Click any tag below to further narrow down your results
Links
This is a sign-up page for Zhipu AI's GLM-Legal API, inviting law firms and legal teams to apply for access and discuss how the tool can integrate into their practice. The pitch covers using it for daily legal work, business operations, and client services.
- GLM-Legal is positioned as a tool for routine legal tasks and evolving legal challenges
- The offering targets three use cases: legal practice, business operations, and client services
- Access requires form submission and direct conversation with the Zhipu team about collaboration terms
New York Governor Kathy Hochul is pushing to close a legal loophole that shields sexual assault perpetrators when victims were intoxicated at the time of the assault, prompted by a high-profile case at Cornell University.
- Current New York law makes it difficult to prosecute sexual assault cases where the victim willingly consumed drugs or alcohol before the assault occurred
- The push follows a specific assault case at Cornell that exposed the gap in state law
- Hochul has committed to legislative action to address this prosecutorial obstacle
AI is collapsing the time needed for legal work, forcing law firms to abandon hourly billing before their revenues crater. Pryce is a SaaS tool that uses historical billing data and AI execution logs to help firms price fixed-fee engagements with confidence and protect margins.
- Corporate clients are now explicitly demanding AI discounts on invoices, making the billable hour model unsustainable for firms that adopt efficiency tools
- Law partners fear fixed-fee pricing because they lack data to accurately scope risk and quote rates—Pryce solves this by benchmarking past human hours against actual AI task completion times
- The platform builds lock-in through a proprietary dataset linking task hours to AI execution logs; as firms use it more, their firm-specific margin models become harder to replicate elsewhere
This article examines the reliability issues of large language models (LLMs) used in AI, highlighting their tendency to hallucinate and produce incorrect information. New research indicates that these problems stem from the models' inherent design, raising concerns about their suitability for high-stakes applications like law and accounting. Investors may need to reconsider the viability of AI business models given these risks.
- Hallucination rates rise sharply with input length: GLM 4.5 went from 1.2% errors at 32K words to 3.2% at 128K, and some models hallucinated in most cases at 200K words.
- Tsinghua research suggests hallucination-causing neurons are baked in during initial training, making the flaw structural rather than a fixable bug.
- NYT investigation found LLMs generating tax form errors serious enough to risk legal consequences like tax evasion, casting doubt on their use in high-stakes fields like law and accounting.
- Fixes for hallucination are being explored but likely years away, undercutting near-term AI business models built on high-reliability use cases.