The Weekly Think: June 26 – July 1, 2026
Memory prices are climbing again, OpenAI shipped a model the government has to approve you for, Anthropic’s banned model inched toward a comeback, and China trained a giant AI sans Nvidia chips.
Well well well, if it isn’t your boy, Eric. June 2026 might go down as the month AI stopped being a fun tech story and turned into a full-on geopolitics story (wish it was a GeoCities story), and boy did this past week’s news start coming (and it didn’t stop coming because, hey now, you’re an all-star). I’m publishing a day early because I’ll be away tomorrow, so consider this your slightly-ahead-of-schedule dispatch from the final stretch of the wildest month I’ve covered yet.
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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