1. The Focus Tax — Eitan Saban

  • Why read: Why cutting busywork drives more revenue than increasing activity.
  • Summary: Top sales teams stand out by what they drop. High activity often dilutes results. To drive revenue, you need speed and strict focus on core priorities. Cut distractions to execute the few strategies that actually matter. You have to build a system around this focus before you can effectively integrate AI.
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2. Two questions every CEO should ask about AI — Substack

  • Why read: How to control the rising costs and strategic risks of enterprise AI.
  • Summary: AI token costs are quietly inflating operating expenses. Stop overpaying for frontier models when cheaper, smaller ones do the job perfectly well. Route AI requests through a control plane that matches the model to the task. Sending proprietary workflows to closed models also risks giving away your edge. Treat AI as a standard input cost, and protect your data by hosting specialized models in-house.
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3. Training design senses — proofofconcept.pub

  • Why read: The active practice required to build sharp design judgment.
  • Summary: As AI tools spread, human taste becomes the main differentiator. Experience takes deliberate practice; time on the job isn't enough. Train your eye by studying the physical world and curating patterns instead of hoarding images. Build manual skill by copying masterworks to understand their spacing and proportions. Close the gap between what you see and what you can actually build.
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4. Before the game — Substack

  • Why read: A simple mindset shift to improve execution and set better standards.
  • Summary: We usually wait until a task is done to judge how we performed. Instead, define your outcome and execution standard before you start. Ask yourself what a top-tier performance looks like up front. This forces you to map out the exact steps needed to hit that mark. Shifting from looking backward to defining the future improves your execution.
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5. The man behind Paw Patrol on how to succeed in business with no experience at all — Be Giant

  • Why read: Why inexperience and a bias for action are early advantages for founders.
  • Summary: Lacking business experience can be an asset because it frees you from risk aversion. Young founders have the energy and fresh eyes to spot opportunities that veterans miss. Don't wait until you have all the answers. Bet on yourself and start building. Success often comes from tapping into current culture, which younger generations understand intuitively. Use your early naiveté to ignore old rules and find blank spaces in the market.
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6. TBM 431: The Denominator That Matters — John Cutler from The Beautiful Mess

  • Why read: How to roll out AI in your company without causing a revolt.
  • Summary: Rolling out AI takes good tech, clear use cases, and a strong social contract. If you mandate AI without promising that productivity gains won't lead to layoffs, employees will naturally resist. Workers need to know their efficiency won't be weaponized against them. Leaders have to prioritize this trust, or rational self-preservation will kill the rollout.
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7. #293 | AI Inference Market, Return of Nuclear, Broker's Edge, & more — Substack

  • Why read: A quick brief on current VC themes, AI markets, and operating tactics.
  • Summary: Two major market shifts are underway: the rise of the AI inference market and the return of nuclear energy to feed AI's power hunger. We are also seeing a split between open-source and closed models. On the operating side, companies must watch out for false product-market fit, which can drain resources before anyone notices. Startups need to update their go-to-market motions to survive in this new infrastructure environment.
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8. You're doing "Activation" wrong — ProducTea with Leah

  • Why read: Why traditional activation metrics fail and how to fix your product-led growth.
  • Summary: Measuring activation from the moment a user signs up is flawed. It ignores everything that happens before the account exists, like how the user evaluates the product. Focusing only on onboarding hides the real problem: acquiring the wrong users. AI can drive top-of-funnel traffic, but it rarely brings in high-intent buyers. Treat activation as a full distribution problem, rather than a narrow metric for the growth team.
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9. Sales Rep Quotas in 2026 — SaaStr

  • Why read: New benchmarks for top B2B sales reps and how AI is changing expectations.
  • Summary: Sales processes haven't changed much with AI, but output expectations have spiked. Top-quartile enterprise reps now carry $2.25 million annual quotas. Mid-market reps aim for $1.35 million, and SMB reps target $750,000. These numbers show AI is actually delivering productivity gains, letting single reps manage bigger pipelines. Companies need to update their quotas and pay structures to match these new baselines.
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10. The Builder's Economy: AI Revenue Benchmarks — SaaStr

  • Why read: How AI is shifting from an experiment to a primary revenue engine in B2B.
  • Summary: AI is moving from a novelty feature to a core business engine. For many B2B software companies, AI products now generate close to half their revenue. The mandate has shifted from proving the tech works to proving it makes money. This growth is heavily concentrated: the Bay Area takes over half of all VC dollars for AI and B2B software. If you aren't integrating AI into production now, you will fall behind.
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11. 🧠 Stripe's $53bn PayPal Gamble — beehiiv.com

  • Why read: What Stripe's massive bid for PayPal means for the future of payments.
  • Summary: Stripe and Advent International have reportedly offered $53 billion to take PayPal private. If it goes through, this creates the largest merchant acquirer in the US. However, a deal this big risks becoming a massive operational distraction for Stripe. In the background, financial infrastructure is still shifting, with traditional assets like US stocks moving toward tokenization. Legacy payment rails are fusing with digital assets, changing how money moves globally.
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12. Netflix CPTO on AI and the future of product and tech roles | Elizabeth Stone — Lenny's Newsletter

  • Why read: How Netflix's CPTO thinks about managing engineering teams when AI generates the code.
  • Summary: When AI writes the code, the volume of output explodes. Netflix's CPTO argues that systems thinking is now the most critical skill for product and engineering. You need to understand how components interact to prevent the codebase from degrading into a mess. AI fluency isn't a niche skill anymore; it's a baseline requirement for everyone. Teams need to shift from generating content to curating and integrating it, acting as editors to maintain quality.
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13. Venkatesh Rao on AI 2040 Plan A — Substack

  • Why read: A critique of how insular subcultures produce flawed AI policy.
  • Summary: Much of current AI policy comes from insular subcultures that mistake their local influence for universal authority. Their proposals sound authoritative but push untested ideologies. By dressing up theory as empirical analysis, these plans ignore how unpredictable technology actually is. Instead of addressing real risks, these frameworks just validate the group's existing beliefs. Policymakers need to spot these narrow agendas before adopting them as long-term strategy.
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14. Kyle Poyar on AI Search Visibility — Substack

  • Why read: How to update your growth strategy as AI chatbots replace search engines.
  • Summary: Traditional SEO is fading: 71% of software buyers now use AI chatbots for research. The new playbook requires tracking an "AI visibility ladder." Since LLMs can't cite what they haven't read, you have to format your content specifically for AI ingestion. Most companies are ignoring this shift and failing to track if they show up in AI answers. Marketers must update their metrics now to capture buyers in these new discovery engines.
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15. Brain Food: Going Viral — Farnam Street

  • Why read: A reminder on the unglamorous work required for mastery.
  • Summary: Greatness takes talent, but mostly it's a "game of tonnage" built on thousands of hours. Often, opportunities come from simply watching others make foolish mistakes. The most successful people share one trait: a purpose strong enough to force them through unglamorous, boring work. As AI takes over standard tasks, disciplined curiosity and the ability to ask good questions matter more than knowing the answers. Notice your mistakes, fix them, and put in the reps.
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Themes from yesterday

  • The AI ROI Check: The hype phase is over. Companies are auditing token costs, demanding actual revenue, and raising sales quotas to match new productivity baselines.
  • The New Go-To-Market: AI chatbots are killing traditional SEO, and standard activation metrics are breaking. Growth teams have to rebuild their distribution playbooks from scratch.
  • Human Taste and Systems: When AI writes the code and copy, human judgment becomes the bottleneck. Systems thinking, design taste, and trust between management and employees are the real differentiators.