Transformative AI Trends to Watch

The next wave of AI transformation is here – and it’s not just about language-based models anymore. The real breakthroughs are happening now with Large Quantitative Models (LQMs) and cutting-edge quantum technologies. This seismic shift is already unlocking game-changing capabilities that will define the future: Materials & Drug Discovery – LQMs trained on physics and chemistry are accelerating breakthroughs in biopharma, energy storage, and advanced materials. Quantitative AI models are pushing the boundaries of molecular simulations, enabling scientists to model atomic-level interactions like never before. Cybersecurity & Post-Quantum Cryptography – AI is identifying vulnerabilities in cryptographic systems before threats arise. As organizations adopt quantum-safe encryption, they’re securing sensitive data against both current AI-powered attacks and future quantum threats. The time to act is now. Medical Imaging & Diagnostics – AI combined with quantum sensors is revolutionizing medical diagnostics. Magnetocardiography (MCG) devices are providing more accurate cardiovascular disease detection, with potential applications in neurology and oncology. This is a breakthrough that could save lives. LQMs and quantum technologies are no longer distant possibilities—they’re here, and they’re already reshaping industries. The real question isn’t whether these innovations will transform the competitive landscape—it’s how quickly your organization will adapt.

Agentic AI trends that are a reality already (or someone's working on it 😄): 1. AI Agents won’t just save time — they’ll make money. AI agents will shift from boosting productivity to generating revenue directly. ⏩️ Example: An agent closes outbound deals, writes term sheets, or wins new clients autonomously. 2. Agents will help phase out legacy systems. Instead of replacing old CRMs or ERPs overnight, agents will quietly absorb and replace them from the outside in. ⏩️ Example: An agent learns your workflow, automates key actions, makes the system obsolete over time, and codes them. 3. Agents can mimic human behavior. New AI agents simulate real personalities and groups — unlocking a new kind of behavioral A/B testing. ⏩️ Example: Test how 1,000 investors might react to your pitch deck before ever sending it. Take a look at the research from Stanford University. Link in the comments. 4. Agents will pay each other. Financially autonomous agents can now manage wallets, pay for APIs, or contract other agents. ⏩️ Example: One agent pays another to complete a task, like gathering market data or translating a deck. Project: Payman Ai 5. AI-native fraud is coming fast. Fake voices, documents, and faces will flood markets — especially in finance, identity, and compliance. ⏩️ Example: A deepfaked CEO voice authorizes a $1M transaction. Detection tools must keep up. 6. AI-native institutions are next. AI VCs already exist - AI banks, PE firms, and hedge funds are on the horizon. ⏩️ Example: An AI agent allocates capital, writes IC memos, and reports to LPs without human input. We are building something fascinating here. But also check out one of the Y Combinator startups I left in the comments. 7. New multimodal AI like GPT-4o changes the game. Agents can now see, hear, and speak - making them more useful in real-world tasks. ⏩️ Example: An agent reads a contract PDF, checks for risks, explains it on a call, and sends a summary. 8. AI agents will be insured. As agents make critical decisions, enterprises will insure them like human employees, but we still need to minimize hallucinations. ⏩️ Example: A credit agent makes a false investment call → insurance covers the loss. ARE WE IN THE FUTURE? #AI

AI is no longer just an experimentation tool. It’s reshaping the entire optimization landscape. With this shift comes many untapped opportunities. Working with Andrius Jonaitis ⚙️, we've put together a growing list of 40+ AI-driven experimentation tools ( https://lnkd.in/gHm2CbDi) Combing through this list, here are the emerging market trends and opportunities you should know: 1️⃣ SELF-LEARNING, AUTO-OPTIMIZING EXPERIMENTS 💡 Opportunity: AI is creating self-adjusting experiments that optimize in real-time. 🛠️ Tools: Amplitude, Evolv Technology, and Dynamic Yield by Mastercard are pioneering always-on experimentation, where AI adjusts experiences dynamically based on live behavior. 🔮 How to leverage it: Focus on learning and developing tools that shift from static A/B testing to AI-powered, dynamically updating experiments. 2️⃣ AI-GENERATED VARIANTS 💡 Opportunity: AI can help you develop hypotheses and testing strategies. 🛠️ Tools: Ditto and ChatGPT (through custom GPTs) can help you generate robust testing strategies. 🔮 How to leverage it: Use custom GPTs to generate test ideas at scale. Automate hypothesis development, ideation, and test planning. 3️⃣ SMARTER EXPERIMENTATION WITH LESS TRAFFIC 💡 Opportunity: AI-driven traffic-efficient testing that gets results without massive sample sizes. 🛠️ Tools: Intelligems, CustomFit AI, and CRO Benchmark are pioneering AI-driven uplift modeling, finding winners faster -- with less traffic waste. 🔮 How to leverage it: Don't get stuck in a mentality that testing is only for enterprise organizations with tons of traffic. Try tools that let you test more and faster through real-time adaptive insights. 4️⃣ AI-POWERED PERSONALIZATION 💡 Opportunity: AI is creating a whole new set of experiences where every visitor will see the best-performing variant for them. 🛠️ Tools: Lift AI, Bind AI, and Coveo are some of the leaders using real-time behavioral signals to personalize experiences dynamically. 🔮 How to leverage it: Experiment with tools that match users with high-converting content. These tools are likely to develop and get even more powerful moving forward. 5️⃣ AI EXPERIMENTATION AGENTS 💡 Opportunity: AI-driven autonomous agents that can run, monitor, and optimize experiments without human intervention. 🛠️ Tools: Conversion AgentAI and BotDojo are early signals of AI taking over manual experimentation execution. Julius AI and Jurnii LTD AI are moving toward full AI-driven decision-making. 🔮 How to leverage it: Be open-minded about your role in the experimentation process. It's changing! Start experimenting with tools that enable AI-powered execution. 💸 In the future, the biggest winners won’t be the experimenters running the most tests, they’ll be the ones versed enough to let AI do the testing for them. How do you see AI changing your role as en experimenter? Share below: ⬇️

🌟 What’s Next for AI Design: Themes for 2025 🌟 As we enter 2025, the landscape of AI design is evolving rapidly, with emerging trends reshaping how we build and interact with technology. Here are some key trends I’m particularly excited about: 🔹 1. Interfaces That Adapt to User Needs We’re moving from static UIs to interfaces that dynamically adapt to context, personalization, and real-time inputs. This means simpler, cleaner, and more intuitive UX that delivers exactly what users need when they need it. 🛠️ Examples: Jordan Singer's work at Mainframe and Beam by @Toby Bream (https://beem.computer/) showcase the future of adaptive design. 🔹 2. Reimagining Data Organization Traditional data structuring feels ancient today. AI is helping us rethink how unstructured data is reorganized and delivered intuitively, in formats tailored to our needs. 💡 Check out @MatthewWsiu's explorations on this (https://lnkd.in/gFADJkXS) 🔹 3. Fluid Media AI is democratizing media creation - transforming text into videos, sketches into 3D models, and more. These capabilities open up a world of immersive, creative possibilities. 🎨 There are many advanced models out there, but here is a classic example I worked on a while back that transforms sketches into animated characters (https://lnkd.in/gPYA7xfP) 🔹 4. Multimodal Interactions Gone are the days of singular inputs. Multimodal AI systems combine voice, visuals, text, and beyond to create richer, more engaging user experiences. Claude artefacts are a good example! 🔹 5. Human-AI Connections AI isn’t just a tool - it’s becoming a partner for advice, journaling, task management, and more. Designing safe, meaningful interactions is key to ensuring this shift feels natural and intuitive. 🤖 e.g. I’ve been using apps like Rosebud (https://www.rosebud.app/) that probably know me better than some of my friends! 🔹 6. Immersive Experiences Adaptive interfaces, fluid media, and multimodal capabilities make immersive experiences more accessible than ever. 🌐 Rooms by Things, Inc. has recently launched some fun examples of this (https://lnkd.in/grcnyRcy) 🔹 7. Empowering Anyone to Build Anything The lines between designer, PM, and engineer are blurring. Tools like Cursor are empowering everyone to create AI apps, breaking down traditional silos. 🚀 Dreamcut.ai by Meng To is a great example of the creative potential unlocked by AI. 🔹 8. AI-First Interaction Patterns As AI capabilities grow, we must develop new design patterns to handle these challenges. For those interested in diving deeper, check out my course (https://lnkd.in/gcVgP3My). The next cohort starts in February, and we’ll explore these trends and more! As a reminder, these are just some themes I'm personally excited about and I'm sure I've missed many. Are there other themes you're excited about? Please share them in the comments!

The 2025 AI Index Report is out, and it provides a comprehensive look at the state of artificial intelligence across various sectors. This report, published by Stanford Institute for Human-Centered Artificial Intelligence (HAI), is essential reading for anyone looking to understand the evolving landscape of AI.    Key trends from this year’s report include: ✔ The rise of smaller, more efficient models, which are becoming more capable while dramatically reducing costs.  ✔ A rapid increase in AI-related incidents, underscoring the growing importance of responsible AI practices.  ✔ A shift in AI regulation, with U.S. states taking the lead as federal policies move at a slower pace.  ✔ AI's growing presence in businesses, with 78% of organizations using AI, up from 55% in 2023.  ✔ Global AI investment is soaring, particularly in generative AI.    This report not only highlights impressive technological progress but also emphasizes the need for thoughtful governance as AI continues to permeate industries and daily life.    The future of AI is bright, with vast opportunities for innovation, growth, and meaningful impact across sectors: https://lnkd.in/geYjvs8z

Stanford University researchers released a new AI report, partnering with the likes of Accenture, McKinsey & Company, OpenAI, and others, highlighting technical breakthroughs, trends, and market opportunities with large language models (LLMs).  Since the report is 500+ pages!!! (link in comments), sharing a handful of the insights below: 1. Rise of Multimodal AI: We're moving beyond text-only models. AI systems are becoming increasingly adept at handling diverse data types, including images, audio, and video, alongside text. This opens up possibilities for apps in areas like robotics, healthcare, and creative industries. Imagine AI systems that can understand and generate realistic 3D environments or diagnose diseases from medical scans. 2. AI for Scientific Discovery: AI is transforming scientific research. Models like GNoME are accelerating materials discovery, while others are tackling complex challenges in drug development. Expect AI to play a growing role in scientific breakthroughs, leading to new materials and more effective medicines. 3. AI and Robotics Synergy: The combination of AI and robotics is giving rise to a new generation of intelligent robots. Models like PaLM-E are enabling robots to understand and respond to complex commands, learn from their environment, and perform tasks with greater dexterity. Expect to see AI-powered robots playing a larger role in manufacturing, logistics, healthcare, and our homes. 4. AI for Personalized Experiences: AI is enabling hyper-personalization in areas like education, healthcare, and entertainment. Imagine educational platforms that adapt to your learning style, healthcare systems that provide personalized treatment plans, and entertainment experiences that cater to your unique preferences. 5. Democratization of AI: Open-source models (e.g., Llama 3 just released) and platforms like Hugging Face are empowering a wider range of developers and researchers to build and experiment with AI. This democratization of AI will foster greater innovation and lead to a more diverse range of applications.

A.I. is not just a tool, but a driving force in reshaping the landscape of science. In today's episode, I dive into the profound implications A.I. holds for scientific discovery, citing applications across nuclear fusion, medicine, self-driving labs and more. Here are some of the ways A.I. is transforming science that are covered in today's episode: • Antibiotics: MIT researchers uncovered two new antibiotics in a single year (antibiotic discovery is very rare so this is crazy!) by using an ML model trained on the efficacy of known antibiotics to sift through millions of potential antibiotic compounds. • Batteries: Similar sifting was carried out by A.I. at the University of Liverpool to narrow down the search for battery materials from 200,000 candidates to just five highly promising ones. • Weather: Huawei's Pangu-Weather and NVIDIA's FourCastNet use ML to offer faster and more accurate forecasts than traditional super-compute-intensive weather simulations — crucial for predicting and managing natural disasters. • Nuclear Fusion: AI is simplifying the once-daunting task of controlling plasma in tokamak reactors, thereby contributing to advancements in clean energy production. • Self-Driving Labs: Automate research by planning, executing, and analyzing experiments autonomously, thereby speeding up scientific experimentation and unveiling new possibilities for discovery. • Generative A.I.: Large Language Models (LLMs) tools are pioneering new frontiers in scientific research. From improving image resolution to designing novel molecules, these tools are yielding tangible results, with several A.I.-designed drugs currently in clinical trials. Tools like Elicit are streamlining the process of scientific literature review over vast corpora, allowing connections within or between fields to be uncovered automatically and suggesting new research directions. The SuperDataScience Podcast is available on all major podcasting platforms and a video version is on YouTube. This is Episode #750! #superdatascience #artificialintelligence #science #innovation #machinelearning

2025 is already on track to be a groundbreaking year for GenAI in business, academia and the public sector. That’s why I am super excited for Deloitte’s latest State of Generative AI in the Enterprise research that provides compelling evidence of the transformative impact GenAI will have on organizations in the year ahead. Three key takeaways jump out for me:   1. Leaders continue to have a positive outlook for GenAI’s immediate and long term impact. More than two-thirds surveyed say that 30% of their initiatives will be fully scaled in the next six months. Even with that progress, 78% of respondents plan to increase their overall AI spending in the next fiscal year signaling a belief we are only just getting started. 2. Almost all organizations report are beginning to experience measurable ROI. Nearly three-quarters of respondents report their most advanced GenAI initiative is meeting or exceeding expectations. GenAI adoption in IT is furthest along, followed by cybersecurity. 3. Agentic AI promises to be the unlock for next wave of enterprise value. The majority of leaders surveyed are exploring adoption of autonomous agents to a large (26%) or some (42%) extent. It will be exciting to watch which innovative applications of Agents will have the highest impact in 2025. The full report offers more insights into where GenAI is delivering value and driving impact. Learn more about the current state of GenAI here: https://deloi.tt/3C7clPb 

It's only midyear, but major changes are already underway in global enterprises. As AI #innovation and #geopolitical disruption are reshaping entire economies, here are three macro trends I'm watching closely:    - #AI drives ROI in business #operations  After years of experimentation, AI is now driving measurable value, with organizations achieving an average ROI of 1.7x. But as Gen AI and Agentic AI projects move from pilot to production-scale, AI strategies must be built with the right goals, processes and change culture in place to deliver lasting value: https://lnkd.in/emiHh-zw    - AI agents are becoming #collaborators They are evolving from tools to teammates, capable of independent action and human collaboration - yet trust in AI has declined this year. As barriers to adoption remain, leaders must embed trust, ethical oversight and risk mitigation into AI projects from the start to ensure meaningful human-AI chemistry: https://bit.ly/4lYBexP - #Reindustrialization reshapes supply chains  A new wave of reindustrialization in Europe and the US promises to pivot towards local resilience and autonomy. Our report provides a roadmap for “rightshoring”, integrating sustainability and resilience, and developing a future-ready talent strategy ahead of the next phase of industrial competitiveness: https://lnkd.in/eMUNFCgV    With the summer holiday period across much of Europe, it's a timely moment to reflect on the structural shifts already shaping the second half of 2025 and influencing the next phase of enterprise transformation.

🌍 AI in 2025: What’s Next for the World? As we step into 2025, the transformative power of AI is reshaping not just industries but everyday life across the globe. From our vantage point in San Francisco—the beating heart of AI innovation—we can feel the pulse of these changes. Maulde Cuérel, AI Program Manager at Swissnex in San Francisco, is reporting on the key trends shaping the future: 💡 Sustainable AI: A single ChatGPT query uses 10x more energy than a Google search, and generating an image consumes as much power as charging your phone. In 2025, the world is focusing on energy-efficient AI, stricter regulations, and sustainable data centers to align innovation with climate goals. 🧠 Reasoning Models: OpenAI’s latest model simulates human-like thinking, breaking complex problems into smaller parts. NVIDIA’s CEO Jensen Huang calls it a breakthrough in scaling AI capabilities. 🤖 AI Agents & Multi-Agent Systems: By 2028, 33% of enterprise software will feature agentic AI, automating 15% of decisions (Gartner). These intelligent tools are driving productivity, innovation, and the need for robust governance frameworks. 🔗 Multimodal & Physical AI: AI is evolving beyond thinking—it’s seeing, hearing, and physically interacting with the world. From Meta’s Ray-Ban glasses to advances in robotics, 2025 will redefine human-machine collaboration. 💬 AI Beyond Work: Generative AI is becoming deeply personal. Whether it’s life coaching, therapy, or even companionship, AI is increasingly integrated into our smartphones and daily lives. 📉 Small Language Models (SLMs): Compact, efficient, and versatile, SLMs are emerging as the go-to for resource-conscious AI applications, broadening the scope of what’s possible. We’d love to hear your thoughts: What do you think will be the most impactful AI trend this year? Share your ideas in the comments! And stay tuned as we continue to report on the trends from Silicon Valley’s frontlines. #AI2025 #GlobalInnovation #GenerativeAI #Swissnex #SanFrancisco