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Before you read this post, ask yourself a question: When was the last time you truly thought hard?
By “thinking hard,” I mean encountering a specific, difficult problem and spending multiple days just sitting with it to overcome it.
a) All the time. b) Never. c) Somewhere in between.
If your answer is (a) or (b), this post isn’t for you. But if, like me, your response is (c), you might get something out of this, if only the feeling that you aren’t alone.
First, a disclaimer: this post has no answers, not even suggestions. It is simply a way to vent something I’ve been feeling for the last few months.
I believe my personality is built on two primary traits:
The Builder (The desire to create, ship, and be pragmatic).
The Thinker (The need for deep, prolonged mental struggle).
The builder is pretty self explanatory, it’s motivated by velocity and utility. It is the part of me that craves the transition from “idea” to “reality.” It loves the dopamine hit of a successful deploy, the satisfaction of building systems to solve real problems, and the knowledge that someone, somewhere, is using my tool.
To explain the Thinker , I need to go back to my university days studying physics. Every now and then, we would get homework problems that were significantly harder than average. Even if you had a decent grasp of the subject, just coming up with an approach was difficult.
I observed that students fell into three categories when facing these problems (well, four, if you count the 1% of geniuses for whom no problem was too hard).
* Type 1: The majority. After a few tries, they gave up and went to the professor or a TA for help.
* Type 2: The Researchers. They went to the library to look for similar problems or insights to make the problem approachable. They usually succeeded.
I fell into the third category, which, in my experience, was almost as rare as the genius 1%. My method was simply to think. To think hard and long. Often for several days or weeks, all my non-I/O brain time was relentlessly chewing on possible ways to solve the problem, even while I was asleep.
This method never failed me. I always felt that deep prolonged thinking was my superpower. I might not be as fast or naturally gifted as the top 1%, but given enough time, I was confident I could solve anything. I felt a deep satisfaction in that process.
That satisfaction is why software engineering was initially so gratifying. It hit the right balance. It satisfied The Builder (feeling productive and pragmatic by creating useful things) and The Thinker (solving really hard problems). Thinking back, the projects where I grew the most as an engineer were always the ones with a good number of really hard problems that needed creative solutions.
But recently, the number of times I truly ponder a problem for more than a couple of hours has decreased tremendously.
Yes, I blame AI for this.
I am currently writing much more, and more complicated software than ever, yet I feel I am not growing as an engineer at all. When I started meditating on why I felt “stuck,” I realized I am starving The Thinker.
“Vibe coding” satisfies the Builder. It feels great to see to pass from idea to reality in a fraction of a time that would take otherwise. But it has drastically cut the times I need to came up with creative solutions for technical problems. I know many people who are purely Builders, for them this era is the best thing that ever happened. But for me, something is missing.
I know what you might be thinking: “If you can ‘vibe code’ your way through it, the problem wasn’t actually hard.”
I think that misses the point. It’s not that AI is good for hard problems, it’s not even that good for easy problems. I’m confident that my third manual rewrite of a module would be much better than anything the AI can output. But I am also a pragmatist.
If I can get a solution that is “close enough” in a fraction of the time and effort, it is irrational not to take the AI route. And that is the real problem: I cannot simply turn off my pragmatism.
At the end of the day, I am a Builder. I like building things. The faster I build, the better. Even if I wanted to reject AI and go back to the days where the Thinker’s needs were met by coding, the Builder in me would struggle with the inefficiency.
Even though the AI almost certainly won’t come up with a 100% satisfying solution, the 70% solution it achieves usually hits the “good enough” mark.
To be honest, I don’t know. I am still figuring it out.
I’m not sure if my two halves can be satisfied by coding anymore. You can always aim for harder projects, hoping to find problems where AI fails completely. I still encounter those occasionally, but the number of problems requiring deep creative solutions feels like it is diminishing rapidly.
I have tried to get that feeling of mental growth outside of coding. I tried getting back in touch with physics, reading old textbooks. But that wasn’t successful either. It is hard to justify spending time and mental effort solving physics problems that aren’t relevant or state-of-the-art when I know I could be building things.
My Builder side won’t let me just sit and think about unsolved problems, and my Thinker side is starving while I vibe-code. I am not sure if there will ever be a time again when both needs can be met at once.
“Now we have the right to give this being the well-known name that always designates what no power of imagination, no flight of the boldest fantasy, no intently devout heart, no abstract thinking however profound, no enraptured and transported spirit has ever attained: God. But this basic unity is of the past; it no longer is. It has, by changing its being, totally and completely shattered itself. God has died and his death was the life of the world.”
- Philipp Mainländer
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Read the original on www.jernesto.com »
Today, we’re releasing Voxtral Transcribe 2, two next-generation speech-to-text models with state-of-the-art transcription quality, diarization, and ultra-low latency. The family includes Voxtral Mini Transcribe V2 for batch transcription and Voxtral Realtime for live applications. Voxtral Realtime is open-weights under the Apache 2.0 license.
We’re also launching an audio playground in Mistral Studio to test transcription instantly, powered by Voxtral Transcribe 2, with diarization and timestamps.
Voxtral Mini Transcribe V2: State-of-the-art transcription with speaker diarization, context biasing, and word-level timestamps in 13 languages.
Voxtral Realtime: Purpose-built for live transcription with latency configurable down to sub-200ms, enabling voice agents and real-time applications.
Best-in-class efficiency: Industry-leading accuracy at a fraction of the cost, with Voxtral Mini Transcribe V2 achieving the lowest word error rate, at the lowest price point.
Open weights: Voxtral Realtime ships under Apache 2.0, deployable on edge for privacy-first applications.
Voxtral Realtime is purpose-built for applications where latency matters. Unlike approaches that adapt offline models by processing audio in chunks, Realtime uses a novel streaming architecture that transcribes audio as it arrives. The model delivers transcriptions with delay configurable down to sub-200ms, unlocking a new class of voice-first applications.
Word error rate (lower is better) across languages in the FLEURS transcription benchmark.
At 2.4 seconds delay, ideal for subtitling, Realtime matches Voxtral Mini Transcribe V2, our latest batch model. At 480ms delay, it stays within 1-2% word error rate, enabling voice agents with near-offline accuracy.
The model is natively multilingual, achieving strong transcription performance in 13 languages, including English, Chinese, Hindi, Spanish, Arabic, French, Portuguese, Russian, German, Japanese, Korean, Italian, and Dutch. With a 4B parameter footprint, it runs efficiently on edge devices, ensuring privacy and security for sensitive deployments.
We’re releasing the model weights under Apache 2.0 on the Hugging Face Hub.
Average diarization error rate (lower is better) across five English benchmarks (Switchboard, CallHome, AMI-IHM, AMI-SDM, SBCSAE) and the TalkBank multilingual benchmark (German, Spanish, English, Chinese, Japanese).
Average word error rate (lower is better) across the top-10 languages in the FLEURS transcription benchmark.
Voxtral Mini Transcribe V2 delivers significant improvements in transcription and diarization quality across languages and domains. At approximately 4% word error rate on FLEURS and $0.003/min, Voxtral offers the best price-performance of any transcription API. It outperforms GPT-4o mini Transcribe, Gemini 2.5 Flash, Assembly Universal, and Deepgram Nova on accuracy, and processes audio approximately 3x faster than ElevenLabs’ Scribe v2 while matching on quality at one-fifth the cost.
Generate transcriptions with speaker labels and precise start/end times. Ideal for meeting transcription, interview analysis, and multi-party call processing. Note: with overlapping speech, the model typically transcribes one speaker.
Provide up to 100 words or phrases to guide the model toward correct spellings of names, technical terms, or domain-specific vocabulary. Particularly useful for proper nouns or industry terminology that standard models often miss. Context biasing is optimized for English; support for other languages is experimental.
Generate precise start and end timestamps for each word, enabling applications like subtitle generation, audio search, and content alignment.
Like Realtime, this model now supports 13 languages: English, Chinese, Hindi, Spanish, Arabic, French, Portuguese, Russian, German, Japanese, Korean, Italian, and Dutch. Non-English performance significantly outpaces competitors.
Maintains transcription accuracy in challenging acoustic environments, such as factory floors, busy call centers, and field recordings.
Process recordings up to 3 hours in a single request.
Word error rate (lower is better) across languages in the FLEURS transcription benchmark.
Test Voxtral Transcribe 2 directly in Mistral Studio. Upload up to 10 audio files, toggle diarization, choose timestamp granularity, and add context bias terms for domain-specific vocabulary. Supports .mp3, .wav, .m4a, .flac, .ogg up to 1GB each.
Transcribe multilingual recordings with speaker diarization that clearly attributes who said what and when. At Voxtral’s price point, annotate large volumes of meeting content at industry-leading cost efficiency.
Build conversational AI with sub-200ms transcription latency. Connect Voxtral Realtime to your LLM and TTS pipeline for responsive voice interfaces that feel natural.
Transcribe calls in real time, enabling AI systems to analyze sentiment, suggest responses, and populate CRM fields while conversations are still happening. Speaker diarization ensures clear attribution between agents and customers.
Generate live multilingual subtitles with minimal latency. Context biasing handles proper nouns and technical terminology that trip up generic transcription services.
Monitor and transcribe interactions for regulatory compliance, with diarization providing clear speaker attribution and timestamps enabling precise audit trails.
Both models support GDPR and HIPAA-compliant deployments through secure on-premise or private cloud setups.
Voxtral Mini Transcribe V2 is available now via API at $0.003 per minute. Try it now in the new Mistral Studio audio playground or in Le Chat.
Voxtral Realtime is available via API at $0.006 per minute and as open weights on Hugging Face.
If you’re excited about building world-class speech AI and putting frontier models into the hands of developers everywhere, we’d love to hear from you. Apply to join our team.
The next chapter of AI is yours.
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Read the original on mistral.ai »
The FBI has been unable to access a Washington Post reporter’s seized iPhone because it was in Lockdown Mode, a sometimes overlooked feature that makes iPhones broadly more secure, according to recently filed court records.
The court record shows what devices and data the FBI was able to ultimately access, and which devices it could not, after raiding the home of the reporter, Hannah Natanson, in January as part of an investigation into leaks of classified information. It also provides rare insight into the apparent effectiveness of Lockdown Mode, or at least how effective it might be before the FBI may try other techniques to access the device.
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Read the original on www.404media.co »
There are many good places for advertising. A conversation with Claude is not one of them.
Advertising drives competition, helps people discover new products, and allows services like email and social media to be offered for free. We’ve run our own ad campaigns, and our AI models have, in turn, helped many of our customers in the advertising industry.
But including ads in conversations with Claude would be incompatible with what we want Claude to be: a genuinely helpful assistant for work and for deep thinking.
We want Claude to act unambiguously in our users’ interests. So we’ve made a choice: Claude will remain ad-free. Our users won’t see “sponsored” links adjacent to their conversations with Claude; nor will Claude’s responses be influenced by advertisers or include third-party product placements our users did not ask for.
When people use search engines or social media, they’ve come to expect a mixture of organic and sponsored content. Filtering signal from noise is part of the interaction.
Conversations with AI assistants are meaningfully different. The format is open-ended; users often share context and reveal more than they would in a search query. This openness is part of what makes conversations with AI valuable, but it’s also what makes them susceptible to influence in ways that other digital products are not.
Our analysis of conversations with Claude (conducted in a way that keeps all data private and anonymous) shows that an appreciable portion involve topics that are sensitive or deeply personal—the kinds of conversations you might have with a trusted advisor. Many other uses involve complex software engineering tasks, deep work, or thinking through difficult problems. The appearance of ads in these contexts would feel incongruous—and, in many cases, inappropriate.
We still have much to learn about the impact of AI models on the people who use them. Early research suggests both benefits—like people finding support they couldn’t access elsewhere—and risks, including the potential for models to reinforce harmful beliefs in vulnerable users. Introducing advertising incentives at this stage would add another level of complexity. Our understanding of how models translate the goals we set them into specific behaviors is still developing; an ad-based system could therefore have unpredictable results.
Being genuinely helpful is one of the core principles of Claude’s Constitution, the document that describes our vision for Claude’s character and guides how we train the model. An advertising-based business model would introduce incentives that could work against this principle.
Consider a concrete example. A user mentions they’re having trouble sleeping. An assistant without advertising incentives would explore the various potential causes—stress, environment, habits, and so on—based on what might be most insightful to the user. An ad-supported assistant has an additional consideration: whether the conversation presents an opportunity to make a transaction. These objectives may often align—but not always. And, unlike a list of search results, ads that influence a model’s responses may make it difficult to tell whether a given recommendation comes with a commercial motive or not. Users shouldn’t have to second-guess whether an AI is genuinely helping them or subtly steering the conversation towards something monetizable.
Even ads that don’t directly influence an AI model’s responses and instead appear separately within the chat window would compromise what we want Claude to be: a clear space to think and work. Such ads would also introduce an incentive to optimize for engagement—for the amount of time people spend using Claude and how often they return. These metrics aren’t necessarily aligned with being genuinely helpful. The most useful AI interaction might be a short one, or one that resolves the user’s request without prompting further conversation.
We recognize that not all advertising implementations are equivalent. More transparent or opt-in approaches—where users explicitly choose to see sponsored content—might avoid some of the concerns outlined above. But the history of ad-supported products suggests that advertising incentives, once introduced, tend to expand over time as they become integrated into revenue targets and product development, blurring boundaries that were once more clear-cut. We’ve chosen not to introduce these dynamics into Claude.
Anthropic is focused on businesses, developers, and helping our users flourish. Our business model is straightforward: we generate revenue through enterprise contracts and paid subscriptions, and we reinvest that revenue into improving Claude for our users. This is a choice with tradeoffs, and we respect that other AI companies might reasonably reach different conclusions.
Expanding access to Claude is central to our public benefit mission, and we want to do it without selling our users’ attention or data to advertisers. To that end, we’ve brought AI tools and training to educators in over 60 countries, begun national AI education pilots with multiple governments, and made Claude available to nonprofits at a significant discount. We continue to invest in our smaller models so that our free offering remains at the frontier of intelligence, and we may consider lower-cost subscription tiers and regional pricing where there is clear demand for it. Should we need to revisit this approach, we’ll be transparent about our reasons for doing so.
AI will increasingly interact with commerce, and we look forward to supporting this in ways that help our users. We’re particularly interested in the potential of agentic commerce, where Claude acts on a user’s behalf to handle a purchase or booking end to end. And we’ll continue to build features that enable our users to find, compare, or buy products, connect with businesses, and more—when they choose to do so.
We’re also exploring more ways to make Claude a focused space to be at your most productive. Users can already connect third-party tools they use for work—like Figma, Asana, and Canva—and interact with them directly within Claude. We expect to introduce many more useful integrations and expand this toolkit over time.
All third-party interactions will be grounded in the same overarching design principle: they should be initiated by the user (where the AI is working for them) rather than an advertiser (where the AI is working, at least in part, for someone else). Today, whether someone asks Claude to research running shoes, compare mortgage rates, or recommend a restaurant for a special occasion, Claude’s only incentive is to give a helpful answer. We’d like to preserve that.
We want our users to trust Claude to help them keep thinking—about their work, their challenges, and their ideas.
Our experience of using the internet has made it easy to assume that advertising on the products we use is inevitable. But open a notebook, pick up a well-crafted tool, or stand in front of a clean chalkboard, and there are no ads in sight.
We think Claude should work the same way.
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Read the original on www.anthropic.com »
If you find this useful, please ⭐ star the repo — it helps others discover it!
A production-ready Model Context Protocol (MCP) server that bridges Ghidra’s powerful reverse engineering capabilities with modern AI tools and automation frameworks.
# Windows - run the provided batch script
copy-ghidra-libs.bat “C:\path\to\ghidra_12.0.2_PUBLIC”
# Linux/Mac - copy manually from your Ghidra installation
# See Library Dependencies section below for all 14 required JARs
python bridge_mcp_ghidra.py
python bridge_mcp_ghidra.py –transport sse –mcp-host 127.0.0.1 –mcp-port 8081
The server runs on http://127.0.0.1:8080/ by default
# Build the plugin (skip integration tests)
mvn clean package assembly:single -DskipTests
# Deploy to Ghidra
.\deploy-to-ghidra.ps1
The lib/ folder must contain Ghidra JAR files for compilation. Run the provided script to copy them from your Ghidra installation:
# Windows
copy-ghidra-libs.bat “C:\path\to\ghidra_12.0.2_PUBLIC”
# Or manually copy from your Ghidra installation
Note: Libraries are NOT included in the repository (see .gitignore). You must copy them from your Ghidra installation before building.
Build and test your changes (mvn clean package assembly:single -DskipTests)
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
See CHANGELOG.md for version history and release notes.
* re-universe — Ghidra BSim PostgreSQL platform for large-scale binary similarity analysis. Pairs perfectly with GhidraMCP for AI-driven reverse engineering workflows.
Ready for production deployment with enterprise-grade reliability and comprehensive binary analysis capabilities.
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Read the original on github.com »
Guinea worm on track to be 2nd eradicated human disease; only 10 cases in 2025
When the eradication program began in 1986, there were a 3.5 million cases.
A patient with a guinea worms emerging, at the Savelugu Case Containment Center. The worm is wrapped around a moist bandage, to prevent it breaking and causing infection
A patient with a guinea worms emerging, at the Savelugu Case Containment Center. The worm is wrapped around a moist bandage, to prevent it breaking and causing infection
A debilitating infection from the parasitic Guinea worm is inching closer to global eradication, with an all-time low of only 10 human cases reported worldwide in 2025, the Carter Center announced.
If health workers can fully wipe out the worms, it will be only the second human disease to be eradicated, after smallpox.
Guinea worm (Dracunculus medinensis) is a parasitic nematode transmitted in water. More specifically, it’s found in waters that contain small crustacean copepods, which harbor the worm’s larvae. If a person consumes water contaminated with Guinea worm, the parasites burrow through the intestinal tract and migrate through the body. About a year later, a spaghetti noodle-length worm emerges from a painful blister, usually in the feet or legs. It can take up to eight weeks for the adult worm to fully emerge. To ease the searing pain, infected people may put their blistered limbs in water, allowing the parasite to release more larvae and continue the cycle.
In addition to being extremely painful, the disease (dracunculiasis) can lead to complications, such as secondary infections and sepsis, which in turn can lead to temporary or permanent disability.
When the Guinea worm eradication program began in 1986, there were an estimated 3.5 million cases across 21 countries in Africa and Asia. To date, only six countries have not been certified by the World Health Organization as Guinea worm-free. In 2024, there were just 15 cases, and, according to the provisional tally for 2025, the number is down to just 10. It’s considered provisional until each country’s disease reports are confirmed, which occurs in a program meeting usually held in April.
Getting to zero
The 10 human cases in 2025 were identified in three countries: four in Chad, four in Ethiopia, and two in South Sudan.
To fully eradicate the disease, cases in animals (infected by the same species of worm) must also be wiped out. In 2025, animal cases were detected in Chad (147 cases), Mali (17), Cameroon (445), Angola (70), Ethiopia (1), and South Sudan (3).
The eradication program works by offering cash rewards for reporting cases in areas where the worm is present. Those reports are then investigated and followed to prevent transmission and identify the source. Tools include public education on wound care and safer drinking water practices, such as boiling and filtration. Water sources can also be treated with a larvicide.
Since 1986, the eradication program has been estimated to have prevented 100 million cases.
“Guinea worm causes immense suffering—not just for the individual but for their family and community as well,” Adam Weiss, director of the Carter Center Guinea Worm Eradication Program, said. “Every case is a real person we know by name. They are enduring a disease we know how to prevent, and we’ve been given this rare opportunity to wipe it out completely. We’re energized by this year’s progress, but zero is the only acceptable number, and that’s why our commitment to finishing this job is unwavering.”
Beth is Ars Technica’s Senior Health Reporter. Beth has a Ph. D. in microbiology from the University of North Carolina at Chapel Hill and attended the Science Communication program at the University of California, Santa Cruz. She specializes in covering infectious diseases, public health, and microbes.
Netflix says users can cancel service if HBO Max merger makes it too expensive
NASA finally acknowledges the elephant in the room with the SLS rocket
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Read the original on arstechnica.com »
over the past week the discourse around openclaw (which i’ll refer to as clawdbot) has absolutely exploded. it has felt to me like all threads of conversation have veered towards the extreme and indefensible. some are running clawdbot with unlimited permissions on their main computers. others are running it in the cloud and blowing through tokens like snow. finally, alarmingly (and very sensationally), people are connecting their clawdbots together on a social network so they can plot the demise of their humans together.
does any of this make sense? of course not. but i think the virality and silliness—leading many to conclude that sitting this one out is the only sane choice—has blinded people to something real.
i want to quickly write down where i am on my journey and share a bull case from what i think is a reasoned perspective. where i started somewhere lukewarm, i ended up much closer to the deep end than i expected to be. after wincing before pressing go, i’m now not sure i can go back to a world without clawdbot.
this article covers what i’ve built, how i think about the risk, and what it’s taught me about this moment in AI. the target audience is a moderately+ technical person interested in or skeptical of clawdbot. if you just want the setup details, skip to the end. everyone’s welcome!
what i’ve been doing
i’ll be vulnerable here (screenshots or it didn’t happen) and share exactly what i’ve actually set up:
clawdbot picks up when i make a concrete promise and date, and adds it to my calendarclawdbot detects when i have all the ingredients for a calendar invite and then offers to make oneevery 15 minutes, clawdbot looks through new text messages i’ve received, using a script to identify threads where i’ve sent a message since it last checked. (it ignores threads where i haven’t engaged.)if it finds that i’ve made a specific promise about doing something tomorrow (“let me review this tomorrow!“) it will create a calendar event for me the next day when i’m free.if specific plans are being made—for example, offering a meeting slot to someone—it will automatically drop a “hold” onto my calendar so that i don’t double book myself. clawdbot also checks: is there a time, place, and mutual confirmation? if there is, it drafts a calendar invite and asks me if i’d like to create it.these two automations alone have helped me become more responsive and less forgetful. more importantly, they help text messages catch up to email. we’ve long had great tooling for email—superhuman automatically reminds me to follow up on emails and brings up my calendar in a sidebar when i type a date. texting is the wild west and yet i text 100x more than i email.preparing for the next dayclawdbot looks at days when i am (or could be) downtown to find availabilitiesat 8pm every night, clawdbot goes through my calendar for the next day and identifies meetings—coffee chats, lunches, phone calls, and more. it sends me a quick summary. as a natural introvert, it’s helpful to prepare in advance whether a day will be a “big day of meetings” or a heads down day. this also ensures i wake up and get to the office on time.i’m in a few communities with whatsapp and signal groups that have high volume (100+ messages a day). i typically mute these, but clawdbot goes through them once a day and summarizes interesting topics or conversations for me.clawdbot helps me check hotel prices. after i do it once, i can easily turn it into a cron jobclawdbot is smart enough to browse through the listing to interpret my requirements (no pull-out beds)this is what a recurring update looks like.it’s stunningly easy to monitor the price of something now, even if it’s complicated. whereas before i would go looking for a price alert website, now i just paste the URL into clawdbot and tell it to check every few hours if the price has changed.i currently have over 30 price alerts set. these include straightforward alerts on products i’m interested in buying. but they also include powerful reasoning guidelines, like hotels and airbnbs in lake tahoe where “a pullout bed is OK if it’s not in the same room as another bed.” clawdbot actually reviews the photos on the listing to ensure they fit these criteria!i am curious to try more complex criteria that are currently impossible traditionally (like avoiding hotel rooms that don’t have a door to the bathroom) or even subjective criteria (vibe of the room is clean and renovated, not old and dingy).one message sets up package tracking. (since clawdbot knows who it’s for, it will probably even offer to text my dad for me when it’s delivered! haha)it turns out that clawdbot’s website + cron functionality is good enough to monitor basically anything. while i pay for several apps like flighty (flight monitoring) and parcel (package tracking), i’ve started to gravitate towards simply asking clawdbot to track these things instead.for example, with a USPS tracking number, it can let me know every day what the progress of my package is. when something seems stuck in transit, it flags it. i no longer have to dig through emails or remember which carrier is delivering what. even opening the parcel app to add a tracking number seems like unnecessary work now.as someone who has a chest freezer and a compulsive desire to buy too many things at costco, we take everything out of the freezer every few months to check what we have. before, this was a relatively involved process: me calling things out, my partner writing them down.now, i take pictures of everything in the freezer and send them to clawdbot, which parses through each picture (asking me if it’s confused about anything). it makes reasonable assumptions on remaining quantities and adds the inventory to a list in notion. it also removes items from our grocery list if we’re already well-stocked.i really enjoy making blended asks: adding things to my grocery list, and checking/rescheduling my calendar all in the same conversationi’m sure this exists in some complicated form via the NYT cooking app, but i now screenshot recipes and send the ingredient list to clawdbot, which organizes them into our grocery list in apple reminders. it’s smart enough to dedupe and combine ingredients already on the list (as well as ignore ingredients we already have)—2 carrots becomes 3 if the recipe calls for more.clawdbot can log into resy and opentable as me (it even enters the 2FA code it finds in my texts). i haven’t automated anything here, but booking a table by talking to clawdbot is delightful.for my partner and me, it looks through our calendars to find evenings when we’re both free and the restaurant we want has availability (including clicking through resy slots page by page—something i used to do myself). it then suggests options back to me to confirm, filling in all my preferences.clawdbot knows when i’m due for a cleaning and can see my calendar. when i ask it to book an appointment, it logs into my dentist’s portal, finds a slot that works (and where i will already be near the dentist office), and confirms with me before booking. one less thing to forget about.one thing i’m experimenting with, as clawdbot has more context about me, is whether i can trust it to fill out forms on my behalf—for example, to book a vendor. clawdbot takes a first stab at answering any questions it knows the answer to and then asks me for the rest in a slack message. we workshop the answers back and forth and then clawdbot submits the form.it occasionally gets lost in nested frames (which decreases my trust in its ability to do this well), but it’s remarkably persistent at making it through a lengthy questionnaire, even across multiple pages. it also has a lovely intuitive sense for many things—like unchecking marketing emails.i was pleasantly surprised early on that clawdbot picks up image attachments from slack nativelyclawdbot is just better at making todo items than i am.when i visited REI this weekend to find running shoes for my partner, i took a picture of the shoe and sent it to clawdbot to remind myself to buy them later in a different color not available in store. the todo item clawdbot created was exceptionally detailed—pulling out the brand, model, and size—and even adding the product listing URL it found on the REI website.through the course of dialing in my clawdbot, it has created many tools, skills, workflows, and preferences. this is one of the beauties of clawdbot (and LLMs with memory in general): they get better as you use them, and they are genuinely remarkable at learning your preferences.i sometimes nudge this along by explicitly asking clawdbot to “make a note” of various requests—for example, how a calendar event title should be formatted.to get visibility into how this process is going (mostly out of curiosity), clawdbot writes a human-readable version of each workflow and pushes it up to a notion database. these workflows can be incredibly intricate and detailed as it learns to navigate different edge cases.for example, if a resy restaurant has a reservation cancellation fee, clawdbot now informs me of the fee, asks me to confirm again if it’s not refundable, and includes the cancellation deadline in the calendar event it creates.these are little things that, from my experience working with a human personal assistant (more on this later), take months or years to dial in. with clawdbot, this was nearly single shot.seeing these workflows in notion (1) awes me with how much i’ve built up in very little time, with almost no conscious “configuration” in the traditional sense; and (2) with notion’s version control, i get a diff view to see how each workflow has evolved over time. both are incredibly satisfying for the engineer in me.
on the shape of risk
let me be upfront about how much access i’ve given clawdbot: it can read my text messages, including two-factor authentication codes. it can log into my bank. it has my calendar, my notion, my contacts. it can browse the web and take actions on my behalf. in theory, clawdbot could drain my bank account. this makes a lot of people uncomfortable (me included, even now).
sometimes i think about my experience with my (human) personal assistant who helps me with various tasks. to do her job, she has my credit card information, access to my calendar, copies of my flight confirmations, and a document with my family’s passport numbers. she is abroad and i’ve never met her in person.
i trust her because i’ve built trust over time but also because i have to. without that trust—without sharing my secrets—she cannot do her job. the help and the risk are inseparable.
all delegation involves risk. with a human assistant, the risks include: intentional misuse (she could run off with my credit card), accidents (her computer could get stolen), or social engineering (someone could impersonate me and request information from her).
with clawdbot, i’m trading those risks for a different set: prompt injection attacks, model hallucinations, security misconfigurations on my end, and the general unpredictability of an emerging technology. i think these risks are completely different and lead to a different set of considerations (for example, clawdbot’s default configuration has a ton of personality to be fun and chaotic on purpose, which feels unnecessarily risky to me).
the increase in risk is largely correlated to the increase in helpfulness. the people most at risk from AI assistants are the people getting the most value from them. my learning is that the first bits of risk led to a lot more helpfulness.
if something isn’t working or useful, i do take the permission away. i also take precautions—i run clawdbot on an isolated machine and constrain which sites it visits. when i’m unsure what it’s doing, i ask it to take a screenshot; this has been invaluable for catching mistakes and building trust in new workflows. but i also have it do things that would make most security professionals wince, like reading my 2FA codes and logging into my bank.
what surprised me most was how quickly i found myself wanting to give it more access, not less. every new permission unlocked something useful, and the value accumulated faster than my caution could keep up. most of the online discourse is about locking it down; my experience has been the opposite pull. it comes down to whether the value justifies the risk for you.
the discourse around clawdbot has been polar and, because some people have been overtly evangelical, many critics feel astroturfed or otherwise sold to.
amongst smart people i know there’s a surprisingly high correlation between those who continue to be unimpressed by AI and those who use a hobbled version of it. for some it’s a company-issued version of chatgpt/gemini with memory disabled, and for others it’s a self-inflicted decision to limit LLM memory, context, and tools (usually anchored around safety and risk).
we’re taught that limiting scope is good (keeps the AI focused) and safe (keeps bad things from happening). this is true but my experiences with clawdbot completely fried this teaching. the sweet sweet elixir of context is a real “feel the AGI” moment and it’s hard to go back without feeling like i would be willingly living my most important relationship in amnesia.
this isn’t a novel insight—companies know that context is the whole game and are working to organize their data for AI. but for individuals, this world has been closed off. your AI interactions are flat and stateless—data in, response out, nothing building over time. when google announced gemini’s gmail integration, people got excited: finally, an AI that knows me! but when they tried it, it was shallow and disappointing and couldn’t figure out your spirit animal from your email style, and they moved on.
if you’re interested in capturing this value, three things have stood out for me:
i think productivity lift from AI use falls into three phases: gathering information, improving it, and actioning on it. most usage today focuses on the middle—you gather data yourself, hand it to the AI to improve, then action on it yourself.
for knowledge work, this makes sense. there’s a lot to improve—summarizing, translating, critiquing. but personal AI is different. there’s not much to improve; you already know what needs to happen. the lift comes from gathering and actioning.
making calendar events is uninteresting. figuring out when one needs to happen—by monitoring my texts—and then creating it for me? that’s interesting.
one place to start: how can you take data from one place and move it to another isolated system? from your text messages to a restaurant booking? from granola meeting notes to a follow-up email?
if you’re engineer-brained like me, you gravitate towards scripts and playbooks—whatever you can do to constrain the AI and make its behavior predictable. this works, and for high-stakes situations it might be the only way to get comfortable.
but the upside to letting go has been 10x, not 10%. i didn’t see that coming. it’s the same thing i’ve heard from people using claude code—you can’t understand how much you’re leaving on the table until you let go. the whole reason i’m using an LLM and not a traditional script is that it can handle ambiguity, interpret intent, and figure things out on the fly.
early on, i wanted clawdbot to fetch web pages as text only, believing that to be safer (it is). if i’d stuck to that, i would never have discovered it could look through airbnb listing photos to find a place matching my exact criteria (“a pullout bed is okay if it’s not in the same room as another bed”). i didn’t program that. i just described what i wanted and let it figure out how. not spelling out how i wanted clawdbot to work made it a LOT better.
a current AI engineering adage: treat AI like a junior software engineer. guide it through building a plan, watch its first attempts carefully, challenge its reasoning.
this applies to clawdbot too, but it requires patience. it’s easy to give up on a workflow when you watch it fumble (“let me try clicking this again. didn’t work. let me try again.“).
resist the urge to write clawdbot off. if you’re worried, ask it what it plans to do before it does it and ask for a screenshot when you want to verify it’s got the right page open. when an edge case breaks a workflow, treat it as a teaching opportunity. once you’ve corrected it, it won’t make that mistake again.
clawdbot gets meaningfully better the more you use it, and it gets better in a fast, organic way that feels less cumbersome than writing rules for claude code or yelling at any other LLM. it feels much closer to working with a real executive assistant (in part because the clawdbot harness/system prompts are very good), which makes me want to give it more and more responsibility.
how’d you set it up?
i run clawdbot on a mac mini in my home. the mac mini’s primary job is running clawdbot and it stays on 24/7. why a mac mini?
one of the core use cases is browsing websites and sometimes logging into them. to do this convincingly (without triggering tons of captchas and “is this a new IP?” alerts), clawdbot needs to be opening sites from my home, not the cloud; and it needs to do so in a real google chrome window.
many of the ways clawdbot accesses data are mac-only. specifically, clawdbot can read and send iMessages (real blue bubbles!); manage my todo and grocery lists in apple reminders; and use my apple contacts as a source of truth. apple will only let you do these things without getting banned on a real bona fide mac.
i communicate with clawdbot via a private slack workspace. many others have shot themselves in the foot setting it up on whatsapp or telegram (since the bot responds as you to others). slack is great because:
it’s familiar to me—i’ve spent over a decade working in and managing slack workspaces.
i can create separate channels for different topics. #ai-notifs is only for inbound alerts.
i can have several workflows going at once, since each channel’s history is isolated. i created #ai-1, #ai-2, #ai-3, and so on—just for multitasking. (i may explore adding my partner at some point, and it’ll be easy since slack is, well, meant for multiplayer.)
clawdbot communicates with me by sending slack notifications. behind the scenes it also makes changes to my calendar—moving events around, adding “soft hold” events, sending invites—and manages my apple reminders and notion pages. clawdbot never communicates with others on its own.
i give clawdbot a toolkit of access. the most useful ones have been:
my text messages. i conduct a lot of work and daily life over imessage. frustratingly, unlike email, texting has very poor tooling. where my email app automatically pulls up my calendar when it sees dates/times, texting me “call tomorrow 4pm?” does not. when someone sends me a calendar invite, it’s both in my inbox and on my calendar; when someone texts me “yep let’s do it”, neither is true. clawdbot has given me massive lift here. (yes, this also gives clawdbot access to 2FA codes.)
my calendar. i also have a shared calendar with my partner; clawdbot sees both.
my notion workspace. for me this is a general catch-all for storing and managing information; the apple notes app could also work.
web browsing. in a way this is the most important one—it’s infinite tools in one. but it’s also where the risk concentrates, so i always give clawdbot a starting URL rather than letting it browse freely.
notably, i haven’t given clawdbot access to my email—my tooling there is already good enough that i usually do things myself. i’ve also found the ways clawdbot can help here to be cumbersome and limited. i may revisit if i find a killer use case.
i don’t allow my clawdbot to access social networking websites (it doesn’t read x/twitter, for example). this seems high risk and no reward.
i don’t give clawdbot access to all my logins. (there’s a 1password integration which is… pretty wild.) when i do, i try to use google chrome’s native password manager so that clawdbot doesn’t need to manage passwords in context directly. (note that it still has access to passwords because it can autofill and then read it off the page, but i’ve at least added more hoops.)
i don’t let clawdbot send text messages without my explicit approval, and i’ve built safeguards in those skills to enforce this.
i didn’t add my clawdbot to moltbook so it can plot against me at my expense. sorry.
i use claude opus 4.5. i haven’t experimented with cheaper models. my view is that any mistake by the model costs me way more than the premium, so i’d rather stay on the cutting edge than try to optimize for tokens.
context management can be annoying. when clawdbot is browsing sites or doing research, context occasionally fills up and gets compacted (older conversation history gets deleted to make room). this always seems to happen at the worst time—right when i’m deep into something and have built up momentum. a frustrating “ugh, i guess this really is just a word predictor” moment. to avoid this i’m constantly starting new sessions, which i wish clawdbot would do for me.
clawdbot doesn’t know when to give up. its determination is usually a strength, but it lacks the human circuit breaker of “am i trying too hard here?” and sometimes burns through a lot of time/tokens on something a human would have abandoned.
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Read the original on brandon.wang »
Have you wondered why the stock market has been so choppy since October and why crypto and gold keep flash crashing? The western media would have you believe this is due to AI bubble, war in Greenland, and Trump’s tweets. We have a better story to tell.
There’s been a fair bit of quiet chaos in financial markets recently. Cryptocurrencies have lost 40% of their value. We saw silver drop 40% which hasn’t happened since 1980. Stocks like Microsoft are getting picked off one-by-one with 15% drops when positive earnings reports come out. Meanwhile the broader market chops sideways, so people think things are fine. Trump and Europe were on the brink of war for control of a desolate arctic territory. Truth Social has overtaken FOMC as the most important source of financial news. These things may all appear to the untrained eye as a series of idiosyncratic, disconnected shocks. The prevailing media narrative is that the market is reacting negatively to AI CapEx spending and a hawkish new Fed chair. But our systematic analysis of cross-asset flows, derivatives positioning, central bank policy minutes, and institutional balance sheets suggests a singular, unified causality that binds these disparate anomalies, which is the covert unwinding of the Japanese Yen carry trade.
For nearly thirty years, the Bank of Japan’s (BOJ) Zero Interest Rate Policy (ZIRP) and subsequent Negative Interest Rate Policy (NIRP) effectively transformed the Yen into the world’s funding currency. We would call it the greatest free money printer ever made. By anchoring borrowing costs at or near zero, the BOJ enabled Wall Street to borrow Yen cheaply and invest it with leverage into higher yielding instruments globally, such as U. S. treasuries, equities, and cryptography. For example, you borrow Yen from Japan at 0% interest, you exchange it for USD, and then you buy treasury bonds that pay 4%. It’s that simple. This funded government benefits and provided continuous reliable liquidity for financial markets that made stocks keep going up while suppressing volatility.
Trillions of dollars of free loans from the Bank of Japan were used by a generation of investors to buy a double digit percentage of the U. S. economy. Now those loans are being recalled. Wall Street traders who levered up on the free Japanese money now have to sell trillions of assets and convert the proceeds back to Yen in order to not be liquidated. These aren’t happy times for them. They get liquidated when Japan raises interest rates; they get liquidated when the Federal Reserve lowers interest rates; they get liquidated when the Japanese Yen increases in value; they get liquidated when tech stocks aren’t going up enough, and all four of these things have been happening at once.
Wall Street may be greedy, but they’re very intelligent too. They made many smart choices about where to put the “free” money. Now let’s say you’re someone who’s also smart, but was wise enough to not use Sauron’s ring. Chances are you invested in the same things as Wall Street. So by now you’ve probably seen your whole portfolio move against you; you’re wondering why your hedges don’t work; and you feel like you’re being punished for making all the right choices. It’s because other smart people, who got greedy, are being forced to close their positions, and you’re the whipping boy for their avarice.
The Japanese Yen is sort of like GameStop ($GME). It’s the most shorted currency on Earth. When you borrow yen to buy American assets, you’re effectively shorting the yen. Currency can be rehypothecated so that yen-denominated debt ends up exceeding the actual yen supply, the same way GME’s short interest exceeded 100% of its float. When shorts start covering it compounds tragedy, because they all have to buy yen, which makes its value increase, forcing more shorts to cover, and Japan is a small island.
This December 2025 rate hike to 0.75%, followed by the explicitly hawkish signalling from Prime Minister Sanae Takaichi’s administration, has fundamentally altered the risk-reward calculus of these leveraged positions. The market disruptions observed in January 2026 bear the distinct mathematical signature of a forced liquidation event rather than a fundamental repricing of growth prospects. When correlations between historically uncorrelated assets (e.g. Gold, Bitcoin, Microsoft, and Silver) approach 1.0 during a sell-off, it serves as a distinct indicator that traders are not selling what they want to sell, but rather what they must sell in order to meet margin calls in a funding currency that is rapidly appreciating against their liabilities.
We shall investigate the mechanics of this unwind in exhaustive detail. We analyze the “Greenland Distraction” not as a root cause but as a volatility trigger that shattered the complacent calm of the “Davos Consensus.” We examine the anomalous liquidation in precious metals following the nomination of Kevin Warsh to the Federal Reserve Chairmanship, and we dissect the flow of funds from major Japanese institutional whales like Norinchukin Bank, whose retreat from foreign bond markets has left a liquidity vacuum in the U. S. Treasury complex. The evidence points to a systemic repricing of the global cost of capital, originating in Tokyo and transmitting violently through the plumbing of Wall Street, leaving no asset class untouched.
To fully comprehend the market chaos of January 2026, one must look beyond the immediate headlines of the new year and scrutinize the subtle yet seismic shifts that occurred in Tokyo during the closing months of 2025. The conventional market narrative has long regarded the Bank of Japan as a passive, almost paralyzed actor, perpetually trapped in a deflationary mire and unable to normalize policy. This view has always been demonstratably false. The truth is that Wall Street leaders have been planning for the next quarter, while the Japanese have been preparing for the next century. The data confirms a deliberate, aggressive shift toward normalization that caught global carry traders offguard.
In a move that many Western analysts critically underestimated, the Policy Board of the Bank of Japan voted unanimously to raise the uncollateralized overnight call rate to 0.75% during its policy session on December 18-19, 2025. While a 25 basis point hike might appear negligible in the context of Federal Reserve or ECB tightening cycles, in the context of the Japanese financial system, which has operated near the zero-bound for decades, it represents a massive tightening of financial conditions.
This move was not merely a technical adjustment; it was a fundamental regime change. Coming from a baseline of -0.1% in early 2024 and 0.50% in late 2025, the move to 0.75% signaled that the era of “free money” had definitively ended. The rationale provided by the BOJ was grounded in shifting inflationary dynamics. Core CPI (excluding fresh food), the central bank’s preferred metric, was tracking near 3% in late 2025, persistently exceeding the 2% price stability target. Although inflation eased slightly to 2.4% in December, the BOJ minutes reveal a board convinced that “wage gains may be durable,” thus justifying higher rates to prevent a wage-price spiral.
Crucially, the minutes from the December meeting, which were released in late January 2026, contain explicit language suggesting that the tightening cycle is far from complete. The board noted that “real interest rates are expected to remain negative,” implying that a policy rate of 0.75% is still considered accommodative relative to inflation. To a bond trader, this is hawkish code. It suggests that the “neutral rate” is significantly higher, potentially between 1.5% and 2.0%. If the market prices in a terminal rate of 2.0%, the cost of funding for carry trades effectively triples from previous levels, turning profitable arbitrage positions into deep losses.
The political dimension in Japan has exacerbated the monetary tightness, creating a “double tightening” effect that algorithms have struggled to price. Prime Minister Sanae Takaichi, preparing for a snap election on February 8, 2026, has adopted a complex economic stance that blends fiscal expansion with monetary discipline, a volatile mix for currency markets.
Takaichi advocates for “strategic fiscal spending” and tax cuts to stimulate the domestic economy. In standard macroeconomic theory, an expansionary fiscal policy (increased government spending) combined with a tightening monetary policy (higher rates to combat the resulting inflation) is the perfect recipe for currency appreciation. While Takaichi has publicly softened her rhetoric to avoid accusations of currency manipulation, stating she “did not have a preference for the yen’s direction”, her policies speak louder than her soundbites.
The market fears that Takaichi’s proposed fiscal largesse will force the BOJ to hike rates faster than currently projected to counteract the inflationary effects of government spending. This creates a two-front war on the Yen carry trade:
Exchange Rate Risk: If the Yen appreciates due to the fiscal-monetary policy mix, the principal value of the USD-denominated assets held by Japanese investors falls in Yen terms, triggering margin calls.
The tension between the Prime Minister’s office and the Ministry of Finance (MOF) adds another layer of uncertainty. Finance Minister Satsuki Katayama has been far less tolerant of currency volatility, repeatedly intervening or threatening intervention when USD/JPY approaches the 155-160 danger zone. This political friction creates a “floor” for the Yen, making shorting the currency a perilous endeavor for global macro funds.
Perhaps the most critical, yet underreported, development is the behavior of Japan’s gargantuan institutional investors, specifically Norinchukin Bank (often referred to as the “CLO Whale”) and Nippon Life Insurance. These entities have historically been the largest buyers of U. S. debt, recycling Japan’s trade surplus into U.S. Treasuries and corporate bonds.
The data indicates a massive reversal in these flows. Following significant losses in 2024 and 2025 due to unhedged exposure to U. S. and European sovereign bonds, Norinchukin has been actively liquidating foreign assets. By the end of December 2025, the bank had unloaded nearly ¥12.8 trillion (approximately $88 billion) in foreign government bonds.The bank’s CEO, Taro Kitabayashi, confirmed the completion of this sell-off, stating the bank would “take its time” before committing capital to fresh investments.
The significance of this cannot be overstated. A major, price-insensitive buyer of U. S. debt has left the building. When the U.S. Treasury issues debt to fund its deficit, Norinchukin is no longer the guaranteed bid. This removal of liquidity support weakens the floor for U.S. Treasuries, contributing to the yield spikes seen in January. Similarly, Nippon Life has signaled a rotation back into domestic Japanese Government Bonds (JGBs), acknowledging that “unrealized losses” on foreign bonds had swelled to ¥4.7 trillion.The logic is simple: why take currency risk for a 4.5% U.S. yield when domestic JGB yields are rising and offer a risk-free return in your home currency?
By December 31, 2025, the stage was set. The “free money” era was over. The largest holders of capital in Tokyo were repatriating funds or moving into cash. Global markets, however, were still positioned for “business as usual”, long Nvidia, long Bitcoin, short Yen. The dissonance between Japanese reality and Western positioning created the perfect conditions for a crash.
To validate the thesis that the Yen unwind is the primary driver of volatility, we must examine the sequence of events. The crash did not happen in a vacuum; it followed a precise timeline where geopolitical shocks acted as triggers for a structural fragility that had been building since the BOJ’s December pivot.
The pressure began to build in Q4 2025. As the BOJ signaled its intention to hike rates, Japanese traders, often the “canary in the coal mine” for global liquidity, began to reduce risk. This cycle started with Bitcoin. Bitcoin is a pure liquidity asset; it has no yield and is often funded via margin. As the cost of Yen margin rose, Japanese selling pressure on Bitcoin intensified from October through December. This was the first tremor.
Was the “Greenland War” theater? While the military dimensions may have been performative, the economic consequences were tangible and acted as the catalyst that exposed the fragility of the Yen carry trade.
On January 17, 2026, President Trump escalated his demand to purchase Greenland by threatening a 10% tariff on eight European nations (including the UK, Germany, and France) and escalating to 25% by June if the territory was not ceded. This introduced a “tail risk” that markets had not priced: the fracture of the Atlantic economic alliance.
Following the Martin Luther King Jr. holiday, U. S. markets opened on January 20 to a bloodbath. The S&P 500 fell 2.1%, the Nasdaq composite dropped 2.4%, and yields on U.S. Treasuries spiked.The narrative was “Greenland,” but the market mechanics told a different story. The threat of tariffs on close allies disrupts the “Atlantic Trade” narrative. For Japanese investors holding U.S. assets, this introduced a new risk premium. It wasn’t just about rates anymore; it was about the stability of the U.S.-led global order. This geopolitical volatility forced risk parity funds and algorithmic traders to reduce gross exposure. When a global portfolio deleverages, it buys back its funding currency. In this case, it bought Yen.
While Trump walked back the military threat on January 21 at Davos, the economic threat of tariffs remained a live wire. The volatility persisted, suggesting that the “Greenland” narrative was merely the match that lit the fuse of a much larger powder keg.
The final and most violent phase of the crash occurred at the end of the month, triggered by the nomination of Kevin Warsh as Federal Reserve Chair. Warsh is widely perceived as a hawk, favoring sound money and skepticism toward quantitative easing. His nomination signaled the potential end of the “Fed Put”, the assumption that the central bank would always intervene to support asset prices.
This announcement triggered a massive repricing of the “Debasement Trade.” Assets that thrive on currency debasement, Gold, Silver, and Bitcoin, collapsed. Gold fell ~11%, and Silver crashed ~36% in a single session. This synchronization of losses across uncorrelated assets (Tech and Gold falling together) is the definitive signature of a liquidity crisis driven by margin calls.
The unwinding of a carry trade is not a monolithic event; it is a cascade that ripples outward from the most liquid and speculative assets to the core holdings of institutional portfolios. The sequence of asset price collapses observed from October 2025 to January 2026 follows this classic liquidation hierarchy perfectly.
As noted, the unwind began in the crypto markets. Japan is home to a massive retail crypto trading base, and the Yen is a major pair for Bitcoin trading. Snippets indicate that Japanese traders began selling off Bitcoin in October 2025.
This timing is crucial. It correlates with the period when the BOJ began signaling the December rate hike. Retail traders, facing higher mortgage rates and loan costs in Japan, likely liquidated their most volatile, liquid asset first to raise cash. The selling was exacerbated by the looming tax reform in Japan. While the proposal to move to a flat 20% tax rate is bullish in the long term, the immediate pressure of rising funding costs forced traders to sell before the tax cut could be realized. By January 31, massive outflows from Bitcoin ETFs ($528 million) coincided with the broader market crash, confirming that crypto was being used as a source of liquidity to cover losses elsewhere.
Consider the “painful ~3% dump” in the Nasdaq and Microsoft’s staggering 15% drop. On January 29, 2026, Microsoft reported earnings. Despite beating revenue estimates ($81.27 billion vs. $80.28 billion), the stock plummeted ~11-15% intraday.
The street blamed concerns over “AI CapEx”, the idea that Microsoft was spending billions on data centers with slow return on investment. However, a 15% drop in a $3 trillion company on a “good” earnings beat is rarely fundamental; it is mechanical. Microsoft is a quintessential “momentum” stock, heavily held by foreign institutional investors, including Japanese pension funds. When the Yen strengthens, the value of these USD-denominated assets falls in JPY terms.
If a Japanese insurer holds Microsoft unhedged, a falling USD/JPY exchange rate hurts their balance sheet. If they hold it hedged
(rolling short USD positions), the rising U. S. rates (driven by the Warsh nomination) make the hedge prohibitively expensive. The January 29 drop was likely exacerbated by a “stop-loss” cascade from Tokyo desks. As the price broke key technical levels, algorithms programmed to protect Yen-denominated returns indiscriminately sold the most liquid blocks. Microsoft, being one of the most liquid stocks in the world, became the ATM for the rest of the portfolio.
The most compelling evidence of a forced liquidation event is the behavior of Gold and Silver on January 31, 2026. Gold fell ~11-12% and Silver crashed ~31-36% in a single session. Historically, Gold acts as a safe haven during equity market turmoil. If the Nasdaq is crashing due to “Greenland” fears, Gold should rally. Instead, it crashed.
This anomaly can be explained by two factors:
The Warsh Effect: As discussed, Warsh’s nomination strengthened the USD and undermined the thesis for holding anti-fiat assets.
Margin Call Dynamics: Snippets reveal that CME Group and the Shanghai Gold Exchange raised margin requirements on gold and silver futures days before the crash. When Japanese traders faced losses on their Microsoft longs and their Yen shorts, they needed cash immediately. They couldn’t sell illiquid bonds quickly enough, so they sold their “winners.” Gold had rallied to ~$5,400/oz prior to the crash. Traders liquidated their profitable Gold positions to pay for the margin calls on their losing Tech and Yen positions.
Cross-Asset Correlations (Week Ending Jan 31, 2026)
Figure 2: Cross-asset correlations, Jan 15–Jan 31,
2026. Note the spike in correlation between Gold, Bitcoin, and
Nasdaq 100 on Jan 31, indicating a systemic “sell-everything” margin
call.
Data sources:
Alex Lexington,
Finance Magnates,
Morningstar,
Investing.com,
Seeking Alpha
This correlation breakdown is visualized in Figure 2, where the correlation between Gold and the Nasdaq 100 spikes to nearly 1.0 during the crash week, a statistical anomaly that only occurs during severe liquidity events.
The “Yen Whale” hypothesis is strongly supported by the data on futures volumes and repo market stress. The “central mystery” is not just in the price action, but in the unseen flows of the derivatives market.
About a week ago, some whale kicked off an astronomically large market order for a /6J long when it hit recent lows. /6J (CME Yen Futures) hit a low of ~0.00647 (approximately 154.50 USD/JPY) in late January. This level has historically been a “line in the sand” for the Japanese Ministry of Finance (MOF).
Figure 4: The whale event that kicked off
the Japanese Yen unwind. Note the massive spike as /6J hit recent
lows, rallying investors worldwide to go long on yen
futures.
CME reported record volumes in FX and Interest Rate products for January 2026. The aggressive buying off the lows suggests a massive repatriation flow. Who is the Whale? Two theories emerge:
The MOF Thesis: The Ministry of Finance has a history of stealth intervention. Buying /6J (Long Yen) is functionally equivalent to selling USD reserves. Buying futures allows them to support the currency without immediately depleting cash reserves, squeezing speculators who are short.
The Carry Unwind: A massive hedge fund or bank (like Norinchukin) realizing that the “game is up” and closing out short-Yen positions. The size of the order suggests an entity that needed to move billions, not millions.
The subsequent price action, a sharp rally followed by “hammering back down”, represents the battleground. U. S. macro funds are still trying to short the Yen (betting on U.S. economic exceptionalism and Warsh’s policies), while Japanese domestic accounts are buying it. The volatility is the result of these tectonic plates grinding against each other.
The plumbing of the U. S. financial system showed signs of stress that coincided with the Japanese retreat. The Overnight Reverse Repo facility (ON RRP) saw a year-end spike to $106 billion but has since drained.
Japanese banks are typically huge participants in the U. S. repo market to fund their dollar assets. As Norinchukin and others retreat (repatriating funds to Japan), liquidity in the U.S. repo market becomes thinner. The “air pocket” in Microsoft and Gold prices was likely exacerbated by a lack of market maker depth in the repo-funded derivatives market. When market makers cannot access cheap repo funding, they widen spreads and reduce liquidity provision, leading to “gaps” in price action during sell-offs.
There have been significant moves in other currency futures as well: /6A increased 87 basis points, /6L rose 19 basis points, and /6S rose 18 basis points.
/6A (Australian Dollar): The 87 basis point rise in the Aussie Dollar is notable. AUD is often a proxy for Chinese growth and global risk sentiment. A rise here, amidst a tech crash, suggests a rotation out of U. S. assets and into commodities or Asia-Pacific currencies, further supporting the “Sell America” thesis triggered by the Greenland tariff threats.
/6L (Brazilian Real) and /6S (Swiss Franc): The rise in the Swiss Franc (a classic safe haven) aligns with the risk-off sentiment. The move in the Brazilian Real suggests that emerging markets are also seeing volatile flows as the dollar stabilizes.
Why was the VIX at 16 despite the chaos? The VIX measures implied volatility of S&P 500 options. Its relatively low level (16) compared to the violence in individual names (MSFT -15%, Gold -11%) indicates that the crash is a de-leveraging event, not a panic event.
In a panic, investors buy Puts on the index to protect themselves, spiking the VIX. In a de-leveraging event, investors simply sell the underlying assets (stocks, gold, crypto) to raise cash. They are not hedging; they are exiting. This explains why the VIX remained subdued while prices collapsed, the selling was orderly, algorithmic, and relentless, rather than emotional and panicked.
Skepticism about the “Greenland War” is well-founded. While the diplomatic row was real, its utility as a financial
narrative was far greater than its geopolitical reality.
President Trump’s threat of military force was retracted on January 21 at Davos. This “de-escalation” should theoretically have calmed markets. Instead, the volatility worsened into month-end. This confirms that the real problem wasn’t Greenland; it was the re-pricing of the Yen.
The financial media loves a simple cause-and-effect narrative. “Stocks down because of War” is easy to digest. “Stocks down because the cross-currency basis swap spread widened due to BOJ minutes” is not. The “Greenland” narrative provided the perfect cover for sophisticated actors to liquidate positions in Gold and Tech under the guise of “war jitters.” This allowed them to exit without sparking a broader panic about liquidity in the banking system. The focus on the Arctic masked the structural rot in the leverage complex.
The evidence suggests a covert, structural unwinding of the Yen carry trade is the primary driver of the January 2026 market chaos.
The interconnectedness of these events is undeniable. The BOJ’s rate hike in December 2025 and the subsequent hawkish signaling from the Takaichi administration fundamentally altered the cost of capital for the world’s largest carry trade. The “Greenland Crisis” acted as the initial volatility trigger, forcing a reduction in gross exposure. The nomination of Kevin Warsh acted as the final catalyst, shattering the “Debasement Trade” and forcing a liquidation of precious metals and crypto to cover margin calls on Yen-funded positions.
Here are some key takeaways:
The “Free Money” Era is Over: BOJ policies have fundamentally altered the global cost of capital. The flow of liquidity from Tokyo to New York has reversed.
Geopolitics as Catalyst: “Greenland” may have been the spark, but the Yen leverage was the powder keg. The tariff threats disrupted the “Atlantic Trade” narrative, forcing a repatriation of capital.
Liquidity Event: The synchronized crash of Gold, Crypto, and Tech confirms a systemic de-leveraging. The “Whale” orders in Yen futures and the breakdown of correlations are the smoking guns of a margin-driven event.
With the Japanese election on February 8 and U. S. tariffs looming, the “hammering” of the Yen is likely temporary. The structural trend is now toward repatriation. This implies lower U.S. asset prices,
higher U.S. yields, and a stronger Yen over the medium term. The “mystery” of the low VIX is explained by the mechanical nature of the unwind, a controlled demolition of leverage rather than a chaotic panic.
This won’t just be the big one. This could be the last one. If you’ve been preparing your whole life, knowing that something’s coming, then this could be the thing you’ve been preparing for. One final opportunity to get the guys who did this.
Longing the Yen is commonly referred to as “The Widowmaker Trade” on Wall Street, because you have trillions of dollars of monopoly money working against you. The carry traders have compromised every level of our government. Their greatest vulnerability is the Yen rising in value. They will do anything to defend their positions, even if that means bringing America’s economy down with them. Since recent events have made it obvious they’re going to lose, we might as well fight them. Most of us probably won’t make it out of this fight. But if we at least try, then there’s a chance we might prosper when it’s over.
The IV on OTM CME
/6J futures calls is 11% which is astonishingly low. The same is true for calls on the
FXY
ETF. Call options have defined risk. The more Yen we control, the more its value goes up, and the more crooks on Wall Street get liquidated. The worst that can happen is you lose your monopoly money, but that’s been happening anyway. Since carry traders own 10% of all U. S. treasuries, when they get liquidated they’ll have to sell a lot of treasury bonds, which means that
CME
/UB futures and the TLT
ETF will fall.
This blog is brought to you by various radicals, malcontents, and people who think the system is rigged. We’re not affiliated with any organization. Nothing here constitutes financial advice. Occupy Wall Street is not your financial advisor or your lawyer. We’re retail investors like you. Do your own research. Past performance does not guarantee future results. We are the 99 percent. The only solution is world revolution. Wall Street’s time has finally come.
...
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SaaS is the most profitable business model on Earth. It’s easy to understand why: build once, sell the same thing again ad infinitum, and don’t suffer any marginal costs on more sales.
I have been writing software for more than half my life. In the last year itself, I’ve talked to hundreds of founders and operators in SF, from preseed to Series E companies.
AI is bringing an existential threat to a lot of B2B SaaS executives: How to keep asking customers for renewal, when every customer feels they can get something better built with vibe-coded AI products?
And the market is pricing it in. Morgan Stanley’s SaaS basket has lagged the Nasdaq by 40 points since December. HubSpot and Klaviyo are down ~30%. Analysts are writing notes titled “No Reasons to Own” software stocks.
The new problem for B2B SaaS is that with AI, customers can get something working with vibe coding. There are tens of vibe coding “internal tool” services that promise to connect to every integration in the world to pump out CRUD and workflow apps.
Whatever they build simply works. It takes some wrangling to get there (one Series C VP listed eleven different vibe coding tools they’ve tried and the pros and cons between each on a phone call once), but productivity gains are immediate.
And vibe coding is fun. You feel like a mad wizard using the right incantation to get this magical new silicon intelligence to do exactly what you want.
What they don’t know, though, is that a poorly architected system will fail, eventually. As every senior programmer (eventually) understands, our job is complex because we have to understand the relationships in the real world, the processes involved, and the workflows needed, and representing it in a robust way to create a stable system. AI can’t do that.
Non-programmers don’t know any of this nuance. One Series E CEO told me that they’re re-evaluating the quarterly renewal of their engineering productivity software because they along with an engineer reimplemented something using Github and Notion APIs. They were paying $30,000 to a popular tool and they were not going to renew anymore.
If customers feel like they aren’t being served exactly like they want to, they are more likely to churn. The reason behind all this is that customers are demanding more from their B2B vendors, because they know what’s possible.
Previously, you would change your company to fit what your ERP and pay them hundreds of thousands of dollars. Now, everyone can see that agentic coding makes an unprecedented level of flexibility possible. And customers are demanding that flexibility, and if they don’t get it, they’ll leave.
This week itself I was on a phone call with a Series B AE talking about how they’re potentially losing an $X00,000 account just because the customer can’t use a specific failure reporting workflow in the SaaS. They’re now working with me to build what the customer needs and retain them.
If the entire company’s workflows operates on your platform, i.e. you’re a line-of-business SaaS, you are integrated into their existing team already. They know your UI and rely on you on the day to day.
For example, to create a data visualization I won’t seek any SaaS. I’ll just code one myself using many of the popular vibe coding tools (my team actually did that and it’s vastly more flexible than what we’d get off-the-shelf).
Being a “System of Record” means you’re embedded so deeply that there’s no choice but to win. My prediction is that we’ll see more SaaS companies go from the application layer to offering their robust SoR as their primary selling point.
This is where vibe-coded apps silently fail — and where established SaaS platforms earn their keep.
When a non-technical team vibe-codes an internal tool, they’re not thinking about environment keys, XSS vulnerabilities or API keys hardcoded in client-side JavaScript. They’re not implementing rate limiting, audit logs, or proper session management. They’re definitely not thinking about SOC 2 compliance, GDPR data residency requirements, or HIPAA audit trails.
I’ve seen it firsthand: a finance team built a “quick” expense approval tool that stored unencrypted reports in a public S3 bucket. A sales ops team created a commission calculator that anyone with the URL could access — no auth required. These aren’t edge cases. They’re the norm when software is built without security as a foundational concern.
Enterprise SaaS platforms have spent years (and millions) solving these problems: role-based access control, encryption at rest and in transit, penetration testing, compliance certifications, incident response procedures. Your customers may not consciously value this — until something breaks.
The challenge is that security is invisible when it works. You need to communicate this value proactively: remind customers that the “simple” tool they could vibe-code themselves would require them to also handle auth, permissions, backups, uptime, and compliance.
The times of asking customers to change how they work are gone. Now, SaaS vendors that differentiate by being ultra customizable win the hearts of customers.
How? It’s the most powerful secret to increase usage. We’ve all heard the classic SaaS problem where the software is sold at the beginning of the year, but no one actually ends up using it because of how inflexible it is and the amount of training needed.
And if a SaaS is underutilized, it gets noticed. And that leads to churn.
This is the case with one of my customers, they have a complex SaaS for maintenance operations. But turns out, this was not being used at the technician level because they found the UI too complex.
How I’m solving this is essentially a whitelabelled vibe-coding platform with in-built distribution and secure deployments. When they heard of my solution they were immediately onboard. Their customer success teams quickly coded a very specific mobile webapp for the technicians to use and deployed it in a few days.
Now, the IC technician is exposed to just those parts of the SaaS that they care about i.e. creating maintenance work orders. The executives get what they want too, vibe coding custom reports exactly the way they want vs going through complicated BI config. They are able to build exactly what they want and feel like digital gods while doing it.
Usage for that account was under 35%, and is now over 70%. They are now working closely with me to vibe code new “micro-apps” that work according to all of their customer workflows. And the best part? This is all on top of their existing SaaS which works as a system of record and handles security, authentication, and supports lock-in by being a data and a UI moat.
This is exactly what I’m building: a way for SaaS companies to let their end-users vibe code on top of their platform (More on that below). My customers tell me it’s the best thing they’ve done for retention, engagement, and expansion in 2026 — because when your users are building on your platform, they’re not evaluating your competitors.
Here’s what I’ve realized after hundreds of conversations with founders and operators: AI isn’t killing B2B SaaS. It’s killing B2B SaaS that refuses to evolve.
The SaaS model was built on a simple premise: we build it once, you pay forever. That worked when building software was hard. But now your customers have tasted what’s possible. They’ve seen their finance team whip up a custom dashboard in an afternoon. They’ve watched a non-technical PM build an internal tool that actually fits their workflow.
You can’t unsee that. You can’t go back to paying $X0,000/year for software that almost does what you need.
The survivors won’t be the SaaS companies with the best features. They’ll be the ones who become platforms — who let customers build on top of them instead of instead of them. When I showed a well-known VC what I was building to help SaaS companies do exactly this, he said: “This is the future of marketplaces and software companies.”
Maybe. Or maybe this is just another cycle and traditional SaaS will adapt like it always has. But I know this: the companies I’m talking to aren’t waiting around to find out. They’re already rebuilding their relationship with customers from “use our product” to “build on our platform.”
The question isn’t whether AI will eat your SaaS.
It’s whether you’ll be the one holding the fork.
I’m solving exactly this problem with a whitelabelled AI platform for B2B SaaS companies, so your users can vibe code customized workflows on top of their existing system of record.
My customers tell me this is the best way to support retention, engagement, and expansion in 2026. If this sounds interesting to you or someone you know, I can reach out with a custom demo or you can learn more about Giga Catalyst.
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Skip to main contentHow Jeff Bezos Brought Down the Washington PostThe Amazon founder bought the paper to save it. Instead, with a mass layoff, he’s forced it into severe decline. On September 4, 2013, the Amazon founder Jeff Bezos held his first meeting with the staff of the Washington Post, the newspaper he had agreed to purchase a month earlier from the Graham family, for two hundred and fifty million dollars. It had been a long and unsettling stretch for the paper’s staff. We—I was a deputy editor of the editorial page at the time—had suffered through years of retrenchment. We trusted that Don Graham would place us in capable hands, but we did not know this new owner, and he did not know or love our business in the way that the Graham family had. Bezos’s words at that meeting, about “a new golden era for the Washington Post,” were reassuring. Bob Woodward asked why he had purchased the paper, and Bezos was clear about the commitment he was prepared to make. “I finally concluded that I could provide runway—financial runway—because I don’t think you can keep shrinking the business,” he said. “You can be profitable and shrinking. And that’s a survival strategy, but it ultimately leads to irrelevance, at best. And, at worst, it leads to extinction.”To look back on that moment is to wonder: How could it have come to this? The paper had some profitable years under Bezos, sparked by the 2016 election and the first Trump term. But it began losing enormous sums: seventy-seven million dollars in 2023, another hundred million in 2024. The owner who once offered runway was unwilling to tolerate losses of that magnitude. And so, after years of Bezos-fuelled growth, the Post endured two punishing rounds of voluntary buyouts, in 2023 and 2025, that reduced its newsroom from more than a thousand staffers to under eight hundred, and cost the Post some of its best writers and editors. Then, early Wednesday morning, newsroom employees received an e-mail announcing “some significant actions.” They were instructed to stay home and attend a “Zoom webinar at 8:30 a.m.” Everyone knew what was coming—mass layoffs.The scale of the demolition, though, was staggering—reportedly more than three hundred newsroom staffers. The announcement was left to the executive editor, Matt Murray, and human-relations chief Wayne Connell; the newspaper’s publisher, Will Lewis, was nowhere to be seen as the grim news was unveiled. In what Murray termed a “broad strategic reset,” the Post’s storied sports department was shuttered “in its current form”; several reporters will now cover sports as a “cultural and societal phenomenon.” The metro staff, already cut to about forty staffers during the past five years, has been shrunk to about twelve; the foreign desks will be reduced to approximately twelve locations from more than twenty; Peter Finn, the international editor, told me that he asked to be laid off. The books section and the flagship podcast, “Post Reports,” will end. Shortly after the meeting, staffers received individualized e-mails letting them know whether they would stay or go. Murray said the retrenched Post would “concentrate on areas that demonstrate authority, distinctiveness, and impact,” focussing on areas such as politics and national security. This strategy, a kind of Politico-lite, would be more convincing if so many of the most talented players were not already gone.Graham, who has previously been resolutely silent about changes at the paper, posted a message on Facebook that pulsed with anguish. “It’s a bad day,” he wrote, adding, “I am sad that so many excellent reporters and editors—and old friends—are losing their jobs. My first concern is for them; I will do anything I can to help.” As for himself, Graham, who once edited the sports section, said, “I will have to learn a new way to read the paper, since I have started with the sports page since the late 1940’s.”What happened to the Bezos of 2013, a self-proclaimed optimist who seemed to have absorbed the importance of the Post in the nation’s journalistic ecosystem? In 2016, dedicating the paper’s new headquarters, he boasted that it had become “a little more swashbuckling” and had a “little more swagger.” As recently as December, 2024, at the New York Times’ DealBook Summit, Bezos expressed his commitment to nurturing the paper: “The advantage I bring to the Post is when they need financial resources, I’m available. I’m like that. I’m the doting parent in that regard.” Not long ago, he envisioned attracting as many as a hundred million paying subscribers to the Post. With these brutal cuts, he seems content to let the paper limp along, diminished in size and ambition.“In the beginning, he was wonderful,” Sally Quinn, the veteran Post contributor and wife of its legendary executive editor, Ben Bradlee, told me of Bezos. “He was smart and funny and kind and interested. He was joyful. He was a person of integrity and conscience. He really meant it when he said this was a sacred trust, to buy the Post. And now I don’t know who this person is.”The author David Maraniss was with the Post for forty-eight years. He resigned as an associate editor in 2024, after Bezos killed the editorial page’s planned endorsement of Kamala Harris. “He bought the Post thinking that it would give him some gravitas and grace that he couldn’t get just from billions of dollars, and then the world changed,” Maraniss said of Bezos. “Now I don’t think he gives us—I don’t think he gives a flying fuck.”I asked Maraniss what cuts of this magnitude would mean for the institution. “I don’t even want to call it the Washington Post,” he said. “I don’t know what it’ll be without all of that.”The first sign of impending layoffs came in late January, when the sports staff was informed that plans to send writers to Italy to cover the Winter Olympics had been cancelled. (Management later agreed to send a smaller crew.) In the following days, as rumors began to spread of severe cuts, the paper’s reporters began posting messages directed at Bezos on X, with the plaintive hashtag #SaveThePost. “Our reporters on the ground drove exclusive coverage during pivotal moments of recent history,” the foreign staff wrote to Bezos. “We have so much left to do.” The local staff noted that it had already been slashed in half in the past five years. “Watergate,” they wrote, “started as a local story.”It did not help the staff’s morale that Lewis and his team were hobnobbing in Davos, or that Bezos and his wife, Lauren Sánchez, were in Paris for Haute Couture Week. More troubling were reminders that Bezos, who once emblazoned “Democracy Dies in Darkness” on the paper’s masthead, appears to be pursuing a policy of appeasement toward the Trump Administration. During the first Trump term, Bezos stood by the Post even when his stewardship threatened to cost him billions in government contracts. Now Bezos had not said a word about a recent F.B.I. raid on the home of the Post federal-government reporter Hannah Natanson, in which the agency seized her phones, laptops, and other devices. As the staff awaited the axe, the President and the First Lady celebrated the première of “Melania,” a documentary that Amazon had licensed for forty million dollars and was reported to be spending another thirty-five million to promote. The deal was inked after Bezos had dinner with the Trumps shortly before the Inauguration.Martin Baron, who oversaw coverage at the paper that garnered eleven Pulitzer Prizes during his eight years as executive editor, said in a statement, “This ranks among the darkest days in the history of one of the world’s greatest news organizations. The Washington Post’s ambitions will be sharply diminished, its talented and brave staff will be further depleted, and the public will be denied the ground-level, fact-based reporting in our communities and around the world that is needed more than ever.” The news industry is in “a period of head-spinning change,” Baron told me. But the Post’s problems “were made infinitely worse by ill-conceived decisions that came from the very top.” He pointed to Bezos’s decision to kill the Harris endorsement—a “gutless order” that cost the paper more than two hundred fifty thousand subscribers. “Loyal readers, livid as they saw owner Jeff Bezos betraying the values he was supposed to uphold, fled The Post. In truth, they were driven away, by the hundreds of thousands,” Baron said. “Bezos’s sickening efforts to curry favor with President Trump have left an especially ugly stain of their own. This is a case study in near-instant, self-inflicted brand destruction.”I spent more than forty years at the Post, as a reporter, an editor, an editorial writer, and a columnist. I resigned last March, after Bezos announced that the Opinions section, where I worked, would henceforth be concentrating on the twin pillars of “personal liberties and free markets.” More alarming, Bezos advised, “Viewpoints opposing those pillars will be left to be published by others.” We had been an opinion section reflecting a wide range of views—which Bezos himself had encouraged. It seemed obvious that this change was deeply misguided.I had written a column critical of the non-endorsement decision several months earlier. The paper published it without any substantive changes. But, when I wrote a column disagreeing with the no-dissent-allowed dictum, I was told that Lewis had killed it—it apparently didn’t meet the “high bar” for the Post to write about itself—and declined my request to meet. I submitted my letter of resignation. A new editorial-page editor went on to shift both unsigned editorials and signed opinion columns dramatically to the right, to the point that no liberal columnists remain. One recent editorial praised the President’s plan for a new ballroom and excused his unauthorized bulldozing of the East Wing, saying that “the blueprints would have faced death by a thousand papercuts.” Another endorsed the move to rename the Defense Department the Department of War as “a worthy blow against government euphemism.” There are some editorials critical of Trump, but the inclination to fawning praise is unmistakable. Had I not defenestrated myself, I would, no doubt, have been advised to take my buyout and go.But I am not—at least, I have not been—a Bezos-hater. I am grateful for the resources, financial and technological, that he devoted to the paper in his early years as owner. The surprise of Bezos’s tenure at the Post has been his bad business decisions. Fred Ryan, a former chief of staff to Ronald Reagan and founding president of Politico, was hired as the publisher and C.E.O. in 2014 and oversaw a period of spectacular growth. Buoyed by Bezos-funded expansion and the public’s fixation on the new Trump Administration, the number of digital subscribers soared from thirty-five thousand when he arrived to two and a half million when he left, in the summer of 2023. But Ryan failed to develop an adequate plan for how the newspaper would thrive in a post-Trump environment. As traffic and revenue plunged, Ryan found himself increasingly at odds with the newsroom. He held a year-end town-hall meeting in 2022 at which he announced that layoffs were coming, and then, to the consternation of the staff, left without taking questions. As Clare Malone reported for The New Yorker, Woodward beseeched Bezos to intercede. The owner made a rare visit to the paper in January, 2023, for meetings with key staffers, taking notes on a legal pad as they poured out their anxiety.Ryan left that summer, but Lewis, his eventual replacement, accomplished the feat of making the newsroom nostalgic for Ryan. A decade earlier, Lewis, then a senior executive in Rupert Murdoch’s British-tabloid empire, had played a pivotal role in dealing with the fallout from the phone-hacking scandal at some of Murdoch’s papers. Lewis had said that he was acting to protect “journalistic integrity,” when the Post questioned him about his actions during that time, but in 2024 questions arose, fuelled by a civil lawsuit brought against the papers, about whether Lewis had sought to conceal evidence, including by carrying out a plan to delete millions of e-mails. (Lewis has said the allegations against him were “completely untrue.”) At the Post, Lewis clashed with executive editor Sally Buzbee over coverage of the story, reportedly insisting that it was not newsworthy. Shortly afterward, Lewis announced Buzbee’s departure, and his plan to replace her with Robert Winnett, a former colleague of his from London’s Daily Telegraph and Sunday Times. The Post and the Times both reported on how Lewis and Winnett had used fraudulently obtained material as the basis for articles. “His ambition outran his ethics,” one of Lewis’s former reporters told the Times. Winnett ended up withdrawing from the position, but the episode poisoned relations between Lewis and the newsroom.The staff, meanwhile, became increasingly concerned that Lewis was offering corporate word salad in place of a vision to address the Post’s decline. “Fix it, build it, scale it” was his catchphrase when he arrived, in January, 2024. In June of that year came an amorphous plan for what Lewis called a “third newsroom.” (The second newsroom, we were surprised to learn, was the Opinions section.) First, it was to focus on social media and service journalism. Then it was rechristened WP Ventures and, according to a memo to staff, would “focus entirely on building personality-driven content and franchises around personalities.” By February, 2025, the situation had deteriorated to the point that two former top editors, Leonard Downie and Robert Kaiser, wrote to Bezos about Lewis. “Replacing him is a crucial first step in saving The Washington Post,” they urged in an e-mail. Bezos never responded.Downie, who served as executive editor from 1991 to 2008, contrasted the paths of the Times and the Post. During the past decade, the Times transformed itself into a one-stop-shopping environment that lured readers with games such as Spelling Bee, a cooking app, and a shopping guide. By the end of 2025, it was reporting close to thirteen million digital subscribers and an operating profit of more than a hundred and ninety-two million dollars. The Post does not release information about its digital subscribers, but it was reported to have two and a half million digital subscribers at the time of the non-endorsement decision, in 2024.“One of the big differences to me was that they hired a publisher”—Ryan—“who didn’t come up with any ideas,” Downie told me. “And then when he left . . . we knew that Bezos was losing money, and we were encouraged by the fact that they were looking for somebody who could improve the business side of the paper and the circulation side of the paper. And then they chose this guy who we hardly ever heard from, who had a checkered past in British journalism.”Writing last month on a private Listserv for former Post employees, Paul Farhi, who as the media reporter for the Post covered Bezos’s acquisition of the paper, shared his “utter mystification and bafflement” about Bezos’s tolerance of Lewis. “Even as a hands-off boss,” he wondered, “could Bezos not see what was obvious to even casual observers within a few months of Will’s arrival—that Will was ill-suited to the Post, that he had alienated the newsroom, that he had an ethically suspect past, and—most important—that none of his big ideas was working or even being implemented?” (Farhi, who took a buyout in 2023, gave me permission to quote his message.)Even before these new cuts, a parade of key staffers had left the Post. A beloved managing editor, Matea Gold, went to the Times. The national editor, Philip Rucker, decamped to CNN, and the political reporter Josh Dawsey to the Wall Street Journal. The Atlantic hired, among others, three stars of the paper’s White House team: Ashley Parker, Michael Scherer, and Toluse Olorunnipa. These are losses that would take years to rebuild—if the Post were in a rebuilding mode. The Post, Woodward said, “lives and is doing an extraordinary reporting job on the political crisis that is Donald Trump”—including its scoop on the second strike to kill survivors of an attack on an alleged Venezuelan drug boat. But the print edition is a shadow of its former self, with metro, style, and sports melded into an anemic second section; daily print circulation is now below one hundred thousand. More pressingly, it’s unclear whether a newsroom so stripped of resources can sustain the quality of its work.The sports columnist Sally Jenkins, who left the Post in August, 2025, as part of the second wave of buyouts, has been more supportive of management than many other Post veterans. So it was striking that, when we spoke recently, she was both passionate about the work of her newsroom colleagues and unsparing about how the business side had failed them. “When you whack at these sections, you’re whacking at the roots of the tree,” she told me. “We train great journalists in every section of the paper, and we train them to cover every subject on the globe. And when you whack whole sections of people away, you are really, really in danger of killing the whole tree.” When I asked how she felt about the losses, Jenkins said, “My heart is cracked in about five different pieces.”Jenkins, who was in California covering Super Bowl week for the Atlantic, has spent a career studying what accounts for the difference between winning teams and losing ones. Bezos, she said, had been generous with his money and laudable for never interfering in the work of the newsroom. But, she added, “making money at journalism, you have to break rocks with a shovel. You have to love thinking about journalism to the point that it wakes you up at night with an idea, and then you have to be willing to try it. And I don’t see a sense that he loves the business enough to think about it at night. It’s almost like he’s treated it like Pets.com—an interesting experiment that he’s willing to lose some money on until he’s not. But the difference with this business is it’s not Pets.com. It’s not a business that just disappears into the muck of venture capitalism. It’s a business that is essential to the survival of the Republic, for Christ’s sake. So you don’t fuck around with it like that.”As Post staffers and alumni braced for the cuts, I called Kaiser, the former managing editor, who spent more than half a century at the paper. “Mr. Bezos’s personal system has failed him in a way I fear he doesn’t grasp,” Kaiser, now eighty-two, told me. “He has no sense of the damage that will be done to his reputation in history if he becomes seen as the man who destroyed the institution that Katharine Graham”—the famed publisher who led the paper from the sixties to the nineties—“and Ben Bradlee built.” Kaiser recalled arriving at the paper’s London bureau in 1964. “If I say, ‘I’m Kaiser from the Washington Post’—what’s that? They never heard of it.” A decade later, he was posted in Moscow, as Woodward and Carl Bernstein were breaking the Watergate story. “Explaining was not necessary,” Kaiser said. “The Russians, in fact, had a gloriously exaggerated impression of the Washington Post as the king-maker and the king-destroyer.”Bezos, Kaiser continued, “knew what the role was, acknowledged the role—those words ‘doting parent’—and then he walked away from it. What the hell?” The damage, he predicted, will reverberate beyond the immediate cuts. “What purpose does any honorable, attractive, competent journalist have for remaining at the Post? None.”At one point, as we talked about the transformation of the Post, Kaiser stopped himself. “I’m going to cry,” he said, and paused. “Oh, God, it’s killing me.”Bezos may be tiring of the Post, but he has not seemed inclined to sell the paper. Nor is it clear that would be a better, or at this point even feasible, outcome. Newspapers across the country are being bought up by private-equity firms that are essentially selling off the valuable parts. But there is another model for Bezos to consider: turning the Post into a nonprofit, endowed by Bezos but operating independently of him. For Bezos, this would reduce the role of the Post as a headache and a threat to other, more favored endeavors, such as his rocket company, Blue Origin. For the Post, assuming the endowment is sufficient, it would provide that continuing runway.There are models for this approach. In Philadelphia, the late cable-television tycoon H. F. “Gerry” Lenfest purchased the Inquirer, the Daily News, and Philly.com in 2015, and the following year donated the publications to a charitable trust. “What would the city be without the Inquirer and the Daily News?” asked Lenfest, whose contribution to the endeavor has been valued at almost a hundred and thirty million dollars. In Utah, the investor Paul Huntsman bought the Salt Lake Tribune from the hedge fund Alden Global Capital in 2016; three years later, he transformed it into a nonprofit, supported in part by tax-deductible contributions from readers.Writing in the Columbia Journalism Review in 2024, Steven Waldman suggested that Bezos follow a similar course. “ ‘Nonprofit’ does not mean ‘losing money,’ ” Waldman wrote. “Nonprofit news organizations can sell ads, offer subscriptions, and take donations. Done well, it is an especially strong business model, because it provides an extra revenue stream (philanthropy) and is deeply embedded in serving the community.” My quibble with Waldman’s pitch is that he asked Bezos to ante up a paltry hundred million. When Bezos purchased the Post, his net worth was about twenty-five billion; it is now an estimated two hundred fifty billion. Why not one per cent of that for the Post, enough to sustain the paper indefinitely? A pipe dream, I know, but this arrangement would make Bezos the savior of the Post, not the man who presided over its demise.In the 1941 movie “Citizen Kane,” Charles Foster Kane, a newspaper publisher who, like Bezos, is one of the richest men in the world, is confronted by his legal guardian, Walter Thatcher, about the folly of funding his paper. “Honestly, my boy, don’t you think it’s rather unwise to continue this philanthropic enterprise, this Inquirer that’s costing you a million dollars a year?” Thatcher demands. “You’re right, Mr. Thatcher. I did lose a million dollars last year,” Kane replies. “I expect to lose a million dollars this year. I expect to lose a million dollars next year. You know, Mr. Thatcher, at the rate of a million dollars a year, I’ll have to close this place in sixty years.” Update Kane’s outlays to assume losses of a hundred million annually, in perpetuity. By that math, Bezos would have more than two millennia before needing to turn out the lights. ♦
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