Headlines: Stanford State of AI report, Q2 Funding growth and deal growth in startup space, IPOs in H2 2024, Framework for LLM Architecture, 20 Opensource AI/ML tools

AI in Businesses

• Stanford University published a State of AI report - with 13 key charts determining the future of AI in government, business and society - which provides a level set on the metric around regulation, investments, hyper scalers, AI patents, AI productivity, job impact and so on. One interesting comparison is that China is well ahead on the number of AI patents issued in a year but the USA still leads by a huge margin on the AI investments (absolute terms). (Stanford University - Human Centered Artificial Intelligence)

• Excellent benchmarking of the AI / ML / Data / Security / Cloud dominated public limited companies (Zachary DeWitt, Notorious @ Substack)

• "As VCs, we see a juxtaposition: a handful of impressive companies continue to raise exuberant rounds well above public market valuations, yet many GenAI startups struggle to find their product-market fit" Substack's article on the high-pitched activity in the generative space concentrated on a handful of LLM companies and start-ups. The investment has doubled from $20B in Q1 to $10B in Q2 (VCs - Vivek Ramaswamy and Sabrina Wu at Madrona)

• Global funding for start-up investments has increased by 16% Q1 to Q2 and 12% YoY, up from $71B to $78B. Much of the increase is driven by $100M+ deals. AI start-up investment globally contributed a 3rd of that at $24B with $1B+ rounds by Scale AI, XAI etc. Full Cruchbase report quarterly investment report (Crunchbase)

• Shift in the demographics and the themes in the Y combinator cohort 'graduating in April 2024. 50% of companies are AI-themed and 30% are college grads - when we see it, it can't be that surprising (Crunchbase)

• IPO's expected in H2 2024. We have some very familiar and anticipated ones like Stripe, Databricks, Revolut etc in this list. All 3 are doing very well from a revenue perspective and showing all signs of proceeding for an IPO imminently (Crunchbase)

LLM landscape & tooling

• A collection of some of the most popular open source AI and ML developer tools, ranked by the number of stars they have on GitHub, for projects active in 2023 and 2024. It focuses on developer applications used to train and deploy ML models and AI agents. Its purpose is to highlight the breadth and diversity of tools and frameworks being built by the open-source AI community and the vast potential in the space.  (Alex Sandu, The Strategy Deck)

• Emerging architectures for LLM applications is a comprehensive documentation of tools and the reference architecture for the emerging LLM app stack. It shows the most common systems, tools, and design patterns used by AI startups and sophisticated tech companies. Authors generously maintain a GitHub repo on the ongoing developments of this stack. (Matt Bornstein, Rajko Radovanovic, Andreessen Horowitz)

Resources

• A poetic writing on Emerging architectures for LLM Applications (Milan Cleetus Morais, Neotera.vc)

• A great resource for understanding 'Deep Learning' technology and underlying concepts - crash course in DL (Jakub Boksansky, AMD)

• RAG focused LLMs from Startup - Vectara Mockingbird - Deepdive (Suleman Kazi & Vivek Sourabh & Rogger Luo & Abhilasha Lodha, Vectara)