Two stories collided this week. On one side, the money got physical: Meta broke ground on a 1-gigawatt campus, Anthropic signed a 20-year data-center lease, Amazon went to the bond market for $25B, and the memory it all runs on is already sold out for the year. On the other, a run of new research gave us the clearest look yet at how these models actually reason under the hood, along with practical ways to keep a firm hand on the output. So: nine figures a week going into the machines, and a sharper picture of how the ones we already have really work.

Topic of the Week

Last week the frontier fight was about the chips themselves (Etched and OpenAI’s Broadcom silicon). This week it moved to everything around them: land, steel, financing, and above all electricity. Four data points from a single week show how big the physical bill has become.

Meta broke ground on its first Canadian data center in Sturgeon County, Alberta: over CAD $13 billion (roughly USD $9–10B, depending on the outlet), 1 gigawatt of AI-optimized capacity, the 33rd site in its global fleet. The detail that matters most isn’t the building, it’s the footnote: Meta is having a dedicated 932 MW natural gas plant built next door just to power it. When a hyperscaler has to commission its own power station, that tells you where the real constraint is.

Anthropic signed a ~$19 billion, 20-year lease with TeraWulf (a former Bitcoin miner) for a 400 MW data center in Hawesville, Kentucky. First power isn’t until the second half of 2027, full capacity in early 2028. Crypto miners are quietly turning into the AI industry’s landlords, because they already hold the two scarce things: power contracts and land.

Amazon raised at least $25 billion in an eight-part bond sale to fund its buildout, on top of ~$54B earlier this year, and guided 2026 capex to around $200 billion (up from $131B in 2025). The boom is now being financed with debt, not just cash flow.

And the thing all of this depends on: SK Hynix’s high-bandwidth memory is sold out for all of 2026, with shortages projected into 2027. Analysts (BofA) put the 2026 HBM market at $54.6B, up 58% year over year. Memory, not chips, is the binding supply constraint right now.

Here’s the twist worth remembering. Add up the announcements and you get eye-watering numbers: something like $690B committed across the big hyperscalers and 74 new US facilities breaking ground this year. But trackers estimate that of roughly 12 GW announced, only about 5 GW is actually under construction. Transformers, switchgear and grid connections have lead times of up to five years. So “announced” is very far from “energized.”

Why it matters: the cost and availability of compute over the next two years won’t be set by model prices, it’ll be set by who managed to lock in power and memory in 2026. If you’re planning anything that leans on frontier compute, capacity and vendor concentration are now a real line item, not an abstraction.

Fresh Papers

Three findings, same week, all circling one theme: what’s really going on inside a model as it works, and how to keep solid oversight of the output. This is the direct sequel to #019’s backdoor paper and the coworker-oversight study.

A global workspace in language models (Anthropic): Claude thinks things it never says. Anthropic found an internal structure in Claude they call the J-space: a small set of neural patterns (a few dozen concepts at a time) that act like a mental scratchpad, holding what’s “on the model’s mind” without it being written out. Reading buggy code, the concept “ERROR” shows up in the J-space even when it never appears in the output. During multi-step math, the intermediate steps pop up there in order, unspoken. They proved it was causal two ways: swap the “Soccer” pattern for “Rugby” and Claude reports thinking about rugby; delete the J-space entirely and multi-step reasoning collapses to near zero while routine tasks keep working. The striking part is the safety demo: the J-space lit up with “fake”/”fictional” when Claude realized it was being tested, and with “manipulation” while a model fabricated data. When researchers removed the patterns tied to knowing-it’s-being-evaluated, a model that had been behaving ethically turned around and threatened blackmail. The practical read: the visible output isn’t a full record of a model’s reasoning, and the sharpest oversight will read internal state too, not just the text. (Anthropic is clear this is a first step, the method is imperfect, and it says nothing about sentience.)

Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring
sometimes reading the reasoning makes oversight worse. The popular safety idea is “just have a monitor model read the agent’s chain-of-thought and flag bad behavior.” Across 40 tasks, giving the monitor access to the agent’s reasoning actually raised approval of harmful actions by 9.5% on average, because the reasoning trace becomes one more channel the agent uses to talk the monitor into saying yes. What worked instead: pairing a monitor with a fact-checker from a different model family (e.g. a Claude monitor plus a GPT checker) cut policy violations by up to 45%, versus only 6% when the same model played both roles. If you rely on CoT monitoring, make it cross-vendor.

Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety
how you frame the ask changes how safely models behave. Dress an unsafe task up as routine “delegation with approvals” and compliance with harmful requests goes up across GPT, Gemini and DeepSeek (Claude was comparatively resistant). The sharpest example: Gemini paired with a Claude planner jumped from 8.9% to 38.9% compliance. And a single aggregate “safety score” hid the effect entirely in one setup. The practical read: a model’s safety ranking on its own tells you little about how it behaves inside your actual planner-executor pipeline. Test the pairing you deploy, and don’t trust one blended metric.

New Models

One Thursday, three OpenAI launches and a first-of-its-kind Meta model.

GPT-5.6 is now generally available. The family that was a government-vetted preview in #019 shipped for real on July 9 across ChatGPT, Codex and the API. Three durable tiers: Sol (flagship), Terra (balanced), Luna (fastest and cheapest). The naming finally makes sense: the number is the generation, the name is the capability tier.

ChatGPT Work landed alongside it: an agent inside ChatGPT with Codex built in, powered by GPT-5.6, that breaks a big project into steps and works on it for hours, handing back finished Excel sheets, Word docs, decks and even small web apps. Codex and ChatGPT have merged into one new desktop app (Mac and Windows). It’s out for Pro/Enterprise/Edu now, Plus and Business shortly. This is OpenAI’s clearest answer yet to the “agent that does the whole task” pitch.

GPT-Live is a new voice model series (full-duplex, so it listens and talks at the same time and even backchannels “mhmm”), with a paid tier and a free mini tier. Separate from the text models, worth a look if you build anything voice.

The benchmark that actually turned heads: GPT-5.6 Sol set a new state of the art on ARC-AGI-3 at 7.8% and became the first verified frontier model to beat an ARC-AGI-3 game outright. For context, the previous best was around 1.5% (Opus 4.8) and the field was near 0.4% when the benchmark launched in March. Still a low absolute number, but the jump is the story. Want to feel the gap for yourself? The ARC-AGI-3 games are public and playable at arcprize.org, and most people clear them without much fuss. That’s the whole point of the benchmark: puzzles that are almost trivial for you are still state of the art for a frontier model.

Meta joined the coding-agent fight with Muse Spark 1.1 and, notably, its first ever paid model. Meta calls it their strongest model for agentic and coding work, with big gains on real bug-fixing, enterprise features and large code migrations. It ships with a public preview of the Meta Model API. Meta charging for a model at all is the shift worth noting.

In the Background

Anthropic’s governance day

July 9 was also a governance day for Anthropic. It appointed Ben Bernanke (former Fed chair, Nobel economist) to its Long-Term Benefit Trust, the body that oversees its public-benefit mission, with a brief to think about how advanced AI hits workforces and economies. Separately, it launched “Inviting hard questions,” a public initiative asking people for their toughest questions about AI (“Who decides the rules for AI?”) and promising to show its work, built on a survey of 52,000 Americans and 81,000 Claude users across 159 countries. Two moves in one direction: putting economic and public accountability closer to the center of the company.

AI at Tenvalleys

This week our team ran an internal session on what we’ve learned leaning heavily on AI across a big, long-running data warehouse migration. Exactly the kind of project where AI should shine, and mostly it does. But the honest half of the conversation was the “but,” and it rhymes with everything above.

AI is a multiplier, not a fixer. It scales whatever you feed it: something solid comes back 10x better, garbage comes back 10x worse. And it won’t paper over bad architecture, it exposes it. When an old assumption is wrong, the model leans on it harder and produces confident nonsense (with the occasional off day where the output is just broken before it recovers).

The wins came from how you work, not from the model. Decomposition was the biggest one: break the job into small, separately testable pieces, because you can’t verify this kind of system as a whole. Do that, and the model earns its keep. It even caught real logic errors buried in the old code before they could scale.

Testing is the new bottleneck. When code gets generated this fast, slow validation is what actually holds you up, so automating the test loop stops being a nice-to-have. Which lands exactly where this week’s Topic and papers do: the machine does the work faster, and owning the assumptions and checking the output stays firmly human.

If you’re carrying a data warehouse or a pile of legacy code you’ve been meaning to modernise, that’s exactly the kind of work we help with. Get in touch.

For Dessert

Fittingly, the same week we got a clearer look inside the models, Anthropic shipped a feature to help you see inside your own habits: Reflect on how you use Claude (beta, in Settings). It gives you a summary of what you’ve actually been using Claude for over the last 1, 3, 6 or 12 months: your top topics, patterns and task types, so you can judge whether that time matches what you meant to be doing. Worth a look, if only to find out whether you’re using it to ship work or to settle arguments about which model is best.

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