AI & I

Most frameworks for working with AI agents assume humans should stay in the loop at every phase. That’s the wrong approach, says Cora general manager Kieran Klaassen.
Kieran is the creator of Every's AI-native engineering methodology, compound engineering. His four-step framework—plan, work, review, compound—rebuilds how engineers work with agents. The insight, worked out with collaborator Trevin Chow, is about when to be in the loop and when to step away and let the model handle it. "LLMs are very good at just following steps, doing deep work, working for hours—days even now," Kieran says. "That thing is kind of solved."
Kieran and Trevin describe an AI workflow as a sandwich. Agents are the workhorse filling, and humans are the bread, responsible for framing the problem at the start and reviewing the outputs at the end. 
Every CEO Dan Shipper talked with Kieran for AI & I about why setting the frame of a problem is still hard for agents, why simulated personas won't replace human judgment, Dan's bar for AGI—an agent worth running 24/7 with no off switch—and what Kieran's background as a classical composer taught him about performance, polish, and finding the parts of work that bring you joy.
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To hear more from Dan Shipper:
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Discover more resources in the episode
Compound engineering plugin: https://github.com/EveryInc/compound-engineering-plugin
Compound engineering guide: https://every.to/source-code/compound-engineering-the-definitive-guide
Compound engineering camp: https://every.to/source-code/compound-engineering-camp-every-step-from-scratch

Timestamps:  
 00:00:00 – Introduction and the AI sandwich metaphor
 00:02:33 – What compound engineering is and how it’s evolved
 00:04:27 – The "work" phase of agentic coding is essentially solved
 00:06:27 – Why humans belong at the beginning and the end of an AI workflow
 00:11:06 – Dan's argument for why agents can't change frames—and how this will keep us employed
 00:16:51 – Full automation is a moving target
 00:23:21 – Musical composition as a model for human-AI collaboration
 00:26:39 – Find your place in an AI-accelerated world by leaning into what brings you joy

Most AI companies are racing to build bigger LLMs. Eve Bodnia thinks that's the wrong approach.
Eve is the founder and CEO of Logical Intelligence, which is developing an alternative to the transformer-based models dominating the industry. Her argument: LLMs’ architecture makes them fundamentally unsuited for some mission-critical tasks. A system that generates output one token at a time, with no ability to inspect its own reasoning mid-process or guarantee its results, shouldn't be trusted to design chips, analyze financial data, or even fly a plane. Her alternative is the energy-based model (EBM), a form of AI rooted in the physics principle of energy minimization, not language prediction. Rather than guessing the next probable word, an EBM maps every possible outcome across a mathematical landscape, where likely states settle into valleys and improbable ones sit on peaks. 

Dan Shipper talked with Bodnia for AI & I about why she believes LLM progress is plateauing, what it means for AI to actually understand data rather than just pattern-match across it, and how her team is building toward formally verified code generated in plain English—no C++ required.

If you found this episode interesting, please like, subscribe, comment, and share!

Head to http://granola.ai/every and get 3 months free with the code EVERY

To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe 
Follow him on X: https://twitter.com/danshipper 

Timestamps: 
00:00:51 - Introduction
00:02:09 - Why correctness and verifiability matter in AI
00:09:33 - What an energy-based model is
00:14:21 - How EBMs construct energy landscapes to understand data
00:19:00 - Why modeling intelligence through language alone is a flawed approach
00:26:54 - What it means for a model to "understand" data
00:37:21 - How EBMs solve the vibe coding problem and enable formally verified code
00:43:21 - Why LLM progress is plateauing
00:49:54 - Mission-critical industries haven't adopted LLMs, and how EBMs could fill that gap

While walking to the office, our COO Brandon Gell had his AI agent call him and go over his emails in his inbox one by one. When he arrived, he opened Gmail and confirmed she'd done everything he'd asked. "My jaw is on the floor," he messaged me.
That was the moment Every got serious about setting up each employee with their own agent. Today, it's a reality—and it has completely changed how we work.
Dan Shipper talked to Every COO Brandon Gell and head of platform Willie Williams for Every's AI & I about what happens when everyone at a company gets their own AI sidekick. 
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To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe 
Follow him on X: https://twitter.com/danshipper 
Visit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI.
Timestamps:  
00:00 Introduction
00:02:21 How Brandon built Zosia, an AI agent to run his household
00:07:09 Brandon's aha moment re: using agents for work
00:09:39 What happened when everyone on the team got their own agent
00:12:42 How agents take on their owners' personalities, and why that matters inside an org
00:23:51 Why it's important for agents to do work in public
00:30:51 What we're still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem
00:40:45 How we built Plus One, our hosted OpenClaw product
00:47:27 The cultural shift required to make agents work at scale

Founded in 2019, Linear is the rare company started pre-ChatGPT to have successfully reinvented itself as an agent-native business.
On this episode of AI & I, Dan Shipper sat down with Karri Saarinen, cofounder and CEO of the product management tool, to discuss building a platform where humans and agents develop software together—and why the "SaaSpocalypse" isn’t coming for all SaaS companies. 
If you found this episode interesting, please like, subscribe, comment, and share! 
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe 
Follow him on X: https://twitter.com/danshipper 
Visit  https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI.
Timestamps:
0:00 Introduction 
2:00 Why Linear waited to ship AI features instead of rushing to chatbots 
5:06 Linear's agent platform and becoming the system that guides AI agents 
7:42 Why "SaaS is dead" is a simplistic narrative 
12:18 How Linear adopted AI coding tools
17:45 AI's impact on product building workflows—speed versus thoughtfulness 
22:18 The value of conceptual work and thinking before shipping 
29:30 How AI is reshaping Linear's product strategy  
37:18 Demo: Linear's agent skills, shared context, and code review workflow 
47:48 The future of product development and the enduring role of human judgment

Mike Krieger built one of the most consequential consumer apps of the last two decades as cofounder of Instagram. He is now at the frontier of determining what makes a breakout AI-native product as co-lead of Anthropic Labs.
Dan Shipper talked with Krieger for Every’s AI & I about how his experience creating Instagram shapes how he thinks about building with AI, including what can be sped up and what remains stubbornly time-intensive. 
If you found this episode interesting, please like, subscribe, comment, and share! 
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe 
Follow him on X: https://twitter.com/danshipper 
Download Grammarly for FREE at grammarly.com
Timestamps 
Introduction: 00:01:39
What's gotten easier—and what hasn't—about building products in the age of AI: 00:02:33
Why vibe coding creates "indoor trees": 00:05:00
How rewrites have become a normal part of the development process: 00:09:00
What "agent native" product design means: 00:11:39
How Mike's labs team is structured and the cofounder model: 00:24:27
The best signal for a product bet is someone with "break through walls" conviction: 00:29:33
Navigating enterprise customers while keeping pace with rapid AI change: 00:38:51
OpenClaw, personal agents, and the product question defining 2026: 00:40:54
Links to resources mentioned in the episode:
Mike Krieger: https://x.com/mikeyk 
Agent-native architecture: https://every.to/guides/agent-native