OpenAI Unveils Hardware Ambition with Jony Ive, Transforms ChatGPT into AI Platform | Tiny Models Punch Above Their Weight; Notion Rebuilds for Agentic AI

OpenAI Unveils Hardware Ambition with Jony Ive, Transforms ChatGPT into AI Platform | Tiny Models Punch Above Their Weight; Notion Rebuilds for Agentic AI

Sleek, minimalist AI hardware device concept, reflecting Jony Ive's design, representing OpenAI's AI platform ambition and efficient models.

Key Takeaways

  • OpenAI announced a multi-year collaboration with legendary designer Jony Ive on new AI-centric hardware, signaling a major push beyond software.
  • ChatGPT is evolving into an “app store” or operating system, allowing developers to build and distribute rich, interactive applications directly within the chat interface.
  • New “tiny” open-source AI models, like Samsung’s TRM (7M parameters) and AI21’s Jamba Reasoning 3B (3B parameters), are outperforming much larger models on specific reasoning tasks and running inference efficiently on local devices.
  • Notion completely overhauled its tech stack to support enterprise-scale agentic AI, enabling autonomous agents to orchestrate tools and perform complex tasks across connected environments.

Main Developments

This week, the AI landscape solidified its trajectory toward ubiquitous intelligence, driven by OpenAI’s ambitious platform expansion and a counter-trend of highly efficient, specialized “tiny” models. The standout news came from OpenAI’s DevDay 2025, where CEO Sam Altman laid out a vision to transition from systems users “ask anything” to systems that “do anything for you,” signaling a profound shift in how we interact with technology.

The centerpiece of OpenAI’s strategy is the transformation of ChatGPT into a dynamic, interactive platform. With the new Apps SDK, developers can now build and deploy full-fledged applications directly within the ChatGPT interface, turning the popular chatbot into an effective operating system. Live demonstrations showcased partners like Coursera, Canva, and Zillow running rich, interactive UIs, even full-screen experiences, all initiated and managed through natural language. This move creates a direct distribution channel to ChatGPT’s over 800 million weekly active users, aiming to make it the de facto entry point into the commercial web. Beyond interactive apps, OpenAI introduced the Agent Kit, a suite of tools for building autonomous AI workers. This integrated development environment allows for designing complex workflows, deploying agents via an embeddable Chat Kit, and rigorous performance evaluation. A compelling demo showed a procurement agent autonomously handling complex requests, reducing weeks-long processes to minutes. Furthermore, OpenAI’s AI coding agent, Codex, now powered by a specialized GPT-5, can autonomously write, review code, create pull requests, and even transform whiteboard sketches into functional mobile apps.

However, the week’s biggest bombshell was the revelation of a three-year collaboration between OpenAI and Jony Ive, Apple’s former chief design officer. Speaking at a private fireside chat, Ive and Altman confirmed they are working on a new family of AI-centric hardware. Ive emphasized that current “legacy products” are inadequate for AI’s “breathtaking” capabilities, expressing a desire to design with “care” to alleviate user “overwhelm and despair.” This announcement confirms OpenAI’s ambition extends beyond software into the physical interfaces of the future. Underpinning these expansive plans is an “unquenchable thirst for compute,” with OpenAI candidly acknowledging massive investments in infrastructure, echoing Walt Disney’s philosophy of making more money to “make more movies” – in this case, more powerful AI models.

While OpenAI pushes the boundaries of scale and platform, a parallel revolution in “tiny” models is challenging the prevailing “scale is all you need” philosophy. Samsung AI researcher Alexia Jolicoeur-Martineau introduced the Tiny Recursion Model (TRM), a neural network with just 7 million parameters. Despite its minuscule size, TRM competes with or surpasses models 10,000 times larger on challenging reasoning benchmarks like Sudoku-Extreme, Maze-Hard, and ARC-AGI. Jolicoeur-Martineau emphasizes that “recursive reasoning, not scale, may be the key to handling abstract and combinatorial reasoning problems,” making high-performance AI more affordable and accessible. TRM’s code is open-source under an MIT License.

Further reinforcing this trend, AI21 Labs unveiled Jamba Reasoning 3B, a “tiny” open-source model designed to run extended reasoning and code generation on edge devices like laptops and mobile phones. With a 250,000-token context window and 2-4x faster inference speeds thanks to its Mamba-Transformer hybrid architecture, Jamba Reasoning 3B tackles complex tasks locally, reducing reliance on expensive data center GPU clusters. AI21 co-CEO Ori Goshen highlighted the economic and privacy benefits of this hybrid approach, where inference occurs on-device, offering enterprises greater control and steerability.

Meanwhile, companies like Notion are practically implementing agentic AI, demonstrating the necessary architectural shifts. For its Notion 3.0 release, the company completely rebuilt its tech stack to support goal-oriented reasoning systems. Rather than simply executing prompt-based workflows, Notion’s new architecture allows agents to autonomously select, orchestrate, and execute tools across Notion, the web, and other platforms like Slack. Sarah Sachs, Notion’s head of AI modeling, emphasized that this re-orchestration enables agents to make multiple decisions and perform tasks concurrently. Notion’s rigorous evaluation framework and focus on “contextual latency”—understanding when users are willing to wait for exhaustive reasoning versus demanding immediate answers—offers a blueprint for responsible enterprise AI deployment.

Analyst’s View

OpenAI’s announcements are nothing short of a paradigm shift. Moving from a model provider to a full-fledged platform and now into hardware with Jony Ive is a clear statement of intent to own the entire AI ecosystem. This isn’t just about better software; it’s about redefining human-computer interaction, potentially leading to a post-screen, ambient AI future. However, this vast ambition remains tethered to an insatiable appetite for compute, a critical choke point for the industry. Simultaneously, the rise of “tiny” yet powerful models like TRM and Jamba Reasoning 3B offers a vital counter-narrative, proving that ingenuity can, in specific domains, trump brute-force scale. This democratization of high-performance AI could accelerate innovation, alleviate compute constraints, and address privacy concerns by enabling more on-device intelligence. The next phase of AI will likely be a dynamic interplay between these two forces: massive, cloud-based foundational models powering platform-level experiences, and highly optimized, specialized models delivering efficient, contextual intelligence at the edge. Enterprises, exemplified by Notion’s bold re-architecture, must be prepared to rebuild and iterate rapidly to harness truly agentic AI.

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