AI Entrepreneurship Series

We are living in the era of GenAI, which has transformed not only the computing industry but also our daily lives. Today, I will discuss our journey building the Together AI Native Cloud — designed to empower next-generation large-scale enterprise Gen AI with customized end-to-end solutions that target the entire AI lifecycle, powered by open source models. I will share insights into some of our innovations in the inference system research space, highlighting how these advancements shape our services and real-world applications. Lastly, I will explore future trends in Gen AI and discuss how Together AI’s cutting-edge technologies are driving the next-generation AI cloud revolution: large-scale agentic system building, multi-silicon adoption and more.

Becoming a hardware supplier for data centers and hyperscalers is highly challenging for a start-up. I will describe my journey of founding and building a start-up to commercialize high-bandwidth optical interconnects for AI and high-performance computing.

In this talk, Sidharth will explore why deploying AI systems in real-world applications presents challenges distinct from model training. He will discuss the engineering complexities behind inference, including serving models securely, reliably, and at scale, and provide insight into the hidden systems work behind a single request to a large language model.

We’ve officially entered the era of non-human adversaries, where AI doesn’t just assist hackers—it executes 90% of the attack autonomously. In this talk, I deconstruct the watershed moments of the AI age, from the first documented AI-orchestrated espionage campaigns to multimillion-dollar deepfake heists.

In this talk, we’ll outline why a new class of agent-native infrastructure is emerging, what problems it is designed to solve, and the core use cases driving it, from autonomous coding agents to large-scale evaluation and training workloads. Daytona is an agent-native control plane designed to orchestrate isolated, stateful sandbox environments at scale. We’ll break down the infrastructure challenges behind isolation, state management, and massive parallelism, and why traditional VM and container stacks fall short. As a concrete example, we’ll walk through scaling RL rollouts, showing how tens of thousands of environments can be provisioned and orchestrated in minutes as part of a high-throughput RL pipeline.

This talk will provide insights into building and scaling enterprise software companies, from early-stage product development to public company growth.

AI progress today is constrained by rising energy use, cost, latency, and complexity. In this talk, I will share the scientific and entrepreneurial journey behind Liquid AI, an MIT spin-off founded to rethink artificial intelligence from first principles. I will describe how breakthroughs in brain-inspired, continuous-time neural networks enabled a new class of Liquid Foundation Models that deliver high-quality multimodal intelligence with dramatically lower compute, memory, and latency. The lecture will explore how these technical advances translate into company-building strategy: designing models around hardware, deploying AI on-device and at scale, and creating defensibility beyond the frontier-model race. I will conclude with lessons on fundraising, partnerships, talent, and building sustainable AI businesses that can operate at planetary scale.

Building an AI startup today is exciting, but also uniquely challenging. In this talk, I’ll offer a transparent, behind-the-scenes look at the realities of modern AI entrepreneurship. We are in one of the fastest-moving technology cycles in history. Drawing from firsthand experience as a repeat founder, I’ll unpack the full spectrum of an entrepreneur’s journey exploring obstacles, opportunities, and outcomes. You will leave with an honest, grounded understanding of what it takes, what to expect, and why, despite the challenges, this may be one of the most exciting times in history to build in AI.

In this presentation, I will describe ongoing work at Traversal, where our team is developing an AI Site Reliability Engineer (SRE) designed to assist enterprises in diagnosing and mitigating production incidents, with the broader goal of improving the resilience of large-scale, mission- critical systems. I will outline why incident troubleshooting is rapidly becoming a central bottleneck in realizing end-to-end automation of the software development lifecycle (SDLC), particularly as organizations adopt increasingly complex cloud-native architectures and integrate AI-driven tooling across their operational workflows.

From a research perspective, automated incident response represents a technically rich and largely underexplored problem space. I will highlight how it brings together challenges at the intersection of agentic system design, large language model evaluation, causal inference on unstructured data, and time-series modeling of high-dimensional telemetry. These domains converge in the task of enabling AI systems to form hypotheses, reason under uncertainty, and propose actionable remediations in environments characterized by incomplete information, noisy signals, and strict reliability constraints. Our goal in this work is not only to build a practical system, but also to surface open problems and novel research opportunities for the broader community.

Autonomous warehouse systems represent one of the most advanced and large-scale examples of embodied multi-agent intelligence operating in the physical world. Thousands of mobile robots collaborate continuously to move, pick, and sort goods in dynamic, uncertain environments— achieving high efficiency, adaptability, and robustness in real time.

In this talk, I will discuss how warehouse robotics offers both a practical foundation and conceptual inspiration for the design of AI systems. I will introduce the market context and technical underpinnings of large-scale robotic automation, then explore how intelligence emerges through coordination, perception, and control across distributed agents. I will also discuss how large language models (LLMs) may extend this paradigm—enabling natural language tasking, high-level reasoning, and seamless human-AI collaboration in embodied systems.

Finally, I will reflect on how lessons from large-scale robotic coordination can inform the broader design of the warehouse automation ecosystems, highlighting new directions for research at the intersection of autonomy, learning, and collective intelligence.

Modern document-heavy work in law, finance, and consulting is repetitive, bespoke, and review heavy. Code generation tools like Cursor have reshaped how developers work, but that shift has not happened in law. Legal tasks are hard to measure and even harder to “unit test,” which is why many AI tools miss the mark.

This talk shows how August is building agents that do better: the right indexing methods, visible state, disciplined tool use, and citations at every step to cleanly automate real professional workflows. We’ll walk through a multi-step research agent (e.g., over SEC filings) that plans the task, retrieves the right sources, routes to the relevant sections, and drafts grounded legal answers.

Attendees will leave with a lightweight design pattern for credence domains like law. We will also share our growth and go-to-market. We are productizing law-firm workflows and, in some cases, selling with the firm to their clients. This is a new model that is gaining traction across professional services.

Join Xiaofeng Wang, Sr. Engineering Manager at LinkedIn’s Agents Platform, as he takes you behind the scenes of the platform powering all of LinkedIn’s member-facing agentic applications at scale. This talk will trace the GenAI product revolution, from simple prompt-in-string use cases to the latest LinkedIn Hiring Assistant. It will show how the Agent Platform evolved to enable these experiences. Xiaofeng will share the technical foundations, organizational challenges, hiring strategies, and hard-won lessons that shaped LinkedIn’s journey, highlighting how the team bridged open-source innovation with enterprise-grade infrastructure, built seamless developer experiences, and enforced trust and responsible AI from day one. Whether you’re a researcher, engineer, product leader, or entrepreneur, this talk offers a front-row look at LinkedIn’s agentic journey, the latest trends in agent development, and what it takes to transform prototypes into impactful, production-grade agents at enterprise scale.

This talk shares lessons learned going from NLP researcher to building Noetica, a platform that analyzes populations of complex contracts to determine market standards for deal terms. Along the way I’ll discuss (1) how LLMs have enabled a new opportunity space for hybrid systems that combine extraction capabilities with formal analytical frameworks, (2) how building these systems still requires deep fusion of subject-matter and technical judgment to identify tractable decompositions of seemingly impossible problems, and (3) how this converges to a new version of the Technical Product Manager role. This role is highly suited to technical researchers willing to tackle sector-specific challenges.

In this talk, Aaron will walk through his journey building in the AI space, detailing his learnings from Instabase, his own AI startup Regression Games, and now his experience at Anthropic. This talk will focus on the importance of focusing on specific problems and navigating frequently-changing technological landscapes, and how to apply cutting-edge research in record time.