Market Overview - Salesforce and ServiceNow are major enterprise software vendors, each generating over $10 billion in annual revenue and possessing a market capitalization exceeding $200 billion [1] - Both companies were founded approximately 25 years ago, pioneering cloud-native CRM and ITSM systems respectively [1] Strategic Opportunity - The rise of SaaS provided an opportunity to create products that were 10 times better than existing solutions [2] - Incumbents struggled to adapt to cloud computing, giving startups a competitive advantage [2][3] - AI presents a similar opportunity for new companies to develop AI-native enterprise software [3] Technological Shift - Future enterprise software systems will integrate AI to enhance employee productivity and accuracy [4] - Incumbents may face challenges in rebuilding their products around AI, creating opportunities for startups [4]

AI Agent Evolution - AI agents are evolving into distributed workflows with many sub-agent calls in a single run [1] - These multi-agent systems are useful for long-running workflows and agentic map reduce jobs [1] - These systems apply human-level judgment to filter and search through large amounts of data in parallel [1] Challenges in Building Multi-Agent Systems - Building these systems requires solving traditional distributed systems problems to ensure high throughput and reliability while controlling costs [2] - New problems include writing effective agent and sub-agent prompts, handling untrusted context, and monitoring and debugging agents [2] Call to Action - The industry is looking for builders who have felt this pain in production and want to create tools to make these systems easier to build and maintain [2] - The industry aims to make operating fleets of agents as routine and reliable as deploying a web service or running a Spark job [3]

Thanks to new AI tools, we believe it's now possible for small high agency teams, even solo founders, to build multi-billion dollar companies with as little as just $500,000 in funding from YC. 15 years ago, cloud computing came along and eliminated the need for spending tons of money on physical server infrastructure, making it easier to build a big company with way less capital. This is happening again right now with new AI tools that make it easier for ambitious founders to scale with far fewer people.Th ...

Technological Advancements - Video generation models are rapidly improving, with Google's V3 producing 8-second photorealistic clips for a few dollars per video [1] - The cost of generating near-perfect video is approaching zero, positioning video as a new basic building block for software [1] Potential Applications & Market Opportunities - Generative video will transform media and entertainment, enabling personalized content creation like custom TV seasons and AI-native successors to TikTok [2] - Online shopping experiences will be enhanced, allowing consumers to visualize themselves using products or wearing clothes [3] - Gaming and simulation will undergo major changes, including video games built without game engines and infinite robotic training data [3] - Video calls with loved ones long after they're gone may become possible [3] Investment & Innovation Focus - The industry is interested in founders who view generative video as a new computing primitive, not just an output [4] - The industry seeks founders building new apps, tooling, and infrastructure for a world with limitless, low latency video [4]

Industry Trend - AI revolution requires significant physical infrastructure buildout, including data centers and semiconductor fabs [1] - Shortage of skilled tradespeople, such as electricians, HVAC technicians, and welders, poses a challenge to infrastructure development [2] - Government's AI action plan emphasizes worker-first agenda and rapid retraining programs for physical labor jobs [2][3] Investment Opportunity - Opportunities exist for startups to build new vocational schools for the AI economy, training people for physical labor jobs [3] - AI can personalize training programs to prepare individuals for jobs in months instead of years [4] - Multimodal AI, including voice AI, AR, and VR, can be used to coach and provide feedback in real-world simulations [4][5] - Employers are willing to pay for well-trained workers in these fields [5] - AI can potentially solve the scalability issues of traditional training businesses by creating effective AI teachers that can scale infinitely [6] Potential Risk - Challenge lies in teaching hands-on skills like welding or pipe fixing via AI, as these skills require real-world practice [4] - Traditional training businesses have struggled to scale due to the difficulty in maintaining the quality of human tutors [6]

I think one of the things that will feel most different about these next 10 years versus these last 10 years is how much a single person or a small group of people with a lot of agency can get done and that is a bigger deal than it sounds like because coordination costs are huge and when we can empower people with more knowledge more tools more resources whatever I think we won't just see like a little bit more stuff get built but because of these kind of coordination costs across people we'll see like a re ...

AI for Science & AlphaFold Overview - AI systems can accelerate scientific discovery and enable new breakthroughs, particularly in healthcare [1] - AlphaFold, a system developed for protein structure prediction, has been cited approximately 35,000 times, demonstrating its impact on scientific research [1] - The speaker's guiding principle is to build tools that enable scientists to make discoveries [1] Protein Structure Prediction & Biological Significance - Proteins, numbering around 20,000 different types in humans, perform nearly every function in a cell [1] - Determining protein structure is exceptionally difficult, often requiring years of effort and significant resources, costing around $100,000 [2] - There are approximately 200,000 known protein structures, with roughly 12,000 new structures being added annually [2] - Protein sequence discovery is happening approximately 3,000 times faster than protein structure determination [2] AlphaFold Development & Key Factors - AlphaFold's success was driven by data (200,000 protein structures), compute (128 TPU V3 cores for two weeks), and, most importantly, research and innovative ideas [2] - Research and novel ideas were approximately 100 times more valuable than the data used in training AlphaFold [3] - Mid-scale ideas, rather than just scaling transformers, are crucial for building transformative AI systems [2][3] Impact & Applications of AlphaFold - AlphaFold has enabled scientists to make discoveries in areas like vaccine and drug development, and understanding how the body works [1] - The release of the AlphaFold database, containing approximately 200 million protein structure predictions, significantly increased its adoption and impact [3] - Researchers are using AlphaFold in unexpected ways, such as predicting protein interactions and engineering proteins for targeted drug delivery [5][6] - AlphaFold is estimated to have accelerated the field of structural biology by approximately 5-10% [9]

Startup Characteristics - The key to startup success lies in deeply understanding a specific user base and providing effective software solutions [1] - A significant competitive advantage (moat) is built by understanding users better than anyone else and having software that works for them [1] - Successful founders typically possess strong engineering/technology skills combined with unique insights into a specific area [1] - The founders of multi-billion dollar startups possess a rare combination of technical expertise and unique understanding [1]

Artificial Intelligence (AI) Potential - Unimaginable super intelligence is expected in 10 to 20 years, barring major setbacks [1] - AI has the potential to vastly increase the rate of new scientific discovery [2] Impact on Science and Economic Growth - AI for science is highlighted as a key area of excitement [2] - Long-term sustainable economic growth relies on scientific discovery and good governance [2] - Increased scientific discovery via AI could lead to incredible improvements in people's lives [2]

A fireside with Satya Nadella on June 17, 2025 at AI Startup School in San Francisco. Satya Nadella started at Microsoft in 1992 as an engineer. Three decades later, he’s now Chairman & CEO, navigating the company through one of the most profound technological shifts yet: the rise of AI. In this conversation, he shares how Microsoft is thinking about this moment— from the infrastructure needed to train frontier models, to the social permission required to use that compute. He draws parallels to the early PC ...