SAP Invests $1.16 Billion in Prior Labs, an 18-Month-Old German AI Startup
On May 5, SAP announced the acquisition of **Prior Labs**, a German AI startup founded just 18 months ago, along with plans to invest $1.16 billion in the company.
What's striking is the speed. Founded in late 2025, Prior Labs builds AI agent infrastructure for enterprises, and SAP is positioning it at the core of its enterprise AI strategy.
At the same time, SAP also revealed a policy of restricting AI agents used on SAP products to **Nvidia's NemoClaw**. It's a move to bring AI usage within its ecosystem under control — a sign that the platform race in enterprise AI has entered a new stage.
The Risk That AI Tools Could Enable Bioterrorism
On May 5, **The Economist** published an in-depth investigation into how AI tools could aid the design and manufacture of biological weapons.
According to the article, while state-of-the-art AI models include safety measures that filter information about harmful pathogens, experts point out that combining multiple AI tools could allow attackers to bypass existing guardrails. Particular concern centers on the risk of pairing open-source biology databases with AI reasoning capabilities.
This issue has been debated in the AI safety community for years, but it has grown more tangible as model capabilities improve — and some argue that regulators haven't kept up.
AI-Generated Share of Scientific Papers Surges — a Nature Investigation
On May 6, **Nature** published a feature article investigating how much of the scientific literature is generated by AI.
According to estimates from multiple research groups, the share of AI-generated text is surging, particularly in computer science and medicine. One study, analyzing specific indicators (the frequency of AI-typical vocabulary such as "delve" and "intriguingly"), reports that a significant portion of papers published since 2025 show traces of AI generation.
The trend is raising concerns about the quality and reliability of research, and the academic community is scrambling to respond. At the same time, observers note how hard it is to draw the line between appropriate use of AI as a "writing aid" and improper automated generation.
HiL-Bench: AI Agents Don't Know How to Ask for Help
A new study published in **Hugging Face Daily Papers**, "HiL-Bench (Human-in-Loop Benchmark)," measured AI agents' ability to judge when they should ask a human for help.
The results were surprising. When given complete information, state-of-the-art agents can solve 89% of SWE and SQL tasks. But in realistic scenarios with incomplete information or ambiguous specifications, even the best models saw their scores plummet to 24%.
When information is missing, agents show a strong tendency to **make overconfident guesses and output wrong answers**, even when a tool for asking humans questions is available. The study also suggests that RL training could improve their questioning ability, but it laid bare the limits of today's agents' capacity for self-awareness.
SubQ Announces a 12-Million-Token Context Window, but Skepticism Mounts
The talk of the local LLM community right now is **SubQ**'s announcement of a 12-million-token context window.
As reported on Reddit's r/LocalLLaMA, the community is asking, "Is this number real?" 12 million tokens is an order of magnitude beyond what even today's state-of-the-art models have achieved, and detailed verification of the benchmark conditions and actual performance is awaited.
With no official technical report released at this point, we'll hold off on a definitive verdict — but as an indicator of what local LLMs can do, it's worth watching.
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