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.

Why a Framework Matters

Somebody asks you “how do you use AI?” and you freeze. You can at least say you use AI, but explain how you use it? In detail? You know that saying “sporadically, I guess” is not the answer you should give anymore.

Also, notice how the question has shifted from “if…” to “how…” over the last couple years? Usage is assumed. People expect to hear more now. Trade tips, anecdotes, etc.

The problem is that most people don’t know how to answer that question, not even the ones asking. There really isn’t a structured way to think about AI proficiency. You’ve “used ChatGPT a few times” but you can’t tell whether that makes you ahead of the curve, behind it, or on par.

Because we have no language for it. No scale. No reference points.

So I stole the best one and repurposed it for AI: Dreyfus’s 5-stage model of skill acquisition, which has shaped how medicine, aviation, and the military train experts for 40+ years. But I added a Level 0, the Bystander, because Dreyfus assumed you've at least started. With AI, half the problem is starting.

That's what this framework gives you: goal posts for structured progression.

So why frame things as structured progression at all? Because it solves the two biggest failure points in learning:

1. The overwhelm problem. You can’t eat the entire elephant in one sitting. Structured stages act as a complexity filter. You only deal with what you’re ready for. Think of it like progressive overload in training: you don’t walk into a gym and load 300 pounds on the bar day one.

2. The plateau problem. Without escalating goals, people settle into comfortable competence and stop growing. And usually, comfortable competence means the beginner stage repeated over and over. Just good enough to tread water. Structured progression builds in goal escalation automatically, with each stage demanding more than the last.

But don’t take my word for it. Here is the evidence.

Goal-setting research (i.e., Locke and Latham, validated across 35+ years and hundreds of studies) tells us specific, challenging goals lead to higher performance 90% of the time compared to vague “do your best” or “figure out what works best for you” goals. A proficiency framework gives you the specific targets and focus that “just get better at AI” never will.

And the stages take inspiration from Stuart and Hubert Dreyfus’s five-stage model of skill acquisition, which go from novice to expert. This model has been applied across medicine, aviation, and professional development for over four decades.

Staged proficiency models work because they make invisible progress visible. You shouldn’t underestimate being able to articulate an answer to: “where are you right now, and what does the next step actually look like?”

And the urgency is real. DataCamp’s 2026 State of Data & AI Literacy report found that 59% of enterprise leaders report an AI skills gap in their organization. And that’s even though 82% of organizations already provide some form of AI training.

The training exists. The progression map doesn’t. So here’s the map.

The AI Fluency Framework

Curious where you land? Take the 5-question quiz now or keep reading.

That’s the staircase. Now let’s zoom in on each step.

Level 0: The Bystander

Not using AI, or tried it once and bounced.

You’re overcome with skepticism, overwhelmed, or just not seeing why it’s a big deal.

Level 0 is meaningfully different from every other level in that it’s a motivation and access barrier. Every other level is a skill barrier.

What this looks like:

• The operations director who’s been told to “look into AI” by leadership but it hasn’t been a priority yet, so they’re scrambling to catch up.

  • There are also leaders who refuse because if they don’t use it, then it can’t replace them.

• The professional who heard about it at a dinner party, tried asking it to do something vague, got a generic answer, and concluded “it’s just a fancy autocomplete”.

• The person who’s genuinely concerned about data privacy and chose not to engage until they understand the risks, which is actually reasonable. They just need the right information to make an informed decision, not a blanket avoidance strategy.

Your next steps toward Level 1:

• Pick one small task you already do regularly, like summarizing a meeting, drafting an email, or explaining a concept to a colleague and try it with ChatGPT, Claude, or Gemini.

• Schedule yourself 15 minutes with a free tool. Have a conversation. Observe what it does and doesn’t do well.

• Read one credible source on what AI actually is and how it works.

  • Recommendation: Large Language Models explained briefly

The danger:

Lack of adoption isn’t free, it’s just a delayed bill.

I’m sure you’ve heard the statement: “Workers won’t necessarily get replaced by AI, but those who use AI.” In that statement, you could swap AI with any new, major technology of the last 100 years and it’d be true.

Economists Autor, Levy, and Murnane studied 40 years of US labor data and found the same pattern at the worker level when computers were invented: workers who adopt it pull ahead, the workers who don’t get left behind.

And the gap between them compounds for decades. A follow-up study by Autor and Acemoglu showed that when computers complemented complex, non-routine tasks, the market’s valuation of educated workers skyrocketed. Following three decades of increase, the college wage premium hit a high water mark in 2008, meaning the average college graduate earned 97% more than the average high school graduate.

Now AI is the new computer.

Same pattern, but a much faster timeline. PwC’s 2025 AI Jobs Barometer found that workers with AI skills now earn a 56% wage premium (more than double last year’s 25%) over workers in the same job without them. And in industries most exposed to AI, wages are rising twice as fast as in industries least exposed.

Level 1: The Explorer

Uses AI casually, like a smarter Google.

You get value, but there’s no system, consistency, and/or understanding of why some prompts produce gold and others produce garbage.

What this looks like:

• The marketing manager who asks ChatGPT “give me 5 subject lines for my email campaign” and uses whatever comes back. Sometimes it’s great, sometimes it’s unusable, and she can’t pinpoint why.

• The account exec who summarizes articles before client calls but types one-sentence prompts and accepts the first response without ever pushing back.

• The project manager who asks AI and Google the same question, compares answers, but never iterates on or refines the AI’s output.

Your next steps toward Level 2:

• Learn the basics of structured prompting: give AI a role, context, a specific task, and a format. This alone will transform your results from coin-flip to predictable.

• Start building a personal prompt library in your preferred structured format. Save your best prompts. Reuse and refine them.

  • Here is a paper on LLM impact from prompt formatting in 2024.

    • My personal preference is markdown. A simple text formatting system that is easy to use, easy to learn.

  • Formatting will matter less as models get better, but for now it’s an advantage.

• Try iterating. Instead of accepting the first response, give feedback. Iterating can drastically improve quality.

  • Caveat: not all iterating helps. A 2025 study found that LLMs lose an average of 39% of their performance when users drift across turns without structure. Iteration works when it's directed, 'make this more concise, add a healthcare example', not when it's exploratory.

• Start noticing patterns. When you get an output just right, ask yourself why. Was it more context? Better stated goal? That feedback loop is the bridge from Explorer to Practitioner.

The danger:

The ‘good enough’ trap.

Most AI users are at this level in the framework. According to OpenAI’s research, 45% of all work-related messages are either getting information, interpreting information, and/or documenting information. So, half of all usage is related to information processing and using AI like a smarter Google.

So why is this a danger? Aren’t those great use cases? Yes, they are.

But these use cases are the safest and most consistent uses of AI. You’re getting some value. Arguably, enough to feel like you’re an adopter, but it’s just enough to tread water.

Think about how fast AI has been evolving. Today’s ‘good enough’ ceiling will be the floor in a year or two.

In fact, METR (AI evaluation lab) found that the length of tasks models can complete autonomously has been doubling every seven months for six years straight. And in the last year, that pace accelerated to every four months. That’s an exponential gain.

I know what you’re thinking: “If AI is improving that quickly, then can’t it eventually meet me where I’m at instead?” And that’s flawed thinking.

Because when the model improves, everyone's baseline results improve with it. What passes as impressive today becomes the default tomorrow. You’re intentionally limiting yourself.

Think about it this way: better models don't close the gap between skilled and unskilled users. They widen it.

The Explorer gets a better baseline result with their one-sentence prompt. The Practitioner gets a result that further exceeds the baseline with a structured, iterative prompt.

The model is the multiplier. Your input is what it multiplies. The premium on skill doesn't shrink as AI improves, it compounds.

Food for thought.

Level 2: The Practitioner

Has a structured prompting approach. Knows enough to use AI intentionally for work.

This is where AI stops being a novelty and becomes a reliable tool. You can delegate more creative and nuanced work with consistent, predictable results. You know when and how to iterate so that you can rise above the dreaded AI slop.

This is where my upcoming book, Prompt to Unlock, guides people.

What this looks like:

• The HR partner who writes structured prompts with role, context, and constraints to draft policy documents and then has a process to iterate 3–4 times until the output matches her organization’s tone and compliance requirements.

• The financial analyst who uses multi-turn conversations to walk AI through a dataset, asking clarifying questions, fact-checking conclusions, and produces a summary for his leadership deck. What may have taken days, now takes a couple of hours.

• The content creator who has saved prompt templates for recurring tasks like “write a LinkedIn post in [voice] about [topic] with [specific structure]” and gets a mostly usable output on the first pass.

Your next steps toward Level 3:

• Experiment with multi-step workflows and managing context: research → outline → draft → edit as four separate, sequential prompts instead of one big ask. The quality difference is significant.

• Explore custom instructions or project-level context (available in ChatGPT, Claude, and Gemini) to give AI persistent knowledge about your role, preferences, and common tasks.

• Explore tool-connected AI that accesses more of your actual data: calendar integrations, file search, database connections, Notion/Obsidian, etc.

  • For regulated industries like healthcare and finance, this step involves your compliance team and that’s worth starting early.

• Start thinking about AI as infrastructure, not just a chat window. What recurring workflows could you automate end-to-end?

The danger:

Plateau by comfort. You’ve crossed the hardest gap in this framework, which is having the ability to repeatably get high-quality results from AI. And honestly, this puts you ahead of at least 50% of people.

And that’s the trap. Unlike Level 0 or 1, the ground won’t suddenly shift out from under you. As a Practitioner, you’re not falling behind.

But there’s an entire other level to this mountain you can’t see from this altitude: you’re still doing everything manually.

Every prompt is hand-typed. Every workflow handled adhoc. You’re saving hours, but you’re spending hours to save them.

The Architect automates what the Practitioner repeats. The difference is the practitioner is saving hours a week and the higher levels are saving weeks a year.

Personally, I was stuck here for over a year. I thought being good at prompting was enough.

I was getting decent outputs, and that was exactly the problem. I didn’t know what I was missing until I saw what an Architect could do.

Here is a list of things I was able to do by moving beyond Level 2. Each is a future guide on this blog.

• Custom tools I ship in an afternoon: the survey below is an example.

• A morning briefing delivered as audio while I make coffee: industry news filtered, calendar previewed, priorities surfaced, all without me opening a single app.

• Pre-meeting briefings in my inbox 30 minutes before every call with the attendees, company, recent news, and my own notes.

• Content atomization from a single source document, in my voice, with my guardrails.

• Quarterly financials reported without me opening a spreadsheet. Transactions pulled, taxes estimated, anomalies flagged, digest in my inbox. I haven’t manually calculated revenue in over a year.

Level 3: The Architect

Builds repeatable AI workflows, not one-off prompts. Engineers context deliberately with custom scaffolding. Connects AI to tools and data sources.

Here’s the shift: at Level 2, you’re great at individual, adhoc AI prompting. You can effectively use AI out-of-the-box and can get to average outcomes pretty fast with AI at level 2.

But you have to spend extra time to take it beyond average. For some tasks, this means it isn’t even worth it to involve AI.

At Level 3, you stop thinking in one-off conversations and start thinking in systems. You learn to augment an AI’s capabilities with tools, data, and custom workflows. This shortens the time to an exceptional and custom outcome.

This is when AI starts to become infrastructure for how you work.

The defining question: are you still starting from scratch every time you open a chat or investing large amounts of time for a quality outcome? If yes, you’re still a Practitioner.

If you’ve built persistent context, connected tools, and repeatable workflows that carry forward across sessions, then you’re an Architect. And honestly, this level likely puts you ahead of 90% of people right now.

What this looks like:

• The product manager who set up a Claude Project with custom instructions loaded with her company’s product specs, brand voice guide, and competitive landscape so every conversation starts with deep context instead of a blank slate.

• The job seeker who built an AI-powered job search OS: one workflow researches target companies, another tailors their resume for each application, a third drafts personalized outreach. All feeding into an application tracking dashboard.

  • Here are examples of what’s possible.

• The small business owner who connected their CRM, email, and calendar through n8n so that when a new lead comes in… and AI is auto-prompted to research the company, draft a personalized follow-up, and schedules it in their outreach sequence. This turns a 20-minute manual process into a 2-minute review-and-send.

Concrete tools at this level: Claude Projects, Claude Cowork, ChatGPT Custom GPTs, Notion/Obsidian integrations, n8n (open-source, self-hostable), Zapier/Make for workflow automation, Google Apps Script for connecting spreadsheets to AI APIs.

Your next steps toward Level 4:

• Ship something small first. One problem, one tool, one user (you). The best Builders iterate on real usage, not hypothetical features.

• You must master prompt chaining: sequences where each prompt’s output feeds the next prompt’s input. This is the cornerstone for the bridge from “architect” to “builder.”

• Start to learn a coding language like Python or Javascript.

• Experiment with vibe coding: Claude Code, Cursor, Replit Agent, or Lovable. These are purpose-built for people who can describe what they want but don’t write code professionally.

  • Identify one problem you deal with every week that still needs to be manual. Then build one script, one automation, or one simple tool to ship for yourself before you ship it for anyone else.

• Learn the basics of version control and deployment. AI can teach you this and it’s the difference between “I built a thing on my laptop” and “I built a thing other people can use.”

The danger:

This biggest risk you’ll have it trying to automate a process you don’t fully understand. This manifests in a couple of ways.

You’re trying to automate a process that you haven’t manually done yourself, at least a few times. Or you build elaborate systems for problems a single well-written prompt would solve (hammer, meet all of these hypothetical nails).

The signal you’ve crossed the line: you’re spending more time building/maintaining your AI ‘infrastructure’ than doing the work it was supposed to replace.

As your capabilities increase, your judgment on problem-solution fit must also be better.

Level 4: The Builder

Creates software and custom tools with AI assistance. Doesn’t just use what exists, builds what doesn’t.

At Level 4, you gain the ability to create the tools themselves. You speak enough of the language of technology to direct AI to build functional applications. And enough is less than you think.

The best part: you don’t need to be a junior developer to start. If you have a clear problem statement and the ability to describe what you want, then most of the time, you won’t need to touch code at all.

The coding basics you picked up help you understand what AI is building, but at this level, AI often writes the code for you. We’ve come that far.

What this looks like:

• The solopreneur with a side business who used Claude to build a Python script that pulls revenue and expense data from Google Sheets, estimates quarterly tax liability based on their state and filing status, and emails them a formatted summary every quarter with what they owe. Total build time: one afternoon

• The marketing director who built a custom Slack bot that monitors brand mentions across social channels, runs sentiment analysis, and posts a daily digest to her team. No engineering team required.

• Alex Finn, a creator with no formal engineering background, used Claude Code to solo-build Creator Buddy, an AI-powered app that hit $300,000 ARR. He described what he wanted, iterated with AI, and shipped a production product in five months without writing a single line of code himself.

  • All of the evidence for this success is documented publicly on his social media and blog.

Your next steps toward Level 5:

• Understand the basics of how high-quality agents function.

  • This is a great starting point: Thin Harness, Fat Skills by Garry Tan.

• Start thinking about persistence. The tools you build at Level 4 run when you trigger them. Level 5 is about tools that run without you. What would it look like if your best tool monitored, decided, and acted on its own?

• Learn to evaluate what you’ve built. Does it actually save time? Does anyone use it? The skill at this level isn’t building, but the judgment to know what’s worth building.

• Learn what it takes to secure AI-built tools and agentic workflows.

The danger:

Bad judgment and blind spots. The discipline at Level 4 isn’t building. It’s the willingness to grind enough to make the skill actually useful.

My top three rules for being a builder with AI.

Rule #1: don’t build without purpose.

The dopamine of shipping with AI is real, and it’s easy to blow through millions of tokens tinkering aimlessly (trust me). Paying to play is fine. Just remember vibe coding is a skill like anything else.

My challenge: are you learning or earning? Pick one. If you're doing neither, you're just paying to play (hello new version of microtransactions).

You could ship a new tool every weekend and never open most of them.

Rule #2: don’t ship code you don’t understand.

AI makes mistakes. You don’t need to be able to write all the code, but you should be able to understand what it’s doing. This starts as simple as understanding what every file does and why.

My challenge: can you explain what your code does in plain English? If not, you can't ship it.

Rule #3: don’t build without guardrails.

And it's not just the code. The AI itself can take wrong actions.

When you’re creating tools that touch real data like API keys, financial records, customer information, you’re also creating opportunity for hackers.

Not to mention the actions of the AI itself. You should never blindly approve what AI wants to do. You don’t want to be this person (and make no mistake, this was a people problem, not AI).

Security and safety aren’t optional if you’re a builder. It's the price of admission.

Build assets, not liabilities.

Level 5: The Orchestrator

Deploys autonomous AI systems that observe, decide, and act without you in the loop for every decision.

Here’s the critical distinction between these latter levels and how they build on each other:

• At Level 3, you learn to manage context and how to improve AI outcomes. But you’re still largely in the loop, reviewing output, managing steps, and triggering workflows manually.

• At Level 4, you can build your own custom tools that automate manual steps or solve problems no existing tool addresses.

• At Level 5, you build systems that can mimic your judgment and taste. They observe conditions, evaluate what matters, and take action on their own within guardrails you define.

In other words, level 5 is when AI can confidently act while you sleep.

Every level is essentially decreasing the time it takes you to get to a quality outcome and increasing how much of that time doesn’t require you at all.

What this looks like:

• The solopreneur running an OpenClaw agent that scans six email accounts every hour, filters out noise, summarizes what’s important, drafts responses, and sends them to their messaging app for approval. And sends a morning briefing with task priorities, weather, and calendar pulled from their project management system, which is delivered as an audio file while they make coffee.

• The founder who deployed agents that monitor competitor pricing, industry news, and customer feedback across multiple channels 24/7. Surfacing only the signals that require a human decision, auto-filing everything else.

• The developer who built a support system where one agent triages incoming tickets, a second drafts responses using the company knowledge base, and a third escalates edge cases to humans. All three running overnight, working autonomously to handle a queue that would take a team of three.

The honest caveat: This is the frontier. Most people operating at Level 5 today are deeply technical, and the tooling is evolving fast.

Frameworks like OpenClaw (an open-source agent orchestrator that hit 100,000 GitHub stars in its first week) are making this more accessible, but it still requires a significant time investment for setup, security awareness, and ongoing maintenance. It’s here because it’s where the technology is heading, and knowing the destination changes how you think about Levels 2, 3, and 4.

Where to go from here: At this level, the “next step” isn’t climbing another rung. It’s pushing the boundary itself through open-source contributions and/or building products that extend what agents can do, or designing the guardrails that make autonomous systems trustworthy.

Just keep building.

The danger:

Agent drift.

Autonomous systems don’t crash, their reliability decays. They start being subtly wrong, and because no human is in the loop for every decision, it compounds before anyone notices. “Set it and forget it” becomes “set it and regret it.”

At Level 5, the work isn’t on rails, it’s unpredictable. So your main job becomes designing the guardrails and evaluation loops that catch drift before it becomes damage.

This level takes serious skill. Not just to build, but to maintain responsibly.

The Honest Assessment

If you think about where we are collectively, the numbers are sobering.

DataCamp’s 2026 report (conducted with YouGov) found that only 17% of employees use AI frequently, despite 42% expecting their role to change significantly because of it within the next year. Meanwhile, 34% of workers feel unprepared for AI-driven changes, and 42% say their employer expects them to figure it out on their own.

Translation: most of the workforce is at Level 0 or Level 1. And most organizations are shrugging and saying “go learn AI” without giving anyone any guidance.

But here’s the flip side, and this is the part that matters. Pairing a time investment with structured upskilling programs is nearly twice as likely to see significant ROI from AI.

So if you’re at Level 0? Your next step is trying one task. (Scroll back up to The Bystander, it’s right there.)

If you’re at Level 1? Your next step is learning to structure a prompt. (It’s in The Explorer section.)

Every level has a next step. And if you don’t know yours, you can take the below quiz.

Find Your Level: The 5-Question Self-Assessment

Below is a link to a survey built with AI. This is a good example of something small that a practiced Builder can do.

You can take the quiz here.

Short on time? Here is the 15-second version of the survey.

What Comes Next

Now you know where you stand. The question is whether you do anything about it.

Every level above has a “next steps” section written for exactly where you are. Scroll back up to your level and pick one action. Not three. One.

If you landed at Level 0 or 1 and want structured help getting to Level 2, that’s exactly what my upcoming book Prompt to Unlock is built to help you achieve. Coming later in 2026.

You can also follow along as I build this framework out into deeper dives into each level, tool breakdowns, real workflows, practical guides, etc. This blog is where it will all live.

Subscribe now

And I’m curious: where did you land, and did it surprise you? Drop a comment below or find me on X.

This article is for informational purposes only and does not constitute professional advice. Please consult with qualified professionals before making career, business, or technology decisions.

Sources:

• Locke, E.A. & Latham, G.P. (2002). “Building a Practically Useful Theory of Goal Setting and Task Motivation.” American Psychologist.

• Dreyfus, S.E. (2004). “The Five-Stage Model of Adult Skill Acquisition.” Bulletin of Science, Technology & Society.

• DataCamp/YouGov (2026). “The State of Data & AI Literacy in 2026.”

• Finn, A. (2025). “How to Build Your First App with AI.” alexfinn.ai

• Autor, D., Levy, F., & Murnane, R. (2003). “The Skill Content of Recent Technological Change: An Empirical Exploration.” Quarterly Journal of Economics.

• Acemoglu, D., & Autor, D. (2011). “Skills, Tasks and Technologies: Implications for Employment and Earnings.” Handbook of Labor Economics.

• Michaels, G., Natraj, A., & Van Reenen, J. (2014). “Has ICT Polarized Skill Demand? Evidence from Eleven Countries Over 25 Years.” Review of Economics and Statistics.

• Chatterji, A., Cunningham, T., Deming, D. J., Hitzig, Z., Ong, C., Shan, C., & Wadman, K. (2025). How people use ChatGPT (NBER Working Paper No. 34255). National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w34255/w34255.pdf

• Kwa, M., West, B., et al. (2025). Measuring AI Ability to Complete Long Tasks. METR. https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/

• He, J., Rungta, M., Koleczek, D., Sekhon, A., Wang, F. X., & Hasan, S. (2024). Does prompt formatting have any impact on LLM performance? arXiv. https://arxiv.org/abs/2411.10541

• PwC (2025). The Fearless Future: 2025 Global AI Jobs Barometer. https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf

• Laban, P., et al. (2025). LLMs Get Lost In Multi-Turn Conversation. arXiv:2505.06120