Wait hold the phone. Frank Chimero is writing again! One of my all-time favorite design writers. Welcome back to my RSS feed, (Internet) friend.

I wanted to get back to walking, reading, and writing. These were the foundational practices during the most prolific and enjoyable parts of my career. I longed to feel generative again and to have ideas with depth, meaning, and pleasant uncertainty, ideas whose remit extended beyond the boundaries of one company. I missed the opportunities of the internet as a common place for finding your people and feeling like a part of a group that actually had ideas instead of opinions or pleas for attention.

Source: Time is On My Side →

Yep this tracks.

Researchers at Gartner have observed that high-performing employees react to a return-to-office mandate as a trust issue, resulting in a 16% lower intent to stay. “High-performing employees are more easily able to pursue opportunities at organizations that offer hybrid or fully remote policies,” said Caitlin Duffy, a director in the Gartner HR Practice. “Losing high performers to attrition costs organizations in terms of productivity, difficulty in backfilling the role, and the overall loss of high-quality talent available to fill critical positions.”

Source: The hidden cost of RTO: Why forcing choice is detrimental to your business →

This is some really interesting data.

In a new paper, several Stanford economists studied payroll data from the private company ADP, which covers millions of workers, through mid-2025. They found that young workers aged 22-25 in “highly AI-exposed” jobs, such as software developers and customer service agents, experienced a 13 percent decline in employment since the advent of ChatGPT. Notably, the economists found that older workers and less-exposed jobs, such as home health aides, saw steady or rising employment. “There’s a clear, evident change when you specifically look at young workers who are highly exposed to AI,” Stanford economist Erik Brynjolfsson, who wrote the paper with Bharat Chandar and Ruyu Chen, told the Wall Street Journal.

Source: The Evidence That AI Is Destroying Jobs For Young People Just Got Stronger →

This is fun research but did they have to use “reach out” in this quote.

According to a new study, using too much jargon in the workplace can hurt employees’ ability to process messages, leading them to experience negative feelings and making them feel less confident. In turn, they’re less likely to reach out and ask for or share information with their colleagues.

Source: Workplace jargon hurts employee morale, collaboration, study finds →

I almost skipped this Michael Lopp piece, but I’m glad I didn’t. It’s one of his best in a long time, especially if you’re a manager. Reframing the act of working with AI as “making the robots dance” is so good. But there’s more to it than that. Just read it, ok?

Robots don’t lie. Lying requires intent to deceive, and when a robot provides you with plausible-sounding, but incorrect statements, it’s either following its programming or making an error. Or both. Humans lie. They boast, they are tragically optimistic, they exaggerate, they forget, I could go on for a long, long while. It’s a list of foibles that make them familiar… that makes them human. What do I do as a leader to work with these troublesome humans? Well, here’s a short, essential list:

• I speak clearly and specifically, so my intent is clear.
• I frame conversations with context so everyone understands my ideas.
• I understand errors are part of the process and work to build tools to prevent them.
• I debate and plan big ideas before I begin.

Source: Every Single Human. Like. Always. →

In South Africa we call this game (where the goal is to keep the rally going as long as possible) “Beach Ball” so I immediately got it. And despite my aversion to “Here’s what X taught me about B2B Sales” posts this one actually got to me. Because Gabrielle is right. The communication goal—especially in big corporations—is not to win, it’s to keep talking until you have a better way forward:

In tennis, you win by hitting shots your opponent can’t return. In Frescobol, you win by setting up your partner to succeed. One earns you points. The other builds momentum. When you default to tennis, every hard conversation becomes a match to win. You come in armed with tactics—rebuttals, logic, bulletproof clarity. But the more you prepare to win, the more you risk breaking the rhythm that makes change possible. Frescobol invites a different question: “What would I say differently if my goal were to extend the rally, not win the point?”

Source: The game you’ve never heard of that taught me a better way to build alignment →

This is some fascinating research.

Researchers at Oxford University and King’s College London tested LLMs using game theory, giving LLMs from OpenAI, Google, and Anthropic prompts that mimicked the setup of the classic Prisoner’s Dilemma.

They found that Google’s Gemini is “strategically ruthless,” while OpenAI is collaborative to a “catastrophic” degree. Their paper, published on the preprint repository Arxiv (and not yet peer reviewed), claims that this is due to OpenAI model’s fatal disinterest in a key factor: how much time there is left to play the game.

Source: Gemini Is ‘Strict and Punitive’ While ChatGPT Is ‘Catastrophically’ Cooperative, Researchers Say →

Don’t let the clickbait title put you off. Related to my link about AI killing jobs in tech, here Cal Newport produces some compelling “both sides” receipts about how AI is helping + hurting software development. His conclusions are solid:

My advice, for the moment:

1. Tune out both the most heated and the most dismissive rhetoric.
2. Focus on tangible changes in areas that you care about that really do seem connected to AI—read widely and ask people you trust about what they’re seeing.
3. Beyond that, however, follow AI news with a large grain of salt. All of this is too new for anyone to really understand what they’re saying.

AI is important. But we don’t yet fully know why.

Source: No One Knows Anything About AI →

I think we’ve all seen the internal Shopify memo on requiring teams to use AI. This is a great article on what happened next. I especially love the internal tools Shopify built to make adoption easier:

Employees can use the LLM proxy to build the workflows they need. They can select from different models, which are updated with the latest versions as soon as they’re released. There’s a collection of MCPs, and all it takes is asking the proxy (or another tool like Cursor) to access them. There’s even a stable of agents already created by other people for anyone to use. It’s a one-stop shop for everything someone needs to use AI.

Source: From Memo to Movement: Shopify’s Cultural Adoption of AI →

There are a lot of these “how to beat the AI cookie monster” posts out there right now, but this one by Benjamin Todd is well-researched and articulated, with lots of practical examples on how to do the one thing that we all need to do anyway: keep learning.

I break this down into four key categories of skills likely to increase in value:

1. Hard for AI: data poor, messy, long-horizon tasks where a person-in-the-loop is wanted
2. Needed for deploying AI: the skills of organising and auditing AI systems, as well as those used in complementary industries such as data centre construction
3. Used to make things the world could use far more of: skills that contribute to improved healthcare, housing, research, luxury goods, etc. – things which people want more of as they get better and cheaper
4. Hard for others to learn: rare expertise that matches your unique strengths

Source: How not to lose your job to AI →