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<title>ConsultingAI · Shreenivas Sangoram</title><link>https://www.consultingai.dev/blog/</link><description>Significant moves in AI, and how to build agents, RAG and evals that hold up.</description><language>en</language><atom:link href="https://www.consultingai.dev/feed.xml" rel="self" type="application/rss+xml"/>
<item><title>Between two Samsung lines, the eyes post: the shortage is now a payout story</title><link>https://www.consultingai.dev/blog/jukan-eyes/</link><guid>https://www.consultingai.dev/blog/jukan-eyes/</guid><pubDate>Thu, 29 Oct 2026 00:00:00 +0530</pubDate><category>AI news</category><description>We could not find a quoted post or an image attached to this one — on the account mirrors it is exactly two emojis, no text. What it sits between is more informative than what it says. Jukan posted it during Samsung’s Q3 call, immediately after “the memory shortage will worsen in 2027” and next to the preferred-share buyback remarks. The subject that repriced that morning was not demand. It was the return plan: KRW 90–110 trillion of payouts announced on top of a market that is sold out through 2028.</description></item>
<item><title>Samsung’s HBM4 sold at “target price” is the memory bill for Rubin</title><link>https://www.consultingai.dev/blog/jukan-hbm4-pricing/</link><guid>https://www.consultingai.dev/blog/jukan-hbm4-pricing/</guid><pubDate>Thu, 29 Oct 2026 00:00:00 +0530</pubDate><category>AI news</category><description>Jukan is right that this is the tell in Samsung’s quarter, and it is worth putting the unit math beside it: mid-to-high $4 per gigabit is roughly $36–40 per gigabyte, which is about $9,000–$10,500 of HBM in a single 288 GB Rubin GPU. Sold out at target price does not mean the shortage eased. It means the price of the shortage is now contractual, and NVIDIA is going to sell it as a product feature rather than absorb it.</description></item>
<item><title>Powertech’s $400M panel fab is where Broadcom’s ASIC volume goes when CoWoS is full</title><link>https://www.consultingai.dev/blog/jukan-powertech-plp/</link><guid>https://www.consultingai.dev/blog/jukan-powertech-plp/</guid><pubDate>Thu, 29 Oct 2026 00:00:00 +0530</pubDate><category>AI news</category><description>The report is right and the timing is the point: Taiwan’s Investment Commission cleared the US$400M investment on 28 October, which turns a July announcement into money that can be spent. Panel-level packaging is the second queue for AI packages, and Powertech is one of the few OSATs with custom-ASIC volume already reported booked on it. If you are modelling Broadcom’s AI revenue, this line matters more than another CoWoS wafer forecast.</description></item>
<item><title>Samsung’s 60–70% LTA target is a mortgage on 2030 capacity</title><link>https://www.consultingai.dev/blog/jukan-samsung-ltas/</link><guid>https://www.consultingai.dev/blog/jukan-samsung-ltas/</guid><pubDate>Thu, 29 Oct 2026 00:00:00 +0530</pubDate><category>AI news</category><description>Jukan is flagging the most important sentence in the whole Samsung call, and it is worth being precise about what it does and does not lock. The contracts lock volume, not price. Twenty customers in a queue for five-year agreements through 2030 means the memory cycle has been converted into a demand schedule with deposits attached — and that schedule, not the spot print, is what now justifies the capex.</description></item>
<item><title>Samsung’s 2028 line is a lead-time statement, not a demand forecast</title><link>https://www.consultingai.dev/blog/jukan-tight-supply-2028/</link><guid>https://www.consultingai.dev/blog/jukan-tight-supply-2028/</guid><pubDate>Thu, 29 Oct 2026 00:00:00 +0530</pubDate><category>AI news</category><description>Jukan is right to lift this line, and it reads better when you put it next to the capex number from the same call. Samsung is guiding 2026 capital spending to KRW 84.4 trillion and still says 2028 is tight, because the money buys cleanroom shells and tools on 12-to-18 month lead times before it buys output. The sentence is not a demand forecast. It is a statement about how long it takes to build a fab in 2026 — and about HBM4E and HBM5 dies that are bigger, with more metal layers, so each wafer yields fewer bits.</description></item>
<item><title>What is DataVendor by HUD, and who pays for your data?</title><link>https://www.consultingai.dev/blog/datavendor-hud/</link><guid>https://www.consultingai.dev/blog/datavendor-hud/</guid><pubDate>Wed, 28 Oct 2026 00:00:00 +0530</pubDate><category>AI news</category><description>DataVendor is a marketplace run by HUD (Human Union Data, Inc., a San Francisco startup) where teams license private codebases, RL tasksets with their environments, and raw data to AI labs. It opened a public beta on 22 Oct 2026. Vendors set their own prices; DataVendor keeps 20% of earnings, charged only when you withdraw to a bank. Selling is self-serve, while buyers join a waitlist. No completed sale prices or buyer names are public yet.</description></item>
<item><title>Can you steer sycophancy out of a small model without retraining it?</title><link>https://www.consultingai.dev/blog/sycophancy-steering-small-models/</link><guid>https://www.consultingai.dev/blog/sycophancy-steering-small-models/</guid><pubDate>Tue, 02 Dec 2025 00:00:00 +0530</pubDate><category>Interpretability</category><description>Yes, partly. On Qwen3-0.6B, a logistic-regression probe finds sycophancy in the hidden states (73.5% accuracy at layer 15). Subtracting the probe&#x27;s direction during generation raised correct answers under social pressure from 12.5% to 53.8% (+41.3 points), with no RLHF or fine-tuning. The effect held on Qwen3-4B (+16.4) and DeepSeek-R1-8B (+23.5), but the best steering strength has to be tuned for each model.</description></item>
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