Jasmeet Bhatia

re:Invent Day 1 is done and it was packed! My day began with a roundtable with some of our best partners talking about how we can better help customers migrate and modernize their AI workloads and use cases. Then Priya Arora and I presented on-stage on how Agentic AI is opening opportunities for partners, from ISVs and SIs, to startups. We shared data from our survey we did with BCG showing the trends in customer adoption of AI, focusing on the industries and use cases with the highest momentum. We also uncovered areas where customers need the most help and support as they roll out their agentic use cases. After that I sat down with 3 key partners, grabbed lunch with a few AWS peers I don't get to see often enough, and had a lot of ad hoc hallway meetings. (It's hard to walk 10 feet at re:Invent without seeing someone you know) But my highlight was launching the new AI Competency and Agentic AI categories for partners. I had a chance to join the AWS OnAir team to talk about it. My team was the driving force behind the launch and I'm proud of what this means for our partner community. We spent months listening to partners tell us they needed a way to stand out in customer conversations around agentic AI. The new AI Competency creates three distinct specialization paths. Agentic AI Applications recognizes partners delivering production-ready autonomous systems. Agentic AI Tools validates partners providing the infrastructure and tooling that makes agent development possible. Agentic AI Consulting Services distinguishes partners with proven expertise helping enterprises design, build, and scale agentic deployments. Each path requires demonstrated technical depth and validated customer outcomes, not just certifications or marketing claims. We launched with 60 partners who achieved the AI specialization. That's the highest number of launch partners in any AWS Competency program so far. Partners like Loka, Anthropic, LangChain, and Mission are already proving their ability to deploy AI systems that handle real business processes autonomously. That validation matters when enterprises are making critical technology decisions. Partners achieving these specializations gain access to funding, co-marketing resources, and priority placement in customer engagements. But what matters most is the market differentiation. When customers are evaluating dozens of partners claiming agentic AI expertise, this competency provides a clear signal about who has actually done the work. For enterprises evaluating partners, this competency provides the differentiation signal you need. These partners have solved the hard problems of moving agents from demo to production, from single use case to enterprise platform. The timing matters. Enterprises are moving past whether to deploy agentic AI and into how to do it at scale. Partners with proven expertise become increasingly valuable as deployment complexity increases. That's what makes this competency so important.

178

5 Comments

Had the absolute pleasure of sitting down with Sriram Raghavan, VP of AI Research at IBM, for the latest episode of Ctrl + Alt + AI, and I learned so much. Sriram has spent over two decades at IBM shaping the direction of AI research. In this episode, we went deep into how enterprise AI is evolving, and what it really takes to build responsible, efficient models that can scale in the real world. A few things we covered: ✅ The surprising performance of IBM’s Granite models, despite being smaller than most foundation models ✅ Why IBM chose to open-source them, and what they've learned from engaging with the OSS community ✅ What makes enterprise data readiness so hard, and how it changes the game compared to internet-scale models ✅ How Guardian AI helps ensure AI systems stay safe and aligned ✅ And Sriram’s take on what comes after general-purpose LLMs, including his excitement around neuro-symbolic reasoning and causal models We also talked about the gap (and opportunity!) between academia and industry, and what both sides can learn from each other in this next chapter of AI. If you’re building or investing in enterprise AI, or just want a peek behind the curtain of one of the most ambitious AI research orgs, this is a conversation worth watching. 🎥 Full episode is live now on my YouTube channel. Would love to hear your thoughts!

143

21 Comments

If there’s one piece this year I’ve read multiple times over, it’s AI as Normal Technology by Arvind Narayanan and Sayash Kapoor. “The statement ‘AI is normal technology’ is three things: a description of current AI, a prediction about the foreseeable future of AI, and a prescription about how we should treat it.” I first heard Arvind unpack this at Open Data Science Conference (ODSC). It crystallized the gap I see between the high investment sentiment in AI and the reality of deployment. Here are three themes that stood out: 1. Value and risk live in deployment, not to be confused with development Most arguments about AI’s imminent impact on the economy (negative and positive) focus on the pace of model advancements. This does not account for the friction involved in deploying AI to production. Some of the most compelling opportunities lie in deploying AI in safety critical areas, like healthcare, financial services, education, and security. We've seen this pattern before. We've had deep learning models in production for a decade, yet most production ML use cases in healthcare, for example, rely on decades old statistical methods. 2. Our tools to measure the capabilities of AI models are necessary but not sufficient for generalization Benchmarks, used to measure the progress of AI models are "quickly becoming saturated and losing utility as we try to measure performance on more general tasks" "This is a problem of ‘construct validity,’ which refers to whether a test actually measures what it is intended to measure." Scoring well on the bar exam doesn’t mean a freshly minted lawyer can sit in a deposition or navigate ambiguity in a contract. The same is true of an LLM. "Intelligence is not the property at stake for analyzing AI’s impacts. Rather, what is at stake is power—the ability to modify one’s environment." And with that lies an opportunity to improve how we measure the utility of AI models on highly generalizable tasks. 3. The future of job growth will be in AI control AI is much more like a bionic arm than a piece of factory equipment on autopilot. It can give us superpowers, but still requires direction and control. "when models are more complex and less intelligible, it is hard to anticipate all possible deployment conditions in the testing and validation process." There will be an awkward period where AI can automate a lot of tasks but we won't have prepared fresh grads with the skills to critique and oversee AI systems effectively. More AI deployment will also mean more testing and validation. And more jobs in these areas, likely also using AI tools! The good news? There's a lot of upside to getting AI right. And there's a lot of work that still needs to be done. You may have heard me say boring AI is the best AI. This article lays out why. If you influence AI strategy, this article is a must-read. https://lnkd.in/gy7EGSKQ

37

3 Comments

India just hosted one of the world's largest AI summits, announcing a staggering ₹10 lakh crore investment in AI over the next seven years 📈. With GPU capacity expanded to 58,000 units and compute priced at just ₹65 per hour, the infrastructure is finally catching up to ambition. The event also showcased homegrown breakthroughs: Sarvam AI's Indic‑language LLMs that outperform global rivals, the launch of BharatGen – India's own multimodal model, and even PM Modi sporting Made‑in‑India AI glasses 👓. The "Create in India" mission promises to build world‑class talent and position the nation as a tech powerhouse. Yet the headlines were dominated by a controversy over a robot dog, turning a historic summit into a gossip cycle. While the world watched India pledge massive AI funding, we were busy debating a professor’s unauthorized comment. This misplaced focus reveals a deeper issue: a collective attention calibrated for scandal, not substance. The uncomfortable truth is clear – a nation gets the media it feeds. If we continue to reward noise over innovation, we will keep lagging behind. Let’s redirect our energy toward the real breakthroughs happening right now. What will you do to support India’s AI ascent? #AI #IndiaAI #TechInnovation #FutureReady #CreateInIndia #BharatGen #SarvamAI Reference: [https://lnkd.in/grE-GRun] 🔄 Share 👍 React 🌐 Visit www.aravind-r.com #AravindRaghunathan

30

3 Comments

Gemini 3 is here! It was incredible reading about Google's new model. It's feels far more than a simple performance bump—it signifies a real shift where AI moves into being a genuine “thought partner”. Few features that stood out: • State-of-the-Art Reasoning: I'm excited that this model is built to genuinely grasp depth and nuance, which means it's phenomenal at "reading the room" for complex problems and is setting a new standard for intelligence. • Advanced Agentic Power: What really caught my attention is Google's claim that this is their most powerful agentic and vibe-coding model yet. It enables planning and executing complex, multi-step workflows, which is a massive leap forward for productivity. • Broad Multimodality: I was amazed to read it's claimed as the best model in the world for multimodal understanding. It can seamlessly synthesize information—vision, code, and text—helping us learn, build, and plan complex tasks in totally new ways. Looking at these features, we are entering a new era of intelligence. I absolutely cannot wait to see what everyone builds with this! #Gemini3 #GoogleAI #AIInnovation #FutureOfWork #LLM #AI https://lnkd.in/guMDTxAU

114

4 Comments

#Training and #upskilling our customers and partners with the latest data and AI skills is incredibly important to Databricks. That’s why we have opened a new Data + AI Academy in #India, which will support the training of more than 500,000+ data and AI professionals over the next three years. With an increasing need for AI talent, the academy will meet the demands for advanced data and AI skill sets and have a big impact on our customer and partner ecosystem in EMEA. Take a look at the full story here 👉https://lnkd.in/dEGaAgJR Ed Lenta, Rochana Golani, Ankush Korla, Vinod Marur, Anna Lewis

151

6 Comments

ROI is always a hot topic with AI. Recently in my conversations with executives I heard the conversation shift away from “if” there is value in AI to “how and where” AI drives value across an organization. And now we have new data on exactly how AI is benefiting enterprises. We partnered with the team at IDC to survey hundreds of  Google Cloud customers to see what quantifiable benefit it is driving. We found a wide array of customer use cases like content creation (32% quicker content editing), application development (50% more productive developers) and data enablement (40% of once manually collected data now automated). It is amazing to see how our customers are putting AI to use! Read more with “How businesses achieve strong ROI with Google Cloud AI.” : https://lnkd.in/g9hZSP8y #GenerativeAI #GoogleCloudAI #GoogleCloudNext

140

Filling the AI Infrastructure Gaps: Sharad Sanghi on What Clients Really Want In this reel, Sharad Sanghi, CEO of Neysa, talks about how he’s addressing the critical gaps in India’s AI infrastructure. He shares key insights into what clients are truly looking for — from scalable GPU access to clients having full control — and how Neysa is uniquely positioned to deliver. Watch the Full Episode here: https://lnkd.in/g2jbfbKy #aipodcasts #ai #technology #techleadership #podcasts #datacenters

18

It is hard to understand Fabric's long-term strategy. On one hand, it was smart to build it around PBI, the leading BI platform. On the other hand, the key to Power BI's success in dethroning Tableau was its' extremely affordable licensing. Competitive licensing is not an element that Fabric's always-on pricing model can boast of at current rates, especially when the likes of Databricks, Snowflake, and other rising platforms are around. Perhaps Microsoft will be content with Fabric always being the 3rd/4th best option?

39

32 Comments

Accuracy doesn’t come from bigger models — it comes from grounding. Microsoft’s Fabric latest release weaves Graph + Maps + MCP into the enterprise stack For CDAOs and data leaders, here’s what matters: 1. Graph + Maps come to Fabric Adding it native to fabric and simplifying use is a reinforcement that context is the new control plane with the rise of AI. 2. Official MCP Server The Model Context Protocol makes the Fabric agent-ready. No more custom plumbing. Fabric assets (tables, pipelines, models) can be more safely exposed to AI agents, right inside and leveraging your Microsoft governance framework. Bonus: It’s already integrated into VS Code and GitHub Codespaces, so developers can build within their daily workflow. 3. LinkedIn Graph under the hood This is the quiet but strategic play. Microsoft is operationalizing LinkedIn’s proven graph tech in Fabric. Today, it grounds agents. Tomorrow, it could bridge LinkedIn’s people/skills/company graph into enterprise solutions and decisioning. 💡 Why it matters: Fabric is shaping up as Microsoft’s answer to the data-to-decision stack: not just unifying data, but grounding AI agents in enterprise context, governance, and graph-rich reasoning. Rivals have MCP servers too, but Microsoft, as it has done in the past, is bundling. Bundling governance, graph, and developer experience into one package. Check out the Microsoft release. I put a link below👇🏻 Question for you: Would grounding your AI agents in a graph and geospatial context change your 2026 roadmap? #Fabric #Microsoft #CDAO #DecisionAutomation #GraphRAG

43

2 Comments

India’s AI journey must focus not just on consumption, but on creation. While global LLMs are powerful, they often miss India’s linguistic and cultural nuances. When a farmer in Punjab asks a question in Punjabi, or a small-town entrepreneur in Bihar seeks legal advice in Hindi, how effective can these models truly be? India’s diversity is vast — but do our AI models reflect this? 22 official languages and over 1,600 dialects Thinking shaped by culture, context, and nuance Data spread across vernacular platforms, regional websites, PDFs, handwritten documents, and more 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜𝗻𝗱𝗶𝗮 𝗻𝗲𝗲𝗱𝘀:  ✅ 𝗟𝗟𝗠𝘀 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗶𝗻 𝗿𝗲𝗴𝗶𝗼𝗻𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀  ✅ 𝗕𝘂𝗶𝗹𝘁 𝗼𝗻 𝗜𝗻𝗱𝗶𝗰 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 (𝗴𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁 𝘀𝗰𝗵𝗲𝗺𝗲𝘀, 𝗹𝗼𝗰𝗮𝗹 𝗰𝗮𝘀𝗲 𝗹𝗮𝘄𝘀, 𝘀𝘁𝗮𝘁𝗲-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗿𝘂𝗹𝗲𝘀, 𝗲𝘁𝗰.)  ✅ 𝗖𝘂𝗹𝘁𝘂𝗿𝗮𝗹𝗹𝘆 𝗮𝗹𝗶𝗴𝗻𝗲𝗱, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹𝗹𝘆 𝗮𝘄𝗮𝗿𝗲, 𝗮𝗻𝗱 𝘀𝗼𝗰𝗶𝗮𝗹𝗹𝘆 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 Imagine a Marathi teacher in Satara creating lesson plans in seconds. A shopkeeper in Kerala receiving tax advice in Malayalam. A student in Assam preparing for UPSC in Assamese. That’s real AI for Bharat. We’ve got the talent. We’ve got the data. Now, we need the intent and investment. #AIforBharat #IndianLLM #GenAI #MakeInIndia #DigitalIndia #LLM #AI #LanguageModels

39

10 Comments

𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗲𝗮𝗻 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗶𝗻 𝗮𝗻 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝘄𝗼𝗿𝗹𝗱? Dived deep into this at the roundtable at Bits n Atoms, hosted by The Product Folks last night — and the conversations stuck with me. Scaling consumer products in an AI world demands thinking across multiple dimensions, and these are just some of the lenses through which our group explored it.  • 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻 — not just smart recommendations, but designing for one and actually impacting behaviour change  • 𝗧𝗿𝘂𝘀𝘁 — especially for this new generation of AI-native products, where UX becomes critical to adoption  • 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 — using AI to boost efficiency and also designing AI systems that can scale without exploding infra or model costs  • 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 — whether through subscriptions or new models that make sense for AI-first experiences  • 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 & 𝗚𝗧𝗠 — this differentiation matters more than ever, in a world where building products is getting faster and cheaper everyday  • 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 — across regions, languages, devices, and modalities. If it only works for the few, does it really scale? We’re in an interesting moment — where product thinking is expanding, not just because of what AI can do, but because of the questions it’s forcing us to ask. Loved the sharp, honest, and wide-ranging perspectives from everyone Shreyanshi C. Kushal Khandelwal Ashish Jain Madhav Bhartia Tanmay Saxena Pranav Agrawal Sachin Kamkar Shashank Raghavendra Nikhil Ankan Huge shout out to Abhay Jani Aditya Mohanty Suhas Motwani for driving these thoughtfully curated and highly impactful events!

92

6 Comments

Building on top of large language models is fun, but getting consistent performance and reliability is extremely challenging. I love this post from Kimberly Patron about how Sierra uses health and performance-based traffic routing and request hedging to get meaningfully better performance and tail latency from high scale LLMs https://lnkd.in/g4qWRCb5

284

13 Comments