Mastercard brings 85+ firms into crypto payments push

The program helps shift digital assets from experimental side rails into...

What I Read This Week: a summary of the content that I consumed this past week…

Caught My Eye…

1) Yann LeCun’s New AI Lab, AMI, Raises $1.03B for World Models

Advanced Machine Intelligence (AMI) launched March 10, 2026, with a record $1.03 billion seed round at a $3.5 billion pre-money valuation, making it Europe’s largest seed round ever. Founded by Yann LeCun (post-Meta) as chairman, the team also includes Alexandre LeBrun (ex-Nabla CEO) as CEO and Saining Xie (ex-DeepMind and Meta) as Chief Science Officer. The lab drew co-leads from Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions.

AMI’s bet is that the next important AI systems will not come from scaling next-token prediction alone. Instead, it pursues world models built on LeCun’s Joint Embedding Predictive Architecture (JEPA). This enables systems to learn from sensory/physical data, maintain persistent memory, reason causally, plan under constraints, and operate safely in the real world.

AMI targets robotics, manufacturing, transportation, and healthcare. Nabla (LeBrun’s prior company and LeCun’s investment) is the first partner to gain early access to agentic clinical AI.

This vision aligns with LeCun’s February 2026 arXiv paper “AI Must Embrace Specialization via Superhuman Adaptable Intelligence“, co-authored with Goldfeder, Wyder, and Shwartz-Ziv. The paper argues AGI concepts are misguided; human intelligence is specialized, not general.

It proposes Superhuman Adaptable Intelligence (SAI): rapid adaptation to surpass humans on any key task, plus tackling human-infeasible domains. SAI favors SSL and world models for grounded reasoning/planning, positioning AMI as a post-LLM path where breakthroughs come from architectural innovation rather than just more compute/data.

2) Netflix Makes Major Bet on AI Filmmaking Tools

Netflix announced on March 5 that it acquired InterPositive, the AI filmmaking technology company founded by Ben Affleck in 2022. The entire 16-person team (engineers, researchers, creatives) is joining Netflix, and Affleck is staying on as a senior advisor.

Financial terms were not disclosed at the announcement, but Bloomberg later reported the deal could be worth up to $600 million, making it one of Netflix’s largest acquisitions. The company had been operating in stealth, building tools specifically “by filmmakers, for filmmakers.” Netflix already piloted the technology on its own production “El Eternauta,” reporting 10x efficiency gains over traditional VFX pipelines.

InterPositive’s core innovation is per-film custom AI models: it ingests raw production dailies, trains a bespoke model on that specific project’s footage and style, then hands filmmakers an autonomous post-production engine.

The tool can relight entire shots, remove stunt wires frame-perfectly, reframe missed angles, enhance or generate backgrounds, insert VFX elements, and fix continuity errors, which can save studios months and millions of dollars. Because the model is trained on the actual film’s data, outputs stay stylistically consistent and director-controlled rather than generic.

The acquisition gives Netflix an in-house post-production AI capability built around project-specific models and filmmaker-controlled workflows. In its announcement, Netflix framed the deal as “innovation for filmmaking, by filmmakers”, keeping human creatives at the center.

At the same time, Affleck described the tools as purpose-built for production environments rather than general-purpose generative media. That makes the transaction less about consumer-facing AI and more about integrating specialized production software into the studio pipeline

3) Mastercard Brings Circle and 85+ Firms Into Crypto Payments Push

Mastercard launched its Crypto Partner Program on March 11, bringing together more than 85 crypto-native firms, payments providers, and financial institutions in a formal collaboration group.

Participants include Circle, Binance, PayPal, Ripple, Gemini, Paxos, SoFi, Worldpay, Polygon, Solana, Fireblocks, and BitGo. Mastercard said the program is designed as a forum for product input, standards, and collaboration. The program helps shift digital assets from experimental side rails into production-grade integration with Mastercard’s 150+ million global merchant network and core card infrastructure.

The company’s stated focus is on practical payment use cases, especially cross-border remittances, B2B transfers, payouts, settlement, and other forms of money movement, where digital assets can add speed and programmability.

Mastercard’s explicit goal is to fuse the “speed and programmability of digital assets” with established card rails so payments feel seamless to end users. Circle’s inclusion is front and center with their Chief Commercial Officer, Kash Razzaghi, emphasizing in the accompanying Mastercard Newsroom interview that stablecoins (starting with USDC) are moving from trading/investing into payments and store-of-value layers, especially in high-inflation markets.

Circle’s role helps explain the stablecoin angle. The partnership explicitly aims to make stablecoin usage feel like typing an HTTP request, where users just send dollars, unaware of the blockchain layer beneath.

This marks an institutional tipping point at which crypto and traditional finance converge to upgrade the underlying financial infrastructure. After years of parallel tracks, regulation has cleared the runway, and giants like Mastercard are now betting that collaborative infrastructure will drive the next leg of growth through cheaper, 24/7, borderless transfers that approach zero marginal cost.

Learn With My Friends and Me…

• All-In: Iran War, Oil Shock, Off Ramps, AI’s Revenue Explosion and PR Nightmare

Other Reading…

• Fed to Loosen Capital Requirements for Big US Banks (Financial Times)

• US Supreme Court Declines to Hear Dispute Over Copyrights for AI-Generated Material (Reuters)

• When Your Life’s Work Becomes Free and Abundant (Aditya Agarwal)

• Challenges and Research Directions for Large Language Model Inference Hardware (ARXIV)

• Autoresearch (Andrej Karpathy)

On X…