Welcome back AI enthusiasts!
In today’s AI Report:
📦a16z Stockpiles Over 20,000 AI Chips to Woo Startups
🧪OpenAI Partners With LANL for Bioscience Research
🤯Microsoft Drops Observer Role on OpenAI’s Board
⚙️Microsoft’s MInference Tackles Long-Context Tasks
🛠5 Trending Tools
💰Venture Capital Updates
💼Who’s Hiring?
Read Time: 3 minutes
🗞RECENT NEWS
ANDREESSEN HOROWITZ (A16Z)
📦a16z Stockpiles Over 20,000 AI Chips to Woo Startups
Image Source: Steve Jennings/Getty Images for TechCrunch
Venture Capital (VC) firm Andreessen Horowitz (a16z) reportedly secured thousands of AI chips to provide for its portfolio companies at below-market rates in exchange for equity.
Key Details:
The initiative is called “Oxygen” because the AI chips “Breathe Life” into AI startups.
Video generation platform Luma AI is an early participant in the “Oxygen” initiative, citing access to AI chips as a key factor in choosing a16z as its lead investor.
a16z has already secured thousands of AI chips, with plans to add over 20,000 Nvidia H100 Tensor Core GPUs to offer accelerated computing power for AI-centric tasks.
Over the last two years, a16z has invested significantly in generative AI (GenAI), including funding for notable companies like xAI, Mistral AI, and OpenAI.
Why It’s Important:
By offering scarce computing resources, a16z is positioning itself as more than just a source of capital.
The VC firm is selling “shovels” during a “gold rush.” a16z is stockpiling AI chips (“shovels”) and offering them to startups (“miners”) at below-market rates without being directly involved in the gold mining (“AI development”) itself.
In exchange for these AI chips, a16z gets equity in the startups, which gives them a stake in the potential payoff (“the gold”) if the startups are successful.
🩺 PULSE CHECK
Is a16z’s “Oxygen” initiative a clever idea?
OPENAI
🧪OpenAI Partners With LANL for Bioscience Research
Image Source: Los Alamos National Laboratory (LANL) Screenshot
OpenAI partnered with Los Alamos National Laboratory (LANL) to understand how scientists can safely use multimodal AI models in laboratory settings.
Key Details:
LANL is a government-funded research facility in New Mexico overseen by the U.S. Department of Energy (DOE).
LANL’s primary focus is national security; nuclear weapons are central to that mission.
OpenAI and LANL are working together to assess how frontier AI models like OpenAI’s GPT-4o (“o” for “omni”) can assist scientists in physical laboratory settings through multimodal capabilities like vision and voice.
Why It’s Important:
Moderna currently uses OpenAI’s technology to augment clinical trial development by building data-analysis GPTs designed to tackle large datasets.
OpenAI’s frontier AI models will assist in performing tasks like transformation (e.g., introducing foreign genetic material into a host organism), cell structure, and cell separation for LANL.
AI DRAMA
🤯Microsoft Drops Observer Role on OpenAI’s Board
Microsoft dropped its observer role on OpenAI’s Board of Directors less than eight months after securing a non-voting seat.
Microsoft obtained an observer role on OpenAI’s Board of Directors by investing $13 billion into developing the company’s products and services.
OpenAI’s new approach toward Microsoft and Apple will involve hosting “regular stakeholder meetings to share progress on our mission.”
The decision comes as antitrust authorities, including the Federal Trade Commission (FTC) and Department of Justice (DOJ), continue investigating “whether investments and partnerships pursued by dominant companies risk distorting innovation and undermining fair competition.”
AI RESEARCH
⚙️Microsoft’s MInference Tackles Long-Context Tasks
Microsoft unveiled MInference, which stands for “Million-Tokens Prompt Inference,” to accelerate how Large Language Models (LLMs) process long-context tasks while maintaining accuracy.
When dealing with massive amounts of text containing millions of tokens, a crucial step in LLM processing called “pre-filling” becomes a bottleneck.
“Pre-filling” refers to processing the entire context of an input text for LLMs to perform tasks.
Traditional LLMs rely on an “attention mechanism” during the “pre-filling” process to compare every token in the input text to understand their relationships.
This comparison process becomes computationally expensive for long-context tasks with millions of tokens. Imagine the mental strain of comparing every word in a long novel to every other word.
Instead of comparing every word in a long novel to every other word, MInference identifies essential sections related to the character’s motivations (e.g., flashbacks, internal monologues, etc.).
MInference only reads the crucial parts of the long novel to process the essential context of an input text and answer user queries quickly with fewer computational resources.
🛠TRENDING TOOLS
📱Prodia adds AI to your app with one API.
🦾MathSolver is an interactive math problem solver.
🌍Crew helps you hire, pay, and manage the world’s top talent.
🕸Reworkd simplifies your data extraction process with end-to-end automation.
🏠Gendo offers architecture designers stunning AI-enabled CGI in minutes.
🔮Browse our always Up-To-Date AI Tools Database.
💰VENTURE CAPITAL UPDATES
Volley raises a $55M Series C for voice-enabled AI games.
Index Ventures raises a $2.3B Third Fund to chase AI breakthroughs.
Iceland’s early-stage investor, Frumtak Ventures, raised a Fourth Fund totaling $87M to use cutting-edge technologies to solve real-world problems.
💼WHO’S HIRING?
HP (Palo Alto, CA): Personal Systems Machine Learning (ML) Intern, Summer 2024
Amazon (New York, NY): Applied Science PhD Intern, Fall 2024
DataVisor (Mountain View, CA): Software Engineer Intern, Summer 2025
Applied Intuition (Mountain View, CA): Product Manager, New Grad
OpenAI (San Francisco, CA): Software Engineer, Backend
🤖PROMPT OF THE DAY
LEADERSHIP
🏄Managing Multi-Generational Teams
Imagine you’re leading a diverse team with members spanning different generations, including Gen Z, Millennials, Gen X, and Baby Boomers.
How would you foster collaboration, understanding, and productivity among team members with varying work styles, communication preferences, and technological comfort levels?
Share strategies for leveraging each generation’s strengths while promoting a culture of mutual respect and effective teamwork.📒FINAL NOTE
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