Somebody else's cover

We're definitely going to get to the point where handwriting code doesn't make sense. Similar to handwriting assembly doesn't make sense. But the analogy breaks down. Developers don't really know assembly and they can't really review assembly. On top of it, the assembly that's produced by compilers is so optimized and confusing that you can't even understand it. For AI the code that's being produced, the developer needs to understand it could have written it themselves but it's just not as efficient or safe to do so. AI is not a new abstraction layer. It is a tool to create something you understand. We still need people to understand the mechanics of coding. We still need people to learn these skills and these are important skills. And even with the power of AI, you can't get away with not knowing these skills if you want to be effective. As always, there'll be people who try to take shortcuts and they'll get short-term gains and then it will fall apart.

Narrative violation and great insight from the latest Citadel Securities banger by Frank Flight: "We illustrated back in February that demand for software engineers, the most AI exposed occupation was accelerating higher, which we argued violates the displacement narrative. Indeed the acceleration in software job postings has continued, now up 18% from the inflection point in May last year."

Tech layoffs are skyrocketing: Tech companies announced 81,747 layoffs in Q1 2026, the highest quarterly total since at least Q1 2024. Layoffs have more than DOUBLED from the previous quarter and have risen +580% since Q4 2025. March alone saw 45,800 announced job cuts, the worst single month for tech layoffs in at least 2 years. Tech layoffs are set to remain elevated with Meta's, $META, recent plans to cut ~8,000 employees. Furthermore, Microsoft, $MSFT, is offering voluntary retirement to ~7% of its US workforce, which could transition into layoffs if participation is low. This comes as tech giants shift spending toward AI chips and data centers, trimming staff to free up capital for infrastructure. US tech employment is rapidly contracting.

Harvard Business Review just published a super interesting piece. AI’s biggest shock may be that nobody can price the future cleanly anymore i.e. we all are staring at a "AI Fog" i.e. the range of outcomes is now so wide that people cannot tell whether today’s prized skill, product, or business model will still pay off a few years from now. AI’s first big economic effect is not automation itself, but the collapse of foresight. The hidden cost of AI may be a collapse in conviction, as its erasing the visibility that modern finance depends on. Modern capitalism runs on the assumption that tomorrow will rhyme with today closely enough to justify big, slow bets. On long bets like degrees, hiring plans, factories, software valuations, and infrastructure, and those bets work only when the future is readable. All these depend on one quiet belief: the future is legible. AI attacks that legibility before it fully rewires any one industry. That hits workers first, because a medical degree, MBA, or coding career looks weaker when AI agents may absorb diagnosis, analysis, drafting, research, and junior software work. That hits companies next, because stock prices depend on durable future cash flow, and terminal value breaks down when AI can erode moats in software, services, and even specialized manufacturing. That changes behavior fast. Students hesitate to buy expensive human capital when the job at the end may be redefined halfway through training, and companies hesitate to hire when junior work, software work, and coordination work are all moving targets. Financial markets feel the same pressure, because once AI casts doubt on a company’s durability, the terminal value carrying much of its valuation starts to look less like math and more like faith. So the immediate economic consequence of AI may be shorter horizons. Less skyscraper, more tent. Less irreversible commitment, more staged investment, modular teams, and organizations built to learn before they lock in. It points to something subtler and probably more important: when institutions cannot see clearly, they stop making the kinds of commitments that built the old economy. --- hbr .org/2026/04/the-future-is-shrouded-in-an-ai-fog

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