Latest Interviews

Former OpenAI CRO Bob McGrew thinks that two jobs of the future will be genius and manager.

Bob McGrew

Future AI-integrated industries will likely be defined by two primary roles: the independent "Genius," who leverages tools for high-impact solo innovation, and the "Manager," a CEO overseeing firms comprised mostly of AI agents. While speakers characterize these emerging pathways as optimistic and engaging, they clarify that AI will serve as the core workforce rather than necessitating the complete displacement of human staff. This shift suggests a transformed business landscape where human leaders and creators coexist with autonomous digital employees to drive efficiency and innovation.

The Future of Software Engineering

Language models are projected to reduce software development costs to near zero while driving demand for human engineers to shift from direct coding to managing autonomous agent teams. These agents are expected to assume specialized responsibilities spanning quality assurance, operations, and compliance audits, fundamentally transforming the engineering workflow. Consequently, the speaker is seeking funding for startups that provide generalist developers with tools to coordinate these large agent forces for high-volume software delivery.

B2A: Software Where the Customers Will All Be Agents

The entity introduces a "b2a" investment thesis targeting companies that build software for AI agents as primary customers, aiming to shift the internet landscape from automated impersonation to first-class agent citizenship. This strategy prioritizes funding infrastructure, logistics, and legal frameworks that enable agents to autonomously handle payments, travel bookings, and commercial contracts. Mirroring the coexistence of human and algorithmic traders in stock markets, the approach projects a massive expansion of AI-to-AI interactions across the entire internet ecosystem.

AI Coding Agent for Hardware-Optimized Code

NVIDIA's market dominance relies on CUDA's hand-optimized code, but emerging reasoning models like DeepSeq R1 can now generate comparable AI-optimized kernels, offering a strategic pathway to bypass the critical shortage of low-level system programmers. Founders who develop tools to automate this kernel generation can enable broader adoption of competing hardware and reduce ecosystem dependency on NVIDIA. In response to this opportunity, Y Combinator is actively soliciting applications from entrepreneurs building AI-generated kernel solutions.

Vertical AI Agents

Sam Altman

Vertical AI agents, which are specialized software applications built on large language models to automate specific workflows, are emerging as a primary pathway to building billion-dollar startups across dozens of distinct market sectors. Industry experts highlight high-potential use cases ranging from automated tax accountants and medical billers to dedicated phone support agents and quality assurance testers. This strategy has received strong validation from Sam Altman, who explicitly stated he would focus on building vertical AI agents if he were to restart his career at age 24.

Devtools for AI Agents

A venture firm has pivoted its two-year strategy to fund startups disrupting legacy players through next-generation AI agents capable of autonomous decision-making. Leveraging recent reasoning breakthroughs from model releases like "01" and "03," the firm targets investments in developer tools and building blocks that enable the creation of specialized agent teams. This initiative aims to integrate these autonomous systems across industries, empowering users to enhance productivity and creativity while inviting founders to collaborate on shaping the future of software.

Inference AI Infra in the World of Test-Time Compute

As capital allocation shifts from pre-training to inference for advanced models like DeepSeq R1 and OpenAI 01, surging API demand creates a critical infrastructure cost bottleneck for AI applications. Startups addressing this challenge by rebuilding the inference stack through optimized software tooling and efficient GPU workload management are positioned to unlock significant market growth. Y Combinator is currently soliciting applications from founders developing these essential solutions to enable scalable application deployment.

AI Personal Staff for Everyone

Historically, personalized knowledge work such as legal, financial, and medical assistance remained exclusive to the wealthy due to the reliance on human labor, while software democratized other services like transportation and photo organization. The organization now aims to bridge this persistent gap by developing advanced AI systems capable of automating these specialized roles for the general public. To achieve this mission, the group is actively recruiting collaborators and employees to build "personal AI staff" products that will deliver elite-level services to billions of users.

Browser/Computer Automation

Following demonstrations by OpenAI's "Operator" and Anthropic, AI agents now possess the capability to browse the web and operate desktop applications, effectively treating all digital interfaces as programmable APIs. This technological leap, supported by emerging open-source tools, enables the full automation of human-executable workflows and is estimated to expand the addressable use cases for AI agents by a factor of ten. Industry observers predict this shift will catalyze significant innovation across applications built upon these newly accessible agent capabilities.

Compliance and Audit

A global workforce of nearly four million compliance professionals faces mounting pressure from complex regulatory frameworks like GDPR and ESG reporting, which rely on inefficient manual audits of unstructured data. Large Language Models are emerging as a transformative solution capable of processing entire datasets to continuously identify anomalies and contradictions across global financial records. This shift enables the transition from limited sampling to comprehensive, real-time auditing, creating a significant market opportunity for developers of automated compliance technologies.

Datacenters

The initiative aims to revolutionize AI infrastructure by deploying fully automated, "lights out" data centers that reduce construction timelines and costs through software-managed site selection, power management, and autonomous robotics. Key figures are currently seeking startup funding to transition from traditional multi-year projects to 24-7 operations where robots handle all facility tasks without human intervention. This approach promises to solve current industry bottlenecks by introducing innovative solutions in cooling, material procurement, and project management to accelerate the deployment of faster and cheaper data centers.

A Secure AI App Store

Y Combinator is seeking founders to build a new operating system layer and app store for AI that centralizes personal data in a shared memory architecture while enforcing strict, user-consented privacy controls. This platform will provide simplified APIs for developers to create context-aware applications, such as travel agents and reading assistants, without handling payments directly. By establishing a vetted marketplace infrastructure, the initiative aims to solve industry-wide distribution challenges and allow startups to lead the creation of secure, intelligent AI tools before major tech giants dominate the space.

America’s First Civil Supersonic Jet to Break the Sound Barrier

Blake Scholl

Boom Supersonic's XB1 aircraft successfully broke the sound barrier, marking the first time an independently developed supersonic jet has achieved this critical milestone. Founder Blake Scholl led the initiative to validate the safety and operational lessons necessary for scaling future commercial supersonic passenger travel. This achievement demonstrates how pursuing ambitious technological challenges can drive breakthrough solutions while navigating the emotional extremes inherent in high-stakes aviation startups.

Zip co-founder Rujul Zaparde on going from 0 to 1 with enterprise sales

Rujul Zaparde

Founders validated their business concept by acquiring their first ten customers exclusively through cold LinkedIn outreach, deliberately avoiding referrals or personal networks. This disciplined strategy, which combined daily connection limits with advisory-style discovery calls, generated a 107-page synthesis of market intelligence while establishing a permanent outbound sales bias that dominates the company's current acquisition model.

How do OpenAI’s o1 and o3 models perform complex reasoning?

OpenAI's O1 model utilizes a "chain of thought" reasoning mechanism to solve complex problems by decomposing them into sequential steps, a strategy originally formalized by Google Brain researchers in 2022. This approach enables the system to dynamically identify errors and adjust its logic rather than relying on simple next-token prediction, significantly improving accuracy in tasks requiring multi-step calculation. By explicitly walking through intermediate stages, such as determining total slices and calculating consumption before finding the remainder, the model mirrors human problem-solving to ensure precise outcomes.