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Most CEOs of $2 trillion companies stopped reading customer emails a long time ago, Jeff Bezos never did. Bezos says one customer complaint isn't a customer service problem, it's a root cause waiting to be fixed for every customer. "My email address is famous and I keep it, it's jeff@amazon. com. I'll get one from a customer and there's a defect. Something seems a little odd about it." "I ask the team to do a case study and find real root causes, usually causes, and then real root fixes." "When you fix it, you're not fixing it for that one customer. You're fixing it for every customer. That process is a gigantic part of what we do." PS. We post daily content strictly for dedicated entrepreneurs, so if you’re one of them, make sure to follow us @entreprneursonx for more. Like and repost if you found value in this post:

Does slow and steady actually build an audience on X, or are we all just waiting to get noticed? Every big account I study seems to have a moment a quote-post from someone huge, one thread that broke containment, a podcast that put them on the map. The “overnight” part of overnight success. For everyone grinding in the replies and posting into the void: did consistency ever pay off on its own, or did everything change the day someone bigger pointed at you?

🌍 GLOBE HUB We're allocating Genesis Hub access to the strongest signals inside the network. Study GLOBE. Understand the model. Synthesize the signal. How to participate: 1. Publish a Twitter thread explaining GLOBE and its network architecture. 2. Join Telegram: ➔ 3. Submit your thread together with your ETH wallet inside Telegram. ⏳ Submissions close in 48 hours. Selection is based on: • Depth of analysis • Original insight • Community engagement Write in any language. The network remembers signal.

Elon Musk just identified the real bottleneck to artificial intelligence on Dwarkesh Patel’s podcast. He didn’t use political science. He used physics. Impedance matching. In electrical engineering, impedance matching means a component adjusts its own resistance to mirror whatever system it’s plugged into. It becomes the thing it’s connected to. Musk: “They impedance match to the government, to the Public Utility Commission. Literally and figuratively.” The companies responsible for powering every data center, every GPU cluster, every training run on Earth didn’t just slow down. They absorbed the exact operational frequency of the federal bureaucracy. They became it. Musk: “They have to do a study for a year. A year later, they’ll come back to you with their interconnect study.” Twelve months. Not to build anything. Not to deliver a single watt. To study whether you’re allowed to plug into the grid. In technology, one year is an evolutionary epoch. NVIDIA ships a new architecture. OpenAI leaps an entire generation. DeepMind publishes frontier breakthroughs quarterly. Inside government, one year is a single administrative pulse. And the friction isn’t accidental. It’s structural. The utility matches the regulator. The regulator matches the legislature. The legislature matches the election cycle. Each one calibrated to the metabolic rate of the next. A feedback loop of institutional inertia with no exit ramp. Every AI lab. Every hyperscaler. Every nation racing toward superintelligence. Same invisible ceiling. A permitting desk. The ultimate bottleneck is not compute. Not data. Not talent. It is the regulatory capture of electricity itself. And nobody with the authority to fix it has any incentive to move faster. The system wasn’t designed to produce outcomes. It was designed to produce process. A year-long interconnect study isn’t a safety measure. It’s a tax on momentum. The race to AGI will not be decided by who builds the best model. It will be decided by who builds the best grid. You cannot impedance match the future to the past. Eventually, the circuit burns out.

New MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality Autonomous AI coding agents raised commits by 180%, but releases rose only 30%. The paper’s main idea is that software production has weak links, so faster code writing does not help as much when humans still need to review, connect, test, package, and ship the work. The authors also check app marketplaces and find more new apps, but no increase in total usage, which means more software appeared without clear evidence that users adopted more software. The marketplace evidence points the same way: more new apps appeared, but total usage did not rise. The authors compare more than 100,000 GitHub developers before and after they start using 3 generations of AI coding tools, from autocomplete to more independent coding agents. Autocomplete raised commits by 40%, interactive coding agents raised them by 140%, and autonomous coding agents raised them by 180%. The 180% commit gain shrank to 50% for the number of projects and 30% for actual releases. The estimated "elasticity of substitution" is 0.25 i.e. for every big improvement in AI’s usefulness, only a small amount of human work can be replaced. Because AI can write code faster, but humans are still needed to decide what to build, check if the code works, connect it with the rest of the product, fix messy edge cases, and actually ship it. --- papers .ssrn.com/sol3/papers.cfm?abstract_id=6859839