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And the #crazy# part is: just a few years ago, $50 or $60 could get a family of four a pretty decent meal at a sit-down restaurant. But now, because inflation has gone up so much while salaries haven’t kept up—and for some people, salaries have effectively gone #down—things# that used to be affordable are no longer affordable. #McDonald’s# becoming a “#luxury”# is already a sign of how much has changed. The same thing is happening with eating out in general. Back then, a working-class family could go out to eat maybe once a month. Now even that can feel like a luxury. Places like Applebee’s or Cheesecake Factory used to be treated as “cheap” or even a joke—people would say #things# like, “If your boyfriend takes you to Applebee’s, dump him.” But today, that attitude doesn’t even make sense anymore, because Cheesecake Factory is expensive too. Eating out has become a middle-class luxury, not something normal.

This is an email I sent earlier today to all employees at Coinbase: Team, Today I’ve made the difficult decision to reduce the size of Coinbase by ~14%. I want to walk you through why we're doing this now, what it means for those affected, and how this positions us for the future. Why now Two forces are converging at the same time. We need to be front footed to respond to both. First, the market. Coinbase is well-capitalized, has diversified revenue streams, and is well-positioned to weather any storm. Crypto is also on the verge of the next wave of adoption, with stablecoins, prediction markets, tokenization, and more taking off. However, our business is still volatile from quarter to quarter. While we've managed through that cyclicality many times before and come out stronger on the other side, we’re currently in a down market and need to adjust our cost structure now so that we emerge from this period leaner, faster, and more efficient for our next phase of growth. Second, AI is changing how we work. Over the past year, I’ve watched engineers use AI to ship in days what used to take a team weeks. Non-technical teams are now shipping production code and many of our workflows are being automated. The pace of what's possible with a small, focused team has changed dramatically, and it's accelerating every day. All of this has led us to an inflection point, not just for Coinbase, but for every company. The biggest risk now is not taking action. We are adjusting early and deliberately to rebuild Coinbase to be lean, fast, and AI-native. We need to return to the speed and focus of our startup founding, with AI at our core. What this means To get there, we are not just reducing headcount and cutting costs, we’re fundamentally changing how we operate: rebuilding Coinbase as an intelligence, with humans around the edge aligning it. What does this mean in practice? - Fewer layers, faster decisions: We are flattening our org structure to 5 layers max below CEO/COO. Layers slow things down and create coordination tax. The future is small, high context teams that can move quickly. Leaders will own much more, with as many as 15+ direct reports. Fewer layers also means a leaner cost structure that is built to perform through all market cycles. - No pure managers: Every leader at Coinbase must also be a strong and active individual contributor. Managers should be like player-coaches, getting their hands dirty alongside their teams. - AI-native pods: We’ll be concentrating around AI-native talent who can manage fleets of agents to drive outsized impact. We’ll also be experimenting with reduced pod sizes, including “one person teams” with engineers, designers, and product managers all in one role. In short: AI is bringing a profound shift in how companies operate, and we’re reshaping Coinbase to lead in this new era. This is a new way of working, and we need to leverage AI across every facet of our jobs. To those who are affected I know there are real people behind these decisions — talented colleagues who have poured themselves into this company and our mission. To those of you who will be leaving: thank you. You’ve helped build Coinbase into what it is today, and I am sincerely grateful for everything you've done. All impacted team members will receive an email to their personal account in the next hour with more information, and an invitation to meet with an HRBP and a senior leader in your organization. Coinbase system access has been removed today. I know this feels sudden and harsh, but it is the only responsible choice given our duty to protect customer information. To those affected, we will be providing a comprehensive package to support you through this transition. US employees will receive a minimum of 16 weeks base pay (plus 2 weeks per year worked), their next equity vest, and 6 months of COBRA. Employees on a work visa will get extra transition support. Those outside of the US will receive similar support, based on local factors and subject to any consultation requirements. Coinbase prides itself on talent density. Our employees are among the most talented people in the world, and I have no doubt that your skills and experience will be highly sought after as you pursue your next chapters. How we move forward To the team that is staying, I know this is a difficult day. We’re saying goodbye to colleagues and friends you've been in the trenches with. But here’s what I want you to know as we move forward together: Over the past 13 years, we have weathered four crypto winters, gone public, and built the most trusted platform in our industry. We’ve made it this far by making hard decisions and by always staying focused on our mission. This time will be no different – nothing has changed about the long term outlook of our company or industry. And most importantly, our mission has never been more important for the world. Increasing economic freedom requires a new financial system, and we’re building it. The Coinbase that emerges from this will be more capable than ever to achieve our mission. Brian

The market is sending two completely different signals at the same time right now. ➛ $BTC is stuck at $77K. Same range it's been in for weeks. ➛ $HYPE just hit a new ATH at $64.13. Up 46% in 7 days. ➛ $SOL up 15% this week. This kind of divergence doesn't happen randomly. There's a read here. ➛ What's actually going on. $BTC is a macro trade right now. We've talked about this before the DXY correlation is at a 4-year extreme. As long as the Fed stays hawkish and the dollar holds, $BTC doesn't move. It's not weak. It's just being held hostage by the same macro pressure that's been sitting on it since February. Meanwhile $HYPE and $SOL are moving on protocol fundamentals, not macro. $HYPE hit ATH because of real inflows 21Shares ETF, Grayscale's $25M, pre-IPO perps for SpaceX and OpenAI bringing a completely new user base onto the platform. These aren't narratives. These are product milestones driving actual revenue. $SOL is moving because Alpenglow is real, Western Union launched USDPT on Solana, and institutional rails are being built on top of it right now. ➛ Here's my actual read on the divergence. When alts outperform $BTC this aggressively in a risk-off macro environment, it usually means one of two things. Either the broader market is about to catch up and $BTC breaks out too. Or the alts are running ahead of themselves and they retrace hard the moment $BTC gets any macro pressure. The difference between those two scenarios comes down to whether the flows are real or rotational. ‣ If institutions are genuinely buying $HYPE spot through ETFs while staying underweight $BTC that's a structural shift. New money entering through a new product. ‣ If it's crypto-native capital rotating out of $BTC into alts chasing momentum that's a top signal, not a breakout signal. Right now it looks like both are happening simultaneously. The ETF flows into $HYPE are new money. The $SOL and broader altcoin moves feel more rotational. And $BTC dominance is still at 58-60%, which means the rotation hasn't gone far enough to call this a real altseason. ➛ What I'm actually watching. If $BTC breaks above $83K with volume, the divergence resolves bullishly everything follows. If $BTC stays range-bound and alts keep pushing, you're looking at a classic distribution setup where early holders exit into retail momentum. The SBR announcement coming "in the next few weeks" is the wildcard. 328K $BTC officially off the market, no-sell policy codified, potentially first open-market purchase signal for Q4. That's a $BTC-specific catalyst that has nothing to do with macro. If that drops while $BTC is still at $77K, you get the breakout. If it drops after alts have already run 50-100% more, you get a very crowded trade. I'm watching $BTC dominance more than price right now. That's the real tell.

While at consensus down in Miami I sat down with @CryptoMichNL for ~45 minutes to discuss all things crypto and ETFs. Was seriously awesome to chat in person for the first time. Here's the full pod:

"The Knicks pulled off a miraculous comeback in the fourth quarter of Game 1 of this year’s Eastern Conference Finals, coming back from 22 points down with a little less than eight minutes left and beating the Cleveland Cavaliers in overtime. And one has to wonder if those eight minutes will be what, ultimately, determines which one of these teams go to the NBA Finals." @johnschuhmann outlines 3 things to watch as Cleveland tries to even the series in Game 2 of the Eastern Conference Finals tonight at 8pm/et on ESPN!

“The Oklahoma City Thunder have been in this position before – down 1-0 in the playoffs – and responded with series victories… That experience allows the Thunder to act with poise and confidence and an understanding that they have found answers in the past and can figure out how to beat the San Antonio Spurs after Monday’s 122-115 Game 1 double-overtime loss.” @JeffZillgitt highlights 3 things to watch as the Thunder look to respond vs. the Spurs tonight at 8:30pm/et on NBC & Peacock!

I explained the Chinese real estate & debt crisis in much detail in 2024 on my Substack. Nothing has changed since. China is in what we call "the largest balance-sheet recession the world has ever seen". And it will take years to get out of it and assuming the CCP's investment-led growth model does not dig the next hole in the meantime - a likely. The FT published added some colour to it two days ago: "Housing is important to every economy. But to China, it’s extra important. According to the PBoC, 96% of urban households own a home, and 41% own at least two. The average household owns 1.5 properties. And as such, property constitutes around 70% of China’s private wealth. The comparable figure for the US is around 30%. So when Chinese property prices fall, the authors make a pretty compelling case that this has all sorts of particularly bad economic spillovers. And fall they have. The negative wealth effect is substantial, and “effects are amplified by elevated household debt, much of which consists of mortgage obligations”. This — and the weaker income expectations that the falls generate — goes some way to suppressing consumption. Moreover, declining land-sale revenues constrain local government budgets, “limiting their capacity to finance developmental projects and maintain existing public infrastructure”. And this is even before any credit impacts from rising non-performing loans and mortgages on bank balance sheets are considered. Tl;dr: bad bad bad. Of course, China isn’t the first soon-to-be-global-economic-hegemon-East-Asian-power staring down demographic oblivion to have piled its savings into a property boom. Back in 1991, the world was fretting over the rise and rise of Japan. And the Japanese were buying Japanese residential real estate at outlandish prices. Japan’s house prices peaked back in 1991 and spent the next 30 years on a downward trajectory. We’re only a few years into the Chinese property bust, and its ultimate trajectory is both unknown and unknowable. But Rogoff and Yang have pulled together some cool data they kindly shared with Alphaville, allowing us to make this chart below. So far, it looks like prices in Chinese cities are falling at around the same pace as they did over the first five-to-10 years of Japan’s bust. Japan’s property crash is associated with a lost decade (or two) of economic growth. In the 10 years leading up to 1991, Japanese real annual GDP growth averaged 4.4%. In the subsequent 10 years it averaged only 0.9% per annum. The same numbers for China, with 2021 marking its property zenith, are 7.0% per year and 4.6% per year (so far). If the IMF’s forecasts turn out right, this latter number will fall to around 4.0% per annum. While the levels are different, the before-and-after drop looks comparable. Was it housing wot dun it? Rogoff and Yang reckon that a 40% decline in house prices translates into a total consumption loss of 2-4% of GDP. Not nothing, but not a single answer explaining life, the universe and wiggles in the decadal pace of real economic growth. To get here, they construct a historical dataset comprising subnational data across 47 prefectures, and input and output data at granular industry levels. They then use this to examine the macroeconomic implications of Japan’s real estate bust. And the authors argue that: a housing bust can generate substantial adverse effects on the economy via real channels. . . . overbuilding during the boom can trigger a demand-driven recession with limited reallocation and low output. Unlike financial channels, which amplify shocks through leverage, bank balance sheets, credit constraints, or fire sales, real channels operate directly through investment, consumption, labour markets, or productivity. In Japan’s case, the housing market collapse depressed activity through three key real channels: investment, consumption, and sentiment. This is all pretty intuitive. But using city-level and household-level Chinese data plus some whizzy maths, they put meat on the bone for these three channels. They find that Chinese cities that overbuilt housing the most are less keen on new building, suppressing investment. Sounds legit. Chinese household consumption is estimated to be more responsive to house price changes than it was in either Japan or the US given its outsized role in private wealth. And it looks to the authors like people have scrambled to rebuild precautionary savings they thought they had amassed in property. Understandable. Then, on the sentiment side, Rogoff and Yang use an LLM to gauge market perceptions of the housing market. And by incorporating city-specific perceptions, they double the estimated effect of house price changes on consumption. Huh. While China is not Japan, 1991 was not 2021, and a *lot* of other things are/were going on, it’s interesting to see that the overall magnitude and pace of property price falls — as well as the aggregate drop in the pace of headline GDP growth — has (so far) been spookily similar. And as for the big question — are we there yet? "If China’s adjustment unfolds in a similar way as Japan’s, it would mean China has not gone half way through the transition. By contrast, if China’s path is eventually comparable to the United States, it appears to have already covered roughly two-thirds of the adjustment before reaching the bottom." So more to come.

Sharing our recently implemented open-source version of Claude Cowork Statica Fully self-hostable, while supporting Claude Code, Codex, GitHub Copilot CLI, OpenCode, Gemini, Cursor Agent, Kimi, Kiro CLI, and more. Statica's goal is to become the coordination layer between coding agents and your team somewhat like how Obsidian works, where everyone can build on top of it based on their own workflows. Statica + development-related plugins → Cursor/Lovable/Conductor Statica + design-related plugins → Lovart Statica + research-related plugins → NotebookLM Statica handles the middle layer: Human + agent collaboration and issue assignment Runtime & daemon orchestration Multi-agent & LLM scheduling Reusable skills that compound over time The name's inspiration comes from the idea of a static, stable foundation that lets dynamic things happen on top of it - agents spinning up and down, tasks routing across runtimes, skills accumulating over time, all without the human having to manage any of it directly. The bet is on compounding. Every task an agent completes can become a skill. Every skill makes the next task cheaper. A two-person team running Statica doesn't feel like two people.

A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper. Her name is Audrey van der Meer. She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth. The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time. Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen. Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task. When the students wrote by hand, the brain lit up everywhere at once. The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected. When the same students typed the same word, that pattern collapsed almost completely. Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG. Same word, same brain, same person, and two completely different neurological events. The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem. Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next. Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve. Van der Meer said it plainly in her interviews. Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad. Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page. A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched. The handwriting group won by a wide margin on every question that required real understanding rather than surface recall. The reason was hiding in the transcripts of what the two groups had actually written down. The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page. That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it. Two studies. Two countries. Same answer. Handwriting makes the brain work. Typing lets it coast. Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth. You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick. The fix is the thing your grandmother already knew. Pick up a pen. Write the thing down. The slower road is the faster one.

I’ve left Google DeepMind after an amazing chapter. I’m incredibly grateful for the people I worked with, the things we built, and the lessons I learned from taking frontier AI research into production. DeepMind shaped how I think about research, product, evaluation, and what it takes to build AI systems at real scale. As I wrap up this chapter, I wrote down something I’ve been thinking about a lot: evals. We’re good at evaluating the models we have. We’re much worse at evaluating the models we’re about to build — especially if they cross into a new capability regime. We will have self-evolving models, but before that, we need self-evolving evaluations.