Podcasts about enterprise ai

Most leaders assume AI and search already see the whole internet. In reality, they all operate on the same tiny slice of the web.In this episode of IT Visionaries, host Chris Brandt sits down with Sudheesh Nair, Co-Founder and CEO of TinyFish and former CEO of ThoughtSpot, to unpack why only a small percentage of the web is indexable and how that cripples enterprise AI.Sudheesh explains why the next breakthrough won't come from bigger models or better search, but from agents that can operate the web at scale, logging in, filling forms, running workflows, and surfacing the long tail of opportunities that never appear on page one. He also shares why human craft, taste, and presence will matter more than ever in an agent-driven world. Key Moments:00:00 - The Deep Web Problem02:48 - The Amazon Search Trap04:26 - Why Search is Broken07:01 - Internet is No Longer a Library08:29 - AI Answers vs Blue Links13:05 - Introducing Tiny Fish's Mission16:00 - Search as a Poor Experience18:29 - The Deep Web: APIs, Workflows & Logins22:11 - Tackling the 93% Problem25:47 - The Eight-Room Hotel Success Story29:04 - Operating the Web vs Skimming It32:42 - Real-Time Personalized Workflows38:31 - Enterprise B2B Strategy40:18 - Taste Over Tools43:08 - AI Freeing Human Experience46:36 - Travel Experiences & Local Discovery50:00 - Democratizing the Internet56:39 - The Waving Guide in China1:01:12 - Optimism for AI's Future -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today's guest is Nina Edwards, Vice President of Emerging Technology and Innovation at Prudential Insurance. With decades of experience driving strategy, innovation, and AI-enabled growth at leading financial and consulting firms, Nina brings deep expertise in applied intelligence and emerging technology. Nina joins Emerj Editorial Director Matthew DeMello to discuss how enterprises can adopt AI safely and effectively, balancing innovation with compliance while mitigating data and copyright risks. She also shares practical takeaways, including implementing instrumented sandboxes, structured licensing, and governance frameworks that boost experimentation confidence, reduce risk, and deliver measurable ROI across business workflows. This episode is sponsored by CCC. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1. Join an exclusive circle of executive leaders shaping the future of AI. Apply to be a guest on the 'AI in Business' podcast at emerj.com/expert2 – share your insights with peers, cement your reputation as a forward-thinking innovator, and have your expertise highlighted to a curated audience of decision-makers.

Kanwal Rekhi first came to the US in the 1960s. He took his company public on Nasdaq in 1987. As a young Indian in the US, he was laid off from his first three jobs. That experience pushed him towards entrepreneurship. At the time, Indians were known and hired for technical and mathematical skills, not as founders building companies on US soil.But Kanwal and his co-founders decided to bet on themselves. They faced rejection from nearly 50 investors before one VC agreed to invest $2 million for 50% of the company. In just five years, the company went public.From being appointed CEO overnight to being removed by the board two months before the IPO for a more “wall street-acceptable” CEO, this is a story of many firsts.After Excelan, Kanwal co-founded TiE in 1992 and has mentored tens of thousands of entrepreneurs. Beyond a personal story, Kanwal Rekhi is a turning point in how Indian founders came to be seen in Silicon Valley.0:00 – Trailer01:11 – How TiE was formed07:11 – DoT Hatao, Desh Bachao11:31 – Career opportunities in the 70s13:41 – When Indians weren't trusted to build companies15:44 – Pioneers in computer networking16:51 – Finding an Investor after 50 rejections20:31 – Becoming CEO overnight23:29 – Spare the story, show the numbers24:17 – The “Wall Street acceptable” CEO for IPO27:30 – Founders have to be financial thinkers28:14 – How Excelan could go public in just 5 years29:27 – Cost is unrelated to pricing in software31:12 – Do Indian companies need Americans to lead?34:05 – Benefits of registering in the US36:53 – $1 trillion to solve India's problems40:49 – Policies for India's startup ecosystem42:01 – Enabling entrepreneurs in villages44:41 – India in the 80s v/s today50:36 – US vs India vs China01:04:52 – How did IITs start allowing donations?01:07:25 – AI investments of Silicon Valley Quad01:18:29 – What Kanwal Rekhi looks for in founders?-------------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

Today, Chris sits down with Joe Schaeppi, co-founder & CEO of Solsten—a deep-tech company mapping human psychology and turning it into actionable AI for creative, targeting, and product personalization. After 8 years of R&D, Solsten's “human context layer” helps enterprises and SMBs understand why people act the way they do—then adapt ads, products, and AI agents to match. Clients like LEGO and Peloton report creative wins and 3× conversion lifts, while a new self-serve product opens the stack to smaller teams.Highlights include...• Building a cognitive-behavioral AI model from clinical-grade psychometrics and authentic behavior data• Why “creative is the new targeting” (Meta's Andromeda) and how psychology-matched creative cuts CPI/raises LTV• Personalization beyond demographics—training AI agents to speak in users' thinking and communication styles• Go-to-market shift: from years of R&D to scale (>$35M raised; investors incl. RedBird & Galaxy)• Use cases across gaming, fintech, health/fitness, hospitality, and more

This episode is our second installment of a special, three-part Reid Riffs miniseries focused on what it actually means to become AI-native. Instead of a news or headline-driven conversation, Reid sits down one-on-one with AI engineer and strategist, Parth Patil, for a deeper exploration of how AI is changing the way people and organizations work. In this second episode, they discuss why most enterprises are still talking about AI without truly integrating it (“AI theater”), and how the real shift begins inside everyday workflows rather than strategy decks. Together, they explore how language models and agents can reduce friction in communication and coordination, reinvent meetings, and turn unstructured information into actionable insight (with examples). They also examine how AI-powered analysis, automation, and parallelized agents are accelerating decision-making, reshaping roles, and moving work from execution toward orchestration. Parth and Reid both highlight how an open mindset and experimentation are required to collaborate effectively with these systems as AI evolves from a productivity tool into a foundational layer for thinking and leadership. Subscribe below to catch the third episode for startup founders and their early teams building AI-native companies.  For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/ 

Prashant Mehrotra, Chief AI Officer at US Bank, discusses how the bank evaluates AI initiatives and scales projects from pilot to production. He explains how to build customer trust through responsible AI design and prepare for the future of autonomous banking in CXOTalk episode 906. This conversation covers key aspects of AI in business and AI implementation within a large banking institution.=======Please support our sponsor Emeritus: Explore executive education programs from Emeritus, in collaboration with top universities: https://cxotalk.partner.emeritus.org/=======Key topics discussed:→ Why AI should transform processes, not simply make them more efficient→ How U.S. Bank cut governance approval times in half by engaging risk partners early→ The critical role of baselines in determining whether AI pilots scale or fail→ Why "AI without data is a hallucination" and how the bank organizes Digital, Data, and AI under one leader→ Building AI literacy across the entire workforce, from executives to frontline associates→ The shift from building models to leveraging external foundation models at scale→ Balancing personalization with privacy in customer interactionsMehrotra emphasizes that the client remains the "North Star" for every AI initiative. He offers practical guidance on metrics, funding pilots through to production, and creating repeatable governance processes that accelerate rather than slow down AI deployment.

In this episode of the AI Agent & Copilot Podcast, John Siefert, CEO of Dynamic Communities, is joined by Crystal Ahrens, Vice President of Solution Delivery and System Architecture at The Heico Companies LLC. Together, they explore how enterprises are moving beyond AI experimentation into real production outcomes, drawing on lessons from implementing agents and Copilot at scale. The conversation also previews what attendees can expect at the 2026 AI Agent & Copilot Summit NA, including real-world use cases, master classes, and community-driven insights.Key TakeawaysFrom readiness to productivity: Many organizations underestimate the effort required to prepare data and systems for agentic AI. The Summit addresses this gap by showcasing companies that have moved from AI readiness to AI productivity. “Everybody's dipping their toes in AI. Everybody's heard of it. But getting AI productive is not that easy to do. Here, we're going to hear the real stories — how you get there and the major pitfalls.”Master classes reveal the real costs: One standout element of the Summit is its master class format, which goes beyond high-level vision to expose real operational details. These sessions openly address run costs, staffing models, and the balance between human and AI labor. Rather than positioning AI as a replacement for people, speakers show how AI augments human intelligence and accelerates outcomes.Human intelligence amplified by AI: AI doesn't just automate tasks, it makes people more effective. Ahrens shares examples where Copilot and agentic AI dramatically reduce manual effort in financial analysis, help desk operations, and portfolio management. By handling exceptions, querying data, and surfacing insights faster, AI allows teams to focus on higher-value work. Visit Cloud Wars for more.

This week we launch our Imperatives for 2026, and I discuss the 11 top issues you face and how HR, as we know it, is going to radically change. Our research shows that 30-40% of today's HR roles will go away, soon to be automated by AI agents and Superagents. Read today's news release for more details. This podcast explains the transformative impact of enterprise AI on human resources, emphasizing the redefinition of HR roles, the emergence of super agents, and the future of work. It highlights the need for organizations to adapt to these changes by focusing on employee engagement and the development of super workers, ultimately leading to enhanced productivity and organizational growth. Major Messages AI is redefining what HR does and how it operates. We are in the early stages of a technology revolution with AI. AI can analyze unstructured data, making HR more strategic. The concept of superagents will change HR technology. Many HR roles will evolve rather than disappear due to AI. Employee engagement is at a low despite advancements in health and longevity. Organizations must continuously care for and support their employees. The workforce is becoming more independent and less tied to a single employer. AI will create opportunities for super workers who leverage technology effectively. Companies must rethink talent management to retain top talent. Your Personal Transformation Each of these 11 topics represent a learning opportunity for business and HR professionals. We've built an entire AI-powered learning experience and Supertutor in Galileo to help. We encourage you to get Galileo to dig in and apply these topics to your job, your company, and your career. Additional Information Imperatives for 2026: What's Ahead for Enterprise AI, HR, Jobs, And Organizations The Collapse And Rebirth Of Online Learning And Professional Development Yes, AI Is Really Impacting The Job Market. Here's What To Do. Get Galileo: The World's AI Agent For Everything HR and Leadership Chapters (00:00:00) - The 2026 Imperatives for Enterprise AI in Human Resources and Human(00:01:00) - The Future of AI Is Here(00:04:32) - The 'Super Agent'(00:05:41) - Will HR Jobs Go Away?(00:06:43) - The second part of the people equation(00:09:00) - The era of superworkers and super-Workers

Winston Weinberg is the CEO and Co-Founder of Harvey, the leading professional services platform engineered with AI for law, tax, and finance. Winston has raised over $980M for Harvey from Sequoia, a16z, GV, Elad Gil and more with a last round price of $9.2BN post-money. Before founding Harvey in August 2022, Winston was an attorney at O'Melveny & Myers LLP, specializing in antitrust and securities litigation.  AGENDA: 04:10 #1 Thing Every Founder Needs to Do Everyday 05:33 Must Do Daily Routines and Productivity Tips for CEOs 12:45 How to Get Sequoia and a16z Term Sheets 15:06 Why VCs Suck at Helping Companies Hire? 27:01 What No One Understands About Enterprise AI Adoption 38:06 AI's Impact on Professional Services 39:26 Future of Law Firms: Do They Die? 43:38 What Everyone Should Know That No One Tells You About Hiring in Europe 47:08 I Have Massive Trust Issues… 54:17 Biggest Lessons on Effective Deal-Making  59:20 Cold Emailing OpenAI and It Leading to a Term Sheet 01:02:33 Quick Fire Round Try NEXOS.AI for yourself with a 14-day free trial: https://nexos.ai/20vc  

Join Simtheory: https://simtheory.ai---Join the most average AI LinkedIn group: https://www.linkedin.com/groups/16562039/It's 2026 and everyone's having an existential crisis. In this episode, we unpack the two camps dominating AI C/Twitter: hype boys claiming "Claude Code can do my washing" vs. software developers doom-scrolling themselves into career panic. We put the agentic hype to the test and discover that no, you can't actually run 8 agents recreating your local business ecosystem while you sleep. Plus, we reflect on why MCP is exhausting, why Gemini 3 Pro is somehow worse than Gemini 2.5 Pro, and why Geoffrey Hinton would rather write his book than answer questions in Tasmania. Also featuring: the $200,000/month enterprise AI problem, why SaaS isn't dead (but it's scared), and our prediction that AI workspaces will become the everything app.CHAPTERS:00:00 Intro - Unpacking the 2026 AI Vibes02:21 Putting Claude Code and Agentic Hype to the Test05:57 Why Twitter AI Demos Never Show the Receipts07:03 Honest Assessment of Where Frontier Models Are At11:19 Building the Everything App with Email, Calendar and Files16:47 Collaborative Mode vs Agentic Delegation in Practice21:29 The Real Cost of Enterprise AI at Scale24:32 Why Cheaper Models Like Haiku and Gemini Flash Matter29:25 Is SaaS Actually Dead or Just Disrupted38:11 The Future of AI Platforms, SDKs and App Stores43:35 The Untapped Opportunity in Paid Proprietary MCPs51:21 Geoffrey Hinton Refuses to Take Questions in Tasmania55:05 2026 Plans and the Still Relevant Tour AnnouncementThanks for listening. Like & Sub. xoxox

In this episode of the AI Agent & Copilot Podcast, John Siefert, President and Founder of AIAC, sits down with Seth Bacon, Director of Innovation at RSM, to explore how organizations can move beyond AI hype and into real, measurable value. Their conversation dives into practical adoption, industry-specific use cases, and what attendees can expect from the 2026 AI Agent & Copilot Summit NA, where real-world outcomes, not proofs of concept, take center stage.Key TakeawaysIndustry-specific use cases: Bacon highlights that outcomes, data sets, and success metrics differ dramatically between sectors like manufacturing, retail, and professional services. By showcasing real industry accelerators, the summit helps attendees see what's possible within their own vertical while also learning from how other industries solve similar problems using the same technologies.Accessible depth for every role: The summit is intentionally designed to support a wide range of maturity levels. Bacon notes that some attendees are just learning how to write better prompts, while others are orchestrating complex, multi-platform agentic systems.The power of in-person connection: While sessions provide structured learning, both speakers agree that the most impactful moments often happen outside the rooms. Bacon emphasizes that hallway conversations, meals, and informal networking create lasting relationships that extend well beyond the event. These connections give attendees trusted peers to call months later when challenges arise, making the summit not just a learning experience, but a long-term support network. Visit Cloud Wars for more.

This week, as part of our 2026 Imperatives launch, I discuss the explosive new world of agents and superagents, and explain why and how you, as an HR or business person, will be “building apps” and “building agents” at work. I also explain why the Superagent architecture, which is explained in our Imperatives research, is going to replace traditional monolithic HR and other applications at a speedy rate. Yes, we're all going to be “Citizen Developers” and we won't necessarily need Vibe Coding apps to do this. Galileo is an app-builder today and the upcoming Mars release is going to take it even further. This important topic is a big and very important shift in your thinking about how you run HR and also how you select, purchase, and implement HR technology of all kinds. Listen in, join in our webinar next week, and get Galileo to learn more and get started. Galileo will show you how to start building solutions today. All this information and much more is part of our 2026 Imperatives and will be embedded into Galileo, so get Galileo and ask Galileo to give you specific examples of how you can apply AI to HR in your particular company. This research includes 30+ prompts to help you understand enterprise AI in detail. Join me in my 2026 Imperatives webinar on January 21 for more details. Like this podcast? Rate us on Spotify or Apple or YouTube. Additional Information Is Oracle's Debt Level Getting Crazy? There's A Method To This Madness. Yes, AI Is Really Impacting The Job Market. Here's What To Do. The Collapse And Rebirth Of Online Learning And Professional Development Imperatives for 2026: What's Ahead for Enterprise AI, HR, Jobs, And Organizations Chapters (00:00:00) - Claude Code and You as a Citizen Developer(00:05:24) - Building a self-contained AI-enabled HR Software(00:11:41) - Machine Learning and the Software Industry

Parable is building an end-to-end intelligence platform that quantifies how organizations spend their collective time—the foundation for measuring real AI impact. With a thousand data connectors ingesting activity and log data across the enterprise software stack, Parable constructs proprietary knowledge graphs that size opportunities and measure outcomes in hard dollars, not adoption metrics. In this episode of BUILDERS, I sat down with Adam Schwartz, Co-Founder & CEO of Parable, to explore why 95% of CFOs see no AI ROI, how his decade running profitable businesses under resource constraints shaped his focus on inputs over outcomes, and why 2026 requires moving AI from CapEx experimentation to measured OpEx. Topics Discussed: Why the 95% CFO stat on AI ROI matters as an arbiter of truth, despite backlash Building knowledge graphs from activity data to quantify collective time allocation across hundreds of people The fundamental problem: enterprises lack quantitative frameworks for operational efficiency pre-AI Running parallel ICP experiments to achieve sales-market fit before product-market fit Why Parable has never lost a POC once leaders see quantitative baselines Market dynamics creating false signals—unprecedented curiosity without buying intent The demarcation between companies treating AI as product work versus those waiting for vendor solutions Why AI transformation demands century-old management structures to be questioned GTM Lessons For B2B Founders: Engineer disqualification in momentum markets: Market-wide AI enthusiasm creates pipeline illusion. Prospects will engage indefinitely for education without purchase intent. Adam's framework: "How do we get people to say no to us and not drag us along... They want to keep talking because they want to learn and they want to know what's going on and they are genuinely interested." In enterprise sales during category shifts, build explicit qualification gates that force prospects to reveal resource commitment or disqualify. Extended evaluation cycles feel like traction but destroy unit economics. Use go-to-market as ICP discovery mechanism: Adam intentionally pursued multiple customer segments simultaneously—different company sizes and AI maturity stages—to let data reveal fit rather than rely on hypothesis. His memo to the team: "We're going to go after these three, you know, many different sizes of companies in order for us to decide like, who we like best." The key insight: get to problem-market fit and sales-market fit validation before optimizing product-market fit. This inverts conventional wisdom but works when TAM is massive and the bottleneck is identifying who feels pain acutely enough to buy now. Qualify on organizational structure, not verbal commitment: Every enterprise claims AI is strategic. Adam's hard filter: "Who in the organization is responsible for AI transformation? And if you don't have a one person answer to that question, you're not serious." Serious buyers have a named owner reporting to C-suite with dedicated budget and team. Buying Gemini, Glean, or other point solutions isn't a seriousness KPI—it's often passive consumption of AI as a byproduct of existing software relationships. Look for companies doing five-year work-backs on industry transformation and cascading effects on their operating model. Target post-experimentation, pre-scale buyers: Adam discovered the sweet spot isn't companies beginning their AI journey—it's those who've deployed initial programs and now need to prove value. "The market of people that have started to build AI into their operating model or into their strategy in like a coherent way, there's a team, there's an owner, there's budget... those are the people that we really want to be talking to." These buyers understand the problem viscerally because they're living it. They do product work daily—talking to stakeholders, generating use cases, building briefs, triaging roadmaps. They need your solution to professionalize what they're already attempting manually. Build measurement into your category narrative: The AI tooling market has over-indexed on soft efficiency claims that won't survive renewal cycles. Adam's warning: "There is too much hand waving around soft efficiency gains... you're going to have to renew and you need NRR and I don't think it's going to be that usage of the tool internally by employees and adoption is going to be enough." The last decade over-rotated to "everything drives revenue" due to VC pressure. This decade requires precision: does your product save time, reduce headcount needs, or accelerate revenue? Quantify it. Partner with measurement platforms if needed. Adam's insight on Calendly is instructive—it clearly saves time, but most buyers can't quantify how much, which weakens renewal economics. // 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 // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Where did the journey of iD Fresh start?It began when a 19-year-old Abdul Nazer decided to run away from home to Bangalore with ₹100 in his pocket. He did any job that came his way: cook, cleaner, conductor and sold anything he could, from clothes and vegetables to spices and peanuts. Along the way, he brought his three brothers to Bangalore.Even with huge losses in business, they never stopped looking for new opportunities. Their first real glimpse of success came from a tea stall run out of a rented room that cost ₹80 a month. Despite strong demand, the tea business was still running at a loss. The turning point came when they started opening the stall at 2 in the morning: a disruptive business model, says PC. Those ₹2 cups of tea taught them lessons they would carry forward.Abdul Nazer and PC Mustafa together share these stories for the first time. Their journey reminds us that no success is overnight, especially not for these brothers. It was at their kirana store in Indiranagar that the idea of iD Fresh was born. Five brothers with no background in food technology spent six months experimenting with recipes before finding their hero product. This is the story of five founders who pushed past their circumstances. Today, iD Fresh is at a scale the founders never dreamed of growing up in Wayanad. 00:00 – Trailer00:55 – Dropping out of studies02:32 – 19 Year Old that Runaway to Bangalore04:35 – First job as a cook11:25 – When Nazer decided to become an entrepreneur14:01 – Huge loss in Vegetable business16:15 – Starting the kirana store that led to iD18:00 – On the verge of shutting down20:10 – How Lambu Tea Stall became profitable24:16 – When PC decided to do business with Nazer25:14 – All the (failed) businesses that led to iD27:04 – Why PC decided to come back to India28:23 – The origin story of the idli batter idea31:40 – The first $50k investment32:57 – Cracking the batter without any food tech expertise34:38 – The first recipe that became iD's hero product35:35 – Why iD failed to sell 100 packets in 6 Months41:15 – The first customer approval43:06 – Building awareness was the biggest challenge44:35 – Lack of cold storage in supermarkets45:22 – The inventory model of iD46:25 – How the initial team was built48:35 – Story of team spirit50:15 – When iD Fresh Chennai & Mumbai Failed52:27 – How the chemistry worked between the five brothers56:52 – The buyout offer of ₹20+ crores57:35 – How founders build the mindset to hire experts1:00:50 – Did the runaway child achieve his dream?1:03:09 – How Nazer's choices directly led to iD1:04:45 – Building within value systems1:07:26 – Summary: what really worked for iD Fresh-------------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 Send us a text

Can AI really reorder how business works - not just automate tasks, but redefine entire markets? In this episode, Frazer Anderson sits down with two leading thinkers - Conor Twomey and Ray Wang. They discuss: How AI exponentials fundamentally rewrite productivity and what that means for founders, enterprises, and investors competing with legacy incumbents. The evolving state of enterprise AI adoption - from prototypes to production‑scale agent workflows that transform business outcomes and unit economics. Why scaling enterprise AI depends less on model quality and more on your data foundation, internal coordination, and ability to operationalize agents across real workflows. — Conor Twomey is an accomplished executive with over 15 years of experience in addressing complex data challenges for leading global corporations. He is currently an AI Co-Founder at Stealth Startup. Conor is the former Head of AI Strategy at KX, a pioneer in real-time data analytics and decision intelligence. Under his leadership, KX successfully transitioned from a time-series database company to the Enterprise AI platform of choice for large-scale AI implementations. Before this role, Conor managed a 400-person organization encompassing Presales, Professional Services, Support, Managed Services, and Customer Success Management. Renowned for his insights on data and AI, Conor is a sought-after speaker and contributor on frontier technology topics, including Data, Analytics, Machine Learning, AI, and Generative AI. — Ray Wang, a tech luminary, is the Co-Founder & Chairman of Constellation Research, a Bestselling Author, and a Keynote speaker. Renowned for his insights into digital transformation and enterprise technology, Ray's expertise stems from influential roles at Altimeter Research and Forrester Research. His bestselling books, including "Disrupting Digital Business" and "Everybody Wants to Rule the World," delve deep into the impact of digital technologies on business models.

What happens when the web browser stops being a passive window to information and starts acting like an intelligent coworker, and why does that suddenly make security everyone's problem? At the start of 2026, I sat down with Michael Shieh from Mammoth Cyber to unpack a shift that is quietly redefining how work gets done. AI browsers are moving fast from consumer curiosity to enterprise reality, embedding agentic AI directly into the place where most work already happens, the browser. Search, research, comparison, analysis, and decision support are no longer separate steps. They are becoming one continuous workflow. In this conversation, we talk openly about why consumer adoption has surged while enterprise teams remain hesitant. Many employees already rely on AI-powered browsing at home because it removes ads, personalizes results, and saves time.  Inside organizations, however, the same tools raise difficult questions around data exposure, credential safety, and indirect prompt injection. Once an AI agent starts reading untrusted external content, the browser itself becomes a new attack surface. Michael explains why this risk is often misunderstood and why the real danger is not internal documents, but external websites designed to manipulate AI behavior. We dig into how Mammoth Cyber approaches this challenge differently, starting with a secure-first architecture that isolates trusted internal data from untrusted external sources. Every AI action, from memory to model connections to data access, is monitored and governed by policy. It is a practical response to a problem many security teams know is coming but feel unprepared to manage. We also explore how AI browsers change day-to-day work. A task like competitive analysis, which once took days of manual research and document comparison, can now be completed in minutes when an AI browser securely connects internal knowledge with external intelligence. That productivity gain is real, but only if enterprises trust the environment it runs in. We touch on Zero Trust principles, including work influenced by Chase Cunningham, and why 2026 looks like a tipping point for enterprise AI browsing. The technology is maturing, security controls are catching up, and businesses are starting to accept that blocking AI outright is no longer realistic. If you are curious to see how this works in practice, Mammoth Cyber offers a free Enterprise AI Browser that lets you experience what secure AI-powered browsing actually looks like, without putting your organization at risk. I have included the link so you can explore it yourself and decide whether this is where work is heading next. So, as AI browsers become the new workflow hub for knowledge workers everywhere, is your organization ready to secure the browser before it becomes your most exposed endpoint, and what would adopting one safely change about how your teams work? If you want to see what an enterprise-grade AI browser looks like when security is built in from day one, Mammoth Cyber is offering free access to its Enterprise AI Browser.  It gives you a hands-on way to experience how agentic AI can automate real work inside the browser while keeping internal data isolated from untrusted external sources. You can explore it yourself and decide whether this is how your organization should be approaching AI-powered browsing in 2026. Useful Links Learn more about the Mammoth Enterprise Browser and try it for free  Connect with Michael Shieh on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

AI systems are moving fast, sometimes faster than the guardrails meant to contain them. In this episode of Security Matters, host David Puner digs into the hidden risks inside modern AI models with Pamela K. Isom, exploring the governance gaps that allow agents to make decisions, recommendations, and even commitments far beyond their intended authority.Isom, former director of AI and technology at the U.S. Department of Energy (DOE) and now founder and CEO of IsAdvice & Consulting, explains why AI red teaming must extend beyond cybersecurity, how to stress test AI governance before something breaks, and why human oversight, escalation paths, and clear limits are essential for responsible AI.The conversation examines real-world examples of AI drift, unintended or unethical model behavior, data lineage failures, procurement and vendor blind spots, and the rising need for scalable AI governance, AI security, responsible AI practices, and enterprise red teaming as organizations adopt generative AI.Whether you work in cybersecurity, identity security, AI development, or technology leadership, this episode offers practical insights for managing AI risk and building systems that stay aligned, accountable, and trustworthy.

Enterprise AI adoption is still stuck in the teens and the gap between the hype and the reality is getting harder to ignore. People are finding pockets of productivity, but they're often keeping the gains to themselves, worried that “using AI well” is just speed-running their way into a layoff. Meanwhile, many leaders treat it like another piece of software without touching the messier truth: AI changes how work actually happens, and it doesn't care about your org chart, your approval chains, or your performance theater. In this episode, Rodney sits down with Section CEO Greg Shove to name what's really blocking adoption and what it takes to break through. They talk about AI as “co-intelligence”, why most “AI layoffs” are PR cover, and the non-negotiables for real transformation. They also get into how to build a robust AI strategy for 2026, Section's own AI disruption, and why the next era may be dominated by super companies built around small human teams + a fleet of agents. Learn more about Greg: His website Section's website Prof.AI AI Truth Serum podcast -------------------------------- Ready to change your organization? ⁠⁠⁠⁠⁠⁠Let's talk.⁠⁠⁠⁠⁠⁠ Get our newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠ Follow us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ -------------------------------- Mentioned references: Edelman's AI creators Chegg's downfall Moderna's AI usage Zapier's AI usage BOX's AI usage Dual Transformation Skunk Works Mary Barra "amazon.bomb" Stanford AI study 00:00 Intro + Check-In: What's something happening in the AI hype cycle that drives you nuts right now? 03:21 Enterprise AI adoption stall out 08:58 AI as truth serum for lies in your company 11:49 Required ingredients for real AI transformation 19:04 Balancing risk with AI usage in startups and large enterprise 24:10 “Head of AI” roles are an uphill battle 27:48 First principles for an AI-lead organization 30:10 Disrupting your business model with AI and dual transformation 35:29 Greg and Section disrupting themselves with AI 37:44 Role of leadership in an AI future 44:40 Future of companies and careers 47:54 Role of companies in future of society 52:07 Wrap up: Leave us a review and share the show with a coworker! Sound engineering and design by Taylor Marvin of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Coupe Studios⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Moiz Kohari, VP of Enterprise AI and Data Intelligence at DDN, breaks down what it actually takes to get AI into production and keep it there. If your org is stuck in pilot mode, this conversation will help you spot the real blockers, from trust and hallucinations to data architecture and GPU bottlenecks.Key takeaways• GenAI success in the enterprise is less about the demo and more about trust, accuracy, and knowing when the system should say “I don't know.”• “Operationalizing” usually fails at the handoff, when humans stay permanently in the loop and the business never captures the full benefit.• Data architecture is the multiplier. If your data is siloed, slow, or hard to access safely, your AI roadmap stalls, no matter how good your models are.• GPU spend is only worth it if your pipelines can feed the GPUs fast enough. A lot of teams are IO bound, so utilization stays low and budgets get burned.• The real win is better decisions, faster. Moving from end of day batch thinking to intraday intelligence can change risk, margin, and response time in major ways.Timestamped highlights00:35 What DDN does, and why data velocity matters when GPUs are the pricey line item02:12 AI vs GenAI in the enterprise, and why “taking the human out” is where value shows up08:43 Hallucinations, trust, and why “always answering” creates real production risk12:00 What teams do with the speed gains, and why faster delivery shifts you toward harder problems12:58 From hours to minutes, how GPU acceleration changes intraday risk and decision making in finance20:16 Data architecture choices, POSIX vs object storage, and why your IO layer can make or break AI readinessA line worth stealing“Speed is great, but trust is the frontier. If your system can't admit what it doesn't know, production is where the project stops.”Pro tips you can apply this week• Pick one workflow where the output can be checked quickly, then design the path from pilot to production up front, including who approves what and how exceptions get handled.• Audit your bottleneck before you buy more compute. If your GPUs are waiting on data, fix storage, networking, and pipeline throughput first.• Build “confidence behavior” into the system. Decide when it should answer, when it should cite, and when it should escalate to a human.Call to actionIf you got value from this one, follow the show and turn on notifications so you do not miss the next episode.

We are entering the year of Enterprise AI, and one of the imperatives we're introducing is the need to think about your AI Architecture. While much of our AI journey has been focused on individual productivity tools, now we have a much bigger opportunity: using AI to rethink how our HR, talent, leadership, and human capital processes are designed. As you'll hear our new Systemic HR® AI Blueprint defines a new set of “Superagents” that help us think through the new workflow automations we can deploy. In this podcast I explain the new AI architecture for HR at a high level and give you a sense of the explosive vendor market, the role of “citizen developers,” and the business case and process for prioritizing where to focus. All this information and much more is part of our 2026 Imperatives launch and will be embedded into Galileo, so get Galileo and ask Galileo to apply these architectural issues to your HR department. Not only do we have massive opportunities to build a more integrated HR department, these new AI architectures enable our companies to scale, grow, and add customer value faster and more profitably than ever. Join me in my 2026 Imperatives webinar on January 21 for more details. Like this podcast? Rate us on Spotify or Apple or YouTube. Additional Information Yes, AI Is Really Impacting The Job Market. Here's What To Do. Imperatives for 2026: What's Ahead for Enterprise AI, HR, Jobs, And Organizations The Collapse And Rebirth Of Online Learning And Professional Development Get Galileo: The World's AI Agent For Everything HR and Leadership Chapters (00:00:00) - Machine Learning in HR: The Future of AI(00:11:51) - AI HR: The New Business Model

Why wasn't 2025 the year of the agents?

Most organizations are sitting on mountains of documents, PDFs, emails, and images they still cannot fully search, organize, or understand.In this episode of IT Visionaries, host Chris Brandt sits down with Tim McIntire, CTO of Hyland, to unpack why unstructured data is still one of the biggest blockers between AI hype and real results. Tim breaks down why so many companies struggle to access the content they already have and what it really takes to make that information usable, trustworthy, and valuable.From building content that is ready for AI to unlocking new context-aware agents and improving governance and transparency, Tim explains how leading organizations are finally turning everyday content into real business impact and why the future of enterprise AI starts with cleaning up what is already in the basement. Key Moments:02:36 - What Hyland Actually Does04:18 - Why 80% of Enterprise Data Is Unusable06:46 - From 30,000 Manual Indexes to Automation07:47 - Vectorization: Making Documents AI-Ready09:45 - The ROI of Eliminating Mundane Work10:55 - AI vs RPA: Why Intelligence Changes Everything13:23 - Federate Don't Migrate: Meeting Customers Where They Are16:29 - Governance Can't Be an Afterthought18:11 - The Explainability Breakthrough20:10 - Day 1 to Day 90: Faster Time to Value22:56 - Enterprise Agent Mesh Explained24:06 - Context Engineering: The New AI Superpower26:36 - Confidence Scores: When Humans Step In28:12 - Right Model for the Right Job31:00 - The 18-Month Prediction: Agents Everywhere -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Lexi Reese has scaled companies at every stage — from building Google's programmatic advertising business, to helping Gusto grow revenue from $10M to $300M. Now she's co-founder and CEO of Lanai, an enterprise AI startup tackling a problem most companies don't even realize they have: they can't actually see how AI is being used inside their organizations, or whether it's driving real outcomes. In this episode, we unpack what it really looks like to build a company from scratch in the AI era. Lexi walks through how she ran more than 200 customer interviews before committing to a product direction, why product-market fit isn't real until someone is willing to pay, and how she's building a 14-person team — plus AI “teammates” — without losing focus or trust. We also talk about fundraising in a tougher 2025 market, why early founders need to resist the urge to build comprehensive solutions too soon, and how organizational design is already changing as AI flattens hierarchies and reshapes work. If you're thinking about starting a company — or you're in the messy middle of finding product-market fit — this conversation offers a practical roadmap for what actually matters. RUNTIME 51:45   EPISODE BREAKDOWN (2:01) What Lanai does (5:05) Lexi's customer discovery process — “definitely 200 interviews” (12:03) Why customer delight should be a founder's obsession metric (15:36) What “AI productivity” actually means (19:12) Lexi's framework for managing small, early-stage teams (26:23) Her take on seed-stage fundraising in late 2025 (31:54) How to integrate customer feedback into product strategy (38:00) The most meaningful proof a first-time founder can show an investor (40:53) Why “trust has a code” when it comes to teamwork (44:08) How Lexi stays obsessed with customers in every meeting (48:15) The final question LINKS Lexi Reese Lanai Steve Herrod Juxtapose General Catalyst Splunk Datadog Why You Will Marry the Wrong Person, Alain de Botton SUBSCRIBE

In this episode of the Shift AI Podcast, Eilon Reshef, Co-founder and Chief Product Officer at Gong, joins host Boaz Ashkenazy to explore how artificial intelligence is revolutionizing revenue operations and transforming the future of sales teams. Reshef shares the decade-long journey of building Gong from a data-driven conversation platform into what they call an "AI OS for Revenue Teams," serving Fortune 10 companies and pioneering the use of AI in go-to-market workflows.From capturing customer conversations to deploying AI agents that can automate mundane tasks, Reshef offers a compelling vision of where sales productivity is heading. The conversation delves into how AI is making revenue teams more efficient and effective, the transformation of traditional sales roles, and why the biggest shift isn't just in technology but in how quickly organizations can execute and adapt. If you're interested in understanding how enterprise AI is moving beyond hype to deliver measurable productivity gains, how unstructured data creates competitive advantages, and what the future holds for revenue professionals in an AI-driven world, this episode offers invaluable insights from someone building the infrastructure that's reshaping B2B sales.Chapters[00:00] Introduction and Welcome[02:15] Eilon's Background and Journey to Gong[05:30] The Origin Story: Starting Gong 10 Years Ago[08:45] First and Worst Jobs[11:00] The ChatGPT Moment and AI Acceleration[14:30] Gong's Unfair Advantage with Unstructured Data[17:00] Three Pillars of AI Transformation[21:15] The Rev Ops Revolution[24:00] Enabling Sales Teams with AI[27:30] What's Hype vs. Reality in AI for Revenue[31:00] The Future of Sales Roles and Team Structure[33:45] The Platform Strategy and Integrations[36:30] Security and Trust in Enterprise AI[39:00] Gong's Growth Trajectory and Future Plans[41:30] Voice Agents and the Future of Customer Interaction[45:00] Two Words for the Future: Efficient and Exciting[48:00] Connecting with Eilon and Hiring at GongConnect with Eilon ReshefLinkedIn: https://linkedin.com/in/eilonreshefConnect with Boaz AshkenazyLinkedIn: https://linkedin.com/in/boazashkenazyEmail: info@shiftai.fm

Most enterprises aren't struggling with AI because of technology. They're struggling because they're trying to scale pilots instead of platforms. In this episode of Technovation, Peter High speaks with Atilla Tinic, CIO of Qualcomm, about how the company is moving beyond one-off AI use cases to build an enterprise AI platform designed for scale. Tinic explains why unified and validated data is essential for AI accuracy, how Qualcomm enables developers and business teams through a centralized AI marketplace, and why security must be embedded into AI architecture from day one. Key topics include: Why data governance is foundational to AI success How Qualcomm structures AI as a reusable enterprise platform The rise of AI agents and autonomous systems Cybersecurity challenges introduced by AI and how AI helps defend against them

Best of 3500 Minutes in 45 Minutes2025 was a great year for The Neon Show. 60 episodes, 72 guests, and thousands of minutes of insightful conversations on everything around building a business.You'll hear perspectives from Founders scaling companies across the world, sharing the real challenges behind building high-growth startups; Investors on how they spot opportunities and make bold bets; and Ecosystem leaders who have navigated multiple cycles and understand what truly lasts.This episode is a carefully curated highlight reel. The sharpest ideas, boldest bets, and timeless lessons that defined this year. Watch it for clear takeaways to carry into 2026 on building companies that last for decades.0:00 – Trailer01:26 – Paras Chopra03:37 – Avanish Bajaj06:53 – Vijay Rayapati08:33 – Ashu Garg11:39 – Kiran Darisi16:40 – Asha Jadeja20:33 – Sanjeev Bikhchandani23:22 – Alok Goyal26:41 – Shiv Shivumar29:34 – Saurya Prakash31:59 – Raviteja37:21 – Ashish Toshniwal43:54 – Bhaskar Gosh47:32 – Somesh Dash-------------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

Welcome to 2026, a year I coin “The Year of Enterprise AI.” As you'll read about (and hear about) in our 2026 Imperatives launch, the coming year is all about AI moving from “assistants” to “agents” to “solutions.” And there are three big considerations to ponder. First, the cost of AI is skyrocketing, so we're going to have to focus on high-value use-cases and business-specific solutions. That's not to say AI assistants and meeting summaries are not valuable, but once you start paying by the token you're going to want to go deeper. As we discuss in our new Systemic HR AI Framework, we're sitting on billions of dollars of real business opportunities now, and they go far beyond individual assistants. (We call these Superagents.) And the cost of AI will accelerate this focus. Second, the data center buildout, energy costs, and political issues with data centers will matter. For corporate users this means understanding the underlying “costs” of AI usage (creating a single high powered image uses as much as 25% of the battery in your phone). I point this out to make you aware that these AI chatbots are not “free” – there are acres of computing campuses being built behind the scenes. And that means your “software providers” are turning into capital intensive companies. (And a new industry of data center companies may take over.) (For those of you in the energy industry, it's a wild time – almost as exciting as I've seen since my early days as an energy engineer during the OPEC Arab Oil Embargo in the late 1970s.) Third is the fast-changing issue of AI's accuracy, trust, and voracious appetite for data. As I discuss, the real opportunity for corporate AI is to take this problem head-on, and focus on your company's data quality, governance, human feedback, and data labeling. The big AI labs are struggling to reduce the “Jaggedness” of AI (it's strange ability to be really good at some things and totally dumb about others), and that encourages us to focus on narrow, domain-specific AI applications. And we all need to learn about RLHF (reinforcement learning with human feedback). Our experience with Galileo proves that an AI solution that focuses on a vertical domain can be infinitely more reliable and intelligent than a general purpose AI. But don't let me argue with Sam Altman, you'll have to figure this out yourself :-). We are launching our 2026 Imperatives research the third week of January, and there will be a special release of Galileo to accompany all the study. Our goal is not to give you a bunch of pithy predictions, but rather to give you a dozen hard-hitting “Must Do's” for the year ahead. I look forward to talking with many of your this coming year as we travel around the world, join us in January for the launch of our 2026 Imperatives research. Like this podcast? Rate us on Spotify or Apple or YouTube. Additional Information Imperatives for 2026: What's Ahead for Enterprise AI, HR, Jobs, And Organizations Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI (NYT bestseller, high... Chapters (00:00:00) - Three Challenges to AI in 2026(00:01:06) - The Cost of AI Infrastructure(00:06:03) - Sustainability in the AI Era(00:12:57) - The Big Story for Human Resources in 2026

Harish Bhat spent 38 years with the Tata Group, working across businesses that reach millions of Indians every day, including Titan, Tanishq, and Tata Tea.He joins Neon Show for a 3rd time and reflects on what it meant to build inside a 150+ year-old institution. The conversation begins in 1991, the year Ratan Tata took over as Chairman, a role he would hold for 21 years. Harish explains how Ratan Tata prepared Tata Sons at a time when the Indian economy was opening up and competition was changing rapidly.We discuss landmark moments in the group's history, including the Tetley acquisition in 2000, the first time an Indian company acquired a major global consumer brand. Harish shares how this decision transformed not only the Tata Group's mindset but also the way ambitious Indian businesses think about their potential.Harish speaks about Ratan Tata not as a distant icon, but as a leader he worked closely with. He shares stories of how decisions were made, how conflicts were handled, and why dignity, compassion, and keeping one's word were always non-negotiable for Ratan Tata.The conversation also draws from his book Doing the Right Thing, where he transfers these experiences into practical lessons on leadership shaped over decades.https://www.amazon.in/Doing-Right-Thing-Bestselling-Tatastories/dp/014347985700:00 — Trailer01:07 — Paying tribute to Mr. Ratan Tata05:53 — The Tata family legacy06:53 — Early childhood and education of Ratan Tata07:48 — The decision to return to India08:44 — How Ratan Tata prepared the Group for a liberalised economy14:35 — How Tata Sons became a global business16:45 — The $450 million Tetley acquisition20:08 — Tata Group's acquisition of Global Brands23:33 — A visionary leader who chose to remain deeply private25:04 — How Ratan Tata dealt with Conflict28:58 — Dignity above all31:29 — The only concern on renovation of Bombay House34:41 — How the Tata Group gives back to Mumbai39:44 — Four lessons from Ratan Tata's Life42:50 — The deeper purpose that drives the Tata Group44:45 — Emotional gestures that speak to people's hearts48:45 — Ratan Tata as a philanthropist51:26 — A life guided by the principle: “Do the right thing”53:06 — The story behind the book-------------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

Send us a textIn our latest WTR Flashcast, host Tim Gerdeman and technology analyst James Kisner break down Water Tower Research's initiation of coverage on C3 AI (NYSE: AI). They explore how C3 serves as an "enterprise AI operating layer" that sits above cloud providers and foundation models, helping large organizations connect AI capabilities to their internal data and workflows. The discussion covers C3's recent financial reset and leadership transition, the growing importance of federal government customers, and how generative and agentic AI are becoming the front door into the platform. James also walks through why C3 has become a credible acquisition candidate for strategic buyers like Microsoft or IBM. 

What if the key to enterprise AI wasn’t a tool, but a mindset? Mark Bloom, Global CIO at AJ Gallagher, joins Technovation to share how the 70,000-person insurance giant is scaling AI by leading with data quality and cultural alignment—not flashy tools. In this episode, Bloom details: How Gallagher eliminated 800+ data silos to centralize insight and enable AI Why crowdsourcing use cases from employees unlocked adoption at scale The shift from efficiency gains to revenue-focused AI How culture helped overcome resistance to data consolidation His dual perspective as both CIO and board member

Ari Morcos and Rob Toews return for their spiciest conversation yet. Fresh from NeurIPS, they debate whether models are truly plateauing or if we're just myopically focused on LLMs while breakthroughs happen in other modalities.They reveal why infinite capital at labs may actually constrain innovation, explain the narrow "Goldilocks zone" where RL actually works, and argue why U.S. chip restrictions may have backfired catastrophically—accelerating China's path to self-sufficiency by a decade. The conversation covers OpenAI's code red moment and structural vulnerabilities, the mystique surrounding SSI and Ilya's "two words," and why the real bottleneck in AI research is compute, not ideas.The episode closes with bold 2026 predictions: Rob forecasts Sam Altman won't be OpenAI's CEO by year-end, while Ari gives 50%+ odds a Chinese open-source model will be the world's best at least once next year. (0:00) Intro(1:51) Reflections on NeurIPS Conference(5:14) Are AI Models Plateauing?(11:12) Reinforcement Learning and Enterprise Adoption(16:16) Future Research Vectors in AI(28:40) The Role of Neo Labs(39:35) The Myth of the Great Man Theory in Science(41:47) OpenAI's Code Red and Market Position(47:19) Disney and OpenAI's Strategic Partnership(51:28) Meta's Super Intelligence Team Challenges(54:33) US-China AI Chip Dynamics(1:00:54) Amazon's Nova Forge and Enterprise AI(1:03:38) End of Year Reflections and Predictions With your co-hosts:@jacobeffron  - Partner at Redpoint, Former PM Flatiron Health@patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn@ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)@jordan_segall  - Partner at Redpoint

Founders are often seen as superhumans. In this new series, we look at the humans behind the superhuman journey. The thrill of building, the guilt of missing out, the learnings, the failures, and why they still do it and would do it all over again.Arpita is a second-time founder, now building Mysa. Her first startup, Mech Mocha, was acquired by Flipkart. Ananda is the Co-Founder and CTO of Astra Security. They are building in two different spaces, finance and cybersecurity, but the journeys are similar, that of a founder.This is an unfiltered conversation between two founders about what building a company really looks like: the choices they didn't make, the people who bet on them early, and how their identities, relationships, and sense of self changed along the way.This episode is for anyone who is building, thinking of building, or simply curious about what being a founder really feels like.0:00 – Becoming a Founder in 20s05:10 – The odd realities of being a founder young07:51 – Placements we got, but never took10:56 – Learning to ask for help as founders16:39 – The people who bet on you early23:05 – Co-founder dynamics as life partners25:40 – Handling co-founder conflict27:21 – Making it to Forbes 30 Under 3031:54 – How the PM award helped during house-hunting34:10 – Being a Topper is Not Important anymore35:45 – How close should founders be to their teams?37:40 – Why advice hasn't worked much for me39:27 – Getting addicted to the thrill of being a founder41:27 – When a founder's identity becomes tied to their company43:18 – Setting boundaries as founders43:40 – Why I don't share my Instagram with my team44:07 – Realising that your team may not be forever49:10 – Startups are marathons, not sprints50:24 – Why founders need to be humanized53:43 – Living life in the limelight as a founder57:55 – Why work friends often don't exist for founders59:09 – Would you do it all over again?01:01:36 – How family react when one decides to be a founder?01:02:32 – Is it easier the second time as a founder?01:03:27 – Why not knowing was actually a gift-------------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

Interview with Dorothy Creaven and Michael Cordner at AWS re:Invent Dublin-based startup Jentic was the first Irish company to complete the AWS Generative AI Accelerator, which concluded recently at AWS re:Invent in Las Vegas. The company is now focused on building enterprise awareness of its platform, supported by the launch of its AI Readiness Scorecard and its listing on AWS Marketplace. Founded in 2024 by Sean Blanchfield, Michael Cordner, and Dorothy Creaven, Jentic applies middleware and enterprise integration engineering to AI adoption, focusing on how APIs are defined, governed and safely executed by automated and agentic systems. AI adoption with API readiness platform Jentic Jentic operates at the integration layer, working with existing enterprise systems and APIs to make them clearer, more structured and more governable. This allows organisations to connect AI systems to real business infrastructure in a controlled and observable way, without replacing existing platforms or bypassing established security and compliance processes. Built on Enterprise Infrastructure Experience The company's approach is shaped by the founders' backgrounds in building large-scale infrastructure. Blanchfield previously co-founded Demonware, acquired by Activision Blizzard, and PageFair. Cordner co-founded Mindconnex, while Creaven previously led Rent the Runway's Irish operations. Speaking to Irish Tech News at AWS re:Invent, Michael Cordner, CTO of Jentic, said many enterprises are now encountering limits in how their systems were originally built. "We got away with cutting corners for 20 years when we were developing APIs for developers," said Cordner . "But now we're trying to let AI loose on those same APIs, and the standards are much more stringent. Even the most intelligent AI in the world is useless without the right information on how to actually use a system." From Jentic's perspective, the current interest in AI exposes long-standing weaknesses in enterprise integration. Automated systems can reason and decide, but they can only act through APIs. If those interfaces are poorly documented, inconsistently structured or weakly governed, behaviour becomes unpredictable. "We're a business logic and infrastructure layer for AI agents," explains Dorothy Creaven, COO of Jentic. "Software has always been built on APIs, but for AI to connect properly to enterprise systems, there has to be something that can make sense of those APIs and turn them into workflows organisations can rely on." Addressing Enterprise Control and Governance A recurring issue Jentic encounters with enterprise customers is organisational hesitation. Senior leadership often wants progress on AI strategy, while technology and security teams are concerned about control, traceability and risk. "Everyone is afraid to let AI loose in their organisation," Creaven observes. "There's a real concern about what systems might do when nobody is watching, whether actions can be traced, and how failures are handled." To address this, Jentic's platform includes a sandboxed execution environment that mirrors production APIs. This allows organisations to test AI-driven workflows, observe behaviour and understand failure modes before anything is connected to live systems. "We provide an environment that mirrors real APIs, but in a way that's safe," Creaven comments. "You can see exactly what's happening, with auditability and logging, and you can only move forward once you're confident the behaviour is correct." Launch of the AI Readiness Scorecard This approach underpins the launch of Jentic's AI Readiness Scorecard, a free, automated assessment tool introduced at AWS re:Invent. The scorecard evaluates APIs across multiple dimensions, including structure, security, documentation quality and discoverability. According to Jentic, its analysis of more than 1,500 well-known APIs highlights repeated gaps. These include missing authentication details, invalid OpenAPI specifications, i...

As organizations race to adopt AI, many discover an uncomfortable truth: ambition often outpaces readiness. In this episode of the ITSPmagazine Brand Story Podcast, host Sean Martin speaks with Julian Hamood, Founder and Chief Visionary Officer at TrustedTech, about what it really takes to operationalize AI without amplifying risk, chaos, or misinformation.Julian shares that most organizations are eager to activate tools like AI agents and copilots, yet few have addressed the underlying condition of their environments. Unstructured data sprawl, fragmented cloud architectures, and legacy systems create blind spots that AI does not fix. Instead, AI accelerates whatever already exists, good or bad.A central theme of the conversation is readiness. Julian explains that AI success depends on disciplined data classification, permission hygiene, and governance before automation begins. Without that groundwork, organizations risk exposing sensitive financial, HR, or executive data to unintended audiences simply because an AI system can surface it.The discussion also explores the operational reality beneath the surface. Most environments are a patchwork of Azure, AWS, on-prem infrastructure, SaaS platforms, and custom applications, often shaped by multiple IT leaders over time. When AI is layered onto this complexity without architectural clarity, inaccurate outputs and flawed business decisions quickly follow.Sean and Julian also examine how AI initiatives often emerge from unexpected places. Legal teams, business units, and individual contributors now build their own AI workflows using low-code and no-code tools, frequently outside formal IT oversight. At the same time, founders and CFOs push for rapid AI adoption while resisting the investment required to clean and secure the foundation.The episode highlights why AI programs are never one-and-done projects. Ongoing maintenance, data validation, and security oversight are essential as inputs change and systems evolve. Julian emphasizes that organizations must treat AI as a permanent capability on the roadmap, not a short-term experiment.Ultimately, the conversation frames AI not as a shortcut, but as a force multiplier. When paired with disciplined architecture and trusted guidance, AI enables scale, speed, and confidence. Without that discipline, it simply magnifies existing problems.Note: This story contains promotional content. Learn more.GUESTJulian Hamood, Founder and Chief Visionary Officer at TrustedTech | On LinkedIn: https://www.linkedin.com/in/julian-hamood/Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Spotlight Brand Story: https://www.studioc60.com/content-creation#spotlight▶︎ Highlight Brand Story: https://www.studioc60.com/content-creation#highlightKeywords: sean martin, julian hamood, trusted tech, ai readiness, data governance, ai security, enterprise ai, brand story, brand marketing, marketing podcast, brand story podcast, brand spotlight Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Episode SummaryThe future of cyber resilience lies at the intersection of data protection, security, and AI. In this conversation, Cohesity CEO Sanjay Poonen joins Danny Allan to explore how organisations can unlock new value by unifying these domains. Sanjay outlines Cohesity's evolution from data protection to security in the ransomware era, to today's AI-focused capabilities, and explains why the company's vast secondary data platform is becoming a foundation for next-generation analytics.Show NotesIn this episode, Sanjay Poonen shares his journey from SAP and VMware to leading Cohesity, highlighting the company's mission to protect, secure, and provide insights on the world's data. He explains the concept of the "data iceberg," where visible production data represents only a small fraction of enterprise assets, while vast amounts of "dark" secondary data remain locked in backups and archives. Poonen discusses how Cohesity is transforming this secondary data from a storage efficiency problem into a source of business intelligence using generative AI and RAG, particularly for unstructured data like documents and images.The conversation delves into the technical integration of Veritas' NetBackup data mover onto Cohesity's file system, creating a unified platform for security scanning and AI analytics. Poonen also elaborates on Cohesity's collaboration with NVIDIA, explaining how they are building AI applications like Gaia on the NVIDIA stack to enable on-premises and sovereign cloud deployments. This approach allows highly regulated industries, such as banking and the public sector, to utilize advanced AI capabilities without exposing sensitive data to public clouds.Looking toward the future, Poonen outlines Cohesity's "three acts": data protection, security (ransomware resilience), and AI-driven insights. He and Danny Allan discuss the critical importance of identity resilience, noting that in an AI-driven world, the security perimeter shifts from network boundaries to the identities of both human users and autonomous AI agents.LinksCohesityNvidiaSnyk - The Developer Security Company Follow UsOur WebsiteOur LinkedIn

Four enterprise AI leaders from Box, Snorkel AI, Sumo Logic, and Talkdesk peel away the hype and share battle-tested strategies for implementing agentic AI at scale.Topics Include:Carol Potts introduces panel featuring AI leaders from Box, Snorkel AI, Sumo Logic, and TalkdeskDiego Dugatkin explains Box serves 120,000 enterprise customers with 1.5 exabytes of secure cloud contentKui Jia shares Sumo Logic processes petabytes daily across 10 AWS regions for intelligent operationsYunjing Ma describes Talkdesk's evolution from contact center to customer experience automation through agentic AIDennis Panos positions Snorkel AI as leader in embedding human knowledge into data-centric applicationsDiego reveals Box uses AI internally for faster development and externally for metadata extraction automationKui explains security teams face overwhelming volumes, sometimes 1,000 signals daily, many AI-generated attacksSumo Logic announces SOC analyst agent in customer beta and query agent in general availabilityYunjing details Talkdesk's multi-agent hierarchy architecture powered by unified TalkDesk Data Cloud platformFour key areas identified: discovery of opportunities, building knowledge-powered agents, optimization, and measurementDennis emphasizes starting with trusted data foundation before adding generative AI capabilities to avoid hallucinationsDiego stresses governance importance: AI guardrails plus traditional data security create comprehensive protection frameworkKui warns POC-to-production gap requires intentional design: different latency, accuracy, and security requirements at scaleYunjing shares customer success: 80,000 daily calls, 11,000 documents, 97% accuracy despite complex compliance rulesKey success factors include prompt engineering optimization and real-time data processing mechanism improvementsDiego advises learning AI tools end-to-end: from ideation through functional demos without traditional prototyping delaysDennis recommends robust evaluation frameworks across system components, similar to software unit testing approachesYunjing reinforces data processing optimization and governance remain essential alongside exciting agentic AI capabilitiesKui urges immediate action: technology evolves rapidly, perfect solutions don't exist, customer focus builds trustFinal advice centers on treating AI as digital teammate, not replacement, enhancing productivity and creativityPlatform partnerships like AWS Bedrock solve heavy lifting, allowing teams to focus on core differentiatorsParticipants:Diego Dugatkin - Chief Product Officer, BoxDennis Panos - Head of Enterprise AI, SnorkelAIKui Jia - VP AI Engineering, Sumo LogicYunjing Ma - VP of Engineering, AI, TalkdeskModerator: Carol Potts - General Manager, ISV Sales Segment, North America, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

A billion-dollar check from Disney. A federal crackdown on state AI laws. And a new model from OpenAI that beats human experts 71% of the time. In Episode 186, Paul and Mike unpack the release of GPT-5.2, Disney's strategic pivot to license its IP for Sora, and President Trump's executive order designed to accelerate "American AI dominance" at all costs. Plus: Is the future of data centers in space? Why is Microsoft Copilot struggling in the enterprise? And a look at Time's "Architects of AI." Show Notes: Access the show notes and show links here Click here to take this week's AI Pulse. Timestamps: 00:00:00 — Intro 00:03:46 — AI Pulse 00:06:27 — GPT-5.2 and OpenAI Turns 10 00:22:43 — Disney-OpenAI Deal 00:32:41 — Trump Executive Order to Override State AI Laws 00:44:17 — OpenAI State of Enterprise AI Report 00:53:03 — Google Cloud ROI of AI Reports 00:56:14 — Microsoft Lowers AI Sales Expectations 01:02:14 — TIME Person of the Year: The “Architects” of AI 01:06:08 — The Economics of AI and Data Centers in Space 01:14:31 — Shopify SimGym 01:18:37 — Research on Teen AI Usage 01:21:11 — OpenAI Certifications This episode is brought to you by AI Academy by SmarterX. AI Academy is your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. You can get $100 off an individual purchase or a membership by using code POD100 at academy.smarterx.ai. Visit our website Receive our weekly newsletter Join our community: Slack LinkedIn Twitter Instagram Facebook Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

Glean started as a Kleiner Perkins incubation and is now a $7B, $200m ARR Enterprise AI leader. Now KP has tapped its own podcaster to lead it's next big swing. From building go-to-market the hard way in startups (and scaling Palo Alto Networks' public cloud business) to joining Kleiner Perkins to help technical founders turn product edge into repeatable revenue, Joubin Mirzadegan has spent the last decade obsessing over one thing: distribution and how ideas actually spread, sell, and compound. That obsession took him from launching the CRO-only podcast Grit (https://www.youtube.com/playlist?list=PLRiWZFltuYPF8A6UGm74K2q29UwU-Kk9k) as a hiring wedge, to working alongside breakout companies like Glean and Windsurf, to now incubating Roadrunner which is an AI-native rethink of CPQ and quoting workflows as pricing models collapse from “seats” into consumption, bundles, renewals, and SKU sprawl. We sat down with Joubin to dig into the real mechanics of making conversations feel human (rolling early, never sending questions, temperature + lighting hacks), what Windsurf got right about “Google-class product and Salesforce-class distribution,” how to hire early sales leaders without getting fooled by shiny logos, why CPQ is quietly breaking the back of modern revenue teams, and his thesis for his new company and KP incubation Roadrunner (https://www.roadrunner.ai/): rebuild the data model from the ground up, co-develop with the hairiest design partners, and eventually use LLMs to recommend deal structures the way the best reps do without the Slack-channel chaos of deal desk. We discuss: How to make guests instantly comfortable: rolling early, no “are you ready?”, temperature, lighting, and room dynamics Why Joubin refuses to send questions in advance (and when you might have to anyway) The origin of the CRO-only podcast: using media as a hiring wedge and relationship engine The “commit to 100 episodes” mindset: why most shows die before they find their voice Founder vs exec interviews: why CEOs can speak more freely (and what it unlocks in conversation) What Glean taught him about enterprise AI: permissions, trust, and overcoming “category is dead” skepticism Design partners as the real unlock: why early believers matter and how co-development actually works Windsurf's breakout: what it means to be serious about “Google-class product + Salesforce-class distribution” Why technical founders struggle with GTM and how KP built a team around sales, customer access, and demand gen Hiring early sales leaders: anti-patterns (logos), what to screen for (motivation), and why stage-fit is everything The CPQ problem & Roadrunner's thesis: rebuilding CPQ/quoting from the data model up for modern complexity How “rules + SKUs + approvals” create a brittle graph and what it takes to model it without tipping over The two-year window: incumbents rebuilding slowly vs startups out-sprinting with AI-native architecture Where AI actually helps: quote generation, policy enforcement, approval routing, and deal recommendation loops — Joubin X: https://x.com/Joubinmir LinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction and the Zuck Interview Experience 00:03:26 The Genesis of the Grit Podcast: Hiring CROs Through Content 00:13:20 Podcast Philosophy: Creating Authentic Conversations 00:15:44 Working with Arvind at Glean: The Enterprise Search Breakthrough 00:26:20 Windsurf's Sales Machine: Google-Class Product Meets Salesforce-Class Distribution 00:30:28 Hiring Sales Leaders: Anti-Patterns and First Principles 00:39:02 The CPQ Problem: Why Salesforce and Legacy Tools Are Breaking 00:43:40 Introducing Roadrunner: Solving Enterprise Pricing with AI 00:49:19 Building Roadrunner: Team, Design Partners, and Data Model Challenges 00:59:35 High Performance Philosophy: Working Out Every Day and Reducing Friction 01:06:28 Defining Grit: Passion Plus Perseverance

Sales, and Matt Hobbs, Cloud Engineering and Data Analytics Platform Leader and Partner at PwC US. Together, they explore how companies can stop overpaying for cloud and instead fund AI innovation by shifting spend from legacy and suboptimal cloud deployments into modern architectures, multi-cloud strategies, and enterprise-grade AI capabilities that actually move the needle on growth, margin, and new business models.Smarter Cloud, Bigger AIThe Big Themes:Built to Cost Less: Oracle entered the cloud market later and designed OCI from the “bare metal up” with off-box virtualization, a low-latency non-blocking network, and significantly lower egress pricing. That means Oracle's own cost to deliver infrastructure is structurally lower, so they don't need to “race to zero” with margin-crushing discounts. When customers compare OCI run-rates to first-generation hyperscalers, it's common to see 40–70% savings at list-to-net, not just in special deals.Turning Technical Debt Into Innovation Budget: Hobbs notes that roughly 40% of internal tech budgets are often tied up in technical debt rather than innovation. PwC sees executives searching for ways to unlock capital for AI and growth initiatives, not just trim expenses. Its “Fit for Growth” program looks at where money is tied up in non-differentiating costs (cloud infrastructure being one of the biggest line items) and reallocates that spend into value-creating initiatives. When PwC runs side-by-side economics, they've seen OCI's promised 40–70% savings show up in real deals.OCI + PwC: budget creation meets execution: The Oracle–PwC collaboration stands out, the guests argue, because both sides are relentlessly focused on the client outcome rather than maximizing any one platform. PwC validates OCI's economics and brings the talent to design and execute migrations, process re-invention, and agentic AI programs; Oracle brings a cost-efficient, multi-cloud-friendly infrastructure designed for price-performance and portability.The Big Quote: “You can burn a lot of money chasing ghosts in this game if you really don't have a very specific use case." Visit Cloud Wars for more.

In 1992, Roopa Kudva walked into CRISIL's CEO Pradeep Shah's office without an appointment, starting her 23-year career there. She spent over two decades at CRISIL, rising from analyst to CEO. Roopa has spent over 3 decades in leadership roles in India and has witnessed three key phases in India's growth: the closed economy in the 80s, the post-liberalisation era, and the rise of tech entrepreneurs.She shares bold decisions that defined her journey. Like when she proposed to the then CRISIL CEO to create the Chief Ratings Officer role and pitched herself for it. She got the role, which set her on the path to becoming CEO. We also discuss the leaders who shaped her thinking, K.V. Kamath of ICICI, Piyush Gupta of DBS, and Katharine Graham of the Washington Post.Throughout the conversation, Roopa returns to one idea: there is no single leadership style or fixed playbook. Her journey shows how ambition and initiative to act at the right moment can define a career and the organizations one builds along the way.0:00 —Trailer01:21 — IIM to IDBI03:54 — Work Culture in the 80s05:58 — Rise of New-Age Companies06:55 — The Aha Moment of Leadership View08:52 — Leaving CRISIL After 23 Years10:49 — Choosing Omidyar & Impact Investing16:03 — India's Evolving Risk Appetite20:40 — Deciding the Next Career Move26:08 — How She Got the CRISIL Job31:09 — Asking for the CRO Role35:48 — Promotions Are Bets on the Future37:37 — The Leader Who Changed Her Philosophy43:40 — ICICI as a Women-CEO Factory45:36 — What Holds Women Back from Rising51:38 — DBS: The Piyush Gupta Transformation55:06 — Entrepreneurs for the Next Half Billion1:02:47 — The New Indian Founder Profile-------------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

Today's episode breaks down new reports from OpenAI and Menlo Ventures that show enterprise AI adoption accelerating quickly, with coding emerging as the first true killer use case, reasoning models driving deeper workflow integration, and the gap between leaders and laggards widening as frontier firms compound their advantages. The conversation also looks at early agent deployments and what these trends signal for the 2026 boom-versus-bubble debate. In the headlines: Anthropic donates MCP as OpenAI, Anthropic, and Block form the Agentic AI Foundation, rumors swirl around GPT-5.2 and a new image model, OpenAI launches AI Foundations certifications, and the US military unveils its GenAI.milBrought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Gemini - Build anything with Gemini 3 Pro in Google AI Studio - ⁠⁠http://ai.studio/build⁠⁠Rovo - Unleash the potential of your team with AI-powered Search, Chat and Agents - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://rovo.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LandfallIP - AI to Navigate the Patent Process - https://landfallip.com/Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.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/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai

For episode 651 of the BlockHash Podcast, host Brandon Zemp is joined by Andrew Sobko, CEO of Argentum AI, an Enterprise level ready AI-powered compute marketplace.Andrew Sobko is a serial entrepreneur with a background in building transformative marketplaces. He founded one of the fastest-growing companies in America, recognized by the Financial Times and honored by Goldman Sachs' Builders & Innovators award. Andrew has raised over $200 million from leading global investors including Sequoia Capital, Brookfield, and others. ⏳ Timestamps: (0:00) SUMSUB(0:44)  Introduction(0:53) Who is Andrew Sobko?(5:50) Argentum Marketplace(9:28) Advantages of Argentum(13:58) Trust & validation for Argentum clients(14:37) SUMSUB(16:00) Argentum Token(18:50) How to contribute compute(19:42) Future of Enterprise AI(22:05) Argentum roadmap for 2026(25:03) Events & conferences(25:30) Website & socials 

In this episode of the Identity at the Center Podcast, hosts Jeff and Jim sit down with Tobin South, co-chair of the OpenID Foundation's AI Identity Management Community Group, to delve into the intricacies of identity management in the age of agentic AI. They discuss the challenges and solutions related to AI agents, the role of the Model Context Protocol (MCP), and the concept of recursive delegation and scope attenuation. Additionally, the conversation covers practical advice for developers and enterprises on preparing for AI-driven identity management and explores the cultural touchstone of coffee from various global perspectives.Connect with Tobin: https://www.linkedin.com/in/tobinsouth/OpenID Foundation: https://openid.net/Identity Management for Agentic AI (OpenID Whitepaper): https://openid.net/wp-content/uploads/2025/10/Identity-Management-for-Agentic-AI.pdfConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.comChapter Timestamps:00:00 – Jeff and Jim banter about unopened iPads and conference season05:55 – Introduction to Tobin South and his AI identity background07:00 – How AI has evolved from machine learning to generative models09:00 – The OpenID AI Identity Management Community Group10:30 – ChatGPT's impact on the AI perception shift12:00 – Users vs. Agents: What's the difference?14:00 – Letting the right bots in: AI agents vs. bad bots17:00 – AI impersonation, delegation, and the risk of shared credentials20:00 – Impersonation vs. Delegation – what practitioners need to know23:00 – Governance, oversight, and delegated authority for agents26:00 – Liability and “who is responsible” in agentic systems30:00 – How developers can prepare for agent identity and access management32:00 – Explaining the Model Context Protocol (MCP)36:00 – Enterprise use cases for MCP and internal automation38:00 – Is MCP the next SAML?42:00 – Recursive delegation and scope attenuation explained46:00 – The one key takeaway for IAM professionals48:00 – Lighter note: Coffee talk – from Sydney to San Francisco54:00 – Wrap-up and where to find more IDAC contentKeywords:IDAC, Identity at the Center, Jim McDonald, Jeff Steadman, Tobin South, OpenID Foundation, AI Identity Management, Agentic AI, Delegated Authority, Impersonation vs Delegation, Model Context Protocol (MCP), Recursive Delegation, Scope Attenuation, Identity Access Management, IAM, AI Governance, AI Standards, Enterprise AI, AI Agents, Identity Security

Peloton welcomes three new instructors to its fitness team.Get the details on major updates to the PSL schedule.A new Club Peloton perk offers members early access to classes.Peloton's Chief Marketing Partnership Director has departed the company.Peloton's CTO sparked conversation with a discussion on ChatGPT integration.The company is hiring a Sr. Director of Digital Innovation & Enterprise AI.New yoga instructor Johanna Ricouz gives a running class a try.Jess Sims has a hilarious NSFW moment on live television.Denis Morton is at the center of "Hairgate 2.0."Instructor Cliff Dwenger has released a new song.DJ John Michael celebrates his 10-year anniversary with Peloton and drops a new remix.The latest Artist Series spotlights the music of Michael Bublé.Wicked: For Good themed classes are now available.Kristin McGee launches her very first program on the Tonal platform.Our TCO Top Five recap of the community's favorite Peloton classes.This Week at Peloton: A rundown of what's happening on the platform.TCO Radar: We highlight upcoming fitness classes you won't want to miss.Bradley Rose & Benny Adami have new rides themed around Stranger Things.A new "Meet Your New Yoga Instructors" challenge is live.Get ready for a new Holiday fitness challenge.Sam Yo's popular Top Gun ride has been removed from the class library.Happy Birthday to Peloton instructor Tunde Oyeneyin on December 5th.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Maribel Lopez reports live from AWS re:Invent 2025 in Las Vegas, unpacking why the AI experimentation phase is officially over. With statistics that say 95% of AI projects are failing and enterprise budgets tightening, 2026 demands production-quality AI—not more proof-of-concepts. This episode explores the critical shift from building agents to deploying them safely at scale.Key ThemesThe Reality Check (2025 Recap)MIT study reveals 95% AI project failure rateMcKinsey and BCG document widespread implementation strugglesBoard-level AI initiatives now demand real ROI, not just innovation theaterThe POC gold rush is over—experimentation budgets are drying upAgentic AI Grows Up The conversation has evolved from "can we build agents?" to "can we trust them in production?" Three critical roadblocks:Security & Orchestration: How agents interact without creating vulnerabilitiesPolicy & Governance: Preventing rogue agents and establishing guardrailsObservability: Real-time monitoring to ensure agents perform as intendedAWS re:Invent 2025 HighlightsAgent Core ImprovementsEnhanced policy frameworks defining agent boundaries and permissionsHuman-in-the-loop controls for high-stakes decisionsBetter cross-stack orchestration for multi-agent workflowsThe Discoverability ProblemAWS Marketplace now features natural language searchUpload requirements documents instead of filling rigid formsAI-suggested prompts help non-technical users navigate complex decisionsSmarter filtering for nuanced needs (performance vs. cost vs. compliance)The Full-Stack MaturityRecognition that AI "takes a village"—no single vendor owns the entire stackGrowing emphasis on open standards (A2A, MCP) for SaaS integrationTools designed for all skill levels, not just data scientistsKey TakeawayEnterprise AI in 2026 isn't about doing more—it's about doing it right. The winners will be organizations that prioritize governance, observability, and practical deployment over flashy demos.Host: Maribel LopezRecorded: AWS re:Invent, Las Vegas, December 2025Follow-up: Stay tuned for next week's deep-dive episode with demos and vendor interviews

Most conversations in startups begin at zero: what's the idea, who's the customer, how big is the market. But the stage before that, when you know you're ready to be a founder yet the direction is still completely undefined. That strange, uncomfortable, high-potential zone Aditya Agarwal calls “minus one.”In this episode, Aditya and Prateek Mehta breaks down what happens in this “figuring out” stage. The questions people avoid, the habits that matter, and why some of the best companies begin long before their founders have any conviction.We get into how this stage is evolving in the AI era. Exploration cycles are faster, technical founders can test more directions than ever, and the gap between “I'm experimenting” and “I'm running a real company” has narrowed. India's builder ecosystem is shifting too: more second-time founders, more people with real outcomes behind them, and far more comfort sitting with ambiguity.Aditya shares his own minus-one moment after Facebook, his startup acquisition, Dropbox's IPO, and Flipkart, and why that transitional period changed the way he thinks about early-stage startups. Prateek brings on-the-ground view from Bangalore, where ambition, technical depth, and the appetite to explore hard problems from robotics to voice models to AI infra are rising.This episode is for anyone who feels they're between missions. Anyone who wants to understand why the most important part of building a company might actually be the time you spend before you even know what you're building.00:00- Trailer01:06- Aditya's journey to starting SPC after Facebook & Dropbox 03:48- A “learning club” for people in figuring-out stage06:23- 3 Northstars of the SPC community07:02- How SPC evolved from a community to a fund10:32- Not everyone should be a founder11:51- 1% selection rate13:53- Building conviction in 1 of 3 outcomes16:36- SPC is at PMF stage18:38- Mismatch of traditional VC's v/s rapid pace startups19:04- How AI has impacted investing at SPC26:32- How AI has changed VC firms29:02- Axis of curiosity replacing thesis30:17- Star Companies of SPC US33:34- Binny Bansal's role in starting SPC India37:16- Questions & confusions as founders in early stage39:50- Number of great entrepreneurs is NOT small41:49- Talent density in India vs Bay Area44:04- Founders don't need a culture of permission45:08- India tier 2 and 3 does invest heavily in AI46:11- AI is truly democratizing tech49:09- Math gives India advantage in AI51:48- A lot of science fiction is coming true-------------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

Episode SummaryAs AI systems become increasingly integrated into enterprise workflows, a new security frontier is emerging. In this episode of The Secure Developer, host Danny Allan speaks with Nicolas Dupont about the often-overlooked vulnerabilities hiding in vector databases and how they can be exploited to expose sensitive data.Show NotesAs organizations shift their focus from training massive models to deploying them for inference and ROI, they are increasingly centralizing proprietary data into vector databases to power RAG (Retrieval-Augmented Generation) and agentic workflows. However, these vector stores are frequently deployed with insufficient security measures, often relying on the dangerous misconception that vector embeddings are unintelligible one-way hashes.Nicolas Dupont explains that vector embeddings are simply dense representations of semantic meaning that can be inverted back to their original text or media formats relatively trivially. Because vector databases traditionally require plain text access to perform similarity searches efficiently, they often lack encryption-in-use, making them susceptible to data exfiltration and prompt injection attacks via context loading. This is particularly concerning when autonomous agents are over-provisioned with write access, potentially allowing malicious actors to poison the knowledge base or manipulate system prompts.The discussion highlights the need for a "secure by inception" approach, advocating for granular encryption that protects data even during processing without incurring massive performance penalties. Beyond security, this architectural rigor is essential for meeting privacy regulations like GDPR and HIPAA in regulated industries. The episode concludes with a look at the future of AI security, emphasizing that while AI can accelerate defense, attackers are simultaneously leveraging the same tools to create more sophisticated threats.LinksCyborgOWASP LLM Top 10Snyk - The Developer Security Company Follow UsOur WebsiteOur LinkedIn

You ever see a new AI model drop and be like.... it's so good OMG how do I use it?

Have you ever wondered what happens when the browser stops being a simple window to the web and starts becoming the control point for how AI touches every part of enterprise life? That was the starting point for my conversation with Michael Shieh, founder and CEO of Mammoth Cyber. What followed was a detailed look at why the browser is turning into the foundation of enterprise AI and why the shift is arriving faster than many expect. Michael shared why employees already spend most of their working lives inside a browser and how this makes it the natural place for AI to support decisions, speed up routine work, and act as the interface between people, applications, and data. But we also spoke about the uncomfortable reality behind that convenience.  When consumer AI browsers rush ahead with features that harvest data or request wide-reaching permissions, the trade off between speed and governance becomes harder to ignore. Michael explained how this gap leaves security teams unable to see where sensitive data is being sent or how shadow AI creeps into daily workflows without oversight. During our conversation he broke down what makes an enterprise AI browser different. We talked about policy controlled access, device trust, identity federation, and the safeguards that protect AI from hazards like indirect prompt injection. Michael also described how the Mammoth team built a multi layer security model that monitors what the AI can view, what it cannot view, and how data moves across applications in real time. His examples of DLP at the point of use, low friction controls for workers, and granular visibility for security teams showed how the browser is becoming the new enforcement boundary for zero trust. We also covered the growing tension between traditional access models like VPNs or VDI and the faster, lightweight deployment Mammoth is offering to large enterprises. Hearing Michael explain how some customers replaced heavy remote access stacks in weeks made it clear that this is more than a new product category. It hints at an early move toward AI shaped workflows running directly at the endpoint rather than through centralised infrastructure. As he looked ahead to the next few years, Michael shared why he expects the browser to operate as a kind of operating system for enterprise AI, blending native AI agents, web apps, and policy controls into a single environment. This episode raises an important question. If the browser becomes the place where AI reads, writes, and interprets information, how should enterprises think about identity, trust, and control when the pace of AI adoption accelerates again next year? I would love to hear your thoughts.

What happens when enterprise AI moves faster than the data foundations meant to support it? That question guided my conversation with Sumit Mehra, CTO and Co-Founder of Tredence, who joined me while travelling between customer meetings on the US West Coast. Sumit has a clear view of what is coming next, and he believes we are entering a phase he calls data Darwinism.  In his view, the next stage of AI advantage will not be won by the companies with the most models or the flashiest demos, but by those with the strongest data habits. Clean, governed, connected data is now the primary fuel for autonomous decision systems, and the enterprises that fail to address this will struggle to move past surface level gains. As we unpacked this shift, it became obvious how much of the real work in AI has only just begun. Over the years, Tredence built a reputation for solving the last mile of analytics by bringing insights out of slide decks and into the hands of the people doing the work. Sumit described that early chapter with a sense of pride, but he was quick to point out that another transition is already here. With agents now influencing and making decisions across supply chains, forecasting, and customer experience, enterprises are moving from reviewing insights to reviewing decisions. That shift demands stronger data platforms, tighter governance, and a cultural adjustment that many organisations are still wrestling with. Sumit spoke openly about how teams need support to trust agent driven outcomes, and how the leadership layer plays a major role in closing the long standing divide between business and technical groups. Our discussion also moved into the rise of real time decision systems, the move toward unified data platforms, and how vertical AI is reshaping expectations inside industries that rely on precision. Whether it was supply chain visibility, marketing personalisation, or the growing need for credible governance models, Sumit emphasised that organisations can no longer rely on siloed data or fragmented strategies. As Tredence expands deeper into regulated industries through its acquisition of Further Advisory, the work ahead touches everything from finance to healthcare. It left me thinking about how ready most companies truly are for this next phase, where every agent is only as reliable as the data beneath it. Where do you stand on data Darwinism, and how prepared do you think your own organisation is for what comes next? Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.