Good morning. It's Monday, March 16, and we're covering Meta’s delayed Avocado AI model, the Anthropic-Pentagon clash over military AI, Runway’s real-time video avatars, and more.

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Runway Launches Real-Time Video AI Avatars: New Characters API turns a single image into expressive conversational video agents powered by GWM-1, enabling brands to deploy customizable avatars for support, training, and marketing across apps.

Meta Weighs Layoffs Up to 20%: Reports say Meta may cut as much as one-fifth of its workforce following recent “low performer” firings. Deeper reductions could reshape the company’s costly AI and metaverse investment strategy.

MIT’s GenCAD Turns Photos Into CAD: Researchers unveil an open-source model that converts a single image into fully editable parametric CAD commands for manufacturing, trained on 840K images and outperforming prior systems in design generation.

Spotify Lets Users Edit Taste Profiles: Spotify’s new beta feature lets Premium users review and modify their algorithmic “Taste Profile,” refining recommendations and fixing issues from shared accounts or non-representative listening.

Claude Adds 1M Token Context Window: Anthropic makes the full 1-million-token context generally available for Opus 4.6 and Sonnet 4.6 at standard pricing, supporting up to 600 images/PDF pages and enabling entire codebases or documents in one prompt.

Random Labs Launches Swarm Coding Agent: Y Combinator-backed startup unveils Slate V1, a “swarm-native” AI coding system that orchestrates multiple models in parallel threads, aiming to tackle complex software projects.

NanoClaw Partners With Docker for Agent Sandboxes: The open-source AI agent platform integrates with Docker Sandboxes using MicroVM isolation, letting enterprises run autonomous agents with stronger security boundaries.

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R.J. Scaringe shares how a childhood dream became a multibillion-dollar EV company—and why AI, autonomy, and robotics will reshape the next decade.

For the first time in fifty years, it's worth asking a simple question: where did the money go? Productivity has more than doubled since 1979. Wages for most workers haven't kept pace. A RAND study put a number on the gap — $79 trillion funneled upward since 1975. That's a policy choice, not a market outcome.

Scott Santens, Founder and CEO of ITSA Foundation, argues there's only one fix that actually addresses the root problem: Universal Basic Income. Not a job guarantee. Not targeted benefits. A direct dividend — to every person — from the economy we all built. His case is sharper than you'd expect, and harder to dismiss. → Read the full article here.

Meta has delayed its next foundational AI model, code-named Avocado, from March to at least May 2026 after internal testing showed it trailing leading systems from Google, OpenAI, and Anthropic. The model beats Meta's previous AI and Google's Gemini 2.5 but falls short of Gemini 3.0 on reasoning, coding, and writing benchmarks — a meaningful gap given the competitive stakes.

The delay adds pressure to CEO Mark Zuckerberg's massive AI push, which includes $600 billion in data-center infrastructure and up to $135 billion in planned 2026 spending. It also follows a rough stretch for Meta's AI ambitions: after Llama 4 disappointed, the company invested $14.3 billion in Scale AI, brought on Alexandr Wang as chief AI officer, and formed TBD Lab to build new models including Avocado and video generator Mango. Even at that scale, keeping pace at the frontier is getting harder. → Read the full article here. (Paywall)

Defense startup General Trajectory introduced Phoenix, an AI autonomy stack that turns low-cost FPV drones into autonomous interceptors designed to collide with hostile drones. The system runs fully onboard using a $250 NVIDIA Jetson edge computer, allowing the drone to detect, track, and pursue targets without a human pilot or radio link.

Phoenix uses a transformer-based perception model trained on more than 200,000 images and operates at over 30 frames per second with under 33 milliseconds of latency. The company argues the approach could dramatically reduce the cost of counter-drone defense compared with missile systems such as the Patriot PAC‑3, which cost millions per interceptor. → Read the full article here.

Researchers including Kevin P. Murphy introduced AutoHarness, a method that lets language models automatically generate a code “harness” to prevent illegal or invalid actions when operating as agents. The system uses iterative code refinement with feedback from an environment to synthesize guardrails around a model’s decisions.

In tests across 145 TextArena games, the harness eliminated illegal moves—an issue that caused 78% of losses for Gemini 2.5 Flash in a Kaggle GameArena chess competition. With the generated harness, the smaller Gemini 2.5 Flash outperformed the larger Gemini 2.5 Pro, and in some cases, researchers generated a full code-based policy that beat both Gemini 2.5 Pro and GPT‑5.2 High on several tasks. → Read the full article here.

Thanks for reading. See you next time!

— Matthew Berman, Nick Wentz & the Forward Future Team