The AI Productivity Paradox: A Central Bank Measured It
The Bank of Korea surveyed 5,512 workers: AI saves 1.5 hours a week, but the correlation with actual output is near zero. Why time saved is not productivity.
Adoption, productivity, publishing and national strategy show where AI creates value and where it only moves it from one balance sheet to another. Measured gains rarely match the claims, search referral traffic is repricing entire publishing models, and countries subsidize compute for reasons that are strategic before they are economic.
This guide frames the system before you move into the newest signals.
The Bank of Korea surveyed 5,512 workers: AI saves 1.5 hours a week, but the correlation with actual output is near zero. Why time saved is not productivity.
The featured guide stays above; the stream below moves as new analysis is published.
OpenAI priced ChatGPT Business Premium exactly like Anthropic Claude Team Premium, but the 2-seat minimum pushes the real floor to $120, not $100.
OpenAI posted first Jalapeño benchmarks: up to 1.9x more throughput per kilowatt than Nvidia. Real numbers, plus the caveats the headlines are skipping.
YMTC now ships more NAND bits than Micron or Kioxia but ranks only fifth by revenue. What that gap means for flash prices in 2026 and 2027.
Samsung, SK hynix and Micron reversed course and committed about 2 trillion dollars to new memory fabs. The first wafers arrive in December 2028.
Three studies measured AI at work and disagreed: 40 percent faster writing, 14 percent more support tickets, 19 percent slower coding.
A layer-by-layer map of AI data center capex: hyperscaler spending, Nvidia revenue, neocloud debt, REIT backlogs and grid orders, from reported figures.
Grok Bot needs a $120, $200 or $300 per month plan while Claude Cowork ships from $20. What the seat price buys and where it breaks even.
Uber and Walmart capped employee AI use after costs surged. Learn what token budgets reveal and how enterprises can measure AI spend, value, and ROI.
OpenAI and Anthropic alumni raised billions in pre-product seeds in 2026. Verified funding for every spinout, and what the money is buying.
CXMT closed 466 percent above its offer price on debut, worth 3.28 trillion yuan. What that valuation implies about the DRAM shortage.
SK hynix says 2027 will be the worst supply year ever for memory and ADATA expects a decade-long shortage. Here is how long the RAM squeeze may last.
An AI data center explained: how it differs from a traditional data center, what it costs per megawatt, and why power is the binding constraint.
Nearly 200 startups urged Trump not to restrict Chinese open-weight AI. Here is what a ban would target, why enforcement is hard, and the stakes.
Chinese AI models already lead on price and open weights. A six-part scorecard shows why broad leadership is plausible by 2028–2030, but not inevitable.
Compare the real monthly cost of tracking your brand across ChatGPT, Gemini, Perplexity and AI Overviews, plus how to choose the right tool.
Current 2026 API and subscription prices for DeepSeek, Qwen, Kimi, GLM, and MiniMax, compared per million tokens and against US models.
How AI cost per token fell roughly 10x per year since 2021, why it collapsed, and why your per-task bill did not drop nearly as fast.
Kimi K3 and DeepSeek V4 are both open-weight Chinese models. K3 leads the AA intelligence index; DeepSeek V4 costs a fraction and leads on SWE-bench.
Moonshot AI, the Chinese lab behind Kimi, is founder-led by Yang Zhilin, with Alibaba its largest backer and a reported 20 billion dollar valuation.
What does CoreWeave do? See how its GPU cloud works, who uses it, how it makes money, its NVIDIA relationship, and the risks behind its rapid growth.
CXMT is the largest DRAM maker in China, now the number four global supplier, closing in on Micron on capacity while its HBM chips stay years behind.
How AI infrastructure gets financed in 2026: Helix (KKR/Nvidia), Apollo/Blackstone (Anthropic), Stargate (OpenAI), and what this means for model pricing.
Kimi K3 topped the WebDev coding arena at a fraction of frontier prices. Full specs, pricing, benchmarks, and the Moonshot business, verified July 2026.
Who is Matei Zaharia? The Apache Spark creator and Databricks co-founder also helped build MLflow. Here is how his work shaped modern data and AI systems.
OpenAI is spending nearly 6.5 billion dollars to build a screenless AI smart speaker. The business logic, the running costs, and the Apple lawsuit risk.
Consumer DDR5 has gone flat while contract and HBM prices keep climbing. Here are the signals that show whether the 2026 memory cycle is peaking.
68% of searches end without a click. AI Overviews cut publisher traffic 40%. What replaces the click economy in the citation era.
AI Overviews cut publisher clicks 40%. Six revenue strategies publishers are deploying now, from per-query monetization to session-level optimization.
OpenAI is shutting Sora down while Chinese models top the independent video leaderboards on quality and cost. The state of AI video, July 2026.
Chinese AI models list output tokens up to 57x below US flagships. The verified economics of efficient training, cheap power and open weights as strategy.
The Trump administration asked OpenAI to stagger GPT-5.6 and approve users one by one. A federal gate now sits between a finished AI model and the public.
AI usage tracks national income, but the standouts break the rule. The economics of why Israel and Singapore use AI far more than their wealth predicts.
Seven Chinese firms now ship AI accelerators, the best near NVIDIA H100 class. A fact-checked 2026 map of who makes China's GPUs and what is real.
SpaceX is buying Cursor for $60B. What changes for your bill, whether your code now trains xAI models, and the real cost per task of switching away.
Lin Junyang, Fei-Fei Li, and Yann LeCun are raising billions for world models in 2026. What a world model is, who is funding it, and why the money is moving.
AI adoption, productivity, publishing, national strategy, funding and the markets being reshaped.
This topic collects 36 analyses, and they are written to be read together rather than one at a time: the featured guide sets out the shape of the problem, and the pieces below work through the individual numbers, tradeoffs and edge cases behind it. Every figure is attributed to a primary source at the point it is used and carries the date it was verified, because prices and benchmark results in this area go stale in weeks rather than years. Where a number is modeled rather than measured, the assumptions are stated so the arithmetic can be checked.
The topics on this site overlap by design. This one runs into Compute infrastructure and AI costs, and a question that starts in one usually ends in another: a pricing decision turns into a hardware decision, a benchmark result turns into a cost question. Follow the links inside the posts rather than treating these archives as separate shelves.
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