Impact of Generative AI

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

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 Consequently, the outdated per-seat model fails to meet the evolving needs of businesses focused on maximizing efficiency.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.

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

Sam Altman, the co-founder and CEO of OpenAI, made a provocative statement at a JP Morgan conference earlier this year. He believes a solo founder will soon reach a billion-dollar valuation without hiring a single employee. This one-person company would instead be powered by AI and “employ” dozens of AI agents to do the work. Not only do I believe this is entirely possible, but I think when it does happen, the company will be one of the fastest-growing unicorns ever. As I invest in AI-powered startups and teach my students how to use AI in their businesses, I have identified 5 general AI use cases that align with critical phases of the startup journey: 1. Research-Driven Ideation: The genesis of any successful startup is a deep understanding of market needs, pain points, and the competitive landscape. My colleague Scott Brady of Stanford calls this process Research-Driven Ideation (RDI). There are now AI-based tools for competitive analysts, automating competitive monitoring for senior managers—effectively Google Alerts on steroids, tracking personnel changes, marketing launches, traffic, and other publicly available data. 2. Customer Persona Development and Market Research: Understanding your target customer is crucial. Gen AI helps founders create multiple hyper-specific customer personas by analyzing customer data and building hyper-realistic, "living" customer personas to test key hypotheses quickly. 3. Experimentation and Validation: Gen AI facilitates rapid experimentation to validate key hypotheses such as CVP, GTM, and PF by enabling deeper business data insights and rapid prototyping. I have a founder friend who lost his technical cofounder and has been using ChatGPT to build his MVP. By learning to be more effective at writing prompts to generate the desired code output, he has been able to continue building as a solo founder. He told me, “The result is that my burn rate is incredibly low, and velocity has shot through the roof.” 4. Marketing and Customer Engagement: Founders will see major productivity boosts in marketing, community building, and sales prospecting. Flybridge has a portfolio company that builds super smart AI agents that can be used for just about anything. One of their customers trained their agent to automatically generate customized sales collateral and follow-up materials based on customer needs that a sales representative inputs into the system after a prospect call—and then the AI agent sends that tailored material to the customer. 5. Continuous Learning and Iteration: The path to PMF is iterative. Gen AI supports continuous learning by analyzing customer feedback and product usage data to improve their product, GTM, and onboarding processes quickly. How are you using AI to build your startup?

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 

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

From Meh to Mind-Blowing: A Kano Model Hack for Generative AI Let's talk about something I've been thinking about lately: how generative AI (Gen AI) can transform businesses and why the Kano Model is the perfect lens to prioritize its adoption. Gen AI isn't new, but its explosion into the mainstream (think ChatGPT, Gemini) has turned it into a game-changer. The real question isn't if to use it, but how to use it strategically. Here's how the Kano Model can guide your approach: 1️⃣ Start with the Basics: "Must-Have" AI Today, simply using Gen AI tools is becoming a baseline expectation. Customers already assume you're leveraging these tools for faster responses, content creation, or data analysis. If you're not here yet, you're already playing catch-up. 2️⃣ Level Up: "Performance-Driven" AI This is where you stand out. By tailoring Gen AI to your business feeding it your data, refining outputs for your audience, or integrating it into workflows you turn a generic tool into a competitive edge. Think smarter chatbots, hyper-relevant marketing, or real-time analytics. 3️⃣ The Magic Moment: "Delightful" AI Here's where you surprise people. Imagine AI that anticipates needs before customers ask, adapts in real-time based on behavior, or creates entirely new experiences. Think self-improving systems or creative solutions that redefine what's possible. This isn't just "innovation" it's future-proofing. Why This Matters Gen AI isn't a trend it's a tidal wave. Companies that treat it as a checkbox or wait for others to innovate ("We use ChatGPT!") will stagnate. Those who reimagine processes, products, and customer journeys around AI will lead their industries. The risk? Waiting too long. Early adopters aren't just gaining efficiency they're shaping expectations. Falling behind could mean playing an endless game of catch-up. My Challenge to You Start small, but think big. Master the basics, then aim for differentiation. And always ask: "How could AI not just meet but redefine what's possible here?" I've seen firsthand how this framework drives real impact. What do you think? Could the Kano Model shape your AI strategy? Let's chat in the comments! 👇 (P.S. If you're stuck at "Where do I even start?", let me know happy to share practical steps)

We're moving from Generative AI to Agentic AI. That shift changes everything about how your job is structured. Generative AI was a tool. You prompted it, it responded. Zero agency. It waited for you. Agentic AI is different. These systems can perceive, reason, make decisions, and execute actions without constant human oversight. We're talking booking logistics, deploying code, processing refunds — end to end. What does that mean for your role? Every job is a bundle of tasks. Some require your judgment, creativity, and context. Others are repetitive and routine. Agentic AI separates the two. The routine gets delegated to agents. The judgment stays with you. This is what the research calls "unbundling." And the people who stay relevant aren't the ones protecting their current job title. They're the ones sharpening the skills that land on the judgment side of that split. → Critical thinking → Complex problem framing → The ability to synthesize and make sense of massive outputs → Knowing when NOT to use AI That last one matters more than people realize. The best AI strategy isn't about automating everything. It's about knowing where human judgment is irreplaceable. Your currency of value is shifting. The question isn't whether AI will change your job. It's whether you'll be ready when it does. What skill are you doubling down on right now? 👇 Follow for real talk on staying relevant in the age of AI — from someone who's been deploying it since 2011. #agenticai #futureofwork #stayingrelevant #workforcetransformation #criticalthinking

From MIT SMR - how 14 companies across a wide range of industries are generating value from generative AI today: McKinsey built Lilli, a platform that helps consultants quickly find and synthesize information from past projects worldwide. The system integrates with over 40 internal sources and even reads PowerPoint slides, leading to 30% time savings and 75% employee adoption within a year. Amazon deploys AI across multiple divisions. Their pharmacy division uses an internal chatbot to help customer service representatives find answers faster. The finance team employs AI for everything from fraud detection to tax work. In their e-commerce business, they personalize product recommendations based on customer preferences and are developing new GenAI tools for vendors. Morgan Stanley empowers their financial advisers with a knowledge assistant trained on over a million internal documents. The system can summarize client video meetings and draft personalized follow-up emails, allowing advisers to focus more on client needs. Sysco, the food distribution giant, uses GenAI to generate menu recommendations for online customers and create personalized scripts for sales calls based on customer data. CarMax revolutionized their car research pages with GenAI, automatically generating content and summarizing thousands of customer reviews. They've since expanded to use AI in marketing design, customer chatbots, and internal tools. Dentsu transformed their creative agency work with GenAI, using it throughout the creative process from proposals to project planning. They can now generate mock-ups and product photos in real-time during client meetings, significantly improving efficiency. John Hancock deployed chatbot assistants to handle routine customer queries, reducing wait times and freeing human agents for complex issues. Major retailers like Starbucks, Domino's, and CVS are implementing GenAI voice interactions for customer service, moving beyond traditional phone menus. Tapestry, parent company of Coach and Kate Spade, uses real-time language modifications to personalize online shopping, mimicking in-store associate interactions. This led to a 3% increase in e-commerce revenue. Software companies are integrating GenAI directly into their products. Lucidchart allows users to create flowcharts through natural language commands. Canva integrated ChatGPT to simplify creation of visual content. Adobe embedded GenAI across their suite for image editing, PDF interaction, and marketing campaign optimization. For more information on these examples and to gain insight into how companies are transforming with GenAI, read the full article here: https://lnkd.in/eWSzaKw4 images: 4 of the 20 I created with Midjourney for this post. #AI #transformation #innovation

New survey: Internal GenAI tools are booming; client-facing use cases are lagging. Here’s why in 2 words: Hallucinations and PR. Leaders are (rightly) spooked by: • Safety issues with GenAI going off the rails • PR disasters from mishaps (Air Canada 👀) But there's actually a deeper problem here (in my opinion): Risk Management and Governance. Without the right structures, you can't afford to launch client-facing GenAI tools. The risk is too high. You need to be investing in things like: • Dedicated AI governance committee • Company-wide AI ethics & code of conduct • Complete, operationalised AI risk management frameworks • Robust data governance policies for quality, provenance, privacy, and security • Controls, audits and risk assessments of AI systems, including third-party tools Speaking with leaders over the last 12 months, most organisations are far behind here. We need to get moving - fast. Because right now, AI innovation isn't limited by capability or compute power. It's limited by poor risk management and governance. Until we bed that down, GenAI will just be a shiny tool for cost-cutting and efficiency - not a tool for transforming how products and services are delivered. -- PS. What do you think? Do you agree that risk management and governacne are issues here? Or is something else going on? Would love to hear your thoughts below.