OpenAI's latest funding round might hit peak circularity. According to The Information, the AI company is in talks with Nvidia, Microsoft, and Amazon about investments totaling up to $60 billion. Nvidia could put in as much as $30 billion, Amazon more than $10 billion—possibly even north of $20 billion—and Microsoft less than $10 billion. On top of that, existing investor SoftBank could contribute up to $30 billion. If these deals go through, the funding round could reach the previously rumored $100 billion mark at a valuation of around $730 billion.
Critics will likely point out how circular these deals really are. Several potential investors, including Microsoft and Amazon, also sell servers and cloud services to OpenAI. That means a chunk of the investment money flows right back to the investors themselves. These arrangements keep the AI hype machine running without the actual financial benefits of generative AI showing up in what end users pay.
AI research institute Allen AI has released SERA, a family of open-source coding agents designed for easy adaptation to private code bases. The top model, SERA-32B, solves up to 54.2 percent of problems in the SWE-Bench-Test Verified coding benchmark (64K context), outperforming comparable open-source models.
SERA outperforms comparable open-source coding agents on the SWE-Bench-Test Verified benchmark with 32K context. | Image: Allen AI
According to AI2, training takes just 40 GPU days and costs between $400 to match previous open-source results and $12,000 for performance on par with leading industry models. This makes training on proprietary code data realistic even for small teams. SERA uses a simplified training method called "Soft-verified Generation" that doesn't require perfectly correct code examples. Technical details can be found in the blog.
The models work with Claude Code and can be launched with just two lines of code, according to Allen AI. All models, code, and instructions are available on Hugging Face under the Apache 2.0 license.
Former Tesla AI chief Andrej Karpathy now codes "mostly in English" just three months after calling AI agents useless
Just last October, Andrej Karpathy dismissed AI agents: “They just don’t work.” Now he says 80 percent of his coding is agent-based and calls it the “biggest change to my basic coding workflow in ~2 decades.” A typically measured voice is joining the agent coding hype, but with some warnings attached.
OpenAI is charging around $60 per 1,000 impressions for its initial ChatGPT ads, far above typical online advertising rates in the low single digits and closer to what advertisers pay for premium TV spots like NFL games, according to The Information. The ads show up below ChatGPT responses in the free and lower-cost "Go" tiers.
Microsoft has unveiled its new AI inference chip, Maia 200. Built specifically for inference workloads, the chip delivers 30 percent better performance per dollar than current-generation chips in Microsoft's data centers, the company claims. It's manufactured using TSMC's 3-nanometer process, packs over 140 billion transistors, and features 216 GB of high-speed memory.
According to Microsoft, the Maia 200 is now the most powerful in-house chip among major cloud providers. The company claims it delivers three times the FP4 performance of Amazon's Trainium 3 while also outperforming Google's TPU v7 in FP8 calculations—though independent benchmarks have yet to verify these figures.
Microsoft's comparison shows the Maia 200 outperforming Amazon's Trainium 3 and Google's TPU v7 across key specifications. | Image: Microsoft
Microsoft says the chip already powers OpenAI's GPT 5.2 models and Microsoft 365 Copilot. Developers interested in trying it out can sign up for a preview of the Maia SDK. The Maia 200 is currently available in Microsoft's Iowa data center, with Arizona coming next. More technical details about the chip are available here.
New study disrupts the narrative that ChatGPT's launch triggered a job decline
The story sounds simple: ChatGPT launched, jobs in AI-exposed fields disappeared. But a new study shows the decline started months before the chatbot arrived. The researchers argue we shouldn’t pin all labor market problems on AI.
