Pawan Deshpande

Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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AI, Longevity and Love - how are they all connected? They make each other better and are essential to each other’s success Lots of fantastic discussions about how AI will bring out the best in humans including augmenting our time and ability to connect at DOC in Napa and Wisdom 2.0 in SF this past week. Huge thanks to Jordan Shlain, MD, Kevin Ryan and John Battelle for organizing leaders across traditional medicine, longevity, academic research, and biotech and Soren Gordhamer and Mark Hyman, MD for bringing together the best of AI and longevity AI shows incredible promise for healing humans and increasing longevity, here are my key takeaways from these events: We can live to 95 today if we focus on longevity and healthspan, there are biotech solutions using AI to epigenetically reprogram cells to reverse aging and disease that will enable us to extend lifespan by 20+ years but it will take for these to be real Fun to see Mark Hyman, MD interview Eric Verdin, they both discussed how they lowered their biological age by 10+ years and feel the best ever There are so many simple things we can do, walking 30 minutes a day reduces cardiovascular, alzheimer’s and cancer risk by 40% but 80% of the US doesn’t even do this Within 5 years we will have a whole new set of FDA approved psychedelic drugs for depression, anxiety, PTSD, etc. Immunotherapies will also expand so we can avoid chemo and living cancer free will be in reach We need the US to move to a functional medicine, integrative care model and get out of this sick care model, this level of innovation is more likely to come from upstarts, the incumbents have too much revenues to lose in this transition AI will not replace physicians, instead those physicians that don’t use AI will be replaced. 50% of physicians are burned out, AI can help! Fantastic connecting with so many brilliant minds, including Mark Hyman, MD, Eric Verdin Neal Khosla Laura Esserman Abby Levy Sheila Lirio Marcelo Leonard Zon Arjun Desai David Eagleman Lois Quam Vinod Khosla Emi Gal and many others #healthcare #longevity #ai

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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Microsoft just unveiled VASA-1, a groundbreaking AI model for generating ultra-realistic talking avatars from a single image 🤯 Hemant Mohapatra and I were amazed by the early synthetic video tech from Rephrase.ai in 2019, but VASA-1 pushes those boundaries even further! Ashray Malhotra Shivam Mangla Nisheeth Lahoti As impressive as the tech is, regulating synthetic media will be crucial. Upcoming election cycle will stress test our ability to identify AI-generated misinformation and deepfakes at scale 😬👀 https://lnkd.in/gGiWRYi5 #syntheticvideo #gan #video #generativeai #vasa #gpt4 #stablediffusion #dalle #gemini #google #azure #deepfake #ethics

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🤯 Imagine the CEO of the world’s hottest company says the next trillion-dollar opportunity is something your startup has already been delivering results with for over a year in stealth! #JensenHuang and #MarkBenioff have the world buzzing about #AIAgents. 🐝  But at #CalvinBallTech, we've been quietly delivering game-changing results with them for over a year!  🤫 Don't get caught up in the hype 🙅‍♂️ Stay tuned. CalvinBall Tech is about to come out of stealth, and we're bringing proven AI Agents that have already solved critical problems for real businesses. 💪 Here’s a quick couple of questions for anyone showing you fancy roadmaps and dazzling demos: Do they have proven results already? Can they start delivering tomorrow? Gurnoor Dhillon Ratu Nadiah KhairunnisaSahal ZainEva ZuliyaFaris -Yoga Wigardo Dharnesh GordhonAntoine de CarbonnelFrançois Walewski #AICanSolveThat #AISuperAgents #AIRevolution #FutureOfWork #EnterpriseAI #CalvinBallTech #StartupJourney  #CalvinballerCode

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Gemini 2.0 handles complex math and planning tasks previously out of reach of LLMs. You can turn on "code generation" in AI Studio and in the APIs. For context, let's talk about function calling and voice AI agents. (I spend a lot of time working on voice AI.) Voice AI agents use function calls for: - Interacting with external systems specific to particular use cases and deployment contexts. - A wide range of RAG-like information lookup tasks. - Conversation persistence, including complex actions like saving a privacy-redacted version of a conversation. - Script following. Some of these uses were obvious before we had built and deployed production voice AI systems. But some of them weren't. (Script following, for example.) We had to build experiments, and then production systems, to figure out what features and tooling these new systems really need. I'm convinced that code generation will be as important as function calling, once we've accumulated some experience building with Gemini 2.0 and similarly capable new LLM versions. Credit for this insight goes entirely to Shrestha Basu Mallick, who shepherds work at Google on the Gemini APIs and AI Studio. When Shrestha first told me that really good code generation was going to make a lot of new things possible, I didn't really get it. But after playing with code generation for a few weeks ... I still don't fully get it, but in a good way, now. It's clear that there are some obvious things to explore: - Context-aware math on demand driven by natural language. - Generative UI. - Better "binding" behavior with function calling. But I'm also convinced now that there is a lot of unexplored, non-obvious (at least to me, yet) territory. Let's take that last one — improved function calling performance. Function calling and code generation go together like peanut butter and jelly. Functions often take arguments that are fairly complicated. With code generation enabled, Gemini has access to this new pattern: natural language ➡️ code that creates argument structs for functions ➡️ multiple function calls Think about how powerful this is. The ability to use even very basic code structures — loops, for example — massively expands what LLMs can do. In fact, my impression is that function calling in Gemini 2.0 is now trained as a subset of the model's code generation capabilities. This feels quite different than how function calling works in other models that I have access to. In other models, function calling feels like a close cousin to structured output. Which is great. But this new "modality" is even better. (More on the prompt in this post, and the meta-prompt for this post, here: https://lnkd.in/gycKtnNX)

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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if you know, you know Amjad Masad of Replit x's. & paul graham of y combinator demonstrate precisely what (ai) OpenAI nor Google DeepMind nor Perplexity nor any other frontier model, can achieve....personal...experiential...unique...at times communal...context & meaning. Amjad Masad discovers the McDonald's in germany. his perspective living in the u.s. is this https://lnkd.in/eT-h86tJ. this is (his) experience as anyone in the u.s. experiences it. right? then a trip. crossing a border. but it's more than that. it's crossing a mindset...a culture...and another way of advertising the same 'ting somewhere else. germany. Amjad Masad discovers that in asking for a quarter pounder there is a blank stare. vaaaat. do. you. vant. sir. a quarter pounder. we don't have that here. what do you mean. this is McDonald's. sorry sir. we don't have that here. wait. wait a minute. there it is. that picture over there. i recognize it. that's what i want. (here image supersedes audio ;) ) ah. royal. wtf says Amjad Masad. royal. wtf. in chimes in paul graham. he replies with a movie reference. clearly one that masad has not seen. here https://lnkd.in/eqs-Zg2U pulp fiction. https://lnkd.in/erGURkRx at the time quintin tarantino https://lnkd.in/enQ6M2H8 became a phenomenon. a former video store employee who had a library of movies. unlimited. he watched all of them. also had a love of video games. and also a love of foreign famous japanese film directors, cinematographers. a retrieval machine can and does have the ability to retrieve these 'tokens'. what it does not in this moment offer is a time machine a time machine where the context and meaning is transferred, transposed over time from one spot to another seemlessly. now that would be magical wouldn't it. and so in this X we have paul graham attempting to take what we experienced at the time. pulp fiction. was a runaway hit. indeed a palme d'or at cannes. the olympics of cinema. quintin. a lover of film. respectfully, artfully, pirately crafting into a new perspective. we all felt it. we all watched it. many times over. yet here we are back at the beginning. which is to say that removing yourself from one cultural bubble you notice the differences. you notice the little 'tings. and with that offers a new experience. Amjad Masad has crossed the cultural chasm. and with that is opening his mind to new euro/german/berlin ways of communicating the same 'ting. ahhhhhh. who will (b)e the one to cross this context, meaning window will rule the world truly. as the greatest communicators are indeed the greatest leaders. clear in their perspective. culturally sensitive. aware of themselves. and when they ask for a quarter pounder in the future in euro land it will immediately jump to. hey. what's your order? royal. yeah. a royal. pg, in euro. a royal. with cheese. as he remains a pulp fiction, quitin fan. ;) < . >

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Watching Vinod Khosla talk with Rocky Yu about exponentials at the AGI House. Vinod said you could see the future of the Internet in the 1970s if you watched carefully. So, here are my guesses on what new fields AGI will revolutionize in what order: 1) Creative industries: image generation, music generation and movies 2) Robotics: foundational models will enable much better robotics to be deployed much more widely 3) Biotech: the creation of new drugs, better therapies etc…

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How should OpenAI price Strawberry or o1 as it is now known. Flash poll results. Total of 74 responses from Friday Sept. 13. Interested in how generative AI applications are being monetized? Join Kyle Poyar from OpenView and Growth Unhinged, Michael Mansard from Zuora and Steven Forth from Ibbaka for an engaging roundtable on Sept. 19. Please register here. https://lnkd.in/gZNZqS6u My own thoughts is that this depends on strategic intent. Strawberry is a very different application than vanilla ChatGPT and it will provide value in new ways. It should not be tied to ChatGPT pricing. I would position it as a separate offer. However, part of the intent of Strawberry seems to be to provide training data for future reasoning models. In that case the more people that use o1 the better and pricing should be neutral. #Strawberry #Openai #generativeAI #genAI #pricing #monetization #o1

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Sure, the opportunity to build AI Agents is there, but existing SaaS companies have a major advantage: their distribution channel. It’s easier to experiment providing agentic solutions (under their tailored business model) if you have an existing customer base and deep domain expertise, otherwise brace for 360° disruptive competition.

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💲The Founders Weekly BOOST - Elon "Patents are for the weak", How VC's Really Works, All the Newly Minted Ca$h Announced by VCs and Lots lots more 🔥 💲SaaS Legend DHH on saving $10m in Cloud Costs 🤯 Can you follow his lead? 💲The 2024 Accel Euroscape - "Is AI Eating Software..😱 Or is AI the Transformation Lever for SaaS? You decide 🫣 💲I've been an LTV/ +ve DRR focused investor for a decade - Tom Tunguz "The Year Of LTV" - better late than never 🤣 who knew keeping customers was a fashion? 🤔 💲Dig in, share with friends & colleagues and always feel free to repurpose the content in my feed or our Newsletter ✅ 🎯 Don't Forget To Subscribe so you never miss a 💲 of value 💙 "The Founders Corner" Rubén & I work hard all week with our collaborators to make this happen & so if you want to collaborate with us feel free to DM and maybe join the gang 🌟

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India is not just catching up in the AI race; we're innovating and leading in ways that are uniquely ours. The Economic Times recently highlighted how our approach to Generative AI goes beyond just Large Language Models (LLMs), addressing the specific challenges and opportunities within our diverse market. Rahul Agarwalla, Managing Partner at SenseAI Ventures, shared his invaluable insights into this AI revolution. His leadership and vision are truly driving innovation in the field, and it's an honour to be part of this journey with him. For anyone interested in the developments in AI and how India is making its mark on the global stage, this is a must-read.

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The launch of ChatGPT was a "Sputnik moment". In making tangible decades of progress it shot AI to the fore of public consciousness. This attention is accelerating AI development as dollars are poured into scaling models. And just like Sputnik, ChatGPT demarcates a starting line, rather than the finish point. What is the next stage in this journey? And where is the destination? I'm privileged to be joined by the brilliant Nell Watson. Nell offers a broad perspective on the possible trajectories. She sits in several IEEE groups looking at AI Ethics, safety & transparency, has founded AI companies, and is a consultant to Apple on philosophical matters. Nell makes a compelling case that we can expect to see agentic AI being soon adopted widely. We might even see whole AI corporations. In the context of these possible developments, she reasons that concerns of AI ethics and safety — so often siloed within different communities — should be understood as continuous. Along the way, we talk about the perils of hamburgers and the good things that could come from networking our minds. https://lnkd.in/gr68g5uC

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I have nothing much more to add to this - this post on Service-as-Software and Systems of Agents is right on the thesis and most comprehensive #AI #agents stack I've seen so far. I'm thinking about how to translate/apply this for the #accounting profession. Sign up on our website if you're interested in following, I'll be writing more on our blog - https://lnkd.in/giGeTzT5 #AI #AIAgents #Innovation #FutureOfWork #AccountingInnovation #AIinAccounting #FinTech #MachineLearning #DataSecurity #Automation #DigitalTransformation #CPATech #FinanceAI #RegTech #AgentCollaboration #accountingTech #CPA #CPAtech [Day 6 of 30-in-30 LI Posts] Day 1: my transition to #consulting https://lnkd.in/gzWU4jdD Day 2: hidden costs of #innovation https://lnkd.in/guSFwiaV Day 3: Ellen speaking at AT #AI Summit https://lnkd.in/gub5NCfw Day 4: firm m&a integration pillars https://lnkd.in/g2-ya-te Day 5: #AI #agents demo day https://lnkd.in/g3i-pmWp

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Notion vs Glean reminds me a lot of Tesla vs Waymo... Just like I think Tesla is going to solve the 'software' problem of autonomous vehicles before Waywmo solves the 'hardware' problem... I think that Notion is going to solve 'enterprise knowledge search' before Glean solves the problem of document and knowledge creation... Why? A couple reasons: 1) Just like Tesla, Notion has a huge advantage when it comes to the amount of underlying data and documents to train on... They already have 100M+ users, and likely 10x that in terms of the number of documents, files, etc. in team's workspaces. 2) It's very hard to change people's behaviour re: where/how they create 'work'. It's why a lot of companies have come and gone in that category... As exciting as Glean's AI writing assistants might seem, most people are just fine (or even prefer) a good old fashioned editor/writer. I could be way off here, but this will be an intersting one to follow. Thoughts? Where am I getting it wrong? #ai #enterprise #knowledge #management #notion #glean

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Service-as-software is a $4.6T opportunity. But no one knows how to actually grab that opportunity yet. Enterprise software has gone from assisting human workflows to now, LLMs giving software the ability to actually perform parts of the workflow. Instead of relying on timely and sometimes inaccurate human inputs of structured data, today's systems actively capture and process both structured and unstructured data. The service-as-software shift was huge—but we're on the edge of one that's even bigger. My Foundation Partners Joanne Chen and Jaya Gupta have now outlined how to put the service-as-software paradigm shift into action: through a System of Agents, which doesn’t just streamline a workflow but actually completes whole tasks, becoming the worker. A System of Agents mirrors a human team, with agents taking on collaborative, specialized tasks and continuously learning from each other, just as human teams do. Advice for founders building with Systems of Agents: → Position at the data source, own the path. Traditional software waits for humans to input data, whereas Systems of Agents capture it at the source. When an agent owns the interface where data is born, the system can then orchestrate every downstream action. → Move beyond software budgets, tap workforce spend. When a System of Agents completes a whole job task, it should be categorized as a personnel cost, not a software expense. → Use AI’s 24/7 nature and scalability to create new workstreams. AI agents working in areas like healthcare can operate 24/7, providing reliable support and an around-the-clock presence. → Stay ahead of pricing and business model shifts. Service-as-software shifts the core business model from seat-based pricing to outcome-based pricing; value is placed on the tangible improvements that AI delivers. In the past 6 months, we’ve heard from hundreds of startups working in this space. After those conversations, we’re announcing the System of Agents 50, which includes the companies you see here. 👇 Full blog in the comments. 

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