Y Combinator Summaries & Notes

• The AI economy has stabilized, with clear layers for model, application, and infrastructure companies, and a developed playbook for building AI-native businesses. • Anthropic has surpassed OpenAI as the preferred LLM provider for Y Combinator-backed startups, moving from ~20-25% usage to over 52% in the Winter 2026 batch, driven by strong performance in coding tools and agents. • Gemini is also climbing in popularity, now at 23% usage among YC applicants, with users impressed by its reasoning abilities and its integration with Google Search for real-time information. • The "AI bubble" concern is compared to the telecom bubble of the '90s; while there's massive infrastructure investment, it creates an opportunity for application-layer startups, much like YouTube emerged from the excess bandwidth. • The AI revolution is in its "deployment phase," following an "installation phase" of heavy capital expenditure, leading to abundance and new opportunities for founders to build applications on top of existing infrastructure. • Despite initial skepticism, companies are exploring space-based data centers and fusion energy to address power generation and land constraints for AI infrastructure, with companies like Google and Elon Musk pursuing space solutions. • The skill set for building AI models is becoming more democratized, with a growing number of individuals possessing the research, engineering, and business acumen required, leading to an increase in applied AI companies and more specialized models. • The trend of AI increasing efficiency is leading to higher customer expectations, driving companies to still hire significantly to meet demand and compete, rather than reducing workforce size. • Companies like Gamma are demonstrating a new "reverse flex" by achieving significant ARR with a small number of employees, indicating a shift towards efficiency and lean operations in the AI startup landscape.

• Intelligence is defined by the ability to learn new things efficiently, not just by excelling at known tasks like chess or Go. • The ARC benchmark, developed by François Chollet, tests an AI's ability to learn new skills rather than just perform on existing ones, with a focus on tasks that average humans can solve. • Early LLMs performed poorly on the ARC benchmark (around 4-5% for GPT-4 base), but recent advancements, particularly with reasoning paradigms, have significantly improved performance (e.g., 21% with 01 preview). • ARC AGI 3, launching next year, will be an interactive benchmark featuring 150 video game-like environments where AI must infer goals without instructions, testing generalization through action and feedback. • Efficiency in AI will be measured not only by accuracy but also by the number of actions and data points required, drawing parallels to human learning efficiency, with ARC AGI 3 normalizing AI performance to average human actions. • Solving ARC AGI is considered necessary but not sufficient for achieving Artificial General Intelligence (AGI); a system that excels at ARC AGI would demonstrate strong generalization capabilities but wouldn't necessarily be AGI itself.

• PostHog helps users debug products and ship features faster, consolidating customer and product data, with around 160 employees and 300,000 customers. • The company's initial successful product was self-hosted open-source product analytics, born from the frustration of repeatedly setting up analytics during multiple pivots, and it gained traction on Hacker News. • PostHog's marketing strategy, including bizarre billboards and a unique website, focuses on standing out and generating awareness through humor and unexpected comparisons rather than direct conversion. • James Hawkins, CEO of PostHog, emphasizes that having a clear, albeit potentially contrarian, plan is crucial when raising funds, and that building a remarkable product and brand requires going significantly beyond the typical 80/20 effort. • PostHog is now doubling down on AI to build "product autonomy," aiming to automate feature development and product management tasks, which is enabled by their multi-product infrastructure and substantial funding.

• Amplitude initially approached AI with skepticism, viewing its capabilities as "jagged" and facing frustration from external pressure to adopt it without a clear strategy. • A turning point for Amplitude was recognizing the transformative effect of AI on software engineering, leading them to seriously invest in AI adoption around October 2024, marked by hiring a new engineering leader and acquiring Command AI. • The company's pivot to AI involved a significant internal effort, including an "AI week" for training and hackathons, to get the existing team bought-in and proficient with AI tools before focusing on building new AI-native products. • Amplitude's core strategy shifted from a customer-driven "faster horse" approach in traditional SaaS to a technology-first understanding of AI capabilities to map them to product solutions, acknowledging that customers cannot always articulate needs for novel AI functionalities. • The transition required organizational restructuring, including reorganizing the engineering, product, and design teams twice within a year and moving away from leaders solely focused on the pre-AI SaaS modality.