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Aman Rangrass
Experienced Vice President and Business Executive in Finance and Operations
Aman Rangrass is a professional with experience in operations and partnerships. He currently holds the position of Vice President of Operations and Partnerships at Skan, a company that appears to be in the technology sector.1
Some key points about Aman Rangrass:
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He joined Skan in 2021 as VP of Operations and Partnerships.1
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In this role, he is responsible for cross-company operations and partnerships at Skan.1
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His LinkedIn profile username is aman-rangrass-10100a5b.
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He is listed as a Member at Pavilion, though the specific nature of this membership is not clear from the available information.
Aman Rangrass seems to have expertise in business operations and developing strategic partnerships. His role at Skan involves overseeing operational aspects of the company and managing partnerships, which suggests he has experience in business development and organizational management.
Without more detailed information from reliable sources, it's difficult to provide additional specifics about his background, previous roles, or areas of expertise. The information available primarily focuses on his current position at Skan and his LinkedIn profile.
Highlights
Great day at Citi’s Gen AI Summit. Very insightful and factual panels, with takeaways consistent with customer conversations we’ve had in the last quarter:
- Most customers are still very much in the Gen AI PoC stage today, and continue to be cautious about deploying anything at-scale in production
- Many of those in production are primarily doing chatbots focused on internal facing application and customer service; feels very much in the early stage of the first innings
- For those in production, the most time is spent in the last 10% of getting a model into production - many last minute wrinkles to iron out
- Counterintuitively, regulated industries are moving faster on Gen AI use cases, because the in-house data is much cleaner (thanks to adherence to regulation)
- If you thought your cloud bill was high.. wait till you see your AI bills - will force enterprises to think doubly hard about use cases
Good seeing Prayank Swaroop Burak Arik and many others there!
Big kudos in pulling this together Matt Carbonara ✅ Vibhor Rastogi Çağla Kaymaz!

Privileged to attend MIT’s Annual AI day Imagination in Action in Boston with my colleagues Avinash Misra and Vinay Mummigatti. It was an amazing gathering of academia, enterprise practitioners, startups, along with the opportunity to co-host many senior enterprise leaders for a closed room dinner post event.
Tough to summarize, but these my top 10 takeaways, besides many of the practical use cases covered by leading industry practitioners.
The optimism 👌 ✅ Take the long view: everything is new, and only v10 will “feel” close to mature, not v4. Use it, feed the data flywheel and expect the rate of growth to be exponential. ✅ Vinod Khosla’s bold predictions - (i) Most use of the internet will be agents, not humans - multiple agents doing specialized things for us - as if each of us had multiple assistants (my favorite), (ii) computers will learn humans vs. humans have to learn computers, every task will have a co-pilot ✅ The leaders in space are starting to separate themselves: companies have anywhere from 30 to 3,000 cross-functional colleagues dedicated to innovating in this space. ✅ Lex Friedman’s trends: web search will be transformed, robotics is the next “AI wave” waiting to happen in the next 3-5 years (in the factory and the home), more open source of LLMs ✅ Meta’s open sourcing Llama with Yann LeCun: 500K more Nvidia chips ordered ($30B in spend, blows my mind!) and scaling up the algorithms to run them in parallel. Llama 3 just launched with the principle that open sourced infrastructure will become better, faster. ✅ Open AI preparedness for risk: Team dedicated to think about the unwanted consequences of AI and to expand the aperture of the risk consequences for the unknown unknowns ✅ Use cases: Boards care. Customer service seems to be a common starting point. More data-mature enterprises have figured workaround on the security considerations. Enterprises are starting with very small targeted use cases, but still feeling their way into larger expansive use cases (think E2E processes).
What lies ahead 🛣️ ✅ Learn to learn: Be ready to change fields rapidly and nothing you learn today may be relevant 10 years from now. Reinvent yourself. ⏸️ Be ready to disappointed (in the near term): While the foundations of AI have been worked on for decades, only in the last 18 months has there been the recognition and recent funding, similar to the dot com era innovation arc, where the hockey stick arc came in 1995. Iconic companies will be built in AI, but be ready for immediate term disappointment as use cases continue to be refined. 🚦AI Governance and Regulation is coming, whether you like it or not (e.g., AI Act in California). Goal is to democratize it but enterprises will be wary, and they are all building their governance approaches as we speak. Your product might very well be the first use case :)
Vibhor Rastogi Alpa Inamdar Ashraf Youssef Preetha Sekharan Cissy Williams Alex Barretto Amit Sawhney Dr. Rodney B. Woods Douglas Kim
