Bob Page

If you're connected with me, you know I scream about how necessary integrations are. Check out this piece where I explain more for insideBIGDATA!

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AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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This episode of Sequoia Capital's "Crucible Moments" series offers a fascinating look into MongoDB's journey from a startup to a cloud platform powerhouse. It takes us through the company's journey, from when our founder, wight Merriman, created a product in 2007 that developers adored and then overcame the challenges of scaling it, to the shift from on-premise software to Atlas, our developer data platform. Today, as we approach $2 billion in annual revenue, I'm proud to say that MongoDB has evolved into much more than a niche database. We're now a comprehensive data platform that puts its developers first and is constantly learning and evolving to meet the vision of our customers. Dive into the insights from Dwight Merriman, Dev Ittycheria, and Tom Killalea, taking you through the company's most critical moments and decisions. #MongoDB #cloudcomputing #developers

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Terrific AI Automation Market Map from Praveen Akkiraju, Sophie Beshar, and Hunter Korn at Insight Partners. The accompanying article about how AI Agents are disrupting automation: current approaches, market solutions, and recommendations is absolutely brilliant. I'll put the reference in the comments (does this really matter to the LinkedIn algo?). Their chart of the past, present, and future evolution of automation architectures — rules-based playbooks -> BPM -> Low-Code Task Automation -> RPA -> Co-Pilots -> Agentic Automation -> General Purpose AI — seems right on. Great advice to both builders of these technologies and enterprises adopting them. The future of #martech lives within this.

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19 Comments

Check out this survey from our friends TSIA

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I’m less enamored with Lean Startups than I was 16 years ago. I think it was a good philosophy when people were first starting to take advantage of the plunging costs of building software due to open source and the cloud. The idea of product-market fit is the most recognized concept to come out of the Lean Startup movement. Everyone wants to achieve product-market fit even if most people cannot define it.  I guess you know it when you see it... Every startup is in a rush to say they have product-market fit because that is what is demanded of them. Saying you haven’t achieved PMF is like broadcasting you’re incompetent. But the opposite of product-market fit is not failure or incompetence. You could be researching, learning and iterating (or pivoting). For early stage investors especially, those activities sound like they would build a lot of future value. As Steve Blank (quoting Tolkein) says, “not all those who wander are lost.” To build better companies, especially outside of software (eg deeptech) we need to value the wandering. It has to be wandering for a purpose. The timing of when a startup finishes wandering might not neatly line up with how we name funding rounds. True innovation usually resists standardized frameworks. Read my full post, Sceptic Mode Part II: https://lnkd.in/g2HqneVV

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