AI’s ‘Moore’s Law’ emerges

Good morning, AI enthusiasts. The AI capability curve just found its “Moore’s Law” moment — with latest research showing task completion abilities doubling every 7 months since 2019.

With systems tackling hour-long human tasks today and potentially month-long projects by 2030, is the world prepared for the automation tsunami quickly approaching?

In today’s AI rundown:

• AI capabilities following ‘Moore’s Law’

• Hollywood against AI copyright proposals

• Improving non-reasoning AI responses

• Nvidia’s open-source reasoning models

• 4 latest AI tools & 4 job opportunities

LATEST DEVELOPMENTS

AI RESEARCH

📈 Study: AI capabilities following ‘Moore’s Law’

The Rundown: Researchers at METR just published latest data showing that the length of tasks AI agents can complete autonomously has been doubling roughly every 7 months since 2019, revealing a “Moore’s Law” for AI capabilities.

The small print:

• The study tracked human and AI performance across 170 software tasks starting from 2-second decisions to 8-hour engineering challenges.

• Top models like 3.7 Sonnet have a “time horizon” of 59 minutes — completing tasks that take expert humans this long with at the least 50% reliability.

• Older models like GPT-4 can handle tasks requiring about 8-Quarter-hour of human time, while 2019 systems struggle with anything beyond just a few seconds.

• If the exponential trend continues, AI systems will likely be able to completing month-long human-equivalent projects with reasonable reliability by 2030.

• Moore’s Law predicts that computing power doubles roughly every two years — explaining why devices get faster and cheaper over time.

Why it matters: The invention of a predictable growth pattern in AI capabilities provides a very important forecasting tool for the industry. Systems that may handle for much longer (months-long tasks for humans) and more complex tasks independently will completely reshape how businesses the world over approach AI and automation.

TOGETHER WITH MONGODB

📘 The hub for constructing higher GenAI

The Rundown: Step into MongoDB’s Generative AI Use Cases Repository to find how MongoDB powers GenAI applications— from text and vector search to robust AI Retrieval methods.

Inside, you’ll find:

• Notebooks on real-world scenarios like advanced RAG techniques and AI agents

• Integration with top LLM providers and frameworks

• Pre-built datasets and embedding tools to speed up your GenAI development

• A central resource and community for developers using MongoDB

Start exploring MongoDB’s AI possibilities today.

CELEBRITIES VS. AI

⭐️ Hollywood against AI copyright proposals

The Rundown: Greater than 400 Hollywood creatives signed an open letter urging the Trump administration to reject OpenAI and Google’s proposals to expand AI training on copyrighted works—arguing that it will allow them to “freely exploit” creative industries.

The small print:

• The letter is a direct response to OpenAI and Google‘s AI Motion Plan submissions, which argued for expanded fair use protections for AI training.

• OpenAI framed AI copyright exemptions as a “matter of national security,” while Google said the present fair use framework already supports AI innovation.

• Ben Stiller, Mark Ruffalo, Cate Blanchett, Paul McCartney, Taika Waititi, and Aubrey Plaza are among the many high-profile creatives who’ve signed the letter.

• They’ve emphasized that AI firms could simply “negotiate appropriate licenses with copyright holders — just as every other industry does.”

Why it matters: Hollywood vs. AI represents a values collision — the tech industry’s “move fast and iterate” mindset vs. Hollywood’s centuries-old IP frameworks. But with AI giants across the globe already ingesting the world’s data even without copyright protections, this fight, in point of fact, could also be more symbolic than action-oriented.

AI TRAINING

🧠 Improving non-reasoning AI responses

The Rundown: On this tutorial, you’ll learn the way to dramatically improve the intelligence of non-reasoning AI models by implementing a structured reasoning approach with XML tags—forcing the model to think step-by-step before answering.

Step-by-step:

1. Structure your prompt with XML tags like and to separate the reasoning process from the ultimate output.

2. Provide specific context and task details, including examples.

3. Force step-by-step reasoning by explicitly instructing the model to “think” first, then answer.

4. Compare results with and without your reasoning framework to see the dramatic improvements in quality.

Pro tip: You should use this system especially when asking AI to match writing styles or analyze complex information before generating content.

PRESENTED BY DAGSTER

🚀 Streamline your AI stack with Dagster

The Rundown: Dagster consolidates your AI capabilities into one powerful orchestrator that developers love — helping reduce costs, eliminate complexity, and ensure reliable pipelines from prototype to production.

With Dagster, you’ll be able to:

• Consolidate all AI capabilities under one intuitive interface

• Save 40%+ on infrastructure costs by optimizing AI workloads

• Ship AI features 3x faster with standardized development practices

Schedule a demo to learn more about how Dagster can simplify your AI platform.

NVIDIA

🧠 Nvidia’s open-source reasoning models

The Rundown: Nvidia released its Llama Nemotron family of open-source reasoning models, designed to speed up enterprise adoption of agentic AI able to complex problem-solving and decision-making.

The small print:

• The brand new model family is available in three sizes: Nano (8B), Super (49B), and Ultra (249B) — each optimized for various deployment scenarios.

• Early benchmarks show impressive performance, with the Super version outperforming each Llama 3.3 and DeepSeek V1 across STEM and gear testing.

• The models feature a toggle that enables AI systems to modify between intensive reasoning and direct responses based on the duty.

• Post-training resulted in 20% higher accuracy than base Llama models and 5x faster speed than rival open reasoners.

• Nvidia can be releasing an “AI-Q Blueprint” framework in April to assist businesses connect AI agents with their existing systems and data sources.

Why it matters: Nvidia’s reasoning models could also be overshadowed by the insane amount of releases over the past 48 hours, however the chipmaking giant has seemingly built every block essential to be a force across your entire AI stack — from essentially the most advanced hardware to high-quality reasoning models ready for the agentic era.

QUICK HITS

🛠️ Trending AI Tools

• 💎 Diamond – Graphite’s agentic AI-powered code review companion

• 📋 Canvas – Gemini’s latest collaborative space for document editing and coding

• 🎥 Stable Virtual Camera – Images into 3D videos with dynamic camera paths

• 🧊 Hunyuan 3D 2.0 MV – Open model for high-quality 3D shape generations

💼 AI Job Opportunities

• 🤝 OpenAI – Strategic Partnerships Lead, Japan

• 🤖 Anthropic – Applied AI, Product Engineer (UK)

• 🧩 Meta – Manager, Technical Program Management

• 🚀 Rad AI – Director of Emerging Products

📰 Every little thing else in AI today

Google AI and UC Berkeley researchers proposed “inference-time search” as a brand new AI scaling method, producing several answers in parallel and choosing the very best option.

LG released EXAONE Deep, a reasoning AI that achieves comparable performance to models like DeepSeek V1 in math, science, and coding with just 32B parameters.

Muse released Muse S Athena, a scarf wearable combining EEG and sensors to measure each brain activity and oxygen levels for AI-powered cognitive fitness training.

Nvidia and xAI are joining Microsoft, BlackRock, and MGX within the AI Infrastructure Partnership, aiming to boost $30B initially and potentially $100B for AI data centers.

xAI debuted its first image generation API featuring the ‘grok-2-image-1212’ model, allowing developers to create multiple JPG images per request at $0.07 each.

Microsoft is partnering with neuroscience AI startup Inait to develop brain-inspired AI that learns from real-world experiences moderately than data patterns.

COMMUNITY

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See you soon,

Rowan, Joey, Zach, Alvaro, and Jason—The Rundown’s editorial team