Y Combinator

Emergent: How Six Months of Tinkering Led To A $100M ARR Company

Mukund Jha, Jared Friedman

Emergent is an AI-driven platform founded by Dunzo alumni Mukund and Madhav that enables non-programmers to build and monetize functional software through natural language interaction. Operating with a 95% Bangalore-based engineering team, the company has scaled to an $100 million annual recurring revenue run rate and serves over 8.5 million users by leveraging a proprietary multi-agent architecture that ranks first on industry coding benchmarks. The platform distinguishes itself by focusing on end-to-end software execution rather than prototypes, having shipped over 10 million applications globally within its first nine months of operation.

We just launched Paxel!

Paxil is a locally executed tool that generates a five-dimensional builder profile analyzing how developers utilize AI coding agents like Cursor within their own Docker environments. Y Combinator encourages Startup School applicants to integrate this data into their submissions to identify high-potential "no-resume" talent by shifting evaluation focus from final outputs to specific building methodologies. Developed to aggregate global patterns on AI-assisted software creation, the service remains free for developers seeking actionable insights into their engineering workflows.

How Legora Went From YC to $100M ARR in 18 Months

Max Junestrand, Gustaf Alströmer

Following its acceptance into the YC Winter 2024 cohort, a ten-person founding team of former consultants and engineers rapidly scaled their legal technology platform to nearly 500 employees and over $100 million in annual recurring revenue. The company distinguished itself in a crowded market through a high-profile campaign featuring Jude Law and by pivoting from task-based augmentation to autonomous agents capable of executing complex end-to-end legal workflows. As the business prepares to expand beyond its legal identity, it leverages proprietary data and specialized workflow integrations to maintain a competitive moat against generalist model providers while addressing the evaluation challenges inherent in complex legal contexts.

Conductor CEO Charlie Holtz Walks Us Through His AI Coding Setup

Charlie Holtz

Charlie, co-founder of Conductor, leads a team developing a macOS application that orchestrates multiple coding agents to generate software primarily in TypeScript and Rust. The platform enforces a strict non-terminal workflow where AI handles code generation while humans intervene only through specific review modes to maintain quality and prevent error cycles. With a strategic focus on high-efficiency model selection and a future roadmap emphasizing malleable, modifiable agents, Conductor positions itself as a tool that shifts software value from code itself to human-authored prompts and descriptions.

Why Two IIT Engineers Turned Down $550K Jobs To Build A Startup

Varun Vummadi, Ankit Gupta

GigaML, a Y Combinator-backed startup founded by IIT Kharagpur alumnus Varun and his Stanford-educated co-founder, has pivoted from EdTech to developing AI agents that achieve 60% to 70% support deflection rates for major clients like DoDash and DoorDash. The company addresses the enterprise bottleneck of manual configuration by launching "AI forward deployed engineers" that automatically iterate policies to improve business metrics, a strategy enabled by their "0.1% talent" recruitment philosophy and heavy reliance on human-coded expertise rather than sales teams. By leveraging the YC trust network to secure early contracts with eight-person operations, GigaML aims to unlock broader enterprise AI adoption through a generic automation layer that eliminates the need for extensive human engineering resources.

Inside YC's AI Playbook

Pete Koomen, Gary, Jared, Diana, Tom, Boris

Y Combinator has redefined its operational model by evolving into a super AI-native organization that treats artificial intelligence as a foundational infrastructure rather than a mere tool, enabling autonomous agents to access a unified PostgreSQL database for complex business logic. This architecture supports a dynamic ecosystem of over 350 specialized tools and a daily "dream cycle" process that refines workflows based on meeting transcripts, effectively compressing employee onboarding and eliminating traditional manual coding bottlenecks. By broadcasting internal agent interactions to public channels and investing heavily in token costs, YC advocates for a decentralized future of just-in-time software where human prompts drive the interface, positioning the firm to leapfrog competitors reliant on conventional co-pilot methodologies.

How The Best Companies Defend Against Mediocrity And Rot

Garry, Eric Ries

Eric Ries exposes how the 1980s doctrine of shareholder primacy systematically undermines long-term company value, citing cases like Jeff Lawson's ouster at Twilio and Saul Price's departure from FedMart as evidence that founder control is often lost to short-term investor demands. To counter this instability, the event advocates for structural reforms such as the industrial foundation model, which legally prioritizes mission over profit, as demonstrated by the sustained success of entities like Novo Nordisk and the mission-preserving safeguards of Anthropic. The presentation concludes by urging founders to immediately adopt Public Benefit Corporation status and design "governance fortresses" to insulate their organizations from the inherent conflicts of standard corporate governance.

OpenAI: $2M in tokens to every YC company in the spring and summer batches.

OpenAI and Y Combinator have announced a coordinated initiative offering $2 million in non-cash token equity to startups in the Spring and Summer batches via an uncapped SAFE. This investment targets companies engaged in high-volume software factory patterns, aiming to compress eighteen months of engineering work into a fraction of the time by eliminating early-stage API costs. Recipients must apply by Memorial Day weekend to receive notifications by June 5th, with the credit fungible for any OpenAI usage including code execution and API consumption.

How to Build a Self-Improving Company with AI

Tom Blomfield

The event argues for replacing traditional hierarchical management with recursive AI architectures where organizations function as self-improving loops composed of sensor, policy, and learning layers. It demonstrates this shift through a YC case study where autonomous agents analyzed failures and updated code overnight, enabling companies to transition from headcount-constrained models to token-limited systems. By mandating universal data recording and treating internal software as ephemeral, the presentation defines humans as peripheral "edge interfaces" while AI assumes the role of the central "company brain" for decision-making and execution.

Zepto: How Two 17-Year-Olds Built India's Largest Seller Of Fruits and Vegetables

Aadit Palicha, Jared Friedman, Keval

Founded in 2020 by college dropouts Adit Ayyal and Keval Karia, Zepto revolutionized India's grocery sector by pivoting from a WhatsApp-based service to a proprietary 10-minute dark store delivery model. Operating as a massive logistics and supply chain network, the company now manages over 200,000 jobs and delivers tens of millions of monthly orders while leveraging AI to optimize forecasting and advertising revenue. With current expansion plans targeting India's top 50 cities, Zepto aims to replicate the scale of major global retailers like Walmart and cultivate a domestic consumer goods ecosystem.

Thanks for a great time, India 🇮🇳

The India AI event drew an overwhelming 25,000 applications, surprising organizers with the unprecedented turnout. This massive participation underscores a pivotal shift where the future of the ecosystem is being defined by the current generation of attendees rather than previous leaders. The scale of engagement signals a transformative moment for the nation's artificial intelligence development.

Startup School Paris

Alexandre Lebrun, Olivier Pomel, Paul Copplestone, James Hawkins

On June 29th, Y Combinator hosts a one-day Startup School Paris event specifically for builders and hackers across France and Europe. The agenda features confirmed speakers from companies like Ami, Datadog, Supabase, and PostHog alongside YC partners and alumni. Attendees will gain direct access to industry leaders and fellow entrepreneurs to accelerate their startup efforts.

Harshil Mathur: AI Is Compressing Every Moat

Harshil Mathur, Jon Xu

Founded by Harshil Mathur in 2015, Razorpay became the first Indian Y Combinator company to pivot from a failed educational focus to a payment infrastructure provider for startups after navigating a twelve-month regulatory gestation period. The firm secured a competitive moat by rejecting early global acquisition offers and becoming the first gateway to support UPI, subsequently processing over $180 billion in gross merchandise value while maintaining capital efficiency that inadvertently generated profits. Today, Mathur continues to lead the company through a strategic reinvention using AI, emphasizing that founder-led conviction in long-term problem-solving remains essential despite advancements in software development speed.

Inference Chips for Agent Workflows

Current AI inference hardware fails to support agentic workflows, which rely on complex loops and tool calling, resulting in low GPU utilization and a critical market gap for specialized silicon. Strategic acquisitions by NVIDIA and innovations like Google's TPU v7 highlight the industry's urgent recognition that successful agentic AI requires native features for fast context switching, speculative decoding, and persistent memory caching across execution graphs. To address this, the speaker is actively seeking collaborators with deep expertise in both chip architecture and agent execution flows to develop the necessary compiler and hardware optimizations for this emerging paradigm.

AI-Native Discovery Engines

Frontier AI models are transforming the traditional scientific discovery loop by advancing from co-pilot tools to autonomous systems capable of executing full design-make-test-analyze cycles. These AI-native engines are already driving breakthroughs in drug discovery, material science, and protein engineering by generating hypotheses and validating them through automated laboratory workflows. As these closed-loop systems mature, they are poised to become the primary drivers of scientific progress alongside human researchers rather than remaining mere assistants.