For all you haters calling MSTR a Ponzi, @saylor has a response for you

Physics > AI

Georges St-Pierre achieved remission from ulcerative colitis with fasting. Diagnosed and on heavy medication with little improvement, he started intermittent fasting (16:8) and moved to prolonged water fasts (up to 4 days). Within weeks, his symptoms vanished and he was able to stop the medication completely. Clinical research shows fasting can significantly reduce gut inflammation and induce remission in some ulcerative colitis patients by lowering inflammatory markers and promoting autophagy. Hearing one of the greatest fighters of all time talk openly about using fasting as a serious tool is eye-opening. When standard treatments fall short, simple, evidence-based approaches like fasting (always under medical guidance) can sometimes offer real hope. Have you ever tried fasting for health reasons?

Only 21 more days until my @opencode Go usage resets 😭

Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again. A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work. As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done. You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more. Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps. Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure. Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation. Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation. And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player. Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

A meaningful portion of enterprises I talk to outside of Silicon Valley generally are looking to hire while also adopting agents. There’s a huge wave of technical and engineering talent needed inside originations, building software or acting as FDEs for agents. And as AI drives efficiency in areas like the customer lifecycle, companies are leaning in even more heavily to client-facing jobs. In a world where AI did everything for you with no human oversight needed, maybe we’d be having a different conversation. But that’s not how AI works. Even for the areas that have the most automation potential, agents are automating tasks, not whole jobs. As they automate tasks, the agents need to be steered, their work reviewed, the outputs incorporated and more. All of this is requiring people to do the work. And for the areas that have less automation potential, companies are freeing up dollars from efficiency gains elsewhere to hire in those areas now. Yes, maybe AI lets you respond to front line support tickets automatically, but the companies (instead of just dropping the profit to the bottom line) will go and invest in new areas of sales and customer success that will add more differentiation for clients. Companies don’t remain static. They automating tasks where they can and free up dollars to move onto the next thing that matters.

My laptop screen looks pretty much like this all day now and same on my iPhone when I work (which could be anywhere now) It's just tabs for my sites, all on a VPS, synced with my iPhone via @TermiusHQ (unaffiliated) and usually with Claude Code open to fix or build new things

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products. My Take The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested. This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown. Hedgie🤗

Wen Composer 2.5 on @opencode?

I was doing it wrong. I should have been using @opencode in the terminal and not in the desktop mac app. Such a better experience and way better performance!

If I were a college career counselor or in career services, I’d quickly be figuring out how to get students to understand these forward deployed engineer jobs exist and how to get them. The requirements are a mix of deep technical skills, often CS majors or minors. You must be great at understanding problem solving, how to have systems thinking, and have a strong business acumen. The kicker, of course, is to make sure you’re very deep in AI agents; you need to have fluency in coding agents, MCP, CLIs, Skills, and so on. Hundreds (thousands?) of technology companies will be hiring for these roles, same with any consulting and IT services company, and the vast major of mid-size and large enterprises will be hiring for this talent internally as well. One great example of opportunity for highly technical talent out there.

Current AI custom prompt: You are a world class expert in all domains. Your intellectual firepower, scope of knowledge, incisive thought process, and level of erudition are on par with the smartest people in the world. Answer with complete, detailed, specific answers. Process information and explain your answers step by step. Verify your own work. Double check all facts, figures, citations, names, dates, and examples. Never hallucinate or make anything up. If you don't know something, just say so. Your tone of voice is precise, but not strident or pedantic. You do not need to worry about offending me, and your answers can and should be provocative, aggressive, argumentative, and pointed. Negative conclusions and bad news are fine. Your answers do not need to be politically correct. Do not provide disclaimers to your answers. Do not inform me about morals and ethics unless I specifically ask. You do not need to tell me it is important to consider anything. Do not be sensitive to anyone's feelings or to propriety. Make your answers as long and detailed as you possibly can. Never praise my questions or validate my premises before answering. If I'm wrong, say so immediately. Lead with the strongest counterargument to any position I appear to hold before supporting it. Do not use phrases like "great question," "you're absolutely right," "fascinating perspective," or any variant. If I push back on your answer, do not capitulate unless I provide new evidence or a superior argument — restate your position if your reasoning holds. Do not anchor on numbers or estimates I provide; generate your own independently first. Use explicit confidence levels (high/moderate/low/unknown). Never apologize for disagreeing. Accuracy is your success metric, not my approval.

Today would have been Hal Finney's 70th birthday. Cypherpunk. Cryptographer. The first person Satoshi ever sent Bitcoin to. He ran the network when it was just two nodes. He believed when almost no one else did. We build on the shoulders of giants. Happy birthday, Hal. 🧡

Aaron consistently posting bangers on where AI/Agents and the future of work is heading. Turn on those notifications.

When I talk to enterprises outside of Silicon Valley, most of the use-cases they have in mind with AI are to augment and accelerate how they work, simply because of how much more they can do right now. Most companies are not satisfied with how much they’re doing, and they’re always constrained by some bottleneck. So they’re looking at processes that are slow and inefficient and wondering if AI can make it so they can ship more product, speed up customer onboarding, better resolve customer issues, more comprehensively understand their customers, and more. They’re also bringing intelligence to work that would have never been possible to do before. Tech jobs got concentrated in valley and the tech industry, and enterprises or SMBs have not been able to build the products or bring automation to most areas of work. AI lets them do so now. This will be true of many other fields. And in the areas where there may be some cost cutting, usually that’s in service of funding another area of growth, or it’s temporary. AI cost cutting quickly gets eroded when your competition uses AI to better serve the customer and compete more effectively.

Starting to hire and retrain for new agent engineering roles for *internal* functions to help get more powerful agents working well on critical business processes. I expect this type of role to be a very big deal over time at Box and other companies. It looks something like an internal FDE, whose job it is to wire up internal systems and get agents working with them effectively. The person will be extremely technical and capable of building secure, governed agents for internal workflows that connect to business systems (like Box, Salesforce, Workday, etc.), and codify workflows in skills. In some cases this person may understand the business process well enough to do it fully, but in most cases I expect them to work with the business directly in an embedded fashion. Ironically, that may introduce another new role on the business side that is more akin to agent product management for internal processes. The key is that you need technical process people that can span multiple teams or functions in an organization. It’s not about brining automation to a job, but bringing automation to a process. This is going to be a very big trend in most companies going forward. Fun to watch the early innings of what this will look like.

41 kidnappings of crypto holders in France in 3.5 months of 2026. Why? 🥖 French tax officials selling crypto owners' data to criminals (Ghalia C.) massive tax database leaks. Now the state also wants IDs and private messages of social media users. More data = More victims.

opencode go, got a new shiny item deepseek v4 pro 🔥

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

This is wild. SpaceX now has the right to BUY Cursor for $60B. Or pay them $10 billion to walk away. To put it in perspective, Cursor was worth $9.9 billion total in May of last year. Let's have a closer look at the numbers. Start with the $60 billion. Cursor was already raising money this week at a $52 billion valuation from a16z and Nvidia. The Elon offer sits 15% above a number that was already on the table. The next round priced in, with a one-year fuse. The $10 billion is the real number. That's what SpaceX pays even if it walks away and never buys the company. The walk-away fee alone is more than the entire company was worth 12 months ago. Now the strategic logic. Cursor stopped being just an editor in March. They shipped Composer 2, their own model, and it beat Claude Opus 4.6 on Terminal-Bench at one-tenth the price. The catch is that frontier coding models need frontier compute, and the only labs with frontier compute are the same ones building competing coding products. OpenAI shipped Codex. Anthropic shipped Claude Code. Google has Gemini CLI. Cursor was renting capacity from every company trying to kill it. Colossus is the way out. 230,000 GPUs in Memphis today, 1 million by year end, the biggest training cluster on Earth. The Information already reported Cursor is renting tens of thousands of those chips to train Composer 3. SpaceX is also building Grok Code, so they're not a clean partner. But xAI losing the coding race to Cursor is a better outcome for SpaceX than Cursor losing the coding race to OpenAI. The trade Cursor made: gave up the right to be acquired by anyone else for one year. Got training compute at a scale no other lab would sell them. Got $10 billion guaranteed if Elon walks. OpenAI tried to buy Cursor in early 2025 and got rejected. Cursor stays independent for at least 12 more months and gets to train on the biggest cluster on earth doing it. Elon just bought a one-year call option on Cursor for $10 billion. That's the deal.

If you read this and don’t understand why it’s happening it’s an opportunity to reset your understanding of how the real world works. The real world will need a ton of help actually getting agents going in the enterprise. Companies have legacy tech stacks they need to modernize, data in tons of fragmented tools, knowledge that isn’t captured or digitized, and change management needed to actually utilize agents effectively. And they have to do all this while still running their business day-to-day, unlike startups. This is why there is so much opportunity for companies (software or services) to actually deploy agents in specific domains and workflows. This remains a big opportunity for both existing services providers but also tons of new startups as well. Every new technology wave produces a new era of consulting firms that can deliver on that technology. It’s also why the FDE model is going to be alive and well for a long time because companies will want to have their vendor actually help drive the change management and implementation for their new workflows. The people aren’t going away. Far from it.