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It is with a heavy heart that we announce we are winding down the Botanix network. This decision is the hardest one we have made in four years, and we want to share the reasoning openly because the people who backed us, built with us, and used what we shipped deserve more than a quiet shutdown notice. First off, an immediate practical consideration for the Botanix community: please withdraw your Bitcoin and other assets before July 9th, 2026. When we started in 2022, the pitch was simple enough to say in a sentence: bring real utility to Bitcoin. What that actually meant in practice, and what we have spent nearly four years building toward, was more ambitious than that sentence made it sound. We were trying to build a Bitcoin-based blockchain that could find genuine product-market fit as a platform for Bitcoin applications, without using token incentives to drive growth, manufacture users, or simulate utility. Almost every chain that has launched in the last cycle has reached for the same playbook (issue a token without PMF, engineer the incentive surface, point at the resulting metrics), and we did not believe this route is a viable strategy in the long term. We wanted to know whether a Bitcoin chain could earn its users on the strength of what was built on top of it, the value it brings in the market with Bitcoin itself as the only meaningful economic primitive in the system. And we built it. The Spiderchain went live and stayed live, a year of mainnet operation with one hundred percent uptime and zero security incidents on a genuinely novel cryptographic architecture. We built Dynafed, a dynamic federation that turned the Spiderchain from a static multisig set into a rotating, decentralized one, the technical milestone that most people in this space said could not be built on Bitcoin without compromising trust assumptions. Twenty-five million transactions, two hundred thousand wallets, and tens of millions of dollars in assets moved across the chain, every single number of that earned organically without a token, without airdrops, without points programs, or any of the manufactured-demand machinery. Chainlink, Morpho, GMX, Dolomite, Fireblocks, Alchemy, Galaxy, OKX Wallet, all integrated. We shipped a Bitcoin neobank with BINK on iOS and Android, with self-custodial email login for Bitcoin (something that had never existed before), native Bitcoin yield, and the lowest borrowing rates against Bitcoin anywhere in the world, all of it downstream of owning the infrastructure. The point of saying this is not to argue with our own conclusion. The protocol works, the product works, and our team and ecosystem worked in concert to do exceptional work. We have run this experiment in earnest, with a working protocol, real applications, and a serious team, for over a year on mainnet and nearly four years in total. The honest answer we have arrived at, after living inside it every day, is that it did not work, at least not in this market and not on this timeline. We want to share what we think we learned, with the caveat that some of this is conviction and some of this is still suspicion, and we would rather be transparent about the difference than pretend to have clarity we do not have. The first thing I've had to sit with is timing. Bitcoin utility, making Bitcoin programmable, productive, and integrated into real financial activity, isn't where the real world users sit right now. The conversation is still on Bitcoin as a reserve asset, on its monetary and political positioning, on base-layer conservatism. Those questions are upstream of the ones a Bitcoin L2 needs people to be asking. I still believe Bitcoin gets there, but belief in the destination is not the same as being able to predict when, and nobody can. It's also possible the destination never materialises at all, and that Bitcoin's role as a reserve asset is simply where it settles. If that's true, there will never be a market for what we were building, and no amount of time or capital would change that. The second is the token question. We intended to eventually launch a token. We saw it, and still see it, as a genuinely new form of equity, something closer to an IPO than an airdrop, to be done when you reach product market fit and the moment is right. That moment never came. What became clear over the last year is that the market largely stopped rewarding even the more considered versions of that playbook. Token launches across the board have broadly underperformed, and those that did go to market with tokens haven't seen the outcomes or PMF that the model is supposed to produce. The third lesson is about where DeFi demand on Bitcoin actually lives. For most use cases that exist today, lending, yield, leveraged exposure, WBTC on a mature general-purpose L2 is genuinely sufficient. Users have voted with their behaviour, and the verdict is that the trust assumptions of a wrapped representation on Ethereum are acceptable to almost everyone who wants Bitcoin-denominated DeFi. Decentralisation matters to people in principle and in conversation; in practice, when something cheaper and easier is in front of them, they use it. The security case for a dedicated Bitcoin L2 is real, but it only matters for a narrower band of applications than our thesis required, one of the clearer lessons this market has taught us. The fourth lesson is structural. The on-chain economy is consolidating around venues that own the user relationship: Hyperliquid, Robinhood, the major CEXes, and now TradFi participants absorbing an ever-larger share of attention, flow, and revenue. Convenience and institutional credibility win, every time, as soon as they're available. As retail participation thins, that concentration only deepens. We were, and still are, believers in decentralisation, but the current direction of on-chain growth is running through distribution, and any team building base-layer infrastructure today is rowing upstream against that current. We were no exception. The fifth lesson is the most concrete. Both of the above played out directly in our economics. The users we attracted were primarily using Bitcoin as a store of value for yield, a legitimate use case, but not the high-frequency transaction volume that drives fee revenue on a network like ours. BINK was our answer to that: a Bitcoin neobank designed to bring daily usage of BTC and stablecoins on-chain, driving the transaction volume the network needed. It was the right strategic instinct, and one we never got the chance to fully test. BINK only landed on both app stores in the last few weeks, a product that by its nature could only be built once the underlying infrastructure was proven and live. When users choose the convenient option and economic gravity pulls toward distribution, what's left on a decentralised infrastructure layer is a user base that costs more to serve than it generates. Infrastructure costs are what they are, and the fee income never came close to covering them. If you would like to see how we were imagining a Bitcoin future and what we have been working on since September, feel free to download BINK and give it a spin: it’s a full-fledged self-custodial Bitcoin Neobank with email login, one click borrowing, a Lightning integration and more. App store: Play store: This UX is where we think Bitcoin is ultimately heading towards although it feels too early. You can use invite code 1SD31R, but remember to remove your funds by July 9th. We could keep going. We have chosen not to, however, because continuing past the point where additional time stops producing additional learning is not conviction, it is something that looks like conviction from the outside while corroding into something else on the inside. We would rather stop now, with integrity intact and resources available to take care of the people who took a chance on us, than push the experiment past the point where it still has something to teach us. Reminder: Please withdraw all your assets by July 9th. After this, the federation will sweep the remaining Bitcoin. Any other assets or tokens on the network from then onwards will unfortunately be unrecoverable. After this, the federation will sweep the remaining Bitcoin. Any other assets or tokens on the network from then onwards will unfortunately be unrecoverable. To our investors, who backed a thesis that was harder to defend than it should have been, to our partners who built alongside us and bet pieces of their own roadmaps on ours, to the developers who deployed on Spiderchain, to our users and the BINK community who showed up for something experimental and stayed, and most of all to the Botanix team who shipped a genuinely novel system with rigour and care and who made every hard day worth the difficulty: Thank you, more than the words available here can carry.

Jensen Huang just handed every AI cloud investor the clearest framework for picking winners and the question is who actually understands what he said (Save this). Compute is not just infrastructure anymore but rather revenue, and performance per watt is the mechanism by which that revenue becomes profit. The argument Jensen made at Computex deserves to be unpacked fully because it completely reframes the neocloud investment thesis. Every AI factory operates inside a fixed power envelope and once your data center is built and your power contracts are signed, that ceiling does not move. One gigawatt means one gigawatt and the only variable that determines how much money you make is how many profitable tokens you can squeeze out of each watt of electricity flowing through your facility. An operator who chooses cheaper, lower efficiency chips because the upfront cost looks attractive is not saving money and they are permanently handicapping their revenue ceiling for the life of that asset. Every watt that produces fewer tokens is a watt that will never recover those lost revenues, for as long as that infrastructure runs. Jensen's second point is about asset longevity and it is equally important to understand. AI software is evolving every few months from CNNs to Transformers to Mixture of Experts to agentic systems and that pace is not slowing down. A hardware architecture that cannot adapt to new software paradigms has a short useful life, and a short useful life means a high total cost of ownership. Infrastructure built on Nvidia's CUDA ecosystem has a built in software longevity advantage because every new model, framework, and optimization is written for CUDA first. Now apply that framework directly to Nebius, which is the most important stock in the neocloud category. Nebius built its entire infrastructure around full Nvidia integration from the ground up. Nvidia and Nebius announced a formal strategic partnership in March 2026 specifically to develop the next generation of hyperscale AI cloud deployments together. Nebius is already offering Blackwell Ultra GB300 NVL72-powered instances to customers, meaning it has the highest-performance GPU currently available commercially running inside its own infrastructure. The token economics follow directly from the architecture. Contracted power has now passed 3.5 gigawatts, with more than 75% of that capacity owned outright rather than leased. The Meta deal alone is worth $27 billion over five years, and the Microsoft agreement is worth up to $19.4 billion. The 2026 plan targets 480 megawatts of live AI cloud capacity, 150,000 GPUs deployed, and $3.7 billion in annualized revenue implying next twelve month revenue growth of roughly 489%. Q1 2026 revenue was $399 million, up 684% year-over-year, and the CEO said on the earnings call that everything Nebius builds gets sold immediately. Fully booked capacity at an AI cloud running Nvidia's best hardware, inside a power-scarce environment where performance per watt is the direct driver of profitability, means Nebius's revenue ceiling moves in direct proportion to the power it can bring online. CoreWeave, a direct comparable, trades at a materially higher multiple on a smaller contracted power base. Nebius owns more of its capacity outright, has a longer-dated and larger contract backlog on a per-gigawatt basis, and is growing revenue at a faster rate. Milk road remains extremely bullish on Nebius and come join Milk Road Pro and get our full Nebius positioning breakdown and our other AI trades for just a dollar. Link down below!

Re-posting the idea from the second half of this post a few months ago (This is very relevant to the options ideas from yesterday) Question: if we're making a synthetic stable, what should it really be stable WITH RESPECT TO? USD is actually far from the best choice. --- What do people who want stablecoins ultimately want? They want price stability. They have some future expenses in mind, and they want a guarantee that will be able to pay those expenses. But if crypto grows on top of USD-backed stablecoins, crypto is ultimately not truly decentralized. Furthermore, different people have different types of expenses. There has been lots of thinking about making an "ideal stablecoin" that is based on some decentralized global price index, but what if the real solution is to go a step further, and get rid of the concept of currency altogether? Here's the idea. You have price indices on all major categories of goods and services that people buy (treating physical goods/services in different regions as different categories), and prediction markets on each category. Each user (individual or business) has a local LLM that understands that user's expenses, and offers the user a personalized basket of prediction market shares, representing "N days of that user's expected future expenses". Now, we do not need fiat currency at all! People can hold stocks, ETH, or whatever else to grow wealth, and personalized prediction market shares when they want stability.

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

Vitalik shared his perspective on where @ethereumfndn is heading. Here is mine, another part of the same story. The EF Mandate from the board was something I proposed late last year. Two main things prompted me. First, debates that were meant to be technical had started to become political and personal, and at times shaped by quieter incentives. Second, as EF grew, more and more versions of "what EF should be" began pulling at the core of the organization from every direction at once. I became convinced that trying to satisfy all of them would leave us achieving nothing at all. It was time for us to restate our role and underlying principles clearly, both the parts that have been clear from the start and those that have been informed by over a decade of experience. We have said it many times: EF is one of many nodes in Ethereum. I know that is hard to hear for some, because EF was the first group, and in the early years it was essential for making things happen. But it was never meant to stay that way. I have been in crypto since 2012, before it became an "industry." I joined Kraken in 2013, shortly before the implosion of Mt. Gox, which I helped to clean up. I am very aware of how real growth works, and also aware of the real risks of centralization. So when I became ED in 2018, I understood that Ethereum growing beyond EF would be essential to fulfill its real promise as a public blockchain. The goal I set for myself was to ensure that this happens. The opposite path has always been untenable: Ethereum's future is too big for any single organization to bring about. So EF made deliberate choices to distribute power. We did incubate and release, like Uniswap and ENS. Support to seed a new norm, like ETHGlobal and the hackathons that are now everywhere. Funding the funders, like Gitcoin and Moloch. We always asked the same question: how does this stand on its own, without us? Those experiments, alongside the work of countless others, contributed to where we are today. Ethereum is now far bigger than anything EF could coordinate alone. EF now holds less than 0.2% of all ETH, and the return on all of that shared work, together with extraordinary people across the ecosystem, has been beyond anything we could have built by ourselves. That is exactly why a focused EF is possible now. The Mandate states simply the one thing EF must keep carrying: preserving and accelerating the properties and goals that keep Ethereum uniquely valuable, competitive, and worth building on. That is: CROPS - for the sake of inalienable user self-sovereignty and self-sovereign coordination. We cannot do it alone, and we do not intend to. But defining this as the north star for the mission, and coordinating with the allies who share it, is the responsibility we are keeping. None of this means EF stops caring about adoption, for everyday users or for institutions. The opposite is true: everything we do is ultimately for the people who use Ethereum. Supporting adoption, including institutional adoption, remains part of our work, pursued in the ways that fit our mission. The value proposition of Ethereum for both everyday users and institutions rests heavily on this. As EF becomes more focused and more opinionated, the team naturally becomes smaller and more concentrated. That is part of the choice. New leaders are already stepping into this mission and growing within it, and you will hear more from our management in the coming weeks, about what they are doing, and about the new structure and strategy taking shape. The mission we carry is not a smaller one, but a clearer one. Special thanks to those who have stepped in to support, defend and advance it.

OpenAI just ordered 100,000 Blackwell GPUs from NVIDIA. A Chinese developer put one of NVIDIA's $3,999 desktop AI boxes on his office desk and ran the same robot simulator the big labs train on $50,000 racks. The demo was a single empty cube floating in an empty world. Fifty frames per second. He posted the clip with one line. Is this how home robotics starts or is this an expensive toy. The clip went viral the same week NVIDIA shipped the second batch of Sparks to developers. 1.8 million views in 72 hours. Every American hardware engineer shared it as proof you could finally own the rig. Every Chinese commenter left the same Mandarin reply: pause at 1:42. Pause at 1:42. Ignore the empty cube on the screen. Ignore the FPS counter. Look at the memory readout in the top bar. 2.4 GiB used. 87.4 GiB available. The cube is sitting in three percent of the memory. The empty 84 gigabytes of memory was not headroom for a future robot scene. The empty 84 gigabytes was already running. ColdMath. $138,168 profit. Joined November 2025. Bio: Edge Compounds. He had not bought the Spark to train robots. He had bought it because robots were the only workload NVIDIA shipped a 128 gigabyte chip for. The slow memory that ruined the box for real robotics was perfect for what he was actually running. Twelve hundred ensemble weather simulations in parallel. Robot training needs fast memory because every frame is a step in a training loop. Weather ensembles need huge memory because every city is a parallel simulation that does not talk to the others. The Spark's chip is six times slower than a gaming card. It is also five times larger. The trade off only matters if you know what you are running. He knew. Wellington 16C on March 28. Tokyo 16C on March 20. Every city in the wallet was a city the ensemble had simulated three hours before the public forecast posted. Comments turned into a detective board. Someone slowed the clip to 0.25x. Someone else compared the wallet's trade timestamps to the timestamps the public forecast services updated their data. Every trade landed during the three hour gap. The Spark had been catching it. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. The developer in the office cubicle had been one of them. He had wired the agent into the Spark the same week NVIDIA shipped his box. The empty cube was not a benchmark. The empty cube was a screen saver running while the agent occupied the other 84 gigabytes. The Isaac Sim install was not the project. The Isaac Sim install was the proof he could justify buying the box on company expenses. The question about whether this was a real tool or an expensive toy was the only thing in the video designed to be answered by the audience. He was not a Chinese developer testing whether home robotics had arrived. He was the first developer to figure out that the box NVIDIA had marketed for the wrong workload was the cheapest weather simulator on the market. The clip is at 1.8 million views. The forum thread is still arguing about the six times memory penalty. The Spark on his desk is still running. The wallet is still hitting cities the public forecast services have not updated yet. The cube is still floating in three percent of the memory. The country with the better robotics demo has the smaller wallet. The dev with the wrong tool for the job has the bigger one. He just had to install a robot simulator for one afternoon.

Yann LeCun sat across from Lex Fridman and quietly proved that intelligence has nothing to do with thinking. He did it with two sentences about a trophy. “The trophy doesn’t fit in the suitcase because it’s too big.” “The trophy doesn’t fit in the suitcase because it’s too small.” Same words. One swap at the end. In the first, “it” is the trophy. In the second, “it” is the suitcase. You solved both before you finished reading. Nobody taught you that. There is no rule for it. No logic chain. No formula. You knew because you’ve held things. Packed things. Felt the resistance of something too large for the space it was meant to fill. LeCun calls this grounding. “A big object doesn’t fit in a small object.” The machine has read that line a billion times. It has never once picked anything up. It knows the word “big.” It has never been small enough to be lifted, or large enough to be the problem. So when the sentence turns, it has nothing to turn on. You didn’t solve that riddle by thinking. You solved it by having lived. Every object your hands ever closed around. Every door you misjudged. Every suitcase you overpacked and forced shut. Decades of physics written into your nervous system so deep you can’t even find it. That is what answered the question. Not your mind. Your life. LeCun: “You have this knowledge of how the world works, of geometry, and things like that.” Now point that at yourself. Most of what you understand, you could never explain. You cannot describe how you catch a ball. How you judge the weight of a bag before you lift it. How you know a staircase is wrong before your foot confirms it. Your deepest intelligence has no language in it at all. We spent centuries convinced that thinking was the highest act of the mind. LeCun is pointing at something underneath it. Something older. Something the body learned long before the mouth could speak. Intelligence was never computation. It was accumulation. The slow, silent record of a life spent touching the world. The machine holds every word ever written about it. It has never once been in it. We keep asking whether it thinks. It cannot even tell us which “it” we mean.

The Scripture in this document teaches us things that Jesus Christ says are essential. So important that the Bible makes it abundantly clear by repeating it three times. The apostle Paul clarifies what it means to live the way Jesus Christ desires. Matthew 22:36-40 Mark 12:28-31 Luke 10:25-28 Romans 13:8-10 Galatians 5:13,14 Teacher, which is the most important commandment in the law of Moses?” Jesus replied, “‘You must love the LORD your God with all your heart, all your soul, and all your mind.’ This is the first and greatest commandment. A second is equally important: ‘Love your neighbor as yourself.’ The entire law and all the demands of the prophets are based on these two commandments.” One of the teachers of religious law was standing there listening to the debate. He realized that Jesus had answered well, so he asked, “Of all the commandments, which is the most important?” Jesus replied, “The most important commandment is this: ‘Listen, O Israel! The LORD our God is the one and only LORD. And you must love the LORD your God with all your heart, all your soul, all your mind, and all your strength.’ The second is equally important: ‘Love your neighbor as yourself.’ No other commandment is greater than these.” One day an expert in religious law stood up to test Jesus by asking him this question: “Teacher, what should I do to inherit eternal life?” Jesus replied, “What does the law of Moses say? How do you read it?” The man answered, “‘You must love the LORD your God with all your heart, all your soul, all your strength, and all your mind.’ And, ‘Love your neighbor as yourself.’” “Right!” Jesus told him. “Do this and you will live!” Let no debt remain outstanding, except for your obligation to love one another. If you love others, you will fulfill the requirements of God’s law. For the commandments say, “You must not commit adultery. You must not murder. You must not steal. You must not covet.” These—and other such commandments—are summed up in this one commandment: “Love others as yourself.” Love does no wrong to others, so love fulfills the requirements of God’s law. For you have been called to live in freedom, my brothers and sisters. But don’t use your freedom to satisfy your sinful nature. Instead, use your freedom to serve one another in love. For the whole law can be summed up in this one command: “Love your neighbor as yourself.”