Founder Weekly (Issue 737 June 24 2026)

In partnership with

Welcome to issue 737 of Founder Weekly. Let's get straight to the links this week.

You already have a take on which AI lab ships next.

Claude or Gemini? OpenAI or Anthropic? GPT-7 before year-end or not? If you read tech newsletters, you've already formed opinions on all of it.

Kalshi has real-money markets on which AI model leads benchmarks this week, which lab ships AGI first, when Anthropic releases Mythos, whether OpenAI raises ChatGPT pricing, and which company has the best coding model at year-end. These aren't abstract questions — they're live markets with real money on both sides, moving as labs ship, benchmarks drop, and announcements land.

The edge belongs to whoever actually follows this space. Not the casual observer — the person who reads model cards, tracks evals, and notices when a new release outperforms the field before the mainstream press catches up.

That person has a genuine edge. If that's you, Kalshi lets you act on it.

Trade the model race

General

Unlocking The New Industrial Revolution

AI needs more than models. To unlock the new industrial revolution, founders must rebuild power, physical AI, and industrial materials.

The founder's playbook: Building an AI-native startup

The guide explores how AI-native startups can move faster with fewer people by leveraging AI across every stage of company building. It offers practical frameworks, workflows, and founder examples for validating, launching, and scaling products in 2026.

Lost confidence

RICE and other confidence-based frameworks are mostly noise. Here’s how to make decisions without pretending to know the unknowable.

The Fake Traction Problem

Why founder excitement often looks stronger than investor evidence.

The 37signals Guide to Making Decisions

Jason Fried shared 37signals' decision-making framework. A company is people + decisions; the post lists dozens of practical questions/philosophies (reversibility, timing, gut vs data, impact, etc.) as helpful frames for better business choices rather than rigid rules.

Decisions and Dollars

How application companies survive the "what if Anthropic builds this" question.

Marketing, Sales and PR

Why AI for GTM Hasn’t Delivered (and How to Fix It)

The article explains that most GTM AI initiatives underperform because they focus on automating execution rather than improving targeting, prioritization, and hypothesis generation. It proposes building a proprietary GTM context layer to turn shared signals into differentiated GTM intelligence.

Monthly plans might be your best option

The hidden cost of long commitment periods.

How to Calculate Compute-adjusted LTV

The article introduces Compute-Adjusted LTV, a metric for AI SaaS companies that incorporates customer-level AI compute and infrastructure costs into lifetime value calculations. It argues that traditional LTV can significantly overstate customer value when users generate vastly different AI costs despite paying the same subscription price.

Money and Finance

Does the Rule of 40 Work for Hardware?

The article explains why the Rule of 40 should be applied differently to hardware companies than SaaS businesses. Rather than focusing on a single quarter's score, investors should evaluate whether margins and economics improve consistently over successive product generations.

Taking Stock of the Seed Stage

Venture capital headlines describe funding flows. The more revealing metric is the stock of active startups and that appears to be contracting at the Seed stage.