Subscriber momentum accelerated meaningfully to start the year, with January and February on track to be the largest months for new subscribers in our history. People use ChatGPT to learn, write, plan, and build. As usage scales, the product improves in ways people feel immediately: faster responses, higher reliability, stronger safety, and more consistent performance.
Running AI infrastructure costs are astronomical. Back in 2023, it was estimated that OpenAI spends around $700,000 per day to run ChatGPT—about 36 cents per query. However, in 2024 with the release of its higher-performing o3 model, some queries cost over $1,000 of computing power. Consequently, OpenAI CEO Sam Altman reports the company is even losing money on its $200 ChatGPT Pro subscriptions.
Enterprises building autonomous agents powered by large language models face new challenges that traditional test approaches were not designed to address. Agents behave probabilistically, integrate deeply with applications, and coordinate across tools, making isolated accuracy metrics insufficient for understanding real-world performance.
Autonomous agents take the first part of their names very seriously and don't necessarily do what their humans tell them to do - or not to do. But the situation is more complicated than that. Generative (genAI) and agentic systems operate quite differently than other systems - including older AI systems - and humans. That means that how tech users and decision-makers phrase instructions, and where those instructions are placed, can make a major difference in outcomes.
Laboratory safety goggles have finally joined the ranks of smart devices. That's the promise behind LabOS, an AI operating system for scientific laboratories built by the Stanford-Princeton AI Coscientist Team, a group led by Stanford University bioengineer Le Cong and Princeton University computer scientist Mengdi Wang, with founding partners that include NVIDIA. Powered by NVIDIA's vision-language models to process visual data, the system is designed to provide AI with real-time knowledge of lab work so it can determine what causes experiments to fail or succeed and rapidly train new scientists to expert levels by guiding them through experimental protocols.
DeepSeek, the Hangzhou-based lab backed by quantitative hedge fund High-Flyer, has forced a reckoning across the AI supply chain by releasing highly capable models at a fraction of the cost charged by OpenAI, Google, and Anthropic. The result: a cascading price war that is reshaping how companies budget for intelligence, how developers choose their tools, and how the entire economics of foundation models may evolve over the next 12 to 18 months.
OpenAI just announced a massive funding round of $110 billion, which is one of the biggest investment rounds in Silicon Valley history. The investors feature many of the usual suspects, including Amazon with $50 billion, NVIDIA with $30 billion and SoftBank with $30 billion. This investment brings OpenAI to a $730 billion valuation.
OpenAI has been clear about its priorities: infrastructure, compute, and talent. CEO Sam Altman has repeatedly pointed to the sheer cost of training and running frontier AI models as the primary bottleneck to progress. According to reporting from Bloomberg, a significant portion of this raise will go toward securing GPU capacity through partnerships with data center providers across the United States, Japan, and the UAE.
The core of the argument is that agentic AI will replace human labor in most white-collar industries and will do so with dizzying speed. The consequent abrupt and massive job displacements will lead to crashes in property values and local tax bases, with devastating impacts on communities and much of the public sector.
Tesla has the most advanced real-world AI, and hopefully, it will be approved soon in Europe. We're told by the authorities that March 20th, it'll be approved in the Netherlands. Hopefully, that date remains the same. But I think people in Europe are going to be pretty blown away by how good the Tesla car AI is in being able to drive.
We believe the best research teams are built through context, taste and a real feel for where the field is headed next; research recruiting is about finding people who will move the frontier forward, not just filling roles.
The main issue, Khan said, was that all apps that are vibe-coded on Lovable's platform are shipped with their backends powered by Supabase, which handles authentication, file storage, and real-time updates through a PostgreSQL database connection. However, when the developer - in this case AI - or the human project owner fails to explicitly implement crucial security features like Supabase's row-level security and role-based access, code will be generated that looks functional but in reality is flawed.
Competition will heat up as AI-native challengers begin to chip away at market leaders across business processes and create new market segments that were previously unaddressed by software. In addition, new entrants are rapidly growing and disrupting the market with leaner operating models.
