Natasha Singer

PI attorneys in Baltimore — here's what your competition is doing that you're not. We audit personal injury law firm websites and online visibility daily. The top-performing PI firms in Baltimore share a few things in common: ✅ They appear in Google AI Overviews for injury-related queries ✅ Their Google Business Profile has 50+ responses, active Q&A, weekly posts ✅ They're cited by name in Perplexity and ChatGPT when asked for PI attorneys in the area ✅ Their site has location-specific pages for every neighborhood and county they serve ✅ They have schema markup for every attorney, every practice area, every office location The gap between firms doing this and firms not doing it is widening every month. AI search doesn't give you a second chance at first impressions. See exactly where your firm ranks in AI search — free report: https://lnkd.in/gn_P5pBE #PersonalInjury #Baltimore #LawFirmMarketing #AISearch #GEO #AEO

A part of the text I've found extremely interesting: In most jurisdictions, automatically generated text does not receive copyright protection at all. The US Copyright Office has stated that legislative protection of “original works of authorship” is limited to works “created by a human being” (17 USC § 102(a)). It will not register works “produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author” (Compendium of U.S. Copyright Office Practices, 3rd edition, 2019) (emphasis added). The word “any” is key and begs the question of what level of human involvement is required to assert authorship (Gervais, 2020; Phelan & Carey, 2023). Abstract Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners of the data that are “scraped” (lawfully or unlawfully, with or without permission) should be compensated for that use. The second question revolves around the ownership of the output generated by AI, which is continually improving in quality and scale. These topics fall in the realm of intellectual property, a legal framework designed to incentivize and reward only human creativity and innovation. For some years, however, Britain has maintained a distinct category for “computer-generated” outputs; on the input issue, the EU and Singapore have recently introduced exceptions allowing for text and data mining or computational data analysis of existing works. This article explores the broader implications of these policy choices, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy, which might be rendered unsustainable. Lessons may be found in the music industry, which also went through a period of unrestrained piracy in the early digital era, epitomized by the rise and fall of the file-sharing service Napster. Similar litigation and legislation may help navigate the present uncertainty, along with an emerging market for “legitimate” models that respect the copyright of humans and are clear about the provenance of their own creations.