AI & I

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.

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Subscribe to Every: https://every.to/subscribe 
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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

Kate Lee has spent her career working with words—first as a literary agent, then in roles at Medium, WeWork, and Stripe. As Every’s editor in chief, she’s been the quiet force behind the newsletter for more than three years. 
Lately, something has shifted in Kate’s work. After years of watching her colleague Dan Shipper evangelize AI from the front lines, Katie has started rewiring how she works and is integrating more and more AI tools in her work. 
We had Kate on to talk about her career path from book deals to tech startups, what it really means to run a newsletter as a small team in the age of AI, and what she thinks the bottleneck to automating copyediting is. Plus: the story of pulling off reviews of two major model releases in 24 hours, and how she’s using her AI-powered browser to help her hire. 
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe 
Follow him on X: https://twitter.com/danshipper 
Timestamps
0:01 – Introduction and Kate's early career as a literary agent
4:45 – From book publishing to tech: Medium, WeWork, and Stripe Press
12:00 – How Kate joined Every and what made the role click
27:00 – What it's like to be a knowledge worker at the frontier of AI
31:00 – The “aha” moment: using AI to manage hundreds of applicants
36:24 – How Every's editorial team uses AI to enforce standards and train taste
45:06 – Publishing two reviews of major model releases on the same day
51:39 – What automating copy editing requires
Links to resources mentioned in the episode:
Proof: https://www.proofeditor.ai/