Sheraz S.

Was chatting with another investor today on a call, and mentioned Tim Urban's Wait But Why Blog post, "The AI Revolution" and how prescient it was. Given that it was written in 2015, decided to flex AI's latest muscles to give it a new venue: a podcast. Podcast here: https://lnkd.in/gW8Dh89U Pretty easy to do: Step 1: Save the posts as PDFs. Step 2: Upload to NotebookLM, have it create a podcast. Step 3: Upload to Descript, give it captions. Step 4: Export, publish to Youtube. Total time to create the podcast: ~25 minutes. To generate this podcast and publish it even a few years ago would have taken >8 hours. Love the future (Original posts: https://lnkd.in/gW-PQXE7, https://lnkd.in/gg-nRvxz) #AIRevolution #GenerativeAI #WaitButWhy #TimUrban #NotebookLM #AIContentCreation #Podcasting #Descript #ArtificialIntelligence #ContentFlex #ProductivityHacks #FutureOfAI #AIinMinutes #TechEfficiency #AIandCreativity #YouTubePodcast #InnovationInContent #vibecap #vibecapital

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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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ex Google CEO Eric Schmidt spoke at Stanford this week. Lots of nuggets here, but most interesting for the near term, Schmidt believes in the next year AI is set to make a big leap forward. He predicts AI will combine three important features: 1. Vast knowledge retention (1 million token models) 2. Text-to-action capability 3. Personal AI agent fleets While the full impact is unknown, everyone may soon command their own AI teams. Here's an example: Imagine you're developing new quantum computing algorithms. Your AI assistants could: - Continuously scan and synthesize global quantum research papers - Generate novel algorithm ideas based on this knowledge - Simulate and test these algorithms across various quantum architectures - Automatically document findings and prepare draft papers - Identify potential collaborators and draft outreach emails All initiated by a command like "Advance our quantum algorithm research." This could dramatically accelerate R&D cycles and scientific breakthroughs.. https://lnkd.in/eJbg6cnD #frontiertech #AI #futurefrontier

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Can probabilistic models, like LLMs and Nate Silver's election forecast model, ever truly be perfect, with no errors? According to Nate Silver: "We don’t talk about it much, but there’s a small amount of error introduced into the model because it’s probabilistic rather than deterministic. The margin of error on 40,000 simulations is about ± 0.5 points of win probability for either candidate; after 80,000, it declines, but only to ± 0.35." Sounds like a familiar refrain around Infactory as we discuss the limitations of LLMs... P.S. Vote!

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LLM Data Engineering Pattern 3: Aggregating Unstructured Insights at Scale Modern financial analysis often requires synthesizing information from a vast array of unstructured sources—regulatory filings (e.g., 10-K, 10-Q), research reports, market commentary, and even audio transcripts of earnings calls. In many cases, these sources must be combined to form a comprehensive view of an industry’s risk profile. Historically, analysts have relied on manual selection and review of documents, a labor-intensive process that does not scale well in today’s data-rich environment. Consider analyzing credit risk in the U.S. commercial real estate sector: 1) Regulatory Filings: An LLM can systematically parse through hundreds of 10-K and 10-Q filings to extract commentary on asset valuations, loan-to-value ratios, or refinancing challenges. 2) Research Reports: It can synthesize qualitative assessments from research houses covering property market trends, default probabilities, or vacancy rates. 3) Earnings Call Transcripts: It can highlight executives’ forward-looking statements regarding rental income stability, interest rate exposures, or occupancy forecasts. By integrating these unstructured data points, an analyst can address questions like: “Given the commentary on sector vulnerabilities in these filings, research reports, and macroeconomic indicators, what are the key risk factors influencing the commercial real estate market?” Financial people, how are you doing this today? #llm #dataengineering #llmdataengineering #financialdata #finance

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LLMs are trained on internet-scale data. But to make AI work for work, models need to be grounded in your enterprise data -- a much harder problem than meets the eye! How do you connect to all your data sources and ingest them frequently enough while managing compute/storage costs? How do you make sure that you only show users results from documents they have access to? How do you rank results without internet-scale click and link data? How do you process the content of documents that are often hidden in PDFs and presentations? These are tricky problems, and it's why enterprise search didn't work for nearly a decade, until Glean came along. Turns out it's the exact same set of challenges needed to give LLMs the right context to work their magic on your enterprise content. Glean spent 5 years building the ingestion, search, and ranking infra needed to solve this problem well. This has given them the right to run the "Work AI" race and delight users at work. CEO Arvind Jain shares more on the latest episode of Training Data. Listen here, or wherever you get your podcasts: YouTube: https://seq.vc/ytd Apple: https://seq.vc/pit Spotify: https://seq.vc/jnz 

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Introducing Morfless, one of Antler's up-and-coming startup investments using AI to cut cloud costs. As cloud adoption continues to accelerate, so does the complexity and cost associated with managing cloud infrastructure. Morfless was founded by Ian Corbally and Domagoj Filipovic to address this issue head-on. The duo realised the pressing need for a platform that could automate the tedious tasks involved in managing cloud spend, thereby improving operational efficiency and reducing costs. Over the past 12 months, the startup has made significant strides, successfully developing and launching its AI-powered platform, gaining traction among a wide range of Australian and New Zealand sectors – from the largest national media websites to marketing agencies and software companies. Read more in Startup Daily here: https://lnkd.in/gHFjU_SM

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Incisive analysis by Sequoia partner David Cahn on the missing $600 billion in AI revenue... Basically, given what companies are spending on Nvidia's chips and infrastructure build out, a certain amount of resulting revenue is implied. There is a $600 Billion gap. This week's The Economist arrived at a similar conclusion. The ROI will likely be realized after 2032! Links below. #ai #chatgpt #nvidia #venturecapital #startups

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From Crypto Ice Age to AI Inferno- Gartner Curves or Extreme Weather Conditions Precipitated by VC? A few thoughts I've been reflecting on recently on learnings from being through many crypto cycles I can apply to this moment in AI investing... https://lnkd.in/d6KSM3EN

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Monetizing generative AI functionality will be a key challenge in 2025. Ibbaka's second AI Monetization Survey digs deep into how companies are approaching this, probing on market strategy, growth motion, investment and pricing. Please take the survey and contribute your insights. The 2024 survey was taken by 300 companies and has been downloaded more than 1,000 times. https://lnkd.in/gWyGzAet #AI #pricing #monetization

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Part II of our blog is out! In this installment Abde Tambawala and I explored usage metrics and pricing models across different AI modalities and discussed how traditional User-based subscriptions will need to be supplemented with usage metrics. Take a look and let us know what you think! #pricingstrategy #ai #monetization

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AI pricing is hard. Customers (esp enterprise) need to do annual budgeting. They won’t sign up for “who knows what it will cost???” But the AI provider (whether OpenAI or something built on it) have highly variable costs based on usage. In addition, if AI increases productivity, it may **reduce the number of user accounts a company buys**. As you provide more value, you (as the AI provider) can see less revenue. No one has entirely solved this.

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This week on the Mobile Dev Memo podcast: I speak with Mike Taylor about his new book, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs, as well as the skill gap in performance marketing. Find the episode by searching for "Mobile Dev Memo" wherever you consume podcasts.

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Instacart is a market leader when it comes to leveraging AI tools to boost productivity at work. I had an amazing conversation with JJ Zhuang, Instacart’s Chief Architect, who has shepherded over 50 AI-driven initiatives to market across the org. We explored what catapulted Instacart to the forefront of AI integration, supporting radically diverse use cases across various functional areas. One striking insight (echoed by Ethan Mollick and others elsewhere) is that you often don't need specialized tools for specialized work. Frontier models like GPT-4 can seamlessly adapt to a wide range of functions and use cases. They're not just for engineers; they're incredibly versatile tools used by marketing, comms, and legal teams. From generating code to proofreading and ideation, GPT-4 empowers every department to leverage AI for enhanced productivity and innovation. By integrating LLM’s like GPT-4 into their workflows, teams can streamline processes, automate repetitive tasks, and unlock bandwidth for new creative possibilities. How can you harness the power of AI in your own work to boost productivity? Share your thoughts below 👇 Want to hear more insights from JJ? Check out the full podcast episode by clicking the link in the comments.

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Announcing our new feature: Clean Up Your Documents with RaijinAI’s Intelligent Formatting! 🌟 We know how much y’all love nice, clean transcripts😎. At Raijin.ai - AI Analysis Assistant, we are constantly pushing ourselves to make the best experience for anyone who wants to do quick + good research and insight mining. We can now automatically format documents and web links that you upload into our platform without any additional prompts or instructions from you. What this means is that you get enhanced readability without the hassle of trying to split your notes or web content into paragraphs manually. The example here is from the recent Fortune magazine interview with NVIDIA's legendary CEO Jensen Huang. This was done as simply as copying and pasting the URL link into our platform. Come try this out by signing up for our Beta Launch (link in comments section)! #AI #generativeAI #LLM #userresearch #discovery #productivitytools #insights #UIUX #productmanagement #productdevelopment #voiceofcustomer

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This week Fullstory announced the launch of our new offering, Data Direct. This is a big deal. Long story short: If you are on a data team and thinking about building models on visit data, give us a shout. We’ve got you covered. Fullstory is now the fastest path to get you the most behavioral data. Why? Fullstory’s unique and patented approach of automatically collecting and organizing ridiculous amounts of first-party behavioral data has always been our superpower. On this data foundation, our customers have logged into Fullstory and discovered patterns and insights previously unimaginable… But the world is changing at breakneck speed thanks to the seemingly sudden availability of GenAI, and our customers are hungry for more data to train their models and power their AI agents. With Data Direct, your comprehensive first-part data is available within your existing ecosystem -- no need to log into Fullstory’s UI. Lots of exciting things are happening here at Fullstory. Major thank you to the teams who have been working tirelessly to ensure that our customers have access to the best, most comprehensive and accurate behavioral insights into their customers. https://bit.ly/4bs1w6n

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Interesting though unsurprising finding from Bain survey on GenAI implementations thus far at large companies: primarily focused on incremental optimizations, which makes sense! Even what Bain characterizes as "growth" is likely dominated by incremental GenAI additions to current products, rather than truly "transformative" applications.

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Excellent study from Damian Rollison and the SOCi, Inc. team which seems to confirm my longtime hypothesis around citations (https://lnkd.in/gxZQwdx3): the only citations that are valuable are the ones that rank in Google for the keywords you want to be known for. https://lnkd.in/g49DMSeN

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PDFs + LLMs will be the QR code of B2B. So much of business-to-business interactions involve data trapped in PDFs — medical data, government data, transaction data. Even though it is "digital", it isn't easily computer readable, until now. Large language models make it easy to convert this into nicely structured data that can be used for decision-making. This means that hospitals, governments, real-estate developers, industrial farms, etc can continue publishing information in PDFs, and the consumers of that information will use LLMs (or apps built on LLMs) instead of painstakingly reading the PDF.

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