🤖 OpenAI’s Three New Audio Models to Build Voice Agents

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In today’s Daily Report:

• ⚙️OpenAI’s Three New Audio Models to Build Voice Agents

• 📈New Metric Discovers Moore’s Law for AI Agents

• 🛠Trending Tools

• 🥪Brief Bites

• 💰Funding Frontlines

• 💼Who’s Hiring?

Read Time: 3 minutes

🗞RECENT NEWS

OPENAI

⚙️OpenAI’s Three New Audio Models to Build Voice Agents

OpenAI introduced a new suite of three Audio Models for developers worldwide to build, develop, and deploy Voice Agents.

Key Details:

1. gpt-4o-transcribe: A speech-to-text AI model powered GPT-4o (“o” for “omni”) to transcribe audio.

2. gpt-4o-mini-transcribe: A faster, more efficient, and lighter-weight speech-to-text AI model that captures nuances in speech like pauses or fillers.

3. gpt-4o-mini-tts: A text-to-speech AI model powered by GPT-4o mini (“o” for “omni”) that converts text to natural-sounding spoken audio. It also adapts to requests of speaking styles in prompts like: “Use a pirate voice!”

Why It’s Important:

• OpenAI also launched OpenAI.fm, an interactive demo that enables developers to try gpt-4o-mini-tts for free.

• This new suite of three Audio Models integrates with Agents SDK, which allows developers to build Multi-Agent Systems (MAS) where multiple voice-enabled AI Agents work collectively to perform complex tasks like generating natural-sounding spoken audio.

AI RESEARCH

📈New Metric Discovers Moore’s Law for AI Agents

Model Evaluation & Threat Research (METR) just proposed “50%-task-completion time horizon,” a new metric for measuring AI Performance based on the length of tasks AI Agents can complete.

Key Details:

• Despite the rapid progress in AI Benchmarks, their real-world meaning remains unclear. They’re designed to measure domain-specific skills by relying on curated datasets and controlled environments, which don’t reflect the chaos and complexity of real-world tasks.

• This new metric compares the length of time it takes a skilled human to complete a real-world task with the length of time it takes an AI Agent to complete the same real-world task with 50% accuracy.

• For example, Anthropic’s Claude 3.7 Sonnet has a “50%-task-completion time horizon” of 59 minutes. This statement means it can successfully complete a real-world task with 50% accuracy that takes a skilled human nearly an hour to complete.

Why It’s Important:

• This new metric has been “exponentially increasing over the past 6 years, with a doubling time of around 7 months.” This trend suggests that, in under 5 years, AI Agents will be able to autonomously complete real-world tasks that take skilled humans weeks to complete.

• AI Experts are referring to this trend as Moore’s Law for AI Performance. In 1965, Gordon E. Moore, the Co-Founder of Intel, made a bold observation that the number of transistors on a microchip doubles roughly every two years.

🩺 PULSE CHECK

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🔮Browse our always Up-To-Date AI Tools Database.

🥪BRIEF BITES

Anthropic announced that you can now use “Claude” to search the internet for up-to-date information relevant to your prompts.

Apple recently shuffled around AI Executive Ranks in an effort to get back on track with integrating Apple Intelligence into Siri.

OpenAI released “o1-pro,” charging developers a whopping $150 per Million Input Tokens and $600 per Million Output Tokens.

Perplexity AI CEO Aravind Srinivas unveiled that he’s upgrading “Deep Research” to think longer, use code execution, and render in-line charts.

💰FUNDING FRONTLINES

• BuildOps secures a $127M Series C to build Mission Control for Contractors.

• Halliday lands a $20M Series A to build AI Agents that operate safely on Blockchain.

• Browser Use raises a $17M Seed Round to make navigating websites easier for AI Agents.

💼WHO’S HIRING?

• SoundCloud (New York, NY): Marketing Analytics Intern, Summer 2025

• Meta (Los Angeles, CA): Data Scientist, Product Analytics, Entry-Level

• Coinbase (San Francisco, CA): Technology Risk Analyst, Mid-Level

• Red Hat (Boston, MA): Senior MLOps Engineer, AI Inference, Senior-Level

📒FINAL NOTE

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