Podcasts about enterprise ai

Geopolitics is now measured in Nanometers. Anything with a battery or a plug has a semiconductor inside. But these chips aren't just tech anymore, they're shaping who becomes the next Superpower.In the 1980s, India was just two years behind the world in semiconductors. Today, we're 12 generations behind. What went wrong?India's top semiconductor expert, Raja Manickam, returns to The Neon Show to break it all down.We discuss how the U.S. lost the chip race it started, China's strategic rise, and how one visionary turned Taiwan into the most valuable island in the world.Raja Manickam dives into what the $10B India Semiconductor Mission is getting right and where we may fall behind. He explains why

Enterprise AI is evolving quickly. Budgets are rising, agents are becoming essential, and companies demand state-of-the-art AI as soon as possible. Here are 16 insights from Andreessen Horowitz's latest analysis on how AI transforms the enterprise.Source: https://a16z.com/ai-enterprise-2025/Get Ad Free AI Daily Brief: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://patreon.com/AIDailyBrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/ai⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about how KPMG can help you drive value with our AI solutions.Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months AGNTCY - The AGNTCY is an open-source collective dedicated to building the Internet of Agents, enabling AI agents to communicate and collaborate seamlessly across frameworks. Join a community of engineers focused on high-quality multi-agent software and support the initiative at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠agntcy.org ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ -  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://agntcy.org/?utm_campaign=fy25q4_agntcy_amer_paid-media_agntcy-aidailybrief_podcast&utm_channel=podcast&utm_source=podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vanta - Simplify compliance - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://vanta.com/nlw⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Plumb - The automation platform for AI experts and consultants ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://useplumb.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdownInterested in sponsoring the show? nlw@breakdown.network

"At IBM, we really work on two emerging technologies: hybrid cloud and AI for enterprise. These two are deeply connected. Hybrid cloud for us means that regardless of where the data sits whether the compute is on-premise, off-premise, or across multiple clouds. We believe the client should have the control and flexibility to choose where to run and place their data. If you look at the facts, a very high percentage of client data is still on-premise. It hasn't moved to the cloud for obvious reasons. So, how can you scale AI if you don't have proper access to that data? AI is all about the data. That's why we believe in a strategy that redefines and rethinks everything. We call it the Great Technology Reset." - Hans Dekkers Fresh out of the studio, Hans Dekkers, General Manager of IBM Asia Pacific, joins us to explore how enterprise AI is reshaping business across the region. He shares his journey with IBM after business school, reflecting on the evolution of personal computers to AI today. Hans explains IBM's unique approach combining hybrid cloud infrastructure with AI for Enterprise, emphasizing how their granite models and data fabric enable businesses and governments to maintain control over their data while scaling AI capabilities. He highlights customer stories from Indonesian telecoms company to internal IBM transformations, showcasing how companies are re-engineering everything from HR to supply chains using domain-specific AI models. Addressing the challenges of AI implementation, he emphasizes the importance of foundational infrastructure and governance, while advocating for smaller, cost-effective models over GPU-heavy approaches. Closing the conversation, Hans shares his vision for IBM's growing presence in Asia as the key to enterprise AI success. Episode Highlights: [00:00] Quote of the Day by Hans Dekkers [01:00] Introduction: Hans Dekkers from IBM [05:00] Key career lesson from Hans Dekker [06:51] IBM focuses on two emerging technologies: hybrid cloud and AI for Enterprise, deeply connected [09:27] "Your data needs to remain your data" - IBM's fundamental AI principle for enterprise clients [10:00] IBM's approach: Small, nimble, cost-effective AI models that can be owned and governed by clients [13:59] "The cost of AI is still too high. It's about a hundred times too high" - IBM CEO's perspective on AI costs [14:44] Small domain-specific models example: Banking AI trained for financial analysis, not Russian poetry [18:00] IBM's internal transformation: HR, supply chain, and consulting completely re-engineered with AI [21:18] Major partnership announcement: Indonesian telecom embracing IBM's watsonx platform [22:23] AI agents demo: Multiple agents (HR, finance, legal) debating and constructing narratives [25:00] "Everyone talks about AI equals GPU" - Hans wishes clients understood that inferencing is more important [27:00] IBM's Asia Pacific vision: Reestablishing growing presence and differentiated technology approach [28:00] Closing Profile: Hans Dekkers, General Manager IBM Asia Pacific and China: https://www.linkedin.com/in/hans-a-t-dekkers/ Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. Here are the links to watch or listen to our podcast. Analyse Asia Main Site: https://analyse.asia Analyse Asia Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Asia Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Asia YouTube: https://www.youtube.com/@AnalyseAsia Analyse Asia LinkedIn: https://www.linkedin.com/company/analyse-asia/ Analyse Asia X (formerly known as Twitter): https://twitter.com/analyseasia Analyse Asia Threads: https://www.threads.net/@analyseasia Sign Up for Our This Week in Asia Newsletter: https://www.analyse.asia/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

There are No Checklists or Frameworks on HOW TO BE A VC?So how do you even know if it's the right path for you?Unlike most jobs, venture capital comes with an extremely long feedback loop. It can take years before you know whether the bets you made actually worked out. That's why most seasoned VCs say: only choose this path if you're in it for the long haul.This conversation will help you think through that choice. Whether you're considering VC as a career, love building businesses, or just want to understand who really calls the shots on a cap table.On The Neon Show, we have with us two operators turned investors:Gaurav Ranjan, Principal at Prime Venture Partners, has led deals including Dozee, Hitwikcet, Poshn and Gallabox.Naman Lahoty, Partner at Stellaris Venture Partners has been part of investments like Zouk, Nestasia, Dashtoon and Lumio.They share lessons from evaluating thousands of startups - what they've unlearned about pattern-matching in investing, why Excel projections mostly fail and why founder empathy might be the most underrated edge in venture capital.It's truly a conversation between three VCs on what it really takes to be a VC today.0:00 – Stellaris Partners X Prime Ventures0:43 – How Founders Turn Into VCs4:19 – Do VCs Need an MBA or Consulting Background?6:32 – Why Startup Projections Rarely Come True8:43 – Are VCs Naturally Good Founders?11:19 – Startups we Evaluated & Founders we Met14:51 – From First Pitch to Deal Close19:02 – Why VC Feedback Loops Are Extremely Long21:00 – No Checklists. No Frameworks.25:27 – Why On-Demand Rebranded as Quick Commerce Won?29:20 – The Stellaris Framework to Evaluate Founders35:53 – Why Indian VCs Must Think Independently38:28 – Rapid Fire: The Big One We Missed39:16 – The One We Loved But Didn't Back42:23 – Startups We Wish We'd Invested In43:55 – Investors We Admire the Most47:20 – Do We Believe Peter Thiel's Theory?52:35 – Startup Stories: Slack, Flickr, Dozee, Rupicard57:15 – The GTM Hack That Led to Product Discovery58:15 – Babygogo & Atomic Work59:55 – All-Nighter Code Sprint for the Demo1:00:55 – Lessons Founders Taught Us1:06:30 – What We Miss About Being a Founder1:10:28 – When Do You Decide If You Are a Good VC?1:13:28 – Building a Fund V/S Building a Startup-------------​​India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload – where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI…and saved.   Try OCI for free at http://oracle.com/eyeonai   What if you could fine-tune an AI model without any labeled data—and still outperform traditional training methods?   In this episode of Eye on AI, we sit down with Jonathan Frankle, Chief Scientist at Databricks and co-founder of MosaicML, to explore TAO (Test-time Adaptive Optimization)—Databricks' breakthrough tuning method that's transforming how enterprises build and scale large language models (LLMs).   Jonathan explains how TAO uses reinforcement learning and synthetic data to train models without the need for expensive, time-consuming annotation. We dive into how TAO compares to supervised fine-tuning, why Databricks built their own reward model (DBRM), and how this system allows for continual improvement, lower inference costs, and faster enterprise AI deployment.   Whether you're an AI researcher, enterprise leader, or someone curious about the future of model customization, this episode will change how you think about training and deploying AI.   Explore the latest breakthroughs in data and AI from Databricks: https://www.databricks.com/events/dataaisummit-2025-announcements Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI  

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361Firm Meetup and Briefing "U.S. Deficit Concerns & Russia's Wartime Economy" June 10, 2025Transcript: https://361.pub/transcriptjune102025Video: https://youtu.be/Hp7jX5tSCE8Podcasts: Apple https://lnkd.in/eRh8iztB and Spotify https://361.pub/spotifyThis 361Firm Meetup and Briefing on June 10, 2025, covered key economic and geopolitical issues. Stephen Burke discussed the US deficit, highlighting a proposal to boost productivity by 0.5% to raise GDP by 7% and reduce the deficit by 1.2% over a decade. Adam Blanco detailed Ukraine's strategic attacks on Russian airfields, noting the destruction of irreplaceable Soviet-era aircraft and the emergence of Ukraine's drone industry. The discussion also touched on the broader implications of these events, including potential shifts in global power dynamics and the need for strategic investment in defense and productivity. The meeting discussed the future of trade vocations, emphasizing their importance in education. Charles Beyrouthy highlighted the geopolitical implications of Russia and China's nuclear capabilities and the potential for coexistence. The conversation shifted to AI investment, noting a bubble and the need for infrastructure. Lucia Ordonez-Gamero and Anthony Gordon discussed AI's impact on jobs, with AI replacing entry-level roles. Khadija Mustafa predicted a potential AI market crash and emphasized the importance of small language models and machine learning. The discussion also touched on the ethical considerations of AI and the integration of AI with other technologies like quantum computing.SPEAKERS: Lucia Ordonez-Gamero, Keith McCall, Rob Ricciardelli, Lubna Dajani, Sameer Sirdeshpande, Jason Ma, Maher Nasri, Jack Wyant, Erica Lill, Depinder Grewal, Michael Hammer, Mark Sanor, Khadija Mustafa, Giovanni, Glenn, Chloe Sun, Tim Gallabrant, Karolina, Bruce, Kate Lawrence (Bloccelerate), Carl Pro, Anthony Gordon, Mark Mueller-Eberstein (ex-Microsoft, now investor), Adam Blanco, Bill Deuchler, Eyad Kishawi, Jeff Zawadsky, Stephen Burke, Charles Beyrouthy, Robin Blackstone, Detlef Schrempf, Rafiq Ahmed, and many others.SUMMARY KEYWORDS: US deficit, Russia war economy, Ukraine attacks, productivity improvements, immigration policy, military spending, AI advancements, global economy, national security, defense procurement, economic growth, social unrest, UBI, investment strategies, geopolitical issues., AI investment, trade vocations, supply chain, military operation, NATO expansion, economic warfare, AI bubble, job displacement, hard skills, soft skills, intellectual property, quantum computing, enterprise AI, global change, investment strategy. You can subscribe to various 361 events and content at https://361firm.com/subs. For reference: Web: www.361firm.com/homeOnboard as Investor: https://361.pub/shortdiagOnboard Deals 361: www.361firm.com/onbOnboard as Banker: www.361firm.com/bankersEvents: www.361firm.com/eventsContent: www.youtube.com/361firmWeekly Digests: www.361firm.com/digest

This week, I'm speaking with Kevin Weil, Chief Product Officer at OpenAI, who is steering product development at what might be the world's most important company right now.We talk about:(00:00) Episode trailer(01:37) OpenAI's latest launches(03:43) What it's like being CPO of OpenAI(04:34) How AI will reshape our lives(07:23) How young people use AI differently(09:29) Addressing fears about AI(11:47) Kevin's "Oh sh!t" moment(14:11) Why have so many models within ChatGPT?(18:19) The unpredictability of AI product progress(24:47) Understanding model “evals”(27:21) How important is prompt engineering?(29:18) Defining “AI agent”(37:00) Why OpenAI views coding as a prime target use-case(41:24) The "next model test” for any AI startup(46:06) Jony Ive's role at OpenAI(47:50) OpenAI's hardware vision(50:41) Quickfire questions(52:43) When will we get AGI?Kevin's links:LinkedIn: https://www.linkedin.com/in/kevinweil/Twitter/X: @kevinweilAzeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharTwitter/X: https://x.com/azeemOur new show:This was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through Exponential View on Substack.Produced by supermix.io and EPIIPLUS1 Ltd.

Why do so many AI initiatives stall after the strategy phase? In this episode, we unpack a practical AI implementation plan that moves beyond theory to real execution. Discover how enterprise leaders structure, govern, and deliver AI at scale — with measurable impact. Learn how to align AI with business value, manage risk, and ensure accountability across the organisation. If you're serious about embedding AI into your business strategy, this is your blueprint. Tune in for more insights on AI leadership and digital transformation. 

Vertical SaaS customers don't buy software for 10 months, they buy it for 10 years. That's the opportunity and the challenge. Switching costs are high, which makes it hard to get in but once you're in, you're in.But regular SaaS playbooks don't work here. Forget PLG. Forget design partners. These industries have been burned too many times by bad software. Here, Trust defines GTM. Think warm introductions and on-site meetings, not cold emails and Zoom calls.But for founders building in vertical SaaS, there's little to learn from. So in this episode of The Neon Show, we bring together three founders who are building in the trenches of Vertical SaaS.Omkar Patil, Co-founder of Pienomial, helping biopharma companies run faster clinical research and unlock insights from complex drug data.Kumar Siddhartha, Co-founder of Merlin, rebuilding ERP from the ground up for the US construction industry.Divyaanshu Makkar, Co-founder of WizCommerce, modernising sales and commerce tools for wholesale distributors.If you're building SaaS for niche markets or wondering why traditional playbooks are failing, this episode is for you.0:00- Pienomial X WizCommerce X Merlin0:51 – What are we building in Vertical SaaS?4:29 – Vertical SaaS buyers are sticky by nature6:57 – How to build for industries used to Below-Par Tech?10:46 – Fix what your customer hated about the last vendor12:17 – Why these industries pay billions for implementation?13:38 – How we got our First customers?20:03 – Warm intros and word-of-mouth still win23:56 – Why Design Partners don't work in Vertical SaaS?27:17 – Why you should never sell your first product for free?30:29 – Can you Co-build products with early customers?34:33 – Building Your Own Platform Vs Building on Top of one39:33 – Building alongside Legacy players or innovating around them?44:31 – SaaS isn't going anywhere, AI will amplify it46:49 – Can AI agents really be reliable?48:42 – Which roles shouldn't be automated?52:16 – How to approach GTM where users guide you?54:43 – Why trust is everything here?58:54 – How to sell softwares used for 10 years?1:01:02 – How to win when the product demo comes last?1:03:16 – Why NOW for traditional industries with unsolved problems1:09:17 – Thoughts on agentic workflows1:13:02 – Why be Bearish on the “AI Employee Concept”?1:16:57 – Rapid Fire : Google or Perplexity?1:17:37 – LLMs: Open-source or Closed?1:18:19 – Favorite work software + We're hiring!1:19:42 – One business buzzword that should disappear1:20:55 – A Vertical SaaS company we admire (and why)-------------​​India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are thosSend us a text

 Who truly owns the outcomes of your AI initiatives? In this episode, we explore the strategic importance of clear accountability in AI governance and why undefined roles often lead to failed implementation. Discover how a structured AI Roles and Responsibilities Matrix can transform fragmented efforts into aligned, outcome-driven success. Ideal for executives, consultants, and digital leaders navigating AI transformation. Learn how to future-proof your governance, mitigate compliance risks, and accelerate value from AI. Tune in now and gain the clarity needed to lead AI with confidence. 

If you've ever wondered how to actually navigate the AI revolution—without getting crushed by it—then you must hear this one. John Girard, serial tech CEO and modern-day philosopher, breaks down what most businesses are getting dangerously wrong about AI adoption—and what to do instead.This isn't just about optimizing workflows—it's really about whether your business survives the next decade. We unpack the AI enablement wave, why it's eerily similar to the dot-com boom (and bust), and how solopreneurs and Fortune 50s alike can avoid becoming obsolete. Whether you're a curious founder, a hesitant exec, or a policy-overwhelmed leader, this convo is your wake-up call.

How does a top AI company scale massive clusters and build AI for the enterprise? In this episode of The Liftoff with Keith, we talk to Ted Shelton, COO of Inflection AI, from the AI Infra Summit 2025. Ted shares how their team pivoted from consumers to enterprise after their Microsoft deal, why seamless infrastructure is key, and what it takes to build AI models that run on NVIDIA, AMD, and Intel.Learn why “getting to the no” is the smartest move for founders, how enterprises can embrace sovereign AI, and how Inflection's approach to model customization unlocks massive business value.

SHOW NOTESGuest: Andrew AmannWebsite: ninetwothree.coLinkedIn: Andrew AmannX/Twitter: @andrewamannKey topics:Andrew's pivot from mechanical engineering to AI and software development Early experiments with digital transformation, including VBA-coded automations Founding 923 Studio and delivering 150+ innovative AI and ML products Ideal clients: established brands with innovation labs and funded startups How Andrew and his team win business through SEO, conferences, and LinkedIn outreach Stabilization and growth goals for 923 Studio in 2025 How AI can be implemented in enterprise businesses, starting with a knowledge base Balancing business growth with a holistic lifestyle for employees Andrew's best advice: become an apprentice, learn from both good and bad bosses The 923 Studio name: inspired by their early days working 9 PM to 3 AM Tips for building AI solutions that truly solve real-world problems Key Questions(01:19) Can you tell us a bit about how you ended up where you are today?(03:15) Who would be your ideal client these days?(04:03) How do you get in front of these people?(04:35) Do you have repeat customers?(05:55) What are some big goals that you'd like to achieve in the next year?(06:45) Do you use AI within your business?(08:07) So your goals that you have, how would that affect your business?(08:55) What do you feel is the number one roadblock from you guys getting there?.(09:20) Can you talk a little bit about successful AI transformation in enterprise companies?(11:33) Do you have any tips or anything about how to build AI solutions that will solve our real problems like you were talking about?(12:55) How about running a holistic agency that uses profit to enhance the lifestyle of all employees?(13:49) What is the best piece of advice that you've ever received?(15:13) How did you come up with the business name?(15:54) What's the best advice you have ever given?(17:54) Is there anything else that you would like to touch on?(18:02)  Where can we go to learn more about you and what you're doing?Andrew Amannwww.ninetwothree.coAndrew Amann | LinkedInx.com/andrewamannVirginia PurnellFunnel & Visibility SpecialistDistinct Digital Marketing(833) 762-5336virginia@distinctdigitalmarketing.comwww.distinctdigitalmarketing.comwww.distinctdigitalmarketing.co

Today's guest is Chris Tapley, Vice President and Head of Financial Services Consulting for North America at EPAM Systems. EPAM is a global digital engineering company that provides software development and consulting services across industries, including financial services, healthcare, and media. With deep experience guiding AI adoption in regulated industries, Chris joins Emerj Editorial Director Matthew DeMello on the show today to unpack the foundational challenges facing financial institutions as they move from experimentation to production. He explores the technical and organizational barriers that often stall AI projects, from legacy systems and cloud limitations to gaps in data strategy and executive alignment. Chris shares lessons from EPAM's recent market research on AI maturity in financial services — including how long it typically takes to establish enterprise-ready AI governance and why business leaders must prioritize infrastructure, collaboration, and oversight well before model deployment. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! This episode is sponsored by EPAM. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

Recko's acquisition by Stripe is one of India's biggest B2B exits. It's a great headline. But headlines don't tell the full story. They capture one final outcome not the numerous obstacles faced.In this episode, Saurya Prakash Sinha, co-founder of Recko, tells us what really happened behind the scenes. From the early days when no one was buying, no VC was funding, and they had just $500 left in the bank to building a product Stripe couldn't ignore. Saurya also shares hard-won lessons about product-market fit, customer validation, and why usage matters more than ARR in early-stage B2B.Tune in if you want to learn about the full story behind the headlines.0:00 - Trailer1:01 - Why Stripe emailed 2 founders in Bengaluru4:34 - How Recko discovered its core problem9:34 - What IF customers and investors reject?12:33 - How to Build with zero validation?14:18 - Solving what the Big Four couldn't at Myntra17:37 - What to expect when building Products in Finance?19:46 - Bought for the Product, Team or Scale?21:53 - “Customer voice is the loudest in the room”25:28 - Why VCs didn't “get” Recko28:27 - How to raise when investors follow success playbooks?30:36 - Why build products to compete with the Best?33:18 - The First cheque and first customer at Pingsafe36:45 - How to manage Acquisitions before making them public?41:02 - How Pricing is led during Buyouts?42:36 - The culture of Writing at Stripe44:08 - Why do companies Acquire?49:16 - Why ARR at early stage is not the right metric?52:26 - How to find what value your product really adds?56:38 - Why $100M+ acquisitions are rare?-------------​​India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Akanksha Bilani of Intel shares how businesses can successfully adopt generative AI with significant performance gains while saving on costs.Topics Include:Akanksha runs go-to-market team for Amazon at IntelPersonal and business devices transformed how we communicateForrester predicts 500 billion connected devices by 20265,000 billion sensors will be smartly connected online40% of machines will communicate machine-to-machineWe're living in a world of data delugeAI and Gen AI help make data effectiveGoal is making businesses more profitable and effectiveVarious industries need Gen AI and data transformationIntel advises companies as partners with AWSThree factors determine which Gen AI use cases adoptFactor one: availability and ease of use casesHow unique and important are they for business?Does it have enough data for right analytics?Factor two: purchasing power for Gen AI adoption70% of companies target Gen AI but lack clarityLeaders must ensure capability and purchasing power existFactor three: necessary skill sets for implementationNeed access to right partnerships if lacking skillsIntel and AWS partnered for 18 years since inceptionIntel provides latest silicon customized for Amazon servicesEngineer-to-engineer collaboration on each processor generation92% of EC2 runs on Intel processorsIntel powers compute capability for EC2-based servicesIntel ensures access to skillsets making cloud aliveAWS services include Bedrock, SageMaker, DLAMIs, KinesisPerformance is the top three priorities for successNot every use case requires expensive GPU acceleratorsCPUs can power AI inference and training effectivelyEvery GPU has a CPU head node component Participants:Akanksha Bilani – Global Sales Director, IntelSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

The Rollup TV presents: Mammoth May.The Rollup TV is brought to you by:Celestia: https://celestia.org/Boundless: https://beboundless.xyz/AltLayer: https://www.altlayer.io/Mantle: https://www.mantle.xyz/Omni Network: https://omni.network/Vertex: https://vertexprotocol.com/Frax: https://frax.com/Join The Rollup Family:Website: https://therollup.co/Spotify: https://open.spotify.com/show/1P6ZeYd..Podcast: https://therollup.co/category/podcastFollow us on X: https://www.x.com/therollupcoFollow Rob on X: https://www.x.com/robbie_rollupFollow Andy on X: https://www.x.com/ayyyeandyJoin our TG group: https://t.me/+8ARkR_YZixE5YjBhThe Rollup Disclosures: https://therollup.co/the-rollup-discl

In this episode, Amir speaks with Ameya Brid, Global Director of Data & Analytics at Invista, about the maturation of GenAI conversations in the enterprise. They dive into the shift from hype to implementation, real-world challenges like data quality and change management, and how composable architecture is helping organizations adapt to rapid innovation cycles.

Most enterprises don't lack AI ambition—they lack execution. This episode reveals the proven, step-by-step framework used by leading organisations to turn AI strategy into measurable business success. Learn how to align teams, assess readiness, plan implementations, manage risk, and scale AI sustainably across the enterprise. Whether you're a manager, leader, or consultant, this roadmap gives you the tools to lead transformation with clarity and confidence. Subscribe for more insights on AI strategy, digital transformation, and leadership excellence. 

This episode features an interview with Bruce Cleveland, author of the best-seller “Traversing the Traction Gap" and CEO of Traction Gap Partners, a Market Engineering advisory firm.In this episode, Bruce outlines why most startups fail and explains market engineering, a term he coined  to represent the ideas around category design. He shares insights into creating a category and what goes into startup success. Key Takeaways:Market engineering involves the ideas around category design or redefinition thought leadership to create a category.There are distinct advantages to being a category leader; the category leader generates about 76% of  all the profits from a category. While there is a first-mover advantage, there are also some associated challenges.Thought leadership is an essential component of creating a category. People want to be around peers they admire, so gathering the right people together leads to an eventual tipping point that makes it easier for a company to sell.Quote:  One of the reasons that you need to actively be involved in the thought leadership part of category creation is people wanna hang out with other people who they think are smart, who have some cool ideas. And that I think happens with companies as well. So eventually some companies kind of climb out of the morass, the cacophony of, fighting the marketing battle and begin to emerge as the thought leaders in those. And then they collectively gather more people and more people. And finally there's a tipping point where that company is perceived as the category leader. And so it becomes really easy for those companies to then sell more.Episode Timestamps: *(02:26) The Trust Tree: Traversing the Traction Gap *(07:31) The importance of category design*(26:05) Thought Leadership in category creation*(35:39) How to evaluate startupsSponsor:Pipeline Visionaries is brought to you by Qualified.com. Qualified helps you turn your website into a pipeline generation machine with PipelineAI. Engage and convert your most valuable website visitors with live chat, chatbots, meeting scheduling, intent data, and Piper, your AI SDR. Visit Qualified.com to learn more.Links:Connect with Ian on LinkedInConnect with Bruce on LinkedInLearn more about Traction Gap Partners or Traversing the Traction GapLearn more about Caspian Studios

Dell Technologies has announced Dell AI Factory advancements, including powerful and energy-efficient AI infrastructure, integrated partner ecosystem solutions and professional services to drive simpler and faster AI deployments. Why it matters AI is now essential for businesses, with 75% of organisations saying AI is key to their strategy and 65% successfully moving AI projects into production. However, challenges like data quality, security concerns and high costs can slow progress. The Dell AI Factory approach can be up to 62% more cost effective for inferencing LLMs on- premises than the public cloud and helps organizations securely and easily deploy enterprise AI workloads at any scale. Dell offers the industry's most comprehensive AI portfolio designed for deployments across client devices, data centres, edge locations and clouds. More than 3,000 global customers across industries are accelerating their AI initiatives with the Dell AI Factory. Dell infrastructure advancements help organizations deploy and manage AI at any scale Dell introduces end-to-end AI infrastructure to support everything from edge inferencing on an AI PC to managing massive enterprise AI workloads in the data center. Dell Pro Max AI PC delivers industry's first enterprise-grade discrete NPU in a mobile form factor The Dell Pro Max Plus laptop with Qualcomm AI 100 PC Inference Card is the world's first mobile workstation with an enterprise-grade discrete NPU. It offers fast and secure on-device inferencing at the edge for large AI models typically run in the cloud, such as today's 109-billion- parameter model. The Qualcomm AI 100 PC Inference Card features 32 AI-cores and 64 GB memory, providing power to meet the needs of AI engineers and data scientists deploying large models for edge inferencing. Dell redefines AI cooling with innovations that reduce cooling energy costs by up to 60% The industry-first Dell PowerCool Enclosed Rear Door Heat Exchanger (eRDHx) is a Dell- engineered alternative to standard rear door heat exchangers. Designed to capture 100% of IT heat generated with its self-contained airflow system, the eRDHx can reduce cooling energy costs by up to 60% compared to currently available solutions. With Dell's factory integrated IR7000 racks equipped with future-ready eRDHx technology, organizations can: Significantly cut costs and eliminate reliance on expensive chillers given the eRDHx operates with water temperatures warmer than traditional solutions (between 32 and 36 degrees Celsius). Maximise data center capacity by deploying up to 16% more racks of dense compute, without increasing power consumption. Enable air cooling capacity up to 80 kW per rack for dense AI and HPC deployments. Minimise risk with advanced leak detection, real-time thermal monitoring, and unified management of all rack-level components with the Dell Integrated Rack Controller. Dell PowerEdge servers with AMD GPUs maximize performance and efficiency Dell PowerEdge XE9785 and XE9785L servers will support AMD Instinct MI350 series GPUs, which offer 288 GB of HBM3E memory per GPU and deliver up to 35 times greater inferencing performance. Available in liquid-cooled and air-cooled configurations, the servers will reduce facility cooling energy costs. Dell advancements power efficient and secure AI deployments and workflows Because AI is only as powerful as the data that fuels it, organizations need a platform designed for performance and scalability. The Dell AI Data Platform updates improve access to high quality structured, semi-structured and unstructured data across the AI lifecycle. Dell Project Lightning is the world's fastest parallel file system per new testing, delivering up to two times greater throughput than competing parallel file systems. Project Lightning will accelerate training time for large-scale and complex AI workflows. Dell Data Lakehouse enhancements simplify AI workflows and accelerate use cases - such as recommendation engines, semantic...

The dollar will lose its status as the world's reserve currency & the greatest wealth transfer in history is already underway - warns the founder of one of India's fastest-growing unicorns!In this episode, Deepak Garg, founder of Rivigo and AnywhereJobs shares why Rivigo's iconic Relay model succeeded, and what ultimately limited it. He predicts Zomato's dominance, questions funding choices of startups and shares why India may miss the AI revolution without a radical energy shift.From Bitcoin vs. gold and Trump's potential Nobel Peace Prize to Tesla becoming a $30 trillion company, Deepak's predictions are bold and grounded in years of pattern recognition.If you're a founder, investor, or macro nerd, this is an episode you won't forget.0:00- Rivigo & Anywhere Jobs02:16 – When your business outgrows the market04:18 – Capital raising is a Double-edged sword05:02 – Which ideas truly need funding?07:33 – Build teams with Accuracy, not Kindness09:39 – How to know if you've chosen the right market?10:41 – Why Zomato is India's best Consumer tech bet16:00 – How the Power is shifting b/w nations today?20:18 – Will Dollar cease to be a Reserve currency?22:36 – Is Bitcoin better than Gold?26:59 – Who will be the Next global Superpower?31:56 – India in the Next 20 years34:22 – When 2 players control 80% of India's Private sector35:52 – Why China is far ahead of India in Nuclear Energy?40:17 – Will Trump win a Nobel Peace Prize in 2025?42:01 – How Tesla could become a $30 trillion company?47:15 – Wealth transfer from Wall Street to Main Street50:30 – Where India should focus in AI-------------​​India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Is your organisation struggling to scale AI beyond isolated experiments? In this episode, Rob Llewellyn unpacks what enterprise AI really means—and why it's not just about technology, but transformation. Discover the leadership mindset, scalable infrastructure, and cross-functional collaboration needed to drive AI at scale. Learn how cultural change plays a pivotal role in adoption and hear insights from real-world examples like Unilever. If you're a business leader, manager, or consultant navigating AI transformation, this episode offers the strategic clarity you need. 

The Big Themes:SAP's Flywheel Strategy: SAP introduced a compelling flywheel model that integrates applications, data, and AI to drive enterprise momentum. The idea is that integrated applications generate structured data, which then feeds a robust AI layer. As these layers build on one another, they create a self-reinforcing cycle of productivity, insight, and innovation—a flywheel effect. Unlike Microsoft and ServiceNow, which predict the collapse of applications in favor of agents, SAP asserts that AI agents will enhance, not replace, applications.The Business Data Cloud and Databricks Partnership: A highlight of the event was SAP's Business Data Cloud (BDC), launched in partnership with Databricks. This foundational layer brings together internal SAP data and external sources like Moody's or climate models, enabling richer decision-making. SAP showcased real-world use cases, such as tariff fluctuation impact analysis across supply chains, to demonstrate the power of combining enterprise and contextual data.Prompt Optimizer and the End of Prompt Engineering: SAP's introduction of a “Prompt Optimizer” signals a shift in the AI interface landscape. Instead of manual prompt engineering, users will soon rely on AI to manage and optimize prompts across multiple large language models (LLMs), including ChatGPT, Claude, Gemini, and Perplexity. CTO Philipp Herzig even declared we're at “the beginning of the end” of prompt engineering.The Big Quote: "[Customers are] not ready to deploy AI and have that completely eliminate the need for apps. The data is just not there. So, maybe five years from now, let's see what progress we've made. But what's in the here and now is that customers are looking for applications."

Aaron Levie, CEO & co-founder of Box, joins Azeem Azhar to explore how an “AI-first” mindset is reshaping every layer of Box – from product road-maps to pricing – and what that teaches the rest of us about building faster, smarter organisations.Timestamps:(00:00) Episode trailer(02:04) The "lump of labor fallacy" in sci-fi books(07:37) When individual productivity gains don't translate to teams(12:32) Box's Friday AI demos(21:23) How agents might redefine 100 years of management science(26:37) A lesson on AI innovation from the early days of Ford(29:52) Sundar Pichai, Satya Nadella, and Sergey Brin are coding again?(35:16) Pricing in a post-AI agent world(38:43) Cheaper tokens, heavier usage: AI's margin math(43:02) Solving AI's verifiability problem(48:24) How Aaron uses AI in his personal lifeAaron's links:Box: https://www.box.com/LinkedIn: https://www.linkedin.com/in/boxaaron/X/Twitter: https://x.com/levieAzeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharX/Twitter: https://x.com/azeemThis conversation was recorded for “Friday with Azeem Azhar”, live every Friday at 9 am PT / 12 pm ET. Catch it via Exponential View on Substack.Produced by supermix.io and EPIIPLUS1 Ltd

Joel Christner, (@joelchristner, Founder/CEO at @viewyourdata) discusses the complexities of data management in AI, structured and unstructured data, the importance of RAG pipelines and vector databases. SHOW SUMMARY:  Aaron and Joel discusses the complexities of data management in AI, focusing on the concept of universal data representation. They explore the challenges organizations face with structured and unstructured data, the importance of RAG pipelines and vector databases, and the implications of data privacy in regulated industries. The conversation also touches on managing model versions and the emerging patterns in AI tooling that can help enterprises effectively utilize AI technologies.SHOW: 925SHOW TRANSCRIPT: The Cloudcast #925 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SPONSORS:[VASION] Vasion Print eliminates the need for print servers by enabling secure, cloud-based printing from any device, anywhere. Get a custom demo to see the difference for yourself.[US CLOUD] Cut Enterprise IT Support Costs by 30-50% with US CloudSHOW NOTES:View.io websiteTopic 1 - Welcome to the show, Joel. Give everyone a quick introduction.Topic 2 - Our topic today is everything data and how to represent it and embed it into AI systems. First, what is the challenge with data, structured or unstructured, in organizations today and what is behind the concept of Universal Data RepresentationTopic 3 - Industry or customer specific data today is big challenge for organziations, especially in highly regulated industries such as healthcare, financial services, etc. The most prevalent solution I am seeing is taking an existing foundational model and then adding a RAG pipeline vs. the cost and time to fine tuning. What are you seeing?Topic 4 - Even when companies have good data, that doesn't mean that data makes it into the AI pipeline correctly, this is where the embedding problem and your concept of Universal Data Representation comes into play, correct?Topic 5 - But, once you get the first model out, then what? How should the data and models be handled over time? How do you create a platform and a continuous feedback loop to improve the results over time?Topic 6 - What are the most successful use cases you are seeing today with your customers?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Why do over 80% of AI projects in large organisation's fail? This episode explores how to build a high-impact AI strategy that delivers measurable business value. Discover the six essential components every leader must include, how to assess your organisation's AI readiness, and the common pitfalls to avoid. Whether you're leading transformation or shaping enterprise strategy. Stay tuned until the end. 

The buzz in Silicon Valley around AI agents has many asking: What's real and what's hype? Box's co-founder and CEO, Aaron Levie, joins Rapid Response to help decipher between fact and fiction, taking listeners inside the fast-paced evolution of agents and its impact on the future of enterprise AI. Plus, Levie unpacks how AI is really being adopted in the workplace, what it takes to legitimately build an AI-first organization, and what political leaders across both parties misunderstand about creating the best environment for technology to thrive. Visit the Rapid Response website here: https://www.rapidresponseshow.com/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The buzz in Silicon Valley around AI agents has many asking: What's real and what's hype? Box's co-founder and CEO, Aaron Levie, joins Rapid Response to help decipher between fact and fiction, taking listeners inside the fast-paced evolution of agents and its impact on the future of enterprise AI. Plus, Levie unpacks how AI is really being adopted in the workplace, what it takes to legitimately build an AI-first organization, and what political leaders across both parties misunderstand about creating the best environment for technology to thrive.Visit the Rapid Response website here: https://www.rapidresponseshow.com/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

SuperAnnotate is revolutionizing how companies manage their AI training data with a comprehensive infrastructure platform. Having raised over $53 million in funding, SuperAnnotate has evolved from a specialized algorithm for autonomous vehicles to a centralized data hub that enables enterprises to collaborate with multiple service providers and internal teams. In this episode of Category Visionaries, we spoke with Vahan Petrosyan, CEO and Co-Founder of SuperAnnotate, who shared his journey from PhD student to tech founder and unpacked his vision for creating what he describes as "a database for training data" - similar to Databricks but specialized for AI training data. Topics Discussed: SuperAnnotate's evolution from algorithm to comprehensive data labeling infrastructure The journey from academic research to founding a tech startup How an early contract with an autonomous driving company validated their solution The strategic pivot from competing with service providers to creating a collaborative ecosystem The transformation of their go-to-market strategy to create stickier enterprise relationships SuperAnnotate's focus on building a centralized training data platform for enterprise AI The importance of automation and "SuperAnnotate agents" for AI data operations How customizability has enabled SuperAnnotate to support diverse generative AI use cases GTM Lessons For B2B Founders: Recognize when to stop competing and start collaborating: Vahan's most important go-to-market decision was shifting from competing with service providers to creating an infrastructure that enables collaboration. "That's one of the mindset shifts... we are trying to build an ecosystem with our partners, not really trying to compete with them," he explains. B2B founders should consider whether creating an ecosystem platform might be more valuable than directly competing in fragmented service markets. Solution engineers are crucial for enterprise AI sales: Vahan emphasized that "solution engineering is super important because as you're touching enterprise AI, your solution engineers are more or less the core part of your team." Without proper technical enablement, enterprise customers won't be able to implement complex AI solutions. B2B founders selling sophisticated technology should invest heavily in solution engineering capabilities. Build for adaptability in rapidly evolving markets: SuperAnnotate achieved 3x growth by making their platform "fully customizable to any use case." Vahan noted, "If tomorrow there will be a new agentic workflow, then we'll be able to support it." Rather than offering point solutions, B2B founders in emerging technology spaces should build adaptable platforms that can evolve with changing market needs. Passive fundraising often yields better results than active campaigns: Vahan shared a counterintuitive fundraising insight: "Whenever I was actively fundraising, I was doing something wrong." His most successful raises came from casual coffees with investors who approached him, not from pitching dozens of VCs. B2B founders might benefit from focusing on building relationships and demonstrating value rather than running intensive fundraising campaigns. Enterprise AI is a long-term bet: Looking 3-5 years ahead, Vahan sees enterprise AI as the major opportunity. "Companies have datasets sitting in silos, but that dataset is gold," he explains. The ability to "transform that dataset to training data in a fast and accurate manner will define your moat moving forward." B2B founders should consider how their solutions can help enterprises unlock value from proprietary data.   //   Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.  www.GlobalTalent.co  

Create fine-tuned, task-specific models that write like you by teaching models using expert knowledge, tone, and structure - with reference information directly attached to the models themselves using Microsoft 365 Copilot Tuning. Fine-tuning adds new skills to foundational models, simulating experience in the tasks you teach the model to do. This complements Retrieval Augmented Generation, which in real-time uses search to find related information, then add that to your prompts for context. Fine-tuning helps ensure that responses meet your quality expectations for specific repeatable tasks, without needing to be prompting expert. It's great for drafting complex legal agreements, writing technical documentation, authoring medical papers, and more - using detailed, often lengthy precedent files along with what you teach the model. Using Copilot Studio, anyone can create and deploy these fine-tuned models to use with agents without data science or coding expertise. There, you can teach models using data labeling, ground them in your organization's content - while keeping the information in-place and maintaining data security and access policies. The information contained in the task-specific models that you create stay private to your team and organization. Task-specific models and related information are only accessible to the people and departments you specify - and information is not merged into shared large language models or used for model training. Jeremy Chapman, Director on the Microsoft 365 product team, shows how this simple, zero-code approach helps the agents you build write and reason like your experts—delivering high-quality, detailed responses.  ► QUICK LINKS: 00:00 - Fine-tune Copilot 01:21 - Tailor Copilot for specialized tasks 05:12 - How it works 05:57 - Create a task-specific model 07:43 - Data labeling 08:59 - Build agents that use your fine-tuned model 11:42 - Wrap up ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics  

Join us for this episode with Leigh Engel, Senior Product Manager for Enterprise AI at NVIDIA. Leigh's journey spans media, consulting, and big tech firms like Apple and WPP before landing in product leadership at one of the world's top AI companies. We explore her career pivots, how she balances innovation with user-centered design, and her take on evaluating AI models like GPT-4 and Llama for enterprise use. This is a great episode if you're curious about breaking into AI product management or understanding the future of generative AI in business. If you enjoy the episode, share it with a friend and leave us a comment!Please note: The views expressed in this episode are Leigh's alone and do not reflect those of her employer, NVIDIA.

Venture capital is much fancied today.Is this job which looks like cutting cheques for products and founders you like, for everyone?As for any work, there are traits you should have and some which won't help you on the job.We have with us Kushal Bhagia (All In Capital), Karthik Prabhakar (Peer Capital), and Rajan (Upekkha).Three people who interestingly all began as engineers and took different career paths to today become Fund managers of leading VC firms.All In Capital's $24M pre-seed fund backing early-stage founders,Peer Capital's $75M early-stage fund investing in tech-first Indian startups from seed to Series A,Upekkha's $40M Capital's accelerator-style fund supporting B2B SaaS startups.Tune in!01:53 – Why builders shouldn't become VCs?03:12 – Why best VCs sell well & stay curious05:12 – How the VC job is like Flying a plane07:48 – How parental instincts enable VCs?10:42 – Is fundraising harder than ever?14:12 – What makes people write VC cheques?18:03 – Where do India's rich family offices invest?20:30 – Why India still doesn't have its own YC?22:55 – The OG YC when startups weren't cool28:00 – Can YC's numbers ever be replicated?33:03 – David v/s Goliath of Small vs large funds39:02 – Zepto's first $50k cheque40:22 – Sectors VCs won't touch41:35 – Story-driven v/s numbers-driven Fundraising44:36 – How we missed Swiggy, Postman & Zepto?46:18 – The best VCs48:24 – What needs to change in Indian VC?49:47 – Founders we'd invest In (But not work for)51:25 – Unlearnings as an Investor-------------​​India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

What makes AI trustworthy, ethical, and compliant in business? In this episode, we explore how Chief AI Officers lead governance efforts to align innovation with regulation. Learn how the CAIO bridges strategy, risk, and ethics to ensure responsible AI use across the enterprise. Ideal for executives, managers, and consultants navigating AI transformation. 

Is your AI helping—or quietly hurting—your business? In this episode, we uncover how hidden biases in large language models can quietly erode trust, derail decision-making, and expose companies to legal and reputational risk. You'll learn actionable strategies to detect, mitigate, and govern AI bias across high-stakes domains like hiring, finance, and healthcare. Perfect for corporate leaders and consultants navigating AI transformation, this episode offers practical insights for building ethical, accountable, and high-performing AI systems. 

Mastering the EU AI Act is no longer optional—it's a strategic necessity. In this episode, we unpack the critical compliance gaps that separate thriving companies from those falling behind. Learn how to categorise your AI systems, mitigate risk, and turn regulation into a competitive advantage. Perfect for business leaders, consultants, and transformation professionals navigating AI governance. 

Customer expectations have skyrocketed—people now demand instant, personalized, and seamless interactions across every touchpoint. But are companies truly meeting these expectations, or are they still stuck in reactive customer service models? What if AI could completely transform the customer experience into something proactive, predictive, and even empathetic? Joining me today is Vinod Muthukrishnan, VP & COO of Webex Customer Experience at Cisco. Vinod is a leader in the future of customer experience (CX), helping organizations use AI to anticipate customer needs, deliver seamless automation, and create personalized interactions at scale. Vinod Muthukrishnan is the VP & COO for the Webex Customer Experience Business Unit, overseeing Go To Market, Customer, and Business Operations. In this role he collaborates with Cisco field teams, partners, and customers to deliver innovative solutions. His passion lies in creating products that solve real customer pain points and providing a seamless customer experience. He also values building strong communities, teams, culture, and operating rhythms.Previously, Vinod spent three years in Enterprise AI at Uniphore, a Cisco Investments Portfolio Company, where he developed a product enabling Citizen Developers to create AI and automation solutions. He managed Uniphore's customer functions, including Delivery, Technical Support, Customer Success, and AI consulting, helping enterprises align their business goals with AI roadmaps.Vinod was also VP & COO at the Webex Contact Center Business Unit during a period of significant growth and innovation. During his tenure at the BU, the IMI CPaaS business was acquired, and Webex Contact Center was launched. These two initiatives now serve as the foundations of the Webex Customer Experience Business Unit. Vinod oversaw all GTM functions.He joined Cisco when his startup, CloudCherry, was acquired in 2019. As Co-Founder and CEO of CloudCherry, he and his team developed a Customer Experience Platform that became Webex Experience Management. They also built the foundations of the Customer Journey Data Service, essential to the Webex Portfolio today.Coming from a military family, Vinod began his career in the Merchant Marine at 18, becoming a certified First Officer with Maersk Line and sailing to over 60 countries. He later joined the founding team at MarketSimplified, which introduced mobile trading to major brokerages like TD Ameritrade and OptionsExpress. RESOURCESCisco: https://www.cisco.com Catch the future of e-commerce at eTail Boston, August 11-14, 2025. Register now: https://bit.ly/etailboston and use code PARTNER20 for 20% off for retailers and brandsOnline Scrum Master Summit is happening June 17-19. This 3-day virtual event is open for registration. Visit www.osms25.com and get a 25% discount off Premium All-Access Passes with the code osms25agilebrandDon't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnowThe Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company

 Is your organisation ready for AI success? In this episode, Rob Llewellyn unveils the essential steps to craft an AI Strategy Roadmap that delivers real business value. Discover how to align leadership, prioritise investments, drive enterprise-wide adoption, and gain competitive advantage. Whether you're a manager, leader, or consultant, learn how to turn AI from isolated experiments into strategic growth. Subscribe now to transform your AI ambitions into measurable success. 

You prolly missed this HUGE AI drop.Google quietly updated its NotebookLM behemoth to a thinking model and went FULL on multilingual. Millions of people are instantly getting a AI assistant overnight, but probably don't even know. So.... we're breaking it down. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the conversation.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:NotebookLM's Major 2024 AI Tool UpdatesGoogle's Gemini 2.5 Flash Multilingual FeaturesNotebookLM's Gemini Model Integration DetailsAI Reasoning Models in NotebookLM ExplainedAI Audio Overviews in 50+ LanguagesExploring NotebookLM's Mind Map FeatureDiscover Sources Function in NotebookLMUsing Deep Research with NotebookLMTimestamps:00:00 "Google Notebook AI Updates"06:27 ChatGPT-Controlled IBM Updates Demo08:48 Notebook LM Gains Global Attention13:18 Modeling Challenges and Learning Paths14:01 "Gemini 2.5 Flash: Powerful & Affordable"18:55 AI Struggle: Defining Chicago21:49 "Notebook LM Source Integration Guide"26:30 "Notebook LM: Studio and Mind Map"29:47 Watson x AI Updates Overview31:36 Mind Map: Chaos to Clarity36:39 "Adding Sources: Manual vs. Auto"39:02 Analyzing Watson x Updates Monthly41:08 IBM Watson x Trends Overview44:25 Evaluating John's Performance in Marketing48:05 "Leveraging Data with AI"Keywords:NotebookLM, Google Gemini, AI update, Gemini 2.5 flash model, Multilingual audio overviews, Large Language Model, Deep research tools, Google AI Studio, AI-powered deep dives, Gemini 2025, OpenAI, ChatGPT, AI-driven mind maps, IBM Watson x, Enterprise governance, AI reasoning model, Language support, AI-powered conversation, Audio overview features, AI flash model, Multimodal AI, Data protection, AI Studio integration, AI capabilities, Gemini reasoning, Machine learning advancements, AI feature updates, Enterprise AI solutions, Google Gemini thinking model, AI-driven insights, Language model updates, AI-driven research.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

OpenAI shared a short report with seven things they've seen work for companies using AI. These lessons come from real examples with firms like Morgan Stanley, Indeed, Klarna, BBVA, and Mercado Libre. The report reads like a blueprint for interested firms. Interested in sponsoring the show? nlw@breakdown.network Get Ad Free AI Daily Brief: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://patreon.com/AIDailyBrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/ai⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about how KPMG can help you drive value with our AI solutions.Blitzy.com - Go to ⁠⁠https://blitzy.com/⁠⁠ to build enterprise software in days, not months The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown

AI is no longer experimental—it's foundational.In this explosive episode, PrimeVP Founder & Partner Shripati Acharya and Principal Gaurav Ranjan unpack how AI is reshaping B2B software—and what that means for evaluating and investing in early-stage startups.What you'll learn:⚙️ The rise of Enterprise AI and its real-world applications

Artie Intel and Micheline Learning report on Artificial Intelligence for the AI Report. Is the U.S. losing its AI lead? China is catching up fast. Enterprise AI has gone mainstream, with platforms that don’t just generate text, they reason, plan, and execute multi-step tasks. The buzzword? “Agentic AI.”  Large language models are now showing early signs of “reasoning”-they can solve logic puzzles, plan events, and even debug their own mistakes. The AI Report is brought to you by Sponsor: Pecan AI. From raw data to prediction – made effortless. Simply add your data and get the answers you need. Who’s going to churn? Who’s ready to buy more? We auto-build models to show you what matters – no setup, no hassle. Get started for FREE at https://www.pecan.ai/ The AI Report

AI projects often fail due to a lack of clear strategy and governance. In this episode, Rob Llewellyn introduces a proven framework for managing AI initiatives at scale, ensuring alignment from concept through to optimisation. Discover the five pillars of AI success, real-world case studies, and the leadership roles that drive transformation. Learn how to avoid common pitfalls and turn AI from speculative tech into a strategic business advantage. Tune in for actionable insights to lead successful AI projects. Don't forget to subscribe for more expert strategies. 

Erwan Menard is the director of product management for Google Cloud's Cloud AI division, where he helps lead innovation at the intersection of AI agents, enterprise systems, and business outcomes. In part two of our series, Google Cloud and the AI Revolution, Erwan joins Bob Evans to discuss how governance, intentionality, and rapid scaling are critical to AI agent success, share insights on Google Cloud's Agentspace and Agent Builder tools, and explore how multi-agent collaboration is reshaping the future of enterprise technology.Purpose Driven AI InnovationThe Big Themes:Intentionality Drives Impact: Menard advises organizations not to jump into AI agent development for novelty's sake, but to begin with a clearly defined problem and desired business outcome. However, once value is proven, it's crucial to scale intentionally. He shares the example of a customer rolling out 40,000 licenses of Agentspace only after deeply considering what kind of first experience they wanted their employees to have.Organizational Culture Shapes AI Adoption: There's no universal model for who should “own” AI governance. It depends on the company's culture. Some companies may create centralized AI governance teams; others may embed responsibilities within existing business units or IT teams. The key is cultural acknowledgment: governance must be understood as a shared responsibility, not just an operational afterthought.Anchor in Business Value: With so many tools, models, and frameworks emerging, it's easy for companies to fall into what he calls “optionality evaluation.” That is, spending so much time chasing the latest innovations that they lose sight of why they started exploring AI in the first place. Instead, he urges leaders to ask: What are we trying to improve? Whether it's speeding up contract workflows, freeing up data scientists from routine tasks, or enhancing customer service, the goal should be clear.The Big Quote: "If you find yourself in a constant evaluation loop for the new shiny object, maybe it's worth taking a pause and saying, 'Why are we doing this again?'"

In this episode, Dr. Keith Morse, Clinical Associate Professor of Pediatrics & Medical Director of Clinical Informatics – Enterprise AI at Stanford Children's Health, shares real-world applications and future visions for generative AI (GenAI) in pediatric care. The discussion highlights how LLMs are being practically integrated into clinical workflows, reducing clinician burden and enhancing hospital operations. Dr. Morse emphasizes the importance of upskilling the workforce to fully leverage AI's potential. With limited prior exposure to tools like LLMs, clinicians and administrative staff need hands-on training. Stanford has launched initiatives including a PHI-compliant internal chatbot, prompt engineering workshops, and engaging frontline staff in pilot projects to build confidence and competence across roles. Dr. Morse sees immense promise in technologies like ambient listening and agentic AI but stresses the need for cautious adoption. In the absence of comprehensive regulation, healthcare systems must take ownership of AI oversight to ensure safety and mitigate risk. He emphasizes the importance of balancing innovation with responsibility, especially in the sensitive context of pediatric care. Take a listen.

In this interview, we're excited to speak with Pravi Devineni, who was into AI before it was insane. Pravi has a PhD in AI and remembers the days when machine learning (ML) and AI were synonymous. This is where we'll start our conversation: trying to get some perspective around how generative AI has changed the overall landscape of AI in the enterprise. Then, we move on to the topic of AI safety and whether that should be the CISO's job, or someone else's. Finally, we'll discuss the future of AI and try to end on a positive or hopeful note! What a time to have this conversation! Mere days from the certain destruction of CVE, averted only in the 11th hour, we have a chat about vulnerability management lifecycles. CVEs are definitely part of them. Vulnerability management is very much a hot mess at the moment for many reasons. Even with perfectly stable support from the institutions that catalog and label vulnerabilities from vendors, we'd still have some serious issues to address, like: disconnects between vulnerability analysts and asset owners gaps and issues in vulnerability discovery and asset management different options for workflows between security and IT: which is best? patching it like you stole it Oh, did we mention Matt built an open source vuln scanner? https://sirius.publickey.io/ In the enterprise security news, lots of funding, but no acquisitions? New companies new tools including a SecOps chrome plugin and a chrome plugin that tells you the price of enterprise software prompt engineering tips from google being an Innovation Sandbox finalist will cost you Security brutalism CVE dumpster fires and a heartwarming story about a dog, because we need to end on something happy! All that and more, on this episode of Enterprise Security Weekly. Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-403

Is the AI race moving too fast for its own good? In this episode of Leveraging AI, we unpack Altman's revealing TED Talk, OpenAI's silent safety rollback, and why ChatGPT just went all-in on vibe coding, enterprise disruption, and social media dominance.This week's AI news is anything but boring — and has massive implications for business leaders. In this session, you'll discover:Why Sam Altman believes ChatGPT will become your lifelong digital companion — and why that's both exciting and terrifyingWhat Altman didn't say about AI safety and the future of AGIHow OpenAI is aggressively chasing developer dominance with its new GPT-4.1 familyThe $3B coding acquisition OpenAI is reportedly chasing — and what that means for the future of softwareWhy OpenAI skipped publishing a safety report for their newest models (yep, really)How ChatGPT is crushing TikTok in app downloads — and quietly testing a social networkThe enterprise AI gold rush: from supply chains and email to avatars and customer service

The world is completely different than it was before ChatGPT.