Impact of Generative AI

NEWS 21/10/25: Department of Homeland Security obtains first-known warrant targeting OpenAI for user prompts in ChatGPT According to a recent article by Forbes, the U.S. Department of Homeland Security (DHS) has secured a federal search warrant ordering OpenAI to identify a user of ChatGPT and to produce the user’s prompts, as part of a child-exploitation investigation. https://lnkd.in/eatmK3zv? Key details: - The warrant was filed by child-exploitation investigators within DHS. - It specifically targets “two prompts” submitted to ChatGPT by an anonymous user. The warrant asks OpenAI for the user’s identifying information and associated prompt history. - This is described as the first known federal search warrant compelling ChatGPT prompt-level data from OpenAI. What this means for privacy: -Prompts are treated as evidence. What users have assumed to be ephemeral or private entries in a chat session with an AI service may now be subject to law-enforcement production. -Scope of data retention and access must be reconsidered. If prompt history can be identified and requested, both users and providers should evaluate how long prompts are stored, under what identifiers, and how anonymised they truly are. - Implications for user trust and provider responsibility. AI companies may face growing legal obligations to disclose user-generated content and metadata, which may affect how the services present themselves (privacy guarantees, terms of service) and how users engage with them. - International context and legal cross-overs. For users in jurisdictions with strong data-protection regimes (for example, the General Data Protection Regulation in the UK/EU), the fact that prompt-data can be subject to U.S. warrant may raise questions about extraterritorial access and data flow compliance. In short: this isn’t just another law-enforcement request. It marks the first time a generative-AI provider has been legally compelled to unmask a user and disclose their prompt history. ============ ↳I track how stories like this shape the ethics and governance of AI. You can find deeper analysis at discarded.ai. #AISafety #AIRegulation #Privacy #Governance #Ethics Image AI Generated

RIP SEO. Today was the first time Google automatically pushed me into AI Mode for a basic search. I wasn’t on Labs. I didn’t opt in. No blue links. Just a clean AI-generated answer, with no website listings in sight. This isn’t an experimental feature hidden away anymore. It’s becoming the new default in our #AnswerEconomy. As someone who’s spent 20+ years leading web strategy and SEO efforts, I’ve never seen such a dramatic shift in how information is presented for search. We began moving beyond traditional SEO to GEO (Generative Engine Optimization) in late 2023. ChatGPT’s launch three years ago shifted the web from a search economy to an answer economy. At first, it felt like a challenge to Google’s dominance. (The number I’ve seen most often is that Google owns about 90% of the search market.) Last year, I asked myself whether “Google it” would still be a verb in 10 years. Now, after seeing Google’s AI Mode in action, I think the answer is yes, but not in the way we’re used to. The traditional cluttered Google search results page is being replaced by an AI-first interface. If your content isn’t structured, factual, and optimized for AI engines, you risk becoming invisible. The future isn’t just about ranking first. It’s about being the trusted source the AI quotes. No clicks. No CTRs. Just answers. Welcome to the next era of search. #SEO #GEO #AIEO #AIsearch #DigitalStrategy #ContentMarketing #GenerativeAI #SearchRevolution #DigitalMarketing #Marketing

Researchers from Google's DeepMind, Jigsaw, and Google.org units are warning us in a paper that Generative AI is now a significant danger to the trust, safety, and reliability of information ecosystems. From their recent paper, "Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data": "Our findings reveal a prevalence of low-tech, easily accessible misuses by a broad range of actors, often driven by financial or reputational gain. These misuses, while not always overtly malicious, have far-reaching consequences for trust, authenticity, and the integrity of information ecosystems. We have also seen how GenAI amplifies existing threats by lowering barriers to entry and increasing the potency and accessibility of previously costly tactics." And they admit they're likely *undercounting* the problem. We're not talking dangers from some fictional near-to-medium-term AGI. We're talking dangers that the technology *as it exists right now* is creating, and the problem is growing. What are the dangers Generative AI currently poses? 1️⃣ Opinion Manipulation through disinformation, defamation and image cultivation. 2️⃣ Monetization through deepfake commodification, "undressing services," and content farming. 3️⃣ Phishing and Forgery through celebrity ad scams, phishing scams and outright forgery. 4️⃣ Additional techniques involving CSAM, direct cybersecurity attacks, and terrorism/extremism. Generative AI is not only an *environmental* disaster due to its energy and water usage, and not only a cultural disaster because of its theft of copyrighted materials, but also a direct threat to our ability to use the Internet to facilitate exchange of information and facilitate commerce. I highly recommend giving this report a careful read for yourself. #GenerativeAI #Research #Google #Cybersecurity #Deepfakes https://lnkd.in/gR99hZhe

We just released a new 90-page market study covering the Generative AI Market globally. 🧠 Generative AI (GenAI) is a novel technology that enables computer systems to produce original, human-like content across text, images, video, audio and software code. The market is evolving rapidly as more capable and intelligent models are continuously being announced. The market is not yet a winner-takes-all; low switching costs have resulted in a commoditised and fragmented landscape. However, companies can still differentiate through unique training methodologies that produce distinct output styles and “personalities”. Berg Insight expects the influx of new GenAI companies to continue in the coming years, followed by a phase of consolidation within three to five years. The winning companies will be those that can leverage strong financial backing while attracting top-tier talent to drive rapid innovation. 📈 In 2024, the GenAI market experienced triple-digit growth rates in all three major segments spanning GenAI hardware, foundation models and development platforms. The market value for foundation models reached an estimated US$ 4.1 billion, excluding end-user applications such as ChatGPT. The figure primarily includes income through API services or license fees as the models are used via development platforms. Meanwhile, the market value for GenAI development platforms reached an estimated US$ 17.0 billion. Furthermore, GPU-based hardware systems used for GenAI workloads generated revenues of US$ 132.3 billion in 2024. 🏢 Berg Insight has identified 31 key foundation model providers spanning LLMs, vision, audio and multimodal models. While many LLMs started as unimodal, nearly all successful LLMs now include multimodal capabilities. Companies with notable cross-modal offerings include US-based Anthropic, Google, Meta, OpenAI and xAI; China-based Alibaba Cloud, Baidu, Inc., ByteDance and Tencent; France-based Mistral AI and Canada-based Cohere. Specialised vision model developers include US-based Midjourney and Runway, and UK-based Stability AI. In audio, key specialists include US-based AssemblyAI and ElevenLabs. The ecosystem is supported by a host of development platform providers offering tools for building GenAI applications. In the US, key providers include cloud giants like Microsoft, Google and Amazon Web Services (AWS), and diversified tech companies such as IBM and Oracle. The landscape also features hardware providers like NVIDIA, data platform specialists such as Databricks and Snowflake, model training platforms like Scale AI, and the open-source library from Hugging Face. European and Asian players also contribute, including Dutch Nebius and the aforementioned Chinese conglomerates. #AI #GenerativeAI #technology #innovation #marketresearch

Recently, a Gartner study estimated that 30% of Generative AI projects will be abandoned after proof of concept by the end of 2025. In the study, Gartner highlights 4 key possible causes for the 30% of projects that will be dropped out to be either: 1- Poor data quality 2- Inadequate risk controls 3- Escalating costs 4- Ambiguous business value As #generativeAI starts to rate lower in the emerging tech “Hype Cycle” – an expected and common phenomenon in the tech world, users and vendors are collectively reaching a realistic stage of the technology adoption lifecycle, where the excitement about potential takes a backseat, and realities of cost, difficulties of implementation and the pressure of achieving business value become more prominent. In my view, this percentage is a realistic estimate given where Generative AI is at the moment. Continuous experimentation and the search for innovation and exciting use cases should continue, but on the same footing, organizations need to realistically assess Gen AI proposed projects to improve the chances that these projects will make it to wide scale adoption as opposed to being dropped out post POC. Focusing on the cost challenge above, one of the reasons I see many Gen AI projects fail is the high cost of deploying Large Language Models (LLMs). As exciting as #LLMs are, the costs associated with running them is high including computational resources needed to run them in terms of storage, data processing capabilities and other operational costs. In many cases, Small Language Models (#SLMs) can be a more effective choice for Gen AI projects due to their efficient use of computing resources, the ability to scale them faster and cheaper as well as the lower cost of training these models as they focus on smaller sets of parameters and data sources.

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

Most enterprise generative AI projects still struggle to show measurable financial returns within their first six months. That tolerance is fading because boards and investors now want AI to add to earnings instead of just serving as a test. The focus has shifted from pilots to impact on profits and losses. Spending on AI is increasing, while control over capital is getting stricter. Leaders who cannot link AI to better margins or increased revenue risk losing their budgets and credibility. What’s changing is how deployment is viewed. Early efforts were exploratory because the technology was new. Now, management teams are focusing on use cases that directly relate to reducing costs or improving measurable efficiency, as vague claims of productivity gains are no longer accepted. This means AI initiatives must connect to financial statements, not just innovation presentations. Another change is the emphasis on readiness. Only a small number of organizations consider their infrastructure or data environment to be ready for AI because outdated systems create obstacles. Companies that are using AI to upgrade their IT are saving money that they can use for further deployment, as improved efficiency builds on itself. This means modernisation and return on investment must progress together to maintain funding. Random or broad AI projects fail because they overlook workflow realities and data limitations. Targeted deployment focused on clear outcomes leads to measurable results. Measuring sentiment or perceived productivity does not work because boards care about contributions to earnings. Tracking costs and cycle times in workflows provides a solid basis for ROI. One good starting point is to choose a workflow that involves a practical starting point is a workflow with frequent decisions. Measure its cycle time and transaction costs first. Then introduce AI support. Avoid using AI in areas where data is scattered or governance is unclear because scaling up will be difficult. #AIROI #EnterpriseAI #AILeadership #DigitalTransformation #DataStrategy #CIO #CEOAgenda #BusinessValue #AIAdoption #TechStrategy #BoardGovernance #AITalent

The more capable AI tools get, the less we talk about how reliable they are. So it is super timely that the always excellent Sayash Kapoor and Arvind Narayanan have just published a paper (and accessible Substack post) to bring some focus back onto reliability. And how it isn’t improving. Yep, you read that right. They tested 14 models across 18 months of releases and found that while accuracy has improved substantially, reliability has barely moved. Consistency scores range from 30% to 75%. Agents can't reliably tell you when they're wrong. Rephrase the same instruction slightly and performance drops. If you’re a power user of AI personal assistants, this probably feels wrong. We don't experience this because we're papering over the gap ourselves. We re-prompt. We double-check the output. We learn what the tool is bad at and route around it. We've become the reliability layer. While reading the paper I realised I’m so used to accommodating Claude that I don’t have a solid feel for how often it makes mistakes that I notice. Let alone those I don’t. And that works fine when I’m using AI to speed up what I’m doing. It breaks down when you move toward automation — unattended workflows, customer-facing agents, anything where a human isn't catching errors in real time. Something we often now do without thought and hence don’t notice. Thinking about and measuring reliability separately from capability isn't a new problem. Sayash, Arvind and co authors draw on how aviation, nuclear, and automotive engineering have been thinking about reliability for decades, and apply those frameworks to AI agents. They decompose reliability into the four dimensions that safety-critical fields independently converged on: Consistency — Does the agent get the same result when you run it again under the same conditions? Robustness — Does performance hold up when conditions aren't perfect? Predictability — Does the agent know when it's wrong? (This is the weakest dimension across the board.) Safety — When the agent does fail, is the damage contained? Substack article and paper both in the comments. Well worth a thorough read. Which dimension is most critical for your AI agent application? How are you testing for it?

It’s easy to think of AI as a time-saver that streamlines workflows and accelerates output. But the deeper opportunity lies in how it’s reshaping the nature of work itself. A new study from Harvard Business School’s Manuel Hoffmann followed more than 50,000 developers over two years, with half using GitHub Copilot. The results were striking: developers shifted away from project management and toward the core work of coding. Not because someone told them to, but because AI made it possible. With less need for coordination, people worked more autonomously. And with time saved, they reinvested in exploration—learning, experimenting, trying new things. What we’re seeing here isn’t just productivity. It’s a shift in how work gets done and who does what. Managers may spend less time supervising and more time contributing directly. Teams become flatter. Hierarchies adapt. This is just one signal of how generative AI is changing our org charts and challenging us to rethink how we structure, support, and lead our teams. The future of work isn’t just faster. It’s more fluid. And if we get this right, it’s a whole lot more human. https://lnkd.in/gaUgXnRY

Goldman Sachs has just released a very good report on Generative AI with some realistic findings. Here’s a deep dive into the key takeaways that could redefine our economic landscape: 💡 Key Insights: 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭, 𝐔𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧 𝐑𝐞𝐭𝐮𝐫𝐧𝐬: ➡ $1 Trillion: The amount tech giants are poised to invest in AI over the next few years. ➡Economic Skepticism: Not everyone is convinced about the returns. MIT’s Daron Acemoglu sees limited US economic upside in the next decade! 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 & 𝐆𝐫𝐨𝐰𝐭𝐡: ➡Generative AI’s Potential: While some predict up to a 15% boost in US labor productivity, Acemoglu's estimates are more conservative, forecasting just a 0.5% increase . ➡Labor Reallocation: AI could lead to significant cost savings by automating tasks, potentially saving thousands per worker annually. 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 & 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: ➡Sectoral Differences: AI adoption remains modest across industries but is expected to grow, especially in tech-driven sectors . ➡New Opportunities: Historical data shows that 60% of today’s workers are in jobs that didn’t exist in 1940, highlighting AI’s potential to create new roles and industries . 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 & 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬: ➡Power Crunch: The growth of AI might be slowed by infrastructure challenges, particularly in power supply and chip production . ➡Investment in Utilities: As AI infrastructure expands, utilities are poised to be significant beneficiaries of this growth. 𝐌𝐚𝐫𝐤𝐞𝐭 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: ➡Stock Market Impact: Only the most optimistic AI scenarios predict above-average long-term returns for the S&P 500. Equity valuations are high, driven by AI optimism . ➡Phase 2 Investments: Companies focused on AI infrastructure are expected to see continued benefits before broader market adjustments take place. 🌟 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: Generative AI isn't just a technological advancement; it's a potential economic powerhouse. ➡This report from Goldman Sachs not only highlights the optimism and investments pouring into AI but also cautions about the realistic timelines and structural challenges ahead. ➡As industries adapt, the ripple effects on productivity, job creation, and market dynamics is to be seen. 𝑾𝒉𝒂𝒕 𝒂𝒓𝒆 𝒚𝒐𝒖𝒓 𝒕𝒉𝒐𝒖𝒈𝒉𝒕𝒔 𝒐𝒏 𝒕𝒉𝒆 𝒇𝒖𝒕𝒖𝒓𝒆 𝒐𝒇 𝑮𝒆𝒏 𝑨𝑰? 𝑫𝒐 𝒚𝒐𝒖 𝒂𝒈𝒓𝒆𝒆 𝒘𝒊𝒕𝒉 𝒕𝒉𝒆 𝒓𝒆𝒑𝒐𝒓𝒕 𝒇𝒊𝒏𝒅𝒊𝒏𝒈𝒔? ------------------- I am Eugina Jordan, a technologist with 12 patents, an award winning CMO, and a new market category creator. Follow me for more insights on Gen AI. ---------------