Over the last few weeks, the two most powerful American AI models both vanished behind a government-approved rollout strategy.

The White House had OpenAI ship GPT-5.6 to about 20 vetted partners instead of the public, and the US Commerce Department finally cleared Anthropic’s Mythos to rollout again, but only for roughly 100 approved institutions. This was two full weeks after pulling it entirely.

Currently, there is a strong signal that the day-one of immediate access to frontier models are over. From a business perspective, this will be a massive advantage for approved companies.

Sources say the approved companies are part of Anthropic's Project Glasswing, which includes well-known companies like AWS, Microsoft, Google, etc. So for the foreseeable future, the playing field will not be level.

But there is still hope with Fable, which the Commerce Department indicated would be rolled out again soon. In case you missed it, both Mythos and Fable 5 use the same underlying model, but Fable 5 has a harness with guardrails for the general public.

The obvious concern is that Fable 5 is never released again. But I’d argue that a re-release with a significantly handicapped would be just as bad.

And while the US gates its best work on security grounds, the cheap and open alternative keeps shipping from China. A locked US tier and an open foreign one: that split doesn’t bode well, and begs the question, “Will America continue to dominate in AI?”

Signal

• The US just put a government gate on its best AI. Mythos came back for about 100 approved institutions and GPT-5.6 shipped to roughly 20, both at Washington’s say-so, on the same day.

  • So what: if you build on a top US model, your access now runs through an approval list you are probably not on yet. Plan for the frontier reaching you late, or through a vendor who got cleared first.

• Anthropic says Alibaba ran the biggest model theft yet: 25,000 accounts, 28.8M exchanges. Distillation: pump a frontier model for millions of answers, train a cheaper clone on them, skip the R&D bill. Anthropic’s whole argument is that this is how billions in US research becomes a cheap foreign product.

  • So what: it landed the same week DeepSeek made its 75% price cut permanent. Copy the capability, undercut on price, win the volume and the underlying data.

• OpenAI’s first custom chip targets 50% cheaper inference. Not all the price news points up. OpenAI and Broadcom unveiled “Jalapeño,” an inference chip built to halve what it costs to serve a model.

  • So what: the cost of running AI is set to drop hard, and that, not export controls, is the durable American answer to cheap Chinese models. Watch this lane. It is how prices come back down for everyone.

• Claude moves into Slack as a shared teammate. Not a private bot each person pokes alone. One Claude in the channel the whole team hands work to and redirects mid-task.

  • So what: while the strategy fights play out above your head, this is the one thing on the list you can actually deploy Monday morning.

• ByteDance’s Seedance 2.5 makes 30 seconds of video in one shot. Thirty seconds of 4K from a single generation, with up to 50 reference images to hold a character steady.

  • So what: the best and cheapest video model right now is Chinese, and it ships in the open. The accessibility race the US just stepped back from is already being won by the other side. (Enterprise beta now, public in July.)

Noise

Skip the Google brain-drain drama, but enjoy it first, because the gossip is genuinely good. Four senior researchers walked out of DeepMind in six days, including a Nobel laureate and a co-author of the paper that kicked off this whole era, reportedly over who got the GPUs. Read it like a soap opera, not a strategy memo. Then skip the scoreboard: Anthropic’s ~$965B IPO chatter, OpenAI’s IPO timing, SpaceX buying Cursor for $60B. Big numbers, fun to watch, zero effect on your Monday.

Think the gating is overblown, or the real story of the year? Hit reply, I read every response.

Anthropic shipped Claude Fable 5 on Tuesday, gave the public a brief, tantalizing look at the first Mythos-class model, and then abruptly rolled it back due to government export control order.

An export control order is a legally binding directive issued by a government that regulates or prohibits the shipment of sensitive goods, software, or technology to foreign destinations.

There are two paths from here: one where Anthropic accommodates the directive to only rollout to American citizens or refuses to roll anything out that if it can’t go to anyone.

Signal

• Fable and Mythos 5 Rolled Back

• Ethan Mollick’s Fable 5 hands-on

• Reality: The Final Eval, with Andon Labs

• Apple’s Spatial Reframing

• The AI code quality paradox

Noise

Skip the lab corporate theater. OpenAI filed a confidential S-1 days after Anthropic did, Anthropic pledged $200M to study what AI does to jobs, and DeepMind dropped an experimental model that writes text in parallel blocks.

Historic, expensive, and worth exactly zero changes to how you work on Tuesday. Unless you’re an underwriter, let the IPO countdown run without you. The new thing worth your attention this week is on the menu above, not in the filings.

Did I call the Noise wrong? Hit reply. I read every one.

The big platforms aren't inventing the future anymore; they are just packaging the open-source community's homework. OpenAI giving ChatGPT a self-updating memory and pushing it to the free tier is a massive shift for normal users, but it is effectively them playing catch-up.

Open-source agentic frameworks like OpenClaw and Hermes, backed by persistent memory layers like Garry Tan's GBrain, have been running continuous, self-improving background loops for months.

So if you’re using these Open Source agents, should you stop? No. You should continue to use these tools with local models, so you’re ready for when the real price squeeze happens (if it does).

Signal

• Do LLMs Dream of Electric Sheep?

  • OpenAI’s “Dreaming” gives ChatGPT self-updating memory

  • If you tried ChatGPT memory a year ago and switched it off, the reason you switched it off is gone. The ‘Finding Dory’ effect seems to mitigate to at least some extent.

• Prompt Injection Threat Continues to Grow

  • ChatGPT adds a “Lockdown Mode”

  • OpenAI shipped a setting that cuts ChatGPT off from the open web. It works by completely disabling live web browsing and image retrieval. The toggle matters less than the reason for it: anything you let an AI read can carry instructions you didn’t write.

  • Essentially, hackers have been using prompt injection to exfiltrate data and hijack systems. Prompt injections are zero-click exploits that require very little technical expertise. Worth understanding before you wire a something like OpenClaw or Hermes into your PC.

• The Cheap Token Era is Over

  • GitHub Copilot switches to pay-per-token

  • Copilot killed the monthly ‘use it all you want’ buffet. Your subscription now just buys a bucket of tokens and once they’re gone, the meter starts running.

Noise

Skip the money fireworks. Anthropic filed to go public near a $965B valuation, SoftBank pledged €75B for data centers in France, and someone stitched together $36B in chip debt. Enormous numbers, zero effect on your Monday.

Whether we’re in a bubble or not, GenAI isn’t going anywhere. The Dot Com bubble popping didn’t end the internet and the AI bubble popping won’t stop this train. This just means we’re still early.

Think I called the Noise wrong? Hit reply. I read every one.

This week Google started showing its work: inside AI Overviews and AI Mode, it began surfacing your “Preferred Sources” and stamping a “Highly Cited” badge on the reporting other articles lean on.

Read that as the discovery layer turning inside-out. For fifteen years the game was click through rate; the game now is being a source cited in the answer.

GEO is quickly becoming the future of web search.

If you depend on Google search to be found by customers, readers, etc., then the question is no longer “what’s my ranking” but “what does the model say when someone asks about me, and who does it cite?”

If you’re trying to find anything on Google, then the job is no longer scanning links to do your own research; it’s auditing the summary to see if you can actually trust the information it just handed you.

Which begs the real question: How much do you think companies will pay to have their sources injected directly into these summaries?

Signals

• Google AI Overview

  • Google brings Preferred Sources and a “Highly Cited” badge into AI answers

  • Google is now labeling, inside the AI answer, which sources it trusts and which reporting everyone else cites. Translation: “rank on Google” is becoming “get cited by Google’s AI.” With AI Overviews already cutting clicks ~38% on the queries they touch, the move that ages well is to check how you show up inside the answer the way you used to check your ranking before your traffic tells you to.

  • Google’s own numbers show a visible 'Preferred' badge doubles your click-through rate inside the AI answer. But the system requires users to explicitly trust you first. It's a flawed design: as of right now, established content giants will become more entrenched, pulling the ladder up on anyone new.

• The Human Edge

  • Ethan Mollick: “Choosing to Stay Human”

  • The one to read slowly this week. When everyone can generate the words, your edge is knowing when not to and Mollick’s point is that staying in the loop and learning is a habit you keep on purpose, not a default you drift into and out of when you don’t feel like working. File it next to anything you publish under your own name. Your personal brand is still the greatest competitive differentiator you can build today.

• AI Video Localization

  • Adobe Firefly now dubs a video into dozens of languages, with lip-sync to match

  • The lip-syncing piece is rolling out in early access on enterprise plans, but the direction is set: translate-and-dub is now automated and better in every way. And this new feature doesn't just translate words; it uses voice cloning to lock down the original speaker's tone, cadence, and acoustic qualities across languages. AI will continue to eat into post-production editing for the foreseeable future.

• AI Video Character Consistency

  • Reference-image character consistency is now the standard workflow, not a hack

  • AI video’s core problem is what the current model generation was built to fix: tools like Runway’s Gen-4 and Veo’s reference inputs anchor a character to reference images so it holds across shots. Define the character once, reuse it everywhere, instead of re-rolling the dice each prompt. If you bounced off AI video because it couldn’t hold a face, solutions to that problem are rapidly improving.

Noise

• Skip the valuation theater

  • Anthropic closed a ~$65B round at a ~$965B valuation this week. A real number that changes nothing about your Tuesday. The enterprise-deployment race (Big Four rollouts, OpenAI’s new consulting arm) has been loud for two weeks; it’s an industry story, not your story so let it go. Skip the data-center-grid doom-posting too: real for the grid, noise for your inbox.

• And yes, there’s a new Claude (Opus 4.8)

  • A solid point-upgrade, but no jaw-dropping changes so mostly hype; the bit worth filing away is that early testers say it flags its own uncertainty more readily, handy if you lean on it for anything you can’t personally fact-check. Let the people who tweet benchmark charts carry the hype.

    • I’ll be testing this model thoroughly during the week as well. So stay tuned on my X for updates.

Think I filed the wrong story under Noise? Hit reply — I read every one.

The compute crisis is getting into full swing. I mentioned last week that compute is still the most important factor in AI growth and this continues to accelerate.

But what is compute? Compute is the processing power to run software. With AI, think of compute the way you’d think of electricity or water: a foundational utility.

And what does compute require? Land, water, and electricity.

Those are also the ‘big three’ humans need to live somewhere. So compute for AI directly competes for resources people need to live.

Think of it as old vs. new. The old system was utilities serving the people who lived near them. The new system is utilities serving compute that lives nowhere.

The bottom line is that compute access is a hard moat and directly correlated to growth and pricing for these companies. McKinsey projects $7 trillion will be spent on new compute by 2030. The deciding factor for AI growth is now a literal substation, a literal county commission vote, a literal Aboriginal-land treaty conversation.

AI political capital is becoming the new technical capital. And the tension already has a variety of symptoms:

• Residential rate hikes for utilities.

• Water depletion in already water scarce areas.

• Political conflicts and legislation.

• Sovereignty fights with Aboriginal and tribal nations.

All of which can escalate fast once the lobbying machine fires up. Federal preemption fights. State-vs-state subsidy wars. Eminent domain claims for substation siting.

The next 18-24 months of AI economics will be decided in jurisdictions most of the industry has never visited. The Compute pick below shows this playing out at opposite ends, in Utah and Australia.

Noteworthy News

Compute is becoming an increasingly geographic and political fight

Main source: multiple sources below

Two events this week showed both sides of that fight:

• Box Elder County, Utah unanimously approved Stratos, which is Kevin O’Leary’s 40,000-acre AI campus that will eventually need 9 GWs. That’s more power than the entire state currently uses.

  • 40k acres is about 62 square miles, or ~120k standard suburban homes (factoring in roads, etc.).

  • Estimates put water consumption anywhere from 50k gallons per day to +2 billion gallons per year. Regardless, the project is requesting 4.8 million gallons per day in water rights to cover extreme-weather scenarios.

• Near Perth, Australia, the Save Mandoon Bilya coalition actively opposed a 96–120 MW facility near the Helena River, which caused the developer to withdraw the plans.

Heatmap reports that local-government opposition to data centers has now killed or stalled more proposals in 2026 than in the previous three years combined.

As of this week, data centers now consume roughly 6% of national electricity in both the US (29.2 GW) and UK (5.8%) per the IDCA’s 2026 Global Data Centre Report. That’s widely predicted to increase to at least 12% by 2030.

My key takeaway:

Every AI company pitch the last 3 years has operated under the assumption that inference costs will continue to decline in a Moore’s law style curve. But this competition with local governments may put a floor under it.

So every user of AI will depend on cheap inference at scale to keep prices reasonable. If that doesn’t happen… you should:

• Bet on distributed and on-device compute as immensely more valuable.

  • If you’re not experimenting with local models, do it now.

• Treat any business model that needs 10x inference scaling for unit economics as a risky bet, not a verified plan.

• Recognize that “AI gets cheaper every year” was a load-bearing assumption that may rapidly collapse.

You should start watching local-government decisions in their primary cloud region the way you watch chip yields.

Sources to read yourself:

• https://eandt.theiet.org/2026/05/15/uk-and-us-data-centres-now-consume-around-6-national-electricity

• https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers

• https://www.techradar.com/pro/utah-just-approved-a-data-center-twice-the-size-of-manhattan-that-will-consume-more-electricity-than-the-entire-state

• https://geographical.co.uk/news/utah-approves-construction-of-data-centre-twice-as-large-as-manhattan

• https://www.theguardian.com/technology/2026/may/15/developer-withdraws-plans-for-perth-datacentre-after-fierce-community-opposition

• https://heatmap.news/politics/local-opposition-data-center-cancellations

Anthropic just made software a second-class citizen

Main source: Anthropic news (May 2026)

Anthropic launched Claude for Small Business with 15 agentic workflows wired across QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365, and Slack. This was at no additional charge beyond existing Claude and partner licenses.

Sources to read yourself:

• https://qz.com/anthropic-claude-small-business-quickbooks-paypal-051326

• https://www.axios.com/2026/05/13/anthropic-claude-small-business-smb

• https://openai.com/index/openai-launches-the-deployment-company/

• https://stratechery.com/2026/the-deployment-company-back-to-the-70s-apple-and-intel/

• https://www.infoq.com/news/2026/05/anthropic-claude-code-auto-mode/

• https://www.geeky-gadgets.com/gemini-workspace-intelligence-features/

My key takeaway:

Two things worth noting.

First, this is more evidence that software isn’t dead, but we’re shifting how we use it. I.e., the modularization of outcomes.

Second, most products will stop being judged primarily by what a human can do inside them. They’ll be judged by how well an agent can stitch together discrete outcomes.

This is a radical shift from “is this UI intuitive?” or what some would call the “dashboardification of software”… to “can an agent use this at 3am while my customer sleeps?”

So any software that can only be operated by a human is on borrowed time (maybe 3-5 years?). Agents are now your primary user class and they’ll interact with your product 20x more than any human.

This is a product category that will continue to grow rapidly over the next few years and it’s the structural transition. And anytime there is a structural transition in any market, there is opportunity.

Example of usage:

Judge Rakoff: your AI chatbot logs are not privileged

Main source: United States v. Heppner (Harvard Law Review)

This was massive.

Background: Bradley Heppner, former chairman of GWG Holdings, was indicted on federal securities and wire fraud charges. During his arrest, the FBI seized documents that Heppner generated by inputting case facts and asking Claude for legal strategy and defense advice.

Instead of suppressing this evidence, SDNY Judge Jed Rakoff issued a first-of-its-kind written opinion holding that prompts and outputs created by a criminal defendant using a public version of Claude were neither attorney-client privileged nor protected as work product. Even though the defendant was synthesizing his lawyers’ input and forwarded the documents to counsel.

Work product doctrine is a legal rule that protects documents, notes, and tangible materials prepared by or for an attorney in anticipation of litigation

The three-pronged reasoning from Judge Rakoff:

• An AI tool is not a lawyer.

• The platform’s privacy policy disclaims confidentiality (training and third-party disclosure).

• The defendant wasn’t communicating with the tool to obtain legal advice.

Essentially, this means that anything you enter into an AI tool that meets the above criteria, is discoverable in a court case.

Sources to read yourself:

• https://www.crowell.com/en/insights/client-alerts/federal-court-rules-some-ai-chats-are-not-protected-by-legal-privilege-what-it-means-for-you

• https://www.gibsondunn.com/ai-privilege-waivers-sdny-rules-against-privilege-protection-for-consumer-ai-outputs/

• https://www.debevoisedatablog.com/2026/02/11/district-court-rules-ai-generated-documents-are-not-protected-by-privilege/

• https://minnlawyer.com/2026/03/09/ai-chatbot-attorney-client-privilege-waiver-rakoff-heppner/

• https://www.harrisbeachmurtha.com/insights/in-a-first-court-finds-using-ai-tools-ends-attorney-client-privilege/

My key takeaway:

This will almost certainly extend to company/enterprise compliance, deal work, or any sensitive/confidential information put into any consumer AI product.

For my money, there is only one solution worth discussing.

Opt-out of any setting that allows them to reuse your data for training or any secondary use. This removes your data from any training pipeline and limits retention.

As an example, this is Claude’s setting:

This is Gemini’s setting:

Side note: you may opt into longer data retention if you give specific responses or chats a thumbs up/down (this is a universal feedback feature for most products).

I would also refrain from putting anything into any of these AI tools that you wouldn’t want anyone to see.

Legit Learnings

This week’s three Learnings are all about one thing: the Claude Code update this month that introduced a command called /goal.

A regular prompt gets you the next response. You’re usually operating in the same loop: prompt → read output → accept/decline it → prompt again. You’re steering every turn.

/goal hands the wheel to the agent. You write what done looks like, submit it once, and the agent works toward it until it gets there or runs out of budget.

What you need to know:

• Tracks elapsed time, turns, and token usage as it runs. Can run for hours.

• Best for: scoped tasks with a measurable done-state (tests passing, lint clean, audit clean).

• Not for: open-ended creative work, unfamiliar codebases, anything where you want to approve each step.

• Best-practice prompt structure: scope + constraints + done condition.

Most coverage is treating this as a developer-only feature (level 4 builders and above). But the underlying principles aren’t only for builders.

Confused on the levels? Read about them here.

For L0–1: Tell AI where to end up, not how to get there

This prompting upgrade that makes every other prompting tip work better.

Most casual AI users push vague prompts in disconnected steps. “Write an email.” Then “make it more formal.” Then “add a CTA.” Again, you’re steering here.

The /goal pattern flips this: describe what done looks like, then let the AI figure out how to get there. You don’t need Claude Code to use this.

My key takeaway: On your next few prompts, try this: before any AI prompt longer than a sentence, complete the phrase “I’ll know this is done when…”

The skill is learning to describe DONE before you describe the WORK. You’ll be manually doing what /goal does autonomously, but scaled down to your level. This will prepare you to move into being a practitioner.

For L2–3: Completion conditions are the new prompt

You already prompt with structure: personas, constraints, examples, etc. But /goal formalizes the next layer, completion criteria.

And you can start adding it today, with or without Claude Code.

The unlock here isn’t the prompt syntax. It’s the discipline of stating a completion condition once and letting the AI run the iteration loop YOU normally run yourself.

Three components separate goals that work from goals that drift:

• Scope. What inputs, files, or surfaces are in play.

• Constraints. What’s off-limits.

• Done condition. What’s measurably true when the work is complete.

If you can phrase a request in those three terms, you've removed yourself as the loop runner. This works in any AI chat (ChatGPT, Claude, Gemini, Copilot). The slash command is Claude Code's syntax for the same pattern.

My key takeaway: Pick one multi-step workflow you currently run step by step and rewrite it as a goal prompt. Here's the template, filled in for a resume rewrite (the most universal example I can think of). Copy it. Swap in your details. Drop it into any AI chat.

GOAL: Rewrite my resume into a one-page version tailored for [target role at target company] that earns a 30-second read from the hiring manager.

— CONTEXT —
· Current role: [your title, company, years]
· Target role: [job title + company + link to JD if you have it]
· Years of relevant experience: [#]
· Top 3 wins worth leading with: [metric 1, metric 2, metric 3]
· Existing materials: [paste current resume below; add LinkedIn URL if relevant]
· Off-limits: [anything I will not lie about, inflate, or omit]

— SUCCESS CRITERIA (ALL MUST BE TRUE) —
1. Fits on one page in standard formatting.
2. Every bullet includes a quantified result (number, %, $, or time saved).
3. Top three bullets map directly to keywords in the job description.
4. No clichés ("results-driven," "team player," "passionate about").
5. No unexplained gaps over three months.
6. Reads like the candidate the JD describes, not a generic candidate.

— OPERATING RULES —
1. PLAN FIRST. Output your tailoring strategy before rewriting.
2. WORK AUTONOMOUSLY. Don't ask clarifying questions unless something genuinely critical is missing.
3. SELF-CHECK. After drafting, re-read against every success criterion one by one.
4. NO PLACEHOLDERS. No "[insert metric]" or "[your achievement here]" bullets. If you don't have a number, ask me once, then keep moving.
5. FIX YOURSELF. If a bullet fails a criterion, rewrite it. Don't hand me a list of "things to fix."
6. STAY ON GOAL. If you spot LinkedIn or cover-letter improvements, note them at the end. Don't pivot mid-task.

— FINAL DELIVERABLE —
✅ Confirmation each success criterion is satisfied.
📄 The rewritten resume, ready to copy-paste.
📝 What you cut, what you kept, and why.
⚠️ Any claim I should double-check before sending.

Begin by outputting your tailoring strategy. Then execute the rewrite end-to-end without checking in unless genuinely blocked.

The first time you use a structure like this, you'll spend ten minutes scoping. By the third use, you'll have a template that runs in two minutes and produces output you might have previously spent an hour iterating toward.

For L4–5: Your agent harness just shrank to /goal. But it's not a contract yet.

You’re not trying to mimic /goal anymore. You’re actually using it. Three implications worth chewing on this week:

1. Simplifying your agent harness. /goal is a turnkey feature for autonomous agents. What can it replace in your stack?

2. Token management. A long /goal run can burn hours and 100K+ tokens. Where are the sharp edges?

3. Evaluating the output. The current default evaluator in Claude Code is Haiku. Codex requires your own eval suite. How are you going to guarantee return-on-investment when using /goal?

My key takeaway: 1 & 2 above are highly dependent on your individual stack. But 3 is something everyone, everywhere needs to consider.

Any evaluator is an upgrade over single-agent self-grading. But what does the evaluator judge? Per Claude Code docs, “It doesn’t run commands or read files independently, so write the condition as something Claude’s own output can demonstrate.”

So it doesn’t independently open the file system, run the tests, or check external state. If the worker hallucinates a passing test result, the judge accepts it on faith.

How many times have you seen a model be 99% confident but wrong?

For a production contract, you need a third party. Someone who walks into the room with their own tools, runs the tests themselves, and judges the work without trusting the worker’s report blindly.

Here are some moves to fix this, ranked by lift.

Pattern A: Run the success criteria as a separate /goal with a clean session. Each /goal invocation starts fresh, so the evaluator can’t be poisoned by the worker’s claims. Something like:

/goal Verify that all six success criteria from the previous run are
currently TRUE in the working tree. Run each test independently with
your own commands. Report pass/fail per criterion with command output
as proof. Do not trust any prior transcript or claim.

Pattern B: If using Claude Code, use the open-source eval tooling. The bkper/claude-eval project ships an LLM-as-judge harness for Claude Code with binary PASS/FAIL outputs per criterion. Drop your success criteria into the config, point it at the working tree, get a structured verdict.

Pattern C: Use a different model as the judge. GPT evaluating Claude’s work, or Gemini evaluating either. Anthropic’s own eval guidance recommends manually scoring 20–30 cases first; aim for 80%+ agreement between your judge model and a human reviewer before you trust the judge at scale.

The principle: an agent that grades its own work is performative. You need an independent model for real results.

Skip this

The “one-person billion-dollar company by 2026” discourse multiple X / Substack threads, week of May 11–17, 2026

Dario Amodei’s “70–80% probability of a one-person billion-dollar company in 2026” framing is recirculating heavily this week, sitting on top of Pieter Levels and Marc Lou’s solo-founder content. FutureDigest even ran a “no longer a prediction. It happened.” post this week to fuel it.

Skip all of it. The number isn’t falsifiable, the framing is engineered to feel inevitable, and most of the people writing about it monetize the prediction. None of it changes what you do at your desk on Monday morning.

Plus, the actual cost-collapse story underneath all this, that one operator can now do the work of a 2019 ten-person team, is real, important, and buried under the cult content. If anyone wants to write the real version of that piece, OpenAI’s B2B Signals data is a better starting point than Amodei’s vibes.

What I cut

A few things that almost made the lineup:

Anthropic + Gates Foundation $200M partnership: real and good, but a generic corporate press release by this issue’s filter. It doesn’t redraw a single operational boundary this week.

Google’s first AI-developed zero-day disclosure (Google Threat Intelligence Group): new risk category, but the Rakoff ruling carries more immediate operational consequence for the average reader.

—-

Do you think that we have a compute shortage in the near future? And do think that pops the ‘AI bubble’ people think we’re in? Hit reply. I read every response.

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.

Subscribe now

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:

• https://www.anthropic.com/news/higher-limits-spacex

• https://x.ai/news/anthropic-compute-partnership

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:

• https://fortune.com/2026/05/05/musk-court-fight-openai/

• https://gizmodo.com/directionally-very-bad-everything-you-missed-during-week-2-of-the-elon-musk-vs-openai-trial-2000756326

• https://techcrunch.com/2026/05/07/elon-musks-lawsuit-is-putting-openais-safety-record-under-the-microscope/

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:

• https://www.anthropic.com/news/enterprise-ai-services-company

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.

I can’t believe this still needs to be said, but you need to fact AI. 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. So 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.