OpenAI Outlines New For-Profit Structure In Bid To Stay Ahead In Costly AI Race
OpenAI, the company that makes ChatGPT, says in blogpost ‘we once again need to raise more capital than we’d imagined’
OpenAI said Friday that in moving toward a new for-profit structure in 2025, the company will create a public benefit corporation to oversee commercial operations, removing some of its nonprofit restrictions and allowing it to function more like a high-growth startup.
“The hundreds of billions of dollars that major companies are now investing into AI development show what it will really take for OpenAI to continue pursuing the mission,” OpenAI’s board wrote in the post. “We once again need to raise more capital than we’d imagined. Investors want to back us but, at this scale of capital, need conventional equity and less structural bespokeness.”
The pressure on OpenAI is tied to its $157 billion valuation, achieved in the two years since the company launched its viral chatbot, ChatGPT, and kicked off the boom in generative artificial intelligence. OpenAI closed its latest $6.6 billion round in October, gearing up to aggressively compete with Elon Musk’s xAI as well as Microsoft, Google, Amazon and Anthropic in a market that’s predicted to top $1 trillion in revenue within a decade.
Developing the large language models at the heart of ChatGPT and other generative AI products requires an ongoing investment in high-powered processors, provided largely by Nvidia, and cloud infrastructure, which OpenAI largely receives from top backer Microsoft. OpenAI expects about $5 billion in losses on $3.7 billion in revenue this year, CNBC confirmed in September. Those numbers are increasing rapidly.
More about OpenAI corporate structure and fundraising
How Tool Calling is Shaping the Future of AI Agents | Neudesic’s Tula Masterman
Dive into the fascinating world of AI agents with Tula Masterman! In this video, Tula breaks down AI agents, focusing on tool calling challenges, evaluation, and some real-life examples. Whether you're just starting out or already know a bit, this video has something for everyone.
The Future Of AI Shouldn’t Be Taken At Face Value | NY Magazine: Intelligencer
It costs a lot to build an AI company, which is why the most competitive ones are either existing tech giants with an abundance of cash to burn or start-ups that have raised billions of dollars largely from existing tech giants with an abundance of cash to burn.
A product like ChatGPT was unusually expensive to build for two main reasons. One is constructing the model, a large language model, a process in which patterns and relationships are extracted from enormous amounts of data using massive clusters of processors and a lot of electricity. This is called training.
The other is actively providing the service, allowing users to interact with the trained model, which also relies on access to or ownership of a lot of powerful computing hardware. This is called inference.
After ChatGPT was released in 2022, money quickly poured into the industry — and OpenAI — based on the theory that training better versions of similar models would become much more expensive.
This was true: Training costs for cutting-edge models have continued to climb (“GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute,” according to Stanford’s AI Index Report for 2024).
More about costs of building an AI company on NY Mag’s Intelligencer
Google Veo 2 Demo | Physics Test
Artificial Intelligence Data Centers Are Becoming 'Mind-Blowingly Large'
Clusters of GPU chips in coming years will have to connect over distances longer than a mile, says the CEO of this fiber-optics firm.
The building of more powerful data centers for artificial intelligence, stuffed with more and more GPU chips, is driving data centers to enormous size, according to the chief executive of Ciena, which makes fiber-optic networking equipment purchased by cloud computing vendors to connect their data centers together.
"Some of these large data centers are just mind-blowingly large, they are enormous," says Gary Smith, CEO of Hannover, Maryland-based Ciena.
"You have data centers that are over two kilometers," says Smith, more than 1.24 miles. Some of the newer data centers are multi-story he notes, creating a second dimension of distance on top of horizontal sprawl.
Even as cloud data centers grow, corporate campuses are straining to support clusters of GPUs as their size increases, Smith said. "These campuses are getting bigger and longer," he says. The campus, which comprises many buildings, is "blurring the line between what used to be a wide-area network and what's inside the data center."
More on the expansion of data center GPU clusters on ZDNET
Unitree’s B2-W Industrial Wheel Upgrade
One year after mass production kicked off, Unitree’s B2-W Industrial Wheel has been upgraded with more exciting capabilities. Please always use robots safely and friendly.
Why DeepSeek's New V3 Artificial Intelligence Model Thinks It's ChatGPT
Earlier this week, DeepSeek, a well-funded Chinese AI lab, released an “open” AI model that beats many rivals on popular benchmarks. The model, DeepSeek V3, is large but efficient, handling text-based tasks like coding and writing essays with ease.
It also seems to think it’s ChatGPT.
Posts on X — and TechCrunch’s own tests — show that DeepSeek V3 identifies itself as ChatGPT, OpenAI’s AI-powered chatbot platform. Asked to elaborate, DeepSeek V3 insists it is a version of OpenAI’s GPT-4 model released in 2023.
The delusions run deep. If you ask DeepSeek V3 a question about DeepSeek’s API, it’ll give you instructions on how to use OpenAI’s API. DeepSeek V3 even tells some of the same jokes as GPT-4 — down to the punchlines.
So what’s going on? Models like ChatGPT and DeepSeek V3 are statistical systems. Trained on billions of examples, they learn patterns in those examples to make predictions — like how “to whom” in an email typically precedes “it may concern.”
DeepSeek hasn’t revealed much about the source of DeepSeek V3’s training data. But there’s no shortage of public datasets containing text generated by GPT-4 via ChatGPT. If DeepSeek V3 was trained on these, the model might’ve memorized some of GPT-4’s outputs and is now regurgitating them verbatim.
More on DeepSeek’s identity crisis on TechCrunch
Optimize Your AI | Quantization Explained
Run massive AI models on your laptop! Learn the secrets of LLM quantization and how q2, q4, and q8 settings in Ollama can save you hundreds in hardware costs while maintaining performance.
In this video, you'll learn:
How to run 70B parameter AI models on basic hardware
The simple truth about q2, q4, and q8 quantization
Which settings are perfect for YOUR specific needs
A brand new RAM-saving trick with context quantization
Thats all for today, however new advancements, investments, and partnerships are happening as you read this. AI is moving fast, subscribe today. Happy Holidays!