Y Combinator

When you’re pre-product market fit, sales is a job for the founders.

The event asserts that early-stage B2B founders possess the unique capability to sell their products before achieving product-market fit, rendering external sales hires premature and ineffective. Success in this phase requires founders to leverage their vision for high-volume experimentation and establish tight feedback loops between sales efforts and product development. This distinction highlights pre-PMF sales as an exclusively entrepreneurial role, fundamentally different from the specialized functions assumed after a company validates its market.

Enterprise Sales | Startup School

Pete Koomen, Pete Kuhman

Optimizely co-founder Pete Kuhman outlines a rigorous enterprise sales framework for technical founders, emphasizing that selling before product-market fit requires experimentation with targeted prospecting and personalized outreach. The strategy prioritizes deep discovery during initial calls, uses product-specific narratives to demonstrate value during demos, and treats pricing as a signal of problem severity rather than a fixed metric. By actively managing implementation roadmaps to ensure customer adoption and navigating procurement hurdles with internal champions, founders can transform sales skills into a transferable superpower for fundraising and hiring.

The artistry is actually in that interface between the human and the technology itself.

The speaker argues that while artificial intelligence will significantly assist with backend development and API writing, it cannot fully automate the creative interface between humans and technology where true software artistry resides. By leveraging strongly typed systems and natural language to translate requirements into specifications, AI serves as a powerful tool that augments human productivity rather than replacing it. Ultimately, the event emphasizes that defining the fundamental scope of products remains a critical human responsibility that transcends mere code generation.

You should probably still learn how to code.

A recent analysis of Large Language Models reveals that their logical development stems from processing code repositories like GitHub, providing empirical proof for the long-suspected link between programming and increased human intelligence. While the ultimate goal of computing is to eventually eliminate the need for manual coding, experts argue that learning to write code remains essential today because the cognitive challenge of programming itself enhances intellectual capacity. This emerging data transforms a historical practitioner belief into a validated conclusion, urging continued education in coding despite advancements in AI-generated software.

10 People + AI = Billion Dollar Company?

Gary, Jared, Harj, Diana, Jensen Huang, Francesc Campoy Flores, Mark Mandelmann, Michael Witwer, Mark Mandelbach, Mark Mandalmann Bennett, Mark Mandellmann Goldberg, Patrick Hollison, Brian Chesky, Mark Pincus, Rick, Lena Kahn, Harjit

A panel challenged Jensen Huang's prediction that natural language interfaces will render computer science education obsolete, arguing instead that AI currently struggles with the complex, real-world engineering frictions that require human intuition and design. While acknowledging that AI benchmarks like SweeBench demonstrate significant progress in automating routine tasks, the discussion emphasized that programming remains a fundamental cognitive process for discovering ideas and solving ambiguous business problems. Consequently, the panel projects that efficiency gains will trigger the Jevons Paradox, fostering thousands of new billion-dollar ventures rather than consolidating power, provided founders maintain the engineering literacy needed to effectively direct AI tools.

Better models, better startups.

B2B companies are leveraging advanced AI models to achieve productivity scales where a single employee performs the equivalent work of ten, while simultaneously upselling premium features to drive year-over-year revenue growth. End-users focus on functional utility rather than model architecture, allowing vendors to incrementally charge for enhanced capabilities as technology improves. This strategy has proven highly effective, with a Y Combinator cohort demonstrating rapid expansion from $6 million to over $30 million in annual revenue within a single batch cycle.

What Is ZIRP And How Did It Poison Startups?

Dalton, Michael

During the Zero Interest Rate Phenomenon, a flood of cheap capital distorted venture capital markets, prompting fund managers to chase assets under management while founders prioritized aggressive hiring and inflated valuations over product-market fit. This environment spawned a wave of unsustainable businesses that collapsed immediately when rates normalized, separating companies with viable profit models from those reliant on infinite liquidity. In response, experienced investors and accelerators like Y Combinator are now urging a return to realistic growth strategies that prioritize operational efficiency and enduring business models over market timing.

It only really matters if you can find a handful of users that use your product habitually.

Early-stage founders are advised to prioritize identifying users who habitually integrate their product into daily workflows over pursuing vanity metrics like total signups. Success is defined by the retention of a core user base that returns repeatedly, rendering other performance data insignificant without this foundational evidence of habitual usage. Consequently, high acquisition numbers remain secondary unless they demonstrate tangible integration into the users' regular routines.

Standing Up For Startups - YC Goes To D.C.

Luther Lowe, Garry Tan

Y Combinator has established a physical presence in Washington, DC, hiring former Yelp executive Luther to lead its advocacy for "little tech," a movement supporting small, high-impact startups against the dominance of established industry giants. This initiative prioritizes policy reforms regarding skilled immigration, labor mobility, and antitrust legislation while safeguarding the legality of open-source artificial intelligence tools to prevent regulatory stagnation. By leveraging authentic narratives from its global founder network, the organization aims to counteract astroturfing and bridge technical literacy gaps within Congress to foster a more balanced innovation ecosystem.

RFS: AI to build enterprise software

Enterprise software development is undergoing a paradigm shift as AI integration replaces costly manual customization with a single, dynamically standardized codebase. This technological transition threatens incumbent firms by rendering their reliance on large sales teams and unique engineering solutions uncompetitive. Consequently, a new hiring initiative targets individual contributors eager to leverage AI for writing enterprise software in the sector's most lucrative market.

Better AI Models, Better Startups

Gary, Jared, Harj, Diana, Melanie Warrick, Mark Mandelmann, Mark Blythington, Joel Morton, Jordan, Francesc Campoy Flores, Carrie Nordlund

The event analyzes a strategic shift where startups can thrive by building specialized vertical B2B tools and niche consumer products rather than competing with major labs on general-purpose interfaces. It highlights how advanced capabilities like massive context windows and multimodal reasoning create new opportunities in sectors such as robotics, legal tech, and personalized agents while maintaining RAG infrastructure for enterprise data control. Ultimately, the consensus advises founders to leverage these model improvements to automate complex workflows, citing historical precedents where specialized players succeeded by avoiding head-on competition with tech incumbents.

Startup Experts Discuss Doing Things That Don't Scale

Paul Graham

Paul Graham's 2013 essay "Do Things That Don't Scale" challenges Silicon Valley orthodoxy by urging early-stage founders to manually solve immediate user problems before prioritizing technical infrastructure, a strategy exemplified by companies like Airbnb and DoorDash. This approach prioritizes rapid learning and product-market fit over theoretical scalability, allowing startups to validate demand through direct customer engagement while avoiding the pitfalls of building unwanted solutions. Although manual operations risk trapping founders in consultancy models, successfully transitioning to automation after securing initial traction provides a critical competitive advantage by ensuring software development addresses genuine market needs.

How New Technology Creates New Businesses

Dalton, Michael

Leveraging historical precedents like the internet and cloud computing, the event argues that emerging technologies like AI drastically reduce capital barriers, enabling individuals to build high-leverage businesses with minimal headcount. By targeting unsaturated "green field" markets within niche online communities, founders can replicate past successes such as Flappy Bird's rapid monetization or the rise of live-streaming entrepreneurs. This strategic shift promises a structural transformation toward widespread self-employment, allowing creators to bypass traditional corporate hierarchies and establish industries before competition saturates the landscape.

Great founders actually build.

An expert evaluator posits that a startup's capacity for founders to internally build product prototypes is a primary indicator of success. Teams lacking technical founders or relying on external development agencies, premature abandonment of coding, or restrictive no-code solutions face significant scalability risks. Ultimately, the ability to construct initial demos directly is framed as a fundamental requirement for sustainable growth.

Probably not.

Founders of free consumer apps are advised to avoid paid user acquisition because it typically generates only temporary metric spikes while failing to address underlying growth limitations. Sustainable scaling instead relies on discovering non-obvious, low-cost distribution channels that bypass the need for expensive marketing campaigns. Paid spending is reserved exclusively for strictly bounded experiments with predefined learning objectives, serving as a tool for insight rather than a strategy for long-term revenue generation.