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New pod: THE SMARTEST CASE AGAINST THE AI JOBS APOCALYPSE AI is the first technology that seems to automate the same cognitive sectors that absorbed work during previous waves of automation. For that reason, many people worry that it will destroy tens of millions of jobs imminently. But after I review the evidence showing that AI is not clearly destroying work today—and might even be stimulating demand for certain tech jobs— I brought on the great @alexolegimas to talk about the best reasons to doubt the doomsday narrative. We talk about all sorts of economic principles—lump of labor fallacy, income elasticity, Jevon's Paradox—but maybe his most interesting point is about the nature of desire and status. Desire is insatiable, and technology will never solve for status. Even in a world where AI can automate many tasks, status might go up rather than down or flat. And status motivates a lot of economic activity. So even in a world where AGI is very good at 99% of existing tasks is still a world where people will want to send their money to things that are perceived as "scarce" and "status-enhancing." You can create a lot of jobs on this basis alone. You could argue that this is how economic transformations have always worked. Our economy is a rough register of human desires. And in a world where artificial intelligence automates some tasks, it might not destroy work so much as it moves dollars and labor toward new desires in new sectors of the economy. The pet care economy wasn't really a thing in 1800. Now it's a >$100 billion business, made possible by the fact that a richer country moved dollars and workers from corn farms to bespoke poodle manicure spas. It is easy to imagine that AI could automate many tasks and even some jobs. What's harder to imagine is that we'll be permanently stuck in an disequilibrium where people with disposable income aren't trying to satisfy their desires and burnish their status. And in a world where AI is abundant, the question we should be asking about the future of work is: What will be scarce? What will be kind of jobs will be produced as desire and status shift, once again? open.spotify.com/episode/74OPgO…

One of the best essays and speeches I've read in a long, long, time. Learn what your moral obligation is here: notboring.co/p/riding-the-l…

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@ScribeHow just crossed $100M ARR. Today, our 90,000 enterprise customers include nearly half the Fortune 500. I shared this news with @jonfortt on @CNBC earlier today, but I still vividly remember our first deal for $7K! I’m proud of our team and grateful for our customers. The reality is, we’re just in the first inning. Most companies are still very far from the AI transformation they imagined. A lot of AI usage at work is still for personal productivity: it’s not locked into where and how an org creates its value. The models aren’t the problem. It’s that they aren’t being taught enough about the business. AI has a context problem. AI gets dropped into companies
without knowing HOW work actually gets done. That’s thousands of workflows AI can’t see. Dozens of decisions it can’t trace or recreate. AI doesn’t understand your org at all.
 Missing workflow context is now an existential problem for the enterprise. Without context, AI can’t function. It just delivers generic output, or confidently gets things wrong. For AI to actually work inside enterprises, something fundamental has to change. That’s why we’re witnessing a new layer of the enterprise stack emerge. To pull ahead companies need to map their context layer, making it legible to both humans and agents. Here's my full thinking on workflow context and why it’s the most urgent need in enterprise AI: scribe.com/library/100-mi…

Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI. It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact. openai.com/index/openai-l…

Investor Cyan Banister (@cyantist) has backed SpaceX, Uber, Niantic, and Flock Safety. A majority of those investments did not emerge from a formal meeting. Uber traced back to a conviction that the taxi medallion system was a racket. Niantic grew out of watching friends charter helicopters for an obscure geolocation game. Flock Safety materialized from reading the public WiFi list at a Four Seasons cafe. Her method is not a framework. It is attention: a lifelong habit of noticing bottlenecks, tracking human obsessions, and poking at reality until it reveals something others missed. In this conversation, she covers: • Why the taxi medallion system was the real Uber insight •How she found a $7 billion company on a public wifi list • The Biz, Tizz, and Rizz framework for identifying legendary founders • Why the age of the polymath is arriving faster than most people expect • How brain-computer interfaces are surfacing thoughts from two weeks prior • Why she believes vibe manufacturing will mint the largest wave of new millionaires in a generation The signals she is reading now are worth understanding. @thegeneralistco Thank you to the partners who make this possible @dottechdomains: An identity for builders at their core: go.tech/thegeneralistnl @brexHQ: The intelligent finance platform: brex.com/mario @withpersona: Trusted identity verification for any use case: withpersona.com/generalist (00:00) Intro (03:51) Never playing the game you appear to be playing (07:18) Practicing childlike wonder as a daily discipline (10:08) Questioning belief after her stroke (13:30) Cyan’s metaphysical experiments (23:24) Non-local consciousness and creativity (27:22) Investing with extreme openness to signals (29:05) The importance of timing in investing (32:26) Meeting Travis Kalanick (34:19) Finding Flock Safety through a chance encounter (38:23) The summer of Pokémon Go (what worked and what didn’t) (39:55) Human nature and what makes something "stick" (42:15) Brain-computer interfaces and AI’s accelerating effect (52:53) “Biz, Tiz, Riz:” her framework for evaluating founders (59:20) Why Cyan lives in a retirement community part-time (1:03:50) A unique way of finding books that speak to you (1:08:44) Final meditations