James High

🦔 Meta is planning layoffs that could affect 20% or more of the company, according to Reuters. No date has been set and the magnitude isn't finalized, but top executives have told senior leaders to begin planning how to pare back. Meta employed nearly 79,000 people as of December. The company says it's speculative reporting about theoretical approaches. Meanwhile, Meta plans to invest $600 billion in data centers by 2028, just acquired Moltbook, and is spending at least $2 billion on Chinese AI startup Manus. My Take The math here is something. Meta is cutting potentially 20% of its workforce to offset costly AI infrastructure bets while simultaneously spending $600 billion on data centers and billions more on AI acquisitions. Zuckerberg says he's seeing projects that used to require big teams now accomplished by a single talented person, but their Llama 4 models had setbacks last year and their new Avocado model is reportedly lagging expectations. So they're laying off people to fund AI that isn't working as well as they hoped. I keep seeing this same move across tech. Amazon cut 16,000 jobs in January. Block cut nearly half its staff with Dorsey pointing to AI efficiency. The framing is always that AI will let them do more with less, but I think what's actually happening is simpler. These companies over-hired during COVID and low interest rates, now they need to cut costs, and AI gives them cover to do it without admitting the over-hiring was bad management. Meta's core business is still a money printer. The layoffs aren't because Facebook stopped being profitable. They're because Zuckerberg wants to spend $600 billion chasing something that hasn't delivered yet. Hedgie🤗

Reuters published a piece. If companies like OpenAI or Anthropic fail, the massive financial ecosystem built around their existence could rapidly collapse. These labs are the primary customers for the $650B that tech giants are spending on new data centers and chips this year. Without their relentless demand for computing power, the expansion of new data centers would violently hit the brakes. It would also leave huge power grid projects and physical infrastructure investments completely abandoned and useless. Banks and private credit lenders who poured roughly $900B into this space would face severe uncertainty and massive potential losses. While a bigger tech company might swoop in to buy the failed labs for cheap, the overall value of the entire AI industry would instantly crash. Ultimately, the failure of just one of these major labs would not be a simple corporate bankruptcy. It would trigger a massive shockwave that drags down cloud providers, chipmakers, and global infrastructure projects all at once. -- reuters. com/commentary/breakingviews/what-happens-if-openai-or-anthropic-fail-2026-03-11/

Tech dudes not being annoying and smug challenge: impossible

Trump at wartime

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I was a 10x engineer. Now I'm useless.

it's not you: since yesterday claude has regressed about 9% on SWE-BENCH-PRO. link below

Jim Cramer did it again

🚨BREAKING: Alibaba tested AI coding agents on 100 real codebases, spanning 233 days each. the agents failed spectacularly. turns out passing tests once is easy. maintaining code for 8 months without breaking everything is where AI collapses. SWE-CI is the first benchmark that measures long-term code maintenance instead of one-shot bug fixes. each task tracks 71 consecutive commits of real evolution. 75% of AI models break previously working code during maintenance. only Claude Opus 4 stays above 50% zero-regression rate. every other model accumulates technical debt that compounds over iterations. here's the brutal part: - HumanEval and SWE-bench measure "does it work right now" - SWE-CI measures "does it still work after 6 months of changes" agents optimized for snapshot testing write brittle code that passes tests today but becomes unmaintainable tomorrow. Alibaba built EvoScore to weight later iterations heavier than early ones. agents that sacrifice code quality for quick wins get punished when consequences compound. the AI coding narrative just got more honest: most models can write code. almost none can maintain it.