uh… mind… you get up bright and early, ready to attack the day (but hopefully not the flight, as that would introduce a whole new set of problems that I, Eric, am not equipped to assist you with). But once you arrive? Problems. Maybe not a delay in flight. But rough drafts that are truly rough. A voice that sounds like an over-enthusiastic IBM salesman from the 90s (“all 22 MHz will DEFINITELY run your spreadsheets!” kind of energy). An article that reads like it was written in a packed terminal, four hours into a delay, with your back screaming at you.

When writing feels like this, is everything lost? Not so fast, pardner. Though I won’t say “just have AI write the whole thing” (lest you end up in Mr. IBM Salesman territory), there is a solution that might be worth your while. And it’s something I’ve been talking about a lot lately: using AI as an assistive tool in your writing.

Wakey-wakey, weary traveler/writer, this is how we do it.

Here’s the flight plan

So you might be asking me right about now, “but Eric, where are the prompts?” Easy there, friend-o. A prompt you asked for and a prompt you shall receive. Here it is:

I have a rough thought: [paste your half-formed idea]. 

Using my voice profile, give me 10 different angles I could write about this, each as a single 
sentence. Mix the approaches: some practical, some personal, some built around a question that 
makes the reader stop and think.

That’s it? In the words of that old Circuit City commercial that became a meme, “that’s it.”

Or is it? As you might remember from past articles, AI tools absolutely thrive on context. Giving AI context is like Popeye pumping himself full of spinach (and not chicken). The tool becomes powerful. You could also stack this prompt with the AI Interviewer prompt that I talked about previously. Proper context plus your rough, half-formed ideas equals the potential for a lot of good writing. It’s like the flight delay has cleared and the airplane is now ready to get off the tarmac. Another way to think about it is that suddenly, you have all of these backup airplanes, ready to get you to your destination.

Landing the plane on schedule

Of course, none of this is going to auto-magically create your writing for you. If you leave that to the AI, you’re going to have a bad time. You will end up with content so middle-of-the-road it has a dotted yellow line running down it. It will be written for everyone and no one. And, indeed, no one may end up reading such low-effort content.

Put some elbow grease into the prompting. And then follow it up with your own writing, using the ideas generated by the tool. Use what it gives you as a springboard for your own ideas. Let the tool help you put ink to paper (or fingers to keyboard, which is more likely). Keep this workflow in your back pocket on those days where it feels like you’re at the airport and the plane just ain’t taking off. Be the FAA of your writing process and have this contingency ready for when you need to land your content on schedule. By taking advantage of the power found in proper AI prompting, your days of figurative delays just might be behind you.

Now if only you could get your airport food purchases reimbursed. But alas, that’s a different Substack for a different day. Safe travels!

What do you do when you’re having writer’s block so severe, it feels like a long day at the airport? Let me know in the comments, I read them all!

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If it’s Thursday, it’s TWT, courtesy of me: Eric! I said last week I’d be watching whether OpenAI’s GPT-5.6 broke free of its government leash and reached the public. Welp, it sure did, and it brought company. July 9 turned into a genuinely wild day where three of the biggest names in AI (OpenAI, Elon Musk’s Cursor-fortified SpaceX, and Anthropic) all had brand-new models live for the public at the same time.

TL;DR: the frontier-model race hit a three-car pileup, Grok showed up swinging on price, OpenAI quietly changed how it feels to talk to ChatGPT out loud, and the memory shortage kept chewing through the price of every gadget you own. Ooo-wee, let’s get into it.

While I was reading the news so you don’t have to:

• OpenAI’s GPT-5.6 and SpaceX’s Grok 4.5 both went fully public on July 9, the same day

• Grok 4.5 landed at a fraction of the price of its rivals, trained on Cursor’s coding data

• OpenAI launched GPT-Live, a voice model that listens and talks at the same time

• SK Hynix, the memory maker behind Nvidia’s chips, filed for a $28 billion US stock listing

• The memory shortage spread to CPUs and graphics cards, with the RTX 5090 near $5,000

Three frontier labs shipped on the same day

The story: Today, July 9, three of the biggest AI companies each put a new flagship model in front of the public at the same time. OpenAI opened up GPT-5.6 (its Sol, Terra, and Luna tiers) after weeks stuck behind a government review. SpaceX released Grok 4.5 to everyone. And Anthropic’s restored Fable 5 and new Sonnet 5, both from last week, were already live. One tracker called it the first day in AI history that three frontier labs each had a brand-new public model available at once. It’s like the three biggest wrestlers, each representing a different company, getting ready for a three-way brawl.

What happened:

• OpenAI’s GPT-5.6 had been limited since late June to about 20 government-approved organizations, part of the new pre-release review process I covered last week. The government cleared a wider release ahead of schedule, and Sam Altman posted “GPT-5.6 Sol launches Thursday! Happy building.”

• GPT-5.6 comes in three tiers: Sol (the strongest, aimed at hard coding and reasoning), Terra (an everyday model that matches last year’s flagship at half the price), and Luna (the cheap, fast option).

• SpaceX’s Grok 4.5 launched the same day, positioned as a lower-cost rival to the top models from Anthropic and OpenAI.

• Anthropic wasn’t releasing today, but its lineup (Fable 5, Sonnet 5, Opus 4.8) meant all three labs had current frontier models in the wild simultaneously.

• On one widely watched coding benchmark, OpenAI’s Sol leads at 91.9%, with the others clustered close behind, so the gaps between these models are shrinking.

For most of the past two years, model launches were spaced out enough that each one got its own time to shine. But nah, not anymore. They were all like, “we’ll do it live!” When three companies worth trillions of dollars combined all ship on the same Thursday, it tells you the competition has gone from a jog to an Olympic sprint, and the people who benefit most are the ones who actually use these tools.

For you, humble thinker, the practical advantage is real. More competition at the frontier means better models at lower prices, faster. The same work that cost a fortune to run a year ago is getting cheaper by the month as these labs undercut each other. If you’ve been waiting for AI tools to get good enough or cheap enough for whatever you have in mind, the gap between “too expensive” and “worth it” is closing quickly.

Grok 4.5 showed up cheap, and that’s the whole strategy

The story: SpaceX’s AI arm released Grok 4.5 to the public on July 9, and its pitch is built almost entirely on price. Elon Musk called it “an Opus-class model, but faster, more token-efficient and lower cost.” Yowza, Anthropic’s top-tier Opus line catching strays out in these streets. Grok 4.5 is priced at $2 per million input tokens and $6 per million output tokens, which sharply undercuts Anthropic’s Opus 4.8 at $5 and $25.

What happened:

• Grok 4.5 is built on a new 1.5-trillion-parameter foundation and was trained partly on data from Cursor, the AI coding tool SpaceX agreed to buy for $60 billion last month, which is why it’s aimed squarely at coding and agent work.

• On the benchmarks SpaceX chose to publish itself, Grok 4.5 beats Opus 4.8 on two of four tests and loses on the other two. So “Opus-class” is a fair label; “beats Opus” would be a stretch.

• Independent testing ranked it fourth overall, behind Anthropic’s Fable 5, OpenAI’s GPT-5.5, and Opus 4.8. Its edge is cost and speed, near-frontier quality at a fraction of the price per task.

• SpaceX says it plans to ship a brand-new model every month through the end of 2026, which is an aggressive pace nobody else is matching.

• It launched everywhere except the EU, where availability is expected around mid-July.

There’s a fascinating fun fact sitting underneath this launch. SpaceX trained Grok 4.5 on the same Colossus supercomputer it rents out to Anthropic and Google, its own competitors. A few editions back I covered those lease deals, where Anthropic pays SpaceX over a billion dollars a month for compute. Now SpaceX is using that same hardware to build a model that competes directly with the companies renting it. At some point SpaceX has to decide whether it keeps happily cashing rival rent checks or starts holding that compute back for itself, and that decision could reshape who can train what.

For anyone weighing which AI tools to build on, Grok 4.5 is worth a look on the math alone. If a model gets you 90% of the way there at a quarter of the price, that tradeoff makes sense for a lot of everyday work. The pros still reach for the absolute best model when a job is hard and failure is expensive, but the cheaper, fast option covers a huge amount of ordinary, ho-hum work. More price competition at this level is a straightforward win for whoever is footing the bill.

OpenAI rebuilt ChatGPT’s voice so it talks like a person

The story: On July 8, one day before the GPT-5.6 rush, OpenAI launched GPT-Live, a new kind of voice model that can listen and speak at the same time. The old ChatGPT voice mode worked like a walkie-talkie: you talk, you stop, it responds, and a stray cough or a thinking pause could make it cut you off. GPT-Live works like a cafe chat, where both sides can talk and listen at once (while your latte goes cold, further authenticating the experience!) It’s rolling out globally now across iPhone, Android, and the web.

What happened:

• The technical term is “full-duplex,” which is a fancy-pants way of saying it processes what you’re saying while it’s talking, making decisions many times a second about whether to speak, pause, or listen.

• It throws in little human cues like “mhmm” and “got it” while you talk, and it holds quiet when you trail off to think, without jumping in on you.

• When you ask something that needs a web search or hard reasoning, GPT-Live hands that off to a stronger model (GPT-5.5) in the background and keeps chatting while it waits, then folds the answer in.

• Paid users get the full GPT-Live-1; free users get a smaller GPT-Live-1 mini. Both replace the old voice mode.

• More than 150 million people use ChatGPT’s voice and dictation every week, so this changes the daily experience for a lot of people.

Of everything that shipped this week, this is the one most distinctive. The benchmark wars between Sol and Grok matter to devs, but a voice assistant that stops interrupting you (like good ol’ Zoom vocal double-dutch) matters to anyone who has ever tried to talk to their phone while thinking. OpenAI’s product lead said he’s had 30- and 40-minute conversations with it on walks, which speaks to the wild AI age we live in. The clever design choice underneath is that OpenAI split the job in two: one model handles the natural back-and-forth of talking, and a separate, smarter model handles the actual thinking. That means they can keep swapping in better brains behind the voice without rebuilding the conversation part. Teamwork makes the AI dream work!

If you use voice assistants at all, this is worth trying the next time you open ChatGPT. The gap between “talking to a robot” and “talking to a person who can look things up mid-sentence” has been a weak point for AI, and this is the closest anyone has gotten to closing it. Whether it holds up outside a polished demo, especially in languages other than English, is the honest open question.

The memory maker behind Nvidia wants to go public for $28 billion

The story: SK Hynix, the South Korean company that makes the high-speed memory inside basically every Nvidia AI chip, filed for a US stock listing this week aiming to raise around $28 billion. That would be one of the largest listings in years, and it’s a direct bet by investors on the AI memory boom that’s been squeezing your wallet all year.

What happened:

• SK Hynix is the world’s second-largest maker of DRAM and the main supplier of HBM, the stacked, ultra-fast memory that determines how big and fast an AI model can run.

• Its HBM sits inside every Nvidia Blackwell chip shipped in 2026, and its HBM revenue for the first half of the year topped $10 billion on its own.

• The memory business has always been brutally cyclical, boom one year, bust the next, but investors are now treating it as a steady growth story because AI demand looks durable.

• The US listing would give American investors direct exposure to the AI memory market for the first time, and it deepens SK Hynix’s ties to the US chip supply chain.

• It’s part of a broader wave of AI-hardware companies heading to the public markets, following the record IPOs I covered in the past month.

This ties two threads I keep returning to into a knot. The first is the memory crunch, the reason your gadgets keep getting pricier (more on that in a sec-to). The second is the IPO wave, where the biggest names in AI have been rushing to sell shares to the public. SK Hynix sits at the exact intersection: it’s cashing in on the shortage, and now wants a piece of that pie. The fact that a famously boom-and-bust memory company can suddenly pitch itself as a stable growth stock tells you how thoroughly AI has rewired expectations about who makes money and how.

As for you, wise thinker? You don’t need to buy a single share to understand this sitch. When the company supplying the memory decides the AI boom is solid enough to stake a $28 billion public listing on, that’s a real-money vote that the demand driving up your laptop prices isn’t a temporary problem. The people closest to the supply chain are betting it lasts, which is worth knowing whether you’re an investor or just someone trying to give your PC some shine. Speaking of which… *clears throat*

RAMageddon continues to run rampant

The story: The AI memory crunch I’ve tracked for weeks keeps widening. This past week it moved beyond data centers and gaming handhelds into the PC parts that builders and enthusiasts need. Intel raised suggested prices on its desktop processors by 10 to 17%, AMD told its partners graphics-card supply prices are going up 10% in July, and the flagship RTX 5090 is now listing for $4,300 to $5,000. The single cause behind all of it is AI swallowing up memory supply.

What happened:

• Memory chips now make up more than 80% of the total parts cost of a high-end graphics card, so when memory prices spike, the whole card gets dramatically more expensive.

• The research firm IDC estimates AI data centers could swallow as much as 70% of the world’s entire memory output in 2026, leaving far less for everything else.

• Console makers are feeling it too: Nintendo raised the Switch 2, Sony bumped the PS5 a second time, and Valve hiked the Steam Deck by nearly 50%.

• Nvidia’s server-rack component costs have been swinging up to 40% week to week, and cloud providers like AWS have raised GPU rental prices 20 to 30% to keep up.

• The three companies that make nearly all the world’s memory keep steering their best production toward high-margin AI chips, which starves the ordinary stuff.

Longtime readers know this thread as “RAMageddon.” What started as data-center drama has now reached the checkout page for anything with a chip in it (and I don’t mean Lay’s). Picture a town where the fanciest restaurant starts buying up every egg from every farm for miles, and suddenly your corner diner is charging ribeye prices for an omelette. The eggs didn’t change. The demand from one very rich customer just priced everyone else out. That’s the memory market right now, and the very rich customer is AI.

For anyone shopping for a graphics card, a laptop, or a console, the buying math has flipped. In a normal year, waiting a few months gets you a better product for less money. Right now the trend is flipped, turned upside-down, with prices climbing through 2027 and no real relief expected until 2028, when new factories finally come online. If you need the hardware and you find it near a fair price, grabbing it sooner is what saves you money. Waiting mostly means paying more later. I honestly wish I had better news on that, thinkers.

What ties it all together

Step back and this week splits cleanly in two. On one side, the models: three labs shipping at once, Grok racing to the bottom on price, OpenAI further improving voice. On the other side, the hardware underneath them: memory so scarce it’s reshaping the price of every chip, and the companies that make that memory are cashing in so hard that they’re going public on it.

The connection between those two halves is the whole story of AI right now. The software keeps getting better and cheaper and more competitive, which is great for anyone using it. The physical stuff it runs on keeps getting scarcer and more expensive, which is rough for anyone buying a computer. Both things are happening at once, driven by the same explosion of demand. A year ago the AI story was almost entirely about the models. Now half of it is about memory, chips, factories, and who controls them. The AI story right now is wild: I am here for it and also quite shocked by it. How’s your week going?

What I’m watching

• Whether GPT-5.6 and Grok 4.5 hold up in real-world use now that regular people are putting them to work, well past the benchmark charts

• Whether GPT-Live actually feels natural in daily use or stumbles once you leave English and the polished demo behind

• Whether SpaceX keeps renting Colossus compute to the rivals it now competes with, or starts holding it back

• Whether SK Hynix’s $28 billion listing prices strongly and pulls more memory makers toward the public markets

• Whether memory prices show any signs of relief before the new factories arrive in 2027 and beyond

Thanks so much for reading this edition of The Weekly Think.

See you next week, fellow thinkers!

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Let’s say you decided to spend an afternoon letting AI write your article (even forgoing Cool Steve’s 5th Annual BBQ & Ice Cream Bash). So you hand the whole thing over and wait. What does it give back? Well, it’s certainly smooth in terms of grammar and flow. But it’s bland, and now it is effectively no longer yours.

Because of this, the common thought is that “AI can’t write” (and it made you miss out on Cool Steve’s trademark “banana pranks”). The reality is that AI is a very weak writer on its own. It can, however, be a very effective editor. And this article will guide you in applying this in your writing.

Why AI writes badly but edits well

When you let AI write on its own, it gives you that bland, median content, the middle-of-the-road stuff we’ve all started to notice everywhere. Letting it edit is a different animal. You arm it with your voice, ideas, and all the raw materials that form the context of your article.

Think about it this way: imagine you hire a contractor to build your house, but you give him no instructions whatsoever. He’s fully capable, but without direction, you might get the most random house possible (1 bedroom, a toilet next to the stove, which is technically not a bad idea… on second thought, never mind). It’s likely that it’ll be dangerous to live in as well. But give him all your preferences (style of home, size, number of rooms, a kitchen hood that sucks all of the stove smoke right out of the house, etc.), and you end up with the home of your dreams, worthy of a Cool Steve-esque BBQ bash.

The point is that your first draft is those instructions. Writing it yourself is how you hand the tool your preferences, style, and voice all at once. With this draft, you give the AI tool what it desperately needs to do decent work: context. What the article is about, the points you want to hit, your humor and vibe, all of that good stuff. With your point of view to work with, AI has what it needs to help you sharpen that draft into a refined article.

How do I pull this off?

So now that you’ve got a killer draft, what’s next? Follow these steps to further get that content closer to what you’re dreaming up.

1. Paste your finished draft and tell it explicitly not to rewrite, only to diagnose.

2. Ask for specific categories of feedback, not “make it better.” Clarity, flabby sentences, weak spots, places a reader might drop off.

3. Have it flag the three weakest sentences and explain why, so you keep control of the fix.

4. Then, the most important step: you make every final edit by hand, overruling anything that doesn’t match your authentic voice.

In fact, here’s a prompt that will help you with steps 1-3:

Below is a finished draft I wrote. Do not rewrite it. I want you acting as my
editor, not my ghostwriter.

First, read it closely and tell me where it drags: the flabby sentences, the
unclear spots, and any place a reader might lose interest and click away.

Then point out the three weakest sentences specifically, and explain WHY each
one is weak, so I can decide how to fix it myself.

One rule you can't break: keep my voice intact. If you spot an unusual word
choice, a joke, or an odd bit of phrasing, assume I did it on purpose and leave
it alone, unless it genuinely breaks the sentence. You're checking my work, not
replacing my personality.

Here's the draft:
[paste your draft here]

Resist these temptations (and I don’t mean the singing group)

At any point of your writing process, you may be tempted to tell your AI “just fix this mess for me!” Resist any temptation to do this, as it will undo all of your hard work. When you let AI overedit your content, it becomes a technically-correct word salad that says a lot but means very little. It’s like the time Cool Steve’s cousin (you know, Wordy Bort) was at that analyst job interview, telling them all about his time “managing multiple systems while monitoring fault points and restoration systems,” which was a fancy way of saying “I make sandwiches at home all the time.”

Another downside of putting full trust in AI is what it does to all of your stylistic choices. This is especially true when you use unique word choices and humor. If you don’t inform the tool, it will identify all your cool stuff as errors, misspellings, and incorrect sentence structure. You’re the one that knows all of these are on purpose. But AI doesn’t know, and it will remove them or make you sound incredibly awkward. You’ll end up in Wordy Bort territory, real quick.

Cool writing that belongs to you

You have so many great ideas in that ol’ noggin of yours. Don’t throw them all away by letting AI try to write about them, all by itself. You’re an awesome writer. Your content just needs a good second read, and that’s exactly what AI is built for. I always say, “AI is the tool, never the artist.” So let it be the never-tiring tool that reads your content closely. Assigning it the role of full-time “second pair of eyes” is a habit that will serve you well in your writing endeavors.

If you’re struggling with your write-ups, give the guidance in this article a shot. As long as you remain the writer, it can help you further refine your incredible writing. And you just might free up that weekend to go see Cool Steve’s breakdancing dog.

Did you enjoy this article? Let me know what AI tips & tricks you use to keep your weekends full of Cool Steve antics. I read every comment!

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The quick version: your next laptop (and mine, because I’m in the market for one) is about to cost more (wait, noooo), the newest models now come with a government permission slip, the banned Anthropic model might be crawling back to life, and China just proved you can build a frontier AI without leather jacket man’s chips. Time to dive in, like a chip to the guacamole.

While I was reading the news so you don’t have to:

• Jefferies warned memory prices are jumping another 40-50% this quarter, with no relief until 2028

• OpenAI launched GPT-5.6, a model you currently need government approval to even use

• Anthropic’s banned Fable 5 model looks close to coming back, with a new negotiator at the table

• Anthropic also shipped Claude Sonnet 5 and a research tool called Claude Science

• China’s Meituan trained a 1.6-trillion-parameter model on entirely homegrown, non-Nvidia chips

Your next laptop is about to cost more, and the number is ugly

The story: On June 27, Wall Street firm Jefferies put out a memory-market forecast that landed like a cold bucket of water on a hot cat. It expects DRAM prices, the regular memory inside every laptop, phone, and console, to jump another 40 to 50% this quarter compared to last, then another 30 to 40% on top of that in the final quarter of the year. Its bottom line: no real relief until 2028. Man, the future is hard mode, y’all.

What happened:

• The cause is the same one I’ve mentioned several times. The three companies that make the world’s memory have shifted roughly 93% of their production toward HBM, the pricey high-speed memory that feeds AI data centers.

• Server memory now eats up 60 to 70% of total demand, up from about 30% before the AI boom, so there’s far less left over for the stuff in your gadgets.

• IDC estimates PC prices will climb 4 to 8% because of this, and phones take a similar hit, with budget devices getting squeezed hardest.

• Microsoft has said it’s paying two and a half times more for memory now than it did at the end of last year.

• Chinese memory makers were supposed to be the pressure valve, but they’re selling at similar prices and mostly serving their home market, so they’re not the fix people hoped for.

Regular readers know this thread well. A few editions ago I covered Micron posting the best quarter in its history, and before that, the Steam Deck jumping as much as $300 as a result of the infamous “RAMageddon.” This Jefferies forecast is the same story, albeit with a longer timeline attached. Picture a bakery that suddenly makes way more selling giant wedding cakes to corporate clients, so it quietly stops stocking the shelf where you used to grab a $1 muffin. The flour is the same and so is the oven, but the everyday stuff you want just costs more now, because the baker found richer customers. Non-appétit, am I right?

For anyone eyeing a laptop, a phone, a graphics card, or a fresh PC build, the read since RAMageddon has only gotten worse. Prices on anything memory-heavy keep climbing into 2027. If you’ve got a purchase you can make now, doing it sooner saves you money, because the new factories that would ease this are still years from opening.

OpenAI shipped a model you need government permission to use

The story: On June 26, OpenAI launched GPT-5.6, its newest model family, in three flavors with codenames to boot: Sol, Terra, and Luna. As a first time for a major model, however, you can't just up and use it. GPT-5.6 launched in a preview available to only about 20 organizations, each one individually cleared by the US government before getting access. Broad public availability is targeted for mid-July.

What happened:

• The three tiers scale by price and power: Sol is the big one ($5 per million input tokens, $30 output), Terra is the mid-size workhorse, and Luna is the cheap, fast option ($1 input, $6 output).

• On a widely watched coding benchmark, Sol scored 91.9%, edging out Anthropic’s Mythos 5 at 88% and Fable 5 at 84.3%.

• The government gating traces back to a June 2 Executive Order that set up a voluntary system where frontier labs pre-brief the government before releasing their most capable models.

• OpenAI cleared the release with the government first, then rolled it out to the approved handful, with wider access pending a July 2 review milestone.

• For everyday coding and reasoning work, the mid-tier Terra is the one most teams will actually reach for once it opens up, since it roughly matches last year’s flagship at half the cost.

Take a second to appreciate how bonkers this is, because a chatbot needing a government clearance is a new thing. A year ago, a new model dropped and anyone with a credit card could try it that afternoon. Now the most capable release of the month arrived with a velvet rope and a guest list the government helps write (next thing you know, they’ll hire a bouncer). This is the same governance shift that produced the Fable 5 ban I covered last week, viewed from the other direction: one company got its model clipped, and now every company briefs the government before switching a model on.

If you build with AI, the practical takeaway is a slower, more staged rollout for the frontier stuff going forward. The bleeding-edge model you read about on launch day may not reach you for weeks, and it may arrive with strings attached (like a delayed game console, getting to you with no controllers). Planning around “I’ll have it when it’s announced” is no longer a safe bet for the most powerful releases.

The banned Anthropic model is back, and the reason for the ban fell apart

The story: Quick catch-up for anyone who missed the last 2 weeks: on June 12, the US government ordered Anthropic to switch off its two most capable models, Fable 5 and Mythos 5, over security concerns. After 18 days dark, it’s over. The Commerce Department lifted the export controls on June 30, and Fable 5 came back online globally on July 1. In the words of LL Cool J, “don’t call it a comeback!”

What happened:

• Commerce Secretary Howard Lutnick sent a letter withdrawing the export-control requirement on June 30, and Fable 5 returned globally on July 1 across Claude.ai, the Claude Platform, Claude Code, and Claude Cowork.

• Anthropic built a new safety filter, developed with the government, that it says blocks the flagged technique in over 99% of cases. If a request gets blocked, the user is told and it’s rerouted to the older Opus 4.8 model.

• Anthropic co-founder Tom Brown took over the negotiations with the Commerce Department. Brown was the lead author of the original GPT-3 research paper back at OpenAI, so his background is deeply technical, which fits a dispute that hinges on model security.

• Fable 5 is back for everyone, but its sibling Mythos 5 stays on a short leash. It was partially switched back on June 26 for roughly 100 vetted US organizations that defend things like power grids and hospitals, and that’s still where it stands.

Here’s where it gets wilder than a hangry French Bulldog. When Anthropic and the government dug into the actual security report, their testing showed that weaker, unbanned models (Opus 4.8, OpenAI’s GPT-5.5, and others) could find the very same software flaw that got Fable 5 pulled (in a very “but Todd’s mom let him!” moment). In plain terms, the capability the ban was meant to contain was already sitting in models nobody banned. The thing that triggered a national-security order turned out to be something a half-dozen cheaper models could already do.

For anyone whose work depends on a specific AI model, the durable takeaway from this whole saga is about fragility. Whatever you think of the ban, it proved that a model your business relies on can go dark on short notice for reasons far outside your control. Keeping a backup option ready stopped being paranoia this month and started being basic planning.

Anthropic also shipped a cheaper model and a science tool

The story: Same company, but a very busy week. On June 30, Anthropic released Claude Sonnet 5, a mid-priced model built to run AI agents more cheaply, and Claude Science, a workbench aimed at professional researchers.

What happened:

• Claude Sonnet 5 reportedly matches or beats the pricier Opus 4.8 on agentic coding tasks while staying in the cheaper tier, which matters for anyone running AI agents in bulk, where cost per task adds up fast.

• Claude Science isn’t a new model; it’s a workspace with 60-plus preconfigured tools across fields like genomics and chemistry, plus a verification agent that checks citations and math.

• Claude Science can run locally or on an institution’s own computers, so sensitive data (patient records, unpublished results) never has to leave the building.

• Notably, Sonnet 5 was reported to underperform US-restricted models on cybersecurity tasks, which several outlets read as a deliberate, compliance-minded design choice given the whole Fable/Mythos drama (which was not saved for its mama).

• It slots into a broader industry pattern of building specialized tools for specific jobs, going deeper than one-size-fits-all chatbots.

The interesting read here is where the industry is putting all its proverbial beans. A cheaper agent model and a science-specific workbench are both making a huge point: the frontier isn’t only about building the single smartest model anymore. It’s also about making AI cheaper to run at scale and shaping it to fit real jobs. The local-computing angle on Claude Science is the part I’d keep my peepers on. A hospital or a drug lab that can use a capable AI without shipping private data to someone else’s servers is a genuinely useful thing, and it maps to a bigger worry a lot of institutions have about handing their data to outside companies.

For researchers and small labs, this is a development worth tracking if you’ve wanted AI help but couldn’t risk your sensitive data leaving your machines. The promise here is capability without the data-transfer tradeoff. Whether it delivers in practice is the open question, but the design goal is aimed squarely at a real problem.

China trained a giant AI with zero Nvidia chips

The story: On June 30, Chinese company Meituan (commonly known for food delivery) released LongCat 2.0, a 1.6-trillion-parameter AI model (though sadly, not an actual long cat). The headline isn’t the size, though; it’s the hardware. Meituan says it trained the whole thing on domestic Chinese chips, with no leather jacket man (aka Nvidia) silicon involved at all.

What happened:

• “Parameters” are the internal dials a model tunes as it learns, and 1.6 trillion of them puts LongCat 2.0 firmly in frontier-scale territory, the same league as the biggest Western models.

• The significance is that training a model this large has, until now, effectively required Nvidia’s chips, which US export controls restrict China from buying.

• The same week, another Chinese firm, DeepSeek, showed off a technique that speeds up AI responses by 60 to 85% using fewer chips, a related push to get more out of limited hardware.

• Both moves read as a response to the export controls: if you can’t buy the best chips, you squeeze more out of the ones you can get, or you prove you can build without them.

• For extra context on how high the stakes run, Taiwanese authorities this week raided the offices of hardware maker Super Micro over alleged Nvidia chip smuggling into China.

This connects to a theme that’s run through The Weekly Think, many a time: Nvidia sells the shovels for the entire AI gold rush, and until now, everyone assumed there was only one shovel worth buying. LongCat 2.0 (I wonder if, internally, it was just called “Longer Cat”) is the first real crack in the idea that you simply cannot do frontier AI without them. Think of it like a famous chef who insists a dish only works with one rare imported ingredient (let’s call it “fromage ala leather jacket”), and then someone across town serves up a version using only what grows locally. Nvidia’s chips are still the best on the market, and one model doesn’t change that. What changed is the belief that they were the only (very leather-scented) game in town, and that belief just took a hit.

For anyone building on AI, this points toward a future with more hardware options underneath you, which over time tends to mean lower costs and less dependence on a single supplier. It’s early, and one model doesn’t rewrite the industry. But the move made here is worth watching, because the entire AI economy has been leaning on the assumption that one company’s chips are irreplaceable, and this week that assumption got tested.

What ties it all together

Step back and this final week of June is just a slice of the bigger, crazier AI pie. The running theme is that AI has become a physical, national, and contested thing. Memory is scarce and that scarcity ain’t goin’ nowhere any time soon. The most powerful models now ship with government approval attached. A banned model came back after heavy negotiations (as if it were Lebron James, choosing his new team). And China just showed it can build frontier AI outside the supply chain the West has spent years trying to control (much to Jensen’s chagrin).

A year ago, most of AI news was about chatbots getting smarter week to week. Now it’s about who controls the chips, who owns the memory, which models the government will let you touch, and which country can build what without the other’s permission. The technology grew up, and the grown-up version comes with borders, price tags, and paperwork. AI is like spice from Dune, and “he who controls the spice controls the universe.”

That’s a wrap on June. Welcome, July!

What I’m watching

• Now that Fable 5 is back, whether its new safety filter trips on ordinary coding work, and whether Mythos 5 ever gets a wider release

• Whether GPT-5.6 opens to everyone in mid-July, or the government review pushes it later

• Whether memory prices bite hard enough to dent PC and phone sales the way analysts expect

• Whether more Chinese labs follow Meituan and show frontier training without Nvidia, or LongCat 2.0 stays an outlier

• Whether other countries follow Austria, which formally invited Anthropic to set up in the EU after the ban, in treating US model access as a sovereignty question

  

Thanks so much for reading this edition of The Weekly Think.

See you next week, fellow thinkers!

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You know the stock photos, right? The four coworkers in pressed button-downs crowded around a single laptop, every one of them howling at something genuinely hilarious happening inside a spreadsheet. The woman laughing alone with her salad. The two businessmen shaking hands so warmly you’d assume one of them had just donated the other a kidney. The rugged fellow staring out of a high-rise window, contemplating either a merger or the full weight of his existence (hard to say which one it is). The team of attractive thirty-somethings doing trust falls at a “workshop” with no discernible purpose. Gotta love that corporate stock photo account.

One confession, though: I made all of those up. I’ve never actually seen any of those. But you pictured them instantly, and you believed they were real for a half-second. That right there is the whole point of this article.

Every one of those images is technically a photograph. Real camera, real people, and wouldn’t you know it, real salad. But not a single one of them means a thing. They were built to be dropped into any brochure, for any company, selling any product, landing on exactly nobody and sticking with exactly no one.

With that in mind, there’s a very good chance your AI’s first draft is the literary cousin to the laughing-salad lady.

The median is a crowded place

This is something you’ve probably felt, even if you never put words to it. You ask one of these tools for help. You get something back. It's smooth, it's grammatical, and you forget it the second you look away. And it is weirdly hollow, like it was written for everybody and therefore landed on nobody.

None of that is a glitch, and none of it is your fault. The tool did exactly what you asked. When you hand it a blank instruction like “write me an intro about productivity,” you’ve given it no reason to sound like anyone in particular, so it does the only thing it can. It reaches for the median. The most expected version of every sentence, the safest word in every slot; in other words, the middle of the road.

And the middle of the road, as it turns out, is where the laughing-salad lady lives. It’s crowded down there. Everyone using the same tool with the same blank instruction is pulling from the same crowded middle, which is exactly why so much of the internet has started to sound like it was catered by the same beige sandwich platter.

“Wouldn’t it be nice to fix that?”

So let me ask the obvious question. You’ve got something to actually say. The tool genuinely does help you say it faster. Wouldn’t it be awesome if all that speed didn’t come at the cost of you sounding like you?

Good news, friends. It’s fixable, and the fix isn’t some galaxy-brained prompt-engineering ritual that requires a certification. It mostly comes down to giving the tool the one thing it has never once had: you.

This is a rough idea of what that looks like.

The first step is to teach it your voice. The tool has never read a word you’ve written. It does not know that you open with a joke, or that you’d sooner eat the salad-lady’s salad than use the word “leverage.” So show it what for. Take a few things you’ve written that sound the most like you, paste them in, and ask it to study them. Something close to this does the job:

Here are a few samples of my writing. Describe my voice in detail: my sentence
length and rhythm, my punctuation habits, the words I reach for, and the words
I'd never be caught using. Write it so I can paste your description back to you
later, to guide how you write for me.

Hang on to whatever it gives you. That little description is the entire difference between a tool that’s guessing about you and a tool that has, in some small way, actually met you.

The second step is to feed it context. Who is this for? What’s the point you’re trying to land? What’s the one thing you absolutely do not want it to do? The more it knows about the job, the less it reaches for that crowded middle.

The third step is the one people tend to skip, and it’s the one that matters most. You stay in the editor’s chair. The tool drafts; you decide. The second it makes you sound like a press release that gained sentience, you overrule it. That's the actual job, and it always was, because you're the only one in the room who knows what you meant.

The part the hype skips

Notice what none of that was. At no point did the plan become “let the robot write the whole thing while you go get a coffee and come back to a finished masterpiece.” That version is the one that produces the salad lady every time.

The reframe that fixed this for me is small but it changes everything. You're doing the work with a very fast, very tireless assistant who happens to have zero taste of its own. The work never actually leaves your hands. You bring the take, the angle, the actual thing worth saying. It brings the speed and the willingness to proofread the same paragraph forty times without sighing at you. You stay the author. It stays the intern who types quickly and needs a firm hand. Get that division of responsibilities right, and the speed stops costing you the soul of the thing.

Hand it the one thing it can't fake

Think back to the stock photos again. The reason the laughing-salad lady never moved a single human being is that she was manufactured for all of them at once. Your writing has the exact opposite assignment. It’s supposed to sound like one specific person had one specific thing to say, to one specific reader, on purpose.

That difference is sitting right there, waiting for you, every single time you open one of these tools. You just have to be willing to hand it the one ingredient it can never fake on its own.

Because the robot can match your grammar all day long. The thing it can’t do is sound like you, right up until the moment you show it how.

And that “how” is more of a system than a single trick. I’ll be getting into it very soon, so keep an eye out.

Enjoy this one? If you’ve got your own way of keeping the salad lady out of your drafts, drop it in the comments. I read every one. And if you found it useful, sharing is caring!

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Welcome back to the stage of AI history! This was a hardware-heavy week, folks! Yes, I mean you, you are the folks. And all those chips and memory are the things affecting you folks. OpenAI revealed it has been quietly cooking up its own spicy silicon, Micron reported a quarter so good (or bad, because “RAMageddon”), and Google kept losing its top researchers to Anthropic and OpenAI.

There was drama too. Anthropic sent a letter to the Senate accusing a Chinese giant of basically photocopying its homework 28.8 million times, and Getty Images made a deal with OpenAI, laughing all the way to the bank as a result. Let’s get into it.

While I was reading the news so you don’t have to:

• OpenAI and Broadcom unveiled Jalapeño, OpenAI’s first home-built chip

• Micron posted a record $41.5 billion quarter on the back of AI memory demand

• Google keeps hemorrhaging top AI researchers to Anthropic and OpenAI

• Anthropic accused Alibaba of the largest model-copying campaign it has ever seen

• Getty Images struck a deal with OpenAI and its stock shot up over 200%

OpenAI built its own chip and named it after a pepper

The story: On June 24, OpenAI and chipmaker Broadcom revealed Jalapeño, OpenAI’s first custom-built processor. It’s designed for one specific job: running AI models that are already trained (the industry calls that “inference,” which is the model answering your questions, the part that comes after all the learning is done). For a company that has spent years eating up Nvidia chips like they were… chips, building its own is a real statement of intent.

What happened:

• Jalapeño is a purpose-built inference chip, so it’s tuned to serve up answers fast and cheap, with six stacks of high-speed HBM memory wrapped around one big compute core.

• The chip is enormous, roughly 840 square millimeters, which is about as large as current manufacturing machines can physically print on a single piece of silicon.

• OpenAI and Broadcom say they went from idea to finished design in nine months, well under the typical year-and-a-half to two years, and they admit they used OpenAI’s own models to speed up parts of the design work.

• Test chips are already running in the lab on one of OpenAI’s coding models, with real deployment planned for late 2026 in data centers built with Microsoft.

• Broadcom claims the chip beats today’s best hardware on power efficiency, but the two companies released zero actual numbers to back that up, so for now that claim is just a promise with no proof attached.

OpenAI is following a path Google, Amazon, and Microsoft already walked, which is to stop renting all your compute from Nvidia and start making some of your own. The plain reason is money (because in this biz, it always is). When you’re spending billions a year on chips from a single supplier, designing your own starts to look less like a vanity project and more like basic math.

If you’re building anything on top of OpenAI’s models, this is a slow-burn detail to file away. Custom chips like this are how the cost of running AI eventually comes down, and cheaper compute underneath you means the tools you build get cheaper to run over time. Nothing changes tomorrow, but the direction of travel is good for the people building on top.

Micron had the best quarter in its history, and your next PC will feel it

The story: On June 24, memory-chip maker Micron reported the biggest quarter in its 48-year history: $41.46 billion in revenue, up from $9.3 billion in the same quarter a year ago. Its profit margin hit a frankly cartoonish 81%, meaning for roughly every dollar of product it sold, about 81 cents was profit (in the words of rapper Mase, “double up”). The reason is obvious: AI data centers are devouring the world’s memory supply like a kid with M&M’s.

What happened:

• Revenue more than quadrupled year over year, and the company’s profit for the quarter came in around $28 billion.

• The driver is HBM, the ultra-fast memory that sits next to every AI chip, and Micron has already sold out its entire 2026 HBM production.

• CEO Sanjay Mehrotra said the company can only fill somewhere between half and two-thirds of what customers are asking for, which is why prices keep climbing.

• DRAM, the regular memory in your laptop and phone, saw prices jump by a mid-60s percentage in a single quarter.

• Only three companies on earth make this memory at scale (Micron, Samsung, and SK hynix), and all three are pouring tens of billions into new factories that won’t be ready until 2027 or later.

A few editions back I walked you through the Steam Deck getting up to $300 more expensive, and the “RAMageddon” nickname the internet slapped on the memory shortage. Micron’s record quarter is the other side of that exact coin. The same shortage making your gadgets pricier is making the memory makers obscenely rich, because they can sell every chip they produce to AI data centers at a premium. When a supplier can only meet two-thirds of demand, the supplier sets the price, and right now the supplier is naming a very high one. To illustrate that, imagine there’s a lemon shortage and the kid on the block is selling lemonade on a hot summer day for $20 (because, obviously, he’s gotta pay for that expensive AI subscription somehow).

For anyone planning to buy a laptop, a phone, a graphics card, or build a PC, the practical read hasn’t changed since RAMageddon: prices on anything with a lot of memory in it are going to stay high into 2027. If you’ve got an upgrade you’ve been putting off, you now have to pay what I like to call the “waiting tax” (more like wailing tax), because the new factories that would ease prices are still a year or more out.

Google keeps losing its best AI minds to the competition

The story: Across the week, Google lost another wave of senior AI researchers to its rivals (in a very “Giannis Antetokounmpo leaving for the Miami Heat” moment). The headline departures: Noam Shazeer, a co-inventor of the core technology behind modern chatbots, left for OpenAI, and John Jumper, who led the Nobel-winning AlphaFold protein project, left for Anthropic. Two more Gemini contributors, Jonas Adler and Alexander Pritzel, are also heading to Anthropic. Alphabet’s stock dropped as much as 7.2% on the news, its worst single-day slide since February.

What happened:

• Shazeer co-wrote the 2017 research paper that introduced the “transformer,” the architecture underneath basically every modern AI model, so losing him is symbolic as much as practical.

• Jumper isn’t just any researcher; he shared a Nobel Prize for using AI to predict protein structures, and now he’s taking that talent to Anthropic.

• Google’s spokesperson pushed back, pointing to DeepMind boss Demis Hassabis calling it the most competitive talent market the tech industry has ever seen.

• The market clearly read the exits as a warning sign, knocking 7.2% off Alphabet’s value in a single session, even though the company is still worth around $4.46 trillion.

• This continues a pattern from earlier in the year, when Anthropic landed researcher Andrej Karpathy, another marquee name in the field.

This connects to something I wrote about a few editions ago, when Karpathy joined Anthropic. That migration didn’t stop; it straight up accelerated like it was Han in the Fast & Furious franchise. The people who build these models are the rarest resource in the entire industry, rarer than chips or data, because there are maybe a few hundred of them on the planet who operate at the frontier. When they move, they take knowledge that money alone can’t quickly replace, which is exactly why a few resignations can knock billions off a company’s value in an afternoon.

If you work in any field being reshaped by AI, you’re probably taking notice. The scarce, valuable thing is the human expertise, the people who actually understand how to build and direct these systems. That’s the part companies are paying enormous sums to keep, and it’s a useful reminder of where to focus your own learning if you want to stay valuable as this all shakes out like a Polaroid picture in the 90s.

Anthropic accused Alibaba of copying its homework 28.8 million times

The story: On June 24, word got out that Anthropic (the company that makes Claude) sent a letter to the US Senate accusing Chinese tech giant Alibaba (the company behind such services as AliExpress, purveyor of cheapo computer products) of running the largest model-copying campaign it has ever caught. Anthropic says operators tied to Alibaba’s Qwen AI lab used roughly 25,000 fake accounts to run 28.8 million conversations with Claude between April 22 and June 5, harvesting its answers to train a cheaper copycat.

What happened:

• The technique is called “distillation,” which means using a strong model’s outputs as a teaching guide to train a weaker, cheaper model to imitate it. Think of it as a student copying a brilliant classmate’s test answers over and over until the student can fake the same results.

• Anthropic says the campaign zeroed in on Claude’s most valuable skills, its coding and step-by-step reasoning, and called it turning American R&D into a subsidy for a competitor.

• For scale, this dwarfs the cases Anthropic flagged back in February, when it accused DeepSeek (about 150,000 exchanges), Moonshot (3.4 million), and MiniMax (13 million) of similar tactics.

• Alibaba was added to a US Defense Department list of Chinese military-linked companies on June 8 and is suing to get removed, calling the label baseless.

• As of this writing, Alibaba hadn’t responded to the specific accusation, so treat this as one company’s claim that still needs proving. Worth keeping that in mind.

One thing to keep in view: this accusation lands the same month the US government switched off Anthropic’s own two newest models over a security concern. So within a few weeks, Anthropic has been on both sides of the security conversation, the subject of one government action and the source of a warning about someone else. Make of all this what you will.

What’s clear for the rest of us is that AI has become a full-blown geopolitical contest, with company letters to the Senate, Pentagon blacklists, and accusations crossing the Pacific. The models you use are now caught up in a much bigger contest between Washington and Beijing. You don’t have to pick a side to notice that the technology stopped being a neutral tool a while ago, and the fights over who gets to build and use it are only getting louder.

Getty made a deal with OpenAI and its stock went bananas

The story: On June 21, Getty Images (the company behind a huge chunk of the photos of your favorite celebs eating hot dogs at the Oscars) announced a deal to put its licensed image library directly inside ChatGPT. When you ask ChatGPT a question where a real photo helps, like a historical event or a famous landmark (or a celeb eating a hot dog), Getty’s licensed images can now show up in the answer. Getty’s stock jumped more than 200% in a single day on the news.

What happened:

• The deal is display-only, meaning ChatGPT shows Getty’s images alongside answers and Getty gets paid for it, with no permission for OpenAI to use the photos to train its models.

• It covers over 400 million images, including premium news, sports, and entertainment photography plus Getty’s lower-cost iStock library.

• Getty had spent the prior couple of years fighting AI companies in court over images used without permission, so this is a notable pivot from suing to partnering.

• The market reaction was enormous, a 200%-plus single-session jump, which tells you investors had basically priced Getty as a company with no AI future and suddenly changed their minds.

• Getty had already done a smaller version of this with another AI search company last year, but the OpenAI deal is far bigger in reach.

This one’s a quieter story, but it points at something real about how the copyright fights over AI might actually end. For two years the dominant approach from media and image companies was to sue, and some of those suits are still grinding through the courts. Getty looked at that road and chose a different one: license the work, get paid every time it appears, and turn ChatGPT into a paying customer (it basically said, “hold up, actually, lemme get paid”). The 200% stock pop suggests the market thinks getting paid beats waiting years for a courtroom maybe-win.

If you make content for a living (photos, writing, music, video), this is a development to watch closely. The Getty deal is an early example of creators and AI companies finding a paid arrangement that works for both sides. Whether that becomes the norm or stays the exception will shape how much the people who actually make things get compensated when AI uses their work. For now, one company just showed that licensing can pay better than litigating.

What ties it all together

Step back and the week sorts into two big themes. The first is that AI is becoming a physical, industrial business: OpenAI is cooking up its own chips, Micron minting record billions while unapologetically fueling “RAMageddon,” the whole thing increasingly about silicon and factories and supply you can measure in tons. The second is that AI is becoming a contest between nations and titans: Google bleeding talent to its rivals, Anthropic and Alibaba trading accusations across the Pacific, Getty cutting a deal that rewrites the rules of a two-year fight.

The common thread is that the easy, abstract phase of AI is ending. A year ago this was mostly about chatbots becoming smarter. Now it’s about who controls the chips, who owns the memory, who employs the rare humans who build the models, and which country’s companies come out ahead. The technology grew up, and grown-up technology wants its license (and a later curfew). Exciting or unnerving?

I’ll let you, the reader at home, decide.

What I’m watching

• Whether OpenAI’s Jalapeño chip actually ships on time in late 2026, and whether those efficiency claims hold up once real numbers exist

• Whether memory prices show any sign of easing, or keep climbing into 2027 as the shortage drags on

• Where Google’s departing researchers land first in Anthropic’s and OpenAI’s next models

• Whether Alibaba formally responds to Anthropic’s accusation, and whether the government acts on it

• Whether more media and creative companies follow Getty’s lead and cut licensing deals with AI firms while the lawsuits fade into the background

Thanks so much for reading this edition of The Weekly Think.

See you next week, fellow thinkers!

Did you enjoy this week’s Think? If so, please consider sharing this and leaving a comment below, to share your thoughts on these latest AI-related rumblings. Hope to hear from you!

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Think about iconic interviews that have stuck with you.

Princess Diana, being painfully honest about what life inside the royal family actually cost her. OJ Simpson, looking into the camera and swearing he had nothing to do with it. LeBron James, sitting down to tell everyone which direction his career was about to go in. Dave Thomas of Wendy’s, solemnly explaining why the SuperBar salad bar had to go (okay, fine, I made that last one up).

Love the person or loathe them, an effective interview hands you something the public eye could never pick up on its own. The good ones get under the surface and pull out the thing that was actually sitting there the whole time.

So here’s a thought I can’t shake. What if you ran an interview exactly like that, except you were the one in the chair, and the thing asking the questions was your chatbot?

Not that kind of AI interview

We’re living in the era of rampant AI use. However you feel about it, the stuff is everywhere now (though thankfully not omnipresent, because boy, would that be a different and much scarier article).

And in all the hype and all the fear, there’s a genuinely useful way to get your actual voice and your actual intent into the work, and it almost never shows up in the headlines. It’s the AI interview.

Not the dystopian kind, to be clear. I don’t mean the bot that screened you for that analyst job and judged your blinking patterns. I mean an interview where you’re the subject, and the whole point is to collect context.

“Thank you for sitting down with me today”

Here’s the trick at its most basic. You ask your chatbot to interview you. That’s the gist of the whole thing that we’re about to get into.

Why would you want that? Maybe you want it to capture your voice so you’ve got a sharper writing partner instead of a generic one. Or maybe you’re trying to build something and you need the tool to actually understand what you’re going for. A perfect-burger-algorithm calculator, for instance (which I need I need to go patent, the moment I finish this sentence).

Here’s roughly how you’d kick one off. You send something like this:

I am looking for ideas for an app I want to code. Give me an in-depth interview to learn my likes, 
dislikes, and preferences when it comes to the apps I use every day. Use the context of this 
interview to create a tastemaking profile for me, then use that profile to give me up to 10 app 
ideas. The interview should be 100 questions, divided into 10 rounds.

You fire it off, the chatbot asks a couple of clarifying questions, and the interview begins. A hundred questions about how you actually use the things you use.

But is that all there is to it? Not quite.

“I want to circle back to something you said earlier”

Bourdain was talking about food, but the man might as well have been talking about the things we build.

Here’s the rule underneath all of this. The more context you give the tool, the better the thing it hands back. And a one round interview, however long, is rarely the whole meal.

Think about ordering a burger. Walk into a good diner and ask for the cheeseburger, and you’ll get something solid. But tell them you also want grilled onions, bacon, and sauteed mushrooms, and now you’ve got a delicious burger built specifically for you (though, AI output can’t be literally delicious, sadly, unless you own some machine I’m not aware of, in which case please DM me immediately). What does this all mean? The more you specify, the more the result actually fits.

So say those 10 app ideas come back, flatter than a smash burger patty. You’re not stuck with them. You send a follow-up that goes something like this:

Look back at all the responses I gave to your questions. With my answers in mind, ask me 
clarifying questions on anything that seemed vague or anything I sidestepped. Once you’ve 
gathered enough clarity, generate a new list of 10 app ideas.

What did that actually do? You told the tool to press you a little harder so it could understand you a little better. And once it has that extra clarity, the second list tends to land in a completely different place than the first.

That’s the difference between “an app that gives you recipes” and “an app that helps you build customized recipes around what’s already in your fridge.” Same person, same starting point, but a few more questions in between. You went from just “okay” to “wait, that’s actually good!”

But I thought this was supposed to make things easier

Right about here, I can hear an objection forming. “Isn’t AI supposed to do all of the work for me? I wanted to type one thing and get a finished thing back. What’s with all the rounds and the follow-ups and the homework? And where’s my burger?*”

Fair points. And yes, you can absolutely have AI crank stuff out on the fly. If that’s your prerogative, I’m not going to get on a soapbox and shout at you with a megaphone.

But the wing-it approach reliably produces wing-it results. That’s just the reality of the thing. You get the bland, forgettable output, and then you blame the tool or you blame yourself, when the real culprit was that nobody ever asked you what you actually wanted.

Here’s the reframe that changed it for me. The questions aren’t the busywork standing between you and the work. The questions are the work. The interview is how you figure out what you’re really after, and most of us almost never slow down long enough to do that on our own.

Think about how rarely anyone asks you a hundred good questions about a thing you care about. A friend might get to three before the conversation drifts (which is fair and does not mean they’re a bad friend, unless it’s weirdo Steve talking about his “sausage orchard” again). Whereas, the AI interview is a hundred, and somewhere around question sixty you start saying things you didn’t know you thought. The app idea, the essay, whatever you came for, the goal you’re chasing but can’t quite name. The thing you actually walk out with is a clear picture of what you wanted the whole time, finally said out loud.

Fred Rogers (yes, Mister Rogers) had a whole song about liking to take your time and do a thing right. I think about that more and more as an adult typing prompts into a robot. Turns out the cardigan master was onto something.

And I’m not pointing you toward any of this because it’s the disciplined, virtuous choice. I’m pointing you toward it because it can work effectively, and because the version of the thing you get on the other side actually serves what you were trying to do, instead of being something generic that makes you say, “it’s gonna be a no from me, dawg.”

“I appreciate your time”

Come back to those interviews for a second. The ones that stick with us aren’t the ones where someone recited their talking points. They’re the ones where a good question, asked at the right moment, drew out something real that we never would have heard otherwise.

That exact thing is sitting right there, available to you, every single time you open one of these tools. You just have to be willing to be the one in the chair (the chAIr, if you will).

Because it turns out that one of the most useful things the AI can do for you isn’t answering your questions. It’s asking you theirs.

^{*Would ya stop asking the AI for a burger? Trust me, I’ve tried.}

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Eric’s back, back again. Eric’s back, tell your friends! I was away last week, figuring the AI world would do its usual thing and crank out a few model updates I could catch up on later. Friends, it didn’t do its usual thing. I come back to find that Elon Musk’s rocket company is now one of the biggest companies on the stock market, it spent $60 billion on a coding tool like it was grabbing an impulse buy at checkout, Apple changed CEOs, and the heads of the major AI labs got seated at a table with world leaders. Good gravy.

Before we get into this week’s big stuff, let me catch us both up on the June 4-10 stretch I missed, because a lot of it matters for what came next.

While I was gone (the June 4-10 bytes):

• Apple said goodbye to Tim Cook. At WWDC on June 8, Apple unveiled a rebuilt, Gemini-powered Siri (Google on an iPhone? Eric Schmidt would never), and made Claude a selectable option too. The keynote was Cook’s last as CEO before he hands the reins to hardware chief John Ternus on September 1.

• OpenAI filed to go public too. On June 8, OpenAI confidentially filed its own paperwork with the government, joining Anthropic and SpaceX in the IPO line. Their announcement basically said “we figured this would leak, so here you go.”

• ChatGPT dropped below 50% market share for the first time. Per a Sensor Tower report, ChatGPT now sits under half the market, with Google’s Gemini at 27.7% and Anthropic’s Claude at 10.3%. The two-horse race became a three-horse race while I was drinking a cold brew.

• Snap announced $2,195 AR glasses. Called Specs, with OpenAI and Gemini built in, aiming to beat Meta’s comparable glasses to store shelves. Two thousand dollars for face computers. We live in the future, and the future demands a home equity line of credit.

Okay. Caught up? Cool! Let’s get into the stuff that happened this week.

SpaceX went public and became the fourth most valuable company in America

The story: On June 12, Elon Musk’s SpaceX (which now includes his AI company xAI, maker of the Grok chatbot) sold shares to the public for the first time. It raised about $75 billion, the biggest stock-market debut in history, and the stock jumped 19% on day one. That pushed the company’s total value above $2 trillion and, after a few more days of climbing (like a crazed mountain man), made it the fourth most valuable company in the United States.

What happened:

• SpaceX priced its shares at $135 and they closed the first day near $161, then kept climbing through the following week.

• The $75 billion raised beat every prior IPO on record. For scale, that’s roughly the entire yearly budget of NASA, raised in a single morning.

• The company is not profitable. It lost close to $5 billion in 2025, and several analysts flat-out called the valuation too high (one slapped a “sell” rating on it the same day).

• Alphabet, Google’s parent, quietly owns about 4.9% of SpaceX, a stake now worth around $105 billion.

• This is the first of three giant AI-related IPOs lined up back-to-back, with Anthropic and OpenAI both having filed their own paperwork in the past two weeks.

A few editions back I wrote about Anthropic, OpenAI, and SpaceX all heading for the stock market at the same time, and how the public was about to get its first real read on whether the AI hype holds up. Well, SpaceX went first, and the read was “investors are very, very interested.” Regular peeps piled in at record numbers. Whether that enthusiasm is wisdom or mania is the question, and the analysts arguing the company is overvalued might be right. Either way, the first domino (tile, not the pizza) fell, and it fell loudly (like me, after eating too much Domino’s… the pizza, I mean).

The part of this that really stands out is who got rich. More than 4,400 current and former SpaceX employees are expected to become millionaires from the IPO, and a lot of them never wrote a line of code or designed a rocket (meanwhile, I invented the MP3 player but… oh forget it). We’re talking welders, machinists, technicians, even cafeteria workers, people who got company stock as part of their pay. One former welder, Juan Hernandez, joined in 2015 at $28 an hour, barely knowing what SpaceX was. His roughly 6,500 shares were worth just over a million dollars(!) when the stock closed on day one, and he’s now teaching his kids how to invest.

Then, four days later, SpaceX bought Cursor for $60 billion

The story: On June 16, fresh off the record IPO, SpaceX announced it’s buying Anysphere, the company behind the popular AI coding tool Cursor, for $60 billion in stock. Cursor is a program developers use to write software with an AI assistant doing a lot of the typing (like a guy with a typewriter following around a smart dude). The deal makes it the most expensive AI software purchase ever announced, and it happened four days after the company went public. In four days, I’m usually wondering if the leftover chicken in the fridge is still okay to eat.

What happened:

• The deal is all-stock, meaning SpaceX is paying with its own newly public shares, no cash required, which it can afford to do precisely because those shares are valued so high.

• Cursor brings in roughly $2.6 billion a year and had been talking to investors about a $50 billion valuation just two months earlier.

• This folds Cursor into xAI, Musk’s AI arm, giving it a direct foothold in coding tools to compete with Anthropic’s Claude Code and OpenAI’s Codex.

• The purchase represents only about 3.4% of SpaceX’s value, which tells you how much a sky-high stock price lets you throw around.

• It briefly pushed SpaceX past Microsoft in total value during Tuesday trading.

Here’s the big, notable thing here. The major AI coding tools have nearly all been swallowed by giants in a remarkably short time (like CEOs swallowing a Limitless pill). Microsoft owns GitHub Copilot. Anthropic has Claude Code. OpenAI has Codex. And now SpaceX has both Grok’s coding tools and Cursor. A year ago these were scrappy independent startups devs loved. Now almost all of them answer to one of a handful of enormous parent companies.

If you’re a dev, this affects which tools stay independent and which start nudging you toward their parent company’s ecosystem. The thing to do is keep your own skills portable. Learn the underlying craft itself, deeper than any one tool’s buttons, so that when the tool you rely on gets bought and changed, you can move without starting over. Ownership shuffles at the top; your ability to actually build is what stays yours.

Apple swapped CEOs and put Google’s AI inside the iPhone

The story: This one technically kicked off during my time away, on June 8, but your boy needs to expand on this. At its developer conference, Apple unveiled a completely rebuilt Siri running partly on a custom version of Google’s Gemini AI, and it was Tim Cook’s final keynote as CEO. He steps down September 1 after about 14 years, handing the company to hardware chief John Ternus.

What happened:

• The new Siri uses a three-tier system: simple stuff runs on your phone, medium stuff on Apple’s private servers, and the heaviest thinking routes to a 1.2-trillion-parameter Google Gemini model running on Nvidia chips in Google’s cloud.

• Apple says it anonymizes every query so neither Apple nor Google can tie a request back to you.

• Special guest star, Claude: Apple made multiple AI assistants selectable, so Anthropic’s Claude can be an iPhone option for the first time.

• Apple also previewed homeOS, the software for a long-rumored smart-home gadget (a HomePod with a screen) expected this fall.

• The Siri rebuild arrives after a $250 million settlement with iPhone buyers who said Apple advertised Siri features back in 2024 that never actually shipped.

The interesting thread here is what it means that Apple, the company famous for building everything itself, decided the fastest way to fix Siri was to put a competitor’s AI underneath it. For two years Apple promised a smarter Siri and kept missing. So it partnered with Google, a company it competes with on phones, search, and maps. Swallowing that much pride tells you how hard building a frontier AI model has become even for the richest company on earth. Somewhere, Eric Schmidt is laughing maniacally.

For the rest of us, this means the AI you use every day is about to show up in places you didn’t install it. If you own an iPhone, Google’s Gemini and possibly Anthropic’s Claude are coming to your home screen this fall whether you sought them out or not. Knowing what’s under the hood, and what each one does with your questions, is becoming basic digital hygiene.

AI wrote so much code it knocked GitHub offline

The story: In a very Obi Wan “you have done that yourself” sorta way, GitHub, the website where the world’s programmers store and share their code (owned by Microsoft), got so overwhelmed by AI coding agents pumping out work that its reliability cratered. On June 16, Microsoft confirmed it’s temporarily borrowing computing power from Amazon, its direct cloud rival, just to keep GitHub running.

What happened:

• GitHub was processing about 275 million code submissions per week, on pace for 14 billion in 2026, up from 1 billion in all of 2025.

• Automated pull requests (changes opened by AI agents) jumped from 4 million last September to over 17 million by March.

• The site logged nine separate service problems in May, and its uptime in June fell to about 88.4%, well under the 99.9% it promises business customers.

• To cope, Microsoft is routing some GitHub traffic through Amazon Web Services while it finishes moving the platform onto its own Azure cloud by 2027.

• This is part of a broader crunch: Google reportedly pays SpaceX $920 million a month for computing power, and Anthropic pays SpaceX $1.25 billion a month for its Colossus supercomputer.

The plain version of what happened: AI code writing is all the hype and it generated more work than the internet’s main code warehouse could physically handle. The humans pointed the tools at their projects, the tools produced an avalanche, and the warehouse buckled. It’s a little like all the kids in the neighborhood absolutely overwhelming the ice cream truck on the hottest day of the summer. The demand is real, the tools are doing what they were asked, and the physical infrastructure underneath is scrambling to keep pace.

The lesson that keeps repeating across these editions is that compute and infrastructure are the real bottleneck now. The thinking ability of the models stopped being the hard part a while ago; the hard part is having enough machines and electricity to run them. Even Microsoft, a company that owns one of the three biggest clouds on the planet, had to call up its archrival Amazon for backup. If you’re building anything on top of these tools, plan for the plumbing to creak. The smart projects right now are the ones designed to work even when the service they depend on is having a rough day. Locally-run AI, anyone?

The government switched off Anthropic’s newest model three days after launch

The story: On June 12, the US Commerce Department ordered Anthropic to cut off access to its two newest, most capable models, Fable 5 and Mythos 5, citing national security. The order specifically barred any foreign national from using them, including Anthropic’s own foreign-born employees. Since Anthropic can’t be all TSA and check the passport of every person sending a request in real time, it did the only thing it could and shut both models off for everyone. The kicker: the models had launched just three days earlier, on June 9. Imagine throwing a party, and the city condemns your house before the guests finish their charcuterie.

What happened:

• The directive landed at 5:21 p.m. ET on a Friday, in a letter from Commerce Secretary Howard Lutnick to Anthropic CEO Dario Amodei, and the letter reportedly gave no specifics about the actual concern.

• Anthropic says that the trigger was a “jailbreak,” a trick that gets a model to do something its safeguards block. The specific one, by Anthropic’s account, amounted to asking the model to read a codebase and point out its security flaws.

• Anthropic says that same capability is widely available from other models that weren’t banned, naming OpenAI’s GPT-5.5 directly (like a kid saying “but mom, Tommy broke the window too!”)

• Every other Anthropic model, including its flagship Opus 4.8, stayed online and unaffected. The takedown hit only the two newest releases.

• This appears to be the first time the US government has forced a publicly deployed frontier AI model offline. Imagine if Microsoft said “turn off the Word, boys.”

The absurd bit, and the reason this story is more interesting than scary, is the mismatch between the hammer and the nail. A government used national-security export powers, the kind of tool aimed at missile guidance systems and nuclear materials, to pull a chatbot over a bug-finding trick that a half-dozen other chatbots can also do. Anthropic basically said as much in its response, arguing that if this became the standard, every frontier model from every company would have to be yanked too. Whatever you think of Anthropic, them dudes have a point.

The reason this matters for everyone, not just Anthropic’s customers, is the precedent. A switch now exists, and a government just proved it’s willing to flip it. That has nothing to do with whether you like Anthropic or trust the government’s call here. It’s a structural fact about how AI works now: the model you build your business on can be switched off by an email sent at 5:21 on a Friday. If you’re building on any single AI provider, the practical lesson is to keep a fallback ready, because the hypothetical “the model just vanished” just got really real.

What ties it all together

I was gone two weeks. In that time the biggest IPO in history happened, the company behind it immediately spent $60 billion on a coding tool, the most valuable company on earth changed CEOs and adopted a rival’s AI, the website that holds the world’s code fell over from AI overuse, and the government switched off the newest models from one of the top AI labs. If you’d told me all of that in advance, I’d have asked what possessed you to generate all of that with AI.

Step back and look at the shape of it. The money is going public, the giants are absorbing the startups, the rivals are partnering when pride used to stop them, the infrastructure is straining under its own success, and the government just showed it can switch a model off whenever it decides to. The common thread is consolidation. Power, money, talent, and infrastructure are all flowing toward a smaller and smaller number of very large players, and it’s happening faster than anyone can write newsletters about it. Any reason for alarm here? I’ll let you decide, peeps.

What I’m watching

• Whether SpaceX’s stock holds its sky-high value or the “overvalued” analysts get proven right

• Whether Anthropic or OpenAI actually pull the trigger on their own IPOs, and how the market receives them

• What happens to Cursor’s independent feel now that it answers to Musk

• Whether GitHub stabilizes, or the AI-code firehose keeps outrunning the servers

• Whether Anthropic gets Fable 5 and Mythos 5 switched back on, and whether the government ever spells out its actual reasoning

  

Thanks so much for reading this edition of The Weekly Think.

See you next week, fellow thinkers!

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I still remember the first time I walked into a buffet. The place was called SmorgaBob’s, which we can all agree is a very good name for a buffet.

I had never seen so many options in one room in my life, at least not outside of a Filipino party. Roast beef, pizza, fried chicken, fried rice, all of it sitting out at the same time, just waiting for me. So I did what any food-driven kid would do. I grabbed one plate and piled it as high as physics would allow.

And then, of course, I got completely full off that one ridiculous plate.

What I had not figured out yet, in my youthful greed, was the buffet mastery technique. You take a little. You go back. You take a little more. You work the whole room in small trips and you maximize your parents’ hard-earned buffet dollars (because let’s be honest, I was not the one paying for this lunch). Live and learn.

I don’t really do buffets these days. My 40-something body has filed several formal complaints about that kind of eating. But wouldn’t you know it, the buffet dilemma found me again, in a completely different room, and this time there’s no roast beef involved.

“A-I can’t believe there’s this many choices”

Here’s the new buffet. It’s AI tools, and I’m standing in front of it as the exact same wide-eyed kid with an empty plate and zero self-control.

Just a few years ago, this was not a problem, because there wasn’t much to pile on. You had ChatGPT and, well, not a whole lot else. Life was simple. You messed around with prompting here and there, you slowly got the hang of it, and you could become a genuine power user of one good tool without breaking a sweat.

Then 2026 rolled around, and now AI is here, it’s there, it’s absolutely everywhere.

Alongside ChatGPT you’ve got Claude, Gemini, Grok, and a bunch of others that don’t get the same spotlight but are also very good. Then come the specialists. Tools for audio. Tools for video. Tools for image editing. Tools that will build an entire AI version of you, which is a sentence I never expected to type in my lifetime, but here we are. Calling the current spread of AI tools “rich” is underselling it by a mile.

And naturally, every single dish looks mouthwatering. Of course you want to try all of them.

“A-I can’t finish all of this”

Maybe you’ve already been here. You’ve got a couple of trials running, a subscription or two, and the cheerful little plan in your head that goes, “I’ll use this one for that, and that one for this, and it’ll all be gravy.”

I hate to be the one to tell you, but what’s true at SmorgaBob’s is true here too. Pile too much on the plate and you can’t actually appreciate any single thing on it.

It gets worse, though, and this is the part that really got me. While you’re busy hopping from one tool to the next, you never stay anywhere long enough to get good at it. Think about what that actually means. You’re not actually collecting skills at all. You’re collecting a bunch of day ones. You’re a perpetual beginner, over and over again, just at a dozen things instead of one. And since new tools keep dropping one after another at this point, the plate doesn’t even get a chance to lighten. It keeps getting heavier while you stand there holding it.

That overstuffed plate feels productive. It feels like you’re on top of things. But mostly it’s a great way to stay mediocre at a whole lot of stuff while feeling busy the entire time.

Now, I’m not saying options are the enemy. A buffet that only served all-you-can-eat mashed potatoes would eventually be a sad place to spend an afternoon. Mashed potatoes are great, but you’d tap out after a plate. So, the options are good. However, the plate is the problem.

Prioritizing your plate

When I get overwhelmed by all of it, I find the fix is to go back to basics. Instead of using every LLM under the sun for every random thing that floats through my head, I figure out which tool actually serves the job in front of me, and I build a small, deliberate plate.

Here’s what’s on mine these days.

Claude does my proofreading and most of my writing-related tasks. ChatGPT is my smarter search, the thing I poke when I’ve got a random question rattling around and I don’t feel like sifting through a pile of links. Nano Banana handles my image editing. And when I’m digging through the notes I keep in OneNote, Copilot turns out to be a surprisingly good helping hand.

That’s pretty much the whole plate. Four dishes, and I actually know what to do with all four.

That’s the part that changed everything for me. Because the plate is small, I get to build up some real mastery instead of treading water. I know what each tool is for and how to get good results out of it, and I’m not splitting my attention fourteen ways and calling it a dope workflow. The small plate is also the thing that lets me relax about trying something new, because I’ve always got my reliable stack to come home to. So now I’m enjoying this AI buffet instead of getting wheelbarrowed out of it.

FOMO: the buffet's final boss

Here’s the part many don’t like to admit, as it’s the part that usually drives the overloading.

The new tools are going to keep coming, fast, and every one of them is going to show up wearing a big flashing sign that says THIS CHANGES EVERYTHING. And every time, that little voice pipes up. What if the dish I didn’t put on my plate was the one that mattered? What if everyone else is eating the good stuff and I’m over here with my sad, sensible little plate?

That fear is the whole reason the plate gets out of control in the first place. So let me say the thing that helps me.

The tools you’re already good at are still very good, and they’re getting better every day without you having to chase a single thing. Skipping the newest hot tool is not the catastrophe the FOMO insists it is. And here’s the sneaky part: the small plate is exactly what frees you to taste-test the new dish whenever you want, because you’re not depending on it to feed you. You can poke at it, decide if it’s any good, and walk away with no harm done.

It really is better to be a master of a few tools than a random user of many.

Enjoying the buffet, sans indigestion

Picture the overstuffed plate for a second. One plate, loaded down with the AI versions of crab, chocolate cake, and steak, all crammed together and touching in ways food was never meant to touch. Soggy. Overwhelming. Impossible to actually enjoy. That’s the plate you build when you let every new tool talk you into one more scoop.

You don’t have to eat that way. And if you’ve been feeling behind because you’re not running fifteen tools at once, hear me on this: using less, on purpose, was always the strategy, never the surrender.

That kid at SmorgaBob's was playing to win his own crazy version of The Hunger Games, and to him, winning meant the tallest plate in the restaurant. It took me a few decades and a few regrettable stomachaches to realize the tall plate was never the prize. I eat a lot smaller now. I also enjoy it a whole lot more.

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When I first started college, I wanted to be a programmer. Not the “casually hacking like I was in the movie Hackers”, either. I had the whole thing storyboarded out, the one where I commute into San Francisco for some big tech company or a hot startup and make a frankly ridiculous amount of money. The 90s were absolutely stuffed with magazines and commercials glorifying that exact guy, and I was sure, so very sure, I was gonna be him.

Programming turned out to be way harder than I bargained for, at least for me. I could not get anything to work past simple text prompts, and since I was bad at math then (and, full disclosure, remain bad at math now), all the deeper concepts just sailed right over my head and waved on the way by. After two wasted semesters and many a serving of cafeteria cheese fries (no regrets, baby!), I switched to something way more my speed: multimedia.

I already loved to draw, so the digital version of creativity was right up my alley, and I went in deep. Graphics, video editing, audio production, illustration, web design. I ate all of it up. And if I can say so without my head swelling too much, I got pretty good.

But, pride comes before the crash.

“Pretty good,” meet everybody else

Naturally I figured, “hey, multimedia, I’ll get a job easily.” Then I met the job market.

Turns out the thing about being pretty good at something is that a whole lot of other people are also pretty good at it (surprise, surprise), a fair number are flat-out better (way better), and every last one of you is fighting over the same handful of jobs. As good as I thought I was, I was one face in a very big crowd, and the outcome I’d been dreaming about did not show up.

So I did what you do when you gots to eat. I went and found a job.

The college I’d graduated from needed help in the computer lab, and I needed income, so I took a gig as a lab technician. Figured it was a stopgap. Welp. Little did I know that one little decision was about to march me down a years-long road through IT that I never once planned on. College lab, then a computer repair shop, then tech support at a middle school, then a promotion into a school district’s IT department, then two more districts, plus a stint right before the pandemic (there were even a couple years of STEM/STEAM curriculum planning crammed in there somewhere, but that’s a story for another day).

The different kind of creative

By the end of all that, I was burned out. Crispy.

I did a little educational admin work to catch my breath, and here’s where I learned something I didn’t have words for at the time. You can technically be “creative” in a job like that. You find a slick new way to reformat a spreadsheet, or you talk your way around a department problem, and sure, that’s a kind of creative. But it’s not the fun kind. At least for me, anyhoo.

There’s a real difference between work that uses your creativity and work that actually feeds it, and you don’t fully feel that difference until you’ve spent a few years on the wrong side of it. I had. My brain was starving for the other kind, the kind that gives something back.

So I started feeling my way toward it. Some freelance video work. A little social media passion project I’ll tell you about another time. But it was clear my brain wanted a brand new outlet, not an old one dusted off and plugged back in.

That’s around when I stumbled onto two things that clicked the whole season into place.

The two things

First was AI.

I won’t get too in the weeds here, because I’m not looking to start a debate, and however you feel about it, I respect it. For my purposes, it turned out to be a great assistive tool for pretty much anything creative. Is it any good at creating things itself? Nope, and that’s a job I’ll never hand it. But as an assistant? For what I need, it works great.

(Quick aside, while I’m here. AI makes coding so much easier now, and part of me does wish it had existed back when I was flunking out of programming. Although, let’s be real with ourselves. I almost certainly would’ve let it do all my hard programming work for me, which in my case was, hrmm, all of my programming work. So I’d have learned absolutely nothing and called it a degree. C’est la vie.)

The second thing was the big one. Writing.

In some ways I’d always been into writing without ever clocking it as a thing I did. I even kept up a serious Yelp review streak for a couple years, which I’m now choosing to count as relevant experience. But doing it professionally? Never crossed my mind. Then I looked up and realized that between ghostwriting, newsletters, and educational email courses, writing had quietly become more fun than it’s ever been. Lucrative, too.

And here’s the part that gets me. I spent years chasing the lucrative creative job on purpose, twice, and missed it both times. The one that finally worked is the one I wasn’t chasing for the money at all. I just liked doing it.

I feel like a creative again

Now here I am, constantly noodling on how the latest update to whatever AI tool I’m using can make my workflow more efficient. Not do the writing for me. Just help me have a good time figuring out the work. After everything, that’s the part I didn’t expect to get back: the having-a-good-time part.

And don’t get me wrong, I’ve got no regrets about the detours. The programming flop (again, no regrets on the cheese fries), the design grind, even the IT adventure I try not to think about too hard; all of it went into building whoever I am now. I needed every wrong turn to recognize the right one when I finally tripped over it.

But do I ever want to go back to work that doesn’t let me flex the creative muscles? To the spreadsheet kind of clever, the kind that leaves you empty? Not a chance. I went the long way around to figure out the difference, and now that I know it, I’m not unknowing it.

I’m having way too much fun over here. Best believe though peeps, I’m planning to keep it that way.

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This edition is sponsored by Nvidia (because apparently, everything is!… just kidding). Computex happened in Taipei this week, which means leather jacket man reminded the entire tech space that it’s trying to run the whole game. There’s so much Nvidia news that I had to make a list to keep track.

The short version: Nvidia wants to be inside your laptop, on the side of your house, at the center of Taiwan’s economy, and on your dinner plate (just kidding… for now…). Meanwhile Anthropic filed paperwork to go public at a valuation that rounds up to a trillion dollars, and Microsoft used its own conference to quietly announce that it’s tired of renting its AI from other people. Woofa-doofa! Let’s get into it!

Here’s what’s covered today:

• Nvidia officially entered the PC market with the RTX Spark

• Nvidia and SPAN want to mount tiny AI data centers on the outside of regular homes

• Anthropic filed confidentially for an IPO at a ~$965 billion valuation

• Microsoft built its own coding AI to lean less on OpenAI and Anthropic

• Nvidia pledged $150 billion a year to Taiwan and showed off a supercomputer the size of a fridge

(I know, seems like a LOT of Nvidia news. Just to be real here, I am in no way affiliated with them. Love ‘em or hate ‘em, they DOMINATED the news this week.)

Nvidia and SPAN want to bolt a tiny data center onto your house

The story: This one sounds made up, and it is very much real. Nvidia is partnering with a smart-electrical-panel startup called SPAN, plus homebuilder PulteGroup, to mount cabinet-sized mini data centers on the outside of regular homes. They’re called XFRA nodes, and the idea is to run AI workloads using the spare electrical capacity your house isn’t using, while knocking money off your power bill. The first 100 pilot units go onto new PulteGroup homes in Q3 2026.

What happened:

• Each XFRA box reportedly holds 16 Nvidia RTX Pro 6000 Blackwell GPUs, 4 AMD EPYC CPUs, and 3TB of RAM, all liquid-cooled, in a unit about the size of an HVAC condenser.

• The hardware is built by Dell and serviced by SPAN, and it mounts outside next to your AC and electrical gear.

• The trick is that the average American home only uses about 40% of its electrical capacity, so XFRA runs compute during the windows when your house is drawing little power.

• Homeowners get a premium smart panel, battery backup, sometimes solar, and discounted (occasionally free) electricity and internet in exchange for hosting a node.

• SPAN claims it can deploy 8,000 of these units roughly six times faster and at one-fifth the cost of building a comparable 100-megawatt data center.

AI runs on sweet, sweet electricity, and the grid can’t keep up. U.S. data centers ate more than 4% of the country’s entire electricity supply in 2024, and that figure could pass 9% by 2030. Building a new power-hungry data center can take over a decade once you count the wait for grid hookups. SPAN and Nvidia found a way around that wait: treat thousands of houses like one big distributed computer, using power that’s already sitting there unused. Think of it like carpooling, except the empty seats are spare amps in your breaker box and the riders are AI workloads (and not a dummy you’re using to scam the toll booth, though some AI critics may argue otherwise).

If you’re a homeowner, this is one to keep an eye on, especially if you’re buying new construction in the Southwest where the pilots are landing. The pitch is free or cheap power in exchange for a humming box on your wall. Whether that trade is worth it is going to depend entirely on the fine print, so read it carefully before you let a chipmaker move into your side yard. And let’s not forget: these may become tempting targets for copper thieves AND those with the anti-AI sentiment.

Nvidia is now coming for your laptop too

The story: On May 31 and June 1 at Computex, Jensen Huang officially unveiled the RTX Spark, an Arm-based superchip for Windows laptops and small desktops. This is the chip the rumor mill spent a year calling “N1X” and “N1.” It packs a 20-core Nvidia Grace CPU and a Blackwell GPU onto one piece of silicon with up to 128GB of unified memory, and it ships this fall in PCs from Dell, HP, ASUS, Lenovo, MSI, and Microsoft.

What happened:

• Huang said this on stage, and he wasn’t being coy: “A long time ago Nvidia used to be a GPU company.” YOU DON’T SAY.

• More than 30 laptops and 10 desktops are slated to launch this fall with the RTX Spark inside.

• Huang called the move the biggest reinvention of the PC since the smartphone reinvented the phone.

• Nvidia stock jumped over 6% on the announcement, Dell climbed over 10%, and HP gained 8%.

• The chip is built to run “agentic AI” locally, meaning AI agents that keep working in the background without phoning home to the cloud every two seconds.

For about two decades, the Windows laptop world has belonged to Intel and AMD, with Qualcomm elbowing in on the Arm side. Now Nvidia walked in carrying the biggest AI-chip business on the planet and proclaimed that it was all that and allllll the bag of chips (figuratively and literally, if you think about it). The reason this matters is the design philosophy underneath. These chips are tuned to run AI locally on your machine, which is the same direction Apple has been pushing with its M-series silicon. A real three-way fight over the future of the personal computer just broke out, and the prize is which company gets to define what an “AI PC” even means (meanwhile, Skynet could not be reached for comment).

If you were planning to buy a Windows PC in the next year, it might be worth holding out until fall to see the first RTX Spark machines, or at least to see what the new competition does to the price of everything else. Will these new PCs be winners or wieners? Stay tuned peeps. It’s gonna be a show, fa-sho!

Anthropic filed to go public at nearly a trillion dollars

The story: On June 1, Anthropic (the company that makes Claude) told the government it wants to start selling pieces of itself to the public (aka errbody can get a slice ‘o the pie, as long as they got the cash to do so). Right before this, Anthropic raised a fresh $65 billion, and that money raise put a price tag on the whole company of about $965 billion. They’re basically $35 billion away from being worth a trillion dollars, and you can’t even buy the stock yet.

What happened:

• That $965 billion price tag is now bigger than OpenAI’s, which sits around $852 billion. Anthropic quietly passed its biggest rival.

• The company is reportedly on pace to bring in about $47 billion a year, up from around $10 billion a year ago

• The $65 billion came from a who’s-who of big investment firms (Sequoia, Altimeter, Dragoneer, and a bunch more).

• On top of that, two firms (Apollo and Blackstone) are lending Anthropic another $36 billion, which pushes the total pile of new money close to $100 billion.

• Anthropic isn’t alone. Elon Musk’s SpaceX is also lining up to go public this week, and OpenAI is expected to follow soon. Everybody’s heading for the stock market at once.

A few weeks back I mentioned that Sam Altman and Elon Musk were both pointing their companies toward the stock market. Three AI heavyweights (Anthropic, Elon Musk’s SpaceX, and OpenAI, the company behind ChatGPT) are all about to let everyday people buy in, around the same time. That’s a big deal, because the stock market is the most public scoreboard there is. If people throw cash at the thing and the prices climb, it’s a giant vote of confidence (in Anthropic and AI, in general). If the prices flop, welp, might not be the greatest look for these intelligences that are artificial.

Of note is how fast this all happened. Anthropic raised $65 billion and filed its “we want to go public” paperwork in the same week. A year ago, a company raising a single billion would’ve been front-page news. The fact that this barely raises an eyebrow now is the part that should actually raise your eyebrow.

Microsoft built its own coding AI because why not

The story: On June 2 at Microsoft Build 2026, Satya Nadella unveiled Project Polaris, Microsoft’s homegrown AI coding model. Polaris is set to replace GPT-4 as the reasoning engine inside GitHub Copilot by August. At the same conference, Microsoft also made Anthropic’s Claude a first-party option inside Azure AI Foundry, sitting right next to OpenAI, DeepSeek, Llama, and Mistral.

What happened:

• Microsoft has spent the AI era as the cloud provider and the deep-pocketed investor in other people’s models, first OpenAI, then Anthropic.

• Polaris is its first serious crack at a proprietary in-house model built to go toe-to-toe with the frontier labs.

• Nadella framed the shift as AI moving from “synchronous assistants” to “async coworkers that can execute long-running tasks.”

• Agent Mode is now the default across Microsoft 365 Copilot, and the Windows Agent Framework was open-sourced under the MIT license.

• The Pentagon also handed Microsoft the lion’s share of a $9.69 billion software consolidation contract. Good week to be Redmond.

Here’s what’s actually going on underneath the announcement, in plain terms. Microsoft is one of the biggest buyers in the world of AI tokens, which are the units of work an AI model does every time you ask it something (like tokens that peeps used to plunk into arcade machines, making many a kiddo’s allowance disappear in an instant). Every prompt run through Copilot costs Microsoft money it pays out to model providers. When your bill runs into the tens of billions a year, eventually you start wondering why you don’t just build your own AI.

When a $3 trillion company looks at its AI costs and decides, “why for we not do that yesterday? DO NOW,” it’s a reason to take notice. Most people and small businesses won’t train their own models, obviously. The principle still holds: the more an AI tool becomes core to how you work, the more it pays to understand what you’re actually spending and whether there’s a leaner way to get the same result. Free trials are great for kicking the tires. The long-term bill decides if you’re gonna drive the thing.

Nvidia (hi, again) pledged $150 billion a year to Taiwan and showed off a fridge-sized supercomputer

The story: Back at Computex, Huang said the quiet part out loud: Nvidia plans to spend as much as $150 billion a year in Taiwan, and it’s opening a new Taipei headquarters. Taiwan, in his words, is the “epicentre of the AI revolution.” The centerpiece of Nvidia’s booth was the Vera (which we talked about last week) Rubin NVL72, a rack-scale supercomputer that just swept multiple Best Choice Awards at the show for the new “AI factory” category.

What happened:

• “Many years ago we had 10 partners. Five years ago, maybe 50 partners. Now we have 150 partners,” Huang said about Nvidia’s Taiwanese supply chain.

• AMD’s Lisa Su also showed up and pledged over $10 billion of her own in Taiwan investment, so this is bigger than just Nvidia.

• Vera Rubin NVL72 is the rack-scale machine behind the “AI factory” concept, which is Nvidia’s pitch for purpose-built data centers that crank out AI models at industrial scale.

• The new Taipei HQ adds a long-term anchor to a relationship already moving roughly $150 billion a year through the same supply chain.

• The show had basically zero cryptocurrency content this year, which tells you exactly where the Taiwanese tech ecosystem has thrown its chips.

Last week, I wrote that Nvidia was DoorDashing its newest chips to four big companies. This week the same company committed to spending more annually in one island than the entire GDP of most countries. The relationship has crossed into something that looks a lot like industrial policy run by a private company. When one firm decides to drop $150 billion a year in one place, the geopolitics around that place shift, the supply chain bends to fit, and every competitor has to draw up plans around the new center of gravity. Might have to update world maps to “Taiwan (aka Leatherjacketmanland), Sponsored by Nvidia.”

Whoever wins the AI model race, the company selling the hardware underneath all of them gets paid either way. That’s the position Nvidia is in, and this week it spent like a company that is desperate to stay there. The hardware roadmap they laid out runs well into 2027. For anyone building on top of AI, the thing to remember is that the compute you depend on is set to keep getting faster and cheaper for a while, which is worth factoring into whatever you’re planning. Worth watching, too, is who (if anyone) ever manages to challenge that position, because a market with one dominant supplier tends to get expensive for everyone downstream eventually.

What ties it all together

This was a week of enormous institutional moves. Nvidia wants in on the PC pie, floated the idea of turning houses into compute nodes, and pledged a country-sized budget to Taiwan. Anthropic took the first real step toward becoming a public company. And Microsoft made its declaration of partial independence.

Step back and look at the shape of it. The AI industry has gotten big enough that it’s now reorganizing every industry around it. Personal computing is being redesigned around AI agents. The power grid is being re-plumbed to feed AI compute. The public markets are about to get their first honest read on AI valuations. The world’s biggest software company is building its own models so it can stop paying the AI fun tax. The common thread is that AI stopped being a product you use and became infrastructure that everything else now has to bend around. Scary or innovative? Perhaps both? I’ll let you, the reader at home, decide.

What I’m watching

• Whether the RTX Spark holds up on performance once reviewers get the first laptops this fall

• Whether anyone actually signs up to host an XFRA node, and what the fine print really asks of them

• How the SEC review of Anthropic’s S-1 unfolds, and whether OpenAI files its own paperwork before the end of June

• Whether Microsoft’s Polaris truly replaces GPT-4 inside Copilot by August, or quietly slips

• Whether the next big memory headline is a price drop or another shortage warning

  

Thanks so much for reading this edition of The Weekly Think.

See you next week, leather jacket man megafans fellow thinkers!

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The year was 1996, and my family could not afford a computer. I wouldn’t get one of my own until college. But my local library, in a move I am forever grateful for, wired all of its machines to the ‘ol (new??) world wide web, and just like that, the door was open.

And man, did I get lost in the sauce.

I discovered GeoCities. I chatted ALLLL the time at a place called the Tokyo Tea Room. I looked up Game Genie codes for games I was definitely going to beat that weekend, and I devoured every scrap of gaming and anime news I could find. It was an addiction in the worst way, my guys, and I loved every single second of it.

Somewhere in all that loafing, surrounded by webpages other people had built, I landed on the thing I actually wanted to do. I didn’t just want to read these pages. I wanted to make my own.

The embarrassing dilemma

Here was the problem. I had a free GeoCities account, which meant I had my own little plot of internet land just waiting to be built on. What I did not have was a computer at home, and home was where I was at my most creative.

Now, the obvious solution here is for someone to lean over and say, “just design them at the library, dummy.” And that’s fair. That’s completely fair. But I was a teenager with unsupervised internet access for the first time in my life, and I was far too busy having the time of my life to do anything as responsible as using my computer hours productively.

So the question stood, and it was for real. How was I supposed to design webpages with no computer to design them on?

My solution was to draw them. In a notebook. By hand.

What was actually in those notebooks

I loved my notebooks back then. Beyond the occasional journal entry, they were stuffed with my random thoughts and a whole lot of sketches, because I was a teen who loved to draw (I still love to draw, but back then I really loved to draw).

As I fell into the internet age, my notebooks fell right in with me. I was copying down cheat codes and gaming news by hand, because the library didn’t have printers yet either. But I was also doing something a little wild. I was sketching out my dream webpages in full, complete with underlined links, hand-drawn logos, and web banners (and, y’all, web banners were a HUGE deal in the GeoCities era, a load-bearing pillar of any self-respecting homepage).

I was designing like a maniac. It was a completely computerless kind of design, with no way to ever actually publish any of it, and I poured myself into it anyway.

That’s the part I want you to sit with for a second, because I didn’t see it clearly myself until recently. I loved the designing so much that I did it with no computer, for no audience, with no way to ship the thing. The making was the entire point. There was no other point. Just me, a pencil, and a notebook full of wild webpage designs.

The candy store, then and now

The thing about the early internet that’s hard to explain to anyone who missed it is the sheer daily wonder of it. Something new and amazing showed up every single day, people were innovating in every direction at once, and the rest of us got to just stand there and watch it happen in real time. You were the kid in the candy store, except the manager had leaned over and said, “bet you can’t eat as much candy as you want, for free.” Welp. Challenge accepted, sir. All day, every day.

I bring this up because, fast forward to 2026, AI right now feels an awful lot like that. The chatbots update on what feels like a constant cycle, the services are generating every kind of content you can imagine, and the thinking models are getting pointed at real problems and actually making progress on some of them. It’s that same sense of something new showing up before you’ve finished marveling at the last thing.

But there’s one big ‘ol difference in this modern era. Back in the 90s, nobody was standing over the new creations calling them garbage. Today, a whole subset of people have decided that anything made with AI gets labeled “slop,” and the heat behind that critique runs hotter than anything I remember from the early internet days. I’m not here to settle who’s right about that. I’ll just note that the wonder and the contempt are sharing the same room right now, and leave it there.

Why I won’t hand the machine the whole thing

So where do I stand on this whole thing?

I don’t mind using AI as an assistive tool one bit. A good amount of what I make these days has AI somewhere in the process. But using it as the one and only tool, the lone artist doing the actual creating? I don’t do that, and not because some online tribe told me not to.

I come from a traditional multimedia background (Macromedia, anybody?… uhh… hello?), and even before that, I was the kid drawing webpages in a paper notebook for the pure joy of it. I love the design process far too much to just hand it over to a machine. The thing AI is offering to skip for me is the exact thing I loved then and still love now.

And that’s the part I keep coming back to. The notebook kid would have been genuinely baffled by the offer. Picture it. You walk up to a teenager who is so in love with designing that he’s doing it on paper, with no computer, for an audience of nobody, and you tell him you’ve got a machine that will do the whole thing for him instantly. He doesn’t want that. He would look at you like you’d offered to eat his Hostess snack cake on his behalf (and ain’t NOBODY eating MY Hostess snack cake). The drawing wasn’t the obstacle between him and the webpage. The drawing was the entire reason he was sitting there.

I’m still that kid. The offer hasn’t gotten more tempting just because the machine got better at accepting it.

I’m bringing the notebook back

As I sit here typing this, I keep thinking about that kid sketching out fanfiction-level webpages he had no way to build, designing with the kind of focus that would’ve made you think he was Doug Funnie with a library card (and yes, Doug eventually got internet episodes, but we don’t talk about those middle-school-era ones that hardly anybody I know watched).

My design process has changed many a time since then. But I don’t think the kid was wrong about anything, and the fear I keep hearing, that AI is going to cost people their creativity and their originality and their authentic self, would not have scared him one bit. It can’t take any of that from you unless you hand it over. I know, because that whole creative fire was burning years before I ever touched a tool, fueled by nothing but a library card, a spiral notebook (and a frankly irresponsible number of Hostess snack cakes).

I kinda wanna break out the notebooks again. Not as some productivity trick, and not as a protest against anything. It’s because that kid at the library understood something I’ve spent thirty years occasionally forgetting, which is that the wonder was never coming from the tool. Not the computer I didn’t have, and not the AI I do. It was coming from me, hunched over a hand-drawn graphic I’d never publish, having the time of my life.

The pencil didn’t make me creative. Neither will the machine. That part was always mine to keep.

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Greetings, fellow humans! It’s your totally human boy, Eric. Last week was all about the money, talent, and lawsuits (in a way, it always sorta is 😵‍💫). This week the conversation got a little bigger, and by bigger I mean a religious leader showed up with thoughts. Pope Leo XIV published the first papal letter in history dedicated to artificial intelligence (though imagine if a pope in the Byzantine times did that), and words were not minced.

The rest of the week kept the theme going. Anthropic planted a flag on the ad question we talked about last time, the memory crunch finally came for a popular PC gaming handheld, an AI model solved a math problem humans had been stuck on, and Nvidia started hand-delivering its newest brains (not Jensen Huang’s brain, to be clear) to the biggest labs. Grab a hot dog, dawg. Let us to begin, humans.

Here’s what’s covered today:

• Pope Leo XIV published the first-ever papal encyclical on AI, with Anthropic on stage

• Anthropic promised Claude will stay ad-free (one week after ChatGPT turned ads on)

• The Steam Deck jumped up to $300 thanks to “RAMageddon” (thanks, Jensen)

• An OpenAI model disproved a longstanding math conjecture

• Nvidia hand-delivered its new Vera chips to Anthropic, OpenAI, and SpaceX (thanks, Jensen?)

Pope Leo XIV wrote 42,000 words on AI, and Anthropic was in the room

The story: On May 25, Pope Leo XIV released Magnifica Humanitas (Latin for “Magnificent Humanity,” not to be confused with my favorite emo rap album of 2005), the first encyclical of his papacy and the first one in Roman Catholic Church history built entirely around artificial intelligence. An encyclical is intended to be an authoritative kind of letter a pope writes, the heavy-hitter format usually reserved for huge moral questions. He picked AI for his first one, which is a notable choice given everything else a new leader could lead with.

What happened:

• The letter runs 42,300 words and took ten years of Vatican dialogue with the tech industry to produce.

• Leo signed it on May 15, exactly 135 years after Pope Leo XIII’s famous 1891 letter on workers’ rights (the name match is on purpose).

• He argues AI should lift up human dignity and the common good, and warns against a pure race for “more powerful algorithms and larger datasets” driven by profit or geopolitical muscle.

• The Vatican launch featured Anthropic co-founder Chris Olah, who leads the team that studies what’s actually happening inside these models.

• One of his notable thoughts: he calls for AI to “help, not hinder” the development of kids.

What makes this newsworthy has nothing to do with whether you give Pope Leo XIV’s words any weight. It’s the signal. A 2,000-year-old institution with over a billion adherents spent a decade in conversation with the tech industry, then put an AI researcher on stage to mark the occasion (the religious leader & an AI man, sharing the stage, sounds like something from an episode of Futurama). When an organization that size and that old decides AI is the subject of its first major statement, a lot of people are going to take their cue from it.

That’s the real story here: AI has officially become big enough that the institutions shaping how billions of people see the world are now staking out positions on it. Governments, courts, and now the largest religious body on the planet. The conversation has left the tech bubble, and that means the rules, norms, and pressures around this technology are about to start coming from places that have nothing to do with Silicon Valley (personally waiting on AI takes from Mayor McCheese of McDonaldland).

Anthropic said Claude stays ad-free (one week after ChatGPT turned ads on)

The story: Remember last week, when I told you OpenAI flipped on a self-serve ad platform inside ChatGPT and basically said “you’ll have both and you’ll like it” to the charge-or-advertise question? Well, this week Anthropic walked up to the same fork in the road and went the other direction. The company published a piece explaining that Claude will stay ad-free, on purpose, for the foreseeable future.

What happened:

• Anthropic’s argument is that advertising incentives pull against being a genuinely helpful assistant.

• The worry is simple: once an ad pays the bills, there’s pressure to keep you scrolling and nudge what the assistant says.

• Anthropic says it plans to widen access to Claude without leaning on ads to do it.

• This lands one week after OpenAI announced it’s chasing $100 billion a year in ad revenue by 2030.

So now we’ve got a real split in the road, and it’s a fun one to watch. One of the two biggest AI companies is copying the Google playbook (don’t tell coach, or it’s 100 laps). The other is betting people will pay for a tool that has zero incentive to keep them glued to the screen. It’s the difference between a free newspaper stuffed with inserts (though Sunday newspapers used to be fire) and a subscription you actually chose. Two takes (that is to say, OpenAI’s and Anthropic’s) on what you want from the thing you ask questions all day.

The practical takeaway is to notice which one you’re using and why. If a tool is free, ask who’s paying for it. That habit is just good media literacy, and it matters more every week as these assistants get woven into how we all work.

The Steam Deck got up to $300 pricier, and AI is the reason

The story: Last week I walked you through Samsung crossing $1 trillion on the back of the memory boom, and I mentioned the bill was eventually coming for the rest of us. Welp, gamers, here we are. On May 27, Valve raised the price of its Steam Deck OLED handheld by as much as $300, blaming rising memory and storage costs directly. The internet has started calling this whole mess “RAMageddon,” which yeah, accurate.

What happened:

• The 512GB model went from $549 to $789, and the 1TB model jumped from $649 to $949.

• Valve was clear the hardware didn’t change at all; only the component costs did.

• The 1TB Steam Deck now costs more than a PS5 Pro, which is a sentence that would have sounded insane a year ago.

• Valve is far from alone: Sony, Microsoft, and Nintendo have all raised console prices recently for the same reason.

• The root cause is the AI data center boom eating up the world’s memory supply (one report says OpenAI alone reserved roughly half of one chipmaker’s entire 2026 RAM output).

Here’s the through-line that connects this to everything else in the newsletter. The same AI buildout minting trillion-dollar chip companies is the reason your gaming handheld costs as much as a laptop now (if a Game Boy had cost as much as a PC in 1989, there would’ve been RIOTS). The memory that would have gone into affordable consumer gadgets is getting routed to data centers instead, because we all need data centers because leather jacket man (aka Jensen) said so. It’s one giant game of musical chairs, and right now the AI labs have bought most of the chairs.

If you’ve been eyeing a Steam Deck, a gaming PC, or honestly any device with a lot of RAM in it, the practical move is to buy soon or buy refurbished. Prices aren’t expected to ease until new memory factories come online, and those are a 2027-or-later situation (might as well call it Cyberpunk 2027). This is the rare case where waiting probably costs you extra.

An AI model solved a longstanding math problem

The story: On May 22, OpenAI announced that one of its models disproved a longstanding conjecture in discrete geometry, a corner of math that deals with shapes, points, and how they fit together. In plain terms: humans had a hunch about how something worked, nobody could prove it either way, and the AI found the counterexample that settled it (like the cyber-nerd it is).

What happened:

• A “conjecture” is an educated guess that hasn’t been proven; disproving one means finding a clear case where it breaks.

• The model didn’t just guess; it produced a result mathematicians could check and confirm.

• This follows a similar feat where another frontier model contributed to a real math proof earlier in the spring.

• These are narrow, specific wins, not “the AI is now smarter than mathematicians” headlines.

This one is worth understanding correctly, because it shows what these tools are actually good for. The common knock on AI models has been that they only ever average out the internet and hand it back to you (sorta like how Taco Bell reconfigures existing ingredients into the latest viral menu item). A math result like this works differently. A person decided which problem was worth attacking, set up the approach, and checked whether the output held up. The machine ran a staggering number of dead-end attempts very fast, the way a calculator crunches numbers a person could technically do by hand. The insight, the judgment, and the verification stayed human start to finish.

If you work in any field with hard, well-defined problems (engineering, research, finance, logistics), here’s the part worth your attention. These tools are good at chewing through a huge pile of possibilities quickly, and they’re useless without a sharp person deciding what’s worth chewing through and whether the answer is any good. So the person who gets ahead is the one who knows which problems are worth pointing the tool at, and who can tell a real answer from a confident-sounding wrong one. The hard part was always the human judgment, and it still is.

Nvidia hand-delivered its newest chips to the biggest labs

The story: Leather jacket man spent the week reminding everyone who actually supplies the shovels for this whole gold rush. Jensen Huang says the company’s new Vera CPU is unlocking what he pegs as a $200 billion market for agentic AI, with $20 billion already sold this year. In fact, the first units were reportedly hand-delivered to Anthropic, OpenAI, SpaceX, and Oracle. It’s like DoorDash, but it’s Jensen and you tip him BILLIONS.

What happened:

• The Vera CPU is the “brain” half of Nvidia’s next-gen Vera Rubin platform, built specifically for AI agents that run continuously.

• Hand-delivering the first chips to your biggest customers is equal parts logistics and showing off, and Nvidia knows it.

• Nvidia’s most recent quarter came in around $81.6 billion in revenue, up roughly 85% from a year ago.

• Anthropic, OpenAI, and SpaceX (the Colossus supercomputer crew from a few weeks back) are all on the delivery list, so the same handful of players keep showing up.

The pattern here is one I keep returning to. No matter which AI lab wins the model race, Nvidia sells the chips to all of them. It’s the picks-and-shovels business, and right now it’s the best seat in the house. When you hand-deliver your newest product to four of the most important companies on earth and they’re all thrilled to get it, you’re not really sweating the competition. You’re the supplier, and the streets love you or hate you.

For the builders and the curious: watch where these Vera chips point, because they’re purpose-built for agents that can run 24/7, 365 days a year, including leap years. That tells you where the whole industry thinks the next year is headed. The bet is on AI that constantly runs, and the hardware is already being trucked to the front door.

What ties it all together

Last week the story was the business of AI: money, lawsuits, talent. This week the story zoomed out to the meaning of AI: Pope Leo XIV weighing in on human dignity, two companies splitting on whether to sell your attention, and an AI quietly solving a problem humans couldn’t.

Step back and check it out. A 2,000-year-old institution and a frontier lab shared a stage. One AI company bet on ads, the other bet against them. A gaming handheld became a casualty of the data center boom. A model cracked a real math mystery. And Nvidia kept calmly selling chips to everyone in the room. The technology is moving fast, but this week was a reminder that the bigger questions (who pays, who benefits, what stays human) are finally getting asked by the kind of people who don’t usually show up to tech keynotes.

A year ago, “religious leader writes a letter about AI” would have sounded like a headline from a satire site. This week, it was literally Monday.

What I’m watching

• Whether other AI companies follow Anthropic’s ad-free stance or OpenAI’s ad-funded one

• Whether memory prices (and gadget prices) climb even higher before those new factories open

• Where researchers crack the next hard problem using AI, and whether it's science, medicine, or math again

• When the Vera chips start showing up inside products the rest of us can actually use

Thanks so much for reading this edition of The Weekly Think.

See you next week, fellow actual humans!

Did you enjoy this week’s Think? If so, please consider sharing this and leaving a comment below, to share your thoughts on these latest AI-related rumblings. Hope to hear from you!

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There’s a part of every creative project that few are actually excited to do. It’s the proofread you’ve already done twice, or the formatting pass where you’re moving commas around for the fourteenth time, or that third read-through where your eyes have officially stopped reading and started skimming over sentences you wrote a week ago. You know exactly the part I’m talking about, because everyone who has ever made anything has felt their brain check out somewhere around that stage of the process (though, no hate if you love proofreading, because you deserve some kind of trophy if that’s the case).

That’s the boring half. And here’s the thing nobody is really telling you about AI, because everyone is too busy arguing about whether it’s going to take over the world by next Tuesday.

AI is genuinely, almost suspiciously good at the boring half.

What it’s actually good at

I’ve worked as a creative for over twenty years, which means I’ve done my fair share of proofreading, light formatting, and tedious cleanup work. So when I say AI is good at this stuff, I’m not guessing about it. It catches the kind of small errors that vanish from your sight after you’ve stared at the same paragraph for too long (ex: “Looks like you meant to say ‘thought’, but you said ‘though’. Don’t worry champ, I got this!”). It handles light formatting work without sighing about it. It will rephrase a clunky sentence for you so you can finally see what you actually meant to say, which is honestly one of the more useful things a writing tool has ever done (for this guy who doesn’t English so well, at times).

The same goes for quick proof passes, small graphic edits, and the kind of cleanup work you do at the end of a project when your creative energy is gone and you just want the thing to be finished. AI is good at all of it, most definitely, and I see no reason to be coy about that.

Some creatives feel weird about admitting they use AI for anything at all, like admitting it costs them their membership to some sort of Creative’s Costco. Worry not, my friends: it doesn't. A calculator is useful, as is spell-check. Saying "this tool helps me power through the unfun parts" doesn't take anything away from the rest of your work. It's just a description of how a tool works.

Now, I’ve spent a good amount of time praising what AI does well. Is it perfect though? Welp…

Where it falls apart

Now here’s the other half of the story, and this is where the discourse gets the whole thing backwards.

AI is bad (not bad meaning good, like they used to say in the 90s), sometimes spectacularly bad, at the part of creative work that is actually creative. The part where you’re figuring out what the thing should even be. The part where your specific point of view, your taste, and your weird particular brain are the entire reason the work has any reason to exist in the first place.

Hand that part over to AI, and what you get back is the literary equivalent of stock footage. It’s technically functional but entirely forgettable, like a playground slide that’s only a foot high. There’s no point of view in it because there was no point of view going into it. You can feel the absence the moment you start reading, even if you can’t articulate exactly what’s missing.

This is the stuff people love to hate, and with good reason. It’s content the machine generated, that the person didn’t bother to read carefully, and that the audience scrolled past without thinking. The whole transaction happened, and the world is now slightly more cluttered than it was before, with no actual meaning added to it.

That isn’t AI’s fault, though. AI didn’t volunteer for the job. Somebody pointed it at a creative decision and asked it to make that decision for them, and AI did the best it could, and its best was a sentence that started with the words “And honestly”.

The skill is knowing which half is which

So what’s a creative rockstar like yourself to do? The real skill of using AI well is being able to look at your own work and know which half is the boring half and which half is the part that’s actually yours.

The boring half is execution. You already know what you’re trying to make, and now you have a seemingly unsurmountable grind ahead of you. Fear not though, Creative King (or Queen… or… Chief); that’s where AI earns its place, and that’s where you should be using it without guilt.

The creative half is something completely different. It’s where the decisions live. What is this thing actually about? Who is it for? What’s the specific observation, the unusual angle, the small true detail that only you would have caught? That part isn’t safe to outsource, because the second you outsource it, the thing you’ve made stops being yours, and people can tell. They might not be able to name what’s missing, but they can feel it.

The trouble most people are running into right now is that they’ve quietly flipped the equation. They’re handing AI the creative decisions and then doing the proofreading themselves, which is the precise opposite of how this should be going. It’s like paying some random dude to paint for you, then taking that painting back to your studio and trying to fix it yourself. You’re gonna have a bad time.

Take back the part you actually love

If you’ve been using AI on the wrong half, here’s the good news. The half you’ve been handing off is still right there, waiting for you to pick it back up (like that cute puppy you’ve been neglecting).

The next time you sit down to make something, try this. Let AI handle whatever part of the project bores you. Hand over the proofread, the formatting, the cleanup, the small graphical work, all of it. Don’t feel weird about giving it the grunt work, and don’t feel like you have to defend it to anyone. That part of the work was always a chore, and the tool exists to handle chores (I wish I had a tool to handle chores when I was 10, but alas…).

Then take back the part you actually love. The opening line that nobody else could have written. The angle that only makes sense because you’ve lived your specific life. The choice of which detail to include and which one to leave out. The voice that makes the thing recognizably yours when somebody reads three sentences of it without your name attached.

That’s the part you got into this for. It’s the part you’ve been quietly losing reps on, and it’s the part the machine cannot do for you, no matter how nicely you ask (*glares at the chatbot*). You don’t have to fight AI to get it back. You just have to stop handing it the part that was yours to begin with, and start using the tool for the part you were never going to enjoy doing anyway.

The boring half belongs to the machine now. The good half is still yours. Go take it back.

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Ahoy, AI news, starboard bound! So, Google I/O came, I/O saw, and I/O dropped a whole lot of Gemini on us. I/O am intrigued (okay, that’s enough of the I/O)! The short version: Google spent the week reminding everyone that the company with two billion users and its own chips (sadly, not Ruffles) is, in fact, still here.

The bigger theme this week ran underneath the product news: people, lawsuits, and money. Anthropic landed one of the most respected researchers alive. Musk’s big swing at OpenAI got tossed by a jury before lunch. And OpenAI quietly turned on the thing everybody claims to hate but secretly keeps the lights on: ads. Let’s get it, y’all.

Here’s what’s covered today:

• Google I/O dropped Gemini 3.5, a 24/7 agent named Spark, and smart glasses

• Andrej Karpathy left the OpenAI orbit and joined Anthropic

• A jury threw out Elon Musk’s lawsuit against OpenAI in under two hours

• ChatGPT opened a self-serve ad platform (your chatbot has a billboard now)

• Samsung crossed $1 trillion, and the memory crunch is minting winners

• And your “oof” moment of the week

Google I/O: Gemini 3.5, a 24/7 agent, and a $100 price cut nobody saw coming

The story: On May 19, Google held its I/O keynote and did what Google does best lately, which is cram Gemini everywhere it can (still waiting on Gemini: The Wholesale Store and $1.25 Polish sausages). The headliners were Gemini 3.5 Flash (available that day), a 24/7 personal agent called Gemini Spark, a new design language called Neural Expressive, and a surprise price drop on its top AI plan.

What happened:

• Gemini 3.5 Flash beats the older 3.1 Pro on coding and agentic benchmarks while running 4x faster, and it’s already powering Search.

• Gemini Spark is a personal agent that runs on Google’s cloud (so it keeps working even when your laptop is closed) and takes actions across Gmail, Docs, and Calendar.

• Google AI Ultra dropped from $250 a month to $100, and the old $250 tier became $200 with the same features. A price cut, in this economy!

• The first Android XR “audio glasses” are coming this fall, built with Samsung and Qualcomm, with frames by Warby Parker and Gentle Monster.

• The glasses pair with both Android phones AND the iPhone, which is a quiet little flex aimed directly at Apple.

Last week, I wondered whether I/O would land a full Gemini 4 or settle for an incremental bump. The answer was sorta neither. Google shipped a 3.5 (“like 3, but with a .5!”), but the real story was Spark. A model is a tool you pick up and put down. An agent is something that keeps working while you sleep. Your phone running little, tireless virtual contractors; Google just made that the centerpiece.

If you use Google’s stuff all day (fair, in this day and age), keep an eye on Spark when it lands for Ultra subscribers next week. The price cut to $100 tells you Google wants this in as many hands as possible, as fast as possible.

(Hrmm. I feel like I missed an Old MacDonald joke opportunity here. Oh well… E, I, E-I/O! Felt good to get that out of my system.)

Andrej Karpathy left the OpenAI world and joined Anthropic

The story: On May 19, Andrej Karpathy announced he’s joining Anthropic. If that name doesn’t ring a bell, here’s the context: he was a founding member of OpenAI, he ran Tesla’s self-driving AI program, and he’s one of the most respected teachers and researchers in the entire field. People learn how neural networks work from his free YouTube lectures. In the AI world, my mans is considered all that and a bag of chips.

What happened:

• Karpathy is starting a team focused on using Claude to speed up its own pre-training research (AI helping build AI; the snake is eating its tail, and it’s thrilled about it).

• Pre-training is the giant, wildly expensive phase that gives a model its core smarts.

• Anthropic also picked up Chris Rohlf, a 20-year cybersecurity veteran from Meta, for its frontier red team.

• This caps a stretch where Anthropic has become a magnet for top talent and acquisitions (it bought developer-tools company Stainless the day before, on May 18).

Here’s why this is more than AI tea. Karpathy could have gone anywhere (or did what Michael Jackson commanded: to be startin’ somethin’ for himself), and he picked the company betting that AI-assisted research, not just raw compute, is how you stay at the frontier. Think of it like a head chef leaving the biggest restaurant in town to go work at the small, indie kitchen down the street. People notice. Other chefs start updating their resumes. Talent follows talent, and right now the talent is pointing at Anthropic.

If you’re early in your own AI learning journey, check out one of Karpathy’s “Zero to Hero” lectures. The guy explains hard things simply, which is exactly how to play this whole AI game.

A jury threw out Musk’s OpenAI lawsuit in under two hours

The story: WELP, the Musk legal saga got its own ending this week (for now, at least). On May 18, a federal jury in Oakland (shout out to the Bay Areaaaaa) took less than two hours to throw out Elon Musk’s lawsuit against OpenAI and Sam Altman. They didn’t even break a sweat.

What happened:

• Musk had accused Altman and OpenAI of “stealing a charity” by turning the nonprofit into a for-profit operation.

• The jury never reached that question. They ruled Musk simply waited too long to sue (the legal term is “statute of limitations,” which is a fancy way of saying “you snoozed, you lose”).

• The verdict was unanimous, and the judge agreed with it.

• Musk says he’ll appeal, calling it a “technicality.”

• Fun footnote: this same week, the new coding tool Cursor shipped a model partly trained on Musk’s Colossus 2 supercomputer, which is the sequel to the Colossus 1 that Anthropic leased last week. Elon’s compute is everywhere, even when his lawsuits aren’t winning.

Both Altman and Musk are steering their companies toward the money right now, in what could be record-breaking IPOs. Not a great time to be handed messy verdicts. For Altman, the timing is a gift. For Musk, it’s a day he’d probably like to forget.

The wild reality of it all: the biggest fights in AI are increasingly happening in courtrooms and boardrooms, not research labs. It’s Three O'Clock High up in here and everyone’s coming to see Jerry VS Buddy.

ChatGPT now sells ads (aka here’s your report, now how about some juicy Chicken Nuggets?)

The story: On May 21, OpenAI launched a self-serve advertising platform inside ChatGPT, basically an Ads Manager where companies can buy, run, and tune ad campaigns (“because if Google can do everything, so can we!”, said Altman in a press release I just now imagined). OpenAI is reportedly targeting $2.5 billion in ad revenue this year and a frankly bonkers $100 billion a year by 2030.

What happened:

• The platform supports both pay-per-view and pay-per-click ads, the same models that built Google and Facebook.

• It plugs into the big ad agencies and ad-tech firms (Dentsu, Omnicom, Publicis, WPP, Adobe, and others).

• OpenAI swears ads won’t influence ChatGPT’s actual answers, only where ads appear around them.

• This pairs with a separate launch on May 15, where ChatGPT added a personal finance dashboard that connects to your bank accounts.

Every free product you’ve ever loved eventually meets the same fork in the road: charge everybody, or sell ads. ChatGPT just said “why not both?” And yeah, it makes sense. Running these models costs a ludicrous amount of money, so somebody has to pay the piper. The interesting question is whether people will tolerate ads inside a tool they treat like a trusted advisor (remember the Claude Super Bowl ads?).

Here’s the practical bit for you: if you market anything, a brand-new ad channel just opened up, and the early movers on a new channel always get the cheap attention before everyone else floods in. Worth a look-a-roo if you rely on search engine ads.

Samsung hit $1 trillion, and the memory crunch is minting winners

The story: Last week I talked about the memory shortage and the doom of rising laptop prices. This week, here’s the flip side of that same, painful coin. On May 21, news broke that the AI memory boom pushed Samsung’s market value past $1 trillion. The same shortage squeezing your wallet is making the chip companies Dr. Evil-levels of wealthy.

What happened:

• Samsung crossed the trillion-dollar mark on the back of soaring AI memory demand.

• Nvidia is already pushing memory makers toward 16-layer HBM (the ultra-dense, ultra-fast memory that feeds AI chips) for delivery as early as late this year.

• Nvidia is also working on something called “GPU-Direct Storage” for its next-gen Vera Rubin chips, plus a newer memory type called HBF (high-bandwidth flash) that offers roughly 10x the capacity of today’s HBM.

• Samsung, SK hynix, and Micron are pouring a combined $50+ billion into new memory factories.

• Those factories take 18 to 24 months to build, so relief for your wallet is still a 2027-or-later situation.

The shortage isn’t just pain. It’s also the single biggest wealth-creation event in the history of the chip business. The companies that make the picks and shovels (memory makers, packaging firms, the whole supply chain) are printing money. Samsung joining the trillion-dollar club is the loudest possible signal that the AI buildout is real and it’s nowhere near done (much to the chagrin of PC enthusiasts).

For the innovators reading this: the constraint is the opportunity (or more like, the problemtunity). Every dollar that memory eats is a dollar that rewards software which does more with less. Lean models, smart caching, efficient inference. The crunch isn’t going away soon, so the skill of squeezing value out of limited compute is about to be worth a whole lot.

The Weekly Whoops: the guy who wrote a book about AI lying… got caught lying with AI

The story: Steven Rosenbaum published a book this month called The Future of Truth: How AI Reshapes Reality. It’s a serious-minded warning about how AI distorts what’s real. There’s just one teensie-weensie, itsy-bitsy problem. The New York Times found that the book itself is sprinkled with more than half a dozen fake or misattributed quotes, apparently generated by the same kind of AI chatbots the book is warning you about. Yes, really.

What happened:

• One fabricated quote was pinned on tech journalist Kara Swisher, who told the Times she “never said that” and that the AI made her “sound like I have a stick up my butt.”

• Neuroscientist Lisa Feldman Barrett got quotes attributed to her that she said “don’t appear in the book and they are also wrong,” the kind of thing she “would never say.”

• Rosenbaum admitted the book had “a handful of improperly attributed or synthetic quotes” and opened an investigation into his own book (which is a sentence I enjoyed typing).

• He disclosed in the acknowledgments that he used ChatGPT and Claude during research and writing.

• His defense, more or less, was that if this serves as a warning about the risks of AI-assisted research, well, that’s the whole point of the book. Bold move, calling the fire your house caught a “fire safety demonstration.”

Here’s the actual lesson buried under the irony. AI is a phenomenal research assistant and a confident liar at the same time (kind of like that one intern, that one summer). The tools will hand you a perfectly phrased, perfectly formatted quote that the “source” never said, and it will look exactly as real as the true ones. There’s a name for it: hallucination. The model isn’t trying to deceive you. It’s pattern-matching what a quote should sound like, and sometimes the pattern wins over the facts.

So if you’re using AI to write anything that has your name on it (a report, an article, a book about truth, or even a weekly newsletter with charming humor that I won’t directly name), verify before it ships. Check every quote, every stat, and every citation against the real source. Do the fact-checking with your own two eyes. That should keep you off the wrong kind of New York Times news page.

What ties it all together

Last week was about distribution. This week was about everything that happens once the distribution land-grab is on: the Clone talent war heats up, the lawsuits get settled, the money gets… money-er, and the companies feeding the whole machine cross into rich Uncle Moneybags territory.

Step back and look at the board. Google is racing to put an agent in two billion pockets (I just checked my pockets for a tiny person). Anthropic is collecting the researchers who build the frontier. OpenAI cleared its biggest legal cloud and turned on the ad money. And Samsung crossed a line only a handful of companies in history ever have, purely because the world cannot get enough AI memory.

The thing worth holding onto is how fast all of this is compounding. Models, agents, talent, money, hardware: each one pulls the others forward. A year ago, any single one of these stories would have been the headline of the month. This week, it’s just business as usual, ‘round these here Weekly Think parts.

What I’m watching

• Whether Gemini Spark actually works as advertised when it hits Ultra subscribers next week, or whether it’s another impressive demo

• Where Karpathy’s pre-training team shows up first in Claude’s capabilities

• Whether Musk’s appeal goes anywhere, or whether this is really the end of the saga

• How people react to ads showing up inside ChatGPT (tolerance or revolt?)

• Whether any other memory maker chases Samsung into the trillion-dollar club this year

  

Thanks so much for reading this edition of The Weekly Think.

See you next week, matey!

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When I was a kid, “AI” meant one thing and exactly one thing. It was the opposition.

The boss at the end of the level. The enemies between you and the boss. The obstacles, the rival racers, the CPU in NBA Jam who somehow knew you were going for that slam dunk. Whatever shape it took, the objective never changed. You were there to beat the AI, no matter how clever it was (if you got frustrated, you lowered the difficulty to “easy”😓).

Those were very cool afternoons. Firing up the SNES to see what tomfoolery would come out of the latest rental from Eastman Video (which later got bought out by Hollywood Video, a sentence/thought that makes my back hurt). A slice of pizza in one hand, a joypad in the other, and a single clear mission for the day.

That’s the thing I keep coming back to lately. The word “AI” was so simple back then. It was a thing you beat on the weekend, in a tiny bedroom that was way too small for 10 kids (sorry, mom & dad).

It has not stayed that simple. The word has worn three completely different costumes across one lifetime, and watching it change clothes explains a lot about why this particular moment feels the way it does.

When AI was the opponent

The first costume was the one I just described. AI lived on the other side of the screen, and your entire relationship with it was adversarial by design.

AI was NOT your friend. You didn’t ask it for help. You studied its patterns, learned where it was weak, and exploited that weakness until the words “GAME OVER” stopped showing up on your side of the screen.

I want to plant that idea here and let it marinate, because it matters much more than meets the eye (they’re robots in disguise!… sorry, that’s a Transformers reference. Better save that for a later post.) The very first relationship most of us ever had with the term was a fight. We learned the word in a setting where defeating it was the whole point.

When AI became the sentient machine

The second costume came courtesy of Hollywood, and it grew up right alongside me.

Somewhere along the way, AI stopped meaning M. Bison from Street Fighter II and started meaning the machine that has an awakening. Sometimes the machine was kind and worked for the good of humankind. Sometimes the machine looked at humankind, did some quick math, and decided, “yeah, gonna get rid of these fleshbags real quick”. You already know the movies.

I’d rather point at the stranger one. There’s a Spielberg film from 2001 called, quite fittingly, A.I. It wasn’t the friendly-robot story and it wasn’t the destroy-all-humans story. It was the uncomfortable middle, a story about a machine that mostly just wanted to be loved and a world that didn’t know what to do with it. Nobody in that movie quite knew which camp to be in.

What a wild time. For this entire stretch, AI was not something you used. It was something you imagined, and mostly something you were a little afraid of.

When AI showed up on your actual desk

Then the third costume arrived, and it’s the one we’re all still adjusting to. AI stopped being imaginary. It became a tool sitting right there on the desk, doing actual work.

It helps researchers move faster through the kind of deep work that used to take months. It shows up in healthcare. It proofreads the email you were too tired to read twice. It sits in the room while you write and offers you a second set of eyes that never gets bored.

I’ll say my own piece plainly, because I’ve earned the right to an opinion here. I’ve worked as a multimedia creative for more than twenty years, and I find AI genuinely useful in my own writing and creative process. It’s one of the most capable creative tools I’ve put my hands on. I’m not embarrassed about that and I’m not going to pretend otherwise.

And in the very same breath, I cannot stand the slop.

You know the slop. The thoughtless, obviously-generated content, churned out by people who clearly didn’t read it before they posted it, recycled endlessly to the benefit of just about no one. It’s lazy and it’s just about everywhere (especially on your parent’s Facebook feed). And it gives the whole technology an off-odor that the technology doesn’t entirely deserve.

Here’s the part that the loud people on both sides have trouble with. I think the tool is remarkable and I think a lot of what it produces is garbage, and I hold both of those opinions without straining. One doesn’t cancel the other. I don’t have to talk myself down from the praise to make room for the criticism, or soften the criticism to keep the praise.

Why we still want to beat it

So here’s the strange place we’ve all ended up at.

One group of people loves this technology and wants to use it for everything. Another group wants to burn it to the ground in all of its forms. The conversation between them is less of a conversation and more of a standoff, and most of us are standing in the middle wishing everyone would lower their voices (I work at 6:00 a.m., thank you).

And I think, in a way, we’re right back in those old video game afternoons. A lot of people, deep down, just want to beat the AI.

That reflex makes a strange kind of sense once you remember where it came from. We met this thing as an opponent. The first definition any of us ever learned was the one where the entire point was to defeat it. So when a genuinely useful tool shows up on the desk decades later, some part of the old wiring still reads it as the boss at the end of the level. Or even a scary thing that threatens to take over the planet. The frame never got updated. The instinct to win against it is a leftover from a version of AI that either doesn’t exist anymore or was made up by some dudes in a writer’s room.

But the thing on your desk isn’t a boss fight. It’s not an overlord. It never picked a side. It’s just sitting there, capable of real help and real garbage depending entirely on who’s holding the controller.

The position nobody will let you hold in peace

The useful stance, the one I keep landing on, is the balanced one. And it happens to be the one neither camp will let you stand on without yelling at you.

You’re allowed to praise it and criticize it in the same sentence. You’re allowed to find it indispensable for your work on Monday and roll your eyes at a slop article on Tuesday. Knowing the nuance of where it actually helps and where it actually hurts is the entire practical skill, and it’s a skill, not a betrayal of one team or the other.

Whether you use it at all is a decision that lands on you, and honestly, it should. Not on the camp that would shun you for touching it, and not on the camp that would high-five you for posting something you didn’t write. It’s all on you.

Maybe one day the two sides drift toward the middle, and AI gets seen the way we eventually see every tool, as a thing that’s neither good nor evil, just useful in some hands and wasteful in others. I’d like to be around for that.

I miss the joypads

Some afternoons I do miss the old definition. The one where AI just meant the latest RPG to outwit, with an SNES controller in one hand and a soda within arm’s reach of the other, treating Mountain Dew like it was the water of life.

Although, on reflection, maybe I’ll leave that last Mountain Dew-fueled part in the past. My 40+ year old body tummy aches, just thinking about it.

The controller is gone either way. The thing I used to spend weekends trying to beat now sits quietly on my desk, waiting to help me write the very sentence you’re reading. Though, I guess a part of my noggin is still saying “beat the AI!”. Maybe that’s the actual work now, for all of us. Not beating it, and not worshipping it. Just finally seeing it for what it is.

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AI news seeker, come on down! Last week, I called what these labs are turning into a sort of FrAInkenstein (note to self: gotta trademark that bad boy). This week kind of proved the point. Nobody shipped a flashy new frontier model. What they shipped instead was distribution: into law firms, small businesses, Microsoft Office, and Beijing. Oh my!

The pattern isn’t hard to recognize. The race has shifted to distribution: who can plant their flag inside the most workflows before anyone else does. And the labs are basically copying Palantir’s old playbook now (more on that in a sec).

Here’s what’s covered today:

• Anthropic launched Claude for Legal AND Claude for Small Business in 48 hours

• OpenAI started a $4B consulting firm and called it the Deployment Company

• Google previewed its “Gemini Intelligence” and a new laptop category called Googlebook

• Jensen Huang got a last-minute call from Trump and ended up in Beijing

• The memory shortage went from “concerning” to “no affordable laptop for you!”

Anthropic went vertical: Legal on Tuesday, Small Business on Wednesday

The story: On May 12, Anthropic launched Claude for Legal: 20+ legal MCP connectors, 12 practice-area plugins, integrations with Westlaw, Thomson Reuters, Harvey, Everlaw, the works. Less than 24 hours later, on May 13, they shipped Claude for Small Business, a one-click install that wires Claude into QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, and Microsoft 365.

What happened:

• Freshfields, one of the world’s biggest law firms, has already deployed Claude to thousands of users.

• Legal became the #1 power-user job function inside Claude Cowork, with 3x the usage of any other vertical.

• Daniela Amodei said small businesses “make up nearly half the American economy” and have never had enterprise-grade tools.

• Anthropic also bundled a free AI fluency course (14 lectures, an hour of video) for the small business launch.

This is what going vertical looks like, friendos. A year ago, Anthropic was a chatbot. Now it’s an orchestration layer that sits between you and your billable hours, or between you and your unpaid invoices. The thing that should jump out at you, though, is the pacing. Two completely different verticals, two days apart, both shipped with real partner integrations already in place. Good gravy! Companies that take eighteen months to launch a single integration should be taking notes.

If you’re a small business owner reading this and you haven’t dug into Claude yet, this is the week to start. An AI course priced at free 99 is a great place to start.

OpenAI launched its own consulting firm (and immediately bought one)

The story: On May 11, OpenAI announced the OpenAI Deployment Company (also known as DeployCo). It’s a $4 billion subsidiary backed by 19 outside investors, with the very specific goal of, quite literally, putting OpenAI engineers inside Fortune 500 buildings. The same day, OpenAI announced it’s acquiring Tomoro, a London/Edinburgh-based AI consulting firm, to staff the whole ding darn thing on day one.

What happened:

• Tomoro brings approximately 150 Forward Deployed Engineers from day one (Palantir invented this role, and it apparently prints money).

• DeployCo launched with TPG as lead investor, plus Bain Capital, Brookfield, Goldman Sachs, SoftBank, and Warburg Pincus on the cap table.

• Three of the largest consulting firms in the world (Bain, Capgemini, McKinsey) also wrote checks.

• Tomoro’s existing client list reads like a fun trivia card: Tesco, Virgin Atlantic, Red Bull, Mattel, the NBA, Supercell.

• Axios reportedly values the unit at $14 billion.

You catch what just happened? OpenAI just got into the consulting business. They’re sending their own engineers into big companies (think Tesco, Virgin Atlantic, the NBA) to build custom AI tools on-site. The money isn’t in having the smartest model anymore. It’s in the hands-on work of making AI actually useful. Anthropic has been doing this with one law firm at a time. OpenAI just hired 150 engineers to do it with hundreds of companies at once.

For software builders, this is the kind of move you should be taking notes on. The deployment problem is so big that one of the biggest AI labs in the world just spun up a 150-person team specifically to solve it for other people. If you can be the person who walks into a company and makes their AI actually work, you’re going to have more work than you can handle for the next few years.

Google previewed Gemini Intelligence, a new laptop, and basically told Microsoft “we’ll take it from here”

The story: On May 12, Google held its Android Show: I/O Edition, a warm-up event a week before the main Google I/O keynote on May 19. They previewed Gemini Intelligence (a suite of agentic features baked into Android), Gemini in Chrome with “auto browse” (the browser, browsing itself, which is very meta if you ask me), and Googlebook, a new laptop category built for Gemini from the ground up.

What happened:

• Googlebooks will be made by Acer, ASUS, Dell, HP, and Lenovo, shipping this fall.

• The new OS underneath them, called Aluminium OS, is essentially Google merging Android and ChromeOS into one stack.

• A feature called Magic Pointer lets you wiggle your cursor at anything on screen, and Gemini suggests what to do with it (one writer compared it to Clippy “but potentially 1,000 times more annoying,” which, like, justice for Clippy bro✊🏽📎).

• Gboard is getting Rambler dictation cleanup, which scrubs up the “uhh… so basically” mess in your voice notes.

• Android 17 is also adding Pause Point, which interrupts you before you can open the doomscrolling app of your choice.

Microsoft tried this with Copilot+ PCs in 2024 and it didn’t exactly catch fire. Engadget had a great line on this: Microsoft “started stripping Copilot features out of Windows apps after users revolted.” So Google is walking into a category that the much bigger software giant already faceplanted in. Confidence is a heck of a placebo.

Two things you should be watching: first, whether Magic Pointer is genuinely useful or just genuinely annoying (try wiggling your finger at grocery store items with people around; see what happens). And second, whether Gemini in Chrome browsing-on-your-behalf is the moment agentic AI stops being a demo and starts being a habit. Speaking of which, I/O is next Tuesday. You better believe I’ll have thoughts.

Jensen Huang got a last-minute call from Trump and ended up on Air Force One

The story: On Monday, May 12, a list of CEOs traveling with Trump to meet Xi Jinping in Beijing went out to the press. Apple’s Tim Cook? Yes. Tesla’s Elon Musk? Yes. Qualcomm’s Cristiano Amon? Yes. Nvidia’s Jensen Huang? Notably… no. The CNBC headline was basically “oof.” Cue the actually-watching-his-own-funeral SpongeBob meme.

What happened:

• After CNBC and others ran the story of Huang’s absence, Trump personally called him.

• Huang flew to Alaska to board Air Force One mid-trip (which is a sentence I’d like to revisit when I’m old).

• Trump posted that opening up China for U.S. businesses would be his “first request” to Xi.

• Huang’s Nvidia made up the entirety of Anthropic’s SpaceX Colossus 1 lease last week (220,000 GPUs of it, anyway).

• Hassam Nasir at Tom’s Hardware noted Nvidia’s most advanced chips have faced tighter U.S. export restrictions on China for four years running.

Imagine being so necessary to global AI infrastructure that the President of the United States personally rerouted you onto a presidential aircraft in Alaska (in a huge “my bad, dawg” situation). Most of us would feel like we just got picked for the kickball team. Jensen probably felt like that was just another Tuesday. There’s a real story here about how the most consequential (and probably polarizing) CEO in tech right now is the one making the picks and shovels for everybody else’s gold rush. It’s reality: without Nvidia, this whole AI party probably wouldn’t be so crazy.

The compute supply situation is going to keep shaping what models you can use, where you can run them, and what they cost. Speaking of compute…

The memory shortage is now everyone’s problem (yes, including yours)

The story: Two reports this week made the memory crunch impossible to ignore. Insurance-Edge published a deep dive on May 13 showing that DRAM contract prices surged ~90% in Q1 2026, with order lead times now running past 40 weeks. Cisco, also on May 13, posted a 15% stock pop on earnings, and announced 4,000 layoffs starting May 14 to refocus on AI demand. Memory and AI are eating everything else.

What happened:

• Counterpoint Technology Market Research clocked memory prices up ~90% in Q1 alone.

• IDC projects 2026 DRAM supply growth at just 16% year-over-year, well below historical norms.

• Samsung’s memory chief Kim Jaejune warned shortages will persist through “at least 2027.”

• Some hyperscalers have already locked in HBM supply through 2027.

• Cisco’s revenue grew 12% on AI demand and they’re still cutting 4,000 jobs to chase it harder.

Here’s why this matters even if you never touch a data center (because, man, I’ve never even SEEN one). When the three companies that make 90%+ of the world’s memory (Samsung, SK hynix, Micron) decide to send their best wafers to Nvidia instead of Dell, the cost of every laptop, every phone, every PlayStation (gamers: “noooooo”), and every smart fridge (produce in the fresh drawer: “noooooo”) goes up. Lenovo, Dell, HP, Acer, and ASUS have all warned of 15-20% price hikes (and look, I love hiking but… oh, not that kind of hike…). Your next PC upgrade is being indirectly funded by Anthropic and OpenAI buying GPUs. (Funny how that works, no? Actually, it’s not funny.)

The flip side is the opportunity. If you’re a software builder or a small business owner, the memory crunch is creating a giant moat around anyone who can squeeze more value out of less compute. Models that run efficiently on local hardware just got a lot more interesting, and open-weights players did too. The companies betting big on running everything in the cloud are going to find out how expensive “the cloud” really is.

What ties it all together

Last week, I said the “AI lab” category was collapsing in real time. This week was the first week where you could actually see what’s replacing it.

You’ve got Anthropic building vertical-specific products (Legal, Small Business, Financial Services) the way a normal enterprise software company would. You’ve got OpenAI standing up a 150-engineer consulting arm to sit inside your office (gonna one heck of a stuffy office; hope there’s enough coffee). You’ve got Google merging two operating systems and shipping a laptop. And underneath all of it, you’ve got Jensen Huang on a plane to Beijing and a memory shortage that’s reshaping the price of every device you own. Whew.

The big tell of the week is what the labs are now selling. It’s integration. They’re embedding inside legal workflows, inside small business stacks, inside Microsoft Office, inside laptops, inside chip-supply diplomacy. The companies (and the people) who figure out how to ride that wave, by getting deployed inside workflows that actually matter, are the ones who win the next 12 months.

What I’m watching

• Whether Google’s I/O keynote on May 19 lands Gemini 4 or settles for an incremental bump

• How fast OpenAI’s DeployCo eats into Accenture/Deloitte’s AI services revenue (early signs not great for the consultancies; Accenture dropped 3% on the announcement)

• Whether Anthropic adds a vertical-specific Claude every week now (Claude for Healthcare? Claude for Real Estate? Claude for Farming??)

• Whether Trump’s China trip produces any actual chip-export movement

• DRAM spot prices, because the whole industry might be about to find out it built on sand (and not rock, like a smart person)

  

Thanks so much for reading this edition of The Weekly Think.

See you next week!

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