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Tokenized Stocks Are Being Won by Distribution As per @tokenterminal , Tokenized stocks stood at $3.56B in the week of Sep 21, up 65% from $2.16B in the week of Jun 29. That is roughly $1.4B added in 12 weeks, going into Q4. @Ondo Finance leads with 25.9% and $928.7M. xStocks follows at 23.8%, @binance bStocks at 20.6% and Securitize at 12.4%. The top four issuers hold 82.7% of the market Ondo's lead is 2.1% over xStocks and 5.3% over bStocks. That makes it the leader in a three-way race rather than a dominant player. @BNBCHAIN holds 29.5% of the market at $1.1B. Ethereum is at 21.5%, Solana at 20.2% and Avalanche at 11.9%. Arbitrum (5.6%), X Layer (5.0%) and Robinhood Chain (4.1%) make up the next tier, and Base is at 1.0%. Binance bStocks at 20.6% of $3.56B is about $734M. If bStocks sits on BNB Chain, roughly 70% of the chain's value is the exchange's own product. It reflects where an exchange with a large existing user base chose to issue, Ethereum and Solana are split across Ondo, xStocks, Securitize and others, and none of those issuers has a single distribution channel of Binance's size. Robinhood Chain at 4.1% and Robinhood as an issuer at 4.9% follow the same pattern at a smaller scale, with a broker issuing on its own rails. Bunt's POV Ondo's 25.9% is the most exposed number in this dataset. Exchange-native issuers grow with their own user base, and Ondo has to win through partners it does not control. I believe the Q4 test is whether Ondo holds its share while the total keeps expanding. If exchange-native issuance takes the share instead, the chain leaderboard will keep reflecting distribution rather than infrastructure.

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The funniest part in this Britney’s drama is that every single person who made a video for Britney is someone she has staked, or is still staking a piece of their action. And all they offered were some carefully vague, say-nothing statements 😁 😄😄😄😄 #hustlercasinolive#

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world.