Y Combinator

YC Partner and Algolia co-founder Nicolas Dessaigne’s advice for founders starting out.

Nicolas Dessaigne

Founders are urged to immediately launch imperfect products rather than waiting for a perfect idea, prioritizing rapid iteration and direct user engagement to validate market assumptions. This approach rejects over-engineering in favor of a "crappy, quick, and dirty" methodology that secures essential feedback through high-touch interactions with early adopters. By accepting short-term technical debt and imperfections, entrepreneurs can quickly pivot to build the correct product and capitalize on the market's forgiving memory.

What David Lieb and his team built eventually became the foundation for Google Photos.

David Lieb

Facing imminent failure with dwindling funds, the founders of a struggling mobile company consulted Y Combinator's Paul Graham after their primary app and a secondary photo-sharing attempt failed to generate revenue. Graham challenged them to abandon their current path and build a comprehensive replacement for the native iPhone Photos app, a scope the founder initially deemed impossible. The team subsequently pivoted to this ambitious vision by leveraging identified flaws in Apple's existing software to secure their survival.

How David Lieb Turned a Failing Startup Into Google Photos | Backstory

David Lieb

David Lieb co-founded the contact-sharing startup Bump, which was eventually acquired by Google in 2015, but his subsequent secret launch of the billion-user Google Photos product resulted in two firings before its 2015 debut. Following a 2020 leukemia diagnosis and a grueling year-long treatment regimen, Lieb transitioned from a product builder to a mentor at Y Combinator. He now leverages his history of strategic pivots and near-death survival to guide and protect the next generation of founders.

2024: The Year the GPT Wrapper Myth Proved Wrong

Jared Harge, Diana, Harj, Lily Yang, Cheng Cheng, Suk Peng, Yiu, Francesc Campoy Flores, Priyanka Vergadia, Anan, Mark Mandelbaum, Melanie Warrick, Mark Mirchandani, Gary Miles, Leslie Kendrick Magnuson, Harjit

The 2024 startup landscape shifted toward capital-efficient growth, where companies like Opus Clip and Perplexity achieved tens of millions in revenue with under $5M in funding by leveraging open-source models and vertical-specific applications. Enterprise adoption accelerated as AI agents attained enterprise-scale reliability, driving a record-breaking aggregate weekly growth rate of 10% for YC batches while converting pilots to revenue at unprecedented speeds. This ecosystem revival was fueled by regulatory relief, a resurgence of in-person Silicon Valley activity, and a strategic pivot from model monopoly to multi-model orchestration that prioritized product execution over raw compute ownership.

Anthropic’s Claude Computer Use Is A Game Changer | YC Decoded

Garry Tan

Anthropic has launched a public beta of "Claude Computer Use," an AI agent that autonomously navigates websites and executes tasks by interpreting screen screenshots to determine precise pixel locations for clicks and keyboard inputs. While the system successfully automates complex workflows like travel planning and safety compliance analysis, it currently faces performance limitations and security vulnerabilities such as prompt injection. This development marks a significant industry shift as competitors race to release similar autonomous agents, signaling a transition toward AI systems that execute entire tasks rather than serving merely as conversational assistants.

How To Start A Dev Tools Company | Startup School

Nicolas Dessaigne

Drawing on Y Combinator's extensive portfolio of companies like GitLab and Stripe, this analysis outlines that successful DevTools founders must be technical builders who validate ideas through rapid prototyping and direct developer engagement. The presentation emphasizes a bottom-up sales strategy where founders personally sell to technical buyers, prioritizing usage-based monetization and open-source trust-building over traditional marketing. Furthermore, it identifies key pitfalls such as premature hiring and non-technical leadership, advocating instead for a founder-led approach until the product reaches approximately $1 million in annual recurring revenue.

Vertical AI Agents Could Be 10X Bigger Than SaaS

Gary, Jared Harge, Diana, Mark Mandelbaum, Aaron Cannon, Mike, Brett Taylor, Parker Conrad, Matt McGinnis, Salient, VAPI, Rippling, Speedy Brand, OpenAI, Claude, Melanie Warrick, Nico, A Priori, Capital.ai, PowerHelp, Giga ML, Zepto, Mementic, Rainforest QA, Triplebyte, Outset, Vector Shift, Mark Benioff, Paul Graham, Travis, Jake Heller, Flo Cravello, Tia, Hashi Roginio, Mark Mirchandani, Francesc Campoy Flores

Jared Harge projects that vertical AI agents will trigger a $300 billion+ market surge by displacing human labor in specialized enterprise functions, mirroring the historical success of B2B SaaS where general-purpose incumbents cannot master niche domain complexities. While competitors like Mark Mandelbaum note exceptions such as Rippling's horizontal platform strategy, startups are advised to target high-value, repetitive workflows—from automated recruiting to debt collection—rather than competing in obvious consumer applications where incumbents like Google retain dominance. This shift from basic software wrappers to full-stack agents is expected to expand organizational leadership span and create unicorns ten times larger than previous software predecessors by integrating complex domain knowledge directly into autonomous decision-making tools.

Twitter vs. X: Product Lessons For Startup Founders

Tom Blomfield, David Lieb

The presentation critiques the trend of optimizing social platforms for singular engagement metrics, arguing that this strategy degrades user satisfaction by creating "engagement farms" filled with low-quality content. Using Twitter's transformation under Elon Musk as a primary case study, the analysis highlights how shifting from a chronological feed to an algorithmic model has diluted content quality, confused verification systems, and likely reduced advertising revenue despite boosting raw usage time. Ultimately, the speaker advises product leaders to define clear "good states" for users and resist growth-at-all-costs pressures to ensure long-term retention and genuine value.

Why The Next AI Breakthroughs Will Be In Reasoning, Not Scaling

Jared, Harj, Sam Altman, Diana Alvear, Mark Mandelmann, Francesc Campoy Flores, Melanie Warrick, Kendrickson Jr., Harshi, Jake Heller

OpenAI's O1 model demonstrates a significant leap in reasoning capabilities by employing reinforcement learning and chain-of-thought evaluation to solve complex engineering and support tasks that previously required thousands of human experts. Companies like Diode Computer, Camphor, and GigaML have leveraged this architecture to automate chip design, optimize mechanical CAD, and reduce customer support error rates from 70% to 5%, respectively. As Sam Altman forecasts AGI within 4 to 15 years, the emerging strategic focus shifts from general model access to proprietary data and precise evaluation suites, enabling startups to tackle hard tech challenges in fields ranging from fusion energy to biotechnology.

LLMs for Chip Design

OpenAI's model "01" demonstrates that large language models with reasoning capabilities can revolutionize the design of application-specific integrated circuits and field-programmable gate arrays by overcoming the prohibitive costs of traditional custom digital system development. This technological shift is projected to deliver order-of-magnitude performance improvements while enabling specialized computations that were previously economically unfeasible. Consequently, the integration of these AI-driven optimization tools creates a high-potential market opportunity for startups seeking to disrupt the semiconductor engineering sector.

AI-Aided Engineering Tools

The event identifies a decades-long stagnation in engineering tools characterized by high computational costs and barriers to expert-level entry, urging a transition to next-generation AI-powered alternatives. It challenges founders to develop systems that integrate advanced reasoning capabilities to solve complex mathematics and physics problems more efficiently. This shift aims to enable engineers to rapidly design and construct high-quality physical infrastructure, ranging from satellites to circuits, without relying on traditional, labor-intensive workflows.

Stablecoins 2.0

Stablecoin payment volumes have surpassed PayPal and now account for 20% of MasterCard's volume, with Stripe's $1 billion acquisition of Bridge signaling strong investor confidence. Following a potential shift toward a crypto-friendly US administration, market stakeholders anticipate imminent legislation to replace the current regulatory uncertainty. This convergence of explosive market adoption and favorable policy creates a prime environment for new ventures, particularly those focusing on corporate holding services and developer tools.

Public Safety Technology

Y Combinator is actively recruiting startups to advance public safety, building on Flock Safety's current achievement of solving 10% of US crimes with its license plate camera technology. The accelerator seeks new ventures focusing on computer vision for emergency response, community-police collaboration tools, and operational efficiency technologies to help meet the industry goal of solving 25% of all crimes within the year. This initiative positions YC as a key resource provider designed to facilitate the growth of companies driving this expanding public safety movement.

Government Software

Palantir's decade-long success selling software to the U.S. government, now valued at $125 billion, highlights a strategic opportunity for new ventures to leverage artificial intelligence amidst rising federal deficits. With billions spent annually on repetitive administrative functions, startups are encouraged to develop AI solutions that simultaneously reduce government spending and boost operational efficiency. This dual-purpose approach aims to capitalize on the current economic climate to transform how the public sector manages its resources.

How To Build The Future: Sam Altman

Sam Altman, Garry Tan

Sam Altman characterizes the current era as the optimal time for launching technology companies, projecting that Artificial Superintelligence could emerge within a decade if deep learning progress continues to compound. He outlines a five-level roadmap where AI evolves from basic chatbots to autonomous organizational managers, emphasizing that energy abundance and rapid iteration are the critical inputs for unlocking material prosperity. While acknowledging past strategic pivots and leadership turbulence at OpenAI, Altman maintains that speed and conviction allow startups to outmaneuver established corporations in achieving AGI.