Sam Altman’s New Venture: From OpenAI to AI Chip Innovation

Sam Altman, the former CEO of the renowned AI startup OpenAI, is charting a new course in the tech world. After an unexpected departure (and as it seems now, no return) from OpenAI, Altman is now focused on raising capital for a groundbreaking venture in the AI chip industry, a project known as Tigris.

The (final?) departure of Altman from OpenAI occurred during a critical phase for the company, amid discussions about a significant share sale offer. According to sources familiar with the situation, Altman’s exit from OpenAI was marked by issues related to transparency in communication with the board and disagreements on AI safety and technology​​.

Tigris, The New Venture for Sam Altman

Post-OpenAI, Altman has not slowed down. He has turned his attention towards establishing an AI-focused chip company, potentially positioning it as a rival to Nvidia, the current leader in AI-related tasks. This move indicates a significant shift in Altman’s career trajectory, but remains close to his continuous commitment to innovation in artificial intelligence​​​​.

Altman’s new venture, Tigris, is poised to make waves in the AI industry. He has been actively engaging with major global investors to secure billions in funding for this new chip company. His business travels have reportedly taken him to the Middle East, among other regions, to discuss funding opportunities for Tigris​​. The potential establishment of a new player in the AI chip market could introduce healthy competition and foster advancements in AI technology.

Altman was apparently also seeking funding for a new version of Worldcoin’s hardware Globe device that he developed with former Apple design chief Jony Ive.

The AI Chip Market is rapidly growing

The AI chip market is a rapidly growing sector with immense potential. The entry of a new company, led by a figure as prominent as Sam Altman, is likely to accelerate innovation and could challenge existing industry dynamics.

AI chips are specialized silicon chips designed to efficiently process AI tasks. These chips are optimized for tasks like deep learning, neural network processing, and machine learning, offering faster processing and more efficient power consumption compared to general-purpose chips.

There are several types of AI chips, including GPUs, FPGAs (Field-Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), and TPUs (Tensor Processing Units). Each type has its unique advantages and is suitable for different AI applications. For example, GPUs are widely used for training deep learning models due to their high computational power, while TPUs, developed by Google, are optimized for both training and inference tasks in neural networks.

The growth is fueled by the widespread adoption of AI across various industries, including healthcare, automotive, finance, and consumer electronics. The demand for AI chips is increasing as more businesses and industries seek to incorporate AI into their operations for better data analysis, decision-making, and automation.

The market is currently dominated by major tech companies like Nvidia, Intel, and AMD, which have developed their own AI chips. Nvidia, in particular, is a significant player due to its strong presence in the GPU (Graphics Processing Unit) market, which is crucial for AI and machine learning tasks. However, many new entrants, including startups and established tech giants, are also making strides in this area.