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

Generative AI is going to change the SaaS pricing model - and that’s a good thing. For years, the "per-seat" model has been the go-to for SaaS companies, which tend to grow in tandem with the companies they serve. With the advent of AI-driven efficiency enhancements, however, the landscape of SaaS pricing is undergoing a seismic shift. The conventional wisdom of scaling alongside customer growth no longer holds true in a world where fewer personnel are needed to achieve higher efficiency levels. Consequently, the outdated per-seat model fails to meet the evolving needs of businesses focused on maximizing efficiency. This realization opens doors for founders to innovate their pricing strategies. No longer bound by the constraints of traditional models, entrepreneurs are embracing the freedom to experiment with new approaches that better align with the value they provide to customers. In this evolving landscape, it’s my opinion that value-based pricing will emerge as the North Star. By tethering pricing to tangible outcomes such as cost savings and customer satisfaction metrics (e.g. CSAT score for customer support interactions), businesses can establish a more equitable exchange of value with their clientele. This customer-centric approach fosters stronger partnerships and ensures that pricing reflects the true impact of the service provided. In essence, companies now have the ability to shift their pricing structure to whatever model makes it easiest for their customers to buy in. And with Generative AI, we have the means to make these solutions more creative and impactful than ever before. By prioritizing customer needs and business objectives, founders can differentiate themselves in a crowded market and solidify their position as industry leaders.

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

Generative AI has taken “AI” out of the hands of specialists and placed it into every industry that has a problem to solve. When teams can build with natural language, integrate with existing systems, and map human intent instead of rigid filters, AI stops being a lab project and becomes a business capability. I was going through some recent case studies from Publicis Sapient and one of them really stood out to me. It captures something important about where we are in this GenAI wave. We finally have AI that is not limited to technical teams. It is being used directly to reshape customer experience in ways that people can actually feel. The Homes and Villas by Marriott Bonvoy project is a great example of this shift. Publicis Sapient and Marriott built a generative search experience using Azure OpenAI that turns natural language intent into real, bookable vacation homes. Not filters, not rigid queries. Actual human intent. A few technical details from the case study that I loved: ✦ Intent parsing over keyword search Travelers can describe feelings or preferences. The system uses LLMs to infer constraints, property attributes, and destination suggestions across 150K listings. ✦ GPT based retrieval pipeline LLMs enrich the query, expand candidates, and rerank results based on nuanced signals which reduces dead ends and increases high confidence matches. ✦ Real time context generation Weather, activities, and travel ideas are synthesized for each result which turns simple search into discovery. ✦ Enterprise scale rollout acceleration Once the pattern was built, Marriott cut expansion time from a year to three months which shows how GenAI lowers the cost of experimentation inside large organizations. If you want to dive deeper into the Marriott project and the system behind it, the full Publicis Sapient customer story is a great read: https://lnkd.in/evGBTTTN

It is natural to think that if you live in Bengaluru, a shopping app would realise it’s 28°C outside and recommend products for your day, like a coffee meeting in Indiranagar. Right? 🤔 But it doesn’t! 😶 Current personalisation is mostly theatre. “People who bought this also bought this!” 🤦 It’s reactive, not proactive. It never answers the one question that actually matters: “Will this actually look good on ME?” I was recently looking at the architecture behind Glance’s Agentic Commerce, and as an engineer, the shift from intent-based to agentic is where the real story is. 🔥 But Glance isn’t just scaling a recommendation engine; they are deploying a multi-agent architecture. Instead of one model guessing your vibe, multiple specialised agents work in parallel, coordinated by an orchestrator agent. An agent analyses your skin tone, undertones, and body type from a selfie. No more buying emerald green only to realise it washes you out. It also has agents that understand real-time Bengaluru microclimates and fabric science. The agent knows if you lean minimalist, and it checks global signals to keep you current. After parallel processing, the orchestrator agent synthesises everything into a unified strategy. It’s not just a list of items; it’s 20+ unique collections built specifically for your life. Instead of stock photos of models who look nothing like us, Glance uses its generative AI model to create 100+ magazine-quality images of YOU wearing the clothes. It’s an agentic styling chat. You talk to it the way you’d talk to a friend who happens to have impeccable taste. The AI doesn’t just understand the words. It understands YOU — your body, your skin tone, your style preferences from prior interactions — and generates a complete, shoppable look in seconds. Here’s what’s happening under the hood: → The Glance agent interprets your natural language request → It factors in your context: weather, trends, occasion, budget → The generative AI creates a magazine-quality image of YOU wearing the look → Every piece in the image is shoppable, right there in the chat Glance is calling it agentic commerce. And I think it changes everything about how people discover and buy fashion. Engineering lesson: In the corporate world, “time to impact” is the only metric that matters. The search bar makes the user do the work — searching, filtering, and hoping. Agentic commerce flips the script: the AI understands you and works for you. 🏋️ Mitron… the search bar is dead. The only question is: are we ready for the conversation? 😬 #Glance #AICommerce #AgenticAI #FutureOfShopping #GenAI

Generative AI continues to generate excitement, but significant challenges are often overlooked. Reports from respected sources such as Harvard Business Review and Goldman Sachs highlight that current expectations may not align with reality. The technology, while promising, has limitations that need to be acknowledged and addressed. In May, Harvard Business Review discussed "AI's Trust Problem," in June, Goldman Sachs raised doubts about whether the expected $1 trillion in AI investment will deliver substantial returns. Their concern: aside from developer efficiency, there may not be enough value to justify such massive spending, especially in the near term. Jim Covello, Goldman Sachs' head of global equity research, pointed out that replacing low-wage jobs with costly technology contradicts earlier tech transitions, which focused on improving efficiency and affordability. A recent analysis from Planet Money echoes this skepticism, listing “10 reasons why AI may be overrated.” Issues like hallucinations (when AI generates false or misleading information) and declining quality in AI-generated outputs raise concerns about its readiness for widespread use. A study by The Washington Post also examined what people ask AI chatbots about, revealing unexpected trends. Along with common academic assistance, some topics raised ethical and personal concerns. 🔍 Reality check: Generative AI can be impressive but often struggles with accuracy, leading to errors or hallucinations. 💸 Investment risks: Financial experts question the value of massive investments in AI and wonder if the technology will offer enough returns in the short term. 📉 Productivity vs. quality: While AI can increase productivity, particularly in coding, research shows that the quality of AI-generated code is often subpar. 📚 Help with homework: Students turn to AI chatbots for homework help, but concerns arise when AI provides direct answers rather than guidance or learning support. ❓ Personal and sensitive queries: Many chatbot users ask about personal topics, including sex and relationships, which raises ethical questions about privacy and appropriate use. These points serve as a reminder that while generative AI is a powerful tool, it’s important to approach it with realistic expectations and a clear understanding of its current limitations. #GenerativeAI #AIEthics #AIRealityCheck #AIinEducation #TechInvestments #AIProductivity #AIChallenges #AIHomework #AIandSex #AIinConservation #AIFuture #AIHype 

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

GenAI Beyond Art, Video, Text: Addressing the World's Challenges   McKinsey & Company Global Institute modelling of trends in AI adoption revealed that AI has the potential to deliver additional global economic output of about $13 trillion by 2030, which would increase GDP by approximately 1.2 percent per year. There are many examples of global AI applications and use by governments to improve social welfare, national health care systems, domestic security and surveillance, and transportation. We have seen during Covid that global interconnectivity for data and treatments is lacking. +Disaster Relief and Infrastructure Development: Generative AI can model natural disasters, generating new patterns to help governments prepare and respond effectively. Real-time text and voice generation ensure efficient communication with affected populations, aiding in disaster relief efforts. +Healthcare: AI applications revolutionize healthcare by diagnosing diseases, recommending treatments, and enhancing patient engagement. Synthetic medical image generation augments datasets, improving diagnostic accuracy. Accelerates drug discovery and molecular design, enabling the development of life-saving medications. Text-generation educates patients effectively. +Education: Generative AI enriches learning experiences by generating quizzes, exercises, and interactive simulations. Personalized learning plans and textbook recommendations. Virtual tutors and language learning companions, powered by image and voice generation, provide adaptive learning experiences. +Wildlife Conservation: With a 69% average reduction in species populations since 1970, generative AI becomes vital. Predicts ecological changes and population dynamics, aiding researchers in creating proactive strategies to protect endangered species. +Financial Inclusion and Human Rights: Generative AI can also contribute to solving challenges in these areas. It helps promote financial inclusion through personalized financial planning and innovative credit scoring models. In human rights, it aids in automated translation, document analysis, and combating online harassment. KOREAN APPROACH TO AI The Korean government released its national strategy for AI on December 17, 2019. The strategy was formed based on the AI ecosystem, AI use, and people-centered AI and consists of 100 government-wide action tasks under nine strategies (Figure 12 see report). With its New Deal strategy, Korea is expected to transform into the smarter country to use data and digital technologies, including AI, and leads the innovative public services. 💜Generative AI's evolving nature and increasing capacity to contribute to global society make it an exciting field. By harnessing its innovative potential, we can address complex challenges, climate, inclusion and create a better future for all of us. What are the use cases you a excited about making a change in your life? #AI #generativeAI #innovation #smartcities #marthaverse

🚀 12 Real Use Cases of Customers using Generative AI at Amazon Web Services (AWS) Many people ask me recurrently, is there a hype around Generative AI? My answer: Yes and no... Here's why! If you look at TV, newspapers, or casual conversations with family and friends, it definitely seems like there’s a Generative AI hype. This buzz is mostly from non-tech people who are just getting familiar with the concept, often starting their AI journey with the release of ChatGPT. But when I talk to clients, the story is different. Generative AI is truly transforming how businesses interact with their end customers or boosting employee productivity. From my perspective at Amazon Web Services (AWS), there’s no hype—just exciting, real-world applications of AI making a big impact. Here are some great examples to illustrate this: Intuit: Intuit Assist is a generative AI-powered assistant that offers personalized insights to help users make smart financial decisions (more info 👉 https://lnkd.in/dbaxwfXd) BT Group leverages GenAI (CodeWhisperer) to provide coding assistance to its software engineers (more info 👉 https://lnkd.in/dgJafDCC) Accor enhances travel planning and booking, offering personalized recommendations and intuitive, conversational advice (more info 👉 https://lnkd.in/dUYhnQeh) Perplexity: reimagining search by providing personalized answers using generative ai, instead of link lists and generic results. (more info 👉 https://lnkd.in/dAUAEv6S) BMW Group: in-Console Cloud Assistant (ICCA) solution designed to empower hundreds of BMW DevOps teams to streamline their infrastructure optimization efforts (more info 👉 https://lnkd.in/dGBYB4NJ) Booking.com: delivering destination and accommodation recommendations that are tailored and relevant to customers (more info 👉 https://lnkd.in/dZnQNX43) Pfizer accelerates research, predicts product yield, and helps it deliver more medicines to patients (more info 👉 https://lnkd.in/dhHd9t6Q) Toyota Motor Corporation uses generative AI to respond in seconds to driver emergencies (more info 👉 https://lnkd.in/djQWfJ4D) United Airlines: intelligent airport operations powered by generative AI (more info 👉 https://lnkd.in/d9WueKtk) Netsmart: HIPAA-eligible service that automatically creates clinical notes from patient-clinician conversations using generative AI (more info 👉 https://lnkd.in/d8JaeDTh) Amazon Pharmacy: Q&A chatbot assistant to empower agents to retrieve information with natural language searches in real time (more info 👉 https://lnkd.in/dM9NmnTd) Amazon Ads: AI-powered image generation to help brands produce richer creative new content (more info 👉 https://lnkd.in/dCn7xG3t) #ai #genai

Metacognition is the master capability. A study of 250 employees of a technology consulting firm showed what made the difference in how AI was used, and how to increase the value of AI in the organization. "We find that generative AI can indeed boost employee creativity, but the gains are not universal. Specifically, employees with stronger metacognition—the ability to plan, evaluate, monitor, and refine their thinking—are more likely to experience creative gains from using generative AI, because they can use it more effectively to acquire the cognitive job resources that fuel creativity." Here are the pragmatic approaches suggested by the four action points in the article: 1️⃣ Help employees use AI to expand the cognitive job resources that fuel creativity. Encourage employees to use AI to gather broader information, test multiple angles, and offload routine tasks that drain mental bandwidth. Set the expectation that AI should be used to widen thinking and create space for higher-value creative problem-solving. 2️⃣ Raise awareness that metacognition is the engine of AI-supported creativity. Teach employees to question, test, and refine AI outputs rather than accept the first answer they receive. Reinforce habits of checking assumptions, probing for alternatives, and treating AI responses as inputs to improve, not endpoints to adopt. 3️⃣ Build metacognitive skills through targeted and scalable training. Provide practical training that helps employees plan how to use AI, monitor the quality of outputs, and evaluate what to keep or change. Use real examples, short exercises, and simple checklists to build stronger day-to-day habits of reflective AI use. 4️⃣ Design workflows that promote active, iterative engagement with AI. Redesign workflows so employees use AI across multiple rounds of idea generation, comparison, critique, and refinement. Build in prompts, discussions, and review steps that require people to engage actively with AI instead of relying on default answers. ------ For more insights into the edge of value from Humans + AI join Humans + AI Explorers community for free https://lnkd.in/gmhxvikq