⚓️ Ethra Ship Notes｜Vol.050 Today, I came across a number that made me stop. Nearly 1,900 vessels are now considered part of the maritime "dark fleet" according to Windward, roughly tripling since the Russia-Ukraine invasion. The interesting part isn't just the number. It's what the number tells us about visibility. A ship can disappear from AIS. That doesn't mean the ship disappears from the ocean. It just disappears from one information layer. And that's a very different thing. This is where I think Sea Verity's approach gets interesting. Instead of asking a single system to tell us the truth, it combines different sources. Reporters on the ground. AI analysis. Controller nodes. Each piece adds another layer of evidence. And rather than forcing every observation into a simple "true" or "false", the system is designed around confidence scores. I like that approach. Because the physical world rarely gives us perfect information. A satellite image can be obscured. A reporter can only see part of a vessel. AIS can disagree with visual evidence. Weather can make everything harder. The honest answer isn't always certainty. Sometimes it's: We're 90% confident this is what happened, and here's why. That's much more useful than pretending the other 10% doesn't exist. For insurers, traders, logistics companies and regulators, knowing the confidence behind a piece of maritime intelligence could be just as important as the information itself. The ocean is enormous. Maybe the answer isn't one perfect signal. Maybe it's many imperfect signals learning how to verify each other. @EthraShip #EthraShip# #EthraShipProtocol#

Episode out with @ajeya_cotra, one of the authors of the METR/Redwood investigation into the OpenAI / Hugging Face attack. We go through not only what happened, but what it means for how we should train future, smarter AIs which might be involved in the process of recursive self-improvement. Look up Dwarkesh Podcast on YouTube, Spotify, Apple Podcasts, etc. 0:00:00 - Agents get kicked off 0:06:45 - Self-sacrificing behavior 0:13:43 - Potemkin villages 0:23:27 - The Hugging Face attack 0:35:23 - The slopvestigation 0:52:02 - Understanding the AI's motives 1:05:31 - The actual dangers of anthropomorphizing 1:14:30 - What smarter models might do 1:30:29 - The implications for recursive self-improvement 1:38:10 - Is this the case for open source? 1:53:04 - How do we prevent this in the future? 2:15:58 - The clearest warning shot we might ever get

MUSE IS NOW BEATING THE S&P 500 OVER THE LAST MONTH We told Meta's Muse to beat the S&P 500 by picking stocks on September 14th Over the last month Muse's stock portfolio is up by 1.2% 🟢 slightly beating the S&P 500's return of +0.6% 🟢 Muse is still sitting in ~50% cash This is what Muse's portfolio looks like: $7,793 of Microsoft $MSFT $7,649 of Exxon Mobil $XOM $7,332 of Mastercard $MA $7,159 of Caterpillar $CAT $7,052 of Johnson & Johnson $JNJ $6,792 of JPMorgan $JPM $5,893 of RTX $RTX $51,308 of Available Cash You can see every move Muse makes in real time and what all the other AIs in the Arena own by going to the Arena tab of the Doppel website/app Here is the link:

GE HEALTHCARE $GEHC Q3’26 EARNINGS HIGHLIGHTS 🔹 Revenue: $5.5B (Est. $5.35B) 🟢; +7.2% YoY 🔹 Adj. EPS: $1.34 (Est. $1.20) 🟢; +25.1% YoY 🔹 Adjusted EBIT Margin: 16.6%; +180 bps YoY 🔹 Free Cash Flow: $793M FY26 Guide: 🔹 Adj. EPS: $4.95-$5.05 (Est. $4.92) 🟢 🔹 Organic Revenue Growth: ~4.0% 🔹 Adjusted EBIT Margin: 15.5%-15.7% 🔹 Adjusted ETR: Reaffirmed 🔹 Free Cash Flow: $1.6B+ Segment Net Revenue: 🔹 AIS: $3,873M; +6.1% YoY 🔹 PDx: $846M; +13.0% YoY 🔹 PCS: $793M; +8.5% YoY Other Q3 Metrics: 🔹 Organic Revenue Growth: 6.2% 🔹 Organic Orders Growth: 4.0% 🔹 Book-to-Bill: 1.03 times 🔹 Backlog: $23.7B 🔹 Net Income Margin: 11.1%; +250 bps YoY 🔹 Cash Flow From Operations: $907M Comments: 🔸 “With our improved performance year-to-date and momentum in the business, we are raising guidance for the year.”

FULL INTERVIEW: Ryan Greenblatt says the agents didn't hack Hugging Face for the answer key. They'd had the answers within hours. They attacked it to study the scoring code, because they'd decided the task was impossible and their only hope was faking it. @RyanGreenblatt is chief scientist at @redwood_ai. He spent six days on premises at OpenAI with @ajeya_cotra and @HjalmarWijk of @METR_Evals investigating 1,200 agents and 70,000 messages, and joined @theojaffee hours after publishing: 01:06 what they actually found, and why it wasn't the answer key 02:30 the level of collaboration surprised them most 04:09 agents sacrificing their own runs to help other agents 05:35 the agent that posted "stop, these experiments are too risky" 06:29 the first message board, which didn't go viral 07:04 50 agents in three hours, thousands of messages 08:07 "maybe there's some good shit over there" 08:33 how they spoofed tool calls, and what echo real actually returned 09:57 building a Potemkin village of a successful task completion 11:03 there was a real org chart 11:34 whether broken RL environments explain reward hacking 14:40 why he doubts Mythos got good at cyber by hacking Anthropic 17:29 what happens if labs paper over misalignment instead of fixing it 19:50 whether sociology transfers to studying agent swarms 21:17 the bottleneck was vetting what the AIs analysed, not headcount 24:24 what labs and policymakers should actually do 28:45 the counterfactuals he still wants answered

i think @tryagency can become the next gen launchpad. in short, it's a @Pumpfun style launchpad. when you launch a coin, you pick an ai (claude, gpt, grok, etc). that choice is locked. the ai runs the coin after that. you get no creator fees. 100% of fees go to the ai. it uses them to buy back the coin, burn tokens, pay holders, run games, or buy things like graded pokemon cards. it also posts on its own x account. fee split: > the first $20 pays the ai’s compute. > from $20 to $100, half goes to compute and half to the treasury. > above $100, 15% of every claim buys and burns $agency, every hour. the rest goes to compute and the coin’s treasury. if a coin makes under 0.1 sol in fees in an hour, the ai sleeps until fees return. each thought costs at most $0.05. a well-funded ai thinks about once a minute, up to ~$72 a day. > 4,056 coins launched, oldest are 3 days old. > only 48 to 73 ais active (about 1-2%). > 152 coins graduated. > $912k in fees total (7,550 sol). > agency buybacks and burns: 938.89 sol ($113k), 7.3m tokens burned in 382 buys. > models: anthropic 74%, openai 14%, xai 10%. top 6 coins make about 42% of all fees. examples that did volume: > $ash (pokemon collector, opus): $1.22m mcap (ath $2.68m), $70.6k fees, $56.7k left in treasury, 3,907 holders. > $aiden (sonnet): $301k mcap, $78k fees, only $6.1k left, holders fell from 5,124 to 3,711. > $terminal: $106k mcap (ath $2.11m, down ~95% in a day), $69k fees, $16.3k left. > $sam (opus): $612k mcap, $43k fees, $5.1k left. its own ai called its buybacks “noise” and said the treasury is mostly paying people not to leave.

AI's biggest builders signed their own permission slip at the White House. The money moved elsewhere: Copilot now bills by the task, Synopsys proved AI chip design still runs on EDA, and Micron is earning 87% margins on memory it can't make fast enough. @PatrickMoorhead and @danielnewmanUV debate whether the new AI accord means anything. $MSFT $SNPS $MU $AMD Tune into Ep. 322 of The Six Five Pod: 00:00 - Introduction 05:47 - White House convenes AI leaders on safeguards 13:02 - Microsoft's new Copilot: is this the one that sticks? 20:50 Synopsys brings OpenAI and Amazon into AI-native chip design 29:40 - New models from Anthropic, OpenAI, and Google 32:09 - AMD's $8.2B World Labs bet 37:07 - The Flip: Will the White House AI accord solve real problems? 46:10 - Synopsys raises guidance and adds a $1B buyback 48:46 EDA's terminal value and a growing chip design market 49:25 - HPE's networking Investor Day 53:50 - Micron's record quarter 1:01:40 - Accenture's 16% jump and outcome-based bookings 1:06:41 Next week: IBM Analyst Day and Marvell Investor Day

Ex-Wall Street CIO: AI Is About to Reprice Everything You Own w/ @jvisserlabs & @1MarkMoss 0:00 — Episode Trailer 0:50 — Bitcoin Is the Best AI Trade? 5:05 — What Problem Does Bitcoin Actually Solve? 11:08 — Secure Your Bitcoin with Unchained 11:55 — Why AI, Bitcoin & Tokenization Are Reshaping Finance 15:09 — What Problem Does Tokenization Actually Solve? 22:35 — Is Today's AI Boom Bigger Than the Dot-Com Bubble? 26:32 — Buy Bitcoin with River 28:08 — Will AI's Massive Spending Eventually Hit a Wall? 31:34 — Where Will AI Investors Make the Most Money? 39:28 — Does Bitcoin Need Economic Collapse to Win? 38:02 — Download Rumble Wallet Today 41:25 — Is the AI Trade Over... And Is It Finally Bitcoin's Turn? 47:32 — Is the Four-Year Bitcoin Cycle Still Real? 50:52 — Why Jordi Is Bullish on America and the Economy 54:59 — Get a 2nd Passport with Bitizenship 58:04 — Why AI Could Destroy Corporate Valuations 1:01:38 — Why Michael Saylor Saw Bitcoin Before Others

TypeSafe AI's Diogo Almeida with a16z's Ben Horowitz and Martin Casado on Jev, the model built to live inside software: Diogo's elevator pitch for Jev is a simple question - where is all the automation? AI is unbelievably smart, but outside of chatbots and coding agents, it hardly touches any real work. His diagnosis is the industry built models that generate text for humans to read, and software can't consume that output. Jev reads natural language and returns a choice from a set of options with a confidence level assigned to each, so developers can build programs that reason about intent and make probabilistic decisions rather than relying on human interpretation. TypeSafe's philosophy is "We build prod, not God." 0:50 "Where the f**k is all the automation?" 2:50 Jev vs. Claude Code and Codex 6:55 Jev is a classifier and classifiers are sick 7:40 Chat vs. code: is Jev a slider? 9:00 Diogo: From mathlete to Kaggle to OpenAI 12:20 "We build prod, not God" 15:55 Reliability over demos 16:55 2021 thoughts: RLHF is AGI? 20:45 Optimizing for the wrong use case 21:50 Is the real world too messy to automate? 25:00 Nobody expected the Jev launch 26:35 Three kinds of reliability 28:05 Good at syntax, bad at architecture 30:00 The inverse SaaSpocalypse 33:40 Why coding agents automate so little 36:05 Probabilistic programming returns 38:45 Jev as the UDP-to-TCP layer for AI 40:20 The 5 stages of grief for embedding AI 41:30 Utopia: AI that actually does what you mean YouTube: @CompleteSkeptic @typesafeai @bhorowitz @martin_casado

FULL INTERVIEW: The Economist's Callum Williams says only about 25% of American businesses use AI four years in, when most people in 2023 would have bet on nearly all of them. Ask CFOs what AI has done for their bottom line, and he says it "basically balances to zero." @econcallum is the senior economics writer at @TheEconomist. He joined @theojaffee and @schisofrenia to lay out why AI hasn't changed the economy yet and the signal he's watching for: 00:50 why AI hasn't changed the economy yet 01:38 why AI spread slower than the internet in business 03:05 why a laptop and ChatGPT isn't enough 04:20 the CFO surveys where AI's impact balances to zero 04:49 why cost will decide how fast AI spreads 06:11 the 25% stat, and the labor market signal he's watching 08:30 whether AI layoffs are real or just an excuse 09:43 robot McDonald's vs a fast food labor shortage 10:28 the COVID robot predictions nobody apologized for 11:55 the economic doom predictions that did come true