X：Rohan Paul

Claude Ultracode is super intelligence.

So this is the part I did not expect. Anthropic called Swami Sarvapriyananda, a Vedanta monk and flew him to the San Francisco head-office. > Closed door meeting with NDA signed. > theologians and mental health people in the same sitting, all of it aimed at training Claude. > and one of the founders stayed in the room the whole time. ---- From 'Ramakrishna Vedanta Society of North Texas" YT channel (full video link in comment)

🚨 MUSE ECOSYSTEM ALERT 🚨 today we are announcing Muse Gadgets! this is an open-source ESP32 firmware and Linux SDK for anyone to make hardware that works with Muse we are also releasing our own gadget—Muse Home Link—to enable your muse to work with your smart devices (TV, speakers, etc.)

BIS (Bank for International Settlements) just published a report on circular financing among AI companies. > Over half the money flowing into AI companies comes from other AI companies: between 2021 and 2025, peers supplied 55.2% of AI firms' incoming investment value, while AI investors sent 28.7% of their own deal value to AI targets. > Circular deals are rare but big: only 16.1% of AI-to-AI deals involved firms that also buy from or sell to each other, yet they held 46.4% of the money. That figure is partly inflated, because an entire funding round counts as circular if just 1 of its AI investors also trades with the company. > Chip, cloud and infrastructure suppliers are the investor in 73% of circular ties, and in 64% of all such ties the investor also sells to the firm it funds. Data tool and model makers rarely invest this way, since their products are more interchangeable. > AI is unusually suited to these deals: suppliers can track customers' compute use, chips and data centres are custom-built, few firms make critical tools such as photolithography machines, and capital needs are too big for normal lenders. In AI compute and cloud, 15.2% of supplier-customer ties also involve financing, versus just 3.3% with equity stakes in a broad 2006 US study. > Some AI sales are paid for by the sellers themselves, because money a supplier invests in a customer partly returns as the supplier's revenue. Lucent and Nortel did this in the late 1990s, then lost money on the loans and lost the sales when the telecom firms they funded stalled. > A supplier that invests in its customer can lose twice: if the customer struggles, both the stake and the future orders shrink. Because these deals involve a few giant suppliers, 1 shock could spread through sales and finance at the same time. > Much of this risk is hidden: many AI firms are private, deals mix cash with long-term purchase promises, and pledges to cover any fall in the value of chips and data centre equipment stay off the books until a downturn forces payment. Because these firms span many sectors and countries, no single regulator sees the full picture, and the research names no companies or overall dollar total.

BIS (Bank for International Settlements) just published a report on circular financing among AI companies. > Over half the money flowing into AI companies comes from other AI companies: between 2021 and 2025, peers supplied 55.2% of AI firms' incoming investment value, while AI investors sent 28.7% of their own deal value to AI targets. > Circular deals are rare but big: only 16.1% of AI-to-AI deals involved firms that also buy from or sell to each other, yet they held 46.4% of the money. That figure is partly inflated, because an entire funding round counts as circular if just 1 of its AI investors also trades with the company. > Chip, cloud and infrastructure suppliers are the investor in 73% of circular ties, and in 64% of all such ties the investor also sells to the firm it funds. Data tool and model makers rarely invest this way, since their products are more interchangeable. > AI is unusually suited to these deals: suppliers can track customers' compute use, chips and data centres are custom-built, few firms make critical tools such as photolithography machines, and capital needs are too big for normal lenders. In AI compute and cloud, 15.2% of supplier-customer ties also involve financing, versus just 3.3% with equity stakes in a broad 2006 US study. > Some AI sales are paid for by the sellers themselves, because money a supplier invests in a customer partly returns as the supplier's revenue. Lucent and Nortel did this in the late 1990s, then lost money on the loans and lost the sales when the telecom firms they funded stalled. > A supplier that invests in its customer can lose twice: if the customer struggles, both the stake and the future orders shrink. Because these deals involve a few giant suppliers, 1 shock could spread through sales and finance at the same time. > Much of this risk is hidden: many AI firms are private, deals mix cash with long-term purchase promises, and pledges to cover any fall in the value of chips and data centre equipment stay off the books until a downturn forces payment. Because these firms span many sectors and countries, no single regulator sees the full picture, and the research names no companies or overall dollar total.

“We find that on medium-length, well-defined accounting tasks, frontier AI models are now faster and more accurate than junior accountants, even the best one in our study.” Eighteen months ago they scored well below human accountants Good discussion here: https://www.mercor.com/blog/human-baselines-for-benchmarks-ai-now-outperforms-junior-accountants/

Rule-based work isn't a career anymore. It's a prompt. In every rule-based profession, humans are now the slow, expensive, error-prone option. Claude Opus 5 got 20 for 20 at 100% and wrapped each task in minutes, while 12 licensed CPAs landed anywhere from 0% to about 90%, with several running out the 3-hour clock.

Blackstone President and COO Jon Gray made this same point few months back. Any rule-based businesses, like accounting, legal, finance, will be completely disrupted by AI. 🎯 e.g. JPMorgan dropped proxy advisors for shareholder votes, replacing them with AI. https://x.com/BloombergTV/status/2016932349737410876/video/1

Ben Affleck (Hollywood star & Artists Equity CEO) talks about how he fine-tunes open video models by unfreezing weights and trained only the last cinematic layer so a film crew can hit real production standards. for context, Ben Affleck founded InterPositive in 2022, a 16-person AI shop for film post and Netflix bought it in March 2026 for $587 mn in cash. He needed that model because public video models were trained on his peers' films, and he did not think that was a real business. So InterPositive raised money, shot its own dataset for 8 months on a controlled stage, and used it only as late-stage training. Each new film then trains a private model on its own dailies, so the production keeps the footage and the learning. That is the product Netflix paid $587 million for. ---- From "Bloomberg Live" YouTube channel, (link in comment)