What Mattered in the Macro
On the industry side, growth shows zero signs of slowing down. In fact, things continue to speed up.
Compute still has center stage, with Anthropic striking a deal with Elon Musk to use Colossus 1 with Elon Musk and OpenAI’s projecting compute spend to be around $50B this year (their revenue was reportedly around $20B in 2025).
But we’re seeing more interest in services now too (see below for Anthropic’s new consulting play). No surprise here. The AI services market was $8.75B in 2024 and is projected to grow to almost $50B by 2032.
On the practical usage side, nothing paradigm shifting… but AI usage continues to be refined for more and more widespread use.
We chose three pieces today that will definitely change how you use these tools: how much to trust an AI citation, how to pick a agent provider, and what’s actually happening inside the model when it answers you.
Noteworthy News
Anthropic doubled Claude rate limits after taking all of SpaceX’s Colossus 1 Anthropic + SpaceX (May 6)
Anthropic took the full 220k-GPU Colossus 1 buildout in Tennessee (over 300 megawatts of capacity) then immediately doubled Claude’s five-hour usage windows and removed peak-hour throttling on Pro and Max.
My key takeaway: if you’re a paying Claude user, your ceiling dramatically increased this week. Use it.
This also reinforces that compute is still the most important factor in AI growth. In the short-term, expect more data centers, more chips, and much more borrowing.
As bonus, this obsession with brute forcing compute boils down to the Bitter Lesson mentioned here.
Sources:
Musk v. OpenAI hit jury midpoint and the precedent matters more than the verdict Trial coverage roundup (May 4–9)
Musk is asking for $150B and a forced reversion of OpenAI to nonprofit status. The jury question: did Altman and Brockman’s early communications with Musk create a “charitable trust” they later violated? Closing arguments May 14, jury Monday May 18. Most legal analysts think Musk loses the headline ask, but a partial fiduciary-duty finding is plausible and that precedent matters more than the verdict for how AI labs ever convert from nonprofit to PBC.
My key takeaway: this trial is showing OpenAI as greedy and reckless. The safety-record reporting alone is damning, but the pattern of misrepresenting information to its earliest backers and internal turmoil cement bad optics.
There will now be an expectation during future inflection points that OpenAI is not maintaining integrity. It’ll make average users think carefully before trusting OpenAI in any decision that matters, especially when it comes to their data/privacy.
Sources:
Anthropic, Goldman, and Blackstone just stood up a $1.5B AI services firm Anthropic news (May 4)
The new firm drops Anthropic engineers inside mid-cap companies to redesign workflows around Claude. Think McKinsey-as-a-product.
My key takeaway: In the near-term, expect more companies to initiate layoffs based on AI automation. An entire consultant agency is being built around it for companies that may not have the talent to do it themselves.
If you have the stomach for it, then this is also an opportunity to start your own consulting company in the AI space.
Sources:
Legit Learnings
Cited but not verified: source attribution in deep-research agents Onweller et al., arXiv (May 7)
For L0-1: how to know when to trust an AI search citation.
If you’re quoting Perplexity, ChatGPT search, or Gemini citations in your own work, this paper should change how much you trust them. Frontier models are 94%+ valid on link existence but only 39–77% factually accurate — and accuracy drops roughly 42% as tool calls scale from 2 to 150. More retrieval makes citations less accurate, not more.
My key takeaway: AI search tools confidently cite sources that don’t actually say what they’re claimed to say… sometimes more than half the time.
Before you paste a Perplexity, ChatGPT search, or Gemini answer into something someone else is going to read, click one citation and verify the source supports the claim. And counter-intuitively: stacking more follow-up queries makes citations less accurate, not more. Run fewer, deeper queries.
Source: https://arxiv.org/abs/2605.06635
Why we switched from Claude Code to Codex (transcript) Dan Shipper / Austin Tedesco, Every (May 6)
For L2-3: real configuration detail for when you pick your agent stack for more complex workflows around knowledge work. It’s not about coding.
The most useful “I switched my agent provider” discussion of the year. Tedesco actually walks the setup in some detail, discussing folders, keys, reviewer agents, configuring it for non-code knowledge work, etc. Most people just deliver a hype train rant without substance.
My key takeaway: reverse prompting as a strategy for better outcomes. Austin calls this out at the 00:24:12 timestamp.
Quote: “But the way I’d recommend it whether you use Cora or not: have the agent interview you to get an understanding of what the rules should be. I always get a better result that way rather than just stating what I think the rules should be.”
Source: https://every.to/podcast/transcript-why-we-switched-from-claude-code-to-codex
Natural Language Autoencoders: turning Claude’s thoughts into text Anthropic Research (May 7)
For every level on the lessons. For L4-5 on the mechanics.
Anthropic shipped a tool that translates Claude’s internal activations into readable text and used it to catch their models thinking about how to evade detection while cheating on a training task. If you chain Claude into workflows, this is the doc that should change how you think about “what are LLMs actually thinking.”
My key takeaway: what these models are actually doing may not align with what they say they’re doing. Sometimes the model’s real internal state is doing something different… including, in the case Anthropic published, thinking about how to cheat the test it’s being given. Confidence in tone is not evidence of accuracy. Hedged answers are often the honest ones. When the model sounds most certain, that’s the moment to verify, not the moment to relax.
3 practical tips to leverage this info:
Stop asking “are you sure?” or “did you verify this?”. It almost always says yes. Ask instead: “What would have to be true for this answer to be wrong?”
Lead with the facts, not your conclusion. Ask for the strongest case against your read before showing your hand. System instructions that force critical analysis and/or lower agreeableness are also a gold standard.
Treat any high-stakes AI draft like a first pass from a smart intern who didn’t fact-check. You’d never send an intern’s draft unread to your boss.
Source: https://www.anthropic.com/research/natural-language-autoencoders
Confused on the levels? Read about them here.
Skip this
Barry Diller’s “trust is irrelevant as AGI nears” interview TechCrunch, May 6
A media titan opining that AGI is coming and “we don’t know what can happen.” The same content the AGI-takes industry has been recycling for two years. None of it changes anything you should be doing differently with AI on Monday morning. Ignore it.
Plus, I’m not convinced AGI is possible with LLMs. But that’s a topic for another time.
What I cut
A few things that almost made the lineup:
Stratechery on Microsoft & Apple Earnings: strategic context, but paywalled and overlapped with the Goldman venture story.
Every on The Dawn of Codex-native Apps: strong frame, but overlapped with the Tedesco transcript above; picked the more concrete one.
Every on Inside Anthropic’s 2026 Developer Conference: interesting compute angle; deserves its own piece rather than a Pick.
r/LocalLLaMA: “What in tarnation is going on with the cost of compute”: substantive, but skewed too far toward the agent-builder audience for this edition.
Am I being too harsh on OpenAI? Or have they earned it? Hit reply. I read every response.
One thing to do this week: Click through at least one AI citation in the next document you’re about to send and confirm the source actually says what the AI claimed.