Managing reputation in the age of synthetic content
Tools such as ChatGPT, Grok, Gemini, and proprietary large language models (LLMs) can pop out client memos, legal alerts, and thought leadership articles at record speed. However, as law firms embrace these technologies, they face an urgent imperative: using the power of generative AI in a way that does not compromise the integrity of their brand.
Generative AI offers law firms a range of amazing efficiencies such as the ability to produce first drafts of documents, analyze vast amounts of data, and generate commentary on case law in minutes. These functions save time and are extremely valuable, particularly in fast-moving regulatory and litigation environments.
As an example, AI tools can quickly draft breaking news client alerts on recently introduced legislation and summarize complex case law. This can free up an attorney's time for more strategic work as well as enhance a firm's responsiveness, positioning it as a leader in a competitive market. Yet, speed could come at a cost. AI lacks an inherent understanding of legal nuance and most importantly, ethical obligations, or jurisdictional differences that a human understands. It can operate without accountability, increasing the risk of producing inaccurate, overly generalized, or fabricated content.
A notable example is a syndicated article published in the Chicago Sun-Times and Philadelphia Inquirer in summer 2025, which included a reading list featuring nonexistent books in the summer 2025 reading list article. As reported in The Washington Post, ("Major newspapers ran a summer reading list. AI made up book titles." May 20, 2025) the author admitted to using AI tools without human editing, leading to factual errors and reputational damage for both the media outlets and the author.
For law firms, similar missteps could erode client trust and tarnish a brand built over decades. The Wall Street Journal certainly could have been thinking of brand awareness when they issued their own recommendations of 14 books to read.
The adoption of generative AI introduces several risks that can undermine a law firm's reputation:
(1) Accuracy and misinformation: AI-generated content is prone to "hallucinations," which are confidently stated but factually incorrect information. In the legal context, publishing flawed analyses, outdated citations, or incorrect interpretations of case law could have disastrous consequences, particularly if the firm's name is attached.
(2) Plagiarism and intellectual property infringement: Many AI models are trained on datasets scraped from the internet. This raises the risk that outputs may inadvertently replicate existing work without proper attribution, which could expose firms to copyright infringement claims or ethical violations, both of which carry significant reputational and legal consequences.
(3) Dilution of expertise and thought leadership: Overreliance on AI for client alerts, bylined articles, or white papers risks diluting a firm's unique voice and eroding its authority. We see too many headlines using the word navigating, do we not? Clients hire lawyers for their cultivated expertise, unique insight, and judgment, not for generic, machine-generated summaries that lack depth or context.
(4) Reputational fallout from internal use: Even internally circulated AI-generated content, such as draft memos or research notes, can create liabilities if leaked or misused. A poorly written or inaccurate draft that reaches the public eye, whether through a data breach or accidental disclosure, can damage a firm's credibility.
From a public relations perspective, generative AI requires a new framework for content governance. Law firms must move beyond enthusiasm for efficiency and embrace disciplined oversight.
Here are five essential steps:
•Establish a human touch policy: All AI-generated legal content should be reviewed and ideally co-authored by a licensed attorney. Firms must make it clear that no AI-generated output goes client-facing without human verification.
•Implement AI disclosures and transparency protocols: Whether, in client alerts or public-facing articles, firms should consider disclosing when AI has been used in content creation. This transparency not only builds trust but protects against future claims of misrepresentation or malpractice.
•Train attorneys and comms teams in AI literacy: Educate your lawyers, marketers, and PR professionals on the wonders, capabilities, and also the limitations of generative AI. Understanding how these tools work and where they can fail is critical to mitigating brand risk.
•Audit for originality and attribution: Use plagiarism detection software such as Grammarly and internal checks to ensure content is original and appropriately sourced. This applies even to internal research memos and pitch materials.
•Define your firm's brand voice and reinforce it: AI can write in any voice. Law firms have a tone of voice. The challenge is ensuring it consistently writes in your voice. Set clear tone, style, and messaging guidelines so that AI-generated drafts align with your firm's brand identity.
Used thoughtfully, generative AI can elevate a firm's ability to communicate, respond, and engage with clients and the media. But it must never replace human insight, editorial judgment, or the firm's hard-earned reputation.
Your brand is your most valuable asset, so guard it accordingly, even when the content comes from a machine. AI should serve as an enhancer, not a substitute, for the qualities that make your firm unique. Thoughtful implementation requires rigorous oversight, ethical considerations, and a steadfast commitment to authenticity.
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The Guardian
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But experts who have spent the past years working to benchmark the energy and resource usage of AI models say those new powers come at a cost: a response from GPT-5 may take a significantly larger amount of energy than a response from previous versions of ChatGPT. OpenAI, like most of its competitors, has released no official information on the power usage of its models since GPT-3, which came out in 2020. Sam Altman, its CEO, tossed out some numbers on ChatGPT's resource consumption on his blog this June. However, these figures, 0.34 watt-hours and 0.000085 gallons of water per query, do not refer to a specific model and have no supporting documentation. 'A more complex model like GPT-5 consumes more power both during training and during inference. It's also targeted at long thinking … I can safely say that it's going to consume a lot more power than GPT-4,' said Rakesh Kumar, a professor at the University of Illinois, currently working on the energy consumption of computation and AI models. The day GPT-5 was released, researchers at the University of Rhode Island's AI lab found that the model can use up to 40 watt-hours of electricity to generate a medium-length response of about 1,000 tokens, which are the building blocks of text for an AI model and are approximately equivalent to words. A dashboard they put up on Friday indicates GPT-5's average energy consumption for a medium-length response is just over 18 watt-hours, a figure that is higher than all other models they benchmark except for OpenAI's o3 reasoning model, released in April, and R1, made by the Chinese AI firm Deepseek. This is 'significantly more energy than GPT-4o', the previous model from OpenAI, said Nidhal Jegham, a researcher in the group. Eighteen watt-hours would correspond to burning that incandescent bulb for 18 minutes. Given recent reports that ChatGPT handles 2.5bn requests a day, the total consumption of GPT-5 could reach the daily electricity demand of 1.5m US homes. As large as these numbers are, researchers in the field say they align with their broad expectations for GPT-5's energy consumption, given that GPT-5 is believed to be several times larger than OpenAI's previous models. OpenAI has not released the parameter counts – which determine a model's size – for any of its models since GPT-3, which had 175bn parameters. A disclosure this summer from the French AI company Mistral finds a 'strong correlation' between a model's size and its energy consumption, based on Mistral's study of its in-house systems. 'Based on the model size, the amount of resources [used by GPT-5] should be orders of magnitude higher than that for GPT-3,' said Shaolei Ren, a professor at the University of California, Riverside who studies the resource footprint of AI. GPT-4 was widely believed to be 10 times the size of GPT-3. Jegham, Kumar, Ren and others say that GPT-5 is likely to be significantly larger than GPT-4. Leading AI companies like OpenAI believe that extremely large models may be necessary to achieve AGI, that is, an AI system capable of doing humans' jobs. Altman has argued strongly for this view, writing in February: 'It appears that you can spend arbitrary amounts of money and get continuous and predictable gains,' though he said GPT-5 did not surpass human intelligence. Sign up to TechScape A weekly dive in to how technology is shaping our lives after newsletter promotion In its benchmarking study in July, which looked at the power consumption, water usage and carbon emissions for Mistral's Le Chat bot, the startup found a one-to-one relationship between a model's size and its resource consumption, writing: 'A model 10 times bigger will generate impacts one order of magnitude larger than a smaller model for the same amount of generated tokens.' Jegham, Kumar and Ren said that while GPT-5's scale is significant, there are probably other factors that will come into play in determining its resource consumption. GPT-5 is deployed on more efficient hardware than some previous models. GPT-5 appears to use a 'mixture-of-experts' architecture, which means that it is streamlined so that not all of its parameters are activated when responding to a query, a construction which will likely cut its energy consumption. On the other hand, GPT-5 is also a reasoning model, and works in video and images as well as text, which likely makes its energy footprint far greater than text-only operations, both Ren and Kumar say – especially as the reasoning mode means that the model will compute for a longer time before responding to a query. 'If you use the reasoning mode, the amount of resources you spend for getting the same answer will likely be several times higher, five to 10,' said Ren. In order to calculate an AI model's resource consumption, the group at the University of Rhode Island multiplied the average time that model takes to respond to a query – be it for a pasta recipe or an offering to Moloch – by the model's average power draw during its operation. Estimating a model's power draw was 'a lot of work', said Abdeltawab Hendawi, a professor of data science at the University of Rhode Island. The group struggled to find information on how different models are deployed within data centers. Their final paper contains estimates for which chips are used for a given model, and how different queries are parceled out between different chips in a datacenter. Altman's June blog post confirmed their findings. The figure he gave for ChatGPT's energy consumption per query, 0.34 watt-hours per query, closely matches what the group found for GPT-4o. Hendawi, Jegham and others in their group said that their findings underscored the need for more transparency from AI companies as they release ever-larger models. 'It's more critical than ever to address AI's true environmental cost,' said Marwan Abdelatti, a professor at URI. 'We call on OpenAI and other developers to use this moment to commit to full transparency by publicly disclosing GPT-5's environmental impact.'
