Intercom

What Lies Beneath the API — Benjamin Cowen, Modal

Jun 2, 2026 · 12:40

In this episode, Ben Cowen from Modal argues that as AI products mature, fine-tuning becomes essential, citing cases like Intercom beating their frontier API at 1/10th the cost. He identifies three signals it's time to fine-tune: paying more for the API than customers pay you, evaluative plateaus, and latency requirements that shared endpoints can't meet. Cowen explains that supervised fine-tuning now fits in 300 lines of Python and that reinforcement learning rollouts can scale to 50,000 sandboxes using serverless platforms like Modal. He contends that frontier labs aim to win at everything, while businesses need to win at their specific logic, making fine-tuning a natural destination. The episode provides practical guidance on when and how to make the leap, emphasizing that building an agent harness and collecting eval data already sets the stage for training.

How Building with AI Can Double the Throughput of Your Engineering Team — Brian Scanlan, Intercom

May 15, 2026 · 21:49

Brian Scanlan of Intercom explains how treating Claude Code like a new hire—onboarding it to a 15-year-old Rails monolith, writing skills for every recurring task, and connecting it to production systems—doubled engineering PR throughput in under a year. By going all in on one platform instead of letting everyone choose their own tool, Intercom achieved a 17.6% automatic PR approval rate with SOC 2 sign-off, and its CI infrastructure collapsed under the volume. The key principle: give agents problems, not tasks. Scanlan recounts a security incident where Claude automatically pulled files, ran analysis, and handed back next steps in two minutes using a skill he didn't know existed. The talk emphasizes that all engineering work—debugging, testing, planning—should be agent-first, and that companies must invest in platform-level adoption and continuous skill improvement.

Mastering AI Pricing — Mayank Pant, Stripe

May 1, 2026 · 24:19

Mayank Pant from Stripe explains that AI companies, growing 3x faster than traditional SaaS, face margin risk from power users and unpredictable compute costs, making hybrid pricing (base fee + usage fee) essential—56% of AI leaders now use it. He presents a five-step framework: define customer-perceived value (e.g., automation, augmentation, enhanced service, improved results), choose a charge metric (consumption, workflow, or outcome-based), adopt hybrid pricing with guardrails like usage caps and automated notifications, and iterate pricing frequently—84% agree fast adaptation is a competitive advantage. Pant illustrates with examples: Gamma charges per deck (not API calls), Intercom prices per resolved ticket. To keep customer-facing prices stable while changing features, he advises abstracting value with credits (e.g., 100 credits/month) that can be internally revalued. Stripe's billing infrastructure supports this iteration, with 78% of AI companies building on Stripe using its subscription, usage, and hybrid billing, plus Metronome for enterprise contracts.

How AI is changing Software Engineering: A Conversation with Gergely Orosz, @pragmaticengineer

Apr 21, 2026 · 26:42

Gergely Orosz explains how AI is reshaping software engineering, from token maxing at Meta, Microsoft, and Salesforce—where engineers use AI tools to inflate token counts out of fear of layoffs and performance reviews—to the broader shift in the engineer's role toward orchestrating agents rather than managing people. He notes that big tech companies like Uber, Airbnb, and Shopify are building custom internal AI infra (MCP gateways, coding agents) to stay ahead, even tolerating high churn for a six-month competitive edge. Shopify secured GitHub Copilot a year early by offering feedback from 3,000 engineers. Gergely also shares how The Pragmatic Engineer hit product-market fit: 100 paid subscribers before publishing, reaching 1,000 in six weeks, and becoming the #1 paid tech newsletter by focusing on two deep-dive articles per week for two years.

Building the platform for agent coordination — Tom Moor, Linear

Jul 28, 2025 · 19:43

Tom Moor, Head of Engineering at Linear, explains how the company is evolving from an issue tracker into an operating system for engineering teams that treats AI agents as first-class teammates. He details Linear's pragmatic AI journey: starting with embeddings and PG vector, then moving to a hybrid search index using TurboPuffer and Cohere embeddings, leading to features like Product Intelligence (query rewriting, reranking, deterministic rules for suggestions), natural language filters, Slack-to-issue creation, and daily audio pulses. The core of the talk is Linear's agent platform, launched two weeks ago, where coding agents like CodeGen, Bucket, and Charlie integrate via OAuth, GraphQL, and new webhooks, allowing users to assign, mention, and interact with agents just like human teammates. Moor emphasizes best practices for builders: respond fast, inhabit the platform's language, move issues to 'in progress,' and clarify plans before acting, all while keeping interactions concise and value-adding. The episode argues that with this platform, engineering teams can build more, faster, and with higher quality by offloading grunt work to infinitely scalable cloud-based teammates.

Building Agents (the hard parts!) - Rita Kozlov, Cloudflare

Jul 23, 2025 · 21:12

Rita Kozlov, VP of Product for Cloudflare's developer platform, presents the building blocks of AI agents—client, AI reasoning, workflows, and tools—arguing that effective agents require all four components. She highlights the Model Context Protocol (MCP) as a standard for exposing APIs to LLMs, and demonstrates Cloudflare's Agents SDK, which simplifies hosting remote MCP servers with built-in OAuth, state management via durable objects, and real-time WebSocket communication. Kozlov cites real-world impact: companies using agents for sales automation see 20% revenue increases, 90% faster support response times, and 50–75% time savings. She walks through a human-in-the-loop credit card approval workflow built with Nok, showing how durable objects maintain long-running state, prevent duplicate actions, and route approvals across Slack, email, or in-app notifications. The talk emphasizes that once an MCP server is deployed, it can be used directly from Cursor, Claude, ChatGPT, or a custom client, including voice interfaces via WebRTC-to-WebSocket translation.

Shipping an Enterprise Voice AI Agent in 100 Days - Peter Bar, Intercom Fin

Jul 18, 2025 · 17:10

Peter Bar, Product Lead at Intercom, details the 100-day build of Fin Voice, an AI voice agent for enterprise phone support. The agent handles knowledge-based queries using a stack of speech-to-text, LLM, text-to-speech, RAG, and telephony, achieving ~1 second latency for simple queries and using filler phrases for longer ones. Key product decisions included focusing on out-of-office hours as an initial wedge, designing conversations for voice differences like answer chunking, and prioritizing integration with human support workflows over model improvements. The team measured success via resolution rate (user confirming resolution or not calling back within 24 hours) and used an LLM-as-judge for quality analysis. Bar argues that voice AI is the next frontier in customer service, citing cost reduction from $7–12 per human-handled call to 3–20 cents per minute with AI.

Anthropic in the Enterprise — Alexander Bricken & Joe Bayley

Apr 13, 2025 · 20:55

Alexander Bricken and Joe Bayley from Anthropic's Applied AI team argue that enterprise AI implementation often fails due to overengineering, poor data infrastructure, or lack of testing—but industry leaders achieve transformative results with Claude. They detail Anthropic's deployment models (API, cloud partnerships, enterprise solutions) and real-world case studies like Intercom's Fin agent, which solved 86% of support volume using Claude. Best practices include building evals early as intellectual property, identifying intelligence/cost/latency trade-offs based on use-case stakes, and avoiding premature fine-tuning by trying prompt caching, contextual retrieval, and agentic architectures first. They also highlight interpretability research and the Model Context Protocol for reliable AI deployments.

The Price of Intelligence - AI Agent Pricing in 2025

Feb 22, 2025 · 20:38

Shitej, co-founder and CTO of Orbe, argues AI agent pricing must continuously evolve, citing Intercom's 99 cent per resolution outcome model, Clay's prospecting credits, and Cursor's tiered usage limits. He stresses aligning pricing with target audience—SMB vs. enterprise—and maintaining simplicity and predictability. Cost structure is key: Character.AI optimized inference to support 100M DAUs, while Jasper leveraged a model decision engine to offer unlimited credits. Shitej emphasizes flexibility, noting OpenAI's price drops force repricing, and predicts 2025 will see more unlimited plans, outcome-based pricing with SLAs, and greater investment in pricing R&D for usage visibility.