Shashank Singh

This poster is missing a basic economic principle that as the cost of producing a good goes down and the availability of the good goes up, the value (price, valuation, etc.) of the good goes down. There may be a few one person AI-driven unicorn companies, but as soon as the next guy can tell his AI agents to replicate what the first guy did, the game is over. As one comment said, Instagram was a $1B acquisition with 13 people, but if Meta or Microsoft has their own agentic AI, why would they pay $1B when they can tell AI agents to build something similar integrated into their app? In markets with a flood of resources, those who have the hard won talent and knowledge to tailor their goods to particular workflows typically win... and this will take more than an AI and a bright idea.

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Building A Winning AI Neobank 💡 Players interested in launching a neobank today should adopt an AI-first mindset and build a holistic set of capabilities in four interconnected layers: 📱 Engagement layer Neobanks need to create experiences and build accompanying capabilities that fulfill a range of customer expectations. First, the engagement layer needs to deliver experiences that are not only easy, intuitive, fast, and responsive but also delightful. Second, these experiences need to be available where customers need them the most. One way to do this would be to build their products and interfaces in a way that they can be seamlessly integrated with various ecosystem partners. Third, neobanks need to set up a product innovation engine that allows for the discovery of unarticulated customer needs and the flexibility to introduce innovative new products. 🤖 AI-and-analytics-led decisioning layer Hyper-personalization requires granular AI-driven decisions across the entire customer life cycle, spanning acquisition, onboarding, servicing, retention, and cross-sell. This requires many machine-learning models across domains including credit analytics, product and channel propensity, fatigue, and risk to identify the next-best action for customers. Some essential capabilities are required to build and effectively deploy these models. The first relates to data gathering. Since ML models are built on data, neobanks need to create data ingestion pipelines (traditional banking customer information, transaction data, and less-traditional data streams). Neobanks also need to be able to rapidly deploy, test, and iterate on their models in live production environments. This requires technical capabilities such as machine-learning operations and close coordination among data, product, technology, and business teams. 👨‍💻 Core technology and data layer Developing experiences in the engagement layer and personalizing the decisioning layer require a modern technology and data core. The goal should be to develop a cloud-based, flexible, and highly configurable banking core that will help engineering teams launch new product variants rapidly through easy configurations. 🚀 Operating-model layer The final layer pertains to how it attracts and organizes talent across its teams in a way that breaks traditional organizational silos. First, a successful neobank should be able to set and embody an aspirational vision and culture for the organization. Second, neobanks should have full-stack platform-based teams with all the skill sets required for their end objective.Finally, the neobanks should calibrate the organizational rhythms and processes that promote innovation and rapid experimentation while adhering to regulation and compliance. Source: McKinsey & Company - https://mck.co/3KANZym #Innovation #Fintech #Banking #Neobanks #EmbeddedFinance #API #CoreBanking #AI #MachineLearning #BigData #Cloud #GenAI #isapexchange #isapwallet iSAP Exchange FZCO

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𝗗𝗶𝗱 𝗝𝗲𝗳𝗳 𝗕𝗲𝘇𝗼𝘀 𝗕𝗥𝗘𝗔𝗞 𝗧𝗵𝗶𝘀 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗥𝘂𝗹𝗲? (You Decide!) We all know Jeff Bezos revolutionized e-commerce, but did he also break a golden rule of leadership? Watch this video where Bezos discusses his approach to team meetings. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘁𝘄𝗶𝘀𝘁: Bezos famously lets his S-Team (Senior Team) silently read a lengthy memo BEFORE any discussion. This flies in the face of the conventional wisdom that says leaders should speak last to encourage diverse viewpoints. 𝗦𝗼, 𝘄𝗵𝗮𝘁 𝗴𝗶𝘃𝗲𝘀? Is Bezos a genius? 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆 𝘀𝗼𝗺𝗲 𝗺𝗶𝗴𝗵𝘁 𝗰𝗿𝗶𝘁𝗶𝗰𝗶𝘇𝗲 𝗵𝗶𝘀 𝗺𝗲𝘁𝗵𝗼𝗱: 📌 𝗦𝘁𝗶𝗳𝗹𝗶𝗻𝗴 𝗷𝘂𝗻𝗶𝗼𝗿 𝘃𝗼𝗶𝗰𝗲𝘀: By prioritizing the S-Team's pre-digested ideas, could new voices be shut out? 📌 𝗚𝗿𝗼𝘂𝗽𝘁𝗵𝗶𝗻𝗸: Reading the same memo beforehand could lead to an echo chamber effect, hindering fresh perspectives. However! Before you write Bezos off, consider these counterpoints: 📌 𝗗𝗲𝗲𝗽 𝗱𝗶𝘃𝗲𝘀: The memo allows for deep, informed discussion on complex issues. 📌 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗻𝗼𝘁 𝗾𝘂𝗮𝗻𝘁𝗶𝘁𝘆: Maybe fewer, well-considered ideas are better than a free-for-all. I found this video thanks to Paul Storm - credit to him for this super interesting take on leadership! 𝗪𝗵𝗮𝘁 𝗱𝗼 𝗬𝗢𝗨 𝘁𝗵𝗶𝗻𝗸? Did Bezos crack the code on leadership meetings, or is his approach flawed? Debate it in the comments below! ♻️ Repost to share the debate ♻️ _________________ Hi, I'm Timothy 👋 I founded Fanbytes at 21 and sold it at 27 in an 8-figure exit. Now I share leadership, growth, and business advice for free in my newsletter “Cheatcodes.” Join 10K leaders reading Cheatcodes → https://lnkd.in/e96t-RkW

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Market size slide often misses the mark due to a standardized approach by pitch coaching. This template mandates the inclusion of TAM, SAM, and SOM in three bubbles. You can find such templates all over the internet. 👀 Everywhere I found that entrepreneur claims “Our global TAM is $X B, but we are going to start in a certain part where our SAM is $Y B. And we project that our SOM is $Z B” It’s clear why entrepreneurs try to pump up their market size. They’ve been told that VC only interested in unicorns, and so they assume that the best way to become a unicorn is to go for the largest market. The thinking is that it's easier to get 2% of a very large market than it is to get 20% of a smaller market. So, they search for market data that allow them to claim that their TAM is $56B, or $256B, or even better, $2.5T. When this slide appears, most investors chuckle. Investors generally don’t give any credence to the market size. We may still ask founders how big their target market is, but the point is to see 'how founders think about segmentation, not to rate the startup based on how big they think.' 💡 Savvy investors prefer startups go after a market that doesn't yet exist. Better to create the market than try to unseat established competitors. Rather than claiming that you can disrupt a existing market, what’s more relevant and compelling is how you answer questions such as: 👇 1. Who are your target customers? 2. How will you reach them? 3. How many can you reach? 4. How will you convert them? 5. How many can you convert? 6. How will you onboard them? 7. How much will that cost? 8. What is a customer worth? 9. How many customers will you acquire this year,Next year,The year after? Most investors want to see market opportunity, not market size. They need to see that you understand more about your approach of reaching & onboard customers. Founders should build a bottom-up model that demonstrates how you will build a big, profitable, customer by customer business. Don’t suggest that your focus is on acquiring market share in a large market. 🙆‍♀️ We’ve heard several pitch on wearable technologies for dogs. They claim - there are 90M dogs in the US alone — at $9.95 a month subscription, that’s a $10B market! But truth is It doesn’t matter if there are only 20M or 200M dogs. What matters is how many dog owners the entrepreneur can convince, one by one, to sign up for this service. Define your target precisely, like "urban professionals, 25-35, earning $80,000+, active Instagram users, and dog owners." Entrepreneur should stop pitching the typical market-size slide. Instead, they should show investors that they understand the market opportunity customer by customer.💡 Check out my free newsletter for more insights: https://lnkd.in/dKZQKHg2 . #startups #funding #fundraising #founders #investors #VC #angelinvestors #founder #fund #money #runway #marketsize #market #entrepreneurship #entrepreneur #customers

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See how SKILLS is a key part of an AI strategy from Boston Consulting Group (BCG) ? Another good find by my man Jonathan Moss regarding Accelerating the GenAI Revolution in sales. I’d love to dig in more broad to GTM teams.. 1. Potential Start by assessing where AI can make the biggest difference in your sales processes. Look at the stages where deals often stall, and analyze customer behaviors that lead to either success or failure. Then, use AI to predict which prospects have the highest potential, optimizing your outreach based on these patterns. If only a team in GTM was equipped and responsible for gao analysis of impact and systems, in my opinion it should be ENABLEMENT! 2. Platform Evaluate your existing data infrastructure. Conduct a fit-gap analysis to ensure your systems—like CRM—are AI-ready. This might mean cleaning up customer data fields or improving how teams log customer interactions, ensuring AI has the data it needs to deliver insights. This is where Revops is key. 3. People You need to address the human aspect of AI integration. Develop a comprehensive change management plan that not only trains your teams on how to use AI but also builds trust in its recommendations. Create “AI champions” who can show the rest of the team how AI insights translate into practical benefits like better forecasting and higher win rates. If only there was an internal team who usually took care of change management and adoption of tech…oh wait, yes there is! —->ENABLEMENT 4. Policy Establish guidelines for the ethical use of AI, focusing on data privacy and regulatory compliance. For instance, ensure AI systems meet GDPR standards by anonymizing data where possible and being transparent with customers about how AI is used. Maybe someone should document this… if only there was someone who could, oh wait! ~~~>ENABLEMENT 5. Operating Model Bake AI into your workflows, making it a part of day-to-day operations rather than a separate tool. For example, create an AI analyst role who regularly collaborates with sales leadership to review AI-driven insights and integrate them into weekly sales reviews or strategy meetings. This is Revops and Enablement working together as the Double Headed Dragon AI powered. As you read through this.. notice how similar the entire framework is to how enablement is (or should) be ran… In my humble opinion, Enablement teams should not only be responsible and lead the charge with this kind of strategy, but be in the room with leaders when decisions are made to execute. If you want a seat at the table of influence.. know and leverage AI. This. Is. The. Way.

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Sam Altman, the co-founder and CEO of OpenAI, made a provocative statement at a JP Morgan conference earlier this year. He believes a solo founder will soon reach a billion-dollar valuation without hiring a single employee. This one-person company would instead be powered by AI and “employ” dozens of AI agents to do the work. Not only do I believe this is entirely possible, but I think when it does happen, the company will be one of the fastest-growing unicorns ever. As I invest in AI-powered startups and teach my students how to use AI in their businesses, I have identified 5 general AI use cases that align with critical phases of the startup journey: 1. Research-Driven Ideation: The genesis of any successful startup is a deep understanding of market needs, pain points, and the competitive landscape. My colleague Scott Brady of Stanford calls this process Research-Driven Ideation (RDI). There are now AI-based tools for competitive analysts, automating competitive monitoring for senior managers—effectively Google Alerts on steroids, tracking personnel changes, marketing launches, traffic, and other publicly available data. 2. Customer Persona Development and Market Research: Understanding your target customer is crucial. Gen AI helps founders create multiple hyper-specific customer personas by analyzing customer data and building hyper-realistic, "living" customer personas to test key hypotheses quickly. 3. Experimentation and Validation: Gen AI facilitates rapid experimentation to validate key hypotheses such as CVP, GTM, and PF by enabling deeper business data insights and rapid prototyping. I have a founder friend who lost his technical cofounder and has been using ChatGPT to build his MVP. By learning to be more effective at writing prompts to generate the desired code output, he has been able to continue building as a solo founder. He told me, “The result is that my burn rate is incredibly low, and velocity has shot through the roof.” 4. Marketing and Customer Engagement: Founders will see major productivity boosts in marketing, community building, and sales prospecting. Flybridge has a portfolio company that builds super smart AI agents that can be used for just about anything. One of their customers trained their agent to automatically generate customized sales collateral and follow-up materials based on customer needs that a sales representative inputs into the system after a prospect call—and then the AI agent sends that tailored material to the customer. 5. Continuous Learning and Iteration: The path to PMF is iterative. Gen AI supports continuous learning by analyzing customer feedback and product usage data to improve their product, GTM, and onboarding processes quickly. How are you using AI to build your startup?

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Microsoft, Google and Amazon added 🗽1 trillion dollars in market cap year-to-date, while the top 67 SaaS companies collectively lost >$140 Bn in value. This stark contrast highlights that hyperscalers are the ones who is truly benefiting from the AI transformation. It also adds yet another reason to take cloud marketplaces seriously. ⚖️ My analysis YTD (June 3rd): 🔺 Hyperscalers Added $1Tr to its Market Cap Google Market cap: $2.14T Growth (Year-To-Date/YTD): 24.45% Market cap increase (YTD): $420.4B Amazon Market cap: $1.84T Growth: 18.10% Market cap increase: $282.0B Microsoft Market cap: $3.05T Growth: 10.84% Market cap increase: $298.3B The total market cap increase for Google, Amazon, and Microsoft since January 1, 2024, is a staggering $1 trillion (1000.7Bn). 🔻 Top SaaS Companies Lost >$140 Bn in Value The best way to compare hyperscaler performance with top SaaS companies is using BVP Nasdaq Emerging Cloud Index (EMCLOUD). It's calculated and maintained by Nasdaq and sponsored by Bessemer Venture Partners. It includes the weighted average of 67 top SaaS companies with >500M in market cap. Among them are such iconic names in software as: Adobe, Atlassian, Box, Cloudflare, Confluent, CrowdStrike, DigitalOcean, HubSpot, MongoDB, PayPal, Salesforce, ServiceNow, Shopify, Snowflake, Workday, Zoom, and Zscaler. EMCLOUD Total market cap: $1.4 trillion Growth (YTD): -10.10% Market cap decrease (YTD): ~$140B 📊 Still More Hyperscalers Grow: Oracle & NVIDIA To further test the assumption that clouds are the main beneficiaries, let’s take a quick look at Oracle and NVIDIA. Oracle, added $40 Bn to its market cap YTD. Nvidia derived 87% of its $26 Bn revenue last quarter from “Data Center” segment. Cloud hyperscalers account for over 50% of their Data Center revenue (more on this in one of my newsletters - link in comments). Nvidia's jump in market cap YTD is even larger outlier - as if it nearly added another Amazon to its value: NVIDIA Market cap: $2.80T Growth (YTD): 136.21% Market cap increase (YTD): $1.6T 💡 What Does It Mean for Software Businesses? The data shows that the AI transformation significantly benefits hyperscalers. Their strategic positioning on the infrastructure layer, their partner ecosystems and their cloud marketplaces allows them to capture immense value from the AI platform shift. As traditional SaaS companies need to keep pace, it opens questions about the need for strategic alignment with the giants driving the AI r/evolution. As a result, nearly all companies from the EMCLOUD, including largest ecosystems, like Salesforce, ServiceNow, Shopify, Snowflake, Workday have committed to grow their sales via cloud marketplaces of Microsoft, Amazon, and Google. __ 🎯 Join Cloud GTM Leader course starting on June 4th to accelerate your #growth in #cloud marketplaces. Last chance to join - https://lnkd.in/edUa-qm2

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The Rapid Adoption of Generative AI Source: https://lnkd.in/gph2xsQ4 #rapid #adoption #genai #ai #impact #overview #productivity #adoption #us Credits: Alexander Bick Adam Blandin David Deming Highlights: Generative AI has rapidly emerged as an important new technology, yet its impact on the economy depends critically on the speed and intensity of adoption. This paper reports results from the first nationally representative U.S. survey of generative AI use at work and at home. Our data come from the Real-Time Population Survey (RPS), a survey that is constructed and weighted to be nationally representative and follows the same survey design as the CPS, a widely used national data source. We find that 39.4 percent of the U.S. population age 18-64 reported using generative AI during August of 2024, with 28.0 percent using at work and nearly one in nine workers reporting daily usage. We compare the speed and intensity of generative AI adoption with two transformative technologies - PCs and the internet. We find that generative AI has been adopted more rapidly than both technologies. We also find that generative AI is used by workers in a broad range of occupations and job tasks. Nearly half of workers in computer and mathematical and management occupations use generative AI, but so do nearly one in four blue-collar workers. We asked respondents about whether generative AI was useful in eleven different job tasks such as writing, administrative support, interpreting and summarizing text or data, and coding. Usage rates at work exceeded 25 percent for all ten tasks in our list. Overall, we find strong support for the idea that generative AI truly is a general-purpose technology We estimate that between 0.5 and 3.5 percent of all work hours in the U.S. are currently assisted by generative AI. Assuming that the productivity gains from recent experimental studies are externally valid, this suggests that generative AI could plausibly increase labor productivity by between 0.125 and 0.875 percentage points at current levels of usage, although we caution that this calculation should be considered highly speculative given the assumptions it requires. Our findings suggest many directions for future work. In particular, it will be important to track the adoption of generative AI as the technology matures and to monitor whether its usage expands broadly across workers, firms, and occupations. Future RPS surveys will include more detailed questions about the frequency and intensity of generative AI adoption so that we can track its evolving impact on the U.S. economy

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Corporate Venture Capital by BCG & Insead: Did you know that Corporate Venture Capital (CVC) is reshaping the startup landscape, delivering not just funding but strategic value to parent companies? Here are some game-changing insights: 1️⃣ **Massive Growth:** In 2020, CVCs poured in around $73 billion, primarily during the Series A and B stages. That’s nearly a quarter of all VC-backed deals! 2️⃣ **Strategic Moves:** Top CVCs nail the balance between strategic and financial gains. They often operate independently, ensuring agility and swift decision-making. 3️⃣ **Industry Focus:** IT and Healthcare are leading the charge, with a significant pivot towards Asia. This shift mirrors broader market trends and untapped opportunities. 4️⃣ **Creating Synergies:** It’s more than capital. CVCs are driving partnerships that boost innovation, build synergies, and create immense value for both startups and parent firms. PS. check out 🔔 for a winning pitch deck the template created by Silicon Valley legend, Peter Thiel https://lnkd.in/ejp-Bhnu

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AI nuggets in Google Q3 earnings: App Layer: - 25% of all new Google code is AI-generated - Germini used by over 1B users - Waymo: 150k paid rides and 1m miles weekly, with Uber / Hyundai partnerships Infra layer: - Cloud revenue $45b ARR growing 35%, 17% op margin. Nice re-acceleration Thomas Kurian James Imtiaz 👏 - Capex - $13b capex 60% for servers 40% for datacenter and networking equipment -90% cost reduction for AI search queries in 18 months

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Pitching to VC is to earn a 2nd conversation Keep it simple: 10 slides, 20 mins, 30 point fonts

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The future is here

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AI is growing fast, but this number⚠️ stopped me in my tracks: Bain expects the AI market will explode from $185 billion to nearly $1 trillion by 2027. Here's where it gets actionable for alliance leaders ⬆️ 5X AI Market Growth Bain & Company forecasts 5X growth in the AI market in just four years. The real question is: Will this reshape the tech industry? Where AI Growth is Happening: ✔️ AI Models are the key driver, expected to grow at an incredible 110%–135% CAGR ✔️ Applications and Marketplaces follow close behind, growing at 60%-85% annually ✔️ IT Services Related to AI are expanding by 40%-50% annually, in lockstep with compute growth. These growth vectors reveal something important: AI isn’t just about advanced models—it’s about how AI gets distributed and implemented. The fact that marketplaces and services are in second and third place emphasizes that how you deliver AI solutions will be as important as the solutions themselves. 🎯 AI Workload Shift: Public Cloud Dominates AI workloads are projected to grow by 25%–35% annually through 2027, driving further expansion of the public #cloud. Bain’s recent survey and forecast show that two-thirds of AI spending (covering fine-tuning, model training, inference, and vector storage) will take place on public clouds. 🧰 The Evolving Role of Tech Services In the medium term, tech services, often delivered by partners, will be critical, especially as businesses face a skills gap in AI deployment and data modernization. However, Bain highlights that over time, significant portions of tech services are likely to be replaced by software. Clients race to design new services to sustain growth. 📊 Three Strategic Imperatives for Alliance Leaders: 1️⃣ Cloud Commitments Will Accelerate AI’s insatiable need for compute power means reliance on cloud services is only going to grow. This is an incredible opportunity for alliance leaders to deepen #partnerships with cloud  hyperscalers to capture more value from this shift. 2️⃣ Cloud Marketplaces Will Boom Already becoming the primary channel for AI solutions, cloud marketplaces are where customers go for convenient purchasing and deployment and to optimize their cloud spending. ISVs and AI vendors will increasingly use these platforms to scale their reach, and smart alliance leaders will find ways to align their offerings with these rapidly expanding ecosystems. 3️⃣ Public Cloud Growth will Sustain As AI models get larger and more complex, public cloud infrastructures will become essential to delivering AI’s full potential. Alliance and tech leaders who will act on these trends will be in prime position to capitalize on the trillion-dollar AI race. Do you agree or have I missed something? 👍 Follow for more insights & share with others PS. Accelerate your #growth in cloud marketplaces with 10+ top experts. Join our 5-week Cloud GTM Leader cohort course starting on October 29th - last cohort this year. https://lnkd.in/edUa-qm2

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