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Noah Shinn - Building Instinct: The Personal Agent - [Invest Like the Best, EP.493]

Invest Like the Best with Patrick O'Shaughnessy1h 26m

Noah Shinn, CEO of Instinct, discusses building a personal AI agent that acts autonomously on behalf of users through natural communication channels like text, email, and voice calls. The company is growing at 10% daily with over $1 billion in transaction volume, and Shinn explores how personal agents will fundamentally transform digital commerce, business operations, and user interaction with technology.

Summary

Noah Shinn founded Instinct about a year ago to build a personal AI assistant that works intuitively for users without requiring a new app or interface. Unlike traditional AI products that accomplish specific tasks, Instinct follows higher-level objectives and learns user preferences over time, handling everything from travel bookings to subscription management to coordinating meetings through a "Trusted Person Network" where agents communicate directly with each other to find optimal meeting times and arrangements.

The product emphasizes understandability and feeling over raw capability, with Shinn stressing that users should always feel in control of their data and share information at their own pace. Within three weeks, 40% of users share credit card information with Instinct, reaching 80% retention rates among users who connect at least one piece of sensitive data. The company operates on an invite-only model, with each user receiving five invites to share, resulting in organic 10% day-over-day growth with zero marketing spend.

Instinct is generating over $1 billion in annual transaction volume, with 50% coming from travel alone. Shinn plans to monetize through transaction take rates rather than ads or subscriptions, arguing this aligns incentives with users' actual interests. He draws comparisons to Apple Pay and Amex, which charge merchants rather than users, positioning Instinct potentially at a higher take rate than current payment infrastructure (2-3%) but lower than app store fees (30%).

The company faces significant technical challenges, primarily around compute scaling. Growing 10% daily means doubling compute needs every week, with compute having 3-4 month lead times to acquire. Shinn spends 40% of his time thinking about this problem. However, Instinct has engineered efficient serving methods, running the same performance as Claude Opus 3.5 at dramatically lower costs by customizing inference deployments and separating background work (completed in minutes or hours) from interactive requests (needing millisecond responses).

Shinn discusses how agents will transform industries by removing friction from transactions and enabling proactive behavior. Restaurant reservations could shift from first-come-first-serve to agents communicating users' special occasions to restaurants that want to cater to significant events. Travel bookings collapse from multiple steps to simply saying "I need to be in New York tonight." Businesses dependent on user attention and ads face risks, while transaction-based businesses may see volumes increase despite reduced time on app.

The company has implemented multiple safety layers including firewalls to intercept malicious content, decoupled monitoring systems to approve actions before execution, and hallucination detection systems. These are separate from the agent architecture itself, creating defensive depth. Shinn emphasizes that Instinct's architecture, following objectives rather than just executing tasks, provides robustness against misaligned user requests.

Future versions will trend toward simpler interfaces despite increased capability. Over 90% of some users' interactions occur through voice. Shinn envisions always-on voice interaction through AirPods where users naturally converse with Instinct during activities like running or hiking. Long-term, individual task execution gives way to pursuing longer-term objectives, such as personal finance goals or fitness targets. Early small businesses are fully operating on Instinct, with autonomous inventory management pursuing higher-level objectives.

Instinct raised $1 billion at a $10 billion valuation, with Sequoia, Benchmark, and others as lead investors. Shinn chose partners experienced with similar technology transformations, prioritizing capital efficiency for a business model that could easily switch to subscriptions but instead targets lower take rates through higher transaction volume and user trust.

About this episode

My guest today is Noah Shinn. Noah is the founder of Instinct, a personal assistant that you text, call, or email the same way you would a person. It has its own phone and its own computer, and it can do almost anything on your behalf that you would do yourself online. Instinct is about a year old, still invite-only, and growing roughly 10% day over day without any marketing spend. We discuss the surprising ways people are already using Instinct, the trusted network that lets two people's agents coordinate directly, and what Noah has learned about how long it takes users to trust an agent with their credit card, inbox, and calendar. He explains why he refuses to build an ad model and why he believes a personal agent should never influence users against their own interests. We also cover the take rate model behind the more than $1 billion a year now flowing through the platform, how travel and food delivery businesses should prepare for a world of agents, why he spends 40% of his time thinking about compute, and why he believes all of software will eventually collapse into one simple interface. Please enjoy my conversation with Noah Shinn. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp’s⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Timestamps: (00:00:00) The Most Exciting Software Race Ever (00:00:49) What Instinct Is (00:04:11) The Wildest Things Users Have Done With Instinct (00:07:15) The Instinct to Instinct Trusted Network (00:13:24) How Agents Will Reorder the Internet (00:14:44) Reinventing Restaurant Reservations (00:17:23) Travel and $1 Billion in Transaction Volume (00:21:00) How Users Learn to Trust an Agent (00:23:25) Keeping Sensitive Data Safe (00:26:08) Alignment and the Business Model (00:31:07) Take Rates and the Payments Stack (00:34:52) When Incumbents Push Back on Agents (00:40:46) How Businesses Should Prepare for Agents (00:44:43) Designing for Understandability (00:48:15) Taste Versus Data (00:50:03) Invite-Only Growth at 10% a Day (00:54:46) How Far Ahead to Buy Compute (00:56:58) The Cost to Serve Each User (00:59:24) How Much Compute Proactive Agents Will Need (01:02:30) Thoughts on Muse (01:03:59) Mistakes and Staying Proactive (01:05:34) The Future of Security (01:08:32) Where the Interface Goes Next (01:12:35) Agents That Pursue Goals (01:14:35) Personality and the Movie Her (01:16:27) Why It's Called Instinct (01:17:34) Allies, Enemies, and Distribution Channels (01:18:43) Raising Capital (01:20:34) The Kindest Thing

Key Insights

  • Instinct achieves 10% daily organic growth through invite-only referrals without any paid marketing, with users voluntarily spending their five invites daily, indicating extremely strong product-market fit and word-of-mouth demand.
  • The core value proposition is reducing friction to near-zero by removing the need for users to learn new interfaces or apps; instead, Instinct integrates into existing communication channels users already trust like iMessage, WhatsApp, and email.
  • Shinn designed Instinct to follow higher-level objectives rather than execute individual tasks, allowing it to robustly decline or reframe misaligned user requests and maintain behavior aligned with actual user interests rather than short-term commands.
  • Within three weeks of use, 40% of users share credit card information with Instinct, and users who share any sensitive data achieve 80% retention rates, demonstrating that trust builds gradually through consistent positive experiences.
  • The Trusted Person Network introduces weighted network effects where agents can communicate directly with each other to coordinate meetings, schedules, and tasks, with varying permission levels and built-in accountability for trust violations.
  • Instinct is generating over $1 billion in annualized transaction volume while still in invite-only early access with a small user base, growing at 10% daily, suggesting the product has discovered a genuine demand wave.
  • 50% of Instinct's transaction volume comes from travel, demonstrating category-specific concentration, but Shinn argues this validates the model's applicability across industries where agents can remove friction and increase transaction volume.
  • Shinn has engineered Instinct to serve at costs comparable to frontier models like Claude Opus 3.5 through deployment shape optimization and batch processing, allowing him to pursue a free or low-cost consumer model rather than high-priced subscriptions.
  • The company faces exponential compute scaling where 10% daily growth requires doubling compute every week, with 3-4 month lead times to acquire it, forcing Shinn to make capital-intensive bets on future demand and potentially acquire 10x excess capacity.
  • Shinn argues that businesses whose revenue depends primarily on user attention and ad impressions face disruption, while transaction-based businesses may see revenue increase despite reduced user time on their platforms as friction falls to zero.
  • The product philosophy emphasizes soft qualities like message formatting, cognitive load, and communication timing learned through staged rollouts and user testing, not just capability—aiming for product that feels good rather than simply powerful.
  • Shinn plans to monetize through merchant-paid transaction take rates (similar to Apple Pay or Amex) rather than user fees or ads, positioning this as a natural alignment of incentives where merchants pay for distribution while users receive a free, powerful service.

Topics

Personal AI agents and autonomous task executionProduct design philosophy emphasizing understandability and user controlBusiness model based on transaction take rates vs. ads or subscriptionsData privacy and security architecture for sensitive informationCompute scaling challenges and inference optimizationImpact on existing industries and digital commerceTrusted Person Network for agent-to-agent coordinationGrowth metrics and viral word-of-mouth expansionLong-term vision for simplified interfaces and higher-level objectivesCapital requirements and fundraising strategy

Transcript

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