weekend ai reads for 2026-06-05

June 05, 2026

šŸ“° ABOVE THE FOLD: CO-PILOTING

I Tried to Sell My House With A.I. / New York Times (17 minute read)

In the end, using A.I. netted me more than $90,000. That includes the premium over the asking price, plus the roughly $36,000 in fees I didnā€™t pay.

He Quit Baidu. But First He Had to Build an AI Version of Himself. ā€” As Chinese firms expand internal AI systems, some employees are being asked to build digital versions of themselves before leaving their jobs. / Sixth Tone (7 minute read)

  • this feels dystopian

 

šŸ“» QUOTES OF THE WEEK

My take on AI is, essentially, everybody whoā€™s against it is too against it and everybody whoā€™s for it is too for it.

Daniel Jalkut (source)

 

My companyā€™s claude account got exhausted.

Now my legendary manager is asking if we can build our own LLM like Claude to reduce costs

sickdotdev (source)

 

šŸ‘„ FOR EVERYONE

The 2ā€“7 problem / Anton Sten (5 minute read)

His point: AI output never leaves the middle. It always lands somewhere between a 2 and a 7. Never a 1. Never an 8.

The line that stuck: AI is bad at making things that are bad.

the solution might be cancelling my AI subscription / David Wilson (6 minute read)

Except for the SaaS, almost none of this is useful and I donā€™t want to maintain any of it. I accidentally run a news outlet which is surely a liability. Sure, it has helped me ā€œlearn AI toolingā€ and I use many of these tools, but I didnā€™t need them. I canā€™t afford to maintain any of them, not in terms of time, commitment, belief, attention or willingness to spend on tokens.

 

šŸ“š FOUNDATIONS

How Do I Use AI ā€” Practical AI Guides & Tutorials

The AI Perception-Reality Gap / Hubspot blog (9 minute read)

After three and a half years of building, shipping, and watching many of our growing customers put AI to work, the AI perspectives we are most certain of at HubSpot are the things almost no one else is saying out loud.

Here are six of them.

The Web Is Being Made Accessible for AI, Not People / Tech Policy Press (9 minute read)

The overlap between what AI agents need and what screen reader users need, however, is narrower than it appears.

 

šŸš€ FOR LEADERS

A.I. Doesnā€™t Have to Mean Layoffs ā€” A French multinational, Schneider Electric, decided to use artificial intelligence in manufacturing to make workers more productive, rather than to replace them. Hereā€™s how thatā€™s going. / New York Times (9 minute read)

  • related, How Box Created 13 New Types of Jobs Because of A.I. ā€” Box, a Silicon Valley software maker, expects to have more employees, not fewer, as it hires A.I. architects, A.I. solutions managers and other new A.I.-related positions. / New York Times (10 minute read)

CAIO and CEO alignment: Why It Matters and What It Takes ā€” Charged with scaling their initial but siloed successes, chief AI officers push to unite the c-suite behind a shared AI vision. / IBM (9 minute read)

The boardā€™s role in managing emerging AI risks ā€” Leading cybersecurity executives and board directors discuss the challenges and opportunities of AI. / McKinsey Technology, archive (17 minute read)

 

šŸŽ“ FOR EDUCATORS

AI cheating accusations: The students hiring lawyers to defend themselves ā€” Faced with terrifying stakes, some students pay a lawyer to help with their defense. / Mashable (11 minute read)

Iā€™m a professor. Heres what Iā€™m thinking about re: AI in the classroom. / The Accidental Beauty Contest, Substack, archive (10 minute read)

Law Professors Prefer AI Over Peer Answers [PDF] / Stanford University (57 minute read)

Participants created 40 representative questions, wrote answers, and judged 2,918 anonymized comparisons between human and LLM responses. Professors rated LLMs far higher than their peers (average win rate = 75.33%), with models performing similarly to the best instructor. LLM responses were also rarely flagged as harmful (3.53%, vs 12.06% for professors).

 

šŸ“Š FOR TECHNOLOGISTS

Fast is better than slow / Patrick Dubroy (6 minute read)

they werenā€™t fast because they were great programmers, they were great programmers because they were fast.

Then thereā€™s the talent pipeline question. ā€œWhen agents handle more of the execution layer, how do junior engineers grow into senior engineers if AI is absorbing much of the entry-level work? What is the role of a designer or product manager in this new world?ā€ Tallapragada writes. Salesforce is experimenting with one-person or three-person units instead of traditional Scrum teams. It doesnā€™t have clear answers yet.

Of Hammers and Nails: What AI Can and Cannot Do for a Data Analyst / Adam Cassar, Substack, archive (5 minute read)

Giving LLMs enough context to analyse metric changes well is often more laborious than doing the work yourself

 

šŸŽ‰ FOR FUN

Continue? Y/N ā€” How carefully do you read AI commands?

  • we, shamefully, did not excel

Wingbits AI ā€” Agents that Scan the Sky & Send Real-Time Alerts

ā€˜Turn Your Texts into a Songā€™ TikToks: Inside the AI Trend ā€” TikTokers everywhere are making songs out of friends and relativesā€™ texts. Is AI music the next Snapchat filter? / Rolling Stone (10 minute read)

 

šŸ§æ AI-ADJACENT

 

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