Y Combinator

We’re still in the early innings with AI.

YC-backed ventures like DoorDash, Instacart, and Uber exemplify smartphone-driven success stories that launched roughly four years after the iPhone's release. These examples demonstrate that determining which ideas will succeed and where value will accumulate requires a significant period of maturation before market dominance can be achieved.

How To Keep Your Users | Startup School

David Lieb

YC Group Partner David Leib leverages his experience scaling Bump and Google Photos to argue that high cohort retention is the definitive quantitative signal of product-market fit. He details precise methodologies for defining user actions, selecting appropriate measurement intervals, and interpreting retention curve shapes to distinguish sustainable growth from a churn-heavy treadmill. By avoiding common analytical pitfalls and applying strategies like user acquisition targeting and onboarding optimization, founders can transform cohort data into a actionable roadmap for building scalable, long-term businesses.

Gmail creator Paul Buchheit on the very first version of Google’s “Did you mean?” feature

Paul Buchheit

A former Google engineer designed the original "Did you mean" spell correction feature after analyzing query logs that revealed roughly one-third of searches contained errors. Although the initial implementation used a standard spell correction library, it frequently produced incorrect suggestions, such as recommending the fish "Turbot" for "TurboTax." These early accuracy failures highlighted the complexity of auto-correction and drove subsequent refinements to the system's reliability.

Sales pre-PMF should be done by the founders.

Early-stage B2B startup founders must personally drive sales efforts prior to achieving product-market fit to validate their business models effectively. Because this pre-PMF selling requires entrepreneurial vision and a tight feedback loop with product development, external sales hires often fail to replicate these critical dynamics. Relying on external sales teams before validation is identified as a suboptimal strategy that risks the venture's ability to succeed later.

Are We In An AI Hype Cycle?

Gary, Jared, Harj, Diana, Mark Mandelmann, Jr., Melanie Warrick, Mark Mandelbacher

Y Combinator is launching its first-ever Fall batch, offering $500,000 in funding with an application deadline of August 27th. The organization analyzes the current AI market through a dual lens of heightened hype cycles and tangible application-layer utility, contrasting speculative valuations against revenue-generating tools that demonstrate clear enterprise efficiency. While acknowledging risks of overinvestment similar to past tech bubbles, YC emphasizes that long-term value will accrue to companies solving specific customer problems rather than relying on foundation model speculation.

How Nothing Founder Carl Pei Built A Multi-Million Dollar Smartphone Brand In Just 2 Years

Carl Pei

Nothing founder Carl Pei details the company's strategic pivot from a bankrupt partnership with a Chinese factory to achieving $600 million in annualized revenue by overcoming a 90% return rate on its "ear one" product. Pei advocates for hardware founders to prioritize operational stability over design innovation, utilizing a "two-second rule" for features like the Glyph Interface to mitigate screen addiction and build brand trust. This approach enabled the startup to successfully validate its supply chain with 600,000 units sold and establish a foundation for future smartphone ambitions in the highly competitive hardware sector.

Gmail Creator Paul Buchheit On AGI, Open Source Models, Freedom

Paul Buchheit, Jared, Harj, Diana, Noam Shazier, Mark Mandelbaum, Mark Blyth, Paul Lewisohn, Zuck Meyer, Melanie Warrick, Gary Illyes, Lyn Alden

Paul Buchheit and Noam Shazier trace Google's evolution from an AI-first innovator to a risk-averse monopoly that stifled tools like Lambda to protect search revenue, while OpenAI emerged as a non-profit counter-movement funded by figures like Elon Musk to keep research open. Buchheit champions open-source models as essential for preserving individual liberty against Big Tech centralization and authoritarian surveillance, predicting that algorithmic efficiency will soon lower barriers for small teams to build AGI. He warns that regulatory overreach like SB 1047 will force excessive censorship and that the future workforce will face displacement by autonomous AI agents capable of deep-faking knowledge work by 2033.

How To Price For B2B | Startup School

Tom Blomfield

Founders are advised to anchor pricing in a collaborative value equation that quantifies customer ROI, setting rates between 25% and 50% of that value to ensure healthy margins while retaining two-thirds of the benefit for the client. The strategy explicitly discourages cost-plus or predatory price wars, instead urging a shift toward recurring revenue models and short, metric-driven pilots to mitigate revenue volatility. By treating cloud credits as real costs and avoiding public price transparency for enterprise deals, this framework enables startups to secure sustainable growth without triggering a race to the bottom.

Tarpit Ideas: The Sequel

Dalton, Michael

YC partners Dalton and Michael define "tar pit" ideas as market spaces that appear highly attractive but historically fail due to a lack of genuine technological shifts or fundamental behavioral changes. While Large Language Models have rendered some previously impossible ventures viable, founders must demonstrate concrete problem-solving capabilities rather than relying on social validation or assuming the first successful iteration is imminent. This dynamic framework warns against behavioral coordination failures and fast wealth arbitrage, urging entrepreneurs to rigorously validate ideas against historical precedents and actual user needs.

Why Startup Founders Should Launch Companies Sooner Than They Think

This presentation dismantles the myth of the single-shot launch, arguing that founders should immediately release imperfect MVPs to gather critical data rather than delaying for perfection. It highlights Y Combinator's peer-pressure strategy and case studies like Airbnb and Brex to demonstrate that early failure provides essential learning while the market rarely remembers product stumbles. By redefining launch goals from revenue generation to information gathering, founders can overcome psychological barriers to build products that deeply serve a small, urgent user base.

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.