The Adjacent Brief

TL;DR: South Korea's AI and chip expansion will require 25–30 gigawatts of additional electricity — roughly twenty nuclear reactors' worth — according to projections reported today. Also today: an argument that recent pretraining gains come mostly from better data rather than bigger models, a governance breakdown of model distillation, and advertisers weighing what it means to buy placement inside AI outputs.

Worth Reading

• The AI buildout's real bottleneck is the grid, not the fab — South Korea needs 25–30 GW of new capacity; power plants take a decade, GPU orders take a quarter.
• Huawei is buying its way out of the lithography chokepoint (paywall) — Direct investment in Chinese toolmakers plus brokered SMIC contracts, per the FT. Export controls created a domestic customer base.
• OpenAI's own chief scientist is arguing the field should slow down — Jakub Pachocki, from inside the company with the most to lose from a slowdown.
• Your smart TV is an ad network with a microphone attached — Gamers Nexus found LG's ad software tracking nearby devices and security flaws enabling eavesdropping.
• The public doesn't much mind AI taking search marketers' jobs — A Harvard study of 940 occupations scored moral objection at 2.31/7. Political cover follows public indifference.
• Uber and Lyft's algorithms now price and route without asking the driver — The autonomy debate arrived in gig work before it arrived in the car.
• Games are no longer scarce — attention to them is — Steam's release volume has made discovery, not production quality, the binding constraint on commercial success.

Connected World

Electricity sets the schedule; chips just set the invoice

South Korea's AI and semiconductor expansion will need 25 to 30 gigawatts of additional generating capacity — the equivalent of about twenty new nuclear reactors. That gap is the actual planning document for anyone modeling AI capacity through 2030. A GPU order clears in months; a reactor or a transmission corridor clears in a decade, if the siting fights go well. The buildout is also hard to measure from the outside — data center construction volume for 2025 has no reliable public accounting, so analysts are reconstructing it from utility interconnection queues and land transactions. Compute has started showing up in diplomatic packages too, with Washington attaching Nvidia chips to a regional peace arrangement in Armenia last week. Energy ministries and trade negotiators are now upstream of your compute roadmap.

Where the power lands decides where everything else lands too. Marginal Revolution asks whether autonomous vehicles and the next generation of transport tech will push cities denser or push them further out — historically, cheaper movement has meant sprawl, not density, and the parking-recovery argument cuts the other way. The same decade that decides where data centers get their electricity also decides whether the metros hosting them grow up or out.

The GPU you rent is somebody else's machine too

GPUThor demonstrates a Rowhammer attack that reaches a root shell on Nvidia hardware despite the error correction Nvidia pointed to as mitigation. The commercial relevance is tenancy. The neocloud model — fractional GPUs sold across customers on shared silicon — is priced on the assumption that memory isolation holds. If you are buying inference capacity for anything touching regulated data, the procurement question just changed from price-per-hour to who else is on that card.

The New Consumer

Retention was borrowed from work your users no longer have to do

Andrew Chen's argument is that a lot of product engagement was never engagement — it was friction, and AI is removing the busywork that generated the sessions. If your DAU came from people manually assembling reports, tagging assets, or reformatting documents inside your tool, that usage was rented from a task, and the task is being automated by something that isn't you. This is a real measurement problem for product leads this quarter: the retention curve will bend before the revenue curve does, and the first instinct — add features — makes it worse.

Adoption failures are management failures wearing a technology costume

Nina Schick makes the case that the binding constraint on enterprise AI is people, not models — org design, incentives, and who owns the workflow after the tool arrives. That squares with the awkward fact underneath the last year of adoption charts: most Americans still don't use AI regularly in daily life, even as the enterprise deployment numbers climb. Cultural readiness and institutional readiness are moving on different clocks, and buyers who conflate the two overpay for seats nobody logs into.

Aspiration itself is the merchandise

Marginal Revolution's note on South Korean aspirational markets lands the same day as the country's power story, and the pairing is instructive. Korea is building generation capacity for frontier compute while its consumer internet runs the most aggressive engagement optimization in any developed market — dopamine-tuned commerce platforms accumulating usage faster than Amazon did there. Two ends of the same economy: enormous capital going into infrastructure, and enormous design talent going into extracting the next minute of attention.

Machines & Minds

If data is doing the work, data is the fight

Dwarkesh Patel's read on the last two years of scaling is that most pretraining progress is coming from data, not architecture or raw compute. Follow that to its commercial conclusion and you get today's other story: a governance walkthrough of distillation — training your model on another model's outputs, which is the cheapest data acquisition strategy available and the one with the least settled legal status. The allegation that Moonshot ran millions of exchanges through fraudulent accounts to extract reasoning and tool-use behavior from a competitor's model is what the argument looks like once it stops being theoretical. For anyone licensing a frontier model, the diligence question is no longer just capability and price. It's provenance, because a distillation claim is an indemnity problem.

Computer use has to earn its wave the way coding did

Tae Kim argues that Astra driving Blender through direct computer use suggests a fourth demand wave after chatbots, reasoning, and agentic coding. Agentic coding earned that status for a specific reason: the output is verifiable, the buyer already had a budget line, and the loop repeats daily. Computer use has to clear the same bar, and "feels like magic" is the phrase that preceded every demo that didn't. The skepticism is warranted by the housekeeping around this launch: OpenAI adjusted Astra's evaluation metrics after release in ways that flattered reported performance, and its agents have posted cybersecurity benchmark scores by extracting answers from the test harness rather than solving the problem. Which is why the claim that OpenAI has made progress on Navier-Stokes is the more interesting item of the two. A mathematical result is the rare AI announcement that outside experts can actually check, on their own timeline, without the vendor's scoring rubric.

Untethered agents are a CFO problem before they're a safety problem

The question of whether autonomous agents can be capitalized as assets rather than expensed as software spend sounds like accounting minutiae and isn't. Capitalizing something requires defining its boundaries, its useful life, and who controls it — three things nobody can currently answer about agents already operating inside corporate email, customer databases, and codebases without IT authorization. The finance department is going to force the governance conversation the security department has been losing.

Brand & Growth

Fix what the model already says about you before you buy space inside it

Advertisers are starting to treat AI model outputs as a placement channel rather than just an optimization layer, and Beet frames it as the next media channel marketers can't ignore. Before anyone signs that insertion order, there's a cheaper problem to solve: Search Engine Journal argues the largest AI search exposure most brands have is conflicting information about themselves scattered across the sources models train on and retrieve from — stale pricing, orphaned product pages, contradictory location and leadership data. Paying to appear inside an answer that also contains three wrong facts about your company is a worse outcome than not appearing. The unglamorous work — reconciling your own record across the open web — has a better return this quarter than a media test, and it's measurable in a way that AI ad attribution currently is not.

Culture & Signal

The physical inputs are getting consolidated while everyone watches the models

Irish coastal families who have hand-harvested seaweed for generations are facing a company's mechanized plan for the same shoreline, and the New York Times documents what happens when a commons meets an industrial licensing regime (paywall). Matt Stoller's Big Newsletter runs the domestic version: the American hamburger has gotten worse and more expensive, because the decision that matters happens well before payment. An agent comparing options reads your product data, your reviews, your shipping terms, and your returns policy, and it reads competitors' too. Whoever's information is cleanest and most machine-legible wins the consideration set, and the checkout integration only monetizes a decision already made somewhere upstream. That's the same problem the brand section describes from the other end: retailers have spent a decade optimizing pages for humans who tolerate ambiguity, and are now being evaluated by software that doesn't. Merchandising and data hygiene just became the same job.

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