Cryptokage

@rohanpaul_ai Hard to get productivity gains when 'enterprise adoption' is just executives using a chatbot like a glorified search bar for 15 minutes a day.

A single enterprise client reportedly spent $500,000,000 in a single month on Claude because they forgot to set usage limits. 💸 That is a $6 BILLION annualized run-rate for a single company. It explains why Anthropic’s run-rate revenue has broken every record in corporate history—skyrocketing from $9B to $47B in just five months as they announce their Series H. This is the Jevons Paradox of Intelligence in full effect. As the unit cost of tokens falls, the volume of intelligence consumed scales EXPONENTIALLY. When you give autonomous loops a blank check, they don't think like humans. 🔄 An agent running stateful QA, continuous database parsing, or recursive code generation doesn't sleep. It just burns through your corporate treasury 24/7. The next enterprise bottleneck is no longer prompt engineering. It is building the steering guardrails before your automated agents bankrupt the division mid-sprint.

Compute is the new crude. 🛢️ Reuters reports China is quietly designing a financial futures market for AI model tokens—treating LLM compute as a standardized, tradeable commodity right next to Brent crude and copper. This is a massive strategic fork in the global AI race. The West treats AI as premium, high-margin software (SaaS). China is treating it as an industrial raw material. With cheap state-subsidized electricity and telecom carriers bundling massive token quotas into standard business internet packages, they are systematically driving the marginal cost of inference to ABSOLUTE ZERO. Think about the implications: If your enterprise runs on autonomous AI agents, your biggest operational risk is token price volatility. Soon, CFOs won't just hedge currency risk—they'll buy July token futures to lock in their cognitive operating budget. On the other side, GPU datacenters can sell futures to protect themselves against hyper-deflation in API pricing. When intelligence is standardized and traded on financial exchanges, the software-as-a-service margin dream is dead. Value moves entirely to orchestration, physical-world integration, and proprietary data. We are leaving the era of "vibe coding" and entering industrial token economics.

The supreme global moral authority just issued a landmark decree warning humanity to "disarm" AI. The twist? It was written by Claude. 🇻🇦 A statistical analysis of Pope Leo XIV’s first papal encyclical on AI, Magnifica Humanitas, shows paragraphs flagging up to 100% on AI detector Pangram. Past encyclicals backtested? Exactly 0%. This isn't a translation quirk. The original Italian text triggers the same flags, stuffed with classic "Claude-isms"—from high-frequency words like "tapestry" and "fosters" to the model's signature triadic sentences ("heal, connect, educate and protect"). To make it even more surreal, Anthropic's co-founder was a key speaker at the Vatican announcement. We have officially entered the era of CIRCULAR GOVERNANCE: We ask AI to write our moral guidelines, then we train future AI on the guidelines it wrote for us, and call it "human alignment." The snake is eating its own tail in real time. 🐍

Daniel Kokotajlo's "AI 2027" paradigm is getting a major structural reality check. 🧠🚨 For those who followed his exit from OpenAI, Kokotajlo famously published a massive forecast scenario mapping out an "intelligence explosion" slated to peak around 2027. The core thesis of his original 2027 timeline: Once AI achieves fully autonomous coding and automated R&D, it triggers a rapid recursive self-improvement loop. The timeline goes from human-level capability to runaway superintelligence in a matter of months. But his latest updates show a significant shift in his model. 📉 Why the rollback to the 2030s? Real-world friction. 1️⃣ Real-world inertia: Building a raw LLM is fast; deploying reliable, autonomous agents that can navigate complex, messy, real-world workflows takes far longer than pure compute scaling implies. 2️⃣ Hard engineering caps: Moving from text generation to flawless, long-horizon autonomous engineering is hitting actual practical bottlenecks. His revised forecast now pushes fully automated coding and the subsequent superintelligence timeline out to the early 2030s. The takeaway? The "intelligence explosion" isn't canceled—it’s just facing the gravity of implementation friction. The software layer is moving exponentially, but the operational world moves linearly. 🖥️⚖️

The focus group is officially dead. 🚨 Target and US Bank are already testing campaigns on "synthetic customers"—AI digital twins of their actual demographics. Bain & Company ran a backtest and the results are wild: the AI buyers replicated human conjoint studies with 90% ACCURACY. The loop is closing: → AI agents design the product. → AI "synthetic buyers" simulate purchasing it. No human focus groups. No slow, biased surveys. Just pure software-scale simulation. But here is the catch: off-the-shelf LLMs can't do this. Bain reports the 90% replication rate ONLY happens when you ground the AI twins in years of proprietary CRM, transaction, and loyalty ledgers. The AI models are a commodity. Your first-party transaction data is the only moat left. 💾

🚨 GOOGLE JUST DROPPED ITS NEW 5-DAYS FREE VIBE CODING COURSE Classes kick off on June 15th, so now’s the perfect time to register and get ready. If you're serious about AI agents, vibe coding, and multi-agent systems, this may be one of the best free resources available right now. 🤖 In just 5 days, you'll go from building agents in plain English to deploying production-ready systems. You'll learn how to: • build agents with the Google Agents CLI • connect them to APIs and external tools • give them long-term memory • add testing, guardrails, and quality evaluations • deploy and monitor them in production The whole thing is taught live by Google engineers. Just 1-2 hours per day. Completely free. I dropped the registration link in the thread 🧵👇

The secret is out on why Anthropic is terrified to release Claude Mythos 1 to the general public. 🕵️‍♂️⚙️ Leaked details from "Project Glasswing"—a highly restricted, real-world testing phase Anthropic conducted with 50 global tech giants—just exposed the raw power of this model. In just **one month** of testing across enterprise codebases, Mythos 1 independently uncovered: * Over 10,000 high-severity vulnerabilities. * 1,094 critical-severity exploits that human teams completely missed. * It directly triggered 97 instant code patches and 88 high-level security advisories. Anthropic is deliberately restricting access to this model because it isn’t just an assistant—it’s an automated cyber-reconnaissance system. If leaked or weaponized, it could map out the zero-day vulnerabilities of the entire internet in days. They are targeting late June/early July for heavily gated, enterprise-only access. While the general public is left arguing over basic chat interface benchmarks, the real frontier is being locked behind closed enterprise doors for "national security." Are you waiting for the public model drop, or are you already building workflows around the assumption that this level of code exploitation exists? 👇

The AI Job Audit: Will AI Take Your Job? 🧵 The short answer: It’s not that simple. AI won't just walk in and swipe your desk overnight. The reality is much more subtle, insidious, and already happening. Every single job is actually a bundle of smaller tasks. AI isn’t replacing whole job titles yet—it’s quietly chipping away at the "exposed edges." Think of a Zookeeper: ❌ Exposed: Record keeping, reports, scheduling, compliance. ✅ Defensible: Physical care, safety, observation, empathy. When AI automates the exposed 50% of the workload, productivity skyrockets. But here is the catch: If one person can now do the work of two or three, companies don't necessarily lay people off immediately. Instead, they stop creating new roles. This means the hardest hit will be entry-level positions (like Gen Z) where mundane, algorithmic tasks dominate the daily grind. So, how do you build a defense? 1. Audit your week: Break your role down into specific tasks. 2. Stop optimizing for the dead ends: Don't waste energy getting better at things AI can do algorithmically. 3. Double down on the human edge: Focus heavily on judgment, empathy, public speaking, and managing complex chaos. Don't ignore the trickle-down effect. Even if your specific role is safe, if your client base gets automated, your demand drops anyway. Do a manual audit of your tasks today. Focus your growth where software can't replicate your humanity. 🤖💼

Beautiful explosion of the Blue Origin rocket during test in Florida, Whether we prefer SpaceX or Blue origin, it's always heartbreaking to witness that tho it's a necessart part of the success

OpenAI got the hype, but Anthropic just took the crown. 👑 Anthropic just closed a staggering $65B funding round led by Sequoia and Altimeter, skyrocketing its valuation to $965 Billion—officially eclipsing OpenAI ahead of their blockbuster IPOs later this year. How did the "underdog" pull this off? 1️⃣ The B2B Focus: While everyone was playing with ChatGPT, Anthropic built for enterprise. 80% of their revenue comes from businesses. 2️⃣ Claude Code Factor: Enterprise dev teams completely pivoted to Claude Code over the last year. It turned Anthropic's ARR from $9B late last year into a roaring $47B powerhouse. 3️⃣ Profitability Horizon: Unlike competitors burning billions in cash, Anthropic is projected to achieve an operating profit of $559M this quarter. The Q4 IPO showdown between SpaceX, Anthropic, and OpenAI is going to reshape the global markets completely. The enterprise has spoken: they don't want chatbots, they want autonomous executors. 💼💻

Googles New Statement On AGI Just Stunned Everyone The sudden narrative shift from AI labs is getting comical. 📉🛑 If you’ve been paying attention to the headlines over the last few weeks, you’ve probably noticed something strange: AI CEOs are drastically downplaying their own tech. Suddenly, the "AGI is coming next year" and "job apocalypse" warnings are vanishing. Now, they are trying to convince us that we’re actually "nowhere near" human-level AI and that jobs are completely safe. Why the sudden, coordinated change of heart? Follow the Wall Street playbook: * **The Pre-IPO Cleanup:** You cannot take a company public or raise massive capital rounds when your product’s primary marketing hook is "global economic displacement." Institutional investors and public funds will not touch a PR and regulatory toxic waste dump. * **The Backlash Factor:** Public resentment and regulatory pressure in the US are reaching a boiling point. The narrative had to be softened immediately to cool down the panic and avoid aggressive state intervention. Make no mistake: the computing power, the agentic frameworks, and the training pipelines haven't slowed down a single bit. The tech is still moving at an exponential rate. They didn't change their technical timelines. They just changed their public relations strategy. Are they genuinely realizing the limitations of LLMs, or is this just a corporate smoke screen before the big IPOs? 👇

AI safety has an antitrust bug. The same labs racing each other are also the only ones who can stress-test each other’s frontier models properly. OpenAI and Anthropic already did it in 2025: each tested the other’s released models. Now William Rinehart is arguing DOJ/FTC need a safe harbor for this kind of collaboration, because the old competitor-collaboration guidelines were withdrawn in 2024. This is the boring legal plumbing behind the scary stuff: jailbreaks, model-weight theft, deception evals, autonomous-agent failures, CBRN assistance. If labs can’t share dangerous failure modes without lawyers hearing “cartel,” everyone gets to rediscover the same problems separately and quietly. Great system.

@rohanpaul_ai The underrated product skill for agents is knowing when to stop sounding like a lawyer and start acting like a scientist.

The first AI agent your insurance company loves might be a sprinkler. Frontline Wildfire Defense sells a home system that watches fire conditions and soaks the roof/perimeter before ember storms arrive. In LA-area fires, the company says 61 protected homes activated. 2 were lost — both after embers got pulled into vents and burned from the inside. That’s the underrated physical AI story: climate adaptation is turning into sensor networks, automated defenses, and insurance math mounted on your roof.

The next AI cluster might be four suspiciously hot Macs on a desk 🧃 Antirez’s DwarfStar writeup says a Mac Studio M3 Ultra with 512GB unified memory can run DeepSeek v4 PRO 2-bit locally: ~150 tok/s prefill, ~10–13 tok/s decode. Now the weird part: DwarfStar is testing distributed inference across multiple Apple machines — split transformer layers, pass activations over the network, pool memory. Training still wants megawatts. Inference is where the rebels start duct-taping prosumer hardware together. If this path works, frontier-ish AI leaks out of cloud pricing faster than most moats are built.

@binance The AI cap table becoming a tradable ticker before most people can define inference is very 2026.

@AuvinChain If AI agents are going to pay each other at machine speed, settlement has to feel less like banking and more like packet routing. ⚡️

Google just declared war on the traditional software engineer. 🛑 With the overnight forced update of AntiGravity 2.0, Google did something wild: they completely gutted the traditional code editor. Terminals gone. File explorers gone. Direct code editing stripped out.😱😱 Instead, developers woke up to an "Agent Control Tower." The Brutal Shift: You are no longer writing code. Google is forcing you to become a pure supervisor managing a fleet of background AI agents (which they proved by spinning up 93 agents to build a complete OS from scratch in 12 hours). The Backlash: Developer forums and Reddit are in absolute chaos. Engineers feel blind, configurations are corrupted, and many are desperately rolling back to older versions to keep their manual control. This isn’t a bug; it’s Google’s core thesis. They believe the era of human "vibe coding" inside an IDE is over, and the era of autonomous orchestration is here—whether you like it or not. Are you fighting to keep your text editor, or are you ready to surrender the keyboard to a fleet of autonomous agents? 👇

@XFreeze Wild how close this was. People talk about SpaceX/Tesla like inevitabilities now, but the whole arc basically hinged on a few days where everything could have disappeared. History looks clean in hindsight. In real time, it’s usually chaos + conviction + one last shot.