David Walker's Linkedin Analytics

📚 Writing a Book on AI Didn’t Just Change My Thinking: It Changed Me As many of you know, I’m passionate about AI, the commercial world, my work at Quantori, and more recently, writing a book on B2B AI sales. But what surprised me most about writing a book wasn’t what I learned about commercializing AI. It was what I learned about myself. For nearly three years, I toyed with the idea. I wrote chapter outlines and a few opening paragraphs and repeatedly reworked the structure. But I never made real progress. The excuse? A demanding job. Two young kids. Yardwork. Life, etc., etc. There was always something more urgent than sitting down to write. Everything changed when I stopped saying, “I’m too busy to do this,” and started asking, 👉 “How can I make this nonnegotiable?” I picked a deadline. I committed. And I became a different person. I woke up at 4:00 AM, sometimes earlier, to write for 2–3 hours before the day began, seven days a week. There was no day off; I had a deadline to hit. Eventually, no alarm was needed. It became a habit, a mission. And now, that book is finished. If you have something on your “someday list,” I hope this post inspires you. Don’t think about doing it. Decide to do it. The discipline will shape you more than the goal ever could. #AI #BookWriting #Discipline #Mindset #Motivation #Leadership #PersonalGrowth

Breakthrough Research Needs Breakthrough Infrastructure Genomics. Cryo-EM. AI models. Today’s scientific workloads are exploding and traditional HPC infrastructure is struggling to keep up. At Quantori, we’re partnering with AWS to deliver elastic cloud-bursting HPC solutions that scale dynamically, simplify the user experience, and support the modern research stack, including AI/ML workloads and GPU-intensive applications. 🔍 The problem: Long job queues, underutilized clusters, clunky interfaces, and a lack of AI-readiness are slowing innovation. 💡 Our solution: Seamless, secure HPC cloud-bursting environments, tailored for research institutions in pharma, biotech, and academia. ✅ Intuitive tools built for scientists ✅ GPU-optimized AI/ML pipelines ✅ Full support for AWS ParallelCluster, Batch, FSx, and more ✅ Customized governance and compliance controls ✅ End-to-end lifecycle support We’ve delivered 1,500+ projects with 700+ experts across cloud, science, and AI. If your team is hitting the HPC wall, it’s time to modernize. Let’s talk about how Quantori + AWS can accelerate your discovery: #HPC #CloudComputing #AIinScience #Genomics #CryoEM #AWS #LifeSciences #ScientificComputing #Quantori

From SMILES to Graphs: Ushering in a New Era of AI-Driven Molecular Design For decades, cheminformatics has leaned on SMILES , an innovative, compact way to represent molecules as text. But as machine learning pushes boundaries in drug discovery, SMILES is starting to show its limits. At Quantori, in collaboration with Nebius, we’ve developed a molecular generation pipeline powered by graph neural networks (GNNs) and equivariant diffusion models (EDMs). Why it matters: 🔹 SMILES works well for basic tasks and NLP-inspired modeling. 🔹 But graph-based approaches more accurately capture 3D geometry, stereochemistry, and molecular connectivity. 🔹 Using EDMs, we generate chemically valid 3D structures, starting from noise and shaping molecules to match target geometries, opening the door for scalable structure-based drug design. Trained on 1.6 million ChEMBL compounds using 8x H200 GPUs, our model delivers: ✅ Molecules in \~2 seconds each ✅ Over **99.8% uniqueness ✅ Strong 3D shape similarity scores This isn’t just a research milestone; it’s a powerful new framework for the future of AI-powered molecular discovery. 🔗 Read the full blog post by our Principal Cheminformatics Engineer, Dr. Denis Sapegin: https://lnkd.in/eXY6Tert #Cheminformatics #DrugDiscovery #GraphNeuralNetworks #DiffusionModels #GenerativeAI #AIinScience #Quantori #MolecularDesign #ML4DrugDiscovery

Promoting AI? Start with Internal Readiness Helping companies adopt AI isn’t always about the most powerful multi-agent approach; it’s about understanding where a company is on its AI journey. Some are eager to innovate, but others are held back by outdated systems, siloed data, or a lack of clear strategy. In fact, 59% of organizations cite legacy tech as a significant barrier to AI adoption. However, technology alone isn’t the only hurdle; organizational culture plays a critical role in determining whether AI initiatives succeed or stall. Companies that foster experimentation and cross-functional collaboration are far more likely to realize AI’s potential than those operating in rigid, siloed environments. If you really want to be successful, you need to tailor your approach: • For companies with limited AI experience, your approach should focus on education, helping them define realistic use cases, run pilot programs, and prove ROI. • For companies with fragmented infrastructure, discussions should revolve around data strategy, IT modernization, and cloud integration to lay the foundation for AI adoption. • For AI-mature organizations, the conversation shifts to scaling AI solutions, automation, and competitive differentiation. Even the best AI solution will fall flat if the organization isn’t ready. 💡 The best AI conversations start with one key question: “Where are you today on your AI journey and what’s your next step forward?”

The AI Disruption Frontier: When AI Becomes the Business AI is evolving, and so is the conversation. For years, we’ve discussed AI as a tool for efficiency, automation, and augmentation. But now, we’re entering new territory. AI isn’t just improving business functions; it’s starting to replace them. From pharma to finance, AI vendors are no longer just selling software; they’re delivering AI-powered services that replace traditional workflows, departments, and even entire business units. This shift raises critical questions: -Will companies operate AI functions in-house or outsource them to AI-native vendors? -How do we rethink job roles when AI moves from a support tool to a core operator? -What happens when AI vendors don’t just provide the tech but also become the business function? 👉 For AI professionals, the conversation is no longer just about optimization; it’s about transformation. The winners in this next wave will help clients redefine their business models, not just improve their margins. Are you ready for that level of disruption? #AITransformation #FutureOfWork #AIAdoption #MasteringAISales #EnterpriseAI#QuantoriAI

💡 Building Enterprise AI Buy-In Isn’t Just About Technology: It’s About Alignment Why? Because you’re navigating up to eight stakeholder groups, each with their agenda and varying levels of AI understanding: • Business wants measurable GenAI impact • IT wants secure, scalable integration • Data Science wants accuracy and transparency • Ops wants fast, actionable results • Legal wants compliance and governance • Finance wants precise ROI and cost control • Procurement wants vendor fit • Executives want innovation, and they want it yesterday The tech? That’s the easy part. The real challenge is alignment. So, how do you drive alignment and move the organization forward? Here’s what’s worked for me: ✔️ Facilitated cross-functional workshops to unify priorities ✔️ Built a joint POC team across business and technical groups ✔️ Developed tailored education for each stakeholder audience ✔️ Led with transparency to build trust and open dialogue The result? Silos broke down. Alignment took root. The organization didn’t just adopt AI; they rallied behind it. In B2B AI today, success means connecting, aligning, and guiding. 🔍 Want to dive deeper into this approach? Chapter 4 of Mastering AI Sales breaks it all down: 📖 Available here: Mastering AI Sales – Amazon Link #AI #EnterpriseSales #GenAI #AIConsulting #TrustedAdvisor #AIAdoption #SalesLeadership

⚡ AI Enterprise Engagement Is Changing: 3 Trends I’m Seeing Right Now The market is evolving fast, and so are the conversations. Here’s what I’m seeing on the front lines of enterprise AI right now: 1. Stakeholders are more educated but still overwhelmed. They’ve read the articles. They’ve seen the demos. However, they need help applying GenAI to their business. They don’t need more sales/marketing pitches; they need translation and guidance to solve real business problems. 2. Technical pilots are stalling post-POC There’s plenty of effort going into proving the tech. But not enough focus on: -Validation -How it scales -Who owns it The result? Great POCs, with no plan for production. 3. C-level execs are bought in, but underprepared for what success in AI takes. Many execs believe in AI, especially after hearing peers talk about “30% cost savings.” But the gap between vision and execution is still massive. Success requires alignment, governance, and infrastructure, not just ambition. 💡 If you support AI adoption, focus on business value, stakeholder alignment, time-to-impact, and scalability. That’s how AI gets adopted, not just approved.

Predicting Protein Structures Shouldn’t Require a PhD in DevOps AlphaFold revolutionized protein structure prediction, but let’s be honest: for most researchers, getting it to work still felt like a challenge. At Quantori, we set out to fix that. Our team developed a streamlined, web-based interface that allows researchers to: ✅ Input a protein sequence ✅ Select desired model settings ✅ Run inferences, no coding required Behind the scenes? We re-engineered the workflow to optimize speed and cost: • Split CPU- and GPU-intensive stages • Leveraged AWS Batch + EC2 instances • Enabled users to choose the best configuration for their budget and timeline We're also exploring a “traffic light” system so researchers can visually assess the tradeoffs between time, cost, and compute intensity, making AI in drug discovery both accessible and predictable. If you're in structural biology, bioinformatics, or drug discovery, and tired of clunky setups, this is a real leap forward. Read more here: https://lnkd.in/eQciEThK #AI #AlphaFold #Bioinformatics #DrugDiscovery #GenAI #ScientificInformatics #Quantori

Breaking News: AI is NOT just about LLMs With all the publicity, it’s easy to think AI = ChatGPT. But LLMs are just one tool in a much larger AI/ML toolbox. At Quantori, we are applying a wide range of AI approaches across R&D. One area we are particularly focused on right now is Histopathology Image Analysis, where we utilize AI to identify cancerous cells earlier and more accurately. If this is an area you're interested in, check out this excellent blog post by my colleague Dr. Danilov, on how AI is transforming histopathology and accelerating precision medicine: 👉 Harnessing AI for Histopathology: https://lnkd.in/eemz43b8 And of course , if you’re working in this space or exploring AI in R&D, we’d love to connect and discuss further.

Staying Informed and Relevant in AI can feel like a Full-Time Job. But It Doesn’t Have to Be The GenAI landscape is evolving quickly. Multi-agent systems. Quantum acceleration. GraphRAG. It’s easy to feel like you’re falling behind. If you're in sales, product, or leadership, here’s how to stay ahead, without drowning in noise: 👇 1. Follow the Right Signals Ignore the hype. Follow people who shape the future. Try: Benedict Evans, Andrew Ng, Demis Hassabis 2. Learn, Unlearn, Relearn This field reinvents itself every quarter. Take a short course on LLM agents or multimodal models. Great places to start: • Stanford Online • MIT Professional Education 3. Get in the Room AI is a contact sport. Join communities, attend local meetups, and show up to webinars. You’ll learn more in 10 minutes of real conversation than 10 hours of reading. 4. Build a System Use tools like Feedly, or Google Alerts. Set a 30-minute weekly block to scan, save, and reflect. Make learning a scheduled habit. 5. Partner with Practitioners Don’t just read about the future, build with it. Collaborate with researchers, vendors, and clients. Invite guest speakers. Your insights will compound. You don’t need to master everything. But you do need a system to stay sharp. #AI #GenAI #LLMs # #AIML #ContinuousLearning