Sequoia Capital
Sequoia Capital is a venture capital firm that works with companies from early ideas through IPO and beyond.
Models Will Absorb Today’s Agent Harnesses Within a Year
Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that the current rush to build agent harnesses may have a short shelf life. In an interview with Sequoia Capital’s Sonya Huang, he says models are absorbing the scaffolding around agents and could make much of today’s custom harness layer less distinctive within about 12 months. Google’s own strategy runs on both sides of that claim: Antigravity has become a shared agent layer across products, while Kilpatrick says the durable advantage for builders will move to focus, domain knowledge, risk tolerance and useful outcomes for users.
AI Makes Customer Understanding the Scarce Input in Product Development
Listen Labs co-founder and CEO Alfred Wahlforss argues that as AI makes software and marketing execution cheaper, the scarce input for companies becomes knowing what customers actually want. He describes Listen as an AI research platform that runs large-scale voice interviews, builds carefully targeted audiences, and uses interview data to simulate how specific customer groups may respond to future questions. Wahlforss’s central claim is that interviews, when designed and tested properly, can provide a richer and more predictive signal than surveys, behavioral logs, or generic personas.
Neuralink Says 20-Patient Scale Is Advancing Brain-AI Interfaces
Neuralink co-founder and president DJ Seo told Sequoia partner Shaun Maguire at AI Ascent 2026 that the company has moved from a single human implant demonstration to more than 20 patients, while still treating its current work as restoration of lost function rather than elective enhancement. Seo argued that Neuralink’s larger aim is not faster computer control but a higher-bandwidth interface between brains and AI, eventually enabling direct, multimodal transfer of concepts. The path there, he said, depends less on a single implant breakthrough than on scaling surgery, robotics, manufacturing, clinical evidence and neural-data models.
Distributed RL Let Composer Match Frontier Coding Models With Smaller-Model Speed
Cursor’s Federico Cassano and Fireworks’ Dmytro Dzhulgakov argue that Composer’s advantage comes from specializing a model for software engineering inside Cursor rather than spending capacity on general-purpose behavior. Starting from an open-source base, Cursor used mid-training and reinforcement learning against its own product environment, while Fireworks supplied the distributed infrastructure needed to make agent rollouts, weight synchronization, and inference efficient enough to run at scale. Their case is that application companies with enough product-specific usage, tools, and feedback can build models that are better, faster, and cheaper for their own workflows than larger general models.
Ivan Zhao Says AI Makes Companies Flatter, Not Hierarchy-Free
Notion founder and CEO Ivan Zhao argues that AI will not make companies hierarchy-free, but can reduce the amount of human routing that makes hierarchy slow. In a conversation with Brian Halligan, Zhao describes Notion’s answer as “jazz mode”: a deliberately decentralized company that still has structure, but relies on high-agency people, ex-founders and model-enabled teams to improvise as product and market conditions change. His broader case is that AI-era leaders have to refound around the technology itself, not just bolt it onto the old SaaS operating model.
Serval Bets Boring IT Controls Will Unlock Enterprise AI
Serval founder and CEO Jake Stauch argues that enterprise AI will be won less by giving models broad autonomy than by constraining them inside permissions, approvals, audits and workflows that companies can trust. In a conversation hosted by Sequoia’s Pat Grady, Stauch describes Serval as a ServiceNow-like system rebuilt for AI: an admin agent generates workflows from natural language, while a help desk agent can act only through tools IT has explicitly approved. He says that same logic extends to Serval’s operating model, where customer insight and “fewer, better” hiring matter more than model access in a market that may force products to be rebuilt every few months.
Suno Bets That Making Songs Can Become a Mass Consumer Medium
Suno founder and CEO Mikey Shulman argues that AI music should not be understood as a cheaper substitute for streaming catalogs, but as a new form of active consumer entertainment. In a conversation with Sequoia’s Sonya Huang, he says Suno’s technical choices — modeling raw sound, prioritizing full songs, and using preference data rather than conventional benchmarks — support a product thesis that making music can be as much the point as listening to it. Shulman also frames partnerships with labels such as Warner as central to building new participatory music formats, not as a concession to incumbents.
Twitter’s Hypergrowth Playbook Replaced Consensus With Accountable Owners
Dick Costolo’s account of running Twitter from 2010 casts the company’s early crisis less as founder drama than as an operating failure: too many decisions required group consent, too few people clearly owned outcomes, and process was allowed to substitute for judgment. In a conversation with Brian Halligan, the former Twitter CEO argues that scaling the company required replacing consensus with accountable decision rights, a bias to yes, direct communication, and faster correction when mistakes or personnel problems became clear.
Data Scarcity, Not Compute, Is the Next AI Bottleneck
At AI Ascent 2026, Flapping Airplanes co-founders Ben and Asher Spector argued that data scarcity, more than compute alone, will determine where AI can create value next. They said the biggest gains so far have come in unusually data-rich domains such as search and coding, while much of the economy — including robotics, trading, science and narrow industrial workflows — lacks comparable datasets. Their proposed answer is to make models far more data-efficient by developing new GPU-level primitives that current frameworks such as PyTorch make hard to express.
Orbital Compute Becomes Cheaper If Launch Costs Fall Below $500/kg
Philip Johnston, Starcloud’s co-founder and chief executive, argues that AI data centers could become cheaper in orbit than on Earth if launch costs fall to about $500 per kilogram. His case rests on continuous solar power in a dawn-dusk orbit, avoiding land and battery costs, and using constellations of optically linked satellites for inference workloads. Starcloud’s plan, he said, starts with an orbital GPU proof point and points toward an 88,000-satellite network delivering roughly 20 gigawatts of compute capacity.
Voice Will Be the Primary Interface for AI Agents and Robots
At Sequoia’s AI Ascent 2026, ElevenLabs co-founder and CEO Mati Staniszewski argues that audio was an overlooked frontier in 2022 because the AI field was focused on text and images, leaving room for a smaller company to build quickly and monetize early. His broader case is that as AI intelligence becomes more capable, voice becomes the interface problem: the way people will use agents, robots, services, education and healthcare. Staniszewski says the next hard problems are emotional intelligence, timing, authentication and workflow, not merely making synthetic speech sound human.
Ricursive Wants AI to Design the Chips That Train AI
At AI Ascent 2026, Ricursive Intelligence co-founders Anna Goldie and Azalia Mirhoseini argued that the next bottleneck in AI is the chip-design process itself, and that AI should be used to design the hardware that trains and serves it. Drawing on their AlphaChip work, which Goldie said has shipped in four generations of Google TPUs, they described Ricursive’s plan to rebuild chip-design tools for fast AI feedback loops and turn that tooling into a platform for custom silicon. Their larger claim is that workload-specific chips, and eventually co-designed chips and models, require moving chip design from yearlong expert workflows to automated optimization.
AI Scaling Faces an Energy Wall Without Physics-First Hardware
At AI Ascent 2026, Unconventional AI founder and CEO Naveen Rao argued that the current AI compute stack is approaching an energy wall because it is built on an 80-year-old digital computing model poorly suited to intelligence. Rao’s case is that GPUs and matrix math cannot close the efficiency gap with biological brains fast enough, and that AI hardware must instead be rebuilt around physical dynamics, time-domain computation, and architectures that blur memory and processing. He presented Unconventional AI’s coupled-oscillator chip prototype as an attempt to move compute closer to the thermodynamic limits of intelligence per watt.
Autonomous AI Hackers Are Already Beating Humans on HackerOne
Oege de Moor, founder and CEO of XBOW, argues that autonomous AI hacking has moved from assistance to real exploitation. In an AI Ascent 2026 talk, he says XBOW’s system reached the top of HackerOne using only black-box access, found a remote code execution flaw in Bing Image Search from a URL alone, and would have been three times more effective with GPT-5. His warning is that defenders have six to nine months before comparable open-weight models make the same capabilities broadly available, including to attackers.