Every’s team breaks down how a new premium tier built around discounted AI tools produced the biggest subscription-revenue jump in the company’s history. The conversation doubles as a snapshot of how builders are using agents to automate the dull parts of work, orchestrate software stacks, and turn expensive experimentation into something more accessible.

Granola CEO Chris Pedregal talks with Every’s Dan Shipper about the knife-fight reality of building through the AI boom, where even breakout products feel precarious. Their conversation widens from meeting notes to the bigger contest over AI-native work: the interfaces, context, and invisible systems that could remake how people think and collaborate on the job.

A writer and technologist describes using AI as both intoxicant and tool, building bespoke software while guarding his mornings and attention so the work that matters does not get flattened into productivity theater. The conversation moves from vibe coding and the collapse of old SaaS moats to the stubborn value of weird books, deep focus and human particularity in an epochal technological shift.

Natalia, Every’s head of consulting, describes how AI agents are moving from novelty to everyday infrastructure: managing sales ops, triaging email, building family care systems, and turning research into personalized learning tools. The conversation also argues for a clearer division of labor, with human judgment and off-the-shelf software still essential even as custom agents absorb administrative work.

Edwin Chen, the founder of data-labeling and evaluation firm Surge, describes training frontier models as a kind of schooling for AGI, where benchmarks now stretch from middle-school math to research-level discovery. The conversation widens into a debate over whether AI should optimize for human flourishing or the same engagement traps that warped social media, and what deeply personal data may be worth in teaching models taste, judgment, and voice.

GitHub’s Kyle Daigle describes a software world remade by coding agents, where pull requests and commits are surging, nondevelopers are building apps, and maintainers need new controls to keep pace. The conversation follows the business and product consequences of that shift, from model routing and token costs to the long game of training tools on a developer’s habits and context.

Mike Krieger describes how living with Anthropic’s new Fable 5 model reshapes work from prompting to delegation, turning AI from a clever assistant into something closer to a teammate. The conversation follows what that shift means for software engineering, verification, workplace collaboration and the widening gap between a person’s intent and their ability to build.

A conversation about the so-called SaaS apocalypse argues that AI will not kill software so much as multiply it, pushing more people into building while preserving the value of products that handle maintenance, context and trust. Along the way, the speakers compare homemade agents, email triage, design systems and Figma’s bet that the future belongs to proactive, personalized tools that move fluidly between code and design.

A spirited debate over whether AI agents will erase jobs or reorganize them argues that automation mostly cheapens yesterday’s expertise, making human judgment, taste and direction more valuable. Drawing on life inside an aggressively AI-native company, the conversation pushes back on layoffs-and-doom narratives and treats adaptation, not retreat, as the real dividing line.

Alex Rattray argues that the internet’s plumbing was built for humans and conventional software, not language models, and that today’s Model Context Protocol often buckles under the weight of context, tool sprawl and weak security. The conversation traces a different path: AI agents that write and execute code against typed APIs, with permissions enforced at the API layer rather than bolted onto chat interfaces.

Noah Breyer describes an unusually practical AI stack: an Obsidian vault synced to a home server, cloud coding agents that search and summarize his notes, and a phone-based workflow that turns dead time into deep work. The conversation broadens into a larger argument about probabilistic software, bureaucratic organizations, and why AI is often more useful as a reader and thinking partner than as a writer.

Anthropic’s platform leads sketch how AI infrastructure is shifting from a bare completion endpoint to managed, long-running agents with memory, tools and cloud scaffolding. They argue that the real bottleneck is no longer prompt craft but the dreary work of production infrastructure, and imagine a near future where Claude chooses its own model, spins up sub-agents and compresses work down to an outcome and a budget.

A once-clunky coding tool is recast here as a desktop command center for modern office work, with hosts arguing that the real contest in AI is over agent interfaces that sit between workers and their apps. The conversation moves from theory to practice, showing how Codex and similar tools now draft emails, triage workflows, assemble strategy docs, and turn fragmented digital labor into something closer to managed delegation.

Stripe’s Emily Glassberg Sands sketches an internet where AI agents are becoming buyers, sellers and developers, forcing payments, fraud detection and software tooling to adapt to machine users. The conversation traces the surge in AI-company revenue, the rise of compute theft and free-trial abuse, and the new pricing and checkout systems emerging for an agent economy.